Search Results (962)

Point-of-care (POC) infectious disease diagnostics are reshaping global health by delivering rapid, decentralized, and clinically actionable results that link bedside testing to population-level surveillance. Valued at approximately USD 53 billion in 2024 and projected to nearly double by 2033, the global POC diagnostics market is driven by infectious disease assays and accelerated by innovations in molecular amplification, biosensors, microfluidics, and artificial intelligence (AI). This review integrates current evidence across technological, clinical, regulatory, and public health domains. Immunoassays remain the backbone of volume deployment, while molecular nucleic acid amplification tests (NAATs) and emerging CRISPR-based platforms achieve laboratory-grade sensitivity at the point of care. AI has transitioned from an experimental tool to an embedded analytical layer that enhances image interpretation, multiplex signal deconvolution, and automated quality control. Rigorous validation, including analytical accuracy, clinical performance in intended-use settings, and usability testing under CLIA guidance, remains central to ensuring reliability in decentralized environments. Regulatory frameworks are adapting in parallel: FDA’s lifecycle oversight of AI-enabled devices, the European IVDR’s expanded evidence requirements, and the WHO Prequalification all emphasize continuous post-market surveillance. From a public health perspective, POC diagnostics have improved early case detection, treatment initiation, and outbreak containment for HIV, tuberculosis, malaria, influenza, RSV, and COVID-19. Yet persistent challenges (including limited harmonization of standards, uneven reimbursement, and scarce real-world data from low- and middle-income countries) continue to constrain equitable adoption. POC infectious disease diagnostics are thus entering a pivotal phase of digitization and regulatory maturity. Addressing remaining gaps in validation, lifecycle monitoring, and implementation equity will determine whether these technologies achieve their full promise as clinical accelerators and as cornerstones of global infectious disease preparedness. Full article

Background: Addressing Missed Opportunities for Vaccination (MOV) contributes to increased vaccination rates in children, reinforcing the need to investigate and intervene in the related factors. Objective: To analyze factors associated with missed opportunities for vaccination in children under one year of age in a Brazilian capital. Methods: This was a cross-sectional, analytical study conducted in seven Basic Health Units in Teresina, Piauí, Brazil. A previously validated questionnaire was applied to parents or guardians of a sample of 316 children. Data were collected from March to June 2025. Multivariable Logistic Regression was performed, and results were expressed as Odds Ratios. Results: Among the children, 53.5% had at least one MOV. The associated factors were: parents with two or more children (95% CI: 1.06–2.96), false contraindications (95% CI: 1.29–8.73), inadequate assessment of vaccination cards by health professionals (95% CI: 1.78–29.00), vaccine shortages in health units (95% CI: 1.57–18.28), and refusal to open multidose vaccine vials (95% CI: 1.81–19.31). Receiving information about vaccination in the previous month was a protective factor against MOV (95% CI: 0.25–0.77). The vaccines most frequently contributing to MOV were BCG (15.8%) and the COVID-19 vaccine, with 15.5% for the first dose and 14.9% for the second. Conclusions: The high prevalence of MOV found in this study indicates weaknesses in the immunization process and suggests the need for implementing measures to interrupt the chain of causes leading to MOV, thereby contributing to the achievement of the objectives of the Brazilian National Immunization Program. Full article

Background and Objectives: Public health needs collaborative, privacy-preserving analytics, but centralized AI is constrained by data sharing and governance. Federated learning (FL) enables training without moving sensitive data. This review assessed how FL is used for disease prevention in population and public health, and mapped benefits, challenges, and policy implications. Methods: Following PRISMA 2020, we searched PubMed, Scopus, Web of Science, IEEE Xplore, and Google Scholar for peer reviewed English-language studies from January 2020–30 June 2025, applying FL to surveillance, outbreak detection, risk prediction, or policy support. Two reviewers screened and extracted data with third-reviewer arbitration. Quality was appraised with a tool adapted from MMAT and AI reporting frameworks. No meta-analysis was performed. Results: Of 5230 records identified (4720 after deduplication), 200 full texts were assessed and 19 were included. Most used horizontal FL across multiple institutions for communicable diseases, COVID-19, tuberculosis and some chronic conditions. Reported gains included privacy preservation across sites, better generalizability from diverse data, near real-time intelligence, localized risk stratification, and support for resource planning. Common barriers were non-IID data, interoperability gaps, compute and network limits in low-resource settings, unclear legal pathways, and concerns about fairness and transparency. Few studies linked directly to formal public-health policy or low-resource deployments. Conclusions: FL is promising for equitable, secure, and scalable disease-prevention analytics that respect data sovereignty. Priorities include robust methods for heterogeneity, interoperable standards, secure aggregation, routine fairness auditing, clearer legal and regulatory guidance, and capacity building in underrepresented regions. Full article

