Search Results (2,051)

Eight Pacific Island Countries and Territories (PICTs) have been validated by the World Health Organization (WHO) as having eliminated lymphatic filariasis (LF) as a public health problem. WHO recommends that these countries implement post-validation surveillance (PVS) to ensure resurgence has not occurred. Some PICTs proactively conducted LF PVS even in the absence of specific recommendations or best-practice guidelines at the time of implementation. We aimed to explore the barriers and facilitators to implementing LF PVS in PICTs, with a view to informing context-specific strategies and regional guidelines. Key informant interviews were held between March and September 2024 with 15 participants involved in LF and/or neglected tropical disease surveillance. Transcripts were analysed thematically using a generalised deductive approach. A conceptual framework was developed to summarise themes with two main streams of barriers identified. Stream One Barriers included limited awareness of, and guidelines for, PVS requirements and competing national health priorities. Stream Two Barriers included cost, resource, and logistical barriers to conducting PVS. Participants called for clearer, contextually tailored guidelines, improved communication from WHO, and integration within existing systems. This study highlights the urgent need for operational guidance, policy advocacy, and capacity strengthening to ensure sustainable LF PVS in PICTs. Incorporating local context and leveraging existing health structures will be essential to prevent disease resurgence and maintain gains achieved through elimination programmes. Full article

Cannabis is one of the most common intoxicants used worldwide. Cannabis is widely consumed worldwide and can lead to visual alterations. However, most of the available information on its effects comes from studies conducted in developed countries, while data remain limited in developing regions such as Morocco, despite its significant role in cannabis cultivation. The aim of this study was to explore multiple visual parameters and self-perceived eyesight in cannabis users in Morocco. A cross-sectional study was conducted between March 2022 and April 2023 in Marrakesh, Morocco, in cannabis consumers. Data collection was performed in two phases. First a hetero-administrated questionnaire was used to collect socio-demographics, intoxicant consumption habit information, and eye health information. Then, several visual acuity tests were performed, including a preliminary examination, a visual function assessment, and an eye health assessment. Ninety-five cannabis users participated in this study. The majority were single (62.1%) males (87.4%). All lived in the Marrakesh-Safi region (100%), and most had daily activities such as having a job or being a student (77.9%). Most had vision conditions like astigmatism or myopia (83.4%). The majority had multiple addictions (66.5%), mainly to tobacco (43.7%). Hashish was the main cannabis type used (57.9%), and smoked cannabis was the principal mode of consumption (94.7%). Many had a family history of cannabis addiction (58.9%). Day light sensitivity (66.3%) and appearance of eye symptoms after cannabis use (90.5%) were declared by the majority. In most cases, no impact on far or near vision or vision impairment due to cannabis use were declared. Our results showed that using cannabis could have significant adverse effects on visual functions. Full article

This study investigates the determinants of turnover intention among real estate sales professionals in Lebanon and the United Arab Emirates (UAE), two markets that represent contrasting economic realities within the MENA region. Drawing on Herzberg’s Two-Factor Theory, Vroom’s Expectancy Theory, and March and Simon’s Push-Pull Model, this study adopts a multidimensional framework to assess the effects of compensation, job stress, career growth opportunities, and work–life balance on employee retention. A quantitative method was employed using a structured questionnaire administered to 832 respondents (425 in the UAE and 407 in Lebanon), and data were analyzed using Structural Equation Modeling. The results reveal that job stress is the most influential predictor of turnover intention, particularly in Lebanon, followed by work–life balance, compensation, and career growth opportunities. These findings underscore the importance of psychological well-being and structural incentives in talent retention. By offering empirical evidence from an underexplored regional labor market, the study contributes to the global turnover discourse and provides comparative insights into the labor dynamics of both a crisis-prone and a high-growth economy. The results carry significant practical implications for HR managers, firm owners, and policymakers, highlighting the necessity of adopting holistic and context-sensitive retention strategies that extend beyond financial rewards to include flexible work arrangements, career development frameworks, and supportive workplace cultures. Full article

Climate change increasingly threatens global food security through disrupted precipitation patterns and extreme weather events, requiring resilient systems for assessing agricultural vulnerability. This study developed and compared machine learning approaches for predicting cropland vulnerability using Earth Observation data, operationalized through NDVI anomalies as a defensible biophysical metric. We employed both a dual-stream deep learning architecture and a Random Forest model to predict 2023 NDVI anomalies across Bangladesh croplands using a 22-year time series (2001–2023) of MODIS vegetation indices, ERA5 climate variables, and static environmental covariates. A spatially aware block cross-validation strategy ensured rigorous, independent performance evaluation. Results demonstrated that the Random Forest model (R² = 0.70, RMSE = 197.03) substantially outperformed the deep learning architecture (R² = 0.02, RMSE = 357.57), explaining 70% of cropland stress variance and enabling early detection of vulnerable areas three months before harvest. Feature importance analysis identified recent climate variables, March precipitation, February NDVI, and vapor pressure deficit as primary vulnerability drivers. Spatial analysis revealed distinct vulnerability patterns, with Natore and Magura districts exhibiting elevated stress consistent with 2023 drought conditions, threatening the productivity of the region’s critical irrigation-dependent rice cultivation. These findings demonstrate that simpler, interpretable models can sometimes outperform complex architectures while providing useful information for early warning systems and precision targeting of climate adaptation interventions. Full article

The abrupt COVID-19 lockdown in early 2020 offered a unique natural experiment to examine vegetation productivity responses to sudden declines in human activity. Although vegetation often responds to environmental changes with time lags, how such lags operate under short-term, intensive disturbances remains unclear. This study combined multi-source environmental data with an interpretable machine learning framework (XGBoost-SHAP) to analyze spatiotemporal variations in net primary productivity (NPP) across the Beijing-Tianjin-Hebei region during the strict lockdown (March–May) and recovery (June–August) periods, using 2017–2019 as a baseline. Results indicate that: (1) NPP showed a significant increase during lockdown, with 88.4% of pixels showing positive changes, especially in central urban areas. During recovery, vegetation responses weakened (65.31% positive) and became more spatially heterogeneous. (2) Integrating lagged environmental variables improved model performance (R² increased by an average of 0.071). SHAP analysis identified climatic factors (temperature, precipitation, radiation) as dominant drivers of NPP, while aerosol optical depth (AOD) and nighttime light (NTL) had minimal influence and weak lagged effects. Importantly, under lockdown, vegetation exhibited stronger immediate responses to concurrent temperature, precipitation, and radiation (SHAP contribution increased by approximately 7.05% compared to the baseline), whereas lagged effects seen in baseline conditions were substantially reduced. Compared to the lockdown period, anthropogenic disturbances during the recovery phase showed a direct weakening of their impact (decreasing by 6.01%). However, the air quality improvements resulting from the spring lockdown exhibited a significant cross-seasonal lag effect. (3) Spatially, NPP response times showed an “urban-immediate, mountainous-delayed” pattern, reflecting both the ecological memory of mountain systems and the rapid adjustment capacity of urban vegetation. These findings demonstrate that short-term removal of anthropogenic disturbances shifted vegetation responses toward greater immediacy and sensitivity to environmental conditions. This offers new insights into a “green window period” for ecological management and supports evidence-based, adaptive regional climate and ecosystem policies. Full article

East Africa’s March–April–May (MAM) rainfall exhibits pronounced variability that strongly influences agriculture, water security, and livelihoods. This study analyzes consecutive wet day (CWD) events using CHIRPS precipitation, GridSat infrared cold-cloud brightness temperature, and ERA5 reanalysis for 1982–2023 to examine rainfall variability and its relationship with atmospheric circulation and convection. CWDs are classified into short (3–5 days), medium (6–10 days), and long (>10 days) events. Results reveal three regional activity centers: the Eastern Congo Basin, Lake Victoria, and Southwest Ethiopia. The Congo Basin emerges as the most convectively active region, sustaining frequent events across all categories and supporting long-duration rainfall through persistent moisture flow and mesoscale convection. On average, CWDs contribute 43% of total MAM rainfall across East Africa, ranging from negligible amounts in arid areas to over 90% in equatorial regions. Short-duration events dominate the seasonal total, while long-duration events, though spatially restricted, contribute up to 52% locally. Composite convection analysis shows a transition from widespread moderate activity during short events to highly localized, intense convection in long events, particularly over the equatorial Congo and Lake Victoria regions. These findings highlight the critical contribution of organized synoptic-scale systems to East Africa’s hydrological cycle, which will have implications for improving sub-seasonal rainfall forecasts. Full article

Background and Objectives: Vaginal natural orifice transluminal endoscopic surgery, vNOTES, has become an increasingly preferred minimally invasive option for benign hysterectomy. General anesthesia is still the routine choice, yet regional methods such as combined spinal epidural anesthesia may support a smoother postoperative course. Although the use of vNOTES is expanding, comparative information on anesthetic approaches remains limited, and its unique physiologic setting requires dedicated evaluation. To compare combined spinal epidural anesthesia with general anesthesia for benign vNOTES hysterectomy, focusing on postoperative nausea and vomiting, recovery quality, and intraoperative physiologic safety. Materials and Methods: This retrospective cohort study was conducted in a single center and identified women who underwent benign vNOTES hysterectomy between March 2024 and August 2025 from electronic medical records. Participants received either combined spinal epidural anesthesia or general anesthesia according to routine clinical practice. All patients were managed within an enhanced recovery pathway that incorporated standardized analgesia and prophylaxis for postoperative nausea and vomiting. The primary outcome was the incidence of postoperative nausea and vomiting during the first day after surgery. Secondary outcomes included time to discharge from the recovery unit, pain scores at set postoperative intervals, early functional recovery, patient satisfaction and physiologic parameters extracted from intraoperative monitoring records. Analyses were performed according to the anesthesia group documented in the medical files. Results: One hundred forty patients met inclusion criteria and were included in the analysis. Combined spinal epidural anesthesia was linked to a lower incidence of postoperative nausea and vomiting, a shorter stay in the post-anesthesia care unit, and reduced pain scores in the first 24 h (adjusted odds ratio 0.32, ninety five percent confidence interval 0.15 to 0.68). Early ambulation and oral intake were reached sooner in the combined spinal epidural group, with higher overall satisfaction also noted. Adherence to ERAS elements was similar between groups, with no meaningful differences in early feeding, mobilization, analgesia protocols or PONV prophylaxis. During the procedure, combined spinal epidural anesthesia produced more episodes of hypotension and bradycardia, while general anesthesia was linked to higher airway pressures and lower oxygen saturation. Complication rates within the first month were low in both groups. Conclusions: In this observational cohort study, combined spinal epidural anesthesia was associated with lower postoperative nausea, earlier recovery milestones and greater patient comfort compared with general anesthesia. Hemodynamic instability occurred more often with neuraxial anesthesia but was transient and manageable. While these findings point to potential recovery benefits for some patients, the observational nature of the study and the modest scale of the differences necessitate a cautious interpretation. They should be considered exploratory rather than definitive. The choice of anesthesia should therefore be individualized, weighing potential recovery benefits against the risk of transient hemodynamic effects. Larger and more diverse studies are needed to better define patient selection and clarify the overall risk benefit balance. These findings should be interpreted cautiously and viewed as hypothesis-generating rather than definitive evidence supporting one anesthetic strategy over another. Full article

Social media addiction (SMA) and social phobia (SP) are significant adolescent mental health concerns. In Saudi Arabia, despite high social media penetration, the association between these two constructs remains under-researched, particularly in the Aseer region. This cross-sectional study, conducted from January to March 2025, recruited 384 Saudi adolescents aged 11–19 from schools in the Aseer region using multistage cluster sampling. Participants completed validated self-report measures, including the Social Phobia Inventory (SPIN) and the Al-Menayes Social Media Addiction Scale. A refined “Core SMA” subscale was created based on expert consensus criteria to enhance measurement precision. The prevalence of moderate-to-severe social phobia was 15.6%. A significant, moderate positive correlation emerged between SP and SMA (Spearman’s ρ = 0.294, p < 0.001). After adjusting for age, gender, and family income, adolescents with moderate social phobia had 2.15 times the odds of probable SMA compared to those with no SP (adjusted odds ratio [AOR] = 2.15, 95% CI: 1.15–4.04, p < 0.05), and this effect was more pronounced for those with severe social phobia (AOR = 2.56, 95% CI: 1.04–6.30, p < 0.05). This study demonstrates a clear relationship between social phobia severity and social media addiction among Saudi adolescents in the Aseer region. These findings support the urgent need for integrated mental health and digital literacy interventions that proactively screen for both conditions. Full article

Background: This study seeks to evaluate the 23 year experience of the İnonu University Liver Transplantation Institute from a public health perspective by examining demographic patterns, etiological factors, and transplantation trends between 2002 and 2025. Aims: This analysis aims to provide insights into the epidemiological landscape of liver transplantation in Türkiye from a public health perspective. Methods: In this retrospective cross sectional study, we analyzed 4011 liver transplant procedures performed between March 2002 and March 2025. Recipient demographics, disease etiologies, donor characteristics, and patients geographic distribution were assessed to delineate regional health needs and service utilization patterns. Results: A total of 4011 patients were included. The cohort comprised 2618 males (65.3%) and 1393 females (34.7%). Recipients were classified as adult (n = 3232, 80.9%) or pediatric (n = 779, 19.1%). Among adults, infectious etiologies were the most prevalent (35.5%), followed by cryptogenic liver cirrhosis (24.7%). In contrast, pediatric patients most commonly presented with toxic etiologies (29.4%), metabolic disorders (22.6%) and bile duct diseases (15.9%). Most liver transplantations were performed using living donors (n = 3481, 86.8%), while deceased donors accounted for 530 procedures (13.2%). Additionally, 244 living donor liver transplantations were performed via liver paired exchange (LPE). Conclusions: These findings may inform resource allocation, health policy development, and the optimization of transplantation services. This center-based model offers a useful framework for characterizing regional health needs and strengthening community health, particularly through prevention, screening, and early intervention strategies for liver diseases. Full article

This study investigates the institutional transformations within Indonesia’s research ecosystem, focusing on the impacts of the National Research and Innovation Agency (BRIN) establishment and the subsequent Work From Office (WFO) policy on the Association of Indonesian Researchers (PPI). The research aims to evaluate these impacts and propose an adaptive institutional revitalization model. Employing a mixed-methods approach, a total of 150 online questionnaires were distributed across 21 regional branches of PPI between February and March 2025. Of these, 87 were completed and valid for analysis, representing a 58% response rate. Findings reveal that the WFO policy has led to a significant decline in member participation, coordination difficulties across regions, and weakened collaboration with local partners such as regional governments and universities. A SWOT analysis of three revitalization options—full agglomeration, bounded agglomeration, and non-BRIN integration—identified a hybrid model as the most adaptive and widely supported alternative (41.5%). This hybrid model combines selective structural efficiency with inclusive membership expansion, aiming to preserve regional identity, enhance collaboration, and strengthen organizational legitimacy. The study offers key insights for developing adaptive governance frameworks rooted in epistemic justice, digital accountability, and cross-sectoral collaboration, applicable to professional organizations navigating decentralization and institutional disruption. The proposed hybrid model serves as a strategic reference for achieving organizational resilience and fostering a more inclusive national innovation ecosystem. Full article

The availability of nighttime satellite imagery provides unique opportunities for monitoring fishing activity in data-sparse ocean regions. This study leverages Visible Infrared Imaging Radiometer Suite (VIIRS) Day/Night Band monthly composite imagery to identify and classify recurring spatial patterns of fishing activity in the Korean Exclusive Economic Zone from 2014 to 2024. While prior research has primarily produced static hotspot maps, our approach advances geospatial fishing activity identification by employing machine learning techniques to group similar spatiotemporal configurations, thereby capturing recurring fishing patterns and their temporal variability. A convolutional autoencoder and a Gaussian Mixture Model (GMM) were used to cluster the VIIRS imagery. Results revealed seven major nighttime light hotspots. Results also identified four cluster patterns: Cluster 0 dominated in December, January, and February, Cluster 1 in March, April, and May, Cluster 2 in July, August, and September, and Cluster 3 in October and November. Interannual variability was also identified. In particular, Clusters 0 and 3 expanded into later months in recent years (2022–2024), whereas Cluster 1 contracted. These findings align with environmental changes in the region, including ocean temperature rise and declining primary productivity. By integrating autoencoders with probabilistic clustering, this research demonstrates a framework for uncovering recurrent fishing activity patterns and highlights the utility of satellite imagery with GeoAI in advancing marine fisheries monitoring. Full article

Background: Several genes and genomic regions have been implicated in COVID-19 susceptibility and severity, but their clinical relevance remains uncertain. We comprehensively assessed both copy number variants (CNVs) and single-nucleotide variants (SNVs) disrupting genes implicated in COVID-19 in a Swedish cohort of ICU-treated COVID-19 patients with detailed phenotype data. Methods: Patients (n = 301) with severe COVID-19 treated in intensive care units (ICU) between March 2020 and January 2021 at two large Swedish university hospitals were included. Whole exome sequencing (WES) was performed to identify both large copy number variations (CNVs) and single-nucleotide variants (SNVs), including small indels, using the Genome Analysis Toolkit (GATK) pipelines. We focused our analyses on variants disrupting coding genes implicated in severe COVID-19, but also assessed variants known to cause human disease. Results: We identified 11 rare CNVs and several SNVs potentially linked to severe COVID-19. Patients carrying a CNV spanning a COVID-19-implicated gene had higher levels of the heart failure marker NT-proBNP (median 4440 [1558–8160] vs. 1170 [329–3152], p = 0.017), worse renal function at ICU admission (p = 0.0026), and a higher need for continuous renal replacement therapy (CRRT) (28% vs. 10%, p = 0.045) compared to patients without a potentially damaging CNV. Conclusions: Although patients with a potentially damaging CNV or SNV exhibited some differences in cardiac and renal markers, our findings do not support broad genetic screening as a predictive tool for COVID-19 severity. Full article

A systematic spatial resilience analysis of population flow networks in underdeveloped mountain towns is essential for sustainable urban–rural integration. Using mobile signaling data from March 2023, this study constructs a population flow network across 69 towns in the Yimeng Mountainous Region. This study proposes a novel targeted-attack framework based on centrality and assesses structural resilience along the three dimensions of efficiency, transitivity, and connectedness. Population flows exhibit a twin-core north–south structure, characterized by a hub-and-spoke system in the south and a self-stabilizing triangular configuration in the north. The network demonstrates strong spatial agglomeration and heterogeneity, with modular clustering revealing four functional modules shaped by administrative boundaries. It exhibits small-world properties, attributed to high transmission efficiency and strong local clustering. The network shows robust resilience to disruptions. Targeted attacks based on betweenness centrality significantly compromise structural resilience; efficiency, transmission, and connectivity change linearly at low attack intensities but decline sharply at higher levels. Full article

Norovirus is the leading cause of acute gastroenteritis worldwide, responsible for up to 90% of viral gastroenteritis outbreaks and an estimated 10.6 billion USD in annual economic losses in the U.S. Despite its well-documented seasonality, wastewater surveillance in the Eastern Upper Peninsula of Michigan reveals persistent GI norovirus detection year-round, diverging from national clinical trends that consistently show far greater GII prevalence. To characterize norovirus dynamics in this region, 250 mL wastewater influent grab samples were collected once per week across 14 sites, concentrated using a PEG-based method, and analyzed via digital droplet PCR (ddPCR) for GI and GII concentrations. Across the study period, the rate of positive sites per month ranged from 57 to 100% for GI and 74 to 97% for GII, with mean positivity rates of 85.4% (GI) and 88.7% (GII), indicating that both genogroups were detected frequently at comparable levels. GI was more prevalent in winter and spring (December–May), whereas GII was more prevalent during spring and summer (March–August). Mean GI gene copies per 100 mL ranged from 12,898 (October) to 532,792 (February), while mean GII concentrations ranged from 29,806 (December) to 1,100,215 (May). These patterns contrast with national clinical data, where GI contributes to a small minority of reported norovirus cases. This study explores potential environmental and behavioral factors contributing to this regional pattern. GI norovirus demonstrates greater resistance to wastewater treatment and environmental stability, which may facilitate its persistence in the region. Additionally, congregate living settings, such as college campuses and correctional facilities, may contribute to sustained GI prevalence through foodborne transmission and asymptomatic viral shedding. Overall, these findings suggest that environmental and social factors influence norovirus seasonality and genogroup distribution in this region, underscoring the need for improved monitoring and expanded multi-site wastewater and epidemiological research to better understand norovirus persistence in similar communities. Full article

Cone beam computed tomography (CBCT) allows for rapid and accessible acquisition of three-dimensional images with a lower radiation dose compared to conventional computed tomography (CT) scans. However, the quality of CBCT images is limited by a variety of artifacts. This systematic review attempts to explore different artificial intelligence-based solutions for enhancing the quality of CBCT scans and reducing different types of artifacts in these three-dimensional images. PubMed, Web of Science, Scopus, Embase, Cochrane, and Google Scholar were searched up to March 2025. Risk of bias of included studies was assessed using the QUADAS-II tool. Extracted data included bibliographic information, aim, imaging modality, anatomical site of interest, artificial intelligence modeling approach and details, data and dataset details, qualitative and quantitative performance metrics, and main findings. A total of 27 papers from 2018 to 2025 were included. These studies focused on five areas: metal artifact reduction, scatter correction, image reconstruction improvement, motion artifact reduction, and noise reduction. Artificial intelligence models mainly used U-Net variants, though hybrid and transformer-based models were also explored. The thoracic region was the most analyzed, and the structural similarity index measure and peak signal-to-noise-ratio were common performance metrics. Data availability was limited, with only 26% of studies providing public access and 15% sharing model source codes. Artificial intelligence-driven approaches have demonstrated promising results for CBCT artifact reduction. This review highlights a wide variability in performance assessments and that most studies have not received diagnostic validation, limiting conclusions on the true clinical impact of these artificial intelligence-based improvements. Full article