Search Results (388)

Background: Stroke is a leading cause of seizures and epilepsy, both of which are linked to increased mortality, disability, and hospital readmissions. Early recognition and management of seizures in acute stroke are crucial for improving outcomes. Electroencephalogram (EEG) is not routinely used for post-stroke seizure monitoring and is typically initiated only after clinical suspicion arises, making bedside recognition essential. This scoping review aimed to map the existing literature on clinical methods used for identifying and observing early post-stroke seizures (EPSSs) at the bedside. Methods: We included literature involving adults with acute ischaemic stroke or primary intracerebral haemorrhage who were diagnosed or suspected of having inpatient EPSS. Searches were conducted in Medline, CINAHL, Embase, and the Cochrane Library for English-language publications up to April 2023. Eligible sources included primary research, case reports, systematic reviews, clinical guidelines, consensus statements, and expert opinion. Reference lists of included articles were also reviewed. Data were charted and synthesised to assess the scope, type, and gaps in the evidence. Results: Thirty papers met inclusion criteria: 17 research studies, six expert opinions, four case reports, and three clinical guidelines. Empirical evidence on clinical methods for seizure recognition and monitoring in acute stroke was limited. No studies evaluated the effectiveness of different approaches, and existing recommendations lacked detail and consensus. Conclusions: Accurate EPSS diagnosis is vital due to its impact on outcomes. This review highlights inconsistency in monitoring methods and a clear need for targeted research into effective clinical identification strategies in acute stroke care. Full article

This initial exploratory study examined the perceptions of teachers and school leaders in the Republic of Ireland regarding diversity in promotions to school principalship, framed by Equity Theory, Organisational Justice Theory, and Legitimacy Theory. A mixed-methods approach was utilised within this study. Data was collected from 123 participants via an online survey comprising Likert-type statements and open-ended questions. This data was analysed using descriptive statistics and quantitative analysis for the Likert-type statements and thematic analysis was used to examine the qualitative responses, allowing for the identification of recurring patterns and themes to complement the quantitative findings. Findings indicated disparities between perceived and desired prioritisation of diversity, alongside varied perceptions of its impact on school performance and leadership. Disability, social class, and religious diversity were perceived as the least prioritised in promotion practices, while gender and cultural diversity received greater support and were more frequently linked to positive leadership outcomes. Participants reported mixed perceptions across diversity dimensions, with gender, age, and cultural diversity associated with the most positive impacts. Concerns about tokenism and the perceived undermining of merit-based promotion were widespread, reflecting the importance of fairness, transparency, and alignment with stakeholder expectations. The study underscored the need for promotion processes that are both equitable and credible, and for organisational cultures that enable diverse leaders to thrive. These findings provided a foundation for further research and policy development to foster inclusive and representative school leadership in Ireland. Full article

Background: First rib stress fractures (FRSFs) are exceptionally rare in skeletally immature athletes and are frequently overlooked because their symptoms mimic more common scapular conditions such as scapular dyskinesis or thoracic outlet syndrome. Early and accurate identification is critical to avoid delayed union, prolonged disability, and misdirected management. Case Presentation: We report a 12-year-old elite baseball pitcher with progressive scapular winging and audible snapping during pitching. Unlike typical posterior-type fractures near the costotransverse joint, imaging revealed a cortical discontinuity precisely at the serratus anterior enthesis, consistent with repetitive traction enthesopathy. High-resolution musculoskeletal ultrasound (MSK-US) identified cortical disruption with periosteal edema, and dynamic ultrasound reproduced the patient’s snapping and pain in real time, establishing a direct clinical–imaging correlation. Conservative three-phase rehabilitation (scapular stabilization, serratus anterior activation, and structured return-to-throwing) led to complete union and pain-free return to sport within 12 weeks. Discussion: This case highlights the superior diagnostic efficacy of MSK-US for FRSFs in adolescents. The posterior scanning approach facilitated bilateral comparison and growth plate assessment. Dynamic examination provided a functional correlation beyond static imaging, identifying a novel snapping mechanism. This underscores the value of MSK-US in visualizing not just anatomy but also pathophysiology. Conclusions: This is among the youngest documented cases of first rib stress fracture diagnosed with dynamic ultrasound. Its novelty lies in the following: (1) occurrence at the serratus anterior enthesis, (2) reproduction of snapping during provocative maneuvers, and (3) expansion of the etiological spectrum of scapular dyskinesis to include rib pathology. Dynamic ultrasound should be considered a frontline modality for adolescent throwers with unexplained periscapular pain. Full article