Neurocognitive symptoms have emerged as notable sequelae of SARS-CoV-2 infection (COVID-19). Although primarily a respiratory virus, SARS-CoV-2 has been associated with central nervous system (CNS) changes observed in both clinical and experimental settings. To better understand these effects and their pathological mechanisms, we conducted a systematic literature search of published studies and employed a qualitative, analytical approach to identify and synthesize key findings from peer-reviewed studies, including large-scale retrospective clinical cohorts, human autopsy reports, animal models (murine, non-human primate), and in vitro brain organoid systems. While viral components were detected in post mortem central nervous system tissues, COVID-19 neuropathology appears to stem primarily from immune-mediated inflammation and vascular injury rather than direct CNS infection. Persistent glial activation and BBB disruption may underlie the long-term neurological symptoms reported in long COVID-19. Although animal models offer mechanistic insight, species-specific differences necessitate cautious extrapolation to human pathology. Further investigation into the chronic effects of SARS-CoV-2 on the brain is essential to guide long-term clinical management and therapeutic development. Full article

Healthcare systems worldwide have faced unprecedented pressure during crises such as the COVID-19 pandemic, exposing limits in managing scarce hospital resources. Many predictive models remain static, unable to adapt to new variants, shifting conditions, or diverse patient populations. This work proposes a dynamic prioritization framework that recalculates severity scores in batch mode when new factors appear and applies them instantly through a streaming pipeline to incoming patients. Unlike approaches focused only on fixed mortality or severity risks, our model integrates dual datasets (survivors and non-survivors) to refine feature selection and weighting, enhancing robustness. Built on a big data infrastructure (Spark/Databricks), it ensures scalability and responsiveness, even with millions of records. Experimental results confirm the effectiveness of this architecture: The artificial neural network (ANN) achieved 98.7% accuracy, with higher precision and recall than traditional models, while random forest and logistic regression also showed strong AUC values. Additional tests, including temporal validation and real-time latency simulation, demonstrated both stability over time and feasibility for deployment in near-real-world conditions. By combining adaptability, robustness, and scalability, the proposed framework offers a methodological contribution to healthcare analytics, supporting fair and effective hospitalization prioritization during pandemics and other public health emergencies. Full article

Urban horticulture as a segment of urban agriculture can take various forms: home gardens, allotment farming, community gardens, community-supported agriculture, vertical farming, etc. After the COVID-19 pandemic in Croatia and neighboring countries, growing horticultural plants in urban and suburban areas became increasingly popular. The aim of the study was to investigate citizens’ attitudes towards the benefits and support of urban horticulture, its relationship to the environment, and needs and relevance in studies in the cities of Šibenik, Split, Mostar and Skopje. The research methods used for the purpose of this study were theoretical analysis method, survey and analytical descriptive and statistical method. The research was conducted online during the first half of 2024 on a sample of 506 respondents. The main goal of the paper was to examine the views of citizens on urban horticulture. With specific objectives, the views of citizens were examined on the benefits of urban horticulture, the relationship between urban horticulture and the environment, urban horticulture and plant protection, support for urban horticulture, and the needs and trends of urban horticulture. and plant protection, support for urban horticulture, needs and trends of urban horticulture. The results showed that citizens are mostly positive towards growing horticultural plants in urban and suburban areas without pollution. In urban horticulture, respondents prefer using ecological principles and products. Female respondents expressed more positive attitudes towards the fashionability and need for urban horticulture. Respondents from Skopje showed the most positive attitudes towards the benefits of urban horticulture and its relationship to the environment. Also, there is no statistically significant difference in attitudes towards urban horticulture with regard to the location of residence. The research contributes to the trend of development and promotion of urban horticulture with a special emphasis on the importance of environmental preservation. It also contributes to the development of an interdisciplinary method that connects natural and social sciences, and develops an empirical approach that can improve urban culture with the aim of preserving the environment. Full article

