Search Results (186)

Background/Objectives: Neurodegenerative proteinopathies, such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and dementia with Lewy bodies (DLB), are increasingly prevalent worldwide mainly due to population aging. These conditions are marked by complex etiologies, overlapping pathologies, and progressive clinical decline, with significant consequences for patients, caregivers, and healthcare systems. This review aims to synthesize evidence on the healthcare complexities of major neurodegenerative proteinopathies to highlight current knowledge gaps, and to inform future care models, policies, and research directions. Methods: We conducted a comprehensive literature search in PubMed/MEDLINE using combinations of MeSH terms and keywords related to neurodegenerative diseases, proteinopathies, diagnosis, sex, management, treatment, caregiver burden, and healthcare delivery. Studies were included if they addressed the clinical, pathophysiological, economic, or care-related complexities of aging-related neurodegenerative proteinopathies. Results: Key themes identified include the following: (1) multifactorial and unclear etiologies with frequent co-pathologies; (2) long prodromal phases with emerging biomarkers; (3) lack of effective disease-modifying therapies; (4) progressive nature requiring ongoing and individualized care; (5) high caregiver burden; (6) escalating healthcare and societal costs; and (7) the critical role of multidisciplinary and multi-domain care models involving specialists, primary care, and allied health professionals. Conclusions: The complexity and cost of neurodegenerative proteinopathies highlight the urgent need for prevention-focused strategies, innovative care models, early interventions, and integrated policies that support patients and caregivers. Prevention through the early identification of risk factors and prodromal signs is critical. Investing in research to develop effective disease-modifying therapies and improve early detection will be essential to reducing the long-term burden of these disorders. Full article

Background/Objectives: Effective teamwork is crucial for minimizing human error in healthcare settings. Medical teams, typically composed of physicians and nurses, supported by auxiliary professionals, achieve better outcomes when they possess strong collaborative competencies. High-quality teamwork is associated with fewer adverse events and complications and lower mortality rates. Based on this background, the objective of this study is to analyze the perception of non-technical skills and immediate learning outcomes in interprofessional simulation settings based on E-CRM items. Methods: A cross-sectional observational study was conducted involving participants from the official postgraduate Medicine and Nursing programs at the Catholic University of Murcia (UCAM) during the 2024–2025 academic year. Four interprofessional E-CRM simulation sessions were planned, involving randomly assigned groups with proportional representation of medical and nursing students. Teams worked consistently throughout the training and participated in clinical scenarios observed via video transmission by their peers. Post-scenario debriefings followed INACSL guidelines and employed the PEARLS method. Results: Findings indicate that 48.3% of participants had no difficulty identifying the team leader, while 51.7% reported minor difficulty. Role assignment posed moderate-to-high difficulty for 24.1% of respondents. Communication, situation awareness, and early help-seeking were generally managed with ease, though mobilizing resources remained a challenge for 27.5% of participants. Conclusions: This study supports the value of interprofessional education in developing essential competencies for handling urgent, emergency, and high-complexity clinical situations. Strengthening interdisciplinary collaboration contributes to safer, more effective patient care. Full article

Background: Type 2 diabetes (T2D) and overweight or obesity are strongly associated, with a high prevalence of these concomitant conditions contributing significantly to global healthcare costs. Given this burden, there is an urgent need for effective interventions. Mobile health (mHealth) technologies represent a promising strategy to address both conditions simultaneously. Objectives: This systematic review and meta-analysis aimed to evaluate the effectiveness of mHealth-based interventions for the management of adults with T2D and overweight/obesity. Specifically, it assessed the quantitative impact of these interventions on glycosylated hemoglobin (HbA1c), body weight, triglycerides, total cholesterol, low-density lipoprotein (LDL), and high-density lipoprotein (HDL) levels. Methods: A systematic search was conducted in PubMed, Web of Science, and Scopus databases from inception to 9 July 2025. The inclusion criteria focused on randomized controlled trials (RCTs) using mHealth interventions in adults with T2D and overweight/obesity, reporting HbA1c or weight as primary or secondary outcomes. The risk of bias was assessed using the Cochrane Risk of Bias tool 2. A total of 13 RCTs met the inclusion criteria. Results: Meta-analysis indicated significant improvements after 6–12 months of intervention in HbA1c (MD −0.23; 95% CI −0.36 to −0.10; p < 0.001; I² = 72%), body weight (MD −2.47 kg; 95% CI −3.69 to −1.24; p < 0.001; I² = 79%), total cholesterol (MD −0.23; 95% CI −0.39 to −0.07; p = 0.004; I² = 0%), and LDL (MD −0.27; 95% CI −0.42 to −0.12; p < 0.001; I² = 0%). Conclusions: mHealth interventions are effective and scalable for managing T2D and obesity, particularly when incorporating wearable technologies to improve adherence. Future research should focus on optimizing personalization, engagement strategies, and long-term implementation. Full article

