Search Results (6,664)

The COVID-19 pandemic compelled higher education to resort to e-learning, posing new challenges to the teaching/learning of pharmacy students worldwide. While digital learning provided flexibility, diverse technological infrastructure and institutional availability of resources greatly influenced the student experience. This scoping review aims to assess the perceptions relating to the pivot to e-learning among pharmacy students in the League of Arab States due to the COVID-19 pandemic and how the shift affected student engagement, learning outcomes, and institutional preparedness. Following PRISMA-ScR guidelines, a comprehensive search across ten databases was conducted to identify relevant studies published between January 2020 and December 2025. Forty studies satisfied the inclusion criteria. Pharmacy students in this region responded to the transition to e-learning in diverse ways. While most appreciated the convenience of online modalities, several challenges were consistently enumerated. These were limited technological infrastructure, reduced interpersonal interaction, and disruption of hands-on practical training. Blended learning approaches were largely favored, particularly for their ability to marry online theoretical instruction with face-to-face experiential learning. Reliability and validity issues of internet-based tests were felt by both faculty and students. Stress and mental health problems among students surfaced. Student complaints in general depicted pharmacy education’s need for pedagogic reform, better infrastructure, and student mental health services during e-learning. Areas identified from this review are instructional technology infrastructure improvement, adopting a blended learning strategy, and the need to consider the mental health of students learning at a distance. Full article

Background/Objectives: The acute complications of COVID-19 have been well characterized and are frequently associated with increased mortality. Although substantial knowledge regarding long COVID has accumulated since the beginning of the pandemic, important uncertainties remain regarding the long-term clinical, functional, radiological, and metabolic consequences of SARS-CoV-2 infection. Identification of post-COVID-19 complications is therefore essential for appropriate recognition and management. This study aimed to evaluate the long-term complications of COVID-19 at 3 and 9 months after infection. Methods: This prospective study was conducted at Inonu University Turgut Ozal Medical Center. Patients who presented with active post-COVID-19 complaints or for routine follow-up were enrolled. Participants were evaluated at the pulmonology outpatient clinic at 3 and 9 months. At each visit, persistent or new-onset symptoms were assessed, and pulmonary function tests (PFT), the six-minute walk test (6MWT), echocardiography (ECHO), and thoracic computed tomography (CT) were performed as clinically indicated. Patients were stratified into three groups according to the severity of acute illness: outpatient, ward-hospitalized, and ICU-hospitalized. Results: A total of 205 patients (120 male, 85 female) were included. Male patients had significantly higher rates of ward and ICU hospitalization than female patients (p = 0.006). At 9 months, 85.3% of patients had at least one persistent symptom; dyspnea (69.6%), cough (35.6%), and chest pain (32.5%) were the most common. FVC showed a statistically significant increase between months 3 and 9 (p = 0.014), and the 6MWT distance improved significantly (423.56 m vs. 464.10 m; p = 0.008). Ground-glass opacity, present in 90.2% of patients at admission, persisted in 44.3% at 9 months (p < 0.001). Reticular opacities, pleuroparenchymal bands, and mosaic perfusion patterns increased over time. ICU patients had significantly lower ejection fraction values compared with ward and outpatient groups at 9 months (p = 0.046). During follow-up, 13 patients developed pulmonary embolism and 7 developed new-onset diabetes mellitus. Conclusions: Despite the well-characterized acute phase, the long-term sequelae of COVID-19 remain a significant clinical challenge. Identification of late complications is critical for reducing morbidity and understanding the long-term societal and healthcare burden of the pandemic. Multidisciplinary long-term follow-up is warranted, particularly for patients who experienced severe acute illness. Full article