Health professionals’ safety is directly linked to organizational culture, and during the COVID-19 pandemic, weaknesses at organizational, emotional, professional, and structural levels became evident. This study aimed to assess health professionals’ perceptions of their safety in facing the COVID-19 pandemic. A cross-sectional, descriptive, and analytical study was conducted with professionals from the public health network of a Brazilian municipality who had taken medical leave due to COVID-19. Data were collected between October and December 2022 using the validated Questionnaire on Health Professional Safety in the COVID-19 Pandemic (QSP COVID-19), which consists of 30 items distributed across four domains: organizational, emotional, professional, and structural. A cut-off score ≥75 was considered a positive perception. Associations were tested using the chi-square test followed by the Bonferroni post hoc test, and multivariate logistic regression was applied to identify associated factors. Significance level: 5%. A total of 235 professionals participated, mostly women (81.7%), working in primary health care (68.1%), and employed under statutory contracts (74.5%). Only 50.6% reported an overall positive perception of safety. The emotional domain showed the highest score (85.5%), followed by the professional domain (74.0%). Organizational (50.6%) and structural (33.6%) domains had the lowest scores, reflecting shortcomings in infrastructure, human resources, and material supplies. In the multivariate analysis, temporary contracts, a 20 h workweek, and specific training were protective factors, whereas belonging to a risk group and being female increased the likelihood of negative perception in the structural dimension. Health professionals’ safety is supported by continuous education, emotional support, adequate infrastructure, professional recognition, participatory leadership, and strengthening of the safety culture. Full article

Rheumatic diseases affect the musculoskeletal system and may also compromise internal organs. The immunosuppressive treatments commonly used in these conditions can weaken the immune system, raising concerns about the efficacy of immunizations, particularly against SARS-CoV-2. This observational and analytical study evaluated the humoral immune response in patients with rheumatic diseases who were vaccinated with a fourth dose of the SARS-CoV-2 vaccine in comparison with a control group and the effect prior COVID-19 infection. The results showed that individuals with a history of COVID-19 infection developed significantly higher levels of neutralizing antibody titers, indicating a stronger immune response. Moreover, the response on the Wuhan strain elicited a more robust humoral response than the Omicron strain in both immunocompromised patients and healthy controls. These findings highlight the impact of previous viral exposure on the effectiveness of immunization in rheumatology patients. Full article

Background and Objectives: The COVID-19 pandemic has caused profound disruptions in socioeconomic, and health domains, with significant implications for mental well-being. The aim of this study was to evaluate the impact of the pandemic on stress, anxiety, and depression, alongside perceived social support, coping flexibility and related factors, and to examine how these issues vary across different age groups. Materials and Methods: A cross-sectional analytical study was conducted in Türkiye between August and December 2020. Data were collected through an online questionnaire including sociodemographic characteristics, pandemic-related concerns, and validated scales: Hospital Anxiety and Depression Scale (HADS), Perceived Stress Scale (PSS), Coping Flexibility Scale (CFS), and Multidimensional Scale of Perceived Social Support (MSPSS). Statistical analyses included descriptive and comparative tests, correlation analysis, multiple linear regression models, and correspondence analysis. Results: Among 1699 participants, 58.0% were female; 24.5% and 42.1% reported anxiety and depressive symptoms above thresholds, respectively. Younger age correlated negatively with stress, anxiety, and depression scores (p < 0.001). Feelings of loneliness, loss of control, ostracism, and sleep or concentration problems were positively associated with anxiety, depression, and stress, but negatively associated with coping flexibility and social support (p < 0.001). The 15–20 age group had the highest anxiety and depression levels and the lowest social support; the 15–30 group showed the highest stress, while the 61–75 group exhibited the lowest coping flexibility. Regression models explained 62.7% of anxiety and 56.6% of depressive symptom variances. Major predictors of anxiety included depressive symptoms, stress, and fear of dying from COVID-19, while depressive symptoms were predicted by age, stress, coping flexibility, social support, and anxiety. Conclusions: The findings highlight the considerable psychological burden and distinct vulnerabilities among age groups. Mental health interventions should be tailored according to age, emphasising the enhancement of social support and coping flexibility to strengthen resilience in future pandemics. Full article