Weeksella virosa (W. virosa) is a rare, non-saccharolytic Gram-negative bacterium initially described in the 1970s, later proposed as a distinct genus in 1986. The genus Weeksella currently contains two species, namely W. virosa and W. massiliensis. Although primarily considered non-pathogenic, recent evidence has linked W. virosa to a limited number of clinical infections, mostly in immunocompromised patients. This review aims to consolidate the current body of knowledge on W. virosa, encompassing its microbiological and biochemical characteristics, involvement in human and animal infections, antimicrobial susceptibility profiles, and a critical evaluation of existing diagnostic methodologies. This review includes 13 case reports detailing 16 human cases retrieved from multiple databases, highlighting diagnostic inconsistencies and a lack of standardized antimicrobial susceptibility testing. Although W. virosa is generally susceptible to most antibiotics with the exception of aminoglycosides, recent reports seem to suggest a possible emerging resistance trend. The presence of this organism in hospital environments raises concerns about its potential transmission within healthcare settings. While biochemical testing appears to offer reasonably accurate identification of W. virosa, molecular confirmation may be warranted in some cases mainly due to the organism’s rarity. The reliability of MALDI-TOF MS for the identification of W. virosa remains currently uncertain. Further studies, including electron microscopy and genome-wide analysis, are urgently needed to clarify the pathogenic potential of this bacterium and guide clinical management. This review underscores the necessity for awareness among clinicians and microbiologists regarding this underrecognized pathogen. Full article

The exponential growth of video data across domains such as surveillance, transportation, and healthcare has raised critical challenges in scalability, real-time processing, and privacy preservation. While existing studies have addressed individual aspects of Video Big Data Analytics (VBDA), an integrated, up-to-date perspective remains limited. This paper presents a comprehensive survey of system architectures and enabling technologies in VBDA. It categorizes system architectures into four primary types as follows: centralized, cloud-based infrastructures, edge computing, and hybrid cloud–edge. It also analyzes key enabling technologies, including real-time streaming, scalable distributed processing, intelligent AI models, and advanced storage for managing large-scale multimodal video data. In addition, the study provides a functional taxonomy of core video processing tasks, including object detection, anomaly recognition, and semantic retrieval, and maps these tasks to real-world applications. Based on the survey findings, the paper proposes ViMindXAI, a hybrid AI-driven platform that combines edge and cloud orchestration, adaptive storage, and privacy-aware learning to support scalable and trustworthy video analytics. Our analysis in this survey highlights emerging trends such as the shift toward hybrid cloud–edge architectures, the growing importance of explainable AI and federated learning, and the urgent need for secure and efficient video data management. These findings highlight key directions for designing next-generation VBDA platforms that enhance real-time, data-driven decision-making in domains such as public safety, transportation, and healthcare. These platforms facilitate timely insights, rapid response, and regulatory alignment through scalable and explainable analytics. This work provides a robust conceptual foundation for future research on adaptive and efficient decision-support systems in video-intensive environments. Full article