While conventional theories posit that stronger institutions buffer economies against crises, the COVID-19 pandemic presents a puzzle: despite substantial variation in institutional capacity, the global economic contraction of 2020 was both severe and widespread. Motivated by this puzzle, we constructed a global panel dataset from 2014 to 2024 and employed two-way fixed-effect estimation with Driscoll–Kraay robust standard errors to examine the differential role of state capacity across COVID-19 crisis phases. The results confirm that the shock caused by the pandemic reduced GDP per capita growth across countries, with the Americas experiencing disproportionately deeper contractions and stronger rebounds relative to other regions. Most importantly, the findings reveal a temporal asymmetry in institutional effectiveness: our constructed composite resource-based measure of state capacity does not mitigate the initial economic contraction but exerts a positive, statistically significant effect on post-pandemic recovery. Unsurprisingly, model re-estimation with the conventional perception-based measure of state capacity fails to replicate this dynamic, underscoring the importance of measurement strategy in institutional research. These results challenge the assumption that institutions uniformly buffer shocks, demonstrating instead that state capacity is more consequential for recovery than crisis prevention. Full article

Alcohol and psychoactive substance use disorders are major public health challenges in Poland. The COVID-19 pandemic affected both mental health and the organization of addiction treatment services. This study assessed changes in the utilization of publicly funded addiction treatment services in Poland before, during, and after the pandemic, with attention to regional differences. National Health Fund data from 2018 to 2023 were analyzed for alcohol use disorder treatment and treatment of other substance use disorders. Indicators included the number of patients, services provided, and total financial value of services. ANOVA, linear regression, and Pearson correlation analyses were performed. In 2020, the number of patients and services declined, particularly for non-alcohol substance treatment, followed by gradual recovery in 2021–2023. Significant regional differences were observed (p < 0.001), while differences between years were not significant. The financial value of services increased significantly over time, and strong positive correlations were found between patients, services, and costs. The pandemic temporarily reduced access to addiction treatment services in Poland. Although service utilization recovered over time, regional inequalities and increasing treatment costs persisted, highlighting the need to improve accessibility and resilience of addiction care systems. Full article

Background: Post-COVID-19 conditions (PCCs) present a significant public health challenge due to a vast array of new or persistent health symptoms across subjects. The complex, multi-systemic nature of PCCs makes these conditions difficult to differentiate from other non-COVID-19 related medical conditions. While the Military Health System Data Repository (MDR) provides a robust supply of population-level encounter data, its high-dimensional structure poses challenges for knowledge discovery and outcome research. Objectives: The primary aim of this study was to identify novel manifestations of PCCs among active-duty service members, and model the probabilistic relationships between PCC-related diagnoses. We propose a machine learning workflow as an effective tool for knowledge discovery to statistically validate candidate PCCs from large datasets. Methods: We conducted a retrospective cohort study using MDR records from July 2018 to June 2023. From an initial pool of 311,367 eligible Active-Duty Tricare beneficiaries, we isolated 101,789 COVID-19 infections and matched them 1:1 with uninfected controls (N = 203,578 total) based on age, sex, and propensity for COVID-19. Encounter data was mapped to 392 clinical categories using the Healthcare Cost and Utilization Project (HCUP) Clinical Classification Software Refined (CCSR). Candidate PCC categories were isolated using a cross-validated lasso regression model optimized with a Tree of Parzen Estimators algorithm. A consensus Bayesian Network structure was fitted to model potential probabilistic dependency structures between identified PCCs and prior COVID-19 diagnosis. Finally, conditional Cox proportional hazards models were used to statistically validate selected novel conditions using larger cohorts drawn from the same initial eligible pool by matching cases 1:2 with controls. Results: Feature selection reduced the diagnosis set by 97.96%, isolating 8 clinical categories from the initial 392. The model confirmed known PCCs, such as respiratory symptoms and malaise, and identified two potentially novel candidate PCCs: tinnitus and personality disorders. Survival analysis validated the selection of tinnitus, showing a significant association with COVID-19 (HR: 1.17, 95% CI: 1.12–1.22). No significant association was found between COVID-19 infection and personality disorders (HR: 1.11, 95% CI: 0.97–1.26). Conclusions: This study demonstrates an effective analytical pathway for addressing the limitations of analyzing complex, high-dimensional healthcare billing data. The methodology successfully generated testable hypotheses, identifying tinnitus as a relevant sequela, and is generalizable to future research involving unknown health outcomes related to prior infection. Full article

Viral vaccine development requires both mechanistic understanding of protective immunity and translational study designs that connect preclinical data with human outcomes. Animal models remain important for early assessment of safety, immunogenicity and protective efficacy, but their predictive value depends on the question being asked, the pathophysiology of infection, the immune mechanisms expected to mediate protection, and the biomarkers chosen to bridge animal and human data. This review focuses on viral vaccines and examines innate and adaptive mechanisms of vaccine-induced protection, including B cell and antibody responses, Fc-mediated functions, Fc glycosylation, T cell memory and CD8+ cytotoxic responses. We discuss common reasons for clinical failure and show how preclinical endpoints can be classified as human-counterpart, surrogate or comparative/mechanistic readouts. Influenza and COVID-19 examples illustrate how different models can be combined across discovery, challenge, transmission and late-stage bridging studies. Emerging tools such as systems serology, omics, AI/ML and new approach methods can improve candidate prioritization, but their value depends on assay standardization, biological validation and cautious interpretation. A mechanism-driven model cascade, paired with human-relevant immunological readouts, can improve preclinical interpretation and reduce the risk of advancing candidates that are unlikely to succeed in clinical trials. Full article

The urban heat island (UHI) effect has become a prominent ecological and energy challenge amid rapid urbanization. This study comprehensively examined the spatiotemporal dynamics of UHI intensity in built-up areas across 216 Chinese cities spanning five climatic zones from 2000 to 2020 and quantified UHI-triggered energy consumption, as well as revealing its driving mechanisms. The results showed a significant increasing trend in UHI intensity across China’s urban built-up areas during summer days, summer nights, and winter nights from 2000 to 2020, with corresponding annual growth rates of 10.23, 5.61, and 5.08 km²·°C·a⁻¹, respectively. However, winter daytime UHI intensity declined dramatically from 4.72 °C in 2000 to −10.21 °C in 2020, which can be attributed to the reduction in socioeconomic activities during the COVID-19 period. UHI intensity intensified significantly across all climate zones, with the largest increases observed in the middle temperate zone and warm temperate zone, reaching 127.23 km²·°C and 116.04 km²·°C, respectively. Spatially, 39.8% of the 216 cities exhibited a significant increasing trend in UHI intensity, while only 2.8% showed a decreasing trend. After 2005, the contribution of large cities to UHI intensity continued to rise, reaching 54% in 2020. This study estimated UHI-induced energy consumption in terms of standard coal equivalent, with the northern and middle subtropical zones jointly accounting for over 61.9% of the annual average consumption. Regression results confirmed that impervious surface expansion served as the dominant positive driver of UHI, while vegetation coverage exerted a strong cooling effect. These findings can facilitate the formulation of region-specific UHI mitigation and energy conservation policies for cities under different climatic conditions and at diverse development scales. Mechanistic analysis further revealed that variations in impervious surface area dominated the rise in UHI intensity, whereas changes in the normalized difference vegetation index exerted a significant mitigating effect. These findings provide a solid scientific basis for targeted UHI mitigation and energy-saving management strategies for cities across different climate zones and urban scales. Full article

Background: Long-term care facility (LTCF) residents are highly susceptible to healthcare-associated infections, and prevention is challenging given frailty, dementia, communal living, and resource constraints. Environmental surface and air contamination contribute to transmission. Novel no-touch automated disinfection technologies have been studied in hospitals, but evidence specific to LTCFs is scarce. This scoping review summarizes recent LTCF-focused interventions, their effectiveness, and implementation considerations. Methods: This scoping review was conducted following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) Checklist. We searched PubMed, Medline, Embase, CINAHL, and Scopus for observational or experimental studies evaluating environmental disinfection in LTCFs/nursing homes, excluding body decolonization, non-LTCF settings, and reviews/protocols. Two reviewers independently screened and extracted data via Covidence. This review has been registered on OSF (Open Science Framework). Results: Of 1491 records, 7 studies met the inclusion criteria (6 from the USA, 1 from Australia): one cluster randomized trial, one interrupted time series studies, three prospective observational studies, and two pre–post designs. Interventions included physical methods (HVAC-integrated UV/UVGI, continuous UVGI) and chemical approaches (dry hydrogen peroxide, room fogging plus chlorine dioxide wipes, hydrogen peroxide wipes). Outcomes were heterogeneous (surface SARS-CoV-2 RNA, COVID-19 attack/case rates, airborne/surface microbial loads, and one clinical endpoint—acute respiratory illness). Several studies reported reductions in environmental or airborne bioburden; however, UV-based studies did not demonstrate statistically significant reductions in clinical infections. Certainty was limited by small numbers, non-randomized designs, and diverse outcome measures. Conclusions: No-touch automated disinfection methods appear promising as supplements to standard infection prevention control bundles for reducing environmental contamination in LTCFs. Nevertheless, consistent clinical benefits are unproven. Rigorous, LTCF-tailored, adequately powered trials with standardized clinical and environmental outcomes, plus implementation and cost-effectiveness evaluations, are needed. Full article

Background: The continued evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) created ongoing challenges for molecular diagnostics and variant surveillance. Assays capable of maintaining diagnostic sensitivity across emerging variants while providing variant-related information remain essential for clinical and public health applications. This study evaluated the performance of the GXT96 X3 extraction kit in combination with the FluoroType^® SARS-CoV-2 varID Q version 1.0 assay (Hain LifeScience SA (Pty) Ltd., South Africa) for the detection, semi-quantitative assessment, and variant characterization of SARS-CoV-2 under laboratory conditions. Methods: A total of 220 samples were evaluated, including residual nasopharyngeal clinical specimens (n = 183), reference materials, and cultured SARS-CoV-2 virus dilutions. Residual specimens collected during multiple COVID-19 waves in South Africa (wild-type, Beta, Delta, and Omicron) were compared against standard-of-care (SOC) molecular assays used for routine diagnosis. RNA extraction was performed using the automated GXT96 X3 platform, followed by amplification on the FluoroCycler^® XT using the FluoroType^® SARS-CoV-2 varID Q assay targeting RdRp and N genes, with additional spike gene mutation detection for variant detection. Diagnostic accuracy, agreement (Cohen’s kappa), precision, linearity, and limit of detection (LoD) were assessed. Results: The assay demonstrated a sensitivity of 98.4% (95% CI: 94.2–99.8) and specificity of 100% (95% CI: 95.9–100.0) compared with SOC assays, with an overall agreement of κ = 0.981. Precision analysis showed acceptable reproducibility with a standard deviation of ≤1.49 and a coefficient of variation of ≤3.83%. Regression analysis demonstrated linearity across the dilution series (R² = 0.9882 for RdRp and 0.994 for N genes). The LoD was ≤100 copies/mL for the RdRp gene and 250 copies/mL for the N gene. Variant-associated spike mutations corresponded broadly with epidemiological wave patterns observed in South Africa. Conclusions: Under the evaluated laboratory conditions, the GXT96 X3 extraction platform combined with the FluoroType^® SARS-CoV-2 varID Q assay demonstrated high diagnostic accuracy and reproducibility for SARS-CoV-2 detection across a range of viral loads with additional spike gene mutation detection as an adjunct feature. Full article

The increased use of technology-assisted distance counseling practices is one result of COVID’s impact on behavioral health, including in counselor education and the delivery of supervision. First, technology-assisted distance supervision needed for “real time” communication grew. Furthermore, there is an emergence of artificial intelligence (AI) technologies that have the potential to contribute to aspects of supervision; however, current evidence remains emerging, context-dependent, and at times mixed, warranting cautious interpretation of their effectiveness. The article offers an overview of using AI in clinical supervision, examines the benefits and potential concerns of AI from different perspectives, and considers the significance of using AI in counseling supervision. The role of AI is discussed as applied to counseling supervision including the use of AI tools, such as chatbots and reasoning AI, to detect and track sessions, note behavioral and emotional cues, aid/monitor communication and feedback, while also attending to ethical and legal consideration for its use. The article will report a range of benefits for supervisors and trainees using AI—for example, by enhancing data-driven supervision decisions, analyzing feedback trends, providing more efficient administrative monitoring, flexible/remote support, skill development, and promoting ethical decisions and self-reflection. Special attention is given to the challenges of using AI in supervision, including risks of undervaluing intuition and qualitative insights, potential for algorithms to reinforce systemic biases, risks of replacing human interaction, as well as non-compliance with HIPAA, FERPA, and ethical guidelines in data storage and privacy. The article will discuss privacy concerns, depersonalized feedback, and increased judgment-driven anxiety despite needed empathy when using AI as a tool for clinical supervision. Recommendations will also be offered for effective, ethical integration of AI in counseling supervision. Full article