Background/Objectives: Multisystem inflammatory syndrome in children (MIS-C), a rare but serious post-acute hyperinflammatory condition that occurs in children 2–6 weeks after Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection or exposure, varies between countries. Despite its serious nature, most children recover without any sequelae. The most frequently reported long-term sequelae are coronary artery aneurysms. This study aimed to describe the epidemiological profile, clinical characteristics (including cardiac manifestations), treatment, and outcomes of multisystem inflammatory syndrome in children (MIS-C) under 14 years of age with SARS-CoV-2 between February 2020 and November 2021 in Kuwait. Methods: Data on sociodemographic factors, co-morbidities, presenting signs and symptoms, as well as laboratory and echocardiography findings were retrieved from the Pediatric COVID registry (PCR-Q8 registry). Results: Of the one hundred and two patients with a provisional diagnosis of MIS-C, eighty-three patients fulfilled the WHO criteria of MIS-C. Thirty-nine of the MIS-C patients were admitted to the intensive care unit, and only one child died due to cardiogenic shock. Sixteen patients from the pediatric MIS-C cohort were diagnosed with cardiac abnormalities. Sixteen patients from the pediatric MIS-C cohort were diagnosed with cardiac abnormalities. Most (63% (10/16)) of the patients had coronary abnormalities, nine patients (56%) had myocardial dysfunction, and six patients (38%) had dual pathologies. Pericarditis occurred in three patients only, whilst six patients (38%) had dual pathologies. Pericarditis occurred in three patients only. Conclusions: MIS-C appears to affect younger children in Kuwait than in other countries; however, the clinical pattern is consistent with other countries. Further studies of an analytical nature are recommended to identify the risk factors associated with MIS-C and its cardiac sequalae to allow for proactive risk reduction. Full article

Urban resilience has gained significant further attention since the COVID-19 pandemic, resulting in various assessments comparing cities’ ability to respond to, and recover from, diverse shocks. This paper responds to the call for grounding urban resilience in context by examining a case study of the city regions on the island of Hainan Province, China, following the onset of the COVID-19 outbreak. After content analysis to trace the lineage of urban resilience in the Chinese context, an exploratory study, including analysis and mapping of statistical data, was conducted to examine the city’s economic and social performance from 2018 to 2021 and beyond. Our study suggests a largely positive trend in the bouncing back and forward of city regions shortly after the pandemic began, as well as a rural–urban gap and growing regional disparities that need to be addressed to enhance resilience for all. This study provides a contextualized understanding of Hainan as it navigates pandemic stresses and builds capacities during state-supported structural transformations in its development as a free trade port. Furthermore, this study suggests a valuable city region analytical lens and a geographical perspective for implementing the urban resilience concept and building urban resilience efforts in China and elsewhere. Full article

Background: During COVID-19 Arab American and Arab/Middle Eastern (AME) international students faced disproportionately higher psychosocial stressors like other racial populations that may influence their mental health and help-seeking behaviors. Objective: This study examined and compared the prevalence of depression, anxiety, and loneliness among Arab American and AME international college students in addition to how loneliness is associated with perceived mental health need and the use of digital mental health (DMH) services. Methods: Data from the 2022–2024 Healthy Minds Study (HMS) comprised a final analytic sample of 3249 AME students (2662 Arab American; 587 AME international). Logistic regression and generalized structural equation modeling (GSEM) were used to examine associations and mediating pathways. Results: Depression and anxiety were prevalent among Arab American students (44.3% and 40.0%) compared to AME international students (40.9% and 37.0%). Rates of loneliness were similar (56.2% vs. 56.9%). Loneliness was bidirectionally associated with both depression and anxiety (p < 0.001). Perceived need for care emerged as a significant mediator linking psychological symptoms to DMH service use (OR = 2.56, p < 0.001), while loneliness did not directly predict DMH utilization. Only 10% of students reported using DMH services. Conclusions: Mental health disparities among AME students exist. Institutions should prioritize culturally responsive approaches to support the mental health needs of diverse student populations. Full article

