Posttraumatic stress disorder (PTSD) has been impacting the functioning of a large number of people in military activities and victims of violence for many generations. However, investments in research aiming to understand the neurobiological aspects of the disorder started relatively late, around the last third of the 20th century. The development of neuroimaging methods has greatly supported further understanding of the structural and functional changes in the re-organization processes of brains with PTSD. This helps to better explain the severity and evolution of behavioral symptoms, and opens the possibilities for identifying individual preexisting structural characteristics that could increase symptom severity and the risk of development. Here, we review the advances in neuroanatomical research on these adaptations in PTSD and discuss how those modifications in prefrontal and anterior cingulate circuitry impact the severity and development of the disorder, detaching the research from an amygdalocentric perspective. In addition, we investigate existing and contradictory evidence regarding the preexisting neurobiological features found mostly in twin studies and voxel-based morphometry (VBM) reports. Traumatic experiences have always been a part of life. They affect people in different ways, depending on how one perceives and processes the experience. It is pertinent to mention that not all who experience trauma will develop a psychiatric dysfunction. Exposure to heightened stress is not solely responsible for neurocircuitry alterations that affect internal thoughts and social behavior. The individual differences present before, during, and after the traumatic event play a determinant role on how this event will be processed . One exaTrauma-related emotional ramifications have been recognized as impacting a person’s quality of life and functioning throughout history. Originally recognized exclusively as combat-related, this collection of symptoms has carried many names, such as “soldier’s heart” , shell shock , and “battle exhaustion” , until it was officially denominated in 1980 in the third version of the Diagnostic and Statistical Measurement of Mental Health Disorders (DSM-3) as posttraumatic stress disorder (PTSD) [Hypervigilance, heightened sensitivity to stimuli perceived as threatening, reexperiencing phenomena, fear-conditioning, and the inability to extinguish learned fear (fear extinction) problems are particular to PTSD, being rarely observed in such synchronicity in other mental health conditions ,7,8. FeaPosttraumatic stress disorder used to be associated with the emotional toll of war, but the condition became well associated with trauma experienced through natural disasters or abuse of any kind, which would elicit such exacerbated emotionally dysregulated responses. Around 5 to 20% of individuals exposed to traumatic events in the United States tend to develop PTSD ,10, and,The processing of heightened stress, the consolidation of trauma-related memories, and the resulting inaptitude to extinguish these fear responses, causing a lack of inhibition of trauma-avoidance reminders, are linked to disorganized resting-state neural activity . NeuroimOver the years, most PTSD and anxiety models focused on amygdalocentric circuitry for its discernible role in the fear response, conditioning, and extinction , with thThe purpose of this review is to remove these amygdalocentric processes and models to take into consideration the role of the various interactions between subcortical systems on cortical regions involved in the encoding, storage, and decoding of trauma-related information that determine behavior. The focus of the analyses of structural and functional changes in the PTSD brain affecting fear extinction, inhibitory control, and, consequently, behavior should lead to the further comprehension of PTSD symptomology and possibly assist in identifying biological variations that could indicate an increased risk of developing the condition in the presence of chronic stress, as well as future prevention and treatment strategies.Twenty-three articles were selected for this review, with a total of 10.981 participants. All papers analyzed cortical and/or subcortical structures and systems affected by exposure to prolonged trauma, either structurally or regarding activation, in participants with posttraumatic stress disorder see . AlthougVoxel-based morphometry is an automated neuroimaging method that was first presented by Ashburner and Friston in 2000 to inquire about macroscopic gray matter changes with high spatial resolution using T1-weighted MRI images, which were then statistically examined on all voxels, determining differences in the volumes of brain structures between groups ,17. Two The prefrontal cortex is associated with executive faculties, playing an overall crucial role in cognitive functions acting on decision-making processes, the processing of intrinsic and extrinsic sensory information, and behavior ,21. ThisIn a recent narrative review on neuroanatomical components involved in emotional regulation in PTSD, Fitzgerald and colleagues 2018) described an overall under-engagement of prefrontal areas during emotional processing in individuals with PTSD . It is k describeThe ventromedial part of the PFC (vmPFC) acts on emotional processing, serving as an extensive interconnected area of the PFC associated with decision making and the perception of self . In manyDue to its part in learning flexibility, extinction recall, and cue discrimination ,28, it iFurther areas of high relevance for studies involving emotional regulation, fear-extinction, and vulnerability to stress are the medial, dorsomedial, and dorsolateral PFC  on account of their connection to the aCC and the limbic system, as well as their role in decision-making, and adaptive and social behavior. During the process of deciding upon social behavior, the mPFC and the AM operate under a mechanism of reciprocal inhibition, modulating emotional responses. Individuals with PTSD seem to present an overall deficit in this process ,5, linkeThe grounds for this issue in functional connectivity can be found in the structural changes in GMV and ReHo in the medial and dorsolateral areas of the PFC ,31. DimiThe ventrolateral PFC (vlPFC) is another area highly involved in behavior, playing a major role in response inhibition, and influencing decision-making that affects the goal-directed response . When coIn view of an optimal circuitry linking the limbic system to areas responsible for decision-making, or, in other words, connecting emotional and rational processes that affect behavior, the anterior cingulate cortex plays a role in the reciprocal inhibition of the mPFC and the AM, and acts like a hub for other behavioral networks ,35,36 reAbnormalities found in the aCC–AM–IC circuitry  have beeReduced aCC activation found in PTSD individuals compared with non-traumatized healthy controls appears to lead to a reduction in GMV within a relatively short period of time . The lowDifferent studies have reported a pattern of hyperactivation of the aCC , especiaIn studies investigating aCC GMV in combat-exposed individuals and victims of abuse—both groups with individuals diagnosed with PTSD—reduced volume was also found, with the reduced caudal aCC volume  being asThe amygdala (AM), described officially as an anatomical structure in 1867 by Theodore Meynert, is one of the most studied subcortical arrangements in individuals with mental health disorders due to its role in emotional processing and emotional memory. This almond-shaped structure is composed of several nuclei, with the basolateral, central, and medial ones being the focus of most studies due to their connections to, among others, the vmPFC, the saCC, and other structures of the limbic system during emotional learning, emotional memory, and fear conditioning and extinction ,46,47.With the purpose of understanding the role of the AM in behavior, a variety of studies have been performed since its discovery, from lesion-based to pharmacological manipulation, with the results reinforcing the AM’s role in social behavior and emotionally driven learning processes . HyperacDespite most studies having described a hyperactivation pattern, one case-controlled study  did not There are overlaps between the symptomology of PTSD and other mental health disorders on the anxiety spectrum, which makes those intersecting symptoms relevant for analysis in experiments on people suffering from PTSD. The atrophy of the AM , with acChanges in the morphology of the AM observed in individuals exposed to a chronic state of anxiety have been associated with elevated levels of fear conditioning, led by overdrawn glucocorticoid responses to stress ,38. ExpoThe hippocampus (HPC) plays a crucial role in declarative memory and cognitive operations, including active systems of social and goal-directed behavior, through its participation in exploration and active learning by coding and storing data for time, space, facts, and events , as wellThe responses of these areas in PTSD may depend on the environmental set and experimental conditions. Decreased activation of HPC and PHG, with decreased blood flow, can be observed in participants when not under emotional stimulation , while, Again, the case-controlled study by Douglas and colleagues (2019) presents contradictory findings, with the authors reporting decreased blood flow to the PHG and superior temporal gyrus during script-driven neuroimaging recordings, raising the question of the validity of script-driven experimental procedures in the investigation of PTSD functional neuroanatomy and physiology. A more reliable investigative neuroimaging method in these cases might be the resting-state VBM, as seen for other regions of interest as well. Decreased HPC and PHG GMV, and decreased volume in all temporal gyri , have beKasai and colleagues (2008) reported a significant volume decrease in the right HPC in a twin study, while Yamasue and colleagues (2003) reported this happening extraordinarily on the left hippocampus ,58. LateDespite controversies regarding the severity of the decreased hippocampal volume, there is a consensus that, in individuals with PTSD, the HPC undergoes a reduction in GMV, possibly caused by exposure to chronic stress itself (overwhelming of the system) or due to comorbid conditions often found in PTSD, such as depression and substance abuse disorder , which cThis sign of atrophy can be seen in other mental health conditions as well, such as affective disorders and stress-related personality disorders ,32. ThisThe hippocampal volume, like other alterations described in this paper, has also been discussed as a preexisting condition that would subject the system to higher vulnerability to the detrimental effects of prolonged exposure to stress conditions. Here again, twin studies can provide important input regarding the matter. When identical twins of combat-related individuals with severe PTSD that were not exposed to combat themselves were investigated in a case-controlled study, they showed HPC volumes comparable to those of their siblings, and significantly smaller than those of veterans without PTSD and their respective siblings . This coThe thalamus (THA) is an important structure of the basolateral circuitry (THA–aCC–mPFC–TP–AM) involved in emotional regulation and, therefore, indirectly involved in decision-making processes involving social behavior through its connections to other subcortical structures of the limbic system and PFC areas. Evidence suggests that emotional visual information processing , an impoComparing activation patterns on the thalamic nuclei requires the consideration of a multitude of aspects, such as possible damage or dysregulation in secondary networks, since those nuclei often act as a hub connecting and distributing information across the cerebral cortex and subcortical structures. A pattern of thalamic hypoactivation  is preseIn a similar way, and due to its participation in hormonal regulatory functions, the hypothalamus (HPT) has been the target of psycho-neuroendocrinology research for many years. Hyperactivity in the hypothalamic–pituitary–adrenal (HPA) axis has been observed in patients with PTSD exposed to symptom provocation, as well as in other mental health conditions, such as depression, linking it to increased systemic responses to stress ,12. The The posterior and midinsular cortices receive and process visceral and other sensory stimuli, transmitting this information later to the anterior portion of the insula, which will additionally process this data and link it to other regions involved in cognitive functions. The anterior insular cortex (aINS) and its connections to the aCC are crucial components of the cinguloopercular network involved in interoceptive and emotional awareness, presenting an unusually complex architecture in humans in comparison with other hominids . This paDuring conditioning, extinction learning, and extinction recall in patients with PTSD, the aINS exhibits hyperactivation , a patteSince their description in 1664 by the physician and neuroanatomist Thomas Willis, the basal nuclei (BN) have been associated with regulatory loops throughout the history of neuroscience. More recent fMRI evidence also suggests a highly important role in cognitive and emotional processes, including the evaluation of possible outcomes, learning, and decision-making processes . More spIncreased psychophysiological responses in the BN were found during symptom provocation experiments in patients with PTSD  when comExposure to high levels of negative emotional stress leads to changes in limbic and limbic-related structures as the exposure develops into chronicity. This has been observed through processes of physiological and morphological reorganization to a perceived threat. It is unclear whether the loss of GMV in medial portions of the PFC, aCC, and aINS is narrowly a result or the sole cause of these adaptive mechanisms. Current evidence regarding limbic dysregulation affecting the HPA axis, their effects on BDNF release, and how this process may have its origins in disturbances in aCC–AM reciprocal inhibition suggests a vicious cycle. This cycle may be an explanation for the chronicity of the disorder, progressively affecting the individual’s ability to maintain social behavior stability.The use of amygdalocentric systems to further understand symptomology is, therefore, although still highly relevant, somewhat insufficient. The collection of symptoms  seems to rely mostly on reciprocal inhibitory processes, located mainly around aCC–mPFC functioning. Traits of the PFC, for example, acting on top-down and bottom-up cingulate regulation of limbic components might be a reasonable focus for future research exploring the functioning of a vicious cycle that leads to and further develops behavioral symptoms in individuals with PTSD.Another important facet of mental health management in society is prevention. Understanding the channels that lead to disorders affecting behavior and quality of life might be the key to reducing the occurrence, or at least the impact, of such disorders, favoring the individual and society at different levels. Reduced HPC volume and the changes in structural integrity observed in the NAcc of individuals with PTSD under successful behavioral treatment might be plausible clues to identify preexisting factors for vulnerability to stress. Further research focusing on these processes might support diagnostic precision, evaluation of treatment methods, and, to a certain level, even the prevention of stress-related mental health disorders.


