critical case study methodology

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Continuing to enhance the quality of case study methodology in health services research

Shannon l. sibbald.

1 Faculty of Health Sciences, Western University, London, Ontario, Canada.

2 Department of Family Medicine, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.

3 The Schulich Interfaculty Program in Public Health, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada.

Stefan Paciocco

Meghan fournie, rachelle van asseldonk, tiffany scurr.

Case study methodology has grown in popularity within Health Services Research (HSR). However, its use and merit as a methodology are frequently criticized due to its flexible approach and inconsistent application. Nevertheless, case study methodology is well suited to HSR because it can track and examine complex relationships, contexts, and systems as they evolve. Applied appropriately, it can help generate information on how multiple forms of knowledge come together to inform decision-making within healthcare contexts. In this article, we aim to demystify case study methodology by outlining its philosophical underpinnings and three foundational approaches. We provide literature-based guidance to decision-makers, policy-makers, and health leaders on how to engage in and critically appraise case study design. We advocate that researchers work in collaboration with health leaders to detail their research process with an aim of strengthening the validity and integrity of case study for its continued and advanced use in HSR.

Introduction

The popularity of case study research methodology in Health Services Research (HSR) has grown over the past 40 years. 1 This may be attributed to a shift towards the use of implementation research and a newfound appreciation of contextual factors affecting the uptake of evidence-based interventions within diverse settings. 2 Incorporating context-specific information on the delivery and implementation of programs can increase the likelihood of success. 3 , 4 Case study methodology is particularly well suited for implementation research in health services because it can provide insight into the nuances of diverse contexts. 5 , 6 In 1999, Yin 7 published a paper on how to enhance the quality of case study in HSR, which was foundational for the emergence of case study in this field. Yin 7 maintains case study is an appropriate methodology in HSR because health systems are constantly evolving, and the multiple affiliations and diverse motivations are difficult to track and understand with traditional linear methodologies.

Despite its increased popularity, there is debate whether a case study is a methodology (ie, a principle or process that guides research) or a method (ie, a tool to answer research questions). Some criticize case study for its high level of flexibility, perceiving it as less rigorous, and maintain that it generates inadequate results. 8 Others have noted issues with quality and consistency in how case studies are conducted and reported. 9 Reporting is often varied and inconsistent, using a mix of approaches such as case reports, case findings, and/or case study. Authors sometimes use incongruent methods of data collection and analysis or use the case study as a default when other methodologies do not fit. 9 , 10 Despite these criticisms, case study methodology is becoming more common as a viable approach for HSR. 11 An abundance of articles and textbooks are available to guide researchers through case study research, including field-specific resources for business, 12 , 13 nursing, 14 and family medicine. 15 However, there remains confusion and a lack of clarity on the key tenets of case study methodology.

Several common philosophical underpinnings have contributed to the development of case study research 1 which has led to different approaches to planning, data collection, and analysis. This presents challenges in assessing quality and rigour for researchers conducting case studies and stakeholders reading results.

This article discusses the various approaches and philosophical underpinnings to case study methodology. Our goal is to explain it in a way that provides guidance for decision-makers, policy-makers, and health leaders on how to understand, critically appraise, and engage in case study research and design, as such guidance is largely absent in the literature. This article is by no means exhaustive or authoritative. Instead, we aim to provide guidance and encourage dialogue around case study methodology, facilitating critical thinking around the variety of approaches and ways quality and rigour can be bolstered for its use within HSR.

Purpose of case study methodology

Case study methodology is often used to develop an in-depth, holistic understanding of a specific phenomenon within a specified context. 11 It focuses on studying one or multiple cases over time and uses an in-depth analysis of multiple information sources. 16 , 17 It is ideal for situations including, but not limited to, exploring under-researched and real-life phenomena, 18 especially when the contexts are complex and the researcher has little control over the phenomena. 19 , 20 Case studies can be useful when researchers want to understand how interventions are implemented in different contexts, and how context shapes the phenomenon of interest.

In addition to demonstrating coherency with the type of questions case study is suited to answer, there are four key tenets to case study methodologies: (1) be transparent in the paradigmatic and theoretical perspectives influencing study design; (2) clearly define the case and phenomenon of interest; (3) clearly define and justify the type of case study design; and (4) use multiple data collection sources and analysis methods to present the findings in ways that are consistent with the methodology and the study’s paradigmatic base. 9 , 16 The goal is to appropriately match the methods to empirical questions and issues and not to universally advocate any single approach for all problems. 21

Approaches to case study methodology

Three authors propose distinct foundational approaches to case study methodology positioned within different paradigms: Yin, 19 , 22 Stake, 5 , 23 and Merriam 24 , 25 ( Table 1 ). Yin is strongly post-positivist whereas Stake and Merriam are grounded in a constructivist paradigm. Researchers should locate their research within a paradigm that explains the philosophies guiding their research 26 and adhere to the underlying paradigmatic assumptions and key tenets of the appropriate author’s methodology. This will enhance the consistency and coherency of the methods and findings. However, researchers often do not report their paradigmatic position, nor do they adhere to one approach. 9 Although deliberately blending methodologies may be defensible and methodologically appropriate, more often it is done in an ad hoc and haphazard way, without consideration for limitations.

Cross-analysis of three case study approaches, adapted from Yazan 2015

Dimension of interest	Yin	Stake	Merriam
Case study design	Logical sequence = connecting empirical data to initial research question Four types: single holistic, single embedded, multiple holistic, multiple embedded	Flexible design = allow major changes to take place while the study is proceeding	Theoretical framework = literature review to mold research question and emphasis points
Case study paradigm	Positivism	Constructivism and existentialism	Constructivism
Components of study		“Progressive focusing” = “the course of the study cannot be charted in advance” (1998, p 22) Must have 2-3 research questions to structure the study
Collecting data	Quantitative and qualitative evidentiary influenced by:	Qualitative data influenced by:	Qualitative data research must have necessary skills and follow certain procedures to:
Data collection techniques
Data analysis	Use both quantitative and qualitative techniques to answer research question	Use researcher’s intuition and impression as a guiding factor for analysis	“it is the process of making meaning” (1998, p 178)
Validating data		Use triangulation	Increase internal validity Ensure reliability and increase external validity

The post-positive paradigm postulates there is one reality that can be objectively described and understood by “bracketing” oneself from the research to remove prejudice or bias. 27 Yin focuses on general explanation and prediction, emphasizing the formulation of propositions, akin to hypothesis testing. This approach is best suited for structured and objective data collection 9 , 11 and is often used for mixed-method studies.

Constructivism assumes that the phenomenon of interest is constructed and influenced by local contexts, including the interaction between researchers, individuals, and their environment. 27 It acknowledges multiple interpretations of reality 24 constructed within the context by the researcher and participants which are unlikely to be replicated, should either change. 5 , 20 Stake and Merriam’s constructivist approaches emphasize a story-like rendering of a problem and an iterative process of constructing the case study. 7 This stance values researcher reflexivity and transparency, 28 acknowledging how researchers’ experiences and disciplinary lenses influence their assumptions and beliefs about the nature of the phenomenon and development of the findings.

Defining a case

A key tenet of case study methodology often underemphasized in literature is the importance of defining the case and phenomenon. Researches should clearly describe the case with sufficient detail to allow readers to fully understand the setting and context and determine applicability. Trying to answer a question that is too broad often leads to an unclear definition of the case and phenomenon. 20 Cases should therefore be bound by time and place to ensure rigor and feasibility. 6

Yin 22 defines a case as “a contemporary phenomenon within its real-life context,” (p13) which may contain a single unit of analysis, including individuals, programs, corporations, or clinics 29 (holistic), or be broken into sub-units of analysis, such as projects, meetings, roles, or locations within the case (embedded). 30 Merriam 24 and Stake 5 similarly define a case as a single unit studied within a bounded system. Stake 5 , 23 suggests bounding cases by contexts and experiences where the phenomenon of interest can be a program, process, or experience. However, the line between the case and phenomenon can become muddy. For guidance, Stake 5 , 23 describes the case as the noun or entity and the phenomenon of interest as the verb, functioning, or activity of the case.

Designing the case study approach

Yin’s approach to a case study is rooted in a formal proposition or theory which guides the case and is used to test the outcome. 1 Stake 5 advocates for a flexible design and explicitly states that data collection and analysis may commence at any point. Merriam’s 24 approach blends both Yin and Stake’s, allowing the necessary flexibility in data collection and analysis to meet the needs.

Yin 30 proposed three types of case study approaches—descriptive, explanatory, and exploratory. Each can be designed around single or multiple cases, creating six basic case study methodologies. Descriptive studies provide a rich description of the phenomenon within its context, which can be helpful in developing theories. To test a theory or determine cause and effect relationships, researchers can use an explanatory design. An exploratory model is typically used in the pilot-test phase to develop propositions (eg, Sibbald et al. 31 used this approach to explore interprofessional network complexity). Despite having distinct characteristics, the boundaries between case study types are flexible with significant overlap. 30 Each has five key components: (1) research question; (2) proposition; (3) unit of analysis; (4) logical linking that connects the theory with proposition; and (5) criteria for analyzing findings.

Contrary to Yin, Stake 5 believes the research process cannot be planned in its entirety because research evolves as it is performed. Consequently, researchers can adjust the design of their methods even after data collection has begun. Stake 5 classifies case studies into three categories: intrinsic, instrumental, and collective/multiple. Intrinsic case studies focus on gaining a better understanding of the case. These are often undertaken when the researcher has an interest in a specific case. Instrumental case study is used when the case itself is not of the utmost importance, and the issue or phenomenon (ie, the research question) being explored becomes the focus instead (eg, Paciocco 32 used an instrumental case study to evaluate the implementation of a chronic disease management program). 5 Collective designs are rooted in an instrumental case study and include multiple cases to gain an in-depth understanding of the complexity and particularity of a phenomenon across diverse contexts. 5 , 23 In collective designs, studying similarities and differences between the cases allows the phenomenon to be understood more intimately (for examples of this in the field, see van Zelm et al. 33 and Burrows et al. 34 In addition, Sibbald et al. 35 present an example where a cross-case analysis method is used to compare instrumental cases).

Merriam’s approach is flexible (similar to Stake) as well as stepwise and linear (similar to Yin). She advocates for conducting a literature review before designing the study to better understand the theoretical underpinnings. 24 , 25 Unlike Stake or Yin, Merriam proposes a step-by-step guide for researchers to design a case study. These steps include performing a literature review, creating a theoretical framework, identifying the problem, creating and refining the research question(s), and selecting a study sample that fits the question(s). 24 , 25 , 36

Data collection and analysis

Using multiple data collection methods is a key characteristic of all case study methodology; it enhances the credibility of the findings by allowing different facets and views of the phenomenon to be explored. 23 Common methods include interviews, focus groups, observation, and document analysis. 5 , 37 By seeking patterns within and across data sources, a thick description of the case can be generated to support a greater understanding and interpretation of the whole phenomenon. 5 , 17 , 20 , 23 This technique is called triangulation and is used to explore cases with greater accuracy. 5 Although Stake 5 maintains case study is most often used in qualitative research, Yin 17 supports a mix of both quantitative and qualitative methods to triangulate data. This deliberate convergence of data sources (or mixed methods) allows researchers to find greater depth in their analysis and develop converging lines of inquiry. For example, case studies evaluating interventions commonly use qualitative interviews to describe the implementation process, barriers, and facilitators paired with a quantitative survey of comparative outcomes and effectiveness. 33 , 38 , 39

Yin 30 describes analysis as dependent on the chosen approach, whether it be (1) deductive and rely on theoretical propositions; (2) inductive and analyze data from the “ground up”; (3) organized to create a case description; or (4) used to examine plausible rival explanations. According to Yin’s 40 approach to descriptive case studies, carefully considering theory development is an important part of study design. “Theory” refers to field-relevant propositions, commonly agreed upon assumptions, or fully developed theories. 40 Stake 5 advocates for using the researcher’s intuition and impression to guide analysis through a categorical aggregation and direct interpretation. Merriam 24 uses six different methods to guide the “process of making meaning” (p178) : (1) ethnographic analysis; (2) narrative analysis; (3) phenomenological analysis; (4) constant comparative method; (5) content analysis; and (6) analytic induction.

Drawing upon a theoretical or conceptual framework to inform analysis improves the quality of case study and avoids the risk of description without meaning. 18 Using Stake’s 5 approach, researchers rely on protocols and previous knowledge to help make sense of new ideas; theory can guide the research and assist researchers in understanding how new information fits into existing knowledge.

Practical applications of case study research

Columbia University has recently demonstrated how case studies can help train future health leaders. 41 Case studies encompass components of systems thinking—considering connections and interactions between components of a system, alongside the implications and consequences of those relationships—to equip health leaders with tools to tackle global health issues. 41 Greenwood 42 evaluated Indigenous peoples’ relationship with the healthcare system in British Columbia and used a case study to challenge and educate health leaders across the country to enhance culturally sensitive health service environments.

An important but often omitted step in case study research is an assessment of quality and rigour. We recommend using a framework or set of criteria to assess the rigour of the qualitative research. Suitable resources include Caelli et al., 43 Houghten et al., 44 Ravenek and Rudman, 45 and Tracy. 46

New directions in case study

Although “pragmatic” case studies (ie, utilizing practical and applicable methods) have existed within psychotherapy for some time, 47 , 48 only recently has the applicability of pragmatism as an underlying paradigmatic perspective been considered in HSR. 49 This is marked by uptake of pragmatism in Randomized Control Trials, recognizing that “gold standard” testing conditions do not reflect the reality of clinical settings 50 , 51 nor do a handful of epistemologically guided methodologies suit every research inquiry.

Pragmatism positions the research question as the basis for methodological choices, rather than a theory or epistemology, allowing researchers to pursue the most practical approach to understanding a problem or discovering an actionable solution. 52 Mixed methods are commonly used to create a deeper understanding of the case through converging qualitative and quantitative data. 52 Pragmatic case study is suited to HSR because its flexibility throughout the research process accommodates complexity, ever-changing systems, and disruptions to research plans. 49 , 50 Much like case study, pragmatism has been criticized for its flexibility and use when other approaches are seemingly ill-fit. 53 , 54 Similarly, authors argue that this results from a lack of investigation and proper application rather than a reflection of validity, legitimizing the need for more exploration and conversation among researchers and practitioners. 55

Although occasionally misunderstood as a less rigourous research methodology, 8 case study research is highly flexible and allows for contextual nuances. 5 , 6 Its use is valuable when the researcher desires a thorough understanding of a phenomenon or case bound by context. 11 If needed, multiple similar cases can be studied simultaneously, or one case within another. 16 , 17 There are currently three main approaches to case study, 5 , 17 , 24 each with their own definitions of a case, ontological and epistemological paradigms, methodologies, and data collection and analysis procedures. 37

Individuals’ experiences within health systems are influenced heavily by contextual factors, participant experience, and intricate relationships between different organizations and actors. 55 Case study research is well suited for HSR because it can track and examine these complex relationships and systems as they evolve over time. 6 , 7 It is important that researchers and health leaders using this methodology understand its key tenets and how to conduct a proper case study. Although there are many examples of case study in action, they are often under-reported and, when reported, not rigorously conducted. 9 Thus, decision-makers and health leaders should use these examples with caution. The proper reporting of case studies is necessary to bolster their credibility in HSR literature and provide readers sufficient information to critically assess the methodology. We also call on health leaders who frequently use case studies 56 – 58 to report them in the primary research literature.

The purpose of this article is to advocate for the continued and advanced use of case study in HSR and to provide literature-based guidance for decision-makers, policy-makers, and health leaders on how to engage in, read, and interpret findings from case study research. As health systems progress and evolve, the application of case study research will continue to increase as researchers and health leaders aim to capture the inherent complexities, nuances, and contextual factors. 7

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Open access
Published: 27 June 2011

The case study approach

Sarah Crowe 1 ,
Kathrin Cresswell 2 ,
Ann Robertson 2 ,
Guro Huby 3 ,
Anthony Avery 1 &
Aziz Sheikh 2

BMC Medical Research Methodology volume 11 , Article number: 100 ( 2011 ) Cite this article

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The case study approach allows in-depth, multi-faceted explorations of complex issues in their real-life settings. The value of the case study approach is well recognised in the fields of business, law and policy, but somewhat less so in health services research. Based on our experiences of conducting several health-related case studies, we reflect on the different types of case study design, the specific research questions this approach can help answer, the data sources that tend to be used, and the particular advantages and disadvantages of employing this methodological approach. The paper concludes with key pointers to aid those designing and appraising proposals for conducting case study research, and a checklist to help readers assess the quality of case study reports.

Peer Review reports

Introduction

The case study approach is particularly useful to employ when there is a need to obtain an in-depth appreciation of an issue, event or phenomenon of interest, in its natural real-life context. Our aim in writing this piece is to provide insights into when to consider employing this approach and an overview of key methodological considerations in relation to the design, planning, analysis, interpretation and reporting of case studies.

The illustrative 'grand round', 'case report' and 'case series' have a long tradition in clinical practice and research. Presenting detailed critiques, typically of one or more patients, aims to provide insights into aspects of the clinical case and, in doing so, illustrate broader lessons that may be learnt. In research, the conceptually-related case study approach can be used, for example, to describe in detail a patient's episode of care, explore professional attitudes to and experiences of a new policy initiative or service development or more generally to 'investigate contemporary phenomena within its real-life context' [ 1 ]. Based on our experiences of conducting a range of case studies, we reflect on when to consider using this approach, discuss the key steps involved and illustrate, with examples, some of the practical challenges of attaining an in-depth understanding of a 'case' as an integrated whole. In keeping with previously published work, we acknowledge the importance of theory to underpin the design, selection, conduct and interpretation of case studies[ 2 ]. In so doing, we make passing reference to the different epistemological approaches used in case study research by key theoreticians and methodologists in this field of enquiry.

This paper is structured around the following main questions: What is a case study? What are case studies used for? How are case studies conducted? What are the potential pitfalls and how can these be avoided? We draw in particular on four of our own recently published examples of case studies (see Tables 1 , 2 , 3 and 4 ) and those of others to illustrate our discussion[ 3 – 7 ].

What is a case study?

A case study is a research approach that is used to generate an in-depth, multi-faceted understanding of a complex issue in its real-life context. It is an established research design that is used extensively in a wide variety of disciplines, particularly in the social sciences. A case study can be defined in a variety of ways (Table 5 ), the central tenet being the need to explore an event or phenomenon in depth and in its natural context. It is for this reason sometimes referred to as a "naturalistic" design; this is in contrast to an "experimental" design (such as a randomised controlled trial) in which the investigator seeks to exert control over and manipulate the variable(s) of interest.

Stake's work has been particularly influential in defining the case study approach to scientific enquiry. He has helpfully characterised three main types of case study: intrinsic , instrumental and collective [ 8 ]. An intrinsic case study is typically undertaken to learn about a unique phenomenon. The researcher should define the uniqueness of the phenomenon, which distinguishes it from all others. In contrast, the instrumental case study uses a particular case (some of which may be better than others) to gain a broader appreciation of an issue or phenomenon. The collective case study involves studying multiple cases simultaneously or sequentially in an attempt to generate a still broader appreciation of a particular issue.

These are however not necessarily mutually exclusive categories. In the first of our examples (Table 1 ), we undertook an intrinsic case study to investigate the issue of recruitment of minority ethnic people into the specific context of asthma research studies, but it developed into a instrumental case study through seeking to understand the issue of recruitment of these marginalised populations more generally, generating a number of the findings that are potentially transferable to other disease contexts[ 3 ]. In contrast, the other three examples (see Tables 2 , 3 and 4 ) employed collective case study designs to study the introduction of workforce reconfiguration in primary care, the implementation of electronic health records into hospitals, and to understand the ways in which healthcare students learn about patient safety considerations[ 4 – 6 ]. Although our study focusing on the introduction of General Practitioners with Specialist Interests (Table 2 ) was explicitly collective in design (four contrasting primary care organisations were studied), is was also instrumental in that this particular professional group was studied as an exemplar of the more general phenomenon of workforce redesign[ 4 ].

What are case studies used for?

According to Yin, case studies can be used to explain, describe or explore events or phenomena in the everyday contexts in which they occur[ 1 ]. These can, for example, help to understand and explain causal links and pathways resulting from a new policy initiative or service development (see Tables 2 and 3 , for example)[ 1 ]. In contrast to experimental designs, which seek to test a specific hypothesis through deliberately manipulating the environment (like, for example, in a randomised controlled trial giving a new drug to randomly selected individuals and then comparing outcomes with controls),[ 9 ] the case study approach lends itself well to capturing information on more explanatory ' how ', 'what' and ' why ' questions, such as ' how is the intervention being implemented and received on the ground?'. The case study approach can offer additional insights into what gaps exist in its delivery or why one implementation strategy might be chosen over another. This in turn can help develop or refine theory, as shown in our study of the teaching of patient safety in undergraduate curricula (Table 4 )[ 6 , 10 ]. Key questions to consider when selecting the most appropriate study design are whether it is desirable or indeed possible to undertake a formal experimental investigation in which individuals and/or organisations are allocated to an intervention or control arm? Or whether the wish is to obtain a more naturalistic understanding of an issue? The former is ideally studied using a controlled experimental design, whereas the latter is more appropriately studied using a case study design.

Case studies may be approached in different ways depending on the epistemological standpoint of the researcher, that is, whether they take a critical (questioning one's own and others' assumptions), interpretivist (trying to understand individual and shared social meanings) or positivist approach (orientating towards the criteria of natural sciences, such as focusing on generalisability considerations) (Table 6 ). Whilst such a schema can be conceptually helpful, it may be appropriate to draw on more than one approach in any case study, particularly in the context of conducting health services research. Doolin has, for example, noted that in the context of undertaking interpretative case studies, researchers can usefully draw on a critical, reflective perspective which seeks to take into account the wider social and political environment that has shaped the case[ 11 ].

How are case studies conducted?

Here, we focus on the main stages of research activity when planning and undertaking a case study; the crucial stages are: defining the case; selecting the case(s); collecting and analysing the data; interpreting data; and reporting the findings.

Defining the case

Carefully formulated research question(s), informed by the existing literature and a prior appreciation of the theoretical issues and setting(s), are all important in appropriately and succinctly defining the case[ 8 , 12 ]. Crucially, each case should have a pre-defined boundary which clarifies the nature and time period covered by the case study (i.e. its scope, beginning and end), the relevant social group, organisation or geographical area of interest to the investigator, the types of evidence to be collected, and the priorities for data collection and analysis (see Table 7 )[ 1 ]. A theory driven approach to defining the case may help generate knowledge that is potentially transferable to a range of clinical contexts and behaviours; using theory is also likely to result in a more informed appreciation of, for example, how and why interventions have succeeded or failed[ 13 ].

For example, in our evaluation of the introduction of electronic health records in English hospitals (Table 3 ), we defined our cases as the NHS Trusts that were receiving the new technology[ 5 ]. Our focus was on how the technology was being implemented. However, if the primary research interest had been on the social and organisational dimensions of implementation, we might have defined our case differently as a grouping of healthcare professionals (e.g. doctors and/or nurses). The precise beginning and end of the case may however prove difficult to define. Pursuing this same example, when does the process of implementation and adoption of an electronic health record system really begin or end? Such judgements will inevitably be influenced by a range of factors, including the research question, theory of interest, the scope and richness of the gathered data and the resources available to the research team.

Selecting the case(s)

The decision on how to select the case(s) to study is a very important one that merits some reflection. In an intrinsic case study, the case is selected on its own merits[ 8 ]. The case is selected not because it is representative of other cases, but because of its uniqueness, which is of genuine interest to the researchers. This was, for example, the case in our study of the recruitment of minority ethnic participants into asthma research (Table 1 ) as our earlier work had demonstrated the marginalisation of minority ethnic people with asthma, despite evidence of disproportionate asthma morbidity[ 14 , 15 ]. In another example of an intrinsic case study, Hellstrom et al.[ 16 ] studied an elderly married couple living with dementia to explore how dementia had impacted on their understanding of home, their everyday life and their relationships.

For an instrumental case study, selecting a "typical" case can work well[ 8 ]. In contrast to the intrinsic case study, the particular case which is chosen is of less importance than selecting a case that allows the researcher to investigate an issue or phenomenon. For example, in order to gain an understanding of doctors' responses to health policy initiatives, Som undertook an instrumental case study interviewing clinicians who had a range of responsibilities for clinical governance in one NHS acute hospital trust[ 17 ]. Sampling a "deviant" or "atypical" case may however prove even more informative, potentially enabling the researcher to identify causal processes, generate hypotheses and develop theory.

In collective or multiple case studies, a number of cases are carefully selected. This offers the advantage of allowing comparisons to be made across several cases and/or replication. Choosing a "typical" case may enable the findings to be generalised to theory (i.e. analytical generalisation) or to test theory by replicating the findings in a second or even a third case (i.e. replication logic)[ 1 ]. Yin suggests two or three literal replications (i.e. predicting similar results) if the theory is straightforward and five or more if the theory is more subtle. However, critics might argue that selecting 'cases' in this way is insufficiently reflexive and ill-suited to the complexities of contemporary healthcare organisations.