Botulism is a severe muscle paralysis disease mediated by the botulinum toxin. Here, we reported a foodborne botulism case caused by Clostridium botulinum subtype A5(b3) from self-packaged vacuum spicy rabbit heads. Treatment for this case was delayed due to misdiagnosis and insufficient diagnostic capacity in three hospitals, which resulted in progressive clinical deterioration, and eventually, the patient was transferred to Shandong Public Health Clinical Center for specialized therapy. The case was suspected as foodborne botulism by the Qilu Medical-Prevention Innovation Integration pathway and multi-disciplinary consultation. An epidemiological investigation and laboratory confirmation revealed that the botulinum neurotoxin originated from vacuum-packaged spicy rabbit heads distributed via interprovincial cold chain logistics. After treatment with botulism antiserum, the patient’s condition significantly improved, and they were discharged after recovery. We revealed that this foodborne botulism outbreak was caused by the Clostridium botulinum A5(b3) subtype from food by whole-genome sequencing and SNP typing. All the strains belonged to Group I carrying the botulinum neurotoxin gene classified as the ha cluster. Toxin A was confirmed by MBA and other methods, while toxin B was non-functional due to the truncated bont/B gene. Other virulence genes and antibiotic resistance genes were also detected. Our findings indicate that self-packaged vacuum meat products represent an emerging risk factor for botulism transmission when stored improperly. Importantly, the recurrent misdiagnosis in this case underscored the urgent need to enhance the training of healthcare professionals in medical institutions to improve the diagnostic accuracy and clinical management of botulism. Full article

The COVID-19 pandemic exposed the urgent need for scalable, reliable telemedicine tools to manage mild cases remotely and avoid overburdening healthcare systems. This study evaluates StepCare, a remote monitoring medical device, during the first pandemic wave at a single center in Spain. Among 35 patients monitored, StepCare showed high clinical reliability, aligning with physician assessments in 90.4% of cases. Patients and clinicians reported excellent usability and satisfaction. The system improved workflow efficiency, reducing triage time by 25% and associated costs by 84%. These results highlight StepCare’s value as a scalable, patient-centered solution for remote care during health crises. Full article

Background: Occupational burnout and fatigue are critical issues affecting the health and performance of healthcare workers (HCWs) globally. These outcomes are often driven by complex and overlapping work-related stressors, which remain insufficiently understood in combination. Objective: To investigate the associations of multiple work-related stressors with occupational burnout and fatigue, and to identify distinct stress patterns and critical stressors among HCWs. Method: A cross-sectional survey was conducted using a self-administered electronic questionnaire among 2695 HCWs in Shanghai, China. Validated questionnaire scales were used to assess work-related stress (self-developed occupational stress scale for medical staff, CSSM), occupational burnout (Maslach Burnout Inventory–General Survey, MBI-GS), and fatigue (Fatigue Scale-14, FS-14). Latent profile analysis (LPA) was employed to identify distinct work-related stress patterns. Generalized linear models (GLMs) were used to explore the associations between individual stressors, stress patterns, and occupational burnout and fatigue. Additionally, weighted quantile sum (WQS) models were utilized to evaluate the combined effects of multiple stressors and identify the main contributors. Results: In this study, 77.0% and 71.2% of participants were classified as experiencing occupational burnout and fatigue, respectively. A strained doctor–patient relationship was the highest-rated work-related stressor. All work-related stressors, including career development, interpersonal relationships, work–life imbalance, physical environment, doctor–patient relationship, social environment, and workload, were significantly associated with burnout (β: 0.444~0.956, p < 0.001) and fatigue (β: 1.384~3.404, p < 0.001). WQS assigned higher weights to career development and workload for burnout, and to workload and work–life imbalance for fatigue. LPA identified two distinct occupational stress patterns. HCWs characterized by higher stress levels in physical environment, career development, workload, and interpersonal relationships exhibited significantly higher burnout scores (β = 0.325, 95% CI: 0.122, 0.528), particularly in the reduced personal accomplishment (PA) dimension (β = 1.003, 95% CI: 0.746, 1.259). Conclusions: This study highlighted the high prevalence of occupational burnout and fatigue among HCWs in Shanghai, China. Occupational stressors were associated with both burnout and fatigue, with higher workload, work–life imbalance, and poorer career development showing particularly significant contributions. These findings emphasized the urgent need for targeted interventions, including workload management, career development programs, and mental health support, to reduce occupational stress and mitigate its adverse effects on HCWs. Full article