It has been recently discovered that some microglial populations can impact the course or severity of neurological conditions, not only via inflammation, but also through a loss of normal microglial function, which hinders the ability to perform usual tasks, particularly in conditions such as Alzheimer's and Parkinson's diseases, mental disorders, and the normal aging process (Mosher and Wyss-Coray, Microglia are innate immune cells of the central nervous system that play crucial roles in maintaining brain health (Colonna and Butovsky, Delbridge et al. demonstrated that brain slice cultures can provide a more physiologically relevant model for studying microglia than in vitro cultures of dissociated brain tissue. The use of RNA sequencing technology to examine microglial heterogeneity in various disease conditions has grown rapidly. RNA sequencing-based gene expression analysis revealed significant differences between the transcriptional signatures of microglia derived from in vitro cultures and acutely isolated adult microglia. Cultured microglia showed elevated expression of many genes that are rarely expressed or not expressed at all in freshly isolated adult microglia; these cultures also revealed low expression of microglia identity-related genes. In contrast, microglia from organotypic brain slice cultures showed greater similarity to adult microglia, with a higher expression of microglia-specific genes and less inflammation. Overall, this article found that organotypic brain slice culture preserves tissue architecture and cellular composition and provides a more physiologically relevant model for studying microglia. Although these findings are relevant, further research is needed to fully understand the advantages and limitations of this model in the context of brain injury and disease.Mercurio et al. showed that Tmem119, considered a protein specifically expressed by microglia, is downregulated in activated microglia after traumatic brain injury in mice. This decrease in Tmem119 immunoreactivity was observed in both amoeboid microglia, which are typically observed in the acute phase of traumatic brain injury, and hypertrophic microglia, which are found in the contused cortex. Surprisingly, while the protein levels of Tmem119 decreased, the expression of Tmem119 mRNA was upregulated. This difference may have occurred because the Tmem119 mRNA was not translated into proteins after traumatic brain injury, or because reactive microglia break down proteins faster after brain injury. Overall, this study highlights the need for novel markers that can accurately identify microglia in the context of brain injury.Another approach to studying microglial states is the use of reliable markers (Jurga et al., Yu et al. provided an update on the role of microglia in ischemic brain injuries. Microglial polarization is regulated by several signaling pathways, including NF-κB, Notch, mTORC1, AMPK, STAT, and PPAR-γ, which play a crucial role in determining the extent of damage caused by ischemic brain injuries. In addition, several drugs, including metformin, resveratrol, and anise alcohol, promote the transition between pro- and anti-inflammatory states. In some cases, microglial depletion using liposomal clodronate or Mac-1-saporin attenuates inflammation after ischemic brain injury. While further research is needed to fully understand these mechanisms and to determine the best manner to target microglia, microglia-specific modulation could display a promising outcome in the treatment of ischemic brain injuries.This Research Topic also includes several review articles in which the authors express their opinions on different microglial states in various disease conditions. Fan et al. suggests that microglia may be a key player in the development of perioperative neurocognitive disorders, and that further research is needed to understand the precise mechanisms underlying microglial involvement in this condition.Perioperative neurocognitive disorders are a group of conditions that affect cognitive function, including memory, attention, and language, in the period surrounding surgical procedures (Kong et al., Plastini et al.. Microglia exhibit a range of responses depending on the stage of multiple sclerosis. In the early stages of the disease, microglia become reactive and release inflammatory molecules that contribute to the destruction of the protective myelin coating around nerve fibers. However, during the chronic phase of the disease, microglia were shown to have protective functions that facilitate the repair process. This review highlights animal models that represent various aspects of multiple sclerosis and discusses the difficulty of accurately depicting the complexity and diversity of microglial responses in multiple sclerosis.The dynamic role of microglia in animal models of multiple sclerosis was reviewed by Gu et al. draws attention to microglial pyroptosis. Pyroptosis is a form of programmed cell death triggered by the activation of caspases, which leads to the degradation of the cell's structural components and the release of pro-inflammatory molecules (Yu et al., The last review of this Research Topic by Mercurio et al.. Another limitation is that these approaches are typically performed ex vivo. This can introduce artifacts and make it difficult to accurately reflect the in vivo state as suggested by Delbridge et al.. In comparison, in situ or in vivo approaches can provide more accurate and relevant information, as they more closely mimic the natural environment and conditions in which the biological processes occur. On the other hand, the use of recently developed microglia-specific markers may not be reliable for identifying these microglia, as shown by Mercurio et al., and it may be necessary to develop novel markers that accurately define distinct microglial states. Overall, the articles included in this Research Topic provide a comprehensive overview of the current knowledge of microglial function and dysfunction, and will be of interest to both glial researchers and the wider community of neuroscientists.Recent technological advances have led to significant progress in the study of microglia. It has become evident that the complex states of microglia are not sufficiently captured by the concept of pro- and anti-inflammatory states. Latest studies have identified multiple microglial states, which include lipid droplet-accumulating microglia, disease-associated microglia, neurodegenerative microglia, dark microglia, proliferative-region-associated microglia, white matter-associated microglia, activated response microglia, necroptotic microglia, late-response microglia, and dystrophic microglia in a variety of neurodegenerative conditions (Wei and Li, AB and KS wrote the manuscript. AO revised and edited the final version. All authors contributed to the article and approved the submitted version.


Ventilator-associated pneumonia (VAP) is the most common ICU-acquired infection among patients under mechanical ventilation (MV). It may occur in up to 50% of mechanically ventilated patients and is associated with an increased duration of MV, antibiotic consumption, increased morbidity, and mortality. VAP prevention is a multifaceted priority of the intensive care team. The use of specialized artificial airways and other devices can have an impact on the prevention of VAP. However, these devices can also have adverse effects, and aspects of their efficacy in the prevention of VAP are still a matter of debate. This article provides a narrative review of how different airway and respiratory devices may help to reduce the incidence of VAP. Ventilator-associated pneumonia (VAP) results from the infection of pulmonary parenchyma in patients under mechanical ventilation (MV) for more than 48 h. It is a very common ICU-acquired infection, occurring in up to 50% of mechanically ventilated patients, and is associated with an increased duration of MV, high antibiotic consumption, increased morbidity, and mortality ,2,3.The protection of the lungs from infections depends on a set of defense mechanisms that prevent the invasion of the lower airways by pathogenic microorganisms. Anatomical barriers (larynx and glottis), cough, mucociliary clearance, and local humoral and cellular immunity are the main defenses that prevent the onset of serious infections such as pneumonia . WheneveAs a result, the prevention of VAP is a fundamental strategy to reduce its incidence and impact on the morbidity and mortality of ventilated critically ill patients . The redThis article provides a narrative review of how different airway and respiratory devices may help to reduce the incidence of VAP.The microaspiration of secretions containing bacterial pathogens into the lower respiratory tract constitutes the main mechanism for VAP occurrence . ImpaireFirst introduced in 1992, subglottic secretion drainage (SSD) constitutes a recommended nonpharmacological prevention strategy for VAP ,14. ThisAlthough the use of SSD is not yet widespread, probably due to poor strength of evidence on certain outcomes, recently updated guidelines on preventive VAP strategies recommend the use of SSD. The 2022 American practice recommendations of the Society for Healthcare Epidemiology in collaboration with the Infectious Diseases Society of America reclassified SSD from an essential practice to an additional approach to VAP prevention . This isp < 0.0001), no improvement in duration of MV, ICU length of stay or mortality  was shown .,48.8,48]To implement the use of these airway and respiratory devices in VAP prevention strategies, their effect on reducing the incidence, cost-effectiveness, and value in multimodal prevention strategies must be evaluated . Rarely In conclusion, SSD is the only system recommended in the guidelines as an additional practice in the prevention of VAP. Other devices such as continuous control of cuff pressure, silver-coated tracheal tubes, low-volume low-pressure tracheal tubes, closed tracheal suctioning system and the Mucus Shaver showed good results; however, the small number of patients or inconsistent methodologies did not allow robust results to be obtained to sufficiently clearly define their real impact on reducing the incidence of VAP. Devices such as the polyurethane-cuffed ETT and tapered-shape cuff, and scheduled ventilator circuit change do not show significant efficacy in preventing VAP, so their use is not recommended.