Background/Objectives: COVID-19, caused by the SARS-CoV-2 virus, can lead to widespread neurological and cognitive complications, even in the absence of significant structural brain abnormalities. Understanding the evolving health concerns in the context of viral infections is critical to service member readiness, fitness, and mission completion. The potential neuroprotective effects of SARS-CoV-2 vaccination remain underexplored. Methods: Using a cross-sectional, non-human primate model (female cynomolgus macaques), we employed magnetoencephalography (MEG) to assess resting-state brain activity following vaccination with escalating doses of a novel psoralen-inactivated SARS-CoV-2 vaccine (PsIV) or a combination of PsIV and a DNA vaccine (prime boost), and subsequent challenge with the Delta variant (SARS-CoV-2 B.1.617.2). MEG scans were acquired 41 days after inoculation. Source series were constructed for 42 regions of interest for each subject, and band power was computed. Results: Band power demonstrated substantial preservation of neural activity across multiple brain regions in vaccinated subjects compared to unvaccinated controls following viral challenge. Significantly lower power was observed across the brain at all bandwidths in the unvaccinated group relative to the prime boost group. As PsIV concentration increased, spectral power increased, with the prime boost group having the greatest power. Conclusions: This approach not only underscores the role of vaccination in mitigating neuropathology but also highlights the capability of MEG to detect subtle yet significant changes in brain function that may be overlooked by other imaging modalities. These findings advance our understanding of vaccine-induced neuroprotection and establish MEG as a powerful tool for monitoring brain function in the context of viral infections. Full article

Background: Respiratory diseases (RDs), including asthma, COVID-19, chronic obstructive pulmonary disease (COPD), and pneumonia, remain a major global health challenge, contributing substantially to global morbidity and mortality. Conventional diagnosis relies heavily on clinicians’ expertise to interpret respiratory sounds and radiographic images, a process that can be subjective, time-consuming, and prone to inter-observer variability. Recent advances in artificial intelligence (AI) and machine learning (ML) have enabled automated diagnostic approaches that can improve the efficiency, consistency, and scalability of respiratory disease detection. However, existing research remains fragmented across different data modalities. Methods: This review systematically analyzes recent studies on AI-based respiratory disease detection using both visual modalities (e.g., chest X-rays, computed tomography (CT) scans, and ultrasound) and audio modalities (e.g., cough and breath sounds). To provide a comprehensive perspective, the reviewed literature is organized using a unified taxonomy that categorizes existing approaches into three main groups: audio-based, visual-based, and audio–visual-based methods. In addition, two conceptual frameworks are proposed to illustrate representative pipelines for audio-based and visual-based respiratory disease classification. Results: The analysis reveals that most existing studies focus on single-modality approaches, while multimodal integration remains relatively underexplored. Only a limited number of studies combine audio and visual data within unified frameworks, primarily due to the scarcity of synchronized multimodal datasets collected from the same patients. The proposed taxonomy and conceptual frameworks provide a structured basis for comparing existing methods, identifying methodological trends, and highlighting key research gaps in multimodal respiratory disease detection. Conclusions: Future research should prioritize the development of multimodal datasets, robust evaluation protocols, and interpretable and lightweight AI models suitable for real-world clinical deployment. Advancing multimodal integration has the potential to significantly enhance the accuracy, reliability, and clinical applicability of AI-driven respiratory disease diagnosis systems. Full article