The integration of edge computing (EC) and Internet of Medical Things (IoMT) technologies facilitates the development of adaptive healthcare systems that significantly improve the accessibility and monitoring of individuals with disabilities. By enabling real-time disease identification and reducing response times, this architecture supports personalized healthcare solutions for those with chronic conditions or mobility impairments. The inclusion of untrusted devices leads to communication delays and enhances the security risks for medical applications. Therefore, this research presents a Trust-Driven Disability-Detection Model Using Secured Random Forest Classification (TTDD-SRF) to address the issues while monitoring real-time health records. It also increases the detection of abnormal movement patterns to highlight the indication of disability using edge-driven communication. The TTDD-SRF model improves the classification accuracy of abnormal motion detection while ensuring data reliability through trust scores computed at the edge level. Such a paradigm decreases the ratio of false positives and enhances decision-making accuracy in coping with health-related applications, mainly the detection of patients’ disabilities. The experimental analysis of the proposed TTDD-SRF model indicates improved performance in terms of network throughput by 48%, system resilience by 42%, device integrity by 49%, and energy consumption by 45% while highlighting the potential of medical systems using edge technologies, advancing assistive technology for healthcare accessibility. Full article

Background/Objectives: Autosomal recessive primary microcephaly is a rare and genetically heterogeneous disorder characterized by congenital non-syndromic microcephaly, with at least 28 causative genes identified to date. Biallelic variants in the CDK5RAP2 gene, an ultra-rare cause of autosomal recessive primary microcephaly, lead to Primary Autosomal Recessive Microcephaly 3 (MCPH3). Methods: We present seven patients from six families diagnosed with MCPH3 in light of clinical and molecular findings using whole-exome sequencing (WES). Furthermore, we investigated the effects of the identified intronic variants on splicing through RNA analysis. Results: Almost all patients had severe microcephaly, mild to moderate intellectual disability, speech delay, and cutaneous pigmentary abnormalities. Four patients presented with postnatal short stature, and two showed weight deficiency. Dysmorphic evaluation revealed that the most prominent features included brachycephaly, hypertelorism, epicanthus, high-arched eyebrows, prominent nasal bridge, and micrognathia. We identified five distinct homozygous CDK5RAP2 variants in our patients, including four novel variants. Segregation analysis verified that the parents were carriers. Two of these variants were intronic (c.3148+5G>C and c.383+4dupA), two were frameshift (c.3168del), and one was a nonsense variant (c.1591C>T). Both intronic variants disrupted splicing, generating a premature stop codon and resulting in a truncated protein. Conclusions: This study broadens the mutational landscape of CDK5RAP2. We also sought to demonstrate the functional consequences of the CDK5RAP2 intronic variants on gene function using RNA analysis. The identification of four novel variants underscores the importance of molecular diagnostics in patients with primary microcephaly and provides valuable data for genetic counseling and future functional studies. Full article

Background: Severe traumatic brain injury (sTBI) in adults remains a leading cause of mortality and disability worldwide. Early identification of reliable predictors of outcome is crucial for risk stratification and ICU management. Disturbances of hemostasis and metabolic factors such as body mass index (BMI) have been proposed as potential prognostic markers, but evidence remains limited. Methods: We conducted a retrospective, multicenter study including 307 adult patients with sTBI (Glasgow Coma Scale ≤ 8) admitted to three tertiary intensive care units in Ukraine between September 2023 and July 2024. All patients underwent surgical evacuation of hematomas and decompressive craniotomy. Laboratory parameters (APTT, INR, fibrinogen, platelets, D-dimer) were collected within 12 h of admission. BMI was calculated from measured height and weight. Predictive modeling was performed using L1-regularized logistic regression and Random Forest algorithms. Class imbalance was addressed with SMOTE. Model performance was assessed by AUC, accuracy, calibration, and feature importance. Results: The 28-day all-cause mortality was 32.9%. Compared with survivors, non-survivors had significantly lower GCS scores and higher INR, D-dimer, and APTT values. Very high VIF values indicated severe multicollinearity between predictors. Classical logistic regression was not estimable due to perfect separation; therefore, regularized logistic regression and Random Forest were applied. Random Forest demonstrated higher performance (AUC 0.95, accuracy ≈ 90%) than logistic regression (AUC 0.77, accuracy 70.1%), although results must be interpreted cautiously given the small sample size and potential overfitting. Feature importance analysis identified increased BMI, prolonged APTT, and elevated D-dimer as leading predictors of mortality. Sensitivity analysis excluding BMI still yielded strong performance (AUC 0.91), confirming the prognostic value of coagulation markers and GCS. Conclusions: Mortality in adult sTBI patients was strongly associated with impaired hemostasis, obesity, and low neurological status at admission. Machine learning-based modeling demonstrated promising predictive accuracy but is exploratory in nature. Findings should be interpreted with caution due to retrospective design, severe multicollinearity, potential overfitting, and absence of external validation. Larger, prospective, multicenter studies are needed to confirm these results and improve early risk stratification in severe TBI. Full article