The selected case study site(s) should allow the research team access to the group of individuals, the organisation, the processes or whatever else constitutes the chosen unit of analysis for the study. Access is therefore a central consideration; the researcher needs to come to know the case study site(s) well and to work cooperatively with them. Selected cases need to be not only interesting but also hospitable to the inquiry [ 8 ] if they are to be informative and answer the research question(s). Case study sites may also be pre-selected for the researcher, with decisions being influenced by key stakeholders. For example, our selection of case study sites in the evaluation of the implementation and adoption of electronic health record systems (see Table 3 ) was heavily influenced by NHS Connecting for Health, the government agency that was responsible for overseeing the National Programme for Information Technology (NPfIT)[ 5 ]. This prominent stakeholder had already selected the NHS sites (through a competitive bidding process) to be early adopters of the electronic health record systems and had negotiated contracts that detailed the deployment timelines.

It is also important to consider in advance the likely burden and risks associated with participation for those who (or the site(s) which) comprise the case study. Of particular importance is the obligation for the researcher to think through the ethical implications of the study (e.g. the risk of inadvertently breaching anonymity or confidentiality) and to ensure that potential participants/participating sites are provided with sufficient information to make an informed choice about joining the study. The outcome of providing this information might be that the emotive burden associated with participation, or the organisational disruption associated with supporting the fieldwork, is considered so high that the individuals or sites decide against participation.

In our example of evaluating implementations of electronic health record systems, given the restricted number of early adopter sites available to us, we sought purposively to select a diverse range of implementation cases among those that were available[ 5 ]. We chose a mixture of teaching, non-teaching and Foundation Trust hospitals, and examples of each of the three electronic health record systems procured centrally by the NPfIT. At one recruited site, it quickly became apparent that access was problematic because of competing demands on that organisation. Recognising the importance of full access and co-operative working for generating rich data, the research team decided not to pursue work at that site and instead to focus on other recruited sites.

Collecting the data

In order to develop a thorough understanding of the case, the case study approach usually involves the collection of multiple sources of evidence, using a range of quantitative (e.g. questionnaires, audits and analysis of routinely collected healthcare data) and more commonly qualitative techniques (e.g. interviews, focus groups and observations). The use of multiple sources of data (data triangulation) has been advocated as a way of increasing the internal validity of a study (i.e. the extent to which the method is appropriate to answer the research question)[ 8 , 18 – 21 ]. An underlying assumption is that data collected in different ways should lead to similar conclusions, and approaching the same issue from different angles can help develop a holistic picture of the phenomenon (Table 2 )[ 4 ].

Brazier and colleagues used a mixed-methods case study approach to investigate the impact of a cancer care programme[ 22 ]. Here, quantitative measures were collected with questionnaires before, and five months after, the start of the intervention which did not yield any statistically significant results. Qualitative interviews with patients however helped provide an insight into potentially beneficial process-related aspects of the programme, such as greater, perceived patient involvement in care. The authors reported how this case study approach provided a number of contextual factors likely to influence the effectiveness of the intervention and which were not likely to have been obtained from quantitative methods alone.

In collective or multiple case studies, data collection needs to be flexible enough to allow a detailed description of each individual case to be developed (e.g. the nature of different cancer care programmes), before considering the emerging similarities and differences in cross-case comparisons (e.g. to explore why one programme is more effective than another). It is important that data sources from different cases are, where possible, broadly comparable for this purpose even though they may vary in nature and depth.

Analysing, interpreting and reporting case studies

Making sense and offering a coherent interpretation of the typically disparate sources of data (whether qualitative alone or together with quantitative) is far from straightforward. Repeated reviewing and sorting of the voluminous and detail-rich data are integral to the process of analysis. In collective case studies, it is helpful to analyse data relating to the individual component cases first, before making comparisons across cases. Attention needs to be paid to variations within each case and, where relevant, the relationship between different causes, effects and outcomes[ 23 ]. Data will need to be organised and coded to allow the key issues, both derived from the literature and emerging from the dataset, to be easily retrieved at a later stage. An initial coding frame can help capture these issues and can be applied systematically to the whole dataset with the aid of a qualitative data analysis software package.

The Framework approach is a practical approach, comprising of five stages (familiarisation; identifying a thematic framework; indexing; charting; mapping and interpretation) , to managing and analysing large datasets particularly if time is limited, as was the case in our study of recruitment of South Asians into asthma research (Table 1 )[ 3 , 24 ]. Theoretical frameworks may also play an important role in integrating different sources of data and examining emerging themes. For example, we drew on a socio-technical framework to help explain the connections between different elements - technology; people; and the organisational settings within which they worked - in our study of the introduction of electronic health record systems (Table 3 )[ 5 ]. Our study of patient safety in undergraduate curricula drew on an evaluation-based approach to design and analysis, which emphasised the importance of the academic, organisational and practice contexts through which students learn (Table 4 )[ 6 ].

Case study findings can have implications both for theory development and theory testing. They may establish, strengthen or weaken historical explanations of a case and, in certain circumstances, allow theoretical (as opposed to statistical) generalisation beyond the particular cases studied[ 12 ]. These theoretical lenses should not, however, constitute a strait-jacket and the cases should not be "forced to fit" the particular theoretical framework that is being employed.

When reporting findings, it is important to provide the reader with enough contextual information to understand the processes that were followed and how the conclusions were reached. In a collective case study, researchers may choose to present the findings from individual cases separately before amalgamating across cases. Care must be taken to ensure the anonymity of both case sites and individual participants (if agreed in advance) by allocating appropriate codes or withholding descriptors. In the example given in Table 3 , we decided against providing detailed information on the NHS sites and individual participants in order to avoid the risk of inadvertent disclosure of identities[ 5 , 25 ].

What are the potential pitfalls and how can these be avoided?

The case study approach is, as with all research, not without its limitations. When investigating the formal and informal ways undergraduate students learn about patient safety (Table 4 ), for example, we rapidly accumulated a large quantity of data. The volume of data, together with the time restrictions in place, impacted on the depth of analysis that was possible within the available resources. This highlights a more general point of the importance of avoiding the temptation to collect as much data as possible; adequate time also needs to be set aside for data analysis and interpretation of what are often highly complex datasets.

Case study research has sometimes been criticised for lacking scientific rigour and providing little basis for generalisation (i.e. producing findings that may be transferable to other settings)[ 1 ]. There are several ways to address these concerns, including: the use of theoretical sampling (i.e. drawing on a particular conceptual framework); respondent validation (i.e. participants checking emerging findings and the researcher's interpretation, and providing an opinion as to whether they feel these are accurate); and transparency throughout the research process (see Table 8 )[ 8 , 18 – 21 , 23 , 26 ]. Transparency can be achieved by describing in detail the steps involved in case selection, data collection, the reasons for the particular methods chosen, and the researcher's background and level of involvement (i.e. being explicit about how the researcher has influenced data collection and interpretation). Seeking potential, alternative explanations, and being explicit about how interpretations and conclusions were reached, help readers to judge the trustworthiness of the case study report. Stake provides a critique checklist for a case study report (Table 9 )[ 8 ].

Conclusions

The case study approach allows, amongst other things, critical events, interventions, policy developments and programme-based service reforms to be studied in detail in a real-life context. It should therefore be considered when an experimental design is either inappropriate to answer the research questions posed or impossible to undertake. Considering the frequency with which implementations of innovations are now taking place in healthcare settings and how well the case study approach lends itself to in-depth, complex health service research, we believe this approach should be more widely considered by researchers. Though inherently challenging, the research case study can, if carefully conceptualised and thoughtfully undertaken and reported, yield powerful insights into many important aspects of health and healthcare delivery.

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Acknowledgements

We are grateful to the participants and colleagues who contributed to the individual case studies that we have drawn on. This work received no direct funding, but it has been informed by projects funded by Asthma UK, the NHS Service Delivery Organisation, NHS Connecting for Health Evaluation Programme, and Patient Safety Research Portfolio. We would also like to thank the expert reviewers for their insightful and constructive feedback. Our thanks are also due to Dr. Allison Worth who commented on an earlier draft of this manuscript.

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What Is a Case Study? | Definition, Examples & Methods

What Is a Case Study? | Definition, Examples & Methods

Published on May 8, 2019 by Shona McCombes . Revised on November 20, 2023.

A case study is a detailed study of a specific subject, such as a person, group, place, event, organization, or phenomenon. Case studies are commonly used in social, educational, clinical, and business research.

A case study research design usually involves qualitative methods , but quantitative methods are sometimes also used. Case studies are good for describing , comparing, evaluating and understanding different aspects of a research problem .

When to do a case study, step 1: select a case, step 2: build a theoretical framework, step 3: collect your data, step 4: describe and analyze the case, other interesting articles.

A case study is an appropriate research design when you want to gain concrete, contextual, in-depth knowledge about a specific real-world subject. It allows you to explore the key characteristics, meanings, and implications of the case.

Case studies are often a good choice in a thesis or dissertation . They keep your project focused and manageable when you don’t have the time or resources to do large-scale research.

You might use just one complex case study where you explore a single subject in depth, or conduct multiple case studies to compare and illuminate different aspects of your research problem.

Case study examples
Research question	Case study
What are the ecological effects of wolf reintroduction?	Case study of wolf reintroduction in Yellowstone National Park
How do populist politicians use narratives about history to gain support?	Case studies of Hungarian prime minister Viktor Orbán and US president Donald Trump
How can teachers implement active learning strategies in mixed-level classrooms?	Case study of a local school that promotes active learning
What are the main advantages and disadvantages of wind farms for rural communities?	Case studies of three rural wind farm development projects in different parts of the country
How are viral marketing strategies changing the relationship between companies and consumers?	Case study of the iPhone X marketing campaign
How do experiences of work in the gig economy differ by gender, race and age?	Case studies of Deliveroo and Uber drivers in London

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Once you have developed your problem statement and research questions , you should be ready to choose the specific case that you want to focus on. A good case study should have the potential to:

Provide new or unexpected insights into the subject
Challenge or complicate existing assumptions and theories
Propose practical courses of action to resolve a problem
Open up new directions for future research

TipIf your research is more practical in nature and aims to simultaneously investigate an issue as you solve it, consider conducting action research instead.

Unlike quantitative or experimental research , a strong case study does not require a random or representative sample. In fact, case studies often deliberately focus on unusual, neglected, or outlying cases which may shed new light on the research problem.

Example of an outlying case studyIn the 1960s the town of Roseto, Pennsylvania was discovered to have extremely low rates of heart disease compared to the US average. It became an important case study for understanding previously neglected causes of heart disease.

However, you can also choose a more common or representative case to exemplify a particular category, experience or phenomenon.

Example of a representative case studyIn the 1920s, two sociologists used Muncie, Indiana as a case study of a typical American city that supposedly exemplified the changing culture of the US at the time.

While case studies focus more on concrete details than general theories, they should usually have some connection with theory in the field. This way the case study is not just an isolated description, but is integrated into existing knowledge about the topic. It might aim to:

Exemplify a theory by showing how it explains the case under investigation
Expand on a theory by uncovering new concepts and ideas that need to be incorporated
Challenge a theory by exploring an outlier case that doesn’t fit with established assumptions

To ensure that your analysis of the case has a solid academic grounding, you should conduct a literature review of sources related to the topic and develop a theoretical framework . This means identifying key concepts and theories to guide your analysis and interpretation.

There are many different research methods you can use to collect data on your subject. Case studies tend to focus on qualitative data using methods such as interviews , observations , and analysis of primary and secondary sources (e.g., newspaper articles, photographs, official records). Sometimes a case study will also collect quantitative data.

Example of a mixed methods case studyFor a case study of a wind farm development in a rural area, you could collect quantitative data on employment rates and business revenue, collect qualitative data on local people’s perceptions and experiences, and analyze local and national media coverage of the development.

The aim is to gain as thorough an understanding as possible of the case and its context.

In writing up the case study, you need to bring together all the relevant aspects to give as complete a picture as possible of the subject.

How you report your findings depends on the type of research you are doing. Some case studies are structured like a standard scientific paper or thesis , with separate sections or chapters for the methods , results and discussion .

Others are written in a more narrative style, aiming to explore the case from various angles and analyze its meanings and implications (for example, by using textual analysis or discourse analysis ).

In all cases, though, make sure to give contextual details about the case, connect it back to the literature and theory, and discuss how it fits into wider patterns or debates.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Normal distribution
Degrees of freedom
Null hypothesis
Discourse analysis
Control groups
Mixed methods research
Non-probability sampling
Quantitative research
Ecological validity

Research bias

Rosenthal effect
Implicit bias
Cognitive bias
Selection bias
Negativity bias
Status quo bias

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Methodology or method? A critical review of qualitative case study reports

Affiliations.

1 Faculty of Health Sciences, La Trobe Rural Health School, La Trobe University, Bendigo, Australia; [email protected].
2 Faculty of Health Sciences, La Trobe Rural Health School, La Trobe University, Bendigo, Australia.
PMID: 24809980
PMCID: PMC4014658
DOI: 10.3402/qhw.v9.23606

Despite on-going debate about credibility, and reported limitations in comparison to other approaches, case study is an increasingly popular approach among qualitative researchers. We critically analysed the methodological descriptions of published case studies. Three high-impact qualitative methods journals were searched to locate case studies published in the past 5 years; 34 were selected for analysis. Articles were categorized as health and health services (n=12), social sciences and anthropology (n=7), or methods (n=15) case studies. The articles were reviewed using an adapted version of established criteria to determine whether adequate methodological justification was present, and if study aims, methods, and reported findings were consistent with a qualitative case study approach. Findings were grouped into five themes outlining key methodological issues: case study methodology or method, case of something particular and case selection, contextually bound case study, researcher and case interactions and triangulation, and study design inconsistent with methodology reported. Improved reporting of case studies by qualitative researchers will advance the methodology for the benefit of researchers and practitioners.

Keywords: Case studies; health research; interdisciplinary research; literature review; qualitative research; research design.

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CASE STUDY METHODOLOGY: FUNDAMENTALS AND CRITICAL ANALYSIS

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This article presents the fundamentals of case study methodology. After a brief history, the presentation is based on a critical analysis to understand the role and the place of case study methodology in scientific research. Thus, both the advantages and the limits of this research method are discussed and the step-by- step procedure is presented and then exemplified in a clinical context.

KEYWORDS: case study research.

I. INTRODUCTION

1. A Brief History

The history of case study methodology as a scientific research procedure is marked by periods of ups and downs. The earliest use of this form of research can be related to psychophysics and medicine. In the United States, this methodology was most closely associated with the University of Chicago. In 1935, there was a public dispute between Columbia University professionals, who were championing the "scientific methods" (i.e., experiment), and the "Chicago School" (Tellis, 1997). The outcome seemed to be in favor of Columbia University and consequently the use of case study methodology as a scientific research method declined (Tellis, 1997).

However, in the 1960s, researchers were becoming concerned with the limitations of quantitative methods. Hence there was a renewed interest in case study, although the case study methodology is not a pure qualitative or quantitative method (Tellis, 1997).

Indeed, a quick PsycInfo based scientometric analysis confirms this history. From 1806 to 1969 about 1319 articles dealing with "case study" and about 11171 articles dealing with "experiment" were published; the ratio is about 1 to 9. From 1960 to present, about 23151 articles dealing with "case study" and about 46069 articles dealing with "experiment" have been published; the ratio is about 1 to 2, which proves an increased interest in this methodology in the psychological field.

The case study research method is defined as "an empirical inquiry that investigates a contemporary phenomenon within its real-life context, when the boundaries between phenomenon and context are not clearly evident, and in which multiple sources of evidence are used" (Yin, 1984, p. 23). Thus, case study methodology uses in-depth examination of single and/or multiple case studies, which provides a systematic way of approaching the problem, collecting and analyzing the data, and reporting the results.

Many proponents of case study methodology argue that it is a...

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Home » Case Study – Methods, Examples and Guide

Case Study – Methods, Examples and Guide

Table of Contents

A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation.

It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied. Case studies typically involve multiple sources of data, including interviews, observations, documents, and artifacts, which are analyzed using various techniques, such as content analysis, thematic analysis, and grounded theory. The findings of a case study are often used to develop theories, inform policy or practice, or generate new research questions.

Types of Case Study

Types and Methods of Case Study are as follows:

Single-Case Study

A single-case study is an in-depth analysis of a single case. This type of case study is useful when the researcher wants to understand a specific phenomenon in detail.

For Example , A researcher might conduct a single-case study on a particular individual to understand their experiences with a particular health condition or a specific organization to explore their management practices. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a single-case study are often used to generate new research questions, develop theories, or inform policy or practice.

Multiple-Case Study

A multiple-case study involves the analysis of several cases that are similar in nature. This type of case study is useful when the researcher wants to identify similarities and differences between the cases.

For Example, a researcher might conduct a multiple-case study on several companies to explore the factors that contribute to their success or failure. The researcher collects data from each case, compares and contrasts the findings, and uses various techniques to analyze the data, such as comparative analysis or pattern-matching. The findings of a multiple-case study can be used to develop theories, inform policy or practice, or generate new research questions.

Exploratory Case Study

An exploratory case study is used to explore a new or understudied phenomenon. This type of case study is useful when the researcher wants to generate hypotheses or theories about the phenomenon.

For Example, a researcher might conduct an exploratory case study on a new technology to understand its potential impact on society. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as grounded theory or content analysis. The findings of an exploratory case study can be used to generate new research questions, develop theories, or inform policy or practice.

Descriptive Case Study

A descriptive case study is used to describe a particular phenomenon in detail. This type of case study is useful when the researcher wants to provide a comprehensive account of the phenomenon.

For Example, a researcher might conduct a descriptive case study on a particular community to understand its social and economic characteristics. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of a descriptive case study can be used to inform policy or practice or generate new research questions.

Instrumental Case Study

An instrumental case study is used to understand a particular phenomenon that is instrumental in achieving a particular goal. This type of case study is useful when the researcher wants to understand the role of the phenomenon in achieving the goal.

For Example, a researcher might conduct an instrumental case study on a particular policy to understand its impact on achieving a particular goal, such as reducing poverty. The researcher collects data from multiple sources, such as interviews, observations, and documents, and uses various techniques to analyze the data, such as content analysis or thematic analysis. The findings of an instrumental case study can be used to inform policy or practice or generate new research questions.

Case Study Data Collection Methods

Here are some common data collection methods for case studies:

Interviews involve asking questions to individuals who have knowledge or experience relevant to the case study. Interviews can be structured (where the same questions are asked to all participants) or unstructured (where the interviewer follows up on the responses with further questions). Interviews can be conducted in person, over the phone, or through video conferencing.

Observations

Observations involve watching and recording the behavior and activities of individuals or groups relevant to the case study. Observations can be participant (where the researcher actively participates in the activities) or non-participant (where the researcher observes from a distance). Observations can be recorded using notes, audio or video recordings, or photographs.

Documents can be used as a source of information for case studies. Documents can include reports, memos, emails, letters, and other written materials related to the case study. Documents can be collected from the case study participants or from public sources.

Surveys involve asking a set of questions to a sample of individuals relevant to the case study. Surveys can be administered in person, over the phone, through mail or email, or online. Surveys can be used to gather information on attitudes, opinions, or behaviors related to the case study.

Artifacts are physical objects relevant to the case study. Artifacts can include tools, equipment, products, or other objects that provide insights into the case study phenomenon.

How to conduct Case Study Research

Conducting a case study research involves several steps that need to be followed to ensure the quality and rigor of the study. Here are the steps to conduct case study research:

Define the research questions: The first step in conducting a case study research is to define the research questions. The research questions should be specific, measurable, and relevant to the case study phenomenon under investigation.
Select the case: The next step is to select the case or cases to be studied. The case should be relevant to the research questions and should provide rich and diverse data that can be used to answer the research questions.
Collect data: Data can be collected using various methods, such as interviews, observations, documents, surveys, and artifacts. The data collection method should be selected based on the research questions and the nature of the case study phenomenon.
Analyze the data: The data collected from the case study should be analyzed using various techniques, such as content analysis, thematic analysis, or grounded theory. The analysis should be guided by the research questions and should aim to provide insights and conclusions relevant to the research questions.
Draw conclusions: The conclusions drawn from the case study should be based on the data analysis and should be relevant to the research questions. The conclusions should be supported by evidence and should be clearly stated.
Validate the findings: The findings of the case study should be validated by reviewing the data and the analysis with participants or other experts in the field. This helps to ensure the validity and reliability of the findings.
Write the report: The final step is to write the report of the case study research. The report should provide a clear description of the case study phenomenon, the research questions, the data collection methods, the data analysis, the findings, and the conclusions. The report should be written in a clear and concise manner and should follow the guidelines for academic writing.

Examples of Case Study

Here are some examples of case study research:

The Hawthorne Studies : Conducted between 1924 and 1932, the Hawthorne Studies were a series of case studies conducted by Elton Mayo and his colleagues to examine the impact of work environment on employee productivity. The studies were conducted at the Hawthorne Works plant of the Western Electric Company in Chicago and included interviews, observations, and experiments.
The Stanford Prison Experiment: Conducted in 1971, the Stanford Prison Experiment was a case study conducted by Philip Zimbardo to examine the psychological effects of power and authority. The study involved simulating a prison environment and assigning participants to the role of guards or prisoners. The study was controversial due to the ethical issues it raised.
The Challenger Disaster: The Challenger Disaster was a case study conducted to examine the causes of the Space Shuttle Challenger explosion in 1986. The study included interviews, observations, and analysis of data to identify the technical, organizational, and cultural factors that contributed to the disaster.
The Enron Scandal: The Enron Scandal was a case study conducted to examine the causes of the Enron Corporation’s bankruptcy in 2001. The study included interviews, analysis of financial data, and review of documents to identify the accounting practices, corporate culture, and ethical issues that led to the company’s downfall.
The Fukushima Nuclear Disaster : The Fukushima Nuclear Disaster was a case study conducted to examine the causes of the nuclear accident that occurred at the Fukushima Daiichi Nuclear Power Plant in Japan in 2011. The study included interviews, analysis of data, and review of documents to identify the technical, organizational, and cultural factors that contributed to the disaster.

Application of Case Study

Case studies have a wide range of applications across various fields and industries. Here are some examples:

Business and Management

Case studies are widely used in business and management to examine real-life situations and develop problem-solving skills. Case studies can help students and professionals to develop a deep understanding of business concepts, theories, and best practices.

Case studies are used in healthcare to examine patient care, treatment options, and outcomes. Case studies can help healthcare professionals to develop critical thinking skills, diagnose complex medical conditions, and develop effective treatment plans.

Case studies are used in education to examine teaching and learning practices. Case studies can help educators to develop effective teaching strategies, evaluate student progress, and identify areas for improvement.

Social Sciences

Case studies are widely used in social sciences to examine human behavior, social phenomena, and cultural practices. Case studies can help researchers to develop theories, test hypotheses, and gain insights into complex social issues.

Law and Ethics

Case studies are used in law and ethics to examine legal and ethical dilemmas. Case studies can help lawyers, policymakers, and ethical professionals to develop critical thinking skills, analyze complex cases, and make informed decisions.

Purpose of Case Study

The purpose of a case study is to provide a detailed analysis of a specific phenomenon, issue, or problem in its real-life context. A case study is a qualitative research method that involves the in-depth exploration and analysis of a particular case, which can be an individual, group, organization, event, or community.

The primary purpose of a case study is to generate a comprehensive and nuanced understanding of the case, including its history, context, and dynamics. Case studies can help researchers to identify and examine the underlying factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and detailed understanding of the case, which can inform future research, practice, or policy.

Case studies can also serve other purposes, including:

Illustrating a theory or concept: Case studies can be used to illustrate and explain theoretical concepts and frameworks, providing concrete examples of how they can be applied in real-life situations.
Developing hypotheses: Case studies can help to generate hypotheses about the causal relationships between different factors and outcomes, which can be tested through further research.
Providing insight into complex issues: Case studies can provide insights into complex and multifaceted issues, which may be difficult to understand through other research methods.
Informing practice or policy: Case studies can be used to inform practice or policy by identifying best practices, lessons learned, or areas for improvement.

Advantages of Case Study Research

There are several advantages of case study research, including:

In-depth exploration: Case study research allows for a detailed exploration and analysis of a specific phenomenon, issue, or problem in its real-life context. This can provide a comprehensive understanding of the case and its dynamics, which may not be possible through other research methods.
Rich data: Case study research can generate rich and detailed data, including qualitative data such as interviews, observations, and documents. This can provide a nuanced understanding of the case and its complexity.
Holistic perspective: Case study research allows for a holistic perspective of the case, taking into account the various factors, processes, and mechanisms that contribute to the case and its outcomes. This can help to develop a more accurate and comprehensive understanding of the case.
Theory development: Case study research can help to develop and refine theories and concepts by providing empirical evidence and concrete examples of how they can be applied in real-life situations.
Practical application: Case study research can inform practice or policy by identifying best practices, lessons learned, or areas for improvement.
Contextualization: Case study research takes into account the specific context in which the case is situated, which can help to understand how the case is influenced by the social, cultural, and historical factors of its environment.

Limitations of Case Study Research

There are several limitations of case study research, including:

Limited generalizability : Case studies are typically focused on a single case or a small number of cases, which limits the generalizability of the findings. The unique characteristics of the case may not be applicable to other contexts or populations, which may limit the external validity of the research.
Biased sampling: Case studies may rely on purposive or convenience sampling, which can introduce bias into the sample selection process. This may limit the representativeness of the sample and the generalizability of the findings.
Subjectivity: Case studies rely on the interpretation of the researcher, which can introduce subjectivity into the analysis. The researcher’s own biases, assumptions, and perspectives may influence the findings, which may limit the objectivity of the research.
Limited control: Case studies are typically conducted in naturalistic settings, which limits the control that the researcher has over the environment and the variables being studied. This may limit the ability to establish causal relationships between variables.
Time-consuming: Case studies can be time-consuming to conduct, as they typically involve a detailed exploration and analysis of a specific case. This may limit the feasibility of conducting multiple case studies or conducting case studies in a timely manner.
Resource-intensive: Case studies may require significant resources, including time, funding, and expertise. This may limit the ability of researchers to conduct case studies in resource-constrained settings.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What the Case Study Method Really Teaches

Nitin Nohria

Seven meta-skills that stick even if the cases fade from memory.

