Search Results (6,744)

Background: Burns represent a public health problem because they generate both physical and psychological damage, especially in the child and adolescent population, and high costs, especially due to the management of scars. Advances in burn care have improved survival and quality of life for this population. New clinical trials have been conducted on the benefits of negative pressure wound therapy (NPWT), showing that it improves the healing of burns and the appearance of scars. Therefore, this study aims to analyze the efficacy of NPWT both alone and as an adjunct to conventional dressings in pediatric and adolescent patients compared with conventional treatments. Methodology: A systematic search was carried out between December 2023 and the last quarter of 2025 in databases such as PubMed, Scopus, CINAHL, and the Cochrane Library. This meta-analysis was performed following the PRISMA statement (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) and was registered in PROSPERO with registration number CRD42024597293. The risk of bias 2 (RoB2) tool was used to assess the risk of bias in the studies. Quantitative meta-analyses using random-model effects were performed only for variables with sufficient comparable data among studies. For other outcomes, where meta-analysis was not feasible due to lack of comparable data or control groups, results were synthesized qualitatively. Results: A total of seven articles (three clinical trials and four retrospective studies), in which a total of 323 subjects participated, were included. The main results demonstrate the efficacy of NPWT, as it decreases the re-epithelialization time, improves the appearance of scars (MD = −1.25 (95% CI between −1.80 and −0.70)), reduces the probability of skin grafts (OR = 0.17 (95% CI between 0.06 and 0.46)), and therefore, as there is less need for surgery and fewer dressing changes, reduces costs. Conclusions: NPWT offers significant clinical benefits in the treatment of burns in children and adolescents. Although a meta-analysis could not be performed due to the lack of a control group in some studies, studies with larger samples and multicenter designs will be necessary to better assess the relevant clinical outcomes. However, the results of this study show that NPWT is effective in treating burns in children and adolescents and that its use in clinical practice may represent a promising adjunctive therapy. Full article

Gammaretroviruses are ubiquitous pathogens, often associated with the induction of neoplasia, especially leukemia, lymphoma, and sarcoma, and with a propensity to target the germline. The latter trait has left extensive evidence of their infectious competence in vertebrate genomes, the human genome being no exception. Despite the continuing activity of gammaretroviruses in mammals, including Old World monkeys, apes, and gibbons, humans have apparently evaded novel infections by the virus class for the past 30 million years or so. Nevertheless, from the 1970s onward, cell culture studies repeatedly discovered gammaretroviral components and/or virus replication in human samples. The last novel ‘human’ gammaretrovirus, identified in prostate cancer tissue, culminated in the XMRV frenzy of the 2000s. In the end, that discovery was shown to be due to lab contamination with a murine gammaretrovirus. Contamination is also the likely source of the earlier findings. Complementation between genes of partially defective endogenous proviruses could have been another source of the virions observed. However, the capacity of many gammaretroviruses to replicate in human cell lines, as well as the presence of diverse infectious gammaretroviral species in our animal companions, for instance in mice, cats, pigs, monkeys, chickens, and bats, does not make a transmission to humans an improbable scenario. This review will summarize evidence for, or the lack of, gammaretrovirus infections in humans in the past, present, and near future. Aspects linked to the probabilities of novel gammaretrovirus infections in humans, regarding exposure risk in connection to modern lifestyle, geography, diet, and habitat, together with genetic and immune factors, will also be part of the review, as will be the estimated consequences of such novel infections. Full article

Numerous factors contributed to coronavirus 2019 (COVID-19) disease recovery and death rates. In many countries, socioeconomics, morbidities, the experience of symptoms and access to healthcare services are major contributors to recovery and death rates. A retrospective cohort study was conducted to determine the morbidity, mortality, survival probability, and risk factors associated with COVID-19 among children in the Free State province, South Africa. A total of 846 patients’ records were used in the study. Using SPSS version 28 software, survival probability was determined using Kaplan–Meier estimation curves and Cox regression was used to determine the effect of sociodemographics and clinical manifestation information on time of death. The COVID-19 mortality rate was 13.12% in our study. There were more female patients (60%) than male patients (40%). In total, 71 patients had two or more morbidities, while 414 patients were asymptomatic. Patients between 5 and 18 years old were at twice the risk of dying of COVID-19, and male children were at a higher risk as well. Having more than one symptom was also a risk for dying in this study. Severe COVID-19 is attributed to numerous factors, and these are closely associated with surrounding environments and public health systems. The findings are important for the clinical management of similar diseases and circumstances in the future. Full article