Introduction: Stroke remains a major cause of disability worldwide, and precise identification of stroke lesions is essential for prognosis and rehabilitation planning. Machine learning has emerged as a powerful tool for automating stroke lesion segmentation and outcome prediction; however, these tasks are often studied in isolation. The two strategies are inherently interdependent since segmentation provides lesion-based features that directly inform prediction models. Methods: This narrative review synthesises studies published between 2010 and 2024 on the application of machine learning in stroke lesion segmentation and recovery forecasting. A total of 23 relevant studies were reviewed, including 10 focused on lesion segmentation and 13 on recovery prediction. Results: Convolutional Neural Networks (CNNs), including architectures such as U-Net, have improved segmentation accuracy on the Anatomical Tracings of Lesions After Stroke (ATLAS) V2 dataset; however, dataset bias and inconsistent evaluation metrics limit comparability. Integrating imaging-derived lesion characteristics with clinical features improves predictive accuracy at a higher level. Furthermore, semi-supervised and self-supervised methods enhanced performance where annotated datasets are scarce. Discussion: The review highlights the interdependence between segmentation and outcome prediction. Reliable segmentation provides biologically meaningful features that underpin recovery forecasting, while prediction tasks validate the clinical relevance of segmentation outputs. This bidirectional relationship underlines the need for unified pipelines integrating lesion segmentation with outcome prediction. Future research can improve generalisability and foster clinically robust models by advancing semi-supervised and self-supervised learning, bridging the gap between automated image analysis and patient-centred prognosis. Conclusion: Accurate lesion segmentation and outcome prediction should be viewed not as separate goals but as mutually reinforcing components of a single pipeline. Progress in segmentation strengthens recovery forecasting, while predictive modelling emphasises the clinical importance of segmentation outputs. This interdependence provides a pathway for developing more effective, generalisable, and relevant AI-driven stroke care tools. Full article

Long COVID, characterized by persistent symptoms following acute SARS-CoV-2 infection, has emerged as a significant public health challenge with wide-ranging clinical and socioeconomic implications. Developing an effective risk assessment strategy is essential for the early identification and management of individuals susceptible to prolonged symptoms. This study uses a quantitative approach to characterize the dose–response relationships between spike protein concentrations and effects, including Long COVID symptom numbers and the release of proinflammatory mediators. A mathematical model is also developed to describe the time-dependent change in spike protein concentrations post diagnosis in twelve Long COVID patients with a cluster analysis. Based on the spike protein concentration–Long COVID symptom numbers relationship, we estimated a maximum symptom number (~20) that can be used to reflect a persistent predictor. We found that among the crucial biomarkers associated with Long COVID proinflammatory mediator, CXCL8 has the lowest 50% effective dose (0.01 μg mL⁻¹), followed by IL-6 (0.39), IL-1β (0.46), and TNF-α (0.56). This work provides a comprehensive risk assessment strategy with dose–response tools and mathematical modeling developed to estimate potential spike protein concentration. Our study suggests persistent Long COVID guidelines for personalized care strategies and could inform public health policies to support early interventions that reduce long-term disability and healthcare burdens with possible other post-infection syndromes. Full article

Traumatic brain injury (TBI), a leading cause of mortality and disability, recognizes a primary, immediate injury due to external forces, and a secondary phase that includes inflammation that can lead to complications such as the post-traumatic confusional state (PTCS), potentially impacting long-term neurological recovery. An earlier identification of these complications, including PTCS, upon admission to intensive rehabilitation units (IRU) could possibly allow the design of personalized rehabilitation protocols in the immediate post-acute phase of moderate-to-severe TBI. The present study aims to identify potential biomarkers to distinguish between TBI patients with and without PTCS. We analyzed cellular and molecular mechanisms involved in neuroinflammation (IL-6, IL-1β, IL-10 cytokines), neuroendocrine function (norepinephrine, NE, epinephrine, E, dopamine), and neurogenesis (glial cell line-derived neurotrophic factor, GDNF, insuline-like growth factor 1, IGF-1, nerve growth factor, NGF, brain-derived growth factor, BDNF) using enzyme-linked immunosorbent assay (ELISA), comparing results between 29 TBI patients (17 with PTCS and 12 non-confused) and 34 healthy controls (HC), and correlating results with an actigraphy-derived sleep efficiency parameter. In TBI patients compared to HC, serum concentration of (1) pro-inflammatory IL-1β cytokine was significantly increased while that of anti-inflammatory IL-10 cytokine was significantly decreased; (2) NE, E and DA were significantly increased; (3) GDNF, NGF and IGF-1 were significantly increased while that of BDNF was significantly decreased. Importantly, IL-10 serum concentration was significantly lower in PTCS than in non-confused patients, correlating positively with an improved actigraphy-derived sleep efficiency parameter. An anti-inflammatory environment may be associated with better prognosis after TBI. Full article

Background/Objectives: Bones are essential components of the human body, providing structural support, enabling mobility, storing minerals, and protecting internal organs. Bone fractures (BFs) are common injuries that result from excessive physical force and can lead to serious complications, including bleeding, infection, impaired oxygenation, and long-term disability. Early and accurate identification of fractures through radiographic imaging is critical for effective treatment and improved patient outcomes. However, manual evaluation of X-rays is often time-consuming and prone to diagnostic errors due to human limitations. To address this, artificial intelligence (AI), particularly deep learning (DL), has emerged as a powerful tool for enhancing diagnostic precision in medical imaging. Methods: This research introduces a novel convolutional neural network (CNN) model, the Multi-Level Feature Fusion Network (MLFNet), designed to capture and integrate both low-level and high-level image features. The model was evaluated using the Bone Fracture Multi-Region X-ray (BFMRX) dataset. Preprocessing steps included image normalization, resizing, and contrast enhancement to ensure stable convergence, reduce sensitivity to lighting variations in radiographic images, and maintain consistency. Ablation studies were conducted to assess architectural variations, confirming the model’s robustness and generalizability across data distributions. MLFNet’s high accuracy, interpretability, and efficiency make it a promising solution for clinical deployment. Results: MLFNet achieved an impressive accuracy of 99.60% as a standalone model and 98.81% when integrated into hybrid ensemble architectures with five leading pre-trained DL models. Conclusions: The proposed approach supports timely and precise fracture detection, optimizing the diagnostic process and reducing healthcare costs. This approach offers significant potential to aid clinicians in fields such as orthopedics and radiology, contributing to more equitable and effective patient care. Full article

Maintaining muscle health is essential for preserving mobility, independence, and quality of life with age. As muscle mass and function decline, the risk of frailty, chronic disease, and disability increases. Sarcopenia, characterized by the progressive loss of muscle mass, strength, and function, is a major contributor to these adverse outcomes in older adults. Early identification and monitoring of sarcopenia are critical for timely intervention to prevent irreversible decline. Muscle biomarkers offer a promising approach for detecting muscle deterioration and guiding treatment strategies. This review explores key biomarkers—including insulin-like growth factor 1 (IGF-1), myostatin, interleukin-6 (IL-6), irisin, interleukin 15 (IL-15), and procollagen type III N-terminal propeptide (P3NP)—that reflect underlying processes such as muscle anabolism, inflammation, metabolism, and remodeling. Alterations in these markers are associated with muscle health status. Furthermore, hormonal status, biological sex, and nutritional factors all modulate biomarker levels, emphasizing the need for personalized assessments. Integrating biomarker analysis into clinical practice has the potential to enhance early diagnosis, inform personalized interventions, and ultimately promote healthy aging by maintaining muscle function and reducing disability risk. Full article

Stroke is a major global cause of death and disability and necessitates both quick diagnosis and treatment within narrow windows of opportunity. CT scanning is still the first-line imaging in the acute phase, but correct interpretation may not always be readily available and may not be resource-available in poor and rural health systems. Automated stroke classification systems can offer useful diagnostic assistance, but clinical application demands high accuracy and explainable decision-making to maintain physician trust and patient safety. In this paper, a ResNet-18 model was trained on 6653 CT brain scans (hemorrhagic stroke, ischemia, normal) with two-phase fine-tuning and transfer learning, XRAI explainability analysis, and web-based clinical decision support system integration. The model performed with 95% test accuracy with good performance across all classes. This system has great potential for emergency rooms and resource-poor environments, offering quick stroke evaluation when specialists are not available, particularly by rapidly excluding hemorrhagic stroke and assisting in the identification of ischemic stroke, which are critical steps in considering tissue plasminogen activator (tPA) administration within therapeutic windows in eligible patients. The combination of classification, explainability, and clinical interface offers a complete framework for medical AI implementation. Full article