It’s been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study method excels in instilling meta-skills in students. This article explains the importance of seven such skills: preparation, discernment, bias recognition, judgement, collaboration, curiosity, and self-confidence.

During my decade as dean of Harvard Business School, I spent hundreds of hours talking with our alumni. To enliven these conversations, I relied on a favorite question: “What was the most important thing you learned from your time in our MBA program?”

Nitin Nohria is the George F. Baker Jr. and Distinguished Service University Professor. He served as the 10th dean of Harvard Business School, from 2010 to 2020.

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Case Study and Narrative Inquiry as Merged Methodologies: A Critical Narrative Perspective

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Introduction to the Nature of Critical Research The way in which we seek to understand the crisis in teachers’ work in this book is through Lather’s (1986) notion of ‘dialectical theory building’, drawing on the approaches of critical ethnography that are given expression here in ‘critical storied accounts’ of teachers’ work. We believe that the idea of dialectical theory building can best be pursued through a stereoscopic view of the relationship between labour process theory and the ideas of critical ethnography. The analysis of teaching needs to start out with the question of how teachers are being controlled (technically, bureaucratically and ideologically), as alluded to in Chapter 2. To fully understand the nature of the educational practices arrived at in different educational sites, requires a research methodology capable of providing depth as well as solidity. The idea of pursuing how the work of teaching is being reshaped, in a context of acknowledging the importance of accessing wider sets of social and political forces, has a good deal of methodological appeal to us.

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Open access
Published: 11 June 2024

Using unfolding case studies to develop critical thinking for Graduate Entry Nursing students: an educational design research study

Rachel Macdiarmid ORCID: orcid.org/0000-0003-4791-7417 1 ,
Eamon Merrick ORCID: orcid.org/0000-0003-4269-6360 2 , 3 &
Rhona Winnington ORCID: orcid.org/0000-0002-6504-2856 1

BMC Nursing volume 23 , Article number: 399 ( 2024 ) Cite this article

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Metrics details

Graduate Entry Nursing (GEN) programmes have been introduced as another entry point to nurse registration. In the development of a new GEN programme, a problem-based approach to learning was used to develop critical thinking and clinical reasoning skills of motivated and academically capable students.

To explore and evaluate the design and delivery of course material delivered to GEN students embedded in authentic learning pedagogy from the perspectives of both GEN students and academic staff using an unfolding case study approach.

An educational design research approach was used to explore the learning experiences of GEN students using an unfolding case study approach situated in experiential pedagogy and the teaching experiences of the academics who designed it. Data were collected through semi-structured interviews with students once they had finished the course and weekly reflective diary recordings by academic staff throughout implementation. Thematic analysis was used to analyse the data.

Student reflections highlighted that this cohort had insight into how they learned and were comfortable voicing their needs to academic staff. While the unfolding case studies were not liked by all participants, for some it offered a unique learning opportunity; particularly when scaffolded with podcasts, simulation labs, tutorials and clinical placements. Staff reflections primarily aligned with student experiences.

The gaps highlighted in the delivery of the course suggest that a blended pedagogical approach to graduate entry nurse education is required. Specifically, GEN students are aware of the learning needs and are happy to express these to academic staff, thus suggesting that engaging with a co-design curriculum approach will benefit future cohorts.

Peer Review reports

Graduate entry nursing students begin their degrees as experienced learners and must develop critical thinking skills within the shortened degree time frame.

What is already known

Graduate entry students are experienced and academically capable learners who begin with a diverse range of life and career experiences.

What this paper adds

Graduate entry students would benefit by being involved in curriculum design to acknowledge the unique skill set that they bring.

Introduction

Graduate Entry Nursing (GEN) degrees, or second degrees leading to eligibility for nursing registration, have recently been introduced to New Zealand. GEN students are known to be academically capable, motivated, and driven, bringing with them a range of life experiences, and have often had significant careers before enrolment [ 1 , 2 ]. Previous research has identified that teaching and learning methods must be carefully planned and innovative [ 1 ].

Pre-registration nursing education programmes prepare nursing students to provide safe nursing care with crucial skills expected of nursing graduates, including critical thinking and clinical reasoning. Clinical reasoning enables students to approach clinical issues with a problem-solving lens that relies on gathering assessment data and intervening and evaluating the patient’s response to the intervention [ 3 ].

Problem-Based Learning (PBL) aligns with the fundamental elements of authentic learning approaches [ 4 ], where learning is situated in real-world contexts [ 5 ]. Problem-based learning is considered to be an experiential teaching and learning approach that helps students develop a critical lens and clinical reasoning skills [ 6 , 7 ]. The use of PBL in nursing education is well established with previous research focused on students’ experiences and satisfaction [ 8 ]; factors that facilitate or hinder students' learning [ 9 ]; and the development of critical thinking skills [ 10 ].

Graduate entry nursing students report enjoyment of the active learning sets that enabled discussion surrounding case studies, scenarios, and practice issues [ 11 ]. Cangelosi’s [ 12 ] phenomenological study found that although time-poor, GEN students welcomed learning opportunities that were not traditional and facilitated their development and growth.

However, there is conflicting evidence regarding the effectiveness of PBL in nursing. For example, McCormick et al. [ 13 ] compared undergraduate student performance using differing teaching approaches, such as unfolding simulation scenarios versus recorded lectures and found these to be of benefit to students. Carter and Welch [ 14 ] compared the results of associate degree nursing students who attended lectures to those whose learning was informed by an unfolding case study. In contrast to McCormick’s et al.’s [ 13 ] earlier positive results, these authors found both groups of students performed worse in the post-test.

As previous research has identified that new graduate nurses do not always have critical thinking skills, using an unfolding case study approach can reflect the reality of clinical practice where not all the relevant information is known at the first encounter with the patient [ 14 , 15 , 16 ].

Nonetheless, while several studies have investigated the use of unfolding case studies in undergraduate preregistration programmes there is little evidence that supports the use of these with more academically capable GEN students. This article reports on a qualitative interpretivist study that used an educational design methodology to explore the experiences of GEN students who participated in the programme of learning and the experiences of the academics who designed it.

Educational Design Research (EDR) is an iterative, pragmatic, and reflective methodology well suited to small projects [ 17 ]. It has arisen from design-based research and can include both quantitative and qualitative data collection methods. EDR was selected as it fitted with our desire to develop new ways of teaching alongside gaining feedback from both academic staff and students. In the first phase of this research, we redesigned the teaching and learning strategies for a component of the GEN programme [ 18 ].

EDR has four phases (Table 1 ) [ 17 ]:

Aims and objectives

The study aimed to explore and evaluate the design and delivery of course material delivered to GEN students embedded in authentic learning pedagogy from the perspectives of both GEN students and academic staff using an unfolding case study approach.

Theoretical framework

To enable the development of clinical reasoning skills a scaffolded learning approach was implemented that involved unfolding case studies designed to represent the health needs of the New Zealand population, thus, encouraging critical thinking. Unfolding case studies reflective of situations that students might face in the future were used to encourage students to consider and analyse information, provoke further questioning and identify the information required to narrow their inquiries [ 14 , 15 ]. Supported by this evidence the academic staff built a learning environment where a regular teaching schedule (two days of lectures and one day of clinical labs per week), was complemented with online resources. Initial questions about the case study were provided on the learning management system. Students attended simulations where they responded to the case and answered questions critical to unpacking the ‘patients’ reality. Alongside the unfolding case studies were podcasts where experts were interviewed on topics related to the case. Tutorials enabled students to collaboratively construct answers and share their perspectives; at the end of each week students shared their answers in an online discussion forum.

Methods and setting

This study was conducted at an education facility in New Zealand offering undergraduate and GEN programmes. The participants are academics involved in the design and delivery of the course and one cohort of students of the GEN programme. This article reports on Phase 2 and 3 of the EDR approach, the academic staff’s reflective diary during course delivery, and students' feedback after the course was completed the first time. The methods were reported using the Consolidated Criteria for Reporting Qualitative Studies (COREQ) [ 19 ].

Participants

Purposeful sampling was used as the researchers were keen to explore the experiences of a specific GEN cohort [ 20 ]. Academic staff involved in the weekly reflective diaries are also the research team ( n = 3). All students in the identified cohort ( n = 7) were invited to participate, totalling ten possible participants. Student participants were approached via an advertisement on the university’s learning management system. Students were asked to contact the research assistant, who was separate from the academic staff and was not involved in the delivery of the GEN programme; five students agreed to participate. A $20 petrol voucher was offered to those who participated.

Data collection and analysis

In keeping with education design methodology, the authors met weekly to reflect on their experiences of delivering the content and guiding students. The weekly reflective conversations, between 60–90 min in length, followed a simple format of ‘what worked, what didn’t work, and what would we (as academic staff) change?’ Face to face student interviews were conducted by the research assistant at a time and place convenient to the students using semi-structured questions that were developed by the research team (see Additional file 1 ).

The semi-structured interviews ( n = 5) and reflective meetings ( n = 9) were recorded and transcribed verbatim by a research assistant who had signed a confidentiality agreement. All identifying information was deleted from the transcripts by the research assistant before the research team reviewed the data; each recording and transcript was allocated a unique identifier, for example ‘participant one’.

Thematic analysis [ 21 , 22 ] was used to analyse the data. First, the research team independently read the transcribed interviews to familiarise themselves with the data and identified initial codes. Second, the researchers met and reviewed all transcripts to identify themes and reached consensus on the themes emerging from the data. Themes were established once more than 50% of the participants stated the same issue/thought/perception. A matrix was developed whereby common themes were identified, with quotes demonstrating the themes collated to establish an audit trail.

Reflexivity

Central to this study given the proximity of staff to this student cohort, a reflexive stance was essential. Reflexivity is an engendered practice and was used in this instance not to influence the direction and outcome of the research but to allow the researchers to engage in the data to produce viable and valuable outcomes for future staff and students. Specifically, this reflexive practice provided a means for the research to be rigorous through the consideration of the vulnerability of the participating student cohort, thus inciting reflection-before-action [ 23 ].

Ethical considerations

Ethical approval for this study was obtained from the Auckland University of Technology Ethics Committee (AUTEC) (19/233). Given the potential power differential in the student/staff relationship present, participants were approached via an online advertisement and followed up by an independent research assistant. This is key to the success of the project, as such research undertakings have the potential for conflict of interest to exist [ 24 ]. The academic staff recordings were also undertaken with the knowledge that these would remain confidential to the participants and transcriber only, with a memorandum of understanding completed to this effect. Participant information sheets were given to students interested in joining the study to ensure they knew what it entailed and how their safety and identity would be managed. Written consent was obtained before the interviews were undertaken, with oral consent obtained at the beginning of each interview.

Three dominant themes emerged, which focused on the experiences of both GEN students and teaching staff. These were:

Reflective learning: Students and staff ability to clarify what worked and what did not work

Evaluation of learning: Students and staff being insightful about their ways of learning and needs

Challenges: Planning and delivering appropriate content for GEN students is challenging for teaching staff.

Within these overarching themes, subthemes were developed and will be presented in the following data results (Table 2 ).

Reflective learning

The exploration of student and staff experiences and responses to the unfolding case studies unearths what worked and what was problematic for both parties.

Unfolding case study as problem-based approach

The student experiences of using an unfolding case study approach were divided. Some students enjoyed the case scenarios but did not necessarily find them beneficial in terms of knowledge advancement as.

“ I personally, like the case studies but personally I didn’t really find that they enhanced my learning in like the clinical setting ” (P1)

or that they were relevant to clinical practice in that.

“… some of it was definitely relatable but I just found it was very different in the clinical setting compared with doing this theoretical case setting ” (P1).

A second student supported this idea that the case studies did not add practical clinical knowledge value as.

“ I mean for me the case studies weren’t challenging…I didn’t think the case studies added anything extra into my practice, they didn’t challenge my clinical reasoning or anything like that ” (P2).

Of note was that those students with previous professional healthcare backgrounds found the use of an unfolding case study approach problematic in that.

“ I found that quite a challenge. I think because with my clinical background I was sort of going straight into, yeah like I wanted more information so you know I probably would have preferred…to have a different case study every week or have all the information…and I’d be like well what about this, what about that? ” (P5).

Participant One, however, noted that while the case studies may not have added knowledge value, they were helpful at times as.

“ …one example is we learnt about arterial blood gases and then I was on placement I came across that literally [on] day one, so was really nice to be able to put something that I’d learnt in class into practice ” (P1).

While some students were less keen on the case study approach and found them hard work, others thought they provided opportunities to encourage discussion, clinical reasoning, and autonomous thinking as.

“ there was no right or wrong answer, you just had to prove your point to say I think it is this because of this, and someone else can say something else and just kind of still prove it because it was a quite grey [area] but I actually found that it really got us thinking ” (P3).

Moreover, the same participant acknowledged that.

“…I think that’s the whole idea of the course [GEN Programme] because at this level they shouldn’t be spoon-feeding you…you should be able to think for yourself and reason things out ” (P3).

Although some discord was present with regard to the case study approach, one participant did acknowledge the value of being able to break down a huge scenario into manageable sections to enhance understanding and clinical decision-making, as.

“ when you break it down it makes it easier to kind of work out what you’re going to do and what steps you’re going to do ” (P4), and that “ because you start looking at the smaller things that you need to do rather than just the big bits ” (P4).

It appears, however, that staff involved in the programme of learning were pleased with the overall notion that problem-based learning approach offered a ‘practical’ means through which to discuss what is the hands-on job of nursing. Specifically,

“ the second session around child abuse and recognising child abuse…took me a bit by surprise as I wasn’t expecting that to go very well and it went extraordinarily well, mostly because it was case based again and story based ” (L1).

Moreover, with regard to encouraging discussion and clinical reasoning at a postgraduate level,

“ I think we’ve really pulled out the difference [of] what we’re expecting of them [GEN students] as opposed to what they may have been used to” (L1).

Use of podcasts

While the use of technology is not necessarily a completely new strategy in tertiary education, here we have linked podcasts recorded with experts in their fields which related to the unfolding case studies, Again, however, there was division in the value of podcast recordings, with some students really enjoying them, saying.

“ I liked the podcasts yeah, I found the podcasts really good especially when there was [sic] different people talking about it, yeah...podcasts are good, like to just chuck on in the car or at the gym ” (P2).

Moreover, some found them easy to listen to because.

“… it’s a different way to learn because like you’ve got YouTube videos and you’ve got books and stuff but podcasts are kind of like easy ” (P2).

Some students found the podcasts particularly engaging saying.

…I just remember listening to it and I think I was in the car and I had stopped because I was on my way home…and I was still listening to it in the garage like when I was home and I was like oh this is a really interesting podcast ” (P2).

Participant three also thought podcasts a positive addition to the resources saying.

“ yeah they were helpful…there was one I listened to…they were talking about dying…I know that [one of the lecturers’] kind of research is kind of talking about death, euthanasia and all this kind of thing, and for some reasons, I don’t know why, maybe that’s why I still remember, I can say it’s the only podcast I really listened to and it was really good because it gave me a good insight as to what is happening… ” (P3)

This positive response was also noted in face-to-face class time as one staff member reported that.

“ they [the students] loved the person who was interviewed, and the feedback was it was really nice to hear a conversation about different perspectives ” (L1).

Yet, not all students were of this opinion, with some advising the podcasts were too long (approximately 60 min each), that they can be distracting, that they preferred videos and images or an in-person discussion, saying.

“ I find podcasts…I tend to switch off a bit, a bit quicker than if I was watching something, I would probably prefer, rather than watching a podcast [sic] I’d rather have an in-class discussion with the person” (P4).

Participant one said that they too struggled with podcasts because.

“ I’m more visual so I like to look at things and see like a slide I guess or what they’re talking about or, so I sort of zone out when it’s just talking and nothing to look at, so that’s what I personally struggle with, they [podcasts] are helpful it’s just I’m more a visual learner ” (P1).

While there were some negative responses to the podcasts, another participant acknowledged their value but offered their own solutions to learning, saying that.

“ I listened to a few podcasts that were put up, because they’re just easy to listen to ” (P2).

but felt that overall there were insufficient resources made available to students and therefore.

“ just went to YouTube and just, any concepts that I was unfamiliar with or stuff in class that we went over and when I went home I was like [I have] no idea what they talked about, I just found my own videos on YouTube… ” (P2).

Evaluation of learning

Learning experiences are unique to each GEN student, as are those experienced by the teaching staff. The data collected highlighted this clearly from both perspectives, offering a particularly strong insight into how this cohort of students’ function.

Approaches to learning

It was evident that these GEN students were aware of their approach to learning and that perhaps the structure of the teaching module did not align with their needs as.

“ I’m not really the best at utilising online things I’m a really hands on learner and things like a lecture…but you know if it’s yeah, more like class time, it’s sort of more my, my learning style [I] guess ” (P5).

A number of students were able to identify that they were visual learners as.

“ I use videos more because I guess I’m more of a visual learner as well and I learn better by seeing things instead of reading a huge article, I think that [videos] it helps me a bit more” (P4).

Another student, however, preferred a discussion based approach as opposed to either videos or podcasts saying that.

“ if it’s interesting, if it’s a topic that you can like relate to [through a podcast] or something it’s fine, but for me I just switch off not really taking a lot of the information [in] whereas in a discussion setting you can ask questions and you can interact with the person, yeah I find that would be a bit more helpful ” (P4).

This approach to learning through discussion was also noted when the teaching staff reflected on their experiences in that in one teaching session the GEN students.

“ were engaged, they were round a table with the second speaker talking and what I think enabled the discussion was that she [the speaker] was using her data as stories and so she was reading them, actually she got them [the students] to read them out” (L3).

The notion of learning styles, however, was not as linear as being visual or auditory or practical, as one student noted that a combination of styles was preferable to enhance learning, saying that.

“ if we weren’t able to have lectures like a recorded lecture so that there was a PowerPoint and just someone actually talking you through it, like I know there’s the YouTube videos…some of them were a little bit helpful, but like I just felt that sometimes we missed the teaching aspect of it. There’s a lot of self-directed stuff but definitely like a recorded lecture every week to go along with the readings and extra videos to watch ” (P5).

Students as insightful and engaged

While GEN students are known for their tenacity and ability to cope with the pressure and fast paced delivery, some students discovered that this did not necessarily equate with their preferred approach to learning. This cohort of GEN students were insightful in terms of their strengths and weaknesses in relation to knowledge acquisition. The use of the unfolding case studies, however, caused some frustrations as.

“ for me it was challenging in the fact that I felt I actually got frustrated because I’m thinking well I want to know this, I want to know that and yeah not getting all the information that I wanted at the time ” (P5).

This participant went further, saying that.

“ I definitely found that difficult [lack of information] I felt like [I] wasn’t getting as much information as I wanted to be able to make my clinical decisions ” (P5),

however this may have been due to the student’s background as their.

“my background is in paramedicine ” where “ we get a lot of information in a very short amount of time ” (P5).

Some fundamental issues were raised by the participants in terms of how much study is required for them to acquire the new knowledge. As one student highlighted,

“ I have a really terrible memory, so I kind of need to listen to things a few times or write it down and then watch a video and do some more reading and then like it’s good having another element to get into your brain you know ” (P2).

For one student, a solution to this was to ensure they did their preparation before attending class as.

“ you’re supposed to have read these things before coming to class, some people don’t but my kind of person, I’d read before coming to class and I tended to answer those questions so the critical, analytical part of me would be trying to find out and come up with a reasonable answer…” (P3).

For another participant, they took an alternative pathway to learning as they.

“ I just watch it and I don’t take [it in], it just sits in the back of my head because sometimes it’s building on top of previous knowledge so just, I just watch it to see if I can gain anything from that, I don’t necessarily take down notes or anything, but I just watch it so that it’s there you know ” (P4).

The pace of content delivery appeared problematic for some students, especially in relation to the practical sessions, with one student highlighting that.

“ personally I didn’t’ really like it and most of the time they were rushing, I was always like can I write this down to go back home to like really make sense of it and then sometimes obviously, sometimes I would have to say can I stay back and practice this thing again [as] I didn’t grab it as quickly as others did and the essence of the labs is that it’s grab all of these things ” (P3).

Challenges: Teaching staff experiences of GEN student learning

While on the whole the teaching staff were able to gauge the learning needs of this GEN cohort, the expectations of both parties did not always align, with one staff member reporting that.

“ the two biggest challenges was [sic] getting them [the students] to unpack already learned behaviour and [to] acknowledge their own limitations or bias ” (L1),

however by the end of the semester the same staff member reported that.

“ I think we made a lot of progress in getting them to acknowledge how they learn ” (L1).

Moreover, the challenges anticipated in teaching GEN students were not those that transpired in that.

“ I actually thought going into the first paper I was pretty excited as to how it was going to roll out, the problems I encountered were not the problems I anticipated ” (L3).

The vocality of this cohort was tangible, however, when content did not meet their needs, interest or expectations with the students saying,

“ that they didn’t do the materials because it wasn’t of interest to them and requested other teaching very much related to the assignment as opposed to anything else …” (L1).

It was expected that the GEN students would be participatory both in class and online irrespective of their ways of learning, but there was a difference in both responses and comfort with this form of engagement. One student that talked about the unfolding case study and the online component of assessment as being problematic said that.

“.. we had to put up about 250 words of something related to the case study every week and then we spoke to someone else, [I] didn’t really like the responses…I didn’t really like having to respond to someone else ” (P3).

Yet in contrast to this statement, the teaching staff were delighted that.

“…actually I got some fantastic questions from one of the students…emailed to me on Monday night about the case that was online for them, questions that I didn’t talk about in [the] lecture, I didn’t introduce the concept…they’re talking about concepts that are currently undergoing international clinical trials” (L1).

This study explored the experiences of both GEN students and academics using unfolding case studies situated in experiential learning pedagogy. The use of unfolding case studies supported with podcasts embraced our idea of developing content situated in real-life contexts. Learning was scaffolded using different teaching approaches such as podcasts, and experiential simulated learning, to offer learners multiple ways of engaging with content. Scaffolding is recognised as learning material being broken into smaller chunks of learning and in this way aligns with case-based learning [ 25 ]. In this way, we hoped that not only would students engage in problem-solving, and develop clinical decision-making skills [ 26 , 27 ], but that they would also achieve deep and lifelong learning and ultimately have an ‘aha’ moment when it all made sense.

Reflections on using an unfolding case study approach

Findings were divided, with some students enjoying the unfolding case studies and others describing them as not sufficiently challenging. The scaffolded learning approach that we developed incorporated a range of teaching approaches that enabled them to engage with the content in a way that fitted in with their lifestyle, even if the teaching method did not align with their individual learning preferences. Students reported differing views about the case studies; some enjoyed the unfolding nature while others wanted more context and direction to feel that they could make an informed clinical decision. Nonetheless, even though they did not like information being presented in smaller chunks one student recognised it meant they analysed the information they received more deeply.

Other learning tools such as podcasts were not always valued by participants and yet, the fact that students were able to provide feedback on their use does indicate that they at least attempted to engage with them.

Student reflections indicate that perhaps the use of unfolding case studies as a learning approach is not the solution to engagement, and that often more traditional teaching methods were preferred Indeed, Hobbs and Robinson’s [ 28 ] study of undergraduate nursing students in the US supported Carter and Welch’s [ 14 ] findings that the use of unfolding case studies were of no direct benefit, whilst Ellis et al.’s., [ 29 ] study confirmed that for final year nurse practitioner students unfolding case studies were beneficial in developing critical thinking and stimulating clinical reasoning. Considering these two conflicting findings, further consideration is needed of how to engage highly motivated GEN students.

As such, our results suggest it can be difficult to predict the needs of the GEN students given the diversity of their previous academic qualifications, career, and often significant life experience they bring to the programme [ 30 , 31 ]. Interestingly students in this study simultaneously demonstrated insight into their needs supporting their previous academic study experience and felt sufficiently secure to voice them, which supports evidence found in D’Antonio et al.’s [ 32 ] study. This suggests that GEN students’ capabilities need to be embraced and incorporated when planning curriculum and scaffolding learning. Anecdotally, we have found that students embrace experiential learning such as that offered in simulation labs whether this involves the use of simulated manikins or not, it seems the hands-on learning offers not only the opportunity to experience simulated reality but also fosters collaboration and problem solving with peers that enables them to dwell in learning of what it is to be a nurse.

Graduate entry students recognised as experienced learners

Our students were not overwhelmingly supportive of the pedagogical approach of unfolding case studies we adopted. As previously recognised GEN students are experienced learners and whilst having differing educational backgrounds bring individual experience and knowledge of their own approach to their learning. Nonetheless, the value of their previous learning experience appears problematic in that those learned behaviours and attitudes need to be refocused to engage with learning how to become a nurse, as demonstrated in the academic staff reflections. Despite this background experience and perceived confidence, some students reflected that online engagement that involved exploring the case studies in discussion forums with colleagues was uncomfortable. This was surprising to the academic staff and contrasted sharply with their reflections on the activity but has been previously noted by Boling et al., [ 33 ].

Implications

Given the disparity that exists between student and academic staff experiences, as demonstrated in our study, co-designing content delivery may offer a progressive solution. By engaging ‘students as partners’ it offers them a much deeper level of involvement in future teaching delivery through collaboration and reciprocation of ideas, thus culminating in appropriate curriculum design [ 34 ]. Collaborating with students in course design might facilitate students learning as they become cognisant of the active engagement of academic staff [ 9 , 10 , 35 ]. In the future, we aim to involve students in any curriculum review and course development to ensure their perspectives influence curriculum design and content delivery.

Even so, our initial intention of scaffolding learning by offering different ways for students to engage with content is supported by recent research by Dong et al. [ 36 ] who found that students performed better academically in a flipped classroom. This point, in association with our findings, suggests that the best approach to content delivery for graduate entry nursing students is to ensure students are involved in curriculum and course design alongside the delivery of learning experiences that are well facilitated and supported by faculty so that students are aware of the expectations, required of them, and importantly how they will be assessed.

Limitations

We acknowledge that the sample size in this study is small in terms of generalisability. However, our findings offer interesting, detailed and in-depth insights into the experiences and needs of both GEN students and the academic staff involved in the development and delivery of educational material. Further work needs to be undertaken to evaluate the experiences of GEN students from a range of educational providers. A longitudinal study has been undertaken to explore the motivations and experiences of GEN students in Australasia [ 37 ], which will also support these findings regarding the learning needs of GEN students.

This study has provided a platform through which academics and GEN students can share their insights of teaching and learning experiences. The results offer a clear insight into what these students expect and need to expedite their learning and how teaching staff must respond. While participants' views were somewhat mixed in relation to the use of unfolding case studies and scaffolded learning these results demonstrate how GEN students are aware of their personal ways of learning and how this translates in terms of education needs. The sharing of these experiences provides an insightful lens through which to re-evaluate pedagogical approaches for GEN students. As such, we suggest that to meet the needs of GEN student’s not only is a blended pedagogical approach appropriate but expanding education design boundaries further through a co-design focused approach to GEN programme design.

Availability for data and materials

The datasets generated and analysed during the current study are not publicly available due privacy and ethical restrictions of the participants, but are available from the corresponding author on reasonable request.

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Macdiarmid, R., Merrick, E. & Winnington, R. Using unfolding case studies to develop critical thinking for Graduate Entry Nursing students: an educational design research study. BMC Nurs 23 , 399 (2024). https://doi.org/10.1186/s12912-024-02076-8

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Research Article

Rehabilitation of brachial plexus injury in contact sport: Where are the data that underpin clinical management? A scoping review

Roles Conceptualization, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft

Affiliation School of Health and Social Care, Edinburgh Napier University, Edinburgh, United Kingdom

Roles Data curation, Methodology, Visualization, Writing – review & editing

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Affiliations School of Health and Social Care, Edinburgh Napier University, Edinburgh, United Kingdom, MAHD National Sports Academy, Saudi Arabia

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Affiliations School of Health and Social Care, Edinburgh Napier University, Edinburgh, United Kingdom, Research Centre for Health, Glasgow Caledonian University, Glasgow, United Kingdom

Rebecca Armstrong,
Tom McKeever,
Michael Leavitt,
Colin McLelland,
David F. Hamilton

Published: June 24, 2024
https://doi.org/10.1371/journal.pone.0298317
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Although a common injury there is a lack of published primary data to inform clinical management of sports related brachial plexus injuries.

A systematic search was completed in Medline, CINAHL, PubMed, SPORTDiscus and Web of Science databases and Google Scholar from inception to August 2023 according to the PRISMA-ScR guidelines. Methodological quality assessment of included articles was with the Joanna Briggs Institute tool. Studies providing primary data as to the rehabilitative management of diagnosed or suspected brachial plexus injuries sustained when playing contact sports were included.

Sixty-five studies were identified and screened, of which, 8 case reports were included, incorporating 10 participants with a mean age of 19.8 (±4.09) years. There was wide heterogeneity in injury severity, injury reporting, physical examination and imaging approaches documented. 9 of 10 participants returned to competitive sports, though follow-up periods also varied widely. Whilst return to play criteria varied between studies, the most consistent indicator was pain-free shoulder range of motion and strength.

Conclusions

There is a distinct lack of data available to inform evidence-based rehabilitation management of sports related brachial plexus injury. Only 8 individual case reports contain published data reporting on 10 athletes. Further reporting is critical to inform clinical management.

Citation: Armstrong R, McKeever T, Leavitt M, McLelland C, Hamilton DF (2024) Rehabilitation of brachial plexus injury in contact sport: Where are the data that underpin clinical management? A scoping review. PLoS ONE 19(6): e0298317. https://doi.org/10.1371/journal.pone.0298317

Editor: Esedullah Akaras, Erzurum Technical University: Erzurum Teknik Universitesi, TURKEY

Received: September 1, 2023; Accepted: January 22, 2024; Published: June 24, 2024

Copyright: © 2024 Armstrong et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Contact sports, such as American football, rugby, or wrestling, by nature, expose participants to physical trauma and can be defined as “a sport in which participants come into bodily contact with another” [ 1 ]. Tackling is an inherent part of these sports and the techniques employed typically involves contact with the opponent via the arm/shoulder, necessarily risking injury to the neck and shoulder region and, by extension, the brachial plexus.

The brachial plexus is a complex anatomical structure, comprising spinal nerves and their terminal branches in the upper extremity [ 2 ]. This includes the C5,6,7,8 and T1 spinal nerves, which provide crucial sensory and motor innervation to the muscles of the upper limb to provide normal function [ 3 ]. Brachial Plexus Injury (BPI) is comparatively rare in the general population, typically occurring in relation to road traffic accidents [ 4 ]. In these instances of high velocity impact, the patient’s injuries can be significant resulting in axonotmesis (axonal damage and Wallerian degeneration) or neurotmesis (complete transection of the nerve), require surgical exploration and intervention [ 5 , 6 ].

Severe BPI injuries involving axonotmesis or neurotmesis are rare in contact sports, however neuropraxia injuries (preserved axonal integrity), which are characterised by transient sensory or motor loss, are comparatively commonplace [ 7 ]. Neuropraxia related BPIs are so well known that the colloquial terminology ‘stinger’ and ‘burner’ used in common sports parlance [ 8 ].

There are three primary mechanisms of injury to the brachial plexus in contact sport; direct compression of the brachial plexus at the supraclavicular region, traction injury due to depression of the ipsilateral shoulder with concomitant side flexion of the neck to the contralateral shoulder, and cervical nerve root compression due to hyperflexion or hyperextension of the neck [ 9 , 10 ]. Any mechanism can result in any severity of injury and the sequalae of these can vary hugely, depending on the degree of nerve damage, ranging from spontaneous resolution to significant functional limitation [ 11 ].

Contact sport governing bodies have made efforts to reduce the risk of cervical spine injuries through rule changes, such as in American football, where ‘spear tackling’ (associated with traction injury) has been banned and players now encouraged to tackle with a ‘head up’ position to limit neck hyperflexion [ 12 ]. Rugby administrators have also banned ‘spear tackling’ and are actively engaged in reducing the contact aspects of the game to mitigate serious collision-based trauma [ 13 ]. Despite this, contact sport athletes remain at risk of BPI due to the inherent tackling positions and impact forces accepted during play [ 10 ]. Multiple studies suggest a ‘stinger’ injury rate of around 2 per 10,000 athlete-exposures in American football [ 14 , 15 ], and cohort studies have reported that more than half of American football players suffered a BPI during their career [ 16 , 17 ]. Similarly, Kawasaki et al. found that in a cohort of 569 rugby players, 33% reported a history of BPI, with a re-injury rate of 37% [ 18 ]. Injury recovery periods were generally short but varied in this group, with 80% reporting full recovery the same day though 6% reported symptoms that lasted beyond 2-weeks. The wider impact of BPI is likely under-reported in this population due to the well know reluctance of athletes to self-report symptoms [ 14 , 19 ].

The primary management of neuropraxia/stinger injuries is through non-operative rehabilitation. Effective rehabilitation management is paramount to minimise the risk of long-term complications or injury recurrence [ 20 ]. Accepting a range in severity of presentation, and requirements of rehabilitation based on individual case presentation, no accepted or recognised rehabilitation management protocols exist for sports related BPI [ 7 , 21 – 23 ]. As such, the rehabilitative management of an individual BPI injury remains somewhat ambiguous, with return to play decisions often difficult for sports team medical staff to make [ 23 ]. Athletes that report full symptom resolution are generally returned to the field at the next opportunity following this, though prolonged symptomology or recurrent injuries may trigger additional cervical imaging and wider diagnostics to inform further management [ 21 , 24 ].

Local rehabilitation approaches will vary and rely on; the injury presentation, the athletes reporting of symptomology, access to clinical diagnostic imaging, and on clinician experience. The quality of the underlying evidence-base which inform the various rehabilitation interventions employed is unclear, with recent reviews of stinger management noting only generic rehabilitative techniques such as ‘stretching’ ‘strengthening’ and ‘electrical stimulation techniques’[ 7 ], or nebulous concepts such as postural correction and myofascial release [ 9 ], with very limited evidence referenced to support these interventions. As such, the aim of this study was to evaluate primary data underlying rehabilitative management approaches for BPI management in contact sport.

A scoping review of the literature was undertaken in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews (PRISMA-ScR) guidelines ( S1 Checklist ) [ 25 ]. Our protocol is available via the open science framework https://osf.io/b6ptu which included our design framework and search strategy (documented here as supplemental data).

Information sources and search strategy

We applied the population, concept, and context (PCC) criteria to inform our search strategy that aimed to find articles that reported the rehabilitative management of brachial plexus injuries sustained while playing contact sports (S2A Table in S1 File ). The search strategy was devised in conjunction with a specialist librarian and an electronic search of the following databases was conducted from inception to 21 st August 2023 in Medline, PubMed, CINAHL, SPORTDiscus and Web of Science. Boolean operators were employed in the searches as detailed in the supplemental data (S2B File in S1 File ). We applied an English language restriction but no other filters to the search. Manual searches of Google Scholar and citation searching of included manuscripts were also completed.

Eligibility criteria and study selection

Due to the nature of the research question, all study types were considered for inclusion provided they were published as peer-reviewed articles and contained relevant information. We sought articles that provided primary data as to the rehabilitative management of diagnosed or suspected brachial plexus injuries sustained when playing contact sports. Contact sport was defined as a sport in which participants come into bodily contact with another [ 1 ]. All grades of competitor were considered, high school, collegiate or professional level. As we were looking for the data underpinning rehabilitative management, we excluded expert opinion or narrative articles that commented on the topic without providing underpinning data to support positions, and also material that was not available as a full text publication, such as conference abstracts.

A three-part screening strategy was employed to identify relevant articles. Two investigators independently carried out the searches and screened by title. Abstracts were reviewed independently by the same two investigators and consensus reached for full text inclusion. In the event of disagreement, or doubt, manuscripts were included for full text review. Full texts were reviewed by the same two reviewers independently and final selection agreed by consensus with a third independent reviewer.

Data extraction and synthesis

We extracted data as to rehabilitation management and also relevant contextual data around the injury history, presentation, diagnosis, diagnostics and outcomes. The following characteristics of each study were extracted to a bespoke excel database: Author, year of publication, country of publication, study type, number of subjects, clinical presentation (mechanism of injury, injury history, physical examination), Additional imaging and diagnostic findings, diagnosis given, medical/surgical management, rehabilitative treatment interventions, return to play recommendations, follow-up timelines and outcomes. Data extraction forms were created, and 2 researchers independently extracted the data from included articles. The extracted data was cross-checked by a third researcher to ensure consistency. The case report nature of the information collected prohibited formal pooling of data. As such the results are presented descriptively.

Quality assessment

To assess internal validity and risk of bias, the Joanna Briggs Institute (JBI) critical appraisal tool for case reports was utilised [ 26 ]. The tool comprises of an 8-point checklist addressing study design and reporting, with: Yes, No, Unclear and Not applicable selection options for each component. There are no accepted thresholds for case report study inclusion within a systematic review, [ 27 ] however, Dekkers et al. [ 28 ] emphasise that the completeness of this quality assessment tool relates to case report reliability. We report the results in this context.

The literature search generated one-hundred and thirty-three articles (Medline:27; PubMed: 15; CINAHL:19; SPORTDiscus:32; Web of Science:35; Google Scholar:5) Following the removal of duplicates, 65 papers were evaluated against the eligibility criteria. After screening, only nine were eligible for full text review, with the majority of exclusions being expert opinion articles. One article was not accessible due to a broken hyperlink, and a further two were conference abstracts. Seven of these were eligible for inclusion alongside a single further article that was found through citation searching of the included publications, bringing the final number of included publications to eight. Full details are displayed in the PRISMA flowchart ( Fig 1 ).

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https://doi.org/10.1371/journal.pone.0298317.g001

Study characteristics

All the included articles were case reports. One article discussed three individual cases [ 29 ], bringing the total number of individuals included in the eight articles to ten athletes. The mean age of subjects was 19.8 years (±4.09). Five individual participants were American football players. [ 11 , 29 – 32 ] two were wrestlers, [ 29 , 33 ] two were rugby union players, [ 34 , 35 ] and one was a basketball player [ 29 ]. Six of the eight reports were from America, [ 11 , 29 – 33 ] one from New Zealand [ 35 ] and one from Italy [ 34 ].

Mechanism of injury and symptomology

Injury characteristics and presentation timelines varied ( Table 1 ). The most commonly stated injury mechanisms were traction (n = 4) [ 11 , 31 – 33 ] and compression(n = 3) [ 29 , 30 , 35 ] the three other reports detailing no clear mechanism. Five participants were reported to have sustained recurrent BPI injuries, [ 11 , 30 – 32 , 34 ] while five were first time presentations [ 29 , 33 , 35 ]. The symptomology recorded primarily incorporated burning pain and altered sensation in the upper limb, [ 11 , 29 – 31 , 33 , 34 ] alongside motor weakness in the upper limb [ 11 , 29 – 31 , 33 – 35 ]. Traction injuries caused biceps brachii motor weakness in all 4 cases [ 11 , 31 , 33 , 34 ] and muscle atrophy in the deltoid region was reported in 3 of 4 cases, [ 11 , 29 , 34 ] whereas compression injuries led to rotator cuff weakness in all 3 cases [ 29 , 30 , 35 ].

https://doi.org/10.1371/journal.pone.0298317.t001

Imaging, diagnoses and surgical interventions

Imaging modalities were reported in seven of the ten cases ( Table 1 ). Radiographs in 4/10, [ 30 , 31 , 33 , 35 ] MRI in 7/10, [ 11 , 30 – 35 ] CT myelogram in 1/10 [ 11 ] and arthrogram in 1/10 [ 29 ]. EMG reports were generated for 6/10 [ 11 , 29 , 32 , 34 ] participants and abnormal nerve conduction in the upper limb musculature was noted in all of these cases. Saliba et al. [ 11 ] utilised the EMG reports to guide rationale for surgical intervention, while the remaining studies utilised EMG reports to guide RTP and for injury prognostics [ 29 , 32 , 34 ].

The injuries and diagnoses reported differed between all case, and were described variously as; ‘Recurrent stinger injuries’, [ 32 ] ‘Brachial plexus neuropraxia’, [ 30 ] ‘Avulsion of C5 and C6 nerve roots’, [ 11 ] ‘Grade 2 Burner’, [ 31 ] ‘Acute Brachial Plexus Neuropathy’, [ 29 ] ‘Postfixed brachial plexus’, [ 33 ] ‘Brachial plexus injury’, [ 35 ] Traumatic paresis of the axillary nerve [ 34 ]. Two cases required surgical intervention [ 11 , 33 ]. with the rest receiving primary rehabilitative management [ 29 – 32 , 34 , 35 ].

Treatment and rehabilitation approach

In all cases, treatment was by both acute and rehabilitative management phases, however the reporting of acute timeframes differed between cases ( Table 2 ). Acute interventions varied, but entailed soft tissue inflammation management, including: cold therapies, [ 11 , 31 , 33 ] rest, [ 29 , 32 , 33 ] use of a hemi-sling, [ 11 , 30 ] soft tissue therapies [ 31 , 33 , 35 ] and cervical mobilisation [ 30 ]. All four cases reporting traction injuries applied cold therapy and rest and/or contraindication of strengthening exercises [ 11 , 33 , 34 ]. All reports describing the management of compression injuries however reported the use of strengthening as an acute treatment approach [ 29 , 30 , 35 ]. Only one study reporting a compression injury reported the use of self-stretching as an acute treatment approach [ 30 ]. The management approaches described in the subsequent rehabilitation stage also varied, encompassing: maintenance of cardiovascular fitness, [ 31 , 33 , 35 ] strengthening of shoulder musculature [ 29 – 31 , 33 – 35 ] strengthening of cervical musculature, [ 31 , 32 ] and scapular stabilisation exercises [ 31 , 34 ].

https://doi.org/10.1371/journal.pone.0298317.t002

Follow-up, outcomes and return to play recommendations

There was no consistent approach across the included papers towards follow-up timelines, outcome reporting or return to play guidance (RTP) ( Table 2 ). The follow-up timescales reflected assessments from 16-days to 5-years post-injury. 1 study did not state a follow-up report [ 30 ]. Objective improvements in clinical presentation were generally noted across the case studies, and nine of ten individuals returned to unrestricted participation in sport [ 29 – 35 ]. RTP recommendations and the criteria for RTP clearance varied between studies. Pain-free shoulder range of motion and strength was the most commonly used indicator for the RTP decision [ 29 – 31 ].

The methodological quality of the studies varied ( Table 3 ). 4/8 studies met all 9 of the evaluation criteria, [ 11 , 31 , 33 , 34 ] and a further 3/8 met at least 75% of items [ 29 , 30 , 35 ]. 1 study failed to reach 50% of the reporting criteria [ 32 ]. Due to the limited availability of published primary research data, the decision was made to include all of these case studies in the review irrespective of reporting quality. Out with the case-report reporting quality assessment evaluation, the complexity of the brachial plexus injury characteristics and variation in management approaches resulted in poorly generalisable data.

https://doi.org/10.1371/journal.pone.0298317.t003

Despite the potential for severe and debilitating outcomes resulting from BPI in the sporting athlete, there is a distinct lack of evidence supporting rehabilitation management approaches or the rationale for setting return to play criteria. Through systematic review of the published primary data for BPI rehabilitation in contact sport, only eight reports, representing ten individual case studies were found. No trials, cohort studies, or even retrospective registry-based studies are available to inform clinical management, which then, necessarily, is driven by expert opinion and the application of basic rehabilitation principles.

There is a substantial difference in scale as to the management approaches required for differently presenting brachial plexus injuries. Transient ‘stingers’ may be isolated events that essentially self-resolve with no need for active treatment, whereas severe neural injuries can require significant medical intervention. Conservative management and rehabilitation will be the typical intervention for mild-modest neuropraxia based injury, whereas the most severe cases involving neural compromise or separation may require surgical intervention and result in disability. The 10 case reports we found reflected this range of presentation.

No two studies applied the same diagnostic terminology making it hard to draw parallels across the cases. Standardisation of injury evaluation and documentation would facilitate pooling of future data to inform clinical management. Variation was also seen in clinical assessment approaches, the use of imaging/ EMG, and in the diagnostic terminology applied. Interestingly, the use of EMG was reported in a number of cases, and used to determine nerve innervation recovery and to inform return to play decisions. It is unlikely that the use of nerve conduction evaluation is reflective of ‘routine’ return to play management following BPI for contact sports players outside of elite sports settings, or those being treated through specialist centres. This may be the result of ‘interesting cases’ being reported in the literature, that may not reflect the more routine situation in clinical practice. This lack of context, and potentially quite limited generalisability, results that caution must applied as to the representativeness of the pooled findings we present for the scant literature in this area.

Despite heterogeneity in injury and in the utilised assessment diagnostics, an interesting finding of this review is differential presentation and subsequent rehabilitative management of compression and traction-based injury. Compression injuries resulted in rotator cuff weakness, whilst traction injuries were associated with biceps brachii weakness. In the acute phases of treatment, traction injuries were managed conservatively, including cold therapy, rest and/or the contraindication of strengthening exercises, whilst athletes with compression injuries were encouraged to participate in active rehabilitation at an earlier stage. Rehabilitative management follows clinical presentation and this likely reflects clinicians treating what they find, as opposed to any specific rationale for differential management of different injury patterns, as this is not otherwise reported.

We are unable to comment on the injury mechanisms leading to BPI, as details as to impact received and the setting of this are scantly reported. There are no specific notes of adverse events or reactions to rehabilitation management. Reported outcomes were generally positive and athletes returned to play following rehabilitation with no ongoing issues in 9 of 10 cases recorded. The exception being the single case involving nerve root evulsion at C5/C6, which, despite surgery, resulted in disability that impeded return to sports and substantially affected the individual’s quality of life. Follow-up timeframes of the cases varied substantially though, and was often limited to a few months following injury. A narrative ‘return to play’ was reported, with little objective context around this statement. Rather ambiguous terminology was also used in relation to the RTP criteria applied, an example being the restoration of ‘normal’ cervical strength [ 4 , 29 ].

Whilst wider literature suggests a comparatively high prevalence of BPI in the contact sport athlete, primary data as to how to manage this injury remains unpublished or unavailable to the scientific community and practising therapists. As highlighted by multiple authors, under-reporting is a concern, and whilst transient symptoms may be a factor, one must question the rigour of current sporting injury data capture and reporting. BPIs will be managed at pitch-side and then in sports and community rehabilitation settings, or they will require medical escalation. From the lack of published data found, it seems no one is publishing the data and progress of these athletes following intervention. Injury reporting remains unpopular for high profile players and teams, who often rely on specific individuals to play when injured or not at their physical peak. Whilst injury data is closely guarded by sporting teams, for fear of competitive disadvantage, the lack of any published data restricts developments and improvements in sporting injury management. Specialist sports treatment centres or national sporting programmes are also likely to collect the BPI injury data that we sought in our review and the scientific community would benefit from publication of injury management and outcomes. The current situation is that rehabilitation professionals lack evidence-based diagnostic criteria, intervention guidelines, reporting guidelines, clinical outcome selection, and return to play criteria for the range of BPI presentations in collision sports.

Limitations

There are various limitations to this work. Despite wide review of four major databases and Google Scholar using pragmatic search terms and a rigorous review methodology, it is possible that relevant articles were missed. We suggest that any further such primary data as to the rehabilitation of sports related BPI is well-hidden, and not readily available to the practicing clinician. Case studies were the only sources that contained relevant data as to rehabilitation interventions for BPI. While disappointing, this is a major finding of this review. As case study data is relied on, we accept the risk that various selection and reporting biases may have influenced the cases presented and rehabilitation themes discussed. The ten case reports included in this review regarded the management of contact sports athletes, with seemingly enhanced access to diagnostic testing, assessment and treatment, it is likely these do not well represent of the wider experience of amateur athletes. Further, these may not be truly representative of the wider management of BPI in contact sport, as published case reports usually pertain to particularly interesting or challenging presentations, or those employing particular diagnostic or treatment technologies. Notably, eight of the ten cases included were reports from the United States of America and health care management may not be widely generalisable.

Although thought to be a relatively common injury, there is a lack of consensus guidance as to the clinical management of sports related brachial plexus injuries. The published primary data as to rehabilitation of sports related BPI is very poor, essentially consisting of 8 case reports relating to 10 individuals. Further data reporting is critical to inform clinical management. Alongside vastly more data, standardised methods of assessment, diagnostic testing, outcome evaluation and reporting across the spectrum of severity of BPI presentation are all needed to facilitate rehabilitation and return to play guidance.

Supporting information

S1 checklist. preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (prisma-scr) checklist..

https://doi.org/10.1371/journal.pone.0298317.s001

S1 File. Search strategy.

https://doi.org/10.1371/journal.pone.0298317.s002

Acknowledgments

We express our gratitude to Maria King, specialist librarian, Edinburgh Napier University, for her assistance with the search parameters.

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Open access
Published: 20 June 2024

Circulating small extracellular vesicles in Alzheimer’s disease: a case–control study of neuro-inflammation and synaptic dysfunction

Rishabh Singh 1 ,
Sanskriti Rai 1 ,
Prahalad Singh Bharti 1 ,
Sadaqa Zehra 1 ,
Priya Kumari Gorai 2 ,
Gyan Prakash Modi 3 ,
Neerja Rani 2 ,
Kapil Dev 4 ,
Krishna Kishore Inampudi 1 ,
Vishnu V. Y. 5 ,
Prasun Chatterjee 6 ,
Fredrik Nikolajeff 7 &
Saroj Kumar 1 , 7

BMC Medicine volume 22 , Article number: 254 ( 2024 ) Cite this article

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Alzheimer’s disease (AD) is a neurodegenerative disease characterized by Aβ plaques and neurofibrillary tangles. Chronic inflammation and synaptic dysfunction lead to disease progression and cognitive decline. Small extracellular vesicles (sEVs) are implicated in AD progression by facilitating the spread of pathological proteins and inflammatory cytokines. This study investigates synaptic dysfunction and neuroinflammation protein markers in plasma-derived sEVs (PsEVs), their association with Amyloid-β and tau pathologies, and their correlation with AD progression.

A total of 90 [AD = 35, mild cognitive impairment (MCI) = 25, and healthy age-matched controls (AMC) = 30] participants were recruited. PsEVs were isolated using a chemical precipitation method, and their morphology was characterized by transmission electron microscopy. Using nanoparticle tracking analysis, the size and concentration of PsEVs were determined. Antibody-based validation of PsEVs was done using CD63, CD81, TSG101, and L1CAM antibodies. Synaptic dysfunction and neuroinflammation were evaluated with synaptophysin, TNF-α, IL-1β, and GFAP antibodies. AD-specific markers, amyloid-β (1–42), and p-Tau were examined within PsEVs using Western blot and ELISA.

Our findings reveal higher concentrations of PsEVs in AD and MCI compared to AMC ( p < 0.0001). Amyloid-β (1–42) expression within PsEVs is significantly elevated in MCI and AD compared to AMC. We could also differentiate between the amyloid-β (1–42) expression in AD and MCI. Similarly, PsEVs-derived p-Tau exhibited elevated expression in MCI compared with AMC, which is further increased in AD. Synaptophysin exhibited downregulated expression in PsEVs from MCI to AD ( p = 0.047) compared to AMC, whereas IL-1β, TNF-α, and GFAP showed increased expression in MCI and AD compared to AMC. The correlation between the neuropsychological tests and PsEVs-derived proteins (which included markers for synaptic integrity, neuroinflammation, and disease pathology) was also performed in our study. The increased number of PsEVs correlates with disease pathological markers, synaptic dysfunction, and neuroinflammation.

Conclusions

Elevated PsEVs, upregulated amyloid-β (1–42), and p-Tau expression show high diagnostic accuracy in AD. The downregulated synaptophysin expression and upregulated neuroinflammatory markers in AD and MCI patients suggest potential synaptic degeneration and neuroinflammation. These findings support the potential of PsEV-associated biomarkers for AD diagnosis and highlight synaptic dysfunction and neuroinflammation in disease progression.

Peer Review reports

The progressive neurodegenerative condition known as Alzheimer’s disease (AD) is characterized by cognitive decline as a result of the formation of amyloid-β (Aβ) plaques, neurofibrillary tangles (NFTs), and chronic neuroinflammation that leads to neurodegeneration [ 1 , 2 , 3 ]. Synapse loss is a crucial pathophysiological event in disease progression, and synaptic proteins have been extensively studied due to earlier perturbations [ 4 , 5 ]. The pathological hallmark of AD, amyloid-β plaques, originates from the imprecise cleavage of the amyloid precursor protein (APP) by β-secretase (BACE1) and γ-secretase generating amyloid-β peptide forms [ 6 , 7 , 8 , 9 ]. Primary amyloid-β peptide forms are Aβ40 and Aβ42, where the majority of the amyloid-β plaques in AD brains are composed of Aβ42 [ 10 ]. Many point mutations in APP and γ-secretase cause familial early-onset AD, favoring Aβ42 formation, causing amyloid-β peptides prone to aggregate as fibrils and plaques [ 9 , 11 , 12 , 13 , 14 ]. Hyperphosphorylation of tau causes the formation of NFTs. The combined effect of accumulation of NFTs, amyloid-β fibrils, and plaques leads to neuronal function loss and cell death [ 15 , 16 ]. Aβ plaques activate immune receptors on microglia, thereby releasing pro-inflammatory cytokines and chemokines that mediate neuroinflammation, which, if it reaches a chronic level, causes damage to brain cells, including axonal demyelination and synaptic pruning [ 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. In addition to these, other proteins, including the neurofilament light (NFL) protein, glial fibrillary acidic protein (GFAP), and synaptic proteins, have also been identified as AD biomarkers [ 24 , 25 , 26 , 27 , 28 ]. Understanding the intricate dynamics of AD in terms of its varied pathophysiological manifestations, such as neuroinflammation, synaptic loss, and proteinopathy, is essential for developing potential therapeutic interventions for AD and biomarker discovery. In clinical practice, cognitive assessment tools such as the Addenbrooke’s Cognitive Examination (ACE-III) and Mini-Mental State Examination (MMSE) are used to diagnose AD. These tools evaluate verbal fluency and temporal orientation, although results may be influenced by subject bias [ 29 , 30 , 31 ].

In recent years, small extracellular vesicles (sEVs) or exosomes have been acknowledged as crucial mediators of communication and signaling within the body, contributing significantly to the transmission of cellular cargo in various health and disease states. They also play a notable role in disseminating protein aggregates associated with neurodegenerative diseases [ 32 ]. sEVs are bi-layered membrane vesicles that have a heterogeneous group of (< 200 nm in diameter) that are found in different human body fluids, including blood, urine, saliva, and ascites, and that are actively released by all cell types [ 33 , 34 , 35 ]. For their functions in various physiological and pathological circumstances, sEVs are the most extensively researched type of EV [ 36 , 37 , 38 ]. sEVs exchange information between cells by transferring bioactive components (nucleic acids and proteins) [ 39 ]. As the sEVs’ composition bears the molecular signature of the secreting cell and bears an intrinsic property of transversing the blood–brain barrier (BBB) in both directions [ 40 , 41 ], they are a target of constant research in neurodegenerative disease. Furthermore, sEVs released by neuronal cells are crucial in transmitting signals to other nerve cells, influencing central nervous system (CNS) development, synaptic activity regulation, and nerve injury regeneration. Moreover, sEVs exhibit a dual function in neurodegenerative processes, as sEVs not only play an essential role in clearing misfolded proteins, thereby exerting detoxifying effects and providing neuroprotection [ 42 ]. On the other hand, they also have the potential to participate in the propagation and aggregation of misfolded proteins, particularly implicated in the pathological spread of Tau aggregates as indicated by both in vitro and in vivo studies [ 43 ]. As a protective mechanism, astrocytes (most abundant glial cells) accumulate at the locations where Aβ peptides are deposited, internalizing and breaking down aggregated peptides [ 44 ]. However, severe endosomal–lysosomal abnormalities arise in astrocytes when a significantly large amount of Aβ accumulates within astrocytes for a prolonged period without degradation [ 45 , 46 ]. Astrocytes then release engulfed amyloid-β (1-42) protofibrils through exosomes, leading to severe neurotoxicity to neighboring neurons [ 44 ]. Additionally, it has been found that the release of amyloid-β by microglia in association with large extracellular vesicles (Aβ-lEVs) damages synaptic plasticity and modifies the architecture of the dendritic spine [ 47 ]. Thus, sEVs can be a compelling subject for the investigation to understand AD’s inflammation and synaptic dysfunction [ 48 , 49 , 50 , 51 , 52 ].

In this study, we reported that protein levels are associated with AD pathology, neuroinflammation, and synaptic dysfunction in plasma-derived small extracellular vesicles (PsEVs). Our objective was to understand the pathophysiological process, neuroinflammation, synaptic dysfunction, and Aβ pathology through sEVs. Our study revealed a significant correlation between the concentration of cargo proteins derived from PsEVs and clinical diagnosis concerning ACE-III and MMSE scores. Furthermore, the levels of these studied proteins within PsEVs could differentiate between patients with MCI and AD. Thus, our study sheds light on the potential of PsEVs in understanding AD dynamics and offers insights into the underlying mechanisms of disease progression.

Subject recruitment

A total of n = 35 AD patients and n = 25 subjects with MCI were recruited from the Memory Clinic, Department of Geriatrics, All India Institute of Medical Sciences, New Delhi, India. Additionally, n = 30 healthy AMC (volunteers) were recruited. The inclusion criteria were as follows: a clinical diagnosis of MCI and AD patients using ACE-III and MMSE tests. The exclusion criteria encompass medical conditions such as cancer, autoimmune disorders, liver disease, hematological disorders, or stroke, as well as psychiatric conditions, substance abuse, or any impediment to participation. Controls were healthy, age-matched adults without neurological symptoms. AMC was 60–71, MCI was 65–79, and AD was 70–80 years of age range (Table 1 ). Neuropsychological scores, viz., ACE-III and MMSE, were recorded before subject selection.

Study ethical approval

The institutional ethics committee of All India Institute of Medical Sciences, New Delhi, India, granted the study ethical permission. The study has been granted the ethical approval number IECPG-670/25.08.2022. Following the acquisition of the written informed consent, all participants were enrolled.

Sample collection

One milliliter of blood was drawn from each participant using venipuncture, and blood collection vials were kept on ice during collection. The blood was centrifuged at 1700 g for 20 min at 4 °C to remove the cells, and the straw-colored plasma was collected. It was further clarified by centrifuging for 30 mi at 4 °C at 10,000 g. Finally, cleared plasma was stored at − 80 °C until further use. The samples were used for the downstream experiment after being thawed on ice and centrifuged at 10,000 g.

Isolation of PsEVs

The PsEVs were extracted by chemical-based precipitation from the plasma samples of AD patients, MCI patients, and AMC, as discussed previously [ 53 , 54 ]. In brief, 180 μL of plasma sample was used and filtered with 0.22 μm filter (SFNY25R, Axiva), followed by overnight incubation with the chemical precipitant (14% polyethylene glycol 6000) (807,491, Sigma). The samples underwent an hour-long, 13,000 g centrifugation at 4 °C the next day. Before being resuspended in 200 μL of 1X PBS (ML116-500ML, HiMedia), the pellet was first cleaned twice with 1X PBS. Before downstream experiments, the sEVs-enriched fraction was further filtered through a 100-kDa filter (UFC5100, Millipore).

Nanoparticle tracking analysis (NTA)

5000-fold dilution in 1X-PBS buffer was used for the NTA of PsEVs. In the ZetaView Twin system (Particle Metrix, Germany) sample chamber, 1 mL of diluted PsEVs sample was introduced. The following parameters were used throughout three cycles of scanning 11 cell locations each, and 60 frames per position were collected (video setting: high, focus: autofocus, shutter: 150, 488 nm internal laser, camera sensitivity: 80, cell temperature: 25 °C. CMOS cameras were used for recording, and the built-in ZetaView Software 8.05.12 (Particle Metrix, Germany) was used to analyze: 10 nm as minimum particle size, 1000 nm as maximum particle size, and 30 minimum particle brightness.

Transmission electron microscopy for morphological characterization

Transmission electron microscopy was employed to investigate PsEVs’ ultrastructural morphology. The resultant PsEVs pellet was diluted with PBS using 0.1 M phosphate buffer (pH 7.4). A carbon-coated copper grid of 300 mesh (01843, Ted Pella) was used to adsorb the separated PsEVs at room temperature for 30 min. After blot-drying, the adsorbed grids were dyed. For 10 s, 2% aqueous uranyl acetate solution (81,405, SRL Chem) as negative staining. After blotting the grids, they were inspected using a Talos S transmission electron microscope (ThermoScientific, USA).

Western blot

Based on the initial volume of biofluid input, all samples were normalized, i.e., 180 μL and the sample loading dye (2 × Laemmle Sample buffer) was mixed with PsEVs sample, and 20 μL equal volume was loaded to run on an 8–12% SDS PAGE [ 53 , 55 ]. After the completion of SDS-PAGE, protein from the gel was subjected to the Wet transfer onto the PVDF membrane of 0.22 μm (1,620,177, BioRad). The membrane-blocking with 3% bovine serum albumin (BSA) (D0024, BioBasic) in Tris (TB0194, BioBasic) base saline containing 0.1% of Tween 20 (65,296, SRL Chem) (TBST) using the BioRad Western blotting apparatus (BioRad, USA). Following this, overnight incubation of primary antibodies of CD63 (10628D, Invitrogen), CD81 (PA5-86,534, Invitrogen), TSG101 (MA1-23,296, Invitrogen), L1CAM (MA1-46,045, Invitrogen), synaptophysin (ADI-VAM-SV011-D, Enzo life sciences), GFAP (A19058, Abclonal), amyloid-β (1–42) oligomer (AHB0052, Invitrogen), phospho-Tau (s396) (35–5300, Invitrogen), interleukin 1β (IL-1β) (PA5-95,455, Invitrogen), tumor necrosis factor α (TNF-α) (E-AB-33121, Elabscience), and β-actin (AM4302, Invitrogen) were done at 4 °C. The membranes were washed with TBST buffer four times before at RT incubating with HRP-conjugated secondary antibodies, anti-rabbit (AB6721, Abcam), anti-mouse (31,430, Invitrogen). The Femto LUCENT™ PLUS-HRP kit (AD0023, GBiosciences) was used to develop the blot for visualizing the protein bands utilizing the method of enhanced chemiluminescence.

Enzyme-linked Immunosorbent Assay (ELISA)

According to the previous protocol, ELISA was carried out. [ 53 ]. PsEV samples were subjected to freeze–thaw cycles; next, PsEVs were ultrasonicated for two minutes, with a 30-s on-and-off cycle, at an amplitude of 25. Following this, they underwent a 10-min centrifugation at 10,000 g, at 4 °C, and the obtained supernatant was used. The samples were kept at 37 °C before loading into the ELISA plates. The bicinchoninic acid (BCA) protein assay kit (23,225, ThermoFisher Scientific) was used to quantify the total protein concentration using BSA (D0024, BioBasic) as a reference. The ELISA kit was used to detect the presence of protein in 100 μL of PsEV sample are as follows: amyloid-β (1–42) (E-EL-H0543, ELabsciences), p-Tau (s-396) (E-EL-H5314, ELabsciences), IL-1β (ITLK01270, GBiosciences), TNF-α (ITLK01190, GBiosciences), GFAP (E-EL-H6093, ELabsciences), and synaptophysin (E-EL-H2014, ELabsciences). The manufacturer’s instructions were followed for every step of the process. A 96-well microplate spectrophotometer (SpectraMax i3x Multi-Mode Microplate Reader, Molecular devices) was used to measure the absorbance at 450 nm.

Data and statistical analysis

The mean age values, ACE-III score, and MMSE score were ascertained using descriptive statistical analysis Table 1 . GraphPad Prism 8.0 was used for statistical data analysis, including NTA concentration, Western blotting densitometric analysis, and ELISA. Unpaired student t -test and ANOVA were used for group analysis, and statistical significance was determined. p < 0.05 was used to assess significance. The Image J software (NIH, USA) was used for the densitometry analysis. The receiver operating characteristic (ROC) curve was used to analyze the efficiency of distinguishing the case from controls. Correlation analysis was conducted between the concentration of PsEVs and the levels of ELISA proteins, including amyloid-β (1–42), p-Tau, IL-1β, TNF-α, GFAP, and synaptophysin, and additionally between the PsEVs-derived levels of amyloid-β (1–42) β1-42, p-Tau, IL-1β, TNF-α, GFAP, and synaptophysin with ACE-III and MMSE values. ROC curve is a probability curve utilized to assess the accuracy of a test. The test’s ability to distinguish between groups is indicated by the area under the curve (AUC), which acts as a quantitative measure of separability. An outstanding test typically exhibits an AUC close to 1, signifying a high level of separability. Conversely, a subpar test tends to have an AUC closer to 0, indicating a poor ability to distinguish between the two classes.

Characterization and validation of isolated sEVs

PsEVs were isolated, characterized, and validated following Minimal Information for Studies of Extracellular Vesicles (MISEV) 2018 guidelines, which suggest a protocol for documenting work specifically with extracellular vesicles [ 56 ]. PsEVs from AMC, MCI, and AD subjects were morphologically characterized by transmission electron microscopy, and spherical lipid bi-layered vesicles were observed in the size range of sEVs (Fig. 1 A–C). In Fig. 1 D–F, the size distribution and concentration of PsEVs were observed in the size range of 30–200 nm in diameter by NTA, which is within the sEVs’ size range. The mean concentration of PsEVs in AMC, MCI, and AD patients were 5.12E + 10, 2.6E + 11, and 3.13E + 11 particle/ml, respectively, with higher concentrations of PsEVs in MCI and AD than in AMC ( p < 0.0001) (Fig. 1 G). To differentiate AD from AMC, ROC and AUC analyses were performed where the AUC = 0.9748, with a sensitivity of 97.14% and specificity of 70.01% (Fig. 1 H), while in AMC versus MCI, AUC = 0.987, sensitivity of 96% and specificity of 86.67% (Fig. 1 I). Furthermore, we could also differentiate between MCI and AD, AUC = 0.629, sensitivity of 60%, and specificity of 56% (Fig. 1 J). Validation of PsEVs was done using immunoblot for sEVs-specific markers (CD63, CD81, and TSG101), which showed a significant increase in expressions in MCI and AD than in AMC (CD63, p = 0.0489, 0.0478 (Additional File 1 : Fig. S1); CD81, p = 0.0172, 0.0133 (Additional File 1 : Fig. S2); TSG101 p = 0.0240, 0.0329 (Additional File 1 : Fig. S3)) for AD and MCI respectively (Fig. 2 A–D). Additionally, higher L1CAM (neuron-associated marker) expression was observed in MCI ( p = 0.0100) and AD ( p = 0.0184) (Additional File 1 : Fig. S4) compared to AMC (Fig. 2 E). All densitometric values were normalized against β-actin, which was used as a loading control (Additional File 1 : Fig. S7).

Isolation and analysis of PsEVs. The isolated PsEV morphology characterize by transmission electron microscopy from age-matched healthy controls (AMC) ( A ), mild-cognitive impairment (MCI) patients ( B ), and Alzheimer’s disease (AD) ( C ). The size distribution of PsEVs subpopulation (nm) versus the concentration (particle/ml) in AMC ( D ), individuals with MCI ( E ), and AD ( F ). Comparison of the sEVs concentration of AD, MCI, and AMC patients ( G ). Receiver operating characteristic (ROC) curve of PsEVs concentration in AMC v/s AD ( H ), AMC v/s MCI ( I ), and MCI v/s AD ( J ) (scale bar 100 nm)

Validation of PsEVs expression analysis of different markers in PsEVs in age-matched controls (AMC), mild cognitive impairment (MCI), and Alzheimer’s disease patients (AD) ( A ). Densitometric analysis of CD63 ( B ), densitometric analysis of CD81 ( C ), densitometric analysis of TSG101 ( D ), densitometric analysis of L1CAM ( E ), densitometric analysis of synaptophysin ( F ), densitometric analysis of GFAP ( G ), and densitometric analysis of amyloid-β (1–42) oligomer ( H ). All densitometric values were normalized against β-actin

Differential expression of amyloid-β (1–42), p-Tau, synaptophysin, GFAP markers, and levels of IL-1β and TNF-α in PsEVs

Using ELISA, we measured levels of amyloid-β (1–42) and p-Tau in PsEVs from AMC, MCI, and AD patients. The significant increase of amyloid-β (1–42) and p-Tau among the groups (Fig. 3 A–H). Amyloid-β (1–42) levels were higher in MCI compared to AMC ( p < 0.0001) and more significant in AD than in MCI and AMC ( p < 0.0001) (Fig. 3 A). Similarly, in comparison to MCI and AMC, p-Tau levels were significantly higher in AD ( p < 0.0001) (Fig. 3 E). Similar levels of both markers were found in their Western blots (Fig. 2 ). We checked GFAP (astrocytic marker) and proinflammatory cytokines (TNF-α and IL-1β) to evaluate neuroinflammation. For proinflammatory markers, IL-1β and TNF-α levels showed a significant increase among the three groups ( p < 0.0001 for IL-1β and TNF-α) (Fig. 3 I, M). When comparing AD to MCI and AMC, the GFAP concentration in PsEVs was significantly higher ( p < 0.0001) (Fig. 3 Q). Similar trends were observed with Western blot analysis (Fig. 2 , Additional File 1 : Fig. S6, S9). Their elevated levels suggest prominent neuroinflammatory conditions contributing to potential neuronal damage. The elevated levels of these neuroinflammatory markers could be due to the activation of astrocytes and microglia and the subsequent increase in the secretion of PsEVs containing proinflammatory proteins, which suggests prominent neuroinflammatory conditions that may contribute to neuronal damage [ 57 ]. While synaptophysin concentration in PsEVs was downregulated in AD and MCI compared to AMC ( p < 0.0001) in ELISA (Fig. 3 U), it shows synaptic dysfunction. We also checked synaptophysin levels in PsEVs in Western blotting, finding it was downregulated in AD compared to MCI and AMC ( p = 0.0045, 0.0142), indicating synaptic degeneration in AD (Fig. 2 , Additional File 1 : Fig. S5). In MCI, synaptophysin levels did not significantly differ from AMC (Fig. 2 F). This aligns with synaptic loss in AD, reflected in lower neuropsychological test scores indicating more pronounced cognitive impairment compared to MCI and AMC.

PsEVs derived amyloid-β (1–42), p-Tau, IL-1β, TNF-α, GFAP, and synaptophysin protein concentration was measured. ELISA results in A shows levels of PsEVs amyloid-β (1–42) in AMC, MCI, and AD and receiver operating characteristic (ROC) curve of PsEVs concentration in AMC v/s MCI ( B ), AMC v/s AD ( C ), and MCI v/s AD ( D ). Similarly, p-Tau concentration in AMC, MCI, and AD ( E ), ROC curve of PsEVs concentration in AMC v/s MCI ( F ), AMC v/s AD ( G ), and MCI v/s AD ( H ). PsEVs derived IL-1β concentration in AMC, MCI and AD ( I ), ROC curve of PsEVs concentration in AMC v/s MCI ( J ), AMC v/s AD ( K ), and MCI v/s AD ( L ). PsEVs derived TNF-α concentration in AMC, MCI and AD ( M ), ROC curve of PsEVs concentration in AMC v/s MCI ( N ), AMC v/s AD ( O ), and MCI v/s AD ( P ). Similarly, GFAP concentration in AMC, MCI, and AD ( Q ), ROC curve of PsEVs concentration in AMC v/s MCI ( R ), AMC v/s AD ( S ), and MCI v/s AD ( T ). For PsEVs-derived synaptophysin concentration in AMC, MCI, and AD ( U ), ROC curve of PsEVs concentration in AMC v/s MCI ( V ), AMC v/s AD ( W ), and MCI v/s AD ( X ). Abbreviations: AMC, age-matched control; MCI, mild-cognitive impairment patients; AD, Alzheimer’s disease patients; TNF-α, tumor necrosis factor-alpha; GFAP, glial fibrillary acidic protein

Determining the diagnostic potential of PsEVs-derived amyloid-β (1–42), p-Tau, IL-1β, TNF-α, GFAP and synaptophysin

We observed the levels of amyloid-β (1–42) and p-Tau in PsEVs, where the increase in amyloid-β (1–42) and p-Tau levels underscores their potential as biomarkers of MCI and AD. The diagnostic efficacy of amyloid-β (1–42) by ROC analysis was observed for AMC vs MCI [AUC = 0.9347, p < 0.0001, sensitivity (Sn) = 92%, specificity (Sp) = 80%] (Fig. 3 B), AMC vs AD (AUC = 0.9862, p < 0.0001, Sn = 91.43%, Sp = 96.67%) (Fig. 3 C), and MCI vs AD (AUC of 0.8457, p < 0.0001, Sn = 80%, and Sp = 72%) (Fig. 3 D). Similarly, diagnostic efficacy of p-Tau by ROC analysis was observed for AMC vs MCI (AUC = 0.8760, p < 0.0001, Sn = 88%, Sp = 83.33%) (Fig. 3 F), AMC vs AD (AUC = 0.9757, p < 0.0001, Sn = 94.29%, Sp = 83.33%) (Fig. 3 G), and MCI vs AD (AUC of 0.9074, p < 0.0001, Sn = 88.57%, and Sp = 92%) (Fig. 3 H). So, we observed that the pathological hallmarks of the disease, viz., amyloid-β (1–42) and p-Tau levels, are increased significantly in PsEVs cargo of AD and MCI groups.

Furthermore, we also checked GFAP, TNF-α, IL-1β, and synaptophysin in PsEVs from MCI and AD groups. The diagnostic efficacy of IL-1β by ROC analysis was observed for AMC vs MCI (AUC = 0.9520, p < 0.0001, Sn = 96%, Sp = 86.67%) (Fig. 3 J), AMC vs AD (AUC = 0.9857, p < 0.0001, Sn = 94.29%, Sp = 90%) compared to AMC (Fig. 3 K), MCI vs AD (AUC = 0.9114, p < 0.0001, Sn = 85.71%, Sp = 92%) (Fig. 3 L). Similarly, diagnostic efficacy of TNF-α by ROC analysis was observed for AMC vs MCI (AUC = 0.8920, p < 0.0001, Sn = 84%, Sp = 80%) (Fig. 3 N), AMC vs AD (AUC = 0.9848, p < 0.0001, Sn = 88.57%, Sp = 96.67%), and MCI vs AD (AUC = 0.9280, p < 0.0001, Sn = 88.57%, Sp = 96%) (Fig. 3 P). So, we observed an elevated expression of neuroinflammatory markers within the PsEVs isolated from the AD and MCI groups.

GFAP is an activation marker of astroglia, and in AD, this activation is associated with synaptic dysfunction [ 58 ]. In PsEVs, the diagnostic efficacy of GFAP by ROC analysis was observed as for AMC vs MCI (AUC = 0.8393, p < 0.0001, Sn = 88%, Sp = 76.67%) (Fig. 3 R), AMC vs. AD (AUC = 0.8814, p < 0.0001, Sn = 91.43%, Sp = 76.67%) compared to AMC (Fig. 3 S); MCI vs AD (AUC = 0.7657, p < 0.0001, Sn = 74.29%, Sp = 72%) (Fig. 3 T). In addition to this, we also checked the level of presynaptic protein, i.e., synaptophysin, within the PsEVs, as the level of synaptophysin correlates with cognitive decline in AD [ 59 ]. The diagnostic efficacy of synaptophysin by ROC analysis was observed as follows for AMC vs MCI (AUC = 0.8507, p < 0.0001, Sn = 80%, Sp = 86.67%) (Fig. 3 V), AMC vs AD (AUC = 0.9738, p < 0.0001, Sn = 88.57%, Sp = 96.67%) compared to AMC (Fig. 3 W); MCI vs AD (AUC = 0.8291, p < 0.0001, Sn = 85.71%, and Sp = 68%) (Fig. 3 X). Table 2 summarizes all the AUC, sensitivity, specificity, and p -value values for all the PsEVs-derived proteins.