Background/objectives: Long COVID is associated with persistent, multi-system symptoms, yet little is known about how it affects individuals with intersecting vulnerabilities, such as a racialized identity and pre-existing mental health conditions. This study aimed to descriptively characterize the symptom burden, functional outcomes and mental health in this population. Methods: A cross-sectional, exploratory study was conducted among 51 adults in Canada who self-identified as racialized and as having a pre-existing mental health condition and reported long COVID symptoms. Participants completed an online survey, including validated measures of symptoms, fatigue, post-exertional malaise, cognitive function, mental health and disability. Descriptive statistics were used to summarize outcomes. Results: Participants reported a slight to moderate overall symptom burden, with the highest scores in respiratory and psychological domains. Functional impairment was moderate across work, social and daily activities (Work and Social Adjustment Scale mean = 17.35; World Health Organization Disability Assessment Schedule 2.0 mean = 16.61; Post COVID-19 Functional Status Scale mean = 2.20). Fatigue and post-exertional malaise were notable (Modified Fatigue Impact Scale mean = 43.39; DePaul Symptom Questionnaire—Post-Exertional Malaise mean = 22.47), and cognitive difficulties were commonly reported (Perceived Deficits Questionnaire mean = 33.43). Anxiety and depression scores were in the mild to moderate range respectively (General Anxiety Disorder-7 mean = 9.27; Patient Health Questionnaire-9 mean = 11.43). Conclusions: Clinically relevant fatigue, post-exertional malaise, and depression were found, alongside moderate functional limitations across life domains. The findings support the conceptualization of long COVID as a syndemic condition and underscore the need for equity-informed research, rehabilitation and public health strategies. Full article

The world has already been facing food, nutrition, and security challenges for the last few decades. The coronavirus 2019, COVID-19, has a significant impact on food security and agriculture, such as affecting food demand and the food supply chain, with the greatest consequences on the most vulnerable population. This review provides a comprehensive overview of the effects of COVID-19 on global agriculture and food security, drawing on recent scientific publications, institutional reports, and policy documents from 2020 to 2026. The review examines the impact of the pandemic on cropping patterns, fruit and vegetable harvests, availability of farm inputs, connectivity of the agricultural system, food supply chains, food demand, and labor availability. Vegetable and fruit markets were most affected due to the spread of COVID-19. Due to the closing of markets and restaurants, produce distributors and farmers were required to transfer supplies entirely from the food production to the marketplace. These effects are additionally being felt in agriculture and food security. Almost 55% of researchers indicated that COVID-19 has the most impact on agriculture and its complete harvest during the season, and an additional 45% stated that COVID-19 has adversely affected food security. However, food has slowed down well to date in numerous nations. The spread of COVID-19 is beginning to disrupt the supply of agricultural products and food to consumers and the marketplace across and within borders. The different spring crops, such as sunflower, canola, maize, barley, spring wheat, and various field vegetables, cannot be grown during COVID-19. Consequently, COVID-19 has had a binding effect on the food supply chain and agriculture due to the disruption, which the government should have addressed promptly. Full article

Background: Post-COVID syndrome represents a significant medical and public health challenge, particularly among older adults and individuals with type 2 diabetes mellitus (T2DM), in whom disturbances in immune and metabolic homeostasis may contribute to the development and persistence of symptoms following SARS-CoV-2 infection. Objective: To investigate the clinical, immunological, and metabolic characteristics of post-COVID syndrome in older adults with T2DM. Methods: A cross-sectional comparative study was conducted involving 141 patients aged ≥ 60 years who were evaluated more than one year after SARS-CoV-2 infection. Clinical data, anthropometric measurements, complete blood count parameters, biochemical markers, glycated hemoglobin (HbA1c), and SARS-CoV-2-specific IgG antibodies were assessed. Statistical analyses were performed using nonparametric methods, while Pearson’s χ² test was applied for categorical variables. A p-value < 0.05 was considered statistically significant. Results: Symptoms consistent with post-COVID syndrome one year after SARS-CoV-2 infection were identified in 53.2% of participants. No significant differences in anthropometric characteristics, hematological parameters, or most biochemical markers were observed between patients with and without post-COVID syndrome. Patients with T2DM exhibited higher fasting glucose, HbA1c, and SARS-CoV-2–specific IgG antibody levels, reflecting underlying metabolic characteristics and differences in humoral immune responses during the late post-COVID period. Conclusions: Post-COVID syndrome symptoms were frequently observed among older adults at the time of assessment, more than one year after SARS-CoV-2 infection, despite normalization of most laboratory parameters. In patients with T2DM, higher glucose, HbA1c, and antibody levels likely reflect underlying metabolic characteristics rather than a direct effect of post-COVID syndrome. Further longitudinal studies are warranted to clarify the long-term clinical significance of the observed metabolic and immunological findings. Full article