Ensuring equitable access to health care is reliant on the strengthening of primary care services. Increasing the utilisation of task-sharing and telehealth models is one strategy to improve patient access and outcomes in primary care. This protocol details the methodology of a proposed scoping review of nurse and midwife involvement in task-sharing and telehealth models in primary care.To identify what task-sharing and telehealth models have been utilised in the primary care setting globally, and to capture the characteristics and health and economic outcomes of the models, and whether they are acceptable and feasible.This protocol was developed in line with the Joanna Briggs Institute (JBI) methodology for scoping reviews and reported according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis Protocols (PRISMA-P).Five databases  will be searched for relevant studies published in English. Articles will be screened for inclusion in Covidence by three authors, with data extracted and synthesised using a chart designed for this review. Evidence will be mapped in both tabular and narrative forms to show characteristics, outcomes, and acceptability of the models of care.Understanding how nurse- and midwife-led models of care may operate is crucial to strengthening service provision in primary care. Evidence on nurse and midwife-led primary care models will be collated and synthesised to inform future models. Task-sharing models of care among primary care teams can be utilised as a means of building capacity to enhance the primary care system. While systematic reviews have been conducted exploring specific aspects of nurse-led task-sharing in primary care, a broader and comprehensive synthesis — one that captures both the characteristics of models of care and outcomes — is required to understand the full breadth of the role of nurses and midwives in task-sharing globally, in order to adapt these models to new clinical areas. Telehealth and task-sharing among primary care teams, especially pertaining to GPs and practice nurses, can be utilised as a means of building capacity to enhance the primary care system.Strengthening primary healthcare services is crucial for ensuring equitable access to essential health care. In 2018, the Declaration of Astana called for action to strengthen primary care as a means of achieving universal health coverage through promoting multisectoral action, empowering local communities, and enhancing capacity and infrastructure to build sustainable primary care systems. Using task-sharing models, in which health care tasks usually completed by physicians are shared with nurses, and adopting nurse-led telehealth models of care in general practice, are opportunities to improve service provision and health outcomes. Data from a literature review on human resources optimisation in health care suggested that 25%–70% of physician tasks could be completed by non-physician health workers in advanced roles, particularly in primary care.Improving strategies to deliver care in primary care settings is critical due to an ageing population, workforce shortages, increasing burden of complex chronic diseases, and inequitable health outcomes and access. The expansion of the role of nurses has arisen from a number of factors, including shortage of family doctors, health system changes, and development of new models of care, with the Netherlands, Spain, the UK, the US, and Switzerland all expanding nurse practice. Australia, Belgium, Hong Kong, Canada, Finland, France, Ireland, Poland, Japan, the Czech Republic, and New Zealand have also employed nurses in advanced roles.Primary health care nurses are suitably placed to collaborate in task-sharing and telehealth models of care and support patients, while reducing the workload for physicians. and satisfaction, while increasing provider job satisfaction. One qualitative study demonstrated that teamwork and high rapport among primary care staff were important contributing factors for patient-centred care. Task-sharing not only directly addresses strategic outcomes in the Declaration of Astana, but also directly benefits staff and patient wellbeing in the primary care setting.The benefits of task-sharing to both patients and staff are numerous, with efficient task-sharing shown to improve patient care A number of studies on telehealth services in primary care have found that telehealth is acceptable to patients, and, in some cases, preferred due to convenience, efficiency, privacy, and comfort. There has been recent increasing evidence that telehealth may exclude certain population demographics, and that patient preference for mode of appointment is important. However, there is still an opportunity to increase access and this has become an increasing necessity, as evidenced during the COVID-19 pandemic, especially in cases where it is the only option for a patient. As evidenced in a systematic review on the impacts and costs of telehealth services, nurse involvement is common, especially in delivering follow-up appointments, and nurse involvement has shown benefits to improving clinical indicators and reducing the need for in-clinic services.Telehealth in primary care, in which appointments are conducted via videoconference or telephone, provides the opportunity to increase accessibility, decrease transportation barriers, and empower patients, especially in rural and regional areas. Due to training and the large sociocultural diversity of the nurse and midwifery workforce, nurse and midwife involvement in task-sharing models may aid in delivering culturally-appropriate and relevant health care, as well as reducing health inequities in primary care.Nurses and midwives are known to empower patients and target interventions that meet the wider social determinants of health through understanding the needs of local populations. However, the research mostly focused on policy, finance, and educational reforms, and the extent to which task-sharing occurred. There was a lack of focus on the characteristics of models of care, and the role of nurses and midwives within these models, which is critical to informing their adaptation to other settings. One Cochrane systematic review explored the impact of nurses working as substitutes for primary care doctors on patient outcomes, processes of care, and utilisation. Despite looking at nurse substitution, a scoping review with broader inclusion criteria would allow for a wider scope of evidence. A further systematic review has been undertaken on the impact of task-sharing on the course of disease. However, although the role of nurses are discussed regarding disease outcomes, there was no discussion on specific models of care. Therefore, a scoping review is required to identify and explain different task-sharing models of care.A cross-country comparative study on task-shifting in primary care was undertaken in 2016.The aim of this scoping review is to synthesise and map current evidence on nurse and midwife involvement in task-sharing and telehealth service delivery models in primary care. This evidence will inform the feasibility and design of nurse-led models of care for provision of early medical abortion (EMA) and long-acting reversible contraception, in order to increase access to these services in the primary care setting. The review will be conducted in accordance with the JBI methodology for scoping reviews and reported against the PRISMA Extension for Scoping Reviews (PRISMA-ScR). The JBI promotes and supports the translation of evidence into practice through the identification of feasible, appropriate, and effective healthcare practices and interventions to improve global health outcomes. The JBI Reviewers' Manual guides the planning, undertaking, and writing of scoping reviews, and was chosen as the methodological framework as the proposed review seeks to inform evidence-based practice and nurse-led models of care in general practice. In line with the JBI guidelines, the protocol will outline the research questions, inclusion criteria, search strategy, study selection, data extraction, analysis of the evidence, and dissemination of results.The protocol has been reported in line with the PRISMA-P.The objective of the scoping review is to ascertain what nurse-led models of care involving task-sharing and telehealth exist in primary care. The proposal for SPHERE was developed by a multidisciplinary team of key experts and clinicians in SRH and primary care. The proposed scoping review aligns with SPHERE goals of addressing access and inequity issues in the provision of EMA services in Australia through primary care.This research is being conducted within the National Health and Medical Research Council (NHMRC) Centre of Research Excellence in Sexual and Reproductive Health (SRH) for Women in Primary Care (SPHERE). The models of care that the study seeks to assess include the involvement of nurses and/or midwives (population), in task-sharing and telehealth models of care (concept) in the primary care setting globally (context).The Population, Concept, Context (PCC) strategy has been utilised to develop research questions.The review will map evidence according to the following research questions:What nurse-led task-sharing and telehealth models have been utilised in the primary care setting?What are the characteristics and the health and economic outcomes of the nurse-led task-sharing and telehealth models of primary care identified?Articles eligible for inclusion are those published in English, as the authors do not have translation resources available. Articles that detail nurse-led models of care are eligible for the review. Articles comprising nurse-led care, in which services are run by nurses, midwives, or nurse practitioners, will be included. This may include working autonomously, managing their own caseloads, physiological assessments and care planning, initiation and delivery of treatment, monitoring of conditions and medications, and referring to specialists where appropriate.Models of care are defined as the way health services are organised and delivered, and may also be described as a 'programme' or 'intervention'. Models of care for any health condition will be eligible for inclusion, as the authors seek an understanding of different models in primary care more broadly. Any nurse-led model of care delivered in the primary care setting will be eligible if it comprises a task-sharing or telehealth component. Task-sharing involves the safe distribution of clinical activities, tasks, and responsibilities, which would otherwise be the domain of a medical physician, to a nurse or a midwife. Telehealth is defined as a clinical consultation conducted by telephone or videoconference. Task-sharing and telehealth models of care performed by a nurse practitioner, practice nurse, midwife or equivalent will be included. For this review, primary care is defined as community health care in which the provider is the first point of contact within the healthcare system, and principle for continued care, such as general practice. Only studies from primary care settings are eligible for inclusion. Studies will be excluded if they have been conducted in settings outside of primary care or do not include a nurse-led component in the delivery of models of care.Outcomes of interest include reported characteristics, health and economic outcomes, and any evidence for feasibility or acceptability of models. Outcomes of the review will be developed iteratively during the review process.Literature from the following sources are eligible for inclusion:Intervention studies including randomised controlled trials, cluster randomised trials and quasi-randomised trials, and pragmatic trials;Observational studies;Qualitative, quantitative, and mixed-methods studies that evaluate or describe different aspects of task-sharing or telehealth models of care; andGrey literature, including government and non-government organisation reports or guidelines that detail task-sharing or