As the supply chain of the electric vehicle (EV) industry becomes increasingly complex and vulnerable, traditional supplier evaluation methods reveal inherent limitations. These approaches primarily emphasize static performance while neglecting dynamic future risks. To address this issue, this study proposes a comprehensive supplier evaluation model that integrates a hybrid Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) framework with the Extreme Gradient Boosting (XGBoost) algorithm, contextualized for the EV sector. The hybrid AHP-TOPSIS framework is first applied to rank suppliers based on multidimensional performance criteria, including quality, delivery capability, supply stability and scale. Subsequently, the XGBoost algorithm uses historical monthly data to capture nonlinear relationships and predict future supplier risk probabilities. Finally, a risk-adjusted framework combines these two components to construct a dynamic dual-dimensional performance–risk evaluation system. A case study using real data from an automobile manufacturer demonstrates that the hybrid AHP–TOPSIS model effectively distinguishes suppliers’ historical performance, while the XGBoost model achieves high predictive accuracy under five-fold cross-validation, with an AUC of 0.851 and an F1 score of 0.928. After risk adjustment, several suppliers exhibiting high performance but elevated risk experienced significant declines in their overall rankings, thereby validating the robustness and practicality of the integrated model. This study provides a feasible theoretical framework and empirical evidence for EV enterprises to develop supplier decision-making systems that balance performance and risk, offering valuable insights for enhancing supply chain resilience and intelligence. Full article

Background/Objectives: Acute ischemic stroke (AIS) associated with cervical carotid artery pathology remains a therapeutic challenge due to uncertainty regarding emergent carotid artery stenting (eCAS) and the need for intensified antithrombotic therapy, which may increase the risk of hemorrhagic transformation (HT). This retrospective cohort study evaluated the functional and safety outcomes of eCAS within an extended treatment time window. Methods: We analyzed 139 consecutive patients with anterior circulation AIS and large vessel occlusion treated with mechanical thrombectomy between 2019 and 2024. Patients were eligible for MT within 24 h based on clinical–core mismatch (DAWN) or perfusion–core mismatch (DEFUSE 3) criteria. Outcomes were compared between patients treated with eCAS and those undergoing MT without stenting. Results: Twenty-five patients underwent eCAS, predominantly for tandem lesions (80%). Median age was 66 years, median baseline NIHSS was 14, and median infarct core volume on DWI/CTP was 15 mL. Baseline characteristics were comparable between groups, except for the site of occlusion (p < 0.001). A good functional outcome (modified Rankin Scale, mRS 0–2 at 90 days) was observed in 60% of patients in the eCAS group versus 43% in the non-stenting group, without statistical significance (p = 0.067). Rates of parenchymal hematoma (12% vs. 18.4%) and symptomatic intracerebral hemorrhage (8% vs. 3.5%) were similar between groups. Conclusions: In this single-center cohort, eCAS performed in an extended time window did not demonstrate a clear signal of increased hemorrhagic risk. However, residual confounding and imbalance between treatment groups persisted despite the application of inverse probability weighting (IPW), and the findings should be interpreted cautiously. Full article

Salmonella, a major global foodborne pathogen, is a leading cause of salmonellosis. Quantitative detection of Salmonella provides a scientific basis for establishing microbiological criteria and conducting risk assessments. The plate count method remains the primary approach for bacterial quantification, whereas the most probable number (MPN) method is commonly used for detecting low levels of bacterial contamination. However, both methods are time-consuming and labor-intensive. Validated digital polymerase chain reaction (dPCR) techniques are emerging as promising alternatives because they enable rapid, absolute quantification with high specificity and sensitivity. Herein, we developed a novel droplet dPCR (ddPCR) assay for identifying and quantifying Salmonella using invA as the target. The assay demonstrated high specificity and sensitivity, with a limit of quantification of 1.1 × 10² colony-forming units/mL in meat samples. Furthermore, the log₁₀ values obtained via ddPCR and plate counting exhibited a strong linear relationship (R² > 0.99). Mathematical modeling of growth kinetics further confirmed a high correlation between plate count and ddPCR measurements (Pearson correlation coefficient: 0.996; calculated bias factor: 0.88). Collectively, these results indicate that ddPCR is a viable alternative to the MPN method and represents a powerful tool for the quantitative risk assessment of food safety. Full article

With the rapid development of the Internet of Things (IOT), 5G, and modern power systems, demand-side loads are becoming increasingly observable and remotely controllable, which enables demand-side flexibility to participate more actively in grid dispatch and emergency support. Under extreme rainstorm conditions, however, component failure risk rises and the availability and dispatchability of demand-side flexibility can change rapidly. This paper proposes a risk-aware emergency regulation framework that translates rainstorm information into actionable multi-load aggregation decisions for urban power systems. First, demand-side resources are quantified using four response attributes, including response speed, response capacity, maximum response duration, and response reliability, to enable a consistent characterization of heterogeneous flexibility. Second, a backpropagation (BP) neural network is trained on long-term real-world meteorological observations and corresponding reliability outcomes to estimate regional- or line-level fault probabilities from four rainstorm drivers: wind speed, rainfall intensity, lightning warning level, and ambient temperature. The inferred probabilities are mapped onto the IEEE 30-bus benchmark to identify high-risk areas or lines and define spatial priorities for emergency response. Third, guided by these risk signals, a two-level coordination model is formulated for a load aggregator (LA) to schedule building air conditioning loads, distributed photovoltaics, and electric vehicles through incentive-based participation, and the resulting optimization problem is solved using an adaptive genetic algorithm. Case studies verify that the proposed strategy can coordinate heterogeneous resources to meet emergency regulation requirements and improve the aggregator–user economic trade-off compared with single-resource participation. The proposed method provides a practical pathway for risk-informed emergency regulation under rainstorm conditions. Full article