Correlations of PsEVs concentration values with protein levels of amyloid-β (1–42), p-Tau, IL-1β, TNF-α, GFAP, and synaptophysin in PsEVs

As we found an elevated number of PsEVs in the diseased condition, we performed a correlation analysis between PsEVs concentration and the amyloid-β (1–42) level, p-Tau, IL-1β, and TNF-α within PsEV. We found that PsEV concentration was positively correlated with all the protein levels except synaptophysin, which showed a negative correlation (Fig. 4 ). In these correlations, amyloid-β (1–42) was positively correlated ( r = 0.7196, p < 0.0001) (Fig. 4 A); p-Tau positively correlates ( r = 0.7960, p < 0.0001) (Fig. 4 B); IL-1β also showed positive correlation ( r = 0.7220, p < 0.0001) (Fig. 4 C); and TNF-α also showed positive correlation ( r = 0.6473, p < 0.0001) (Fig. 4 D). GFAP showed a weak correlation with PsEVs concentration ( r = 0.5155, p < 0.0001) (Fig. 4 E), and synaptophysin showed a weak correlation ( r = 0.5752, p < 0.0001) (Fig. 4 F).

Correlation analysis between PsEVs concentration and PsEVs derived AD pathology markers. The correlation between PsEVs concentration with the amyloid-β (1–42) ( A ), p-Tau ( B ), IL-1β ( C ), TNF-α ( D ), GFAP ( E ), and synaptophysin ( F ). Abbreviations: p-Tau, Phospho-Tau, TNF-α, tumor necrosis factor-alpha; GFAP, glial fibrillary acidic protein. Spearman correlation was used for correlation analysis

Correlations of ACE-III and MMSE scores with protein levels of amyloid-β (1–42), p-Tau, IL-1β, and TNF-α in PsEVs

We performed a correlation analysis between ACE-III and MMSE values with the level of amyloid-β (1–42), p-Tau, IL-1β, TNF-α, GFAP, and synaptophysin (Fig. 5 ). We found that ACE-III and MMSE values were negatively correlated with all the protein levels except synaptophysin, which showed a positive value for the correlation coefficient. ACE-III values showed a negative correlation with amyloid-β (1–42) ( r = − 0.5107, p < 0.0001) (Fig. 5 A), p-Tau ( r = − 0.5055, p < 0.0001) (Fig. 5 B), IL-1β ( r = − 0.5684, p < 0.0001) (Fig. 5 C), and TNF-α ( r = − 0.6110, p < 0.0001) (Fig. 5 D). ACE-III values showed a negative correlation with GFAP ( r = − 0.5024, p < 0.0001) (Fig. 5 E), while synaptophysin showed a positive correlation ( r = 0.5036, p < 0.0001) (Fig. 5 F). In the case of MMSE, the values were as follows: for amyloid-β (1–42) ( r = − 0.5276, p < 0.0001) (Fig. 5 G), p-Tau ( r = − 0.6081, p < 0.0001) (Fig. 5 H), IL-1β ( r = − 0.5743, p < 0.0001) (Fig. 5 I), TNF-α ( r = − 0.5522, p < 0.0001) (Fig. 5 J), GFAP ( r = − 0.4596 p = 0.0002) (Fig. 5 K), and synaptophysin ( r = 0.5428, p < 0.0001) (Fig. 5 L). Table 3 summarizes all the values of Correlation coefficients for all the PsEVs-derived proteins.

Correlation between neuropsychological test (ACE-III and MMSE) and PsEV-derived AD pathology markers. Amyloid-β (1–42) β, p-Tau, IL-1β, TNF-α, GFAP, and synaptophysin protein concentration. A – F Correlation between ACE-III scores and amyloid-β (1–42) ( A ), pTau ( B ), IL-1β ( C ), TNF-α ( D ), GFAP ( E ), and synaptophysin ( F ) protein concentration. G – L A correlation between MMSE Score and amyloid-β (1–42) ( G ), p-Tau (H), IL-1β ( I ), TNF-α ( J ), GFAP ( K ), and synaptophysin ( L ) protein concentration. Abbreviations: ACE-III, Addenbrooke Cognitive Examination; MMSE, Mini-Mental State Examination; p-Tau, Phospho-Tau; TNF-α, tumor necrosis factor-alpha; GFAP, glial fibrillary acidic protein. Spearman correlation was used for correlation analysis

In this study, we aimed to investigate the capacity of PsEVs to mirror pathological processes linked to AD and MCI. sEVs are extensively documented in the propagation of pathological processes associated with neurodegenerative and metabolic disorders [ 60 ]. The increased secretion of sEVs, coupled with the transmission of disease-related pathologies through sEVs-associated cargo, makes sEVs a viable candidate for understanding the physiological state of their originating cells, which is reflected in sEVs cargo [ 61 ]. To isolate the PsEVs, we employed a combined approach involving chemical precipitation followed by ultrafiltration, which effectively eliminates co-precipitants and minute protein contaminants such as albumin and LDL. We employed the neuronal protein L1CAM as a marker to ascertain the neuronal origin, although there is a debate surrounding its specificity for neuronal origin [ 62 ]. Nevertheless, in our study, the L1CAM marker is used to check for protein markers and not to confirm L1CAM affinity-based isolation. A two-step filtration procedure was used to accompany the sEV isolation method in our study to ensure high purity. Spherical lipid bi-layered vesicles within the typical size range of small extracellular vesicles (30–150 nm) were observed across AD, MCI, and AMC subjects (Fig. 1 A–C). NTA was employed to study the size distribution of sEVs in AD, MCI, and AMC. We observed that the isolated PsEVs come within the size range of < 200 nm, and there was a notable increase in the number of particles in diseased conditions compared to the control group. (Fig. 1 D–G).

Validation using sEVs-specific markers (CD63, CD81, and TSG101) demonstrated a noteworthy upregulation in MCI and AD, indicating PsEVs numbers are increased in disease conditions (Fig. 2 A–D). Levels of sEV-specific markers in AD and MCI are elevated because PsEV numbers are increased in the disease condition. As documented by various studies in MCI and AD, there is an increase in cross-talk between different pathophysiological processes, which leads to an increase in sEVs number and sEVs specific marker as a cellular response to heightened cellular stress aggravating neuronal damage and synaptic dysfunction [ 33 , 63 , 64 ]. Neuroinflammation, a characteristic feature of AD and MCI, may lead to the release of sEVs with inflammatory markers. Synaptic dysfunction, evidenced by synaptic degeneration, could contribute to the increased sEV-specific markers, reflecting vesicle release in response to altered synaptic activity [ 9 , 65 ]. Additionally, cells undergoing stress might activate compensatory mechanisms, and the elevated sEV-specific markers could signify communication for potential repair or damage mitigation. Therefore, the increase in sEV-specific markers may be linked to the progression of neurodegenerative processes, indicating ongoing pathological changes in the brain as the disease progresses. Additionally, the elevated expression of L1CAM, a neuron-associated marker, in MCI and AD further strengthens the association between PsEVs and neurodegenerative processes (Fig. 2 E). Furthermore, our observations extend beyond AD and MCI, showing increased concentrations of sEVs in other health conditions where higher levels of these vesicles correlate with elevated levels of disease markers [ 53 , 54 , 55 ]. The results of our research provide valuable insight into the characterization, validation, and functional implications of plasma-derived small extracellular vesicles (PsEVs) in the context of AD and MCI. Our comprehensive analysis encompassed morphological, biochemical, and functional aspects, shedding light on the potential role of PsEVs as biomarkers and contributors to neurodegenerative processes.

For this purpose, we performed the ELISA of amyloid-β (1–42) in PsEVs, where we observed higher protein concentrations of amyloid-β (1–42) in MCI. At the same time, in AD, the concentration also significantly increased (Fig. 3 A). In a similar study by A. Manolopoulos et al. [ 66 ], they studied levels of Aβ42, total Tau, and pro-brain-derived neurotrophic factor (BDNF) in both plasma neuron-derived extracellular vesicles (NDEVs) and plasma. The study reported a lack of correlation between the plasma and NDEVs, substantiating concerns about levels of the Aβ42 and total Tau measured in plasma originating from non-CNS sources. Multiple studies support the involvement of extracellular vesicles (EVs) in AD pathogenesis, where Aβ and Tau are released in association with EVs, influencing neuronal cell death and trans-synaptic spreading of the disease [ 7 , 15 , 54 , 67 ]. A progressive elevation in PsEV levels of p-Tau was observed in MCI, reaching a significantly higher AD concentration (Fig. 3 E). Previous research has revealed that p-tau alone effectively differentiates Frontotemporal Dementia (FTD) from AD with high specificity [ 68 , 69 ]. In our study, the alone analysis of p-Tau and amyloid-β (1–42) proved effective in distinguishing patients with MCI from AMC (Table 2 ). Consequently, studies have reported that the elevation of p-Tau suggests the future likelihood of AD development [ 70 ]. This dual elevation in amyloid-β (1–42) and p-Tau levels highlights their potential utility as concurrent biomarkers associated with MCI and AD diagnosis, as indicated by our ROC analysis. Therefore, the investigation into PsEV content revealed significant alterations in key markers associated with AD pathology, viz., amyloid-β (1–42) and p-Tau, which are a well-established marker of AD and exhibit an elevated level in PsEVs from AD and MCI patients compared to AMC in our study.

Synaptic dysfunction is considered a core feature of AD. It is suggested to precede other pathophysiological events of AD rather than neurodegeneration, which manifests during the later stages of the disease [ 71 ]. Synaptic dysfunction interacts with other core pathophysiology events of AD, such as the amyloid-β cascade, tau pathology, and neuroinflammation, eventually progressing to irreversible neurodegeneration and atrophy [ 72 , 73 ]. In this context, the synchronized exchange of proteins involved in these pathological processes between the CNS and neuronal-derived sEVs highlights the potential of sEVs as reliable carriers of pathophysiological cascade occurring at the pathological site [ 74 ]. In Fig. 3 U, we observed downregulated synaptophysin levels, a synaptic vesicle marker, in AD PsEVs compared to MCI and AMC. This suggests synaptic degeneration, which has also been discussed in several studies [ 59 , 63 , 64 ]. Synaptic damage induced by amyloid-β deposition triggers a response from the glia to eliminate impaired synapses. As amyloid-β accumulates, the severity of synaptic dysfunction intensifies, leading to tau hyperphosphorylation and the formation of tau tangles. Our study’s findings contradict J. Utz et al. (2021), which showed increased synaptophysin levels in microvesicles isolated from cerebrospinal fluid (CSF) in AD [ 28 ]. This discrepancy could be due to different biofluid sources, cellular origins, or clearance mechanisms for synaptophysin in these compartments. Our study also differs from Utz J et al. (2021) as we have studied PsEVs compared to microvesicles; both differ in biogenesis, structure, and functions. Moreover, our study aligns with existing studies that reported lower synaptophysin levels in plasma neuronal-derived EVs. Goetzl et al. [ 75 ] investigated the synaptic protein levels in neuronal-derived exosomes in plasma (NDEs) of patients with FTD and AD, where the authors found significantly lower levels of synaptopodin, neurogranin, synaptophysin, and synaptotagmin-2 in both conditions compared to controls. Furthermore, our results also align with the overall synaptic loss seen in AD patient’s brains, where lower levels of synaptophysin in the hippocampus have been reported to correlate with cognitive decline in AD [ 59 ]. Our study found that no significant difference in synaptophysin levels between MCI and AMC was observed, indicating that synapse dysfunction is more pronounced due to neuronal loss in the advanced disease stage, and its indication is reflected in PsEVs. Since the PsEVs pool also contains neuronal-derived EVs, we interpolate that the reduction in synaptic proteins in brain tissue is reflected in our results.

IL-1β, a potent immunomodulating cytokine, has previously been identified as a trigger for various inflammatory mediators in astrocytes and neurons [ 76 ]. Consistent evidence from post-mortem AD brain studies indicates the prevalent overexpression of IL-1β, with immunohistochemical analyses revealing its localization to microglia around plaques [ 77 ]. Moreover, pro-inflammatory markers (IL-1β and TNF-α) were significantly higher in PsEVs from AD and MCI subjects, as evidenced by ELISA and Western blot findings in our study (Fig. 3 I and M). Table 3 summarizes the correlation between PsEVs and neuroinflammatory markers. IL-1β plays a direct role in the pathophysiological changes associated with AD owing to its specific expression in the vicinity of plaques, and this localization suggests IL-1β as a mediator in the formation of plaques and tangles, thereby contributing to AD pathology [ 65 ]. TNF-α, another pro-inflammatory cytokine primarily secreted by activated macrophages and microglia, is recognized for its dual role in promoting cell survival and death in the central nervous system [ 78 , 79 ].

The cytoskeletal GFAP is found in astrocytic cells [ 80 ]. Increased plasma GFAP levels could result from “reactive astrogliosis,” another term for aberrant astrocytic function brought on by damage to neurons [ 81 ]. According to research on animal and cell models, reactive astrocytes encircle and penetrate amyloid-β plaques, contributing to the amyloid-β pathological process [ 82 , 83 ]. Research has demonstrated a correlation between amyloid-β burden, cognitive decline, and plasma GFAP [ 83 ]. PsEVs of GFAP were elevated in AD [ 27 ] and MCI (Fig. 3 Q). It is well known that sEVs play a pivotal role in the progression of disease pathologies in neurodegenerative and metabolic diseases [ 33 , 84 ]. The high levels of neuro-inflammatory markers (GFAP, TNF-α, and IL-1β) in PsEVs from MCI and AD subjects suggest a potential role of PsEVs in neuroinflammation. This activation of astrocytes and microglia precedes increased secretion of pro-inflammatory PsEVs and may contribute to neuronal damage and progressive cognitive impairment. Diseased conditions involve an increased secretion of sEVs and the cargo they carry, including pathological hallmark proteins or immunomodulatory cytokines [ 33 ].

Correlation analyses unveiled positive associations between PsEVs concentration and the protein levels of amyloid-β (1–42), p-Tau, IL-1β, TNF-α, GFAP, and synaptophysin (Fig. 4 ). Furthermore, our study also analyzed the correlation between cognitive examination scores (ACE-III and MMSE) and PsEV-associated protein levels (Fig. 5 ). The negative correlations observed imply that lower cognitive scores align with elevated levels of amyloid-β (1–42), p-Tau, IL-1β, and TNF-α in PsEVs Table 3 . This implies a strong connection between PsEV biomarkers and cognitive decline, reinforcing that PsEVs could serve as valuable diagnostic and prognostic tools. These findings underscore the potential of PsEVs as reliable disease progression and pathology indicators. The robust correlations further support the hypothesis that PsEVs may actively participate in disseminating neurodegenerative signals.

Our study extensively studied the multiple pathophysiological processes associated with AD by checking the protein levels involved in these processes within PsEVs, including amyloid-β (1–42), p-Tau, neuroinflammatory markers (IL-1β, TNF-α, GFAP), and synaptic protein levels. This comprehensive approach enhances diagnostic accuracy by considering the synergistic effects of these processes, providing valuable insights into disease progression from MCI to AD. We have also performed a systematic comparison with MCI, which was lacking in previous studies. We observed a significant correlation between these investigated protein levels within PsEVs and neuropsychological tests, thus filling a research gap addressing the clinical relevance of these dysregulated pathophysiological processes. The observed downregulated synaptophysin levels in AD PsEVs compared to MCI and control subjects shed light on the combined role of neuroinflammation and proteinopathy in the cognitive decline observed as the disease progresses. This finding suggests that PsEVs may reflect synaptic degeneration, opening avenues for further exploration into the role of PsEVs in synaptic damage and dysfunction in neurodegenerative diseases.

Our study provides a multifaceted examination of PsEVs, offering compelling evidence of their potential as biomarkers and functional contributors in AD. We have comprehensively discussed the synergistic role of synaptic dysfunction and neuroinflammation and their association with amyloid-β and tau pathologies within the PsEVs in AD progression. The pathophysiological conditions in the MCI and AD brain are reflected in PsEVs, as observed by the increased concentration of PsEVs containing disease-associated markers and markers for synaptic dysfunction and neuroinflammation. Therefore, the PsEVs can be exploited to understand the pathophysiological process involved in the progression and severity of MCI and AD.

Availability of data and materials

No datasets were generated or analysed during the current study.

Abbreviations

Addenbrooke Cognitive Examination

Alzheimer’s disease

Age-matched controls

Glial fibrillary acidic protein

Interleukin-1β

Mild cognitive impairment

Mini-Mental State Examination

Phospho-Tau

Tumor necrosis factor-alpha

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Acknowledgements

We express our gratitude to the Electron Microscopy Facility, Sophisticated Analytical Instrumentation Facility (SAIF) at AIIMS, New Delhi.

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Open access funding provided by Lulea University of Technology. The Indian Council of Medical Research (ICMR, funding number 2020–1194), Council of Scientific and Industrial Research (CSIR, funding number 09/006(0533)/2021-EMR-I), and Department of Health Research (DHR, funding numbers GIA/2020/000595, YSS/2020/000158) provided funding for this research manuscript.

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Rishabh Singh, Sanskriti Rai, Prahalad Singh Bharti, Sadaqa Zehra, Krishna Kishore Inampudi & Saroj Kumar

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S.K. conceptualized and designed the study. R.S., S.R., P.S.B., and S.Z. performed the acquisition and analysis of data. R.S., S.R., P.S.B., S.Z., and P.K.G. performed the drafting the text or preparing the figures. R.S., S.R., P.S.B., N.R., K.D., K.K.I., P.C., V.V.Y, G.P.M., F.N., and S.K. performed the initial revision and proofreading of the manuscript. All authors read and approved the final manuscript.

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The ethical clearance for this study was obtained from the institutional ethics committee of All Institute of Medical Sciences, New Delhi, India. The ethical clearance number is IECPG-670/25.08.2022. All subjects were recruited for the study after obtainment of the written informed consent form. A detailed written participant information sheet and participant informed consent form were provided to the subjects to take part in this study and their signatures were obtained.

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12916_2024_3475_moesm1_esm.docx.

Additional file 1: Fig S1. [CD63 expression in age-matched controls (AMC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) and their densitometric analysis]. Fig S2. [CD81 expression in age-matched controls (AMC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) and their densitometric analysis]. Fig S3. [TSG101 expression in age-matched controls (AMC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) and their densitometric analysis]. Fig S4. [L1CAM expression in age-matched controls (AMC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) and their densitometric analysis]. Fig S5. [Synaptophysin (SYP) expression in age-matched controls (AMC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) and their densitometric analysis]. Fig S6. [Glial Fibrillary Acidic Protein (GFAP) expression in age-matched controls (AMC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) and their densitometric analysis]. Fig S7. [β-Actin expression in age-matched controls (AMC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) and their densitometric analysis]. Fig S8. [Amyloidβ-42 Oligomer expression in age-matched controls (AMC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) and their densitometric analysis]. Fig S9. [IL1β (A) and TNFα (B) expression in age-matched controls (AMC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) and their densitometric analysis]. Fig S10. [p-Tau expression in age-matched controls (AMC), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) and their densitometric analysis].

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Singh, R., Rai, S., Bharti, P.S. et al. Circulating small extracellular vesicles in Alzheimer’s disease: a case–control study of neuro-inflammation and synaptic dysfunction. BMC Med 22 , 254 (2024). https://doi.org/10.1186/s12916-024-03475-z

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DOI : https://doi.org/10.1186/s12916-024-03475-z

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Reliability of early warning indicators of critical transition in stochastic Van der Pol oscillators with additive correlated noise

Published: 24 June 2024

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Neha Vishnoi 1 ,
Vikrant Gupta 2 ,
Aditya Saurabh 3 &
Lipika Kabiraj 1

We conduct a numerical investigation to assess the effect of noise characteristics on early warning indicators (EWIs) of critical transition in Van der Pol oscillators undergoing supercritical and subcritical Hopf bifurcation. Our study primarily focuses on the effects of additive correlated noise modeled by the OU process in the stable (subthreshold) region and corresponding trends in EWIs based on signal amplitude distribution, frequency spectra, fractal, and complexity measures as the system is brought closer to bifurcation, as a function of noise color and intensity. Our results indicate that the coherence factor is a reliable indicator for the entire range of investigated noise color, while variance and decay rates of the autocorrelation function are reliable when noise correlation times are either much smaller or larger than the system time scale. Kurtosis, permutation entropy and Jensen-Shannon complexity can be effectively employed in systems where noise exhibits minimal correlation time (resembling white noise). While the Hurst exponent proves a reliable indicator in systems where noise has correlation times much larger than the time scale of the system, multi-fractal spectrum width and skewness are deemed unsuitable as EWIs. These insights enhance our understanding of the effectiveness and limitations of various EWIs in forecasting critical transitions within dynamical systems.

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This work was financially supported by MHRD and the institute seed funding, IIT Ropar through grant no. 9-277/2017/IITRPR/4854.

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Appendix A: Algorithm for numerical integration

Equations 4 and 5 can be re-written as,

where, $f(x,\dot{x},t)$ is the deterministic part of the system and $\xi (t)$ is the OU noise, given as,

The algorithm for numerical integration can be written as [ 102 , 103 , 104 ]:

Initialize the time step size ( $\Delta t$ ), state vector ( $x_0$ ) and its first derivative ( $u=\dot{x}_0$ ), stochastic term ( $\xi _0$ ), and time ( $t_0$ ).

For each time step ( n ) from 1 to N :

Compute the deterministic increments:

Calculate the stochastic increment through Euler-Maruyama approach:

Update the state vector:

Increment the time by the time step size, $\Delta t$ .

Repeat the steps until the desired number of time steps is completed.

This general formulation combines the deterministic part (using the classic 4th order Runge–Kutta method) and the stochastic part (using the Euler-Maruyama method) to solve the second-order ODE with a stochastic term.

Appendix B: Comparison between numerical and analytical results

The stochastic Van der Pol oscillators (Eqs. ( 4 ) and ( 5 )) can be written as,

It is convenient to recast the Van der Pol model using amplitude-phase coordinates. This substitution is valid under the assumption of weakly amplified/damped systems [ 1 ], which implies that $|v|\ll \omega _0$ and hence the right-hand side of the above equation is small compared to the left-hand side. Assuming,

the amplitude-phase coordinates can be given as,

Taking the time derivative of Eq. ( 31 ) and ( 32 ) and considering Eq. ( 29 ), we obtain,

This is a generic expression that is valid for any nonlinear function f . Now considering the case of Van der Pol oscillators, for supercritical system

and for subcritical system,

Performing deterministic and stochastic averaging [ 105 ] for the two oscillators, yield the following stochastic differential equations (SDE) for the amplitude A ,

for white noise driven systems, while

for OU noise driven systems.

The Eqs. ( 36 ) and ( 37 ) can be re-written as,

where, $\mathscr {V}(A)$ is the potential governing the amplitude dynamics. We now compute the Fokker Planck equation [ 102 ] associated with the amplitude equation as,

Considering that when $A \rightarrow \infty $ , the probability density vanishes, we can write that the stationary probability density of the acoustic envelope is solution of the following equation,

The analytical solution for stationary probability density can be then computed as,

Comparison between numerical (markers) and analytical (lines) results for amplitude distribution supercritical ( a , b ) and subcritical ( c , d ) systems respectively in the stable ( a , c ) and limit cycle ( b , d ) regions

For supercritical system

and for subcritical system

where, $\mathscr {N}$ is the normalization constant given as $\int _0^{\infty } P(A) d A=1$ .

To verify the numerical simulations, Fig. 13 shows the comparison between the amplitude distribution from simulations (markers) and analytical expression (lines) given by Eqs. ( 44 )–( 46 ) for the two Van der Pol systems in stable and limit cycle regions when driven by both white and OU noise. We can observe good agreement between simulations and analytical results which provides confidence in the methodology adapted in the present work.

2D contour map of skewness ( S ) as the control parameter ( v ) and noise correlation time ( $\tau _c/T_0$ ) are varied at two noise intensities ( $\sigma _b(1)$ ( a , c ) and $\sigma _b(3)$ ( b , d )) for subcritical Van der Pol systems at $\kappa =4$ ( a , b ) and $\kappa =12$ ( c , d ). The dashed grey line separate the plots into categories of low and moderate to high noise correlation times. (Color figure online)

Appendix C: Effect of $\kappa $ variation on skewness and kurtosis

Figure 14 shows the variation of skewness as a function of v , $\tau _c/T_0$ , $\sigma _b$ and $\kappa $ for subcritical Van der Pol system. We can observe that at $\kappa =4$ (Figs. 14 a, b), skewness decreases with increase in noise color for all v and $\sigma _b$ ; while it increases with increase in noise intensity for all v and $\tau _c/T_0$ . Skewness also decreases as the system approaches the saddle-node point. However, at $\kappa =12$ (Fig. 14 c, d), although skewness increases with increase in both noise color and intensity but it remains relatively constant as the system approach the saddle-node point.