telehealth models of care with nurse or midwife involvement. The following databases will be searched for eligible studies: Ovid MEDLINE, Embase, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Cochrane Library.A preliminary search was conducted in August 2020 on Ovid MEDLINE (see Supplementary Table 1). The search strategy was developed in line with the PCC criteria and research questions. Key relevant articles were identified and checked for any synonymous key terms to add to the search strategy. A medical research librarian assisted in refining the strategy. The search included a combination of Medical Subject Heading (MeSH) terms and key terms for concepts including model, delivery, health service, primary health, general practice, nurse, midwife, mid-level provider, task-shifting, task-sharing, telehealth, and telemedicine. The search strategy was further refined iteratively as the authors became more familiar with the literature, and in collaboration with the SPHERE Abortion Working Group. The authors used the JBI three-step search strategy to improve search sensitivity.Citations identified through the search will be uploaded to Covidence, where duplicates will be removed. Studies will undergo title and abstract screening, and then full-text screening for remaining eligible articles. Three authors  will independently conduct the evaluation and review of articles. If there are conflicts between any two reviewers, the third reviewer will help reach a consensus through discussion. Reference lists of included articles will be scanned for additional relevant articles. A PRISMA-ScR flow chart figure will be provided to detail the study selection process. will be adapted specifically for this review. The pilot form will be trialled on two or three sources by two reviewers to ensure all relevant information is extracted, which is a method favoured by other authors of scoping review conduct. If any additional information is required, the charting table will be updated.A data-charting table will be developed to record key information of the source including (but not limited to) the following: author, aims, country of origin, population, methodology, intervention type (if relevant), outcomes, and key findings relevant to the research question. The JBI template data extraction instrument Results will be presented in a number of ways to clearly and logically characterise the findings.The aim of charting scoping review data is to identify, illustrate, and summarise evidence, including identifying gaps in research. Model of care characteristics, reported outcomes, impact, and implications will be presented in tabular form.A narrative approach will be taken when summarising and presenting the results. Evidence will be mapped based on the key research questions, including the characteristics of the models of care, reported outcomes and impact, and any evidence on acceptability of models. When reporting outcomes of the articles, consideration will be given to the nature of primary care in included countries. Evidence mapping will also be utilised to display data. Evidence maps are a visual presentation of available evidence, and aid in identifying evidence gaps.A table detailing each phase of the scoping review will also be included, from defining a research question to study selection, data extraction, and synthesising and disseminating findings.The findings will be disseminated to relevant key stakeholders including researchers, healthcare providers, SRH organisations, and policymakers. The review will also be disseminated in publications, conference presentations, and via the SPHERE distribution network.Increasing access to primary care is a major public health priority, and thus understanding the characteristics of successful nurse and midwife-led models of care is crucial to strengthening service provision in this setting. Strengths of the review include the use of the JBI methodology for scoping reviews and the broad search. The search strategy aims to identify articles in all countries to capture the role of nurses across different health systems and innovative care in both high and low-to-middle income countries. One limitation of taking a scoping review approach is that the review will be documenting what has currently been undertaken in telehealth, but not necessarily assessing appropriateness of telehealth delivery of these services. Another limitation of the study is that the broad search strategy may elicit a high number of eligible articles across multiple settings, which may make inferences regarding commonalities more difficult. However, the authors believe that the broad search strategy will allow for a more thorough exploration of nurse-led care in line with scoping review methodology. Evidence on nurse- and midwife-led primary care models will be collated and synthesised to inform the development of such models in new clinical content areas.


Although a healthy diet and physical activity have been shown to prevent or delay cardiovascular disease (CVD) hospitalizations and deaths, most adults do not meet current guidelines. Provision of coronary artery calcification (CAC) and carotid ultrasound (CUS) imaging results may motivate beneficial lifestyle changes. We scoped the existing literature for studies providing non-invasive vascular imaging results and reporting diet, physical activity, and/or anthropometric measures to identify knowledge gaps and opportunities for further research.A systematic search was performed across three electronic databases, in line with PRISMA ScR guidelines and Arksey and O'Malley's scoping review framework.Twenty studies  examining the impact of provision of CAC/CUS imaging results on diet and/or physical activity behaviors were included. Nearly half the studies did not clearly state whether participants received dietary and physical activity advice along with vascular imaging results, and these were secondary outcomes in most studies, with data assessment and reporting being inconsistent.Well-designed clinical trials with consistent and clear messaging based on detailed subjective and objective measures of diet and physical activity are needed to determine whether this approach may stimulate long-term dietary and physical activity change. Cardiovascular disease (CVD) accounts for almost 1 in 3 deaths globally, with the majority 85%) attributable to either ischemic heart disease or cerebrovascular disease , 3. Subo% attribuVascular health is commonly assessed in the coronary arteries  or in the carotid arteries (carotid ultrasound [CUS]). CAC testing has been increasingly used in clinical practice to screen asymptomatic patients for advanced atherosclerosis , 15 and Although the impact of provision of vascular health imaging on medication adherence is well-known, the effects on diet and physical activity behaviors are less certain due to the lack of well-designed clinical trials with a focus on those outcomes. A systematic review and meta-analysis including six studies with a total of 11,256 participants on the impact of provision of CAC results on medication initiation and continuation, as well as on preventive lifestyle changes, observed improvements in the use of aspirin  . In addiThe aim of this scoping review was to map the literature to understand the evidence to date and identify opportunities to implement diet and physical activity interventions to support changes in these areas. We sought to summarize the literature to identify the nature of study participants, understand how messages were conveyed to participants and which recommendations were provided, as well as to highlight which tools were used to measure the outcomes of interest, and the duration of follow-up period. The outcomes of this study may allow us to identify gaps in the literature to guide the design of high-quality RCTs.Our study was guided by Arksey and O'Malley  scoping A scoping review question was established with the view of broadly scoping the evidence in the literature that suggests provision of non-invasive carotid or coronary vascular imaging can promote healthy diet and physical activity change, with best practices being not clear:By scoping the literature to identify studies in asymptomatic adults that provided non-invasive carotid or coronary vascular imaging and measured diet and/or physical activity, can we determine which approaches may lead to healthy long-term changes in diet and physical activity, in whom and why?The search for studies focused on the effects of knowledge of vascular health across the adult lifespan on changes in diet and physical activity, and anthropometric measures . Full text original articles available in English with no restriction on year of publication were included. The inclusion criteria for studies were:  adult me*). No limits or filters were used for year of publication. Only articles published in English and including adults (age ≥18 years) were considered. Only data related to pre-established outcomes of interest were extracted.A comprehensive online literature search of scientific papers listed on Medline, Embase, and CINAHL was undertaken from database inception until January 24th, 2022. A hand search of references and gray literature sources were also performed , to ensure that all relevant articles were included. n = 188). Screening of the remaining 637 studies was conducted in the following order: (i) article title; (ii) abstract; and (iii) full text. Two authors (SRB and AKG) screened all articles for specific outcomes of interest. The following words were used to identify the outcomes of interest for this review: diet, exercise, physical activity, exercise, nutrition, weight, BMI, body mass index, body mass, body composition, waist circumference. Discrepancies between the screening authors were resolved through discussions with a senior author (JRL) until consensus was reached. A total of 20 studies were eligible and therefore included in this scoping review . Pertinent data were retrieved from eligible articles including authorship, publication year, and country; population details ; methods and intervention details; outcomes of interest (for RCTs); follow-up period; and significant findings for the outcomes of interest, for both observational  and RCT Twenty studies , 36–40 mThe outcomes of interest found in the eligible studies comprised: overall diet composition , 31, 34,The mean age of participants across studies ranged from 45 to 69 years, and the sample sizes varied from 30 to 2,608. The changes in diet, physical activity or anthropometric measures were investigated over a period of 1 month to 6 years following provision of results. For clarity, the characteristics and findings of the studies are presented separately for observational studies and RCTs, due to the particular characteristics of each type of study.In the observational studies, participants were asymptomatic  and incln = 5) of the studies provided participants with specific recommendations for lifestyle change  provided counseling with focus on risk factor management and lifestyle without explicitly mentioning diet and physical activity  , 36, 40,Findings from seven RCTs included in this scoping review  showed tCompared to participants who were not informed of their scan results, provision of vascular imaging did not lead to reductions in body weight , 38, 40 When examining the changes within groups according to CAC/CUS scores in the EISNER study , particiA range of instruments were used to assess diet and physical activity in the eligible studies, with many being self-reported or single-item questions . However, well-designed clinical trials with particular focus on improving diet and physical activity and reducing anthropometric measures (rather than focused on CVD risk factor management) are needed to strengthen quality of evidence regarding the impact of CAC and CUS imaging results on these particular lifestyle behaviors. Secondly, the outcomes of interest in the present review were mainly secondary or