The recent COVID-19 pandemic outbreak has demonstrated all the limitations of modern healthcare information systems in preventing and controlling pandemics, especially following an unexpected event. Existing approaches often fail to integrate real-time data and adaptive learning mechanisms, leading to inefficient response strategies and resource allocation challenges. To address this gap, in this paper, we propose PROTECTION, an innovative data-centric process-modeling-managing-and-mining framework for pandemic control and prevention that is based on the new paradigm that we name Knowledge-, Decision- and Data-Intensive (KDDI) processes. PROTECTION adopts Business Process Model and Notation (BPMN) as a standardized approach to model and manage complex healthcare workflows, enhancing interoperability and formal process representation. PROTECTION introduces a structured methodology that integrates Big Data Analytics, Process Mining and Adaptive Learning Mechanisms to dynamically update healthcare processes in response to evolving pandemic conditions. The framework enables real-time process optimization, predictive analytics for outbreak detection, and automated decision support for healthcare. Through case studies and experimental validation, we demonstrate how PROTECTION can effectively deal with the complex domain of pandemic control and prevention. Full article

Lower respiratory tract infections (LRTIs) remain a major cause of global morbidity and mortality, yet accurate pathogen identification and risk stratification continue to pose clinical challenges. Lipidomics—the comprehensive analysis of lipid species within biological systems—has emerged as a promising tool to unravel host–pathogen interactions and reveal novel diagnostic and prognostic biomarkers. This systematic review synthesizes evidence from nine original studies applying mass spectrometry-based lipidomic profiling in human LRTIs, including community-acquired pneumonia (CAP), ventilator-associated pneumonia (VAP), and coronavirus disease 2019 (COVID-19). Across diverse study designs, sample types, and analytical platforms, consistent alterations in lipid metabolism were observed. Perturbations in phospholipid classes, particularly phosphatidylcholines (PCs) and lysophosphatidylcholines (LPCs), were frequently associated with disease severity and immune activation. The ratios of PC to LPC and phosphatidylethanolamine (PE) to lysophosphatidylethanolamine (LPE) emerged as markers of inflammatory remodeling. Sphingolipids—including sphingomyelins (SMs) and sphingosine-1-phosphate (S1P)—were identified as key modulators of monocyte and neutrophil activation. Fatty acid–derived lipid mediators such as oxylipins (e.g., 12,13-epoxyoctadecenoic acid and 15-hydroxyeicosatetraenoic acid) and acylcarnitines reflected pathogen-specific immune responses and mitochondrial dysfunction. Several lipid-based classifiers demonstrated superior diagnostic and prognostic performance compared to conventional clinical scores, including the CURB-65 and pneumonia severity index. However, significant heterogeneity in experimental design, lipid identification workflows, and reporting standards limits inter-study comparability. While preliminary findings support the integration of lipidomics into infectious disease research, larger multi-omic and longitudinal studies are required. This review provides the first comprehensive synthesis of lipidomic alterations in human LRTIs and highlights their emerging translational relevance. Full article

This research provides valuable insights into the application of mathematical modeling to real-world scenarios, as exemplified by the COVID-19 pandemic. After data collection, the preparation stage included exploratory analysis, standardization and normalization, computation, and validation. A mathematical model initially developed for COVID-19 dynamics in Romania was subsequently applied to data from Italy and Switzerland during the same time interval. The model is structured as a multiple-input single-output (MISO) system, where the inputs underwent a neural network-based training stage to address inconsistencies in the acquired data. In parallel, an ARMAX model was employed to capture the stochastic nature of the epidemic process. Results demonstrate that the Romanian-based model generalized effectively across the three countries, achieving a strong predictive accuracy (forecast accuracy > 98.59%). Importantly, the model maintained robust performance despite significant cross-country differences in testing strategies, policy measures, timing of initial cases, and imported infections. This work contributes a novel perspective by showing that a unified data-driven modeling framework can be transferable across heterogeneous contexts. More broadly, it underscores the potential of integrating mathematical modeling with predictive analytics to support evidence-based decision-making and strengthen preparedness for future global health crises. Full article