Identification of temporal relations among suicide mortality and economic/political implementations provides important information for not only planning suicide prevention but also socioeconomic/psychosocial measures. This cross-sectional observation study analyzed temporal fluctuations and causalities of suicide mortalities of working-age individuals, disaggregated by age/gender/social standing (employed/unemployed), in Japan from 2009 to 2024, using government databases, by joinpoint and vector-autoregressive analyses. Suicide mortality among total and employed females decreased until the COVID-19 pandemic outbreak but sharply increased, synchronized with the pandemic outbreak, before resuming a downward trend. Among males, the decreasing trends attenuated from 2016, followed by a transient increase in 2022. Unemployed males aged 40–69 exhibited four joinpoints: 2016 (decreasing–increasing), 2018 (increasing–decreasing), 2022 (decreasing–increasing), and 2023 (increasing–stable). In contrast, suicide mortality among unemployed females aged 40–69 sharply increased in 2022 and maintained the high level. Among individuals aged 30–39, suicide mortality reversed from decreasing to increasing in 2016 (males) and 2018 (unemployed females). Economic expansion was protective for employed individuals but had no significant effect on unemployed populations. The government management instability (AENROP) index was positively associated with suicide mortality among employed and unemployed males and employed females. Unemployed females aged 30–39 were sensitive to AENROP but not economic conditions, while those aged 40–69 were largely unaffected by either. Increasing employment of individuals with psychiatric disabilities was positively associated with suicide mortality among unemployed males (30–69) and females under 40. Positive impacts of the employment rates of individuals with psychiatric disabilities and unemployment enhanced from 2016 and 2022, respectively, whereas the impacts were inconstantly affected by political rather than economic factors. Suicide mortality among unemployed individuals has emerged as a critical public health concern in Japan, with rates more than doubling among males and tripling among females in the 2020s. These findings underscore the need for integrated suicide prevention policies that address both labor market vulnerabilities and psychosocial determinants. Full article

Background: By leveraging advanced wireless technologies, Healthcare Industry 5.0 promotes the continuous monitoring of real-time medical acquisition from the physical environment. These systems help identify early diseases by collecting health records from patients’ bodies promptly using biosensors. The dynamic nature of medical devices not only enhances the data analysis in medical services and the prediction of chronic diseases, but also improves remote diagnostics with the latency-aware healthcare system. However, due to scalability and reliability limitations in data processing, most existing healthcare systems pose research challenges in the timely detection of personalized diseases, leading to inconsistent diagnoses, particularly when continuous monitoring is crucial. Methods: This work propose an adaptive and secure framework for disability identification using the Internet of Medical Things (IoMT), integrating edge computing and artificial intelligence. To achieve the shortest response time for medical decisions, the proposed framework explores lightweight edge computing processes that collect physiological and behavioral data using biosensors. Furthermore, it offers a trusted mechanism using decentralized strategies to protect big data analytics from malicious activities and increase authentic access to sensitive medical data. Lastly, it provides personalized healthcare interventions while monitoring healthcare applications using realistic health records, thereby enhancing the system’s ability to identify diseases associated with chronic conditions. Results: The proposed framework is tested using simulations, and the results indicate the high accuracy of the healthcare system in detecting disabilities at the edges, while enhancing the prompt response of the cloud server and guaranteeing the security of medical data through lightweight encryption methods and federated learning techniques. Conclusions: The proposed framework offers a secure and efficient solution for identifying disabilities in healthcare systems by leveraging IoMT, edge computing, and AI. It addresses critical challenges in real-time disease monitoring, enhancing diagnostic accuracy and ensuring the protection of sensitive medical data. Full article

Stroke rehabilitation requires comprehensive assessments aligned with the International Classification of Functioning, Disability, and Health (ICF) biopsychosocial model. Linking assessment tools to the ICF helps integrate this approach by identifying aspects of functioning they address. This study aimed to analyze the content of the most used assessment tools for post-stroke rehabilitation through systematic linkage with the ICF. A scoping review was conducted, including (1) the identification of clinical trials on post-stroke rehabilitation published between 2014 and 2024 in the PubMed, LILACS, SciELO, and PEDro databases to select the most commonly used assessment tools, followed by (2) the ICF linkage methodology to map the most cited tools to the content of ICF categories and domains. From the 897 studies reviewed, 29 tools were identified—21 were newly linked and 8 had pre-existing ICF links. The analysis identified 261 ICF categories: 53% related to Activities, 31% to Body Functions, 15% to Participation, and 1% to Environmental Factors. No tool covered the Body Structure domain. The findings highlight a focus on Activities and Body Functions, reinforcing the need to integrate Participation and Environmental Factors into post-stroke rehabilitation assessments. The results offer an overview of ICF categories covered by each tool, supporting informed decisions in rehabilitation research and practice. Full article