2D contour map of kurtosis ( k ) as the control parameter ( v ) and noise correlation time ( $\tau _c/T_0$ ) are varied at two noise intensities ( $\sigma _b(1)$ ( a , c ) and $\sigma _b(3)$ ( b , d )) for subcritical Van der Pol systems at $\kappa =4$ ( a , b ) and $\kappa =12$ ( c , d ). The dashed grey line separate the plots into categories of low and moderate to high noise correlation times. (Color figure online)

Similarly, Fig. 15 shows the variation of kurtosis as a function of v , $\tau _c/T_0$ , $\sigma _b$ and $\kappa $ for the subcritical system. We can observe that at $\kappa =4$ (Fig. 15 a, b), kurtosis increases with increase in noise color for all v and $\sigma _b$ ; while it decreases with increase in noise intensity and control parameter for all $\tau _c/T_0$ . Whereas, at $\kappa =12$ (Fig. 15 c, d), at low noise level, kurtosis decreases with increase in noise color, while it exhibit increasing-decreasing trend with noise color at high noise levels. Further, at low noise levels, the trends in kurtosis becomes indistinguishable for all $\tau _c/T_0>1$ . Kurtosis also shows different trends with respect to control parameter at different noise levels: kurtosis increases as the system approaches the saddle-node point at low noise level; while at high noise level, it decrease for $\tau _c/T_0<0.1$ and increases for all $\tau _c/T_0>0.1$ as the system approach the Hopf bifurcation.

Figures 14 and 15 show that both skewness and kurtosis not only depends on noise characteristics and control parameter but also depends on constant equation parameters, hence they can not serve as reliable EWIs.

The variation in $\kappa $ value do not affect the qualitative trends of other EWIs discussed above.

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Vishnoi, N., Gupta, V., Saurabh, A. et al. Reliability of early warning indicators of critical transition in stochastic Van der Pol oscillators with additive correlated noise. Nonlinear Dyn (2024). https://doi.org/10.1007/s11071-024-09831-1

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DOI : https://doi.org/10.1007/s11071-024-09831-1

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Early warning indicators
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Md Mamunur Rashid, Kumar Selvarajoo, Advancing drug-response prediction using multi-modal and -omics machine learning integration (MOMLIN): a case study on breast cancer clinical data, Briefings in Bioinformatics , Volume 25, Issue 4, July 2024, bbae300, https://doi.org/10.1093/bib/bbae300

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The inherent heterogeneity of cancer contributes to highly variable responses to any anticancer treatments. This underscores the need to first identify precise biomarkers through complex multi-omics datasets that are now available. Although much research has focused on this aspect, identifying biomarkers associated with distinct drug responders still remains a major challenge. Here, we develop MOMLIN, a multi-modal and -omics machine learning integration framework, to enhance drug-response prediction. MOMLIN jointly utilizes sparse correlation algorithms and class–specific feature selection algorithms, which identifies multi-modal and -omics–associated interpretable components. MOMLIN was applied to 147 patients’ breast cancer datasets (clinical, mutation, gene expression, tumor microenvironment cells and molecular pathways) to analyze drug-response class predictions for non-responders and variable responders. Notably, MOMLIN achieves an average AUC of 0.989, which is at least 10% greater when compared with current state-of-the-art (data integration analysis for biomarker discovery using latent components, multi-omics factor analysis, sparse canonical correlation analysis). Moreover, MOMLIN not only detects known individual biomarkers such as genes at mutation/expression level, most importantly, it correlates multi-modal and -omics network biomarkers for each response class. For example, an interaction between ER-negative-HMCN1-COL5A1 mutations-FBXO2-CSF3R expression-CD8 emerge as a multimodal biomarker for responders, potentially affecting antimicrobial peptides and FLT3 signaling pathways. In contrast, for resistance cases, a distinct combination of lymph node-TP53 mutation-PON3-ENSG00000261116 lncRNA expression-HLA-E-T-cell exclusions emerged as multimodal biomarkers, possibly impacting neurotransmitter release cycle pathway. MOMLIN, therefore, is expected advance precision medicine, such as to detect context–specific multi-omics network biomarkers and better predict drug-response classifications.

The advent of high-throughput sequencing technologies has revolutionized our ability to collect various ‘omics’ data types, such as deoxyribonucleic acid (DNA) methylations, ribonucleic acid (RNA) expressions, proteomics, metabolomics and bioimaging datasets, from the same samples or patients with unprecedented details [ 1 ]. By far, most studies have performed single omics analytics, which capture only a fraction of biological complexity. The integration of these multiple omics datasets offers a more comprehensive understanding of the underlying complex biological processes than single-omic analyses, particularly in human diseases like cancer and cardiovascular disease, where it significantly enhances prediction of clinical outcomes [ 2 , 3 ].

Cancer is a highly complex and deadly disease if left unchecked, and its heterogeneity poses significant challenges for treatment [ 4 ]. Standard treatments, including chemotherapy with or without targeted therapies, aim to reduce tumor burden and improve patient outcomes such as survival rate and quality of life [ 5–7 ]. However, even for the most advanced therapies, such as immunotherapies, treatment effectiveness varies widely across cancer types and even between patients with same diagnosis [ 8 ]. This heterogeneity is believed to be due to tumor microenvironment heterogeneity and their effects on the resultant complex and myriad molecular interactions within cells and tissues [ 9 , 10 ]. This variability underscores the urgent need to identify precise biomarkers to predict individual patient responses and potential adverse reactions to a particular therapy [ 11 ]. This can be made possible through multi-omics data integration analyses at the individual patient scale [ 12 ].

To assess treatment response, such as pathologic complete response (pCR) and residual cancer burden (RCB), current clinical practice relies on clinical parameters (e.g. tumor size/volume and hormone receptor status), along with genetic biomarkers (e.g. TP53 mutations) [ 13–15 ]. However, these approaches do not fully capture the complex intracellular regulatory dynamics [ 16 , 17 ] or the tumor-immune microenvironment (TiME) interactions that influence outcomes [ 18 , 19 ]. Thus, to enhance personalized cancer treatments, we need novel methodologies that can handle large, complex molecular (omics) and clinical datasets. Machine learning (ML) methods integrating multi-omics data offer a promising avenue to improve prediction accuracy and uncover robust biomarkers across drug-response classes [ 20 ], which may be overlooked by single-omics analytics. This approach can predict patients benefiting from standard treatments and those requiring alternative plans like combination therapies or clinical trials.

The current drug-response prediction methods can be broadly categorized into ML-based and network-based approaches. ML methods often analyze each data type (e.g. mutations and gene expression) independently using univariable selection [ 21 , 22 ] or dimension reduction methods [ 23 ]. These results are then integrated using various classifiers or regressors [e.g. support vector machine, elastic-net regressor, logistic regression (LR) and random forest (RF)] [ 24–26 ] and ensemble classifier to make predictions [ 9 ]. However, these methods often overlooked the crucial interactions among different data modalities. Deep learning methods, while gaining popularity, are limited by the need for large clinical sample sizes to achieve sufficient accuracy [ 27 ]. Recent ML advancements have focused on integrating multimodal omics features with patient phenotypes to improve predictive performance [ 28 , 29 ]. To discover multimodal biomarker, techniques such as multi-omics factor analysis (MOFA) and sparse canonical correlation analysis (SCCA), including its variant multiset SCCA (SMCCA) offer realistic strategies for integrating diverse data modalities [ 30–32 ]. However, although these methods are suitable for classification tasks, they are unsupervised and do not directly incorporate phenotypic information (e.g. disease status) to integrate diverse data types. As a result, they are limited to identify phenotype-specific biomarkers.

Recently, advanced supervised approaches like data integration analysis for biomarker discovery using latent components (DIABLO) by Sing et al. (2019) have emerged to overcome these limitations [ 28 ]. DIABLO is an extension of generalized SCCA (GSCCA), considers cross-modality relationships and extracts a set of common factors associated with different response categories. Network-based methods, like unsupervised network fusion or random walk with restart approaches construct drug–target interaction and sample similarity networks that are effective for patient stratification [ 20 , 33 ]. However, these methods lack a specific feature selection design, limiting their utility for identifying biomarkers for patient classification. Nevertheless, none of these ML methods are rigorous in terms of task/class-specific biomarker discovery and interpretability, and both SMCCA and GSCCA struggle with gradient dominance problem due to naive data fusion strategies [ 34 ]. Therefore, it is essential to develop novel interpretable methods for identifying robust multimodal network biomarkers across diverse data types to advance our understanding of the complex factors that influence drug responses.

In this study, we introduce MOMLIN, a multi-modal and -omics ML integration framework to enhance the prediction of anticancer drug responses. MOMLIN integrates weighted multi-class SCCA (WMSCCA) that identifies interpretable components and enables effective feature selection across multi-modal and -omics datasets. Our method contributes in three keyways: (i) innovates a class-specific feature selection strategy with SCCA methods for associating multimodal biomarkers, (ii) includes an adaptive weighting scheme into multiple pairwise SCCA models to balance the influence of different data modalities, preventing dominance during training process and (iii) ensures robust feature selection by employing a combined constraint mechanism that integrate lasso and GraphNet constraints to select both the individual features and subset of co-expressed features, thereby preventing overfitting to high-dimensional data.

We applied MOMLIN to a multimodal breast cancer (BC) dataset of 147 patients comprising clinical features, DNA mutation, RNA expression, tumor microenvironment and molecular pathway data [ 9 ], to predict drug-response classes, specifically distinguishing responders and non-responders. Our results demonstrate MOMLIN’s superiority in terms of outperforming state-of-the-art methods and interpretability of the underlying biological mechanisms driving these distinct response classes.

Overview of our proposed method for treatment response prediction

The workflow of our proposed method MOMLIN for identifying class- or task-specific biomarkers from multimodal data is shown in Fig. 1 . The core of this pipeline involves three stages: (i) identification of response-specific sparse components, in terms of input features and patients, (ii) development of drug-response predictor using latent components of patients and (iii) interpretation of sparse components and multi-modal and -omics biomarker discovery.

Schematic representation of the proposed framework. In stage 1, multimodal datasets from cancer patients (e.g. BC) were sourced from a published study [ 9 ]. This dataset comprises clinical features, DNA mutations, and gene expression from pre-treatment tumors, alongside post-treatment response classes (pCR, RCB-I to III). TiME and pathway activity were derived from transcriptomic data using statistical algorithms. For identifying class-specific correlated biomarkers, class binarization and oversampling were used to balance between classes. WMSCCA models the multimodal associations across different biomarkers and identifies response-specific sparse components on diverse input features and patients. In stage 2, a binary LR classifier then utilizes these patient latent components for predicting response to therapies, evaluated by AUROC. Next in stage 3, class–specific sparse components are shown in a heatmap, highlighting key signatures (non-zero loading) in colors. Finally, the identified multi-modal and -omics signatures then formed a correlation network, revealing pathways associations with multi-modal and -omics biomarkers for each response class. Nodes with colors in the network indicate multimodal features.

The rationales underpinned of this approach is that effective biomarkers are: (i) response–related multimodal features including genes, cell types and pathways, and (ii) features that demonstrate prediction capabilities on unseen patients. The first stage, a ‘feature selection step’ that selects multimodal features on the generated sparse components based on their relevance to drug-response categories (pCR and RCB-I to III). Features with high loading identified are considered as potential biomarker candidates. The second stage, a ‘classification step’, validates these biomarkers by assessing their predictive power in distinguishing responders from non-responders to anticancer therapy; any predictions indicating chemo-resistant tumors should be considered for enrolment in clinical trials for novel therapies. The third stage, an ‘interpretation step,’ analyzes the candidate biomarkers in a multi-modal and-omics network associated with relevant biological pathways. This step aims to elucidate the underlying biological processes differentiating between drug–response phenotypes.

Stage 1. Identification of response-associated sparse components in terms of input features and patients

Multi-modal and -omics data overview and preparation.

This study utilized clinical attributes, DNA mutation and gene expression (transcriptome) data from147 matched samples of early and locally advanced BC patients (categorized as pCR, n = 38, RCB-I, n = 23, or RCB-II, n = 61, or RCB-III, n = 25), obtained from the TransNEO cohort at Cambridge University Hospitals NHS Foundation [ 9 ]. The dataset includes clinical attributes (8 features, summary attributes are available in Supplementary Table S1 available online at http://bib.oxfordjournals.org/ ), genomic features (31 DNA mutation genes, applying a strict criterion of genes mutated in at least 10 patients) and RNA-sequencing (RNA-Seq) features (18 393 genes), covering major BC subtypes-normal-like, basal-like, Her2, luminalA and luminalB. Although DNA mutation genes typically represent binary data, we used mutation frequencies to construct a mutation count matrix. Initial data pre-processing involved a log2 transformation on the RNA-Seq features after filtering out less informative features at 25th percentile (in terms of mean and standard deviation) using interquartile range. For integrative modeling, we used the top 40% of variable genes (3748 genes, based on median absolute deviation ranking) from the RNA-Seq datasets. Finally, each feature was normalized dividing by its Frobenius norm, adjusting the offset between high and low intensities across different data modalities.

To characterize TiME and pathway markers, we applied various statistical algorithms on the RNA-Seq data. The GSVA algorithm [ 35 ] calculated (i) the GGI gene sets [ 36 ] and (ii) STAT1 immune signature scores [ 37 ]. For immune cell enrichment, three methods were used: (i) MCPcounter [ 37 ] with voom-normalized RNA-Seq counts; (ii) enrichment over 14 cell types using 60 gene markers, employing log2-transformed geometric mean of transcript per million (TPM) expression [ 38 ]; and (iii) z -score scaling of cancer immunity parameters [ 39 ] to classify four immune processes (major histocompatibility complex molecules, immunomodulators, effector cells and suppressor cells). Additionally, the TIDE algorithm [ 40 ] computed T-cell dysfunction and exclusion metrics for each tumor sample using log2-transformed TPM matrix of counts, which can serve as a surrogate biomarker to predict the response to immune checkpoint blockade. Pathway activity scores for each tumor sample were computed using the GSVA algorithm with input gene sets from Reactome [ 41 ], PIP [ 42 ] and BioCarta databases within the MSigDB C2 pathway database [ 43 ].

Sparse multiset canonical correlation analysis

In this study, lowercase letters denote a vector, and uppercase ones denote matrices, respectively. The term |${\left\Vert .\right\Vert}_{1,1}$| denotes the matrix |${l}_1$| -norm, and |${\left\Vert .\right\Vert}_{gn}$| denotes the GraphNet regularization. The sparse multiset canonical correlation analysis (SMCCA) is an extension of dual-view SCCA, proposed to model associations among multiple types of datasets [ 31 ]. Given the multiple types of datasets, let |$X\in{\mathcal{R}}^{n\times p}$| represent gene expression data with |$p$| features, and |${Y}_k\in{\mathcal{R}}^{n\times{q}_k}$| represent the |$k$| -th data modality (e.g. clinical, DNA mutation and tumors microenvironment) with |${q}_k$| features. Both |$X$| and |${Y}_k$| have |$n$| samples, and |$k=\left(1,\dots, K\right)$|⁠ , where |$K$| denotes the number of different data modalities. The objective function of SMCCA is defined as follows:

where |$u$| and |${v}_k$| are the canonical weight vectors corresponding to |$X$| and |${Y}_k$|⁠ , indicating the importance of each respective biomarkers. The term |${\left\Vert .\right\Vert}_1$| represents the |${l}_1$| regularization to detect small subset of discriminative biomarkers and prevent model overfitting. |${\lambda}_u,{\lambda}_{vk}$| are non-negative tuning parameters balancing between the loss function and regularization terms. The term |${\left\Vert .\right\Vert}_2^2$| denotes the squared Euclidean norm to constraint weight vectors |$u$| and as unit length |${v}_k$|⁠ , respectively.

However, SMCCA has limitations: (i) it is naturally unsupervised, meaning SMCCA cannot leverage phenotypic information (e.g. disease status and drug-response classes); (ii) pairwise association among multiple data types can vary significantly and can lead to gradient dominance issues during optimization; and (iii) SMCCA mines a common subset of biomarkers for classifying different tasks, which diminishes its relevance, as each task might require distinct features sets.

Weighted multi-class sparse canonical correlation analysis

To address the above limitations, here we propose weighted multi-class SCCA (WMSCCA), a formal model for class/tasks-specific feature selection, different from the conventional SMCCA. Throughout this study, we used the terms tasks/classes/drug-response classes interchangeably. WMSCCA includes phenotypic information as an additional data type, employs a weighting scheme to resolve the gradient dominance issue and innovates traditional class–specific feature selection strategies through the one-versus-all strategies into its core objective function. In this study, the underlying motivation is WMSCCA can jointly identify drug-response class–specific multimodal biomarkers to improve drug-response prediction. For ease of presentation, we consider |$n$| patients with data matrices |${X}_c\in{\mathcal{R}}^{n\times p},{Y}_{ck}\in{\mathcal{R}}^{n\times{q}_k}$|⁠ , and |$Z\in{\mathcal{R}}^{n\times C}$| from C different drug-response classes. Here, |${X}_c$| denotes |$p$| features from gene expression datasets, |${Y}_{ck}$| denotes |${q}_k$| features from |$k$| -th data modality (e.g. mutation, clinical features, TiME and pathway activity), |${Z}_c$| denotes |$c$| response class, and |$k=\left(1,\dots, K\right)$|⁠ , |$K$| denotes the number of data modalities. The WMSCCA optimization problem can be formulated as follows:

where |$U\in{\mathcal{R}}^{p\times C},{V}_k\in{\mathcal{R}}^{q_k\times C}$| are canonical loading matrices correspond to |$X$| and |${Y}_k$|⁠ , representing the importance of candidate biomarkers for each class |$C$|⁠ , respectively. In this equation, the first term models associations among |$X$|⁠ , and |${Y}_k$| datasets; the second- and third terms correlate class labels |${Z}_c$| with |$X$| and |${Y}_k$| data modalities for each |${C}^{th}$| class, aiming to identify class-specific features and their relationships; |$\psi (U)$| and |$\psi \left({V}_k\right)$| represent sparsity constraints on |$U$| and |${V}_k$|⁠ , to select a subset of discriminative feature. As mentioned in Equation ( 1 ), to address gradient dominance, the adjusting weight parameter |${\sigma}_{xy}$|⁠ , |${\sigma}_{xz}$| and |${\sigma}_{yz}$| can be defined as:

where |$k=\left(1,\dots, K\right)$|⁠ , |$K$| denotes the number of data modalities. |${\sigma}_{..}$| adjusts a larger weight if the non-squared loss (denominator term) between datasets is small and vice versa.

Given high-dimensional datasets, the model in Equation ( 2 ) encounters an overfitting problem. Therefore, the use of a sparsity constraint is appropriate to address this issue. We hypothesized that gene expression biomarkers can be either single genes or co-expressed sets; thus, a combined penalty is designed for the |$X$| dataset. Therefore, |$\psi (U)$| for |$X$| takes the following form:

where, |${\mathrm{\alpha}}_u,\beta$| are nonnegative tuning parameters. |$\beta$| balances between the effect of co-expressed and individual feature selection. The first sparsity constraint is matrix |${l}_{1,1}$| -norm, which is defined as follows:

This penalty promotes class-specific features on |$U$|⁠ . The second sparsity constraint GraphNet regularization, defined as follows:

where |${L}_c$| represents the Laplacian matrices of the connectivity in |$\boldsymbol{X}$| matrices. The Laplacian matrix is defined as |$L=D-A$|⁠ , where |$D$| is the degree matrix of connectivity matrix |$A$| (e.g. gene co-expression or correlation network). This penalty term promotes a subset of connected features to discriminate each response on |$U$|⁠ .

Besides, neither every mutation marker nor every clinical/TiME/pathways involves in predicting response classes, therefore, the |${l}_{1,1}$| -norm is used on the |${Y}_k$| datasets to select individual markers, i.e. |$\psi \left({V}_k\right)$| for the |${\boldsymbol{Y}}_k$| data modalities take the following form:

where |${\mathrm{\alpha}}_{vk}$| is non-negative tuning parameter.

Finally, we obtained C pairs of canonical weight matrices |$\big({U}_c{V}_{ck}\big)\left(c=1,\dots, C;k=1,\dots, K\right)$| using an iterative alternative algorithm by solving Equation ( 2 ) [ 44 , 45 ]. Detected features with non-zero weights in each class in the weight vectors were extracted as correlated sets.

The WMSCCA method involves parameters |${\mathrm{\alpha}}_u,\mathrm{\beta}, and\ {\mathrm{\alpha}}_{vk}$| |$\left(k=1, \dots, K\right)$|⁠ . Given the limited number of samples, we applied a nested cross-validation (CV) strategy on training sets and evaluated the maximum correlation on the test datasets. Optimal values for the regularization parameters were determined within each training set via internal five-fold CV.

Stage 2. Drug-response prediction using latent components of patients

To predict drug-response categories, we trained LR classifier using the latent components of patients (or raw multimodal features) generated by MOMLIN in Fig. 1 : stages 1 and 2. We used a binary classification scheme, distinguishing pCR versus non-pCR, RCB-I versus non-RCB-I, RCB-II versus non-RCB-II and RCB-III versus non-RCB-III, to evaluate model performance. In addition, we performed analyses with existing multi-omics methods, including SMCCA+LR, MOFA+LR, DIABLO and latent principal component analysis (PCA) features, with LR classifiers. To assess prediction performance for the response to treatment in an unbiased manner, we used five-fold cross-validated performance and repeated the process over 100 runs. The partitioning of data was kept consistent across all models for fair comparisons. The accuracy of response prediction was evaluated using area under the receiver operating characteristic curve (AUROC).

Stage 3. Interpretation of sparse components and multi-omics biomarker discovery and their networks

After learning sparse latent components of features across different data modalities using MOMLIN, we identify the most relevant feature based on the loading weight of genes, TiME and pathways, which reveal underlying interactions for discriminating response classes. The larger the loading weight, the more important the pair of features in discriminating response categories. We then use these selected features to construct a sample correlation network, or a relationship matrix based on their canonical weights [ 46 ]. In this network, nodes represent selected features, and the edge weights between two interconnected features indicate correlation or relatedness. The generated network is visualized using the ggraph package in R ( https://cran.r-project.org ). Finally, we prioritize multi-omics biomarkers based on their degree centrality within the interconnected correlation network.

Derivation of response-associated latent components from BC data with MOMLIN

We applied MOMLIN to analyze a breast cancer (BC) dataset to predict treatment response and gain molecular insights. The dataset comprised 147 BC patients with early and locally advanced pretherapy tumors [ 9 ], categorized as follows: pCR with 38 patients, RCB-I (good response) with 23 patients, RCB-II (moderate response) with 61 patients and RCB-III (resistance) with 25 patients. After preprocessing and filtering least informative features, the final dataset comprised 3748 RNA genes (top 40% out of 9371 genes), 31 mutation genes, 8 clinical attributes, 64 TiME and 178 pathways activities ( Fig. 1 : stage 1). Supplementary Table S1 available online at http://bib.oxfordjournals.org/ summarizes overall clinical characteristics by patients’ response classes.

While our proposed framework offers general applicability for identifying context-specific multi-omics biomarkers, this study specifically focused on discovering drug-response–specific biomarkers to enhance the prediction of pCR and RCB resistance. MOMLIN decomposed the input multimodal data into response-associated sparse latent components of input-features and patients. These sparse components reveal patterns of how various features (e.g. genes and mutations) and clinical attributes related to treatment outcomes ( Fig. 1 : stage 1–3), and their effectiveness was evaluated by measuring prediction performance. We assessed the predictive ability of MOMLIN through five-fold CV repeated 100 times. In each iteration, the dataset is divided into five-folds, with one random fold assigned as the held-out test set, and the remaining folds used as the training set. MOMLIN was trained using the training dataset, including detection of predictive marker candidates, and its performance was evaluated on the ‘unseen’ test set. This process was repeated for all five-folds to ensure robust evaluation of MOMLIN’s generalizability. Performance was measured by the AUROC matrices ( Fig. 1 : stage 2).

Performance comparison with existing methods for drug-response prediction

To evaluate the prediction capability of MOMLIN, we modeled each response category as a binary classification problem and compared its prediction accuracy to existing multi-omics integration algorithms. For comparison, we randomly split the dataset into a training set (70%) and a test set (30% unseen data), with balanced inclusion of response classes. We employed LR as the classifier to assess predictive performance of multimodal biomarkers. We compared MOMLIN with four other classification algorithms for omics data: (i) SMCCA, which integrates multi-omics data by projecting it onto latent components for discriminant analysis; (ii) MOFA, which decomposes multi-omics data into common factors for discriminant analysis; (iii) sparse PCA; and (iv) DIABLO, a supervised integrative analysis method, represent the state-of-the-art in classification. All methods were trained on the same preprocessed data.

The classification results showed that MOMLIN outperformed the compared multi-omics integration methods in most classification tasks on unseen test samples ( Fig. 2A ). Notably, DIABLO, the next best performer, was 10 to 15% less effective than our MOMLIN. Additionally, we compared the performance of component-based LR models against raw feature-based LR models to predict RCB response classes. Although raw feature-based models showed improved prediction, their performance was notably dropped compared to component-based models ( Fig. 2B ). This indicates the superior adaptability and effectiveness of component-based models in leveraging multi-omics data for predictive purposes.

Performance comparison with existing methods and detection of informative data combination. All results in the plots depict test AUROC over five-fold CV obtained from 100 runs. (A) Box plots comparing response prediction performance of MOMLIN against existing state-of-the-art multi-omics methods. (B) Performance comparison between predictors based on latent components and those utilizing a selected subset of multimodal features. (C) Comparing AUROCs for the models with different data subset combinations (clinical, clinical + DNA, clinical + RNA and clinical + DNA + RNA) using MOMLIN.