Reliability of medium-voltage (MV) cable systems, for distribution to industrial and renewable plants, is becoming an issue for various reasons, among which are increased global aging, unconventional voltage waveforms, and insufficient commissioning tests. The major component undergoing premature failures is splices, and most of those failures can be associated with flaws in installation, commissioning and, in general, workmanship. One of the topics of an ongoing Department of Energy (DOE) Advanced Research Projects Agency-Energy (ARPA-E) project, GOPHURRS, is, indeed, to increase splice reliability through simpler design and installation procedures, which can minimize assembly and aging risks. This paper deals with design and testing techniques, which can allow scaling up to MV, a type of splice design and assembly that has been successful in low-voltage (LV) applications. A new design paradigm, the three-leg approach, is applied for the first time to LV splices to evaluate their operation likelihood and reliability up to 30 kV nominal voltage, allowing intrinsic life to reach the specified target (e.g., 30 years at a failure probability of 5%) and preventing extrinsic aging, namely, partial discharge occurrence. Design principles and validation, including accelerated aging and forensic observations, are presented and discussed. Full article

Progressive rock slope destabilization poses significant geohazard risks, necessitating advanced monitoring frameworks to detect precursory failure signals. This study presents a comprehensive time-dependent evaluation of the displacement probability (CTEDP) model, which integrates GNSS-derived spatiotemporal data with multi-parameter reliability indices to enhance landslide risk assessment. Five monitoring points on a destabilizing rock slope were analyzed from mid-November 2024 to early January 2025 using kinematic metrics (velocity, acceleration, and jerk), statistical measures (e.g., moving averages), and reliability indices (RI₀, RI₁, RI₂, and RI_combined). Point 1 exhibited the most critical behavior, with a cumulative displacement of ~60 mm, peak velocities of 34.5 mm/day, and accelerations up to 1.15 mm/day². The CTEDP for active points converged to 0.56–0.61, indicating sustained high risk. The 90th percentile displacement threshold was 58.48 mm for Point 1. Sensitivity analysis demonstrated that the GNSS-derived reliability indices dominated the RI_combined variance (r = 0.999, explaining 99.8% of variance). The first- and second-order reliability indices (RI₁, RI₂) at Point 1 exceeded the 60-index threshold, indicating a transition to Class B (“Low Risk—Trend Surveillance Required”) status, while other points showed coherent deformation of 37–45 mm. Results underscore the framework’s ability to integrate spatiotemporal displacement, kinematic precursors, and statistical variability for early-warning systems. This approach bridges gaps in landslide prediction by accounting for spatial heterogeneity and nonlinear geomechanical responses. Full article

Coal mine ventilation systems face coupled and systemic risks characterized by structural interconnection and disaster chain propagation. In order to accurately quantify and evaluate this overall system risk, this study presents a new method of risk assessment of the coal mine ventilation system based on fuzzy polymorphic Bayesian networks. This method effectively addresses the shortcomings of traditional assessment approaches in the probabilistic quantification of risk. A Bayesian network with 44 nodes was established from five dimensions: ventilation power, ventilation network, ventilation facilities, human and management factors, and work environment. The risk states were divided into multiple states based on the As Low As Reasonably Practicable (ALARP) metric. The probabilities of evaluation-type root nodes were calculated using fuzzy evaluation, and the subjective bias was corrected by introducing a reliability coefficient. The concept of distance compensation is proposed to flexibly calculate the probabilities of quantitative-type root nodes. Through the verification of the ventilation system of H Coal Mine in Shanxi, China, it is concluded that the high risk of the ventilation system is 18%, and the high-risk probability of the ventilation system caused by the external air leakage of the mine is the largest. The evaluation results are consistent with real-world conditions. The results can provide a reference for improving the safety of the ventilation systems. Full article

Reliable probabilistic wind speed forecasts are essential for integrating renewable energy into power grids and managing operational uncertainty. This study compares Quantile Regression Forests (QRF), Bayesian Additive Regression Trees (BART), and Gaussian Process Regression (GPR) under at-site and regional pooled frameworks using 21 years (2000–2020) of daily wind data from eleven stations in New South Wales and Queensland, Australia. Models are evaluated via strict year-based holdout validation across seven metrics: RMSE, MAE, R², