Search Results (446)

Acute cholecystitis (AC) is one of the most prevalent gastrointestinal emergencies, with laparoscopic cholecystectomy representing the definitive treatment per current guidelines. However, a substantial proportion of patients—particularly elderly individuals with major comorbidities, multiorgan dysfunction, or advanced malignancy—are considered poor surgical candidates in whom operative intervention carries prohibitive risk. Although percutaneous transhepatic gallbladder drainage (PT-GBD) has traditionally served as the first-line non-surgical alternative, its significant morbidity and technical limitations in patients with coagulopathy, massive ascites, or an unsafe percutaneous window have driven the development of endoscopic drainage modalities. This review critically appraises the comparative evidence for two endoscopic gallbladder drainage strategies: endoscopic transpapillary gallbladder drainage (ET-GBD), performed via endoscopic retrograde cholangiopancreatography, and endoscopic ultrasound-guided gallbladder drainage (EUS-GBD), most commonly using lumen-apposing metal stents (LAMS). In appropriately selected high-risk surgical candidates, EUS-GBD with LAMS has emerged as a preferred endoscopic option, supported by high technical success rates, low recurrent AC rates, AGA expert guidance, and FDA regulatory reclassification of the AXIOS stent for gallbladder drainage. Nevertheless, ET-GBD retains distinct clinical indications, including concurrent biliary intervention, large-volume ascites, high bleeding risk, anticipated surgical candidacy, and resource-limited settings. Optimal management requires a systematic, algorithm-driven multidisciplinary approach integrating comorbidity profile, biliary anatomy, cystic duct patency, and institutional expertise. Full article

Municipal social services remain among the least examined public functions in the local-government efficiency literature, owing to the scarcity of structured administrative data and the difficulty of measuring their outputs. Addressing that gap, this study provides the first Data Envelopment Analysis (DEA)-based efficiency assessment of municipal primary social-care departments in the Valencian Community (Spain), drawing on a purpose-built dataset that combines 2023 administrative survey, budgetary, and socio-demographic information. Its methodological contribution is an integrated evaluation architecture articulating well-established components—input-oriented DEA under variable and constant returns to scale, the Banker–Morey treatment of non-discretionary contextual variables across an exhaustive lattice of specifications, and a super-efficiency ranking—into a single design that disentangles managerial, scale, and environmental sources of inefficiency. Applied to 18 municipalities, the analysis detects substantial technical inefficiency but shows that much of it reflects scale constraints and structurally demanding socio-demographic environments rather than managerial underperformance; several municipalities with high social vulnerability and below-average income attain full efficiency once context is incorporated, while a small set of units emerges as consistent benchmarks. The architecture yields objective evidence for inter-municipal benchmarking, offers a replicable template for data-poor public services, and cautions against context-blind efficiency metrics in allocating social-policy resources. Full article

Robotic systems for pediatric rehabilitation must provide precise mechanical assistance while ensuring clinically appropriate risk control for vulnerable populations. In low- and middle-income countries (LMICs), academic medical robotics projects frequently fail to progress beyond intermediate Technology Readiness Levels (TRLs 3–5) due to limited translational planning. This study proposes and evaluates an integrated governance framework for academic pediatric rehabilitation robotics in LMIC settings, applied through the development of the AMCOR robotic orthosis for pediatric arthrogryposis multiplex congenita (AMC). The framework combines multiple national and international medical devices development standards and a dual regulatory pathway separating academic development from future translational stages. The framework is structured around four principles—auditability, TRL-proportional documentation, binding decision criteria, and regulatory separation—and is operationalized through a six-gate process. Across the first two gates (G0–G1), 38 traced requirements and 12 failure modes were documented. Five internal audits confirmed operational implementation of the quality management structure. The framework application also shaped core engineering decisions. The low amplitude and poor signal-to-noise ratio of sEMG signals observed in pediatric AMC patients rendered the original single-layer control strategy inadequate, prompting a framework-governed redesign toward a three-layer adaptive architecture based on signal quality thresholds and fallback safety logic. These findings demonstrate that a prospective, multi-standard governance model can improve early-stage academic medical robotics in resource-constrained settings. Generalizability beyond the single-center, two-gate application reported here requires further validation; however, the framework provides a replicable foundation for adoption in comparable LMIC contexts. Full article

This study explored communication access and barriers from the perspectives of 96 hospital staff across Canada in February 2025. A cross-sectional observational design was used, with an online survey distributed through social media platforms. Statistical analyses, including binary logistic regression, were conducted using IBM SPSS Statistics version 29. Most respondents identified as female (89.0%) and were between 19 and 49 years old. One in ten hospital staff reported that patients always received communication support from staff, and fewer than 5.0% reported consistent access to communication devices. Support from communication professionals was limited, with 20.2% indicating that this support was never available. Environmental barriers included difficulty accessing devices independently (24.8%), lack of privacy (21.7%), poor Wi-Fi or cellular connectivity (17.2%), and noise (14.7%). When communicating with older adults, staff cited time constraints (53.0%), unclear explanations (13.0%), language barriers (13.0%), and emotional stress (7.0%) as key challenges. Hospital staff appeared committed to supporting patient communication; however, barriers such as limited time, staffing availability, access to communication devices, and environmental challenges may affect the consistency of this support. Communication training was associated with staff perceptions of communication needs and inequities among patients. These findings highlight perceived communication gaps and may support further assessment of communication resources, staff support, and organizational approaches to improving patient-centered communication in hospital settings. Full article

This comprehensive meta-analysis reviews 45 studies published from 2000 to 2024 that use quantitative methods to examine how educational poverty—limited resources, poor-quality teaching, and disadvantaged school settings—affects students’ academic outcomes. Following PRISMA guidelines, effect sizes were calculated using random-effects models to account for variability across studies. Educational poverty was associated with a moderate, significant negative impact on achievement (Hedges’ g = −0.45, 95% CI: −0.50 to −0.40); this likely reflects a performance gap of about 10–15% between disadvantaged and non-disadvantaged students. Variability across studies led to subgroup and meta-regression analyses. The negative effects were more pronounced for primary students (g = −0.50) than secondary students, especially in high-inequality regions like North America (g = −0.55), and were largest for cognitive outcomes such as standardized test scores (g = −0.50), compared to non-cognitive metrics like attendance and graduation. Meta-regression showed that location, age group, and outcome type significantly affected effect sizes. Sensitivity tests and bias assessments confirmed consistent results. Overall, the studies highlight that educational poverty remains a major barrier to academic success, with the strongest effects seen early in education. These findings underscore the importance of early, targeted, and context-specific policies to reduce inequalities and improve learning conditions for underprivileged students. Full article

Objective: To identify criteria which can be used locally to assess the quality of care for thalassaemia patients, leading to quality improvement measures. In low-resource settings, there is often minimal support for services, and the investigations used in patient monitoring are very basic. In order to select standards which can serve quality assessment, consideration is given to what is available in most centres. Importance is given to the need for the local service provider to self-assess the quality of care according to evidence-based minimal standards. Methods: A search in the recent literature was performed to identify measures of quality care in thalassaemia, selecting those which can be used in resource-poor settings. They are then compared to the standards listed in internationally accepted guidelines. Results: Twelve criteria were selected based on the routine information recorded by most centres. These include the following: clinical criteria: mean age (excluding paediatric clinics), pre-transfusion Hb < 9 g/dL, serum ferritin, MRI availability, heart iron (where available) >20 ms, LIC (where available) <3 mg/kg dw, LIC > 15 mg/kg dw, combination chelation within the last year, and BMI < 18.5 kg/m². Social criteria (for adults): completed tertiary education, married/cohabiting, and employed full or part-time. Each is assigned a score with a total range from 0 to 10. Conclusions: Annual scoring according to achievements allows service providers to compare with previous years and conclude which of the basic services need to be further upgraded to achieve quality improvement. Scoring also allows for comparison with standards published in international guidelines. The clear aim is to aim for higher scores each year, indicating better patient outcomes. Full article

Stroke remains a leading cause of death and long-term disability worldwide, making prevention strategies a global health priority. Emerging technologies—including artificial intelligence (AI), wearable devices, digital health applications, and drone-assisted emergency systems—are increasingly being explored to improve stroke prevention and early management. In primary prevention, machine learning models can identify individuals at high risk of stroke using clinical and behavioral data with high reported predictive accuracy, although most models are derived from retrospective, single-center datasets and still require prospective external validation. Digital devices and wearable technologies enable continuous monitoring of cardiovascular risk factors and support behavioral interventions aimed at reducing vascular risk. In secondary prevention, AI-based tools are being developed to predict stroke recurrence, identify modifiable risk factors, and detect patients at risk of poor medication adherence. In the acute setting, AI-assisted neuroimaging platforms are already integrated into clinical and telestroke workflows, supporting rapid triage and treatment decisions. In parallel, drone-based emergency systems may contribute to improved outcomes by reducing prehospital delays and facilitating telemedicine-based triage in remote or resource-limited settings, although current evidence is derived largely from out-of-hospital cardiac arrest pathways rather than stroke-specific trials. Although advanced neurotechnological systems capable of real-time neurophysiological monitoring and closed-loop neuromodulation exist in other neurological disorders, their role in stroke prevention remains largely theoretical. Overall, these technologies offer promising opportunities to reshape the continuum of stroke prevention and care, but further validation, integration into clinical workflows, and evidence of real-world effectiveness are required before widespread implementation. Full article

Background/Objectives: Initiation of empiric antibiotic therapy for neonatal early-onset sepsis (EOS) is prudent to prevent morbidity and mortality, particularly in low- and middle-income countries (LMICs). Inappropriate or prolonged antibiotic exposure in neonates is associated with poor clinical outcomes. Antimicrobial stewardship programs (ASPs) have been shown to optimize antibiotic use, but data from LMICs are limited. In this study, we aimed to evaluate adherence to a locally developed and adopted guideline for antibiotic use in EOS. Methods: We conducted a retrospective before-and-after study during the pre- (June 2024–January 2025) and post-implementation (May–December 2025) of ASP guideline for EOS. The intervention involved consolidating best practices—previously shared verbally and applied variably into a locally united written guideline, then provide training to neonatologists, pharmacists, and nurses. Adherence to best practices was evaluated by indication, dosing, timing, and duration of antibiotic therapy. Results: In a cohort of 388 neonates with EOS (i.e., 205 pre- and 183 post-implementation), the median gestational age was 38 (IQR: [37–39]) weeks, with the median birthweight of 3000 (IQR: [2800–3400]) grams, and 63% were male. The total adherence improved from 2.0% to 65.6% (p < 0.001) from pre- to post-implementation of ASP. In the post period, adherence rates were 96.7% for empiric antibiotics indication, 95.6% for antibiotics indication after culture results are obtained, 88.5% for antibiotic dosing, 83.1% for timely antibiotic initiation, and 89.1% for appropriate discontinuation of antibiotics. The median days of therapy and length of therapy significantly decreased by 139 per 1000 patient-days, from 1806 (IQR: [1556–2083] to 1667 (IQR: [1400–2000]; p < 0.001)] patient-days; and from 1000 (IQR: [875–1000]) to 875 (IQR: [769–1000]; p < 0.001) patient-days in the pre- versus the post-implementation, respectively. Median length of hospitalization of 8 [7–12] days and recovery (~93%) from EOS were similar pre- and post-implementation. Conclusions: The results support the effectiveness of ASP implementation in improving guideline adherence and reducing antibiotic exposure among neonates with EOS in low-resource settings. In-hospital clinical outcomes, including mortality at discharge, were similar between periods; however, further studies with longer follow-up are needed to better evaluate clinical outcomes. Full article