non-specified outcomes for the studies included. These studies were largely designed to answer a broader question, focusing mainly on estimated cardiovascular risk or smoking cessation. Finally, the methodologies used varied significantly. For instance, few studies included counseling sessions or other well-recognized behavior change techniques, such as methods for self-monitoring . StudiesTwenty studies examining the impact of provision of CAC/CUS imaging results on diet and/or physical activity were reviewed. Of these, twelve studies focused on CAC  and eight studies focused on CUS . Half of the participants were free of CVD and the other half were at high risk for CVD . Most studies reported providing written/verbal/imaging results but only some studies clearly stated they provided dietary and physical activity advice when providing vascular imaging results. None of the RCTs had dietary or physical activity as a primary outcomes and assessment and reporting of the outcomes were suboptimal and inconsistent among studies. The considerations discussed below can potentially help with the design of future studies where participants receive appropriate guidance and support after provision of vascular imaging, which can be translated into significant changes to their diet and physical activity.We observed that the primary outcomes of most of the studies included in this scoping review focused on managing blood pressure and smoking cessation, rather than changing diet, physical activity and anthropometric measures. Hence, the recommendations for behavior changes were focused on medication and other therapies specific to manage those outcomes. Although some RCTs provided information on risk factor management , 38, 39,In general, the methodologies of the studies included in this review varied greatly, making it difficult to determine which study design would have been potentially more promising at leading to positive changes in diet and physical activity. These included: disparities in randomization methods , instruments used to assess diet and physical activity , how the messages were conveyed (including a lack of clarity in how this was performed), which recommendations for lifestyle change were provided , and by whom . Some observational studies reported physicians had delivered the results to participants, although in most of the observational studies, this was not specified. Only one RCT clearly stated that a physician provided the participants with their results; in all others, this information was not reported or not specified. Previous studies have shown that interventions are more likely to be unsuccessful if not delivered by a clinician . In addiOverall, behavior change has been shown to be possible but needs to be deliberately included and designed, taking advantage of the “teachable moment” . These an = 4) were more successful compared with interventions using six or less BCTs  appears to positively impact the effectiveness of behavior change interventions. A meta-analysis investigating e-health interventions to increase fruit and vegetable intake observed that interventions using 7–8 BCTs  made comparison difficult. Furthermore, the terms exercise and physical activity were used interchangeably in many studies . As physical activity and dietary intake data was mostly self-reported, these data should be interpreted with caution.The results of this scoping review suggest the need for well-structured interventions with the objective of motivating individuals to make positive changes to their diet and physical activity. Although there is consistent evidence that provision of non-invasive vascular imaging results can encourage individuals to make positive behavior changes in relation to medication adherence and other CVD risk factors, evidence for its impact on diet and physical activity remains very weak. However, this is largely due to the lack of studies designed to address this question. Well-designed clinical trials are warranted to further clarify and strengthen the beneficial results mainly seen in observational studies. Future clinical trials should consider a consistent and clear message on how to achieve positive diet, physical activity and other lifestyle changes, preferably be delivered by a trained counselor, and include a follow up session to reinforce advice, ensure understanding, and to provide further encouragement to support positive diet and physical activity changes.The original contributions presented in the study are included in the article/SR-B searched the literature and retrieved the articles. SR-B and AKG screened the papers for eligibility. SR-B, MAK, and JRL participated in the writing of the manuscript. SR-B, AKG, MAK, MS, LCB, CPB, BJ, JD, MPS, JMH, and JRL contributed to the work's conception and design, data interpretation, critical revision of the manuscript, and approved the version of the manuscript being submitted. All authors contributed to the article and approved the submitted version.This study received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. The salary of MS was supported by a Royal Perth Hospital Research Foundation Career Advancement Fellowship (ID: CAF 130/2020) and an Emerging Leader Fellowship from the Western Australian Future Health Research and Innovation Fund. The salary of LCB was supported by a National Health and Medical Research Council (NHMRC) of Australia Emerging Leadership Investigator Grant (ID: 1172987) and a National Heart Foundation of Australia Post-Doctoral Research Fellowship (ID: 102498). The salary of CPB was supported by a Royal Perth Hospital Research Foundation Lawrie Beilin Career Advancement Fellowship (ID: CAF 127/2020). The salary of JMH was supported by a National Health and Medical Research Council of Australia Senior Research Fellowship (ID: 1116973). The salary of JRL was supported by a National Heart Foundation of Australia Future Leader Fellowship (ID: 102817). None of the funding agencies had any role in the conduct and management of the study, data interpretation, preparation, review, or approval of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Machine Learning is increasingly used to predict rehabilitation outcomes in stroke in the context of precision rehabilitation and patient-centered care. However, predictors for patient-centered outcome measures for activities and participation in stroke rehabilitation requires further investigation.This study retrospectively analyzed data collected for our previous studies from 124 participants. Machine Learning models were built to predict postintervention improvement of patient-reported outcome measures of daily activities  and participation . Three groups of 18 potential predictors were included: patient demographics, stroke characteristics, and baseline assessment scores that encompass all three domains under the framework of International Classification of Functioning, Disability and Health. For each target variable, classification models were built with four algorithms, logistic regression, k-nearest neighbors, support vector machine, and random forest, and with all 18 potential predictors and the most important predictors identified by feature selection.Predictors for the four target variables partially overlapped. For all target variables, their own baseline scores were among the most important predictors. Upper-limb motor function and selected demographic and stroke characteristics were also among the important predictors across the target variables. For the four target variables, prediction accuracies of the best-performing models with 18 features ranged between 0.72 and 0.96. Those of the best-performing models with fewer features ranged between 0.72 and 0.84.Our findings support the feasibility of using Machine Learning for the prediction of stroke rehabilitation outcomes. The study was the first to use Machine Learning to identify important predictors for postintervention improvement on four patient-reported outcome measures of activities and participation in chronic stroke. The study contributes to precision rehabilitation and patient-centered care, and the findings may provide insights into the identification of patients that are likely to benefit from stroke rehabilitation. Stroke is a leading cause of disability that requires long-term post-stroke care and rehabilitation . Along tTwo recent trends in rehabilitation are precision rehabilitation and patient-centered care. Clinical decision making in the context of precision rehabilitation involves identifying the characteristics of patients who would likely benefit from rehabilitation programs. Machine learning (ML) is increasingly used for the task of understanding predictors for rehabilitation outcomes by the construction of models that can predict outcomes when given new data. ML is a branch of artificial intelligence that uses algorithms to find patterns in the input data and generate models to predict target variables. Through pattern-finding, the models identify the most important “features,” or potential predictors, for the “target,” or the predicted variable. The advantages of ML include its ability to take a large amount of features at once, to conduct multidimensional data analyses, and to learn from the data without substantial a priori knowledge about the features .In stroke rehabilitation, studies have investigated the feasibility of ML models for the prediction of postintervention outcomes. Most studies focused on patients in the subacute stage. The predicted outcome measures in these studies represent the three domains of the World Health Organization’s International Classification of Functioning, Disability and Health (ICF) , and ranThe other recent trend in medicine and rehabilitation, patient-centered care, aims at engaging the patients, family, and caregivers in the clinical decision-making process. To achieve this goal, patient-reported outcome measures (PROMs) for activities and participation should be incorporated in the assessment in addition to therapist-rated and impairment-level measures. However, most of the existing ML predictive studies on stroke rehabilitation outcomes investigated therapist-rated outcome measures such as the Barthel Index , 6 and tAnother common practice found in the literature has been the inclusion of only one predicted outcome measure. However, given the heterogeneous nature of the stroke population, including multiple predicted outcome measures in research studies was recommended . In factThe MAL was designed to measure the use of the affected upper-limb in basic activities of daily living (ADL). Patients are asked to rate how much  and how well  they use the affected arm for a number of given ADL. The NEADL measures instrumental ADL and assesses functional independence in community living. The SIS measures patients’ health-related quality of life and includes items for participation; one of its domains is Activities of Daily Living (SIS-ADL). Assessing multiple outcome measures to provide multifaceted clinical information about potential prognosis could empower the patients and their families to make appropriate decisions that are most relevant and meaningful to the patient. However, most predictive studies only reported one outcome measure. There is a need to expand the repertoire of outcome measures in research studies to meet clinical applications.This study used ML to build predictive models to predict postintervention outcomes and identify the most important predictors for these outcome measures in stroke rehabilitation. We have expanded on previous findings to use multiple PROMs for activities and participation in consideration of clinical applications and recent trends in stroke rehabilitation.This study is a retrospective analysis of data collected for previous studies conducted by our research team; available results have been published elsewhere , 22. TheThe participants received one of the following therapy programs: InMotion robotic-assisted therapy, Bi-Manu-Track robotic therapy , roboticParticipants’ level of ADL was measured by three assessment tools with four PROMs: the MAL-AOU and MAL-QOM, NEADL, and SIS-ADL. For each measure, participants who achieved the minimal clinically important difference (MCID) from pretest to posttest were labeled as responders, and those who did not were labeled as non-responders. For MAL, we adopted an MCID of 0.5 of average change, corresponding to 10% of the rating scale –25. The We included 18 potential predictors, called “features” in ML terminology, in the ML models. The potential predictors can be grouped into three categories: (1) participant demographics: age, sex, and years of education; (2) stroke characteristics: time since stroke, the National Institute of Health Stroke Scale (NIHSS) score, side of hemiplegia, and diagnosis (hemorrhagic or ischemic); and (3) baseline assessment scores: FMA-UE, Box and Block Test (BBT), Wolf Motor Function Test-Time (WMFT-Time), Wolf Motor Function Test-Quality , Chedoke Arm and Hand Activity Inventory (CAHAI), MAL-AOU, MAL-QOM, NEADL, FIM, SIS-Total, and SIS-ADL. The baseline assessment scores were selected to encompass all three domains under the ICF framework: body function, activities, and participation.The potential predictors and the target variables were used to build ML models. The objective of the ML programs was to find patterns to classify the samples into responders and non-responders. For each PROM, four ML algorithms were used to find the patterns: logistic regression (LR), k-nearest neighbors (KNN), support vector machine (SVM), and random forest (RF). KNN and SVM were selected because they were frequently reported to yield high performance in existing predictive studies of stroke rehabilitation outcomes , 13, 27.In addition to models with all 18 features, in consideration of clinical parsimony, we also built predictive models with the four, five, and six most important features, which we identified by feature selection procedure (see details in the next paragraph). Therefore, for each target variable, 16 models were built .Figure During model construction, the Synthetic Minority Over-sampling Technique (SMOTE)  was usedAfter the models were constructed, model performance was tested using the testing set. Model performance was primarily assessed by classification accuracy and the area under the receiver operating characteristic curve (AUC). We also calculated specificity, sensitivity, negative predictive value (NPV), and positive predictive value (PPV).Descriptive statistics and normality checks were performed with R 4.0.3 software . The conA total of 128 participants were located in our data base; four participants dropped out before the postintervention assessment, resulting in missing data, and were excluded from the study. The study included 124 participants. Table Table Figure ML is increasingly used in the prediction of postintervention prognosis in stroke. Previous studies have investigated prognostic predictors as well as the performance of predictive models. However, most studies were on acute to subacute stroke, and few studies exist on chronic stroke. Further, studies on postintervention improvements in subacute stroke included measures of motor function and measures of activities and participation, whereas few studies on chronic stroke investigated activities and participation. In addition, most studies have included only one predicted outcome measure and focused on therapist-rated measures. The use of PROMs is attracting more attention in recent years as health care shifts toward patient-centered care, but few studies have investigated postintervention improvements measured by PROMs in chronic stroke.This current study extended from the existing literature by investigating the most important predictors for MCID achievements on multiple PROMs for activities and participation in chronic stroke using ML. We identified different sets of the most important predictors for the target variables, reflecting the distinct, albeit related, aspects of ADL assessed in the four PROMs. We also obtained good model performances for the target variables, demonstrating the feasibility of ML for predicting postintervention improvement on PROMs of activities and participation in chronic stroke. In addition, we were able to build parsimonious models with smaller sets of predictors that performed similar or just slightly worse than the full models, which could benefit clinical practice in the selection of prioritized assessments.Emerging research has reported the feasibility of ML for the prediction of postintervention outcome in stroke. However, in the field of health care research, achieving the sample size of big data analysis is often difficult. This is because of a variety of limitations, such as patient privacy policies, the heterogeneity of disease manifestation, the variability in care plans, and cost and time for intervention and data collection, to name just a few. Findings of this current study, however, indicates the feasibility of using ML for the prediction of postintervention outcome with a limited sample size. Despite the relatively smaller sample size, we were able to obtain high classification accuracies and acceptable to outstanding  AUCs usiThe practice of feature selection contributed to clinical parsimony. Clinically, it is more efficient if accurate prediction of prognosis can be obtained by assessment results from fewer tools. In our results, at least one of the models with fewer features for each target variable was able to achieve similar performance compared with 18 features. The results provided support for the clinical application of ML by finding that highly accurate predictions of postintervention outcomes in stroke can be achieved with only a few clinical assessments and patient information.Another issue working with our data set was class imbalance, where one of the classes (responders versus non-responders) outnumbered the other. In a data set with imbalanced classes, the learning machine may focus on finding patterns in the majority class when striving to increase classification accuracy. This usually results in a bias toward the majority class . ConsideOur results showed that the baseline scores of a given PROM were among the important features for classifying responders versus non-responders on that measure. This was similar to findings of predictive studies of UL motor function in chronic stroke using ML , 14 and Iwamoto et al.  found thLin et al.  analyzedBaseline UL motor function, namely, the BBT and the FMA-UE, were found to be important predictors for achieving the MCID on all target variables. The finding was consistent with existing literature. In chronic stroke, baseline FMA-UE was found to predict postintervention UL motor function in two studies using ML , 14. ForDemographic and stroke characteristics were frequently included as potential predictors in rehabilitation outcome prediction. For example, in chronic stroke, age was previously reported as a predictor for postintervention UL motor function  and UL aStroke characteristics, i.e., time since stroke, side of hemiplegia, NIHSS scores, and diagnosis  were identified as important predictors for one or two target variables. Previous studies also reported that time since stroke predicted functional outcomes in the subacute stage , 44 and Note that, methodologically, feature selection could be conducted before model construction from the cohort or after model construction for specific models. This study identified the most important predictors a priori from the cohort, instead of post hoc from specific models. This decision took in considerations of the steps adopted by previous studies in stroke rehabilitation , 13, 14,Despite some overlapping predictors for the four target variables, the four sets of predictors were different. We chose these assessments because they include items for different aspects of activities and participation. There is also a hierarchy among these assessments. The MAL assesses the more basic ADL. The NEADL considers mobility and community living activities. The SIS-ADL considers the daily activities of higher complexities, and some of the activities may require the collaboration of other body parts and/or use of instruments. The important predictors for each target variable likely reflected what each particular assessment tool captures. The findings highlight the importance of using different sets of predictors for these ADL assessments and support the use of feature selection to screen for the most relevant and meaningful predictors in future studies.Among the four PROMs, postintervention achievement of MCID in SIS-ADL appeared to be predicted well across algorithms and numbers of features used. On the contrary, MCID achievement in NEADL required the more complex method, RF, to achieve good prediction performance. The NEADL concerns mobility and community living activities in an extended context, and may involve aspects not as well captured by the predicting variables we used. Regardless, good prediction performance was achievable with the combination of a more complex prediction method and predictors that cover a wider range of aspects, such as general stroke severity (NIHSS) and overall impact of the stroke (SIS). These measures include items for cognition, mobility, and emotion, among others, that may contribute to the extended aspects of activities and participation.The major limitation of this study is the limited sample size; however, we have made an effort to minimize model bias and variance that could result from it by using SMOTE, reducing dimensionality through feature selection, and ensuring that the data used to test model performances did not affect model construction. Through these efforts, we were able to construct at least one model with acceptable to excellent metrics for each target variable. In fact, low specificity and/or sensitivity are commonly seen in the literature using ML to predict stroke rehabilitation outcomes with relatively small sample sizes.Although we would recommend future studies use larger sample sizes, achieving the size of big data in health care is often difficult and/or costly. Future studies may use more advanced techniques to minimize the effects of small sample sizes.Further, the accurate prediction of postintervention ADL outcomes may be more complex and involve predictors that were not included in this study. For example, nutritional status , aphasiaFinally, ML is characterized by its data-driven nature, and therefore the results of this study, as well as many other studies using ML, may not be readily generalized to data from other facilities or other patient characteristics. However, this study and previous studies have repeatedly confirmed the feasibility of ML in predicting postintervention outcomes in the stroke population. Further, some predictors were repeatedly reported and may be important to consider in future studies, such as UL motor function, selected demographic and stroke characteristics, and baseline scores of assessments used to quantify the outcomes. We recommend that health care facilities develop their own models by taking findings of this and previous studies as references.In this study, we obtained high accuracies and AUCs using ML to predict postintervention PROMs for activities and participation in chronic stroke, demonstrating the feasibility of ML methods for this research task. We also identified the most important predictors for achieving MCID on these PROMs. Consistent with existing literature, UL motor function, selected demographic and stroke characteristics, and the baseline scores of the PROMs were important predictors across the four PROMs. Individual predictors identified for the PROMs also reflected the characteristics and contexts of the ADL that these assessments capture. The study findings may contribute to precision rehabilitation by providing insights into the identification of patients that are likely to benefit from stroke rehabilitation.