Large language models (LLMs) are transforming the capabilities of medical chatbots by enabling more context-aware, human-like interactions. This review presents a comprehensive analysis of their applications, technical foundations, benefits, challenges, and future directions in healthcare. LLMs are increasingly used in patient-facing roles, such as symptom checking, health information delivery, and mental health support, as well as in clinician-facing applications, including documentation, decision support, and education. However, as a study from 2024 warns, there is a need to manage “extreme AI risks amid rapid progress”. We examine transformer-based architectures, fine-tuning strategies, and evaluation benchmarks specific to medical domains to identify their potential to transfer and mitigate AI risks when using LLMs in medical chatbots. While LLMs offer advantages in scalability, personalization, and 24/7 accessibility, their deployment in healthcare also raises critical concerns. These include hallucinations (the generation of factually incorrect or misleading content by an AI model), algorithmic biases, privacy risks, and a lack of regulatory clarity. Ethical and legal challenges, such as accountability, explainability, and liability, remain unresolved. Importantly, this review integrates broader insights on AI safety, drawing attention to the systemic risks associated with rapid LLM deployment. As highlighted in recent policy research, including work on managing extreme AI risks, there is an urgent need for governance frameworks that extend beyond technical reliability to include societal oversight and long-term alignment. We advocate for responsible innovation and sustained collaboration among clinicians, developers, ethicists, and regulators to ensure that LLM-powered medical chatbots are deployed safely, equitably, and transparently within healthcare systems. Full article

Background/Objectives: Clinical simulation-based training has become established as an effective strategy to improve healthcare quality and patient safety. This pre–post observational study presents an innovative experience implemented at the Catalan Health Institute (ICS) through the evaluation of a training intervention based on Simulated Clinical Stations in Quality and Patient Safety. The main objective is to improve the competencies of Primary Care Teams (PCT) professionals in managing critical and urgent situations and to assess the impact of the intervention on knowledge and satisfaction using an immersive methodology based in active practice. Methods: More than 8.916 professionals participated in 285 training sessions at the Balmes Primary Care Center (CAP Balmes) simulation center (Barcelona). Knowledge data were collected before and after the training, along with satisfaction levels, showing significant improvement. Results: The analyses show a significant improvement in the knowledge acquired and a high level of participant satisfaction, reinforcing the value of clinical simulation as a key training tool. Conclusions: The study reinforces clinical simulation as an essential, scalable, and adaptable educational tool across different healthcare settings, establishing itself as a key resource for the continuous training of healthcare professionals. Full article

The ongoing improvement in healthcare standards and the frequent occurrence of epidemic outbreaks have led to a significant rise in medical waste (MW) generation, revealing weaknesses in China’s existing management system and disposal technologies for MW. This situation underscores the urgent need to reassess the effectiveness of MW management and disposal practices. This study analyzes China’s centralized MW disposal capacity, the distribution of facilities, the evolution and characteristics of disposal technologies, and the current policy framework. Challenges such as limited technological applicability, inadequate disposal capacity in rural and remote areas, and a lack of emergency disposal capabilities have been identified. Given the new challenges and complexities in the MW industry, two strategies are proposed, including comprehensive whole-process management and the integration of routine and emergency disposal. This research offers a systematic solution for MW disposal, aiming to improve management practices, enhance disposal efficiency, and strengthen emergency response capabilities. Full article

Magnetic Resonance Imaging (MRI) services in high-complexity hospitals often suffer from operational inefficiencies, including suboptimal MRI machine utilization, prolonged patient waiting times, and inequitable service delivery across clinical priority levels. Addressing these challenges requires intelligent scheduling strategies capable of dynamically managing patient waitlists based on clinical urgency while optimizing resource allocation. In this study, we propose a novel framework that integrates a digital twin (DT) of the MRI operational environment with a reinforcement learning (RL) agent trained via Deep Q-Networks (DQN). The digital twin simulates realistic hospital dynamics using parameters extracted from a MRI publicly available dataset, modeling patient arrivals, examination durations, MRI machine reliability, and clinical priority stratifications. Our strategy learns policies that maximize MRI machine utilization, minimize average waiting times, and ensure fairness by prioritizing urgent cases in the patient waitlist. Our approach outperforms traditional baselines, achieving a 14.5% increase in MRI machine utilization, a 44.8% reduction in average patient waiting time, and substantial improvements in priority-weighted fairness compared to First-Come-First-Served (FCFS) and static priority heuristics. Our strategy is designed to support hospital deployment, offering scalability, adaptability to dynamic operational conditions, and seamless integration with existing healthcare information systems. By advancing the use of digital twins and reinforcement learning in healthcare operations, our work provides a promising pathway toward optimizing MRI services, improving patient satisfaction, and enhancing clinical outcomes in complex hospital environments. Full article