Moreover, to test and demonstrate generalizability of this framework, we applied MOMLIN to a preprocessed multi-omics dataset of colorectal adenocarcinoma (COAD) with 256 patients [ 47 ]. This dataset included gene expression, copy number variations and micro-RNA expression data, which we used to classify COAD subtypes such as chromosomal instability (CIN, n = 174), genomically stable (GS, n = 34) and microsatellite instability (MSI, n = 48). The performance results shown in Supplementary Table S2 available online at http://bib.oxfordjournals.org/ and Supplementary Figure S1 available online at http://bib.oxfordjournals.org/ , indicate that MOMLIN outperformed all state-of-the-art methods tested in classifying COAD subtypes. Moreover, when comparing the raw feature-based accuracies with sparse components-based (features derived from MOMLIN) accuracies, we found that raw feature-based classifier was superior against existing methods ( Figure S1A and B ), but lower than the components-based classifier. This consistent observation supports our findings with BC drug-response performances.

Importance of different omics data for treatment response prediction

To assess the added value of integrating multimodal data for predicting treatment response, we trained four prediction models with different feature combinations: (i) clinical features only, plus adding (ii) DNA, (iii) RNA and (iv) both DNA and RNA. We found that adding different data modalities improved prediction performance across all response classes ( Fig. 2C ). Notably, the models that combined clinical data with either RNA or both DNA and RNA demonstrated superior and comparable performance with an average AUROC of 0.978. In contrast, the model based on clinical features alone had much lower AUROC, ranging from 0.51 to 0.82. These results suggest that RNA transcriptome is the most informative data modality in this dataset. Thus, integrating gene expression with clinical features could significantly improve our ability to predict treatment outcomes in BC.

Interpretation of response-associated sparse components identified by MOMLIN

To understand the molecular landscape of treatment response in BC, we used MOMLIN to model response–specific bi-multivariate associations across multiple data modalities. We observed stronger correlations between RNA gene expression and both TiME ( r = 0.701) and pathway activity ( r = 0.868), indicating greater overlap or explained information between them. Conversely, moderate correlations were found between RNA gene expression and DNA mutations ( r = 0.526), or clinical features ( r = 0.488), indicating partially overlapping or independent information. These results suggest that multimodal biological features provide complementary information in a combinatorial manner.

When investigating the importance of each feature to predict response classes, MOMLIN identified four distinct loading vectors corresponding to pCR and RCB response classes, highlighting distinct weight patterns for pCR versus non-pCR and RCB versus non-RCB classes ( Fig. 3 ). For example, in the pCR (complete response) components—taking the top five molecular features across different modalities revealed distinct molecular patterns. Specifically, gene expression analysis showed that downregulation of FBXO2 and RPS28P7 inhibits tumor cell proliferation, and potentially may enhance treatment efficacy, and the upregulation of C2CD4D-AS1, CSF3R, and SMPDL3B genes may promote immune response, increasing tumor cell vulnerability and therapeutic effect ( Fig. 3A ). Mutational analysis revealed negative associations of marker genes HMCN1 and GATA3, but a positive association for COL5A1 ( Fig. 3C ). Additionally, tumor mutation burden (TMB), and homologous recombination deficiency (HRD)-Telomeric AI signatures were higher in pCR patients, suggesting high genomic instability compared to RCB patients [ 9 ]. TiME analysis showed reduced immunosuppressive mast cells and extracellular matrix (ECM), along with increased infiltration of neutrophils, TIM-3 and CD8+ T-cells ( Fig. 3D ). Subsequently, the pathway analysis further revealed potential downregulation of the PDGFRB pathway, involved in stromal cell activity and associated with improved patient response [ 49 ], while upregulation of pathways for antimicrobial peptides, FLT3 signaling, ephrin B reverse signaling and potential therapeutics for SARS ( Fig. 3E ), suggesting enhanced immune surveillance and interaction with tumor cells. In summary, MOMLIN reveals distinct genomic landscape with higher immune activity and genomic instability in pCR that characterizes its favorable treatment response.

Heatmaps illustrate the features importance on response-associated components identified by MOMLIN. Each row in the heatmap represents a drug-response class, pCR, RCB-I , RCB-II and RCB-III, with columns representing features across different data modalities. The color gradient indicates feature loading or importance, representing the strength of association with response classes. The sign (negative or positive) of gradient denotes the association directions to response classes. All results in the heatmaps depict an average over 100 runs of five-fold CV. (A–E) represents the response-associated candidate biomarkers detected in latent components in (A) gene expression data (highlighting DE genes), (B) clinical features, (C) DNA mutations (highlighting mutated genes), (D) TiME cells and (E) functional pathway profiles (highlighting altered pathways).

Similarly, in the RCB-I (good response) components—RNA expression analysis revealed that lower expression of genes GPX1P1 and HBB are linked to less aggressive tumors [ 48 ], while those of thiosulfate sulfurtransferase (TST), NPIPA5 and GSDMB were overexpressed, linked to enhanced immune response and therapeutic effectiveness [ 49 , 50 ]. Mutational analysis showed positive association for therapeutic targets signatures TP53, MUC16 and RYR2 [ 51 , 52 ], but a negative in NEB, and CIN scores. TiME analysis demonstrated increased infiltration of Tregs, cancer-associated fibroblast (CAF), monocytic lineage and natural killer (NK) cells, indicating more active of immune environment [ 9 ], with reduced TEM CD4 cells. Pathway analysis further identified downregulation of NOD1/2 signaling, EPHA-mediated growth cone collapse and toll-like receptor (TLR1, TLR2) pathways, involved in inflammation and immune response, with the upregulation of allograft rejection, and G0 and early G1 pathways. In summary, tumors that achieve RCB-I is marked by distinct genomics marker, active immune response, and lower CIN.

In RCB-II (moderate response) components: RNA expression analysis revealed overexpression of RPLP0P9, FTH1P20, RNF5P1 pseudogenes, following accumulation of overexpressed ERVMER34-1, and PON3 genes play an oncogenic role in BC [ 53 ]. Mutation analysis revealed positive association of HRD-LOH, RYR1 and MT-ND4, but negative association of MACF1 and neoantigen loads, in line with previous reports [ 54 , 55 ]. Analysis of TiME features demonstrated increased infiltration of IDO1 and TAP2, with reduced CTLA 4, NK cells and PD-L2 cells, indicating a less suppressive immune environment. Pathways analysis further revealed downregulation pathways of G1/S DNA damage checkpoints and TP53 regulation, highlighting DNA repair issues, with the upregulation of PDGFRB pathway, E2F targets and signaling by Hedgehog associated with cell proliferation. In summary, RCB-II patients display distinct genomics markers including pseudogenes, lack of suppressive immune environment and active proliferation.

In RCB-III (resistant) components: RNA gene expression analysis revealed lower expression of therapeutic target PON3, and FGFR4 [ 56 ], and flowed accumulation of lower expressed lncRNAc ENSG00000225489, ENSG00000261116 and RNF5P1. Mutation signature analysis identified a positive association of MT-ND1, but a negative association in therapeutic targets TP53, and MT-ND4 [ 7 , 52 ]. Neoantigen loads were higher following lower TMB indicate reduced tumor suppressor activity. TiME analysis revealed reduced activity of T-cell exclusion, and HLA-E, with increased ECM, HLA DPA1 and LAG3, suggesting an immune suppressive tumor environment. Pathway analysis revealed upregulation of pathways involved in neurotransmitter release, cell-cycle progression (RB-1) and immune system diseases, suggesting active cell signaling and proliferation, with downregulation of EPHB FWD pathway and nucleotide catabolism. In summary, patients that attained RCB-III, characterized by low mutational burden and an immune suppressive environment, leading to treatment resistance.

Linking biology to treatment response through biomarker network analysis

To further extract multimodal network biomarkers and understand the complex biological interactions in patients with pCR and RCB, we performed cross-interaction network analysis using candidate signatures identified by MOMLIN across different modalities. This analysis included clinical features, DNA mutations, gene expression, TiME cells and enriched pathways, aiming to elucidate the underlying biology associated with specific treatment responses. Figure 4 shows the interaction networks of selected multimodal features for each RCB class. To identify potential biomarkers associated with pCR and RCB response, we specifically focused on the top ten multimodal features based on network edge connections. For example, tumors that attained in pCR, the network analysis revealed co-enrichment of mutations in HMCN1 and COL5A1 genes, particularly in estrogen receptor (ER)-negative patients. HMCN1 and COL5A1 therapeutic targets like molecules encode proteins for ECM structure, and mutations of these genes regulate tumor architecture and cell adhesion, potentially facilitating immune cell infiltration [ 52 ]. We also observed elevated expressions of FBXO2, CSF3R, C2CD4D-AS1 and RPS28P7 genes, alongside increased infiltration of CD8+ T-cells [ 9 , 57 ]. FBXO2 is a component of the ubiquitin-proteasome system, which regulates protein degradation and influences cell cycle and apoptosis [ 58 ], while CSF3R plays a vital role in granulocyte production and immune response [ 59 ]. These gene expression patterns, coupled with increased CD8+ T-cell infiltration, suggest a robust anti-tumor immune response. Furthermore, these molecular perturbations may be linked to antimicrobial peptide pathways and FLT3 signaling, potentially contributing to the favorable outcome in achieving pCR [ 60 , 61 ]. Future work could specifically search for these complex interactions across different molecules to gain more clinically relevant insights into pCR tumors. Supplementary Table S3 available online at http://bib.oxfordjournals.org/ presents the more detailed list (top 30) of the multi-modal and -omics biomarkers identified using the MOMLIN pipeline.

Multimodal network biomarkers explain drug-response classes. The multimodal networks detail the candidate biomarkers and their interactions for each response class, (A) the pCR patients (B) the RCB-I patients (good response), (C) the RCB-II patients (moderate response) and (D) the RCB-III resistance patients. Nodes in the network represent candidate biomarkers derived from clinical features, DNA mutations, gene expression, enriched cell-types and pathways, each indicated in different colors in the figure legend. Negative edges are light green; positive edges are in light magenta. Edge width reflects the strength of the interaction between features. Node size corresponds to the number of connections (degree), and the font size of node labels scales with degree centrality, highlighting the most interconnected biomarkers.

Similarly, RCB-I tumors exhibited co-enriched mutations in MUC16 and TP53, particularly in HER2+ cases [ 14 ]. MUC16 (CA125) is therapeutic molecule associated with immune evasion and tumor growth [ 51 ], while TP53 mutations can lead to loss of cell cycle control and genomic instability [ 62 ]. We also observed elevated expression of TST involved in the detoxification processes and GPX1P1 [long non-coding RNA (lncRNA)] involved in oxidative stress response. The immune landscape of these tumors showed increased infiltration of TEM CD4 cells (adaptive immunity), monocytic lineage cells (phagocytosis and antigen presentation) and NK cells (innate immunity), as well as CAFs. This immune landscape, coupled with potential perturbations in the allograft rejection pathway, suggests an active but potentially incomplete immune response against the tumor, resulting in minimal residual disease.

RCB-II tumors had lower neoantigen loads compared to pCR, both in ER-negative and HER2+ patients. This reduced neoantigen load might contribute to a weaker immune response. Gene expression analysis showed elevated levels of specific lncRNAs, including FTH1P20 (associated with iron metabolism), RNF5P1 (potentially affecting protein degradation) and RPLP0P9 (involved in protein synthesis), along with ERVMER34-1, which can influence gene expression and immune response in BC patients. Numerous studies have underscored the key regulatory roles of lncRNAs in tumors and the immune system. Notably, increased expression of the immune checkpoint protein IDO1 negatively regulates the expression of CTLA-4, both known to modulate antitumor immune responses [ 63 ]. The combined effect of these molecular alterations suggests potential tumor survival mechanisms, including immune evasion and dysregulation of G1/S DNA damage [ 64 ] contributing to moderate residual disease.

In RCB-III tumors, we observed the reduced prevalence of TP53 and MT-ND4 mutations, typically associated with genomic instability and aggressive tumor behavior [ 51 ], coupled with a higher neoantigen load, suggesting an alternative mechanism (pathways) that drives tumor progression. Despite the higher neoantigen loads, increased expression of HLA-E immune checkpoints and T-cell exclusion in the tumor microenvironment hindered effective anti-tumor immune responses. Additionally, the low-expressed genes PON3, ENSG00000261116 (lncRNA) and RNF5P1 are involved in detoxification, gene regulation and protein degradation, respectively, represents an adaptive response to cellular stress in these tumors. Clinical markers indicating lymph node involvement suggest a more advanced disease state [ 9 ]. These findings, along with potential perturbations in the neurotransmitter release cycle pathway, collectively portray RCB-III tumors as genetically unstable, yet effectively evading immune surveillance, contributing to their significant treatment resistance. Overall, further investigation of these interactive molecular networks, comprising both positive and negative interactions offers a more depth understudying of these potential candidate biomarkers for distinguishing treatment-sensitive pCR and resistant RCB tumors.

The advent of multi-omics technologies has revolutionized our understanding of cancer biology, offering unprecedented insights into the complex molecular interactions that shape tumor behavior and treatment response. In this study, we presented MOMLIN (multi-modal and -omics ML integration), a novel method to enhance cancer drug-response prediction by integrating multi-omics data. MOMLIN specifically utilizes class-specific feature learning and sparse correlation algorithms to model multi-omics associations, enables the detection of class-specific multimodal biomarkers from different omics datasets. Applied to a BC multimodal dataset of 147 patients (comprising RNA expression, DNA mutation, tumor microenvironment, clinical features and pathway functional profiles), MOMLIN was highly predictive of responses to anticancer therapies and identified cohesive multi-modal and -omics network biomarkers associated with responder (pCR) and various levels of RCB (RCB-I: good response, RCB-II: moderate response and RCB-III: resistance).

Using MOMLIN, we identified that pCR is determined by an interactive set of multimodal network biomarkers driven by distinct genetic alterations, such as HMCN1 and COL5A1, particularly in ER-negative tumors [ 9 , 65 ]. Gene expression signatures, including FBXO2 and CSF3R were associated with the immune cell infiltration (CD8+ T-cells), which has been previously reported as a key determinant of response [ 57 ]. The association of these biomarkers with antimicrobial peptide and FLT3 signaling pathways suggests a robust immune response [ 61 ] as a critical driver of complete response. Additionally, C2CD4D-AS1, an lncRNA was identified, and its exact role with these complex molecular interactions in BC remains to be elucidated. Future work could specifically search for these complex interactions across different molecules to gain more clinically relevant insights into pCR tumors.

RCB-I tumors, despite responding well to response, were associated with a distinct multimodal molecular signature. These tumors were enriched for mutations in the therapeutic target MUC16 (CA125), known for its role in immune evasion [ 51 ], and the tumor suppressor gene TP53, particularly in HER2+ cases [ 14 ]. Elevated expression of TST and GPX1P1 (lncRNA involved in oxidative stress response) were associated with increased infiltration of diverse immune cells, including Tem CD4+ cells, monocytes and NK cells [ 10 ]. This active immune landscape and the intricate interactions of these signature with the potential perturbations in the allograft rejection pathway, suggests a robust yet potentially incomplete anti-tumor immune response, contributing to the minimal residual disease observed in this subtype.

RCB-II tumors showed lower neoantigen loads compared to pCR, which could contribute to a weaker immune response, particularly in ER-negative and HER2+ subtypes. Increased expression of lncRNAs, such as FTH1P20, RNF5P1, RPLP0P9 and ERVMER34–1, were associated with the immune checkpoint protein IDO1, and negatively regulate the CTLA-4 protein expression, suggests immune evasion and alterations in tumor cell metabolism and proliferation. These molecules altered intricate interactions implicate dysregulation of G1/S DNA damage as a possible mechanism for moderate treatment response [ 64 ].

RCB-III tumors, classified as resistant, were associated with a distinct multimodal molecular landscape driven by reduced TP53 and MT-ND4 mutations [ 52 ], accompanied with higher neoantigen loads compared to other response groups. This suggests an alternative mechanism driving tumor progression and immune evasion. Despite the high neoantigen load which could potentially trigger immune response, these tumors exhibited immune evasion through increased HLA-E immune checkpoints and T-cell exclusion [ 40 , 55 ]. Also, the downregulation of genes like PON3 and the lncRNA ENSG00000261116, along with lymph node involvement, pointed to advanced disease and cellular stress adaptation [ 9 ]. The presence of these complex interactions, including potential perturbations in the neurotransmitter release cycle pathway, could contribute to treatment resistance in RCB-III tumors. Future studies targeting these immunosuppressive mechanisms and exploring novel pathways could offer promising avenues to overcome resistance in this aggressive subtype.

These findings above emphasize the potential of MOMLIN to enable deeper understanding of complex biological mechanism correspondence to each response class, ultimately paving the way for personalized treatment strategies in cancer. MOMLIN also demonstrated the best prediction performance for unseen patients by utilizing these identified sets of network biomarkers. By identifying response-associated biomarkers, researchers can stratify patients based on their likelihood of achieving pCR or experiencing RCB to anticancer treatments, facilitating more informed treatment decisions and potentially improving patient outcomes. Moreover, the identified biomarkers could serve as valuable targets for the development of novel therapeutic interventions and new biological hypothesis generation. However, the clinical translation of multimodal biomarkers necessitates addressing the potential economic burden associated with multi-omics testing. Developing targeted biomarker panels and prioritizing key hub molecules from the large-scale candidate multimodal network biomarkers identified by MOMLIN could be a viable strategy for reducing costs while maintaining predictive accuracy. Furthermore, ongoing advancements in sequencing and diagnostic technologies are expected to make multi-omics testing more accessible and affordable over time.

In conclusion, our study demonstrates MOMLIN’s capacity to uncover nuanced molecular signatures associated with different drug-response classes in BC. By integrating multi-modal and -omics datasets, we have highlighted the complex interplay between genetic alterations, gene expression, immune infiltration and cellular pathways that contribute to treatment response and resistance. Future research in this direction holds promise for refining risk stratification, optimizing treatment selection and ultimately improving patient outcomes.

While MOMLIN demonstrates promising results as shown, a key limitation lies in its reliance on correlation-based algorithms for multi-omics data integration. These algorithms are great at identifying associations, but they can fall short when it comes to inferring causality between different omics layers. This is a challenge faced by most current state-of-the-art methods [ 28 , 30 ]. In the future iterations of MOMLIN, we aim to incorporate causal inference methodologies alongside sparse correlation algorithms to better understand the complex causal relationships within multi-omics datasets.

We proposed MOMLIN, a novel framework designed to integrate multimodal data and identify response-associated network biomarkers, to understand biological mechanisms and regulatory roles.

MOMLIN employed an adaptive weighting for different data modalities and employs innovative regularization constraint to ensure robust feature selection to analyze high-dimensional omics data.

MOMLIN demonstrates significantly improved performance compared to current state-of-the-art methods.

MOMLIN identifies interpretable and phenotype-specific components, providing insights into the molecular mechanisms driving treatment response and resistance.

We thank Dr Yoshihiro Yamnishi and Mr Chen Yuzhou for their technical help.

This work was supported by the core research budget of Bioinformatics Institute, ASTAR.

Supplemental information and software are available at the Bib website. Our algorithm’s software is available for free download at https://github.com/mamun41/MOMLIN_softwar/tree/main

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Defnition: A case study is a research method that involves an in-depth examination and analysis of a particular phenomenon or case, such as an individual, organization, community, event, or situation. It is a qualitative research approach that aims to provide a detailed and comprehensive understanding of the case being studied.
22 Case Study Research: In-Depth Understanding in Context
Abstract. This chapter explores case study as a major approach to research and evaluation. After first noting various contexts in which case studies are commonly used, the chapter focuses on case study research directly Strengths and potential problematic issues are outlined and then key phases of the process.
Case study research and critical IR: the case for the extended case
Second, since critical theory is self-reflective, and knowledge is situated, 42 a critical methodology for case studies should pay attention to the evolving nature of power relations and social forces, and the way in which they shape societies while in turn being constituted by them. 43 Methodologically, this entails a focus on complex, rather ...
What the Case Study Method Really Teaches
What the Case Study Method Really Teaches. Summary. It's been 100 years since Harvard Business School began using the case study method. Beyond teaching specific subject matter, the case study ...
RWJF
Critical Case Sampling. The process of selecting a small number of important cases - cases that are likely to "yield the most information and have the greatest impact on the development of knowledge" (Patton, 2001, p. 236). Why use this method? This is a good method to use when funds are limited. Although sampling for one or more critical cases ...
Methodology or method? A critical review of qualitative case study reports
Definitions of qualitative case study research. Case study research is an investigation and analysis of a single or collective case, intended to capture the complexity of the object of study (Stake, Citation 1995).Qualitative case study research, as described by Stake (Citation 1995), draws together "naturalistic, holistic, ethnographic, phenomenological, and biographic research methods ...
Case Study and Narrative Inquiry as Merged Methodologies: A Critical
This article offers an explanation of how a. critical perspective on case study and narrative inquiry as an embedded methodology unearthed the interplay between structure. and agency within ...
The Critical Case Study Method
ABSTRACT. Introduction to the Nature of Critical Research The way in which we seek to understand the crisis in teachers' work in this book is through Lather's (1986) notion of 'dialectical theory building', drawing on the approaches of critical ethnography that are given expression here in 'critical storied accounts' of teachers ...
PDF The Case Study Method: Critical Reflection
Fahad Alnaim. Abstract- This paper tackles different aspects of the case study method. This covers a critical discussion about the definition; including its meaning in both of qualitative and quantitative. The paper reveals key criticisms of this method; particularly the concerns about generalizability and subjectivity.
Using unfolding case studies to develop critical thinking for Graduate
Methods and setting. This study was conducted at an education facility in New Zealand offering undergraduate and GEN programmes. The participants are academics involved in the design and delivery of the course and one cohort of students of the GEN programme. ... Bowman K. Use of online unfolding case studies to foster critical thinking. J Nurs ...
Methodology or method? A critical review of qualitative case study reports
Differences between published case studies can make it difficult for researchers to define and understand case study as a methodology. Experienced qualitative researchers have identified case study research as a stand-alone qualitative approach (Denzin & Lincoln, 2011b). Case study research has a level of flexibility that is not readily offered ...
Understanding "patient deterioration" in psychotherapy from depressed
Objective:This study scrutinizes the meaning of deterioration in psychotherapy beyond the widely used statistical definition of reliable symptom increase pre-to-post treatment.Method:An explanatory sequential mixed-methods multiple case study was conducted, combining quantitative pre-post outcome evaluation of self-reported depression symptoms and qualitative analysis of patients' interviews.
UCLA School of Law Critical Race Studies Fellowship
UCLA School of Law proudly presents the Critical Race Studies Fellowship, first launched in 2011 under the guidance of Professor Kimberlé Crenshaw.Since its inception, the program has provided lawyers and activists, many of whom came from Latin America, with a unique educational and professional experience to assist in their work against racial discrimination at home.
Rehabilitation of brachial plexus injury in contact sport: Where are
Background Although a common injury there is a lack of published primary data to inform clinical management of sports related brachial plexus injuries. Methods A systematic search was completed in Medline, CINAHL, PubMed, SPORTDiscus and Web of Science databases and Google Scholar from inception to August 2023 according to the PRISMA-ScR guidelines. Methodological quality assessment of ...
Circulating small extracellular vesicles in Alzheimer's disease: a case
Background Alzheimer's disease (AD) is a neurodegenerative disease characterized by Aβ plaques and neurofibrillary tangles. Chronic inflammation and synaptic dysfunction lead to disease progression and cognitive decline. Small extracellular vesicles (sEVs) are implicated in AD progression by facilitating the spread of pathological proteins and inflammatory cytokines. This study investigates ...
Reliability of early warning indicators of critical ...
We conduct a numerical investigation to assess the effect of noise characteristics on early warning indicators (EWIs) of critical transition in Van der Pol oscillators undergoing supercritical and subcritical Hopf bifurcation. Our study primarily focuses on the effects of additive correlated noise modeled by the OU process in the stable (subthreshold) region and corresponding trends in EWIs ...
Advancing drug-response prediction using multi-modal and -omics machine
In this study, we presented MOMLIN (multi-modal and -omics ML integration), a novel method to enhance cancer drug-response prediction by integrating multi-omics data. MOMLIN specifically utilizes class-specific feature learning and sparse correlation algorithms to model multi-omics associations, enables the detection of class-specific ...

Continuing to enhance the quality of case study methodology in health services research

Stefan Paciocco

Introduction

Purpose of case study methodology

Approaches to case study methodology

Defining a case

Designing the case study approach

Data collection and analysis

Practical applications of case study research

New directions in case study

The case study approach

Introduction

What is a case study?

What are case studies used for?

How are case studies conducted?

Defining the case

Selecting the case(s)

Collecting the data

Analysing, interpreting and reporting case studies

What are the potential pitfalls and how can these be avoided?

Conclusions

Pre-publication history

Acknowledgements

Author information

Corresponding author

Additional information

Authors' contributions

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What Is a Case Study? | Definition, Examples & Methods

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CASE STUDY METHODOLOGY: FUNDAMENTALS AND CRITICAL ANALYSIS

Content area

Suggested sources

Case Study – Methods, Examples and Guide

Types of Case Study

Single-Case Study

Multiple-Case Study

Descriptive Case Study

Instrumental Case Study

Case Study Data Collection Methods

Observations

How to conduct Case Study Research

Examples of Case Study

Application of Case Study

Business and Management

Social Sciences

Law and Ethics

Purpose of Case Study

Advantages of Case Study Research

Limitations of Case Study Research

About the author

Muhammad Hassan

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