The transcriptomic analysis of microarray and RNA-Seq datasets followed our own bioinformatic pipeline to identify a transcriptional regulatory network of lung cancer. Twenty-six transcription factors are dysregulated and co-expressed in most of the lung cancer and pulmonary arterial hypertension datasets, which makes them the most frequently dysregulated transcription factors. Co-expression, gene regulatory, coregulatory, and transcriptional regulatory networks, along with fibration symmetries, were constructed to identify common connection patterns, alignments, main regulators, and target genes in order to analyze transcription factor complex formation, as well as its synchronized co-expression patterns in every type of lung cancer. The regulatory function of the most frequently dysregulated transcription factors over lung cancer deregulated genes was validated with ChEA3 enrichment analysis. A Kaplan–Meier plotter analysis linked the dysregulation of the top transcription factors with lung cancer patients’ survival. Our results indicate that lung cancer has unique and common deregulated genes and transcription factors with pulmonary arterial hypertension, co-expressed and regulated in a coordinated and cooperative manner by the transcriptional regulatory network that might be associated with critical biological processes and signaling pathways related to the acquisition of the hallmarks of cancer, making them potentially relevant tumor biomarkers for lung cancer early diagnosis and targets for the development of personalized therapies against lung cancer. Lung cancer is an unrestrained tumor cell growth that can invade and affect other tissues . Lung caThe transcriptomic studies that look for the complete gene expression scenario, such as microarrays and RNA-Seq, have provided biological information about dysregulated genes involved in cancer . We haveThe selection microarray and RNA-Seq datasets of all possible types and subtypes of lung cancer will reach all deregulated genes and transcription factors specific to lung cancer, regardless of their histopathologic classification. Moreover, the construction of co-expression, gene regulatory, coregulatory, and transcriptional regulatory networks can allow us to identify synchronized biological clocks of deregulated genes co-expressed in lung cancer, which are controlled by transcription factors inside the fibration symmetries of the gene networks , leadingOur bioinformatic pipeline seeks to identify a unique transcriptional regulatory metafirm of lung cancer transcription factors associated with the acquisition of the hallmarks of cancer during the lung tumoral process. Moreover, the lung cancer metafirm can be characterized by the formation of functional blocks of genes co-expressed in lung cancer that are regulated by a group of transcription factors important for the regulation of gene expression during the acquisition of the hallmarks of cancer; therefore, they may be interesting candidates that could be used as biomarkers for the development of diagnostic tools and specific treatments against lung cancer.http://ualcan.path.uab.edu/analysis.html accessed on 10 January 2023). The DAVID (Version 2021) functional and enrichment analyses with the official gene symbol of the most frequent LC DEGs identified those related to biological processes and signaling pathways that might be related to the acquisition of the hallmarks of cancer, through an enrichment analysis of gene ontology, signaling pathways, and gene–disease association terms . . 12]. ThThe CCPs formed between lung cancer and PAH have FOXM1 and FOXF1 co-expressed with the most frequent DEGs in microarrays and RNA-Seq datasets, respectively. The CCP formed between lung cancer co-expression networks of microarray datasets have TCF21, FOXM1, and MYBL2, while the CCP of lung cancer RNA-Seq datasets have TCF21, FOXM1, FOXF1, SOX17, TAL1, LMO2, KLF2, and TBX4 co-expressed with some of the most frequent LC DEGs. Moreover, P53, integrin-mediated, and Wnt signaling pathways have some nodes involved in LC-PAH CCPs, and ATR, FoxO, P53, and BMP signaling pathways have some nodes involved in LC CCPs . CoexnetThe comparison of the LC co-expression networks with Gedevo identified eight alignments that appeared in at least 40% of the possible alignments, all of them also appear in the Coexnet CCPs, and four are statistically significant (median >0.5) . ASPM anIn the 375 most frequent DEGs, there are 257 that are also deregulated in PAH, and 117 are deregulated only in lung cancer  Figure . AccordiThe NSCLC and SCLC GRNs were made with the most frequent LC DEGs dysregulated in eight lung cancer datasets, and the most frequent LC DEGS dysregulated in eight lung cancer datasets and PAH, considering the most frequently dysregulated TFs . The GRNEight of the most frequent TFs regulate DEGs related to angiogenesis, of which seven regulate DEGs in SCLC and PAH, and seven regulate DEGs in NSCLC and PAH. Eighteen of the most frequent TFs regulate DEGs related to limitless replicative potential, of which eleven regulate DEGs in SCLC and PAH, ten regulate DEGs in SCLC, thirteen regulate DEGs in NSCLC and PAH, and sixteen regulate DEGs in NSCLC. Fifteen of the most frequent TFs regulate DEGs related to invasion and metastasis, of which eight regulate DEGs in SCLC and PAH, eight regulate DEGs in SCLC, eight regulate DEGs in NSCLC and PAH, and twelve regulate DEGs in NSCLC. Thirteen of the most frequent TFs regulate DEGs related to growth signaling, of which seven regulate DEGs in SCLC and PAH, six regulate DEGs in SCLC, seven regulate DEGs in NSCLC and PAH, and ten regulate DEGs in NSCLC. Thirteen of the most frequent TFs regulate DEGs related to apoptosis, of which ten regulate DEGs in SCLC and PAH, and twelve regulate DEGs in NSCLC and PAH. Eleven of the most frequent TFs regulate DEGs related to inflammatory and immune responses, of which seven regulate DEGs in SCLC, two regulate DEGs in NSCLC and PAH, and four regulate DEGs in NSCLC. Sixteen of the most frequent TFs regulate DEGs related to the dysregulation of metabolism, of which eleven regulate DEGs in SCLC, nine regulate DEGs in NSCLC and PAH, and ten regulate DEGs in NSCLC . FurtherThe NSCLC and SCLC coregulatory networks were made based on the GRNs showing important functional associations between two, three, and six of the most frequent TFs specific to every type of lung cancer . The corThe RTN analysis showed the importance of the regulatory function of the top seven deregulated transcription factors  in the rThe Kaplan–Meier plotter analysis of the top deregulated TFs revealed a statistically significant association between the expression levels of the top five TFs deregulated also in PAH  and the top two TFs deregulated only in lung cancer (KLF4 and SOX17), with the survival of lung cancer patients .The selected datasets represent an important number of studies that allow the gene expression of lung cancer cases and non-tumor tissue controls to be compared, in order to identify a differentially expressed gene metafirm of the disease. Datasets of the two types (NSCLC and SCLC) and subtypes (LAC and SCC) in different populations were searched to represent, as completely as possible, the dysregulation in transcriptional control processes in lung cancer. The pulmonary arterial hypertension (PAH) dataset stood out for the large number of dysregulated genes , much hiThe common connection patterns (CCPs) formed between the most frequent LC DEGs co-expressed in LC and PAH suggest that there is an important group of genes and TFs (FOXM1 and FOXF1) that act as a group in all types and subtypes of lung cancer during the acquisition of the hallmarks of cancer, while others  are important during lung cancer progression. The importance of FOXM1 and MYBL2 in regulating the biological processes that trigger lung cancer can be seen from the smallest CCP formed when comparing all co-expression networks, to the largest CCP formed among RNA-Seq studies of lung cancer . MoreoveAccording to the DAVID analysis, FOXM1 is related to the regulation of cell growth and proliferation, while FOXM1 and MYBL2 are related to cellular senescence and the positive regulation of transcription . FOXM1 hASPM, TPX2, TOP2A, and CENPF are the most significant Gedevo alignments . These aCENPF (Centromere protein F) is a gene related to chromosome segregation during mitosis that reduces overall survival in lung cancer and is a hub gene  overexpressed in cancer patients . CENPF iFOXF1 (forkhead box F1) is a stemness reprogramming mediator  which is also related to the inhibition of cell growth, proliferation, and migration ; therefoTCF21 (transcription factor 21) is a tumor suppressor gene that is methylated and downregulated in lung cancer and is related to cell viability, proliferation , apoptosLMO2 (LIM-domain only 2) is an important regulator of embryonic hematopoiesis  and angiSOX17 belongs to sex-determining region Y (Sry), a box-containing family of transcriptional regulators that are essential for stem cell maintenance, lung morphogenesis, and tissue homeostasis . SOX17 iThe GRN analysis of lung cancer types associated the hallmarks of cancer with the target DEGs of twenty-three of the most frequently dysregulated TFs . Eight oDLX5  is mainly related to embryonic and postnatal development and cell differentiation, and it is overexpressed in lymphomas and lung cancer . DLX5 isSOX17 represses and SOX4-enhanced canonical Wnt signaling  to keep ID4 belongs to the DNA-binding (ID) protein family, which are dominant negative inhibitors of basic helix–loop–helix (bHLH) transcription factors, regulating developmental processes and promoting stem cell survival, differentiation, and epigenetic inactivation of gene expression in late cancer stages . ID4 is NR2F1 or Coup-TF1  is an orphan nuclear receptor of the retinoic acid receptor family, known as a tumor dormancy marker, which is downregulated in cancer to promote cell proliferation  and metaEPAS1 ) is related to vascular network remodeling , tumor aZBTB16 , or zinc finger protein 145 (ZFP145)) is a tumor suppressor gene that is downregulated by promoter hypermethylation, which stimulates cancer cell proliferation, migration, invasion , metastaMNDA (myeloid cell nuclear differentiation antigen) is related to innate immunity . Its dowHOXC6 (homeobox C6) is overexpressed in lung cancer, regulating the expression of genes related to cell proliferation, migration, and invasion in NSCLC . HOXC6 rKLF4 (Krüppel-like factor 4) induces pluripotent stem cells, controls cell fate reprogramming and self-renewal of embryonic stem cells  156]. K. K156]. MEIS1 (Meis homeobox 1) downregulation is related to lung adenocarcinoma cell proliferation , anchoraThe transcriptional regulatory network of most frequently dysregulated TFs showed consistent evidence of its ability to accomplish their regulatory function throughout the course of lung cancer, in the CCP, regulatory, coregulatory, and fibration symmetries analysis, by means of a cooperative and coordinated function during the acquisition of each hallmark of cancer , each biIn NSCLC, for RTN, there seems to be a regulatory function of the top deregulated TFs over the other TFs A, suggesThe Kaplan–Meier plotter analysis of the top deregulated TFs revealed a strong association of their expression with a decreased probability of long-term survival . The KM In the last decade, new strategies have been developed for targeting oncogenic and rescuing tumor-suppressive TF functions, modulating their expression or degradation by blocking protein/protein interactions, and preventing its DNA binding either through a binding pocket or at the DNA-interacting site; some of these inhibitors are currently being used or evaluated for cancer treatment . CortezoOur bioinformatic pipeline allowed for the identification and analysis of the positively and negatively dysregulated genes in most of the lung cancer datasets, which are dysregulated only in lung cancer, or dysregulated also in PAH, to identify an essential group of co-expressed TFs, which might be related to the overall tumoral process, independently of the type or subtype of lung cancer. There is experimental evidence that supports that the transcriptional regulatory network of TFs identified perform an important number of key functions during the acquisition of the hallmarks of cancer, through the regulation of the gene expression of oncogenes and tumor suppressors associated specifically with the tumoral process of the lung. Two of the most frequently dysregulated transcription factors are co-expressed in lung cancer and PAH (FOXM1 and FOXF1), while the other six frequently deregulated transcription factors are co-expressed only in lung cancer . Moreover, twenty-four of the most frequently dysregulated transcription factors regulate the most frequent DEGs, from which eight are also in the co-expression analysis. The coregulatory analysis identified sixteen of the most frequently dysregulated transcription factors capable of forming protein–protein complexes to regulate gene transcription in a cooperative manner. The fibration symmetries analysis identified groups of genes that regulated up to fourteen of the frequently deregulated transcription factors, which might regulate gene expression in a cooperative and/or coordinated manner. The seven top deregulated transcription factors  consistently appear to be co-expressed with other frequent DEGs and transcription factors throughout the analysis in lung cancer and PAH. Their regulatory function might be related to the formation of co-regulatory complexes and biological functional blocks or fibers, which seem to be able to regulate the function of the other frequent transcription factors, at least in NSCLC, and are associated with the survival of lung cancer patients. The functional analysis and experimental evidence show an association between the frequently deregulated transcription factors with the control of signaling pathways related to the acquisition of the hallmarks of cancer. Moreover, there is evidence of the functional association between the hub TFs, as cooperators or coregulators, supporting a strong transcriptional regulatory network connected with lung cancer. The analysis of the overall transcriptomic changes in the lung cancer oncogenic process, which was performed through the creation of co-expression, regulatory, coregulatory, and transcriptional networks of the most frequently dysregulated DEGs in lung cancer, allowed for the identification of potential biomarkers for lung cancer diagnosis for the future development of specific and more efficient anticancer therapies. The coordinated and cooperative biological function of the transcriptional regulatory network must be evaluated experimentally to fully understand and validate their importance in the regulation of signaling pathways related to the acquisition of the hallmarks of cancer, and to use them as specific biomarkers for the diagnosis and treatment of complex tumor diseases in the lung.