In the face of the limitations in pharmacological and surgical interventions for neurological conditions such as Parkinson’s and Alzheimer’s disease, patients are increasingly turning to non-pharmacological and alternative therapies to manage their symptoms and improve their quality of life. This shift underscores the urgent need for accessible, effective, and affordable treatments. This literature review examines a range of alternative and personalized therapies, including game therapy, animal-assisted therapy, dance therapy, art therapy, music therapy, aroma therapy, and shinrin-yoku therapy. These modalities have demonstrated promising results in mitigating symptoms and enhancing well-being among individuals grappling with neurological disorders. Moreover, these therapies offer a holistic approach that complements traditional medical interventions, underscoring the importance of integrating diverse treatment modalities. Despite their historical roots in non-clinical settings, their potential in modern clinical practice remains untapped. The findings suggest the necessity for further research, particularly large cohort studies, to validate the efficacy of these personalized therapies and advocate for their widespread adoption. In an era marked by escalating healthcare costs, the exploration of alternative therapies presents a compelling avenue for enhancing patient care while simultaneously addressing economic challenges within the healthcare system. Full article

Sex workers and formerly incarcerated people with a cervix face significant structural, interpersonal, and emotional barriers to cervical cancer screening, despite being at elevated risk for HPV infection. HPV self-sampling (HPV-SS) is a validated, user-directed method that has the potential to address these barriers, yet it remains excluded from Ontario’s organized screening program. This qualitative study explored the lived experiences of structurally marginalized individuals with a cervix who were offered HPV-SS as part of a mixed-methods pilot in the Greater Toronto Area. Five virtual focus groups were conducted with 34 participants, including both those who used the HPV-SS kit and those who declined it. Using inductive thematic analysis, we identified barriers to traditional screening including fear, stigma, mistrust of healthcare providers, logistical constraints, and a lack of accessible information. HPV-SS was widely described as more acceptable, empowering, and emotionally manageable, offering participants autonomy, privacy, and control over their care. Concerns included swab design, uncertainty about correct use, and unclear follow-up after positive results. Participants offered concrete, community-informed recommendations to improve HPV-SS implementation, including opt-in distribution models, gender-affirming language, and trauma-informed educational materials. The findings highlight the urgent need to integrate HPV-SS into organized screening programs to advance equitable access to cervical cancer prevention for marginalized populations. Full article

Background/Objectives: The COVID-19 pandemic has highlighted the urgent need for rapid, accurate, and accessible diagnostic testing to effectively manage and contain the spread of SARS-CoV-2. RT-PCR is widely recognized as the gold standard for SARS-CoV-2 detection due to its high sensitivity and specificity. However, RT-PCR testing requires specialized laboratory equipment, highly trained personnel, and extended processing times, which limits its feasibility for large-scale screening and point-of-care applications. This study aims to systematically evaluate the diagnostic performance of RT-PCR and a colloidal gold immunochromatographic assay (GICA). Methods: By comparing these two methods, we seek to determine a GICA’s effectiveness as a complementary or alternative diagnostic tool, particularly in resource-limited settings and scenarios requiring rapid, large-scale testing. We assessed the following key clinical parameters: sensitivity, specificity, NPV, PPV, and accuracy. Additionally, we investigated the correlation between GICA signal intensity and RT-PCR Ct values using regression analysis, receiver operating characteristic curve analysis, and the calculated area under the curve. Results: Our findings indicate that while RT-PCR exhibits superior sensitivity, GICA results demonstrate a strong correlation with RT-PCR results and provide a rapid, cost-effective alternative for SARS-CoV-2 detection. Unlike RT-PCR, which requires extensive resources and prolonged turnaround times, a GICA delivers results within 20 min, making it a viable option for decentralized testing and real-time public health interventions. Conclusions: These results suggest that a GICA can serve as a complementary diagnostic tool alongside RT-PCR, particularly in resource-limited settings and high-throughput screening scenarios. By integrating GICAs into broader testing strategies, healthcare systems can enhance early detection efforts, improve accessibility to diagnostics, and strengthen pandemic response measures. Full article