Background/Objectives: Type 2 diabetes (T2D) remission can be defined as a return to a HbA1c < 6.5% (<48 mmol/mol) sustained without ongoing treatment for at least 3 months. Prevalence estimates and factors associated remain unknown for LMIC and resource-limited settings. Methods: We conducted a retrospective observational analysis of electronic medical records from 8463 adults who received multidisciplinary care at Mexico’s primary care specialized units (UNEMES-EC) between 2015 and 2019 and who were referred for inadequate metabolic control. Remission was defined per 2021 ADA criteria as HbA1c <6.5% sustained for ≥3 months without glucose-lowering medications. After estimating the prevalence of T2D remission, logistic regression models were used to evaluate its sociodemographic and clinical predictors, with particular attention to weight change and baseline adiposity interactions. Results: RT2D prevalence was 0.87% (95% CI: 0.68–1.10) over a median 393-day follow-up. Weight loss ≥10% (adjusted OR 2.75; 95% CI: 1.21-6.27) and systolic blood pressure (tertile 3 vs tertile 1: OR 2.49; 95% CI: 1.17–5.26) were positively associated with RT2D, while elevated baseline HbA1c (tertile 3 vs. tertile 1: OR 0.09; 95% CI: 0.02–0.33), triglyceride levels (tertile 3 vs. tertile 1: OR 0.49; 95% CI: 0.24–0.98) and intensive pharmacotherapy were inversely associated with RT2D. No associations with HDL and total cholesterol were found. Age, sex, educational attainment, and income demonstrated no independent associations with remission. Among lifestyle-treated patients achieving ≥5% weight loss, remission prevalence reached approximately 11%. No significant interaction between baseline BMI and weight change was detected (p = 0.60). Conclusions: This first large-scale Mexican study establishes RT2D as an achievable endpoint in patients with poor baseline metabolic control. The findings suggest that remission could be achieved with equity-focused, weight-centered interventions even in resource-constrained health systems and populations. Full article

Background: Biliary tract cancers (BTCs), encompassing cholangiocarcinoma and gallbladder carcinoma, are aggressive malignancies with poor prognosis and increasing incidence in selected regions worldwide. Advances in imaging, biomarker profiling, immunotherapy, and targeted therapies have improved treatment options but have also increased the economic pressure on health systems. Understanding the economic evidence on BTC is therefore important for resource allocation and health technology assessment. Methods: We systematically searched PubMed/MEDLINE, Embase, Scopus, and Web of Science for peer-reviewed economic studies of BTC published from January 2010 to March 2025. Eligible studies included cost-effectiveness, cost–utility, cost–benefit, cost-of-illness, and resource-use analyses. The review followed PRISMA reporting principles. Reporting completeness was assessed using CHEERS 2022, and methodological credibility was appraised using the Drummond framework. Results: Twenty studies were included: 13 cost-effectiveness or cost–utility analyses and seven cost-of-illness or resource-use studies. Conventional chemotherapy strategies, including gemcitabine plus cisplatin in some settings and other cytotoxic combinations in selected jurisdictions, generally produced more favorable economic results than newer systemic therapies, although findings varied by country, threshold, comparator, and price assumptions. First-line immunotherapy combinations and biomarker-directed targeted therapies frequently produced ICERs above jurisdiction-specific willingness-to-pay thresholds at current prices, often requiring substantial price reductions to approach cost-effectiveness. Real-world studies showed high resource use and costs, particularly with hospitalizations and later treatment lines. Evidence on screening and prevention was limited, with one study suggesting that ultrasound surveillance may be cost-effective in a liver fluke-endemic region of Thailand. Discussion: The available economic evidence suggests that affordability and jurisdiction-specific value assessment are central to BTC policy decisions. Current prices for several immunotherapy and targeted agents limit cost-effectiveness in published models, while evidence on prevention, early detection, and care-pathway interventions remains sparse and context-specific. Full article

Background: Hydatid disease, caused by Echinococcus granulosus, remains a significant public health concern in endemic regions. This study aimed to evaluate the role of ultrasound in the diagnosis, staging, and clinical management of liver hydatid cysts in the eastern city of Jalalabad, Afghanistan. Method: A cross-sectional study was conducted between February and November 2024 among 159 patients diagnosed with liver hydatid cysts. Demographic, clinical, laboratory, and imaging data were collected. Cysts were classified according to the WHO Informal Working Group on Echinococcosis (WHO-IWGE) and Gharbi systems. Ultrasound findings were compared with computed tomography (CT), and their association with treatment decisions was assessed. Result: A total of 159 patients with liver hydatid cysts were included in the study. Among them, 91 (57.2%) were female, 80 (50.3%) were aged 20–39 years, and 128 (80.5%) resided in rural areas. Most patients presented with a single cyst (144/159, 90.6%), while multiple cysts were observed in 15 (9.4%). The majority of cysts measured 5–9.9 cm in diameter (43.4%), followed by 1–4.9 cm (42.1%) and ≥10 cm (14.5%). According to the WHO-IWGE classification, CE1 (25.8%) and CE4 (24.5%) were the most common stages, followed by CE2 (17.6%), CE3a (13.8%), CE3b (11.3%), and CE5 (7.0%). Common exposure-related factors included dog ownership, poor hygiene practices, and consumption of raw vegetables. Ultrasound accurately identified cyst stages and demonstrated a significant association between WHO-IWGE staging and treatment modality (χ² = 63.56, p < 0.001). Almost perfect agreement was observed between ultrasound and CT for cyst classification (Cohen’s κ > 0.90), although CT provided additional anatomical information in selected complex cases. Conclusions: Ultrasound is an accessible, accurate, and reliable imaging modality for the diagnosis, staging, and management of liver hydatid cysts. In resource-limited settings, it serves as the primary imaging modality for guiding clinical decision-making, with CT reserved for complex or uncertain cases. Full article

The einkorn wheat group, comprising ancient diploid species (2n = 14, AA), including Triticum monococcum, Triticum boeoticum, and Triticum urartu, represents a valuable source of genetic variation for improving disease resistance in wheat. To develop a practical platform for introgressing powdery mildew resistance into bread wheat, we screened 21 einkorn accessions with Blumeria graminis f. sp. tritici (Bgt) race E09 and identified seven resistant donors. Because direct hybridization between diploid einkorn (AA) and hexaploid wheat (AABBDD) is constrained by genomic divergence and poor cross-compatibility, we crossed resistant einkorn accessions with susceptible durum wheat and induced chromosome doubling in the F₁ hybrids to generate synthetic durum–einkorn amphiploids. Nine amphiploids were obtained. Chromosome counts and genomic in situ hybridization confirmed the expected genomic constitution (AABBAA) in most lines, with limited variation in chromosome number in two amphiploids. Phenotyping against Bgt race E09 showed that three amphiploids retained high resistance, four showed moderate resistance, and two were moderately susceptible. Marker analysis identified five einkorn accessions contain known Pm genes such as Pm60, Pm60b, and PmNCA6/Pm37, as well as their derived amphipliods. Two einkorn accessions and their derived amphiploids may harbor novel Pm genes. Field evaluation of the agronomic traits of these amphiploids indicated some improvement in tillering, spike length, and seed weight. Moreover, these amphiploids had better seed-setting rates in crosses and backcrosses with common wheat. These synthetic durum–einkorn amphiploids thus offer a new bridging platform for transferring alien genes from diploid einkorn to hexaploid common wheat, providing valuable genetic resources for wheat-breeding programs. Full article

Background: Women remain the primary caregivers globally, especially in rural, low-resource settings plagued by poverty, unemployment, low education and poor infrastructure. These factors limit caregiving capacity, heighten vulnerability and increase the risk of food insecurity in female-headed households. Objective: To establish a baseline profile of caregivers of primary school children. Methods: Phase 1 (baseline) of the study was conducted using a quantitative, exploratory cross-sectional survey design among 75 female caregivers of children aged 7–13 years in Qwa-Qwa, Free State Province. Participants were recruited using convenience sampling. Data were collected with a structured, pre-validated questionnaire on socio-demographics, alongside anthropometric measurements. Data were analysed using descriptive statistics. Results: Most participants were unemployed (73.3%) and had low educational attainment, with 86.7% having completed primary school or less. A substantial proportion of households (80.0%) reported a monthly income below R1000. Food insecurity was common, with 69.3% of caregivers reporting experiences of food shortages. Household infrastructure was limited, particularly in refuse removal services (96.0% without access). Despite these socio-economic constraints, a high prevalence of overweight and obesity (72.5%) was observed amongst the participants. Conclusions: Caregivers experience severe, overlapping socio-economic and environmental vulnerabilities alongside a high prevalence of overweight and obesity. The study highlights the need for multi-sectoral interventions focused on poverty reduction, rural infrastructure development, improved service delivery, women’s empowerment and strengthened livelihood opportunities to improve household nutrition and resilience. Full article

RNA aptamer sensors are promising for environmental and clinical detection, but their poor stability limits practical application. Here, we developed a tRNA-fused strategy to enhance the stability of unmodified RNA aptamer sensors. The tRNA scaffold was fused to the 3′ end of Spinach aptamer to construct tRNA-Spinach. In vitro stability assays showed that tRNA-Spinach retained 50% of its initial fluorescence for 84 days at 25 °C (60% humidity), a 7-fold improvement compared with native Spinach (12 days). The tRNA-fused strategy also doubled the in vivo half-life of Spinach from 20 min to 40 min in KM mice. Based on this strategy, a tobramycin sensor was constructed, which exhibited a LOD of 30 nM, a linear range of 30–100 nM (R² = 0.9905). The biosensor could be detected with a handheld UV lamp within 10 min. This tRNA-fused strategy enables room-temperature storage of RNA aptamer sensors without chemical modification, providing a scalable and cost-effective platform for point-of-care diagnostics in resource-limited settings. Full article

Diabetic retinopathy is a leading cause of vision loss worldwide, particularly impacting individuals in low- and middle-income countries with limited healthcare access. Early detection through automated screening systems is essential for improving outcomes, as timely intervention can prevent severe vision impairment. However, most of the available AI models have not been evaluated in low-resource settings. Hence, this study presents an evaluation of the efficacy of advanced deep learning architectures for detecting rDR across diverse population datasets. A dual-phase validation approach was employed to assess model performance. Internal validation utilised the BrSET dataset to establish baseline performance metrics, while external validation was conducted on the MoDRIA dataset, which encompasses various conditions and demographics, to evaluate model robustness. Key performance metrics, including accuracy, specificity, sensitivity, F1-score, and calibration scores, were systematically recorded and analysed. Internal validation revealed high accuracy across all models, EfficientNetB0 achieved the highest classification accuracy (0.9561; 95% CI 0.9490–0.9630), EfficientNetB3 demonstrated superior overall discriminative performance, achieving the highest AUROC (0.9892; 95% CI 0.9841–0.9934) highest sensitivity (0.9573), and lowest Brier score (0.0168). Meanwhile, DenseNet exhibited the most balanced clinical screening performance, achieving the highest F1-score (0.7259; 95% CI 0.6797–0.7669) and Youden Index (0.2381), indicating improved balance between sensitivity and specificity. In contrast, external validation revealed substantial deterioration in model performance across all architectures, highlighting major limitations in cross-population generalisability. Although EfficientNetB0 achieved the highest external accuracy (0.8821; 95% CI 0.8746–0.8898), AUROC values declined markedly across models (0.5140–0.6104), accompanied by poor sensitivity, reduced F1-scores, and substantial calibration instability. EfficientNetB3 achieved the highest external sensitivity (0.5939), whereas calibration analyses demonstrated unreliable probability estimation under domain-shift conditions. These findings suggest that AI models trained on geographically homogeneous retinal imaging datasets may not generalise reliably across underrepresented populations. Population differences and imaging variability substantially affected external model performance, highlighting the need for diverse datasets, rigorous external validation, and adaptive recalibration before clinical deployment of AI-driven DR screening systems. Full article