Dysphagia is one of the most common problems among older adults, which might lead to aspiration pneumonia and eventual death. It calls for a feasible, reliable, and standardized screening or assessment method to prompt rehabilitation measures and mitigate the risks of dysphagia complications. Computer-aided screening using wearable technology could be the solution to the problem but is not clinically applicable because of the heterogeneity of assessment protocols. The aim of this paper is to formulate and unify a swallowing assessment protocol, named the Comprehensive Assessment Protocol for Swallowing (CAPS), by integrating existing protocols and standards. The protocol consists of two phases: the pre-test phase and the assessment phase. The pre-testing phase involves applying different texture or thickness levels of food/liquid and determining the required bolus volume for the subsequent assessment. The assessment phase involves dry  swallowing, wet swallowing of different food/liquid consistencies, and non-swallowing . The protocol is designed to train the swallowing/non-swallowing event classification that facilitates future long-term continuous monitoring and paves the way towards continuous dysphagia screening. Dysphagia is a medical term used to describe swallowing difficulties . DysphagEstablishing an effective swallowing assessment is imperative to prompt older adults who are at risk to take rehabilitative measures and to evaluate the effectiveness of the rehabilitative measures. Clinically, the fiber-optic endoscopic evaluation of swallowing (FEES) and the video-fluoroscopic swallowing study (VFSS) are the gold standards for dysphagia diagnosis . The FEENon-instrumental swallowing evaluations are routinely conducted and play an important role in bedside dysphagia screening . These cThere are other drawbacks of the standard instrumental diagnosis and routine screening tools. These tests are typically conducted once at a single timepoint because frequent and continuous assessments are often not feasible or affordable. In fact, the occurrence of dysphagia is a gradual process, along with the deterioration of cognitive functions, especially in the dementia population ,17. ContEmerging wearable technology might provide the opportunity to enable continuous monitoring. Swallowing difficulties manifest physical characteristics that could be directly or indirectly measurable, including longer swallowing times and delayed pharyngeal initiation accompanied by poor epiglottic inversion, hyolaryngeal movement, and pharynx clearance . ExistinTo this end, the objective of this study is to develop a Comprehensive Assessment Protocol for Swallowing (CAPS) that is dedicated but not exclusive to wearable technology with machine learning or other classifier algorithms. The premise of this protocol was built upon an integration of our previous review synthesis , the IntThe protocol consists of two phases: (1) the pre-test phase and (2) the assessment phase. It should be completed in a single visit by trained personnel or clinicians, such as nurses, speech therapists, and occupational therapists, to oversee any risk of choking and other adverse events. The pre-test phase involves wet swallowing tasks to determine the size of bolus intake. The assessment phase involves dry  swallowing, non-swallowing, and wet swallowing tasks in sequence. Measurements and assessments are carried out in this phase to screen for individuals with potential dysphagia or swallowing abnormalities. The participants can request a break at any time and should be reminded to pause the task and report any discomfort to the observer.The overall framework and sequence of the protocol are illustrated in The purpose of the pre-test phase is to determine the appropriate size of bolus intake of foods and liquids for the next assessment phase. Participants are asked to avoid eating and drinking for 2 h before the test. The session starts with the participant sitting in a comfortable position and fully alert. They are then asked to drink 10 mL of water to moisturize their mouth. The pre-test begins with liquid samples and is followed by food samples.The pre-test phase continues sequentially with wet swallowing tasks based on different textures and thicknesses of liquids and foods/transitional foods, as shown in For swallowing tasks based on foods/transitional foods, the procedures are very similar. The participant starts with Level 4 (extremely thick food or pureed food) and then progresses through Level 5, Level 6, and Level 7 (easy-to-chew or regular food). For each level, the participants are given 5 g of sample to consume in a single swallow. S/he then attempts to consume an increasing volume  until s/he notes difficulty. Similarly, the maximum amount of sample that the participant can swallow comfortably is noted and applied in the assessment phase. We propose some examples of food or drink according to the IDDSI levels in The assessment phase continues after the pre-test and consists of the following tasks in sequence: (1) dry  swallowing tasks; (2) non-swallowing tasks; and (3) wet swallowing tasks.For dry  swallowing, participants are instructed to gather saliva in their mouths and to swallow once. The non-swallowing tasks involve six maneuvers: throat clearing, yawning, sniffing, coughing, humming, and pronouncing vowels, as shown in The design of the protocol is intended to be generic for different bedside screening techniques and therefore should accommodate the existing evaluation methods, such as clinical examinations and questionnaires.Moreover, the protocol design is devoted to a computer-aided dysphagia screening tool using wearable technology and machine learning techniques or other classifier algorithms. In such a case, the signals of sensors corresponding to each swallowing maneuver can either be manually labelled by the professional watching the video recording, or be labelled by asking the participant to press a button or pedal during the swallowing episode ,22. The The proposed Comprehensive Assessment Protocol for Swallowing (CAPS) accounts for the evaluation of swallowing, dry  swallowing, and non-swallowing events. It integrates the IDDSI framework and other existing protocols in an attempt to assess possible otolarynpharyngeal biomotions. The significance of this protocol is two-fold. Firstly, it is more comprehensive and precise than the traditional one-step approach, in that it considers a variety of biomotions and tests based on different bolus textures and thicknesses. Secondly, the protocol facilitates the training process of the machine learning model for computer-aided dysphagia screening. Once the model is trained, professionals might not need to perform standalone swallowing assessments using CAPS or other bedside screening tests. Instead, the wearable system can monitor the swallowing process and objectively evaluate the risks continuously, even over a prolonged period of time. It should be noted that this protocol paper proposes tasks for the swallowing assessment but not the instrument of the assessment. Regardless, the accuracy, reliability, and validity of different instruments should be well-evaluated. The diagnosis of dysphagia should also be confirmed by standard instruments  after screening.The wet swallowing tasks draw on the IDDSI framework. In the framework, the texture and thickness levels are chosen to represent various physical characteristics of common foods given to dysphagia patients across all age groups, clinical settings, and cultural contexts . CorrespOn the other hand, particularly thick liquids and solid food contents may demand more force from the tongue to push the content through the oropharynx. Residues might remain in the pharynx following a swallow when an individual has weaker tongue muscles or pharyngeal muscles ,28,29. SThe dry  swallowing and non-swallowing tasks are drawn from our previous review synthesis . Dry . We also anticipated that some older adults might refuse to comply because they do not like a particular food, necessitating a switch to other foods/liquids with the same level of texture or thickness. Future studies could consider integrating the continuous or frequent computer-aided swallowing assessment with dysphagia training for those who are at risk, for example, using virtual reality with biofeedback systems . In the This paper proposes the Comprehensive Assessment Protocol for Swallowing (CAPS) based on our existing review, protocols from existing papers, and existing standards, including the IDDSI framework and water swallowing test, etc. The protocol consists of a pre-test phase to determine the bolus volume and an assessment phase based on dry  swallowing, wet swallowing, and non-swallowing tasks. The protocol is designed for the development of computer-aided screening tool, in which the swallowing maneuvers can be classified using a machine learning model or other classifier algorithms. Nevertheless, the protocol design is generic, and we anticipate that it could also be applied to other non-instrumented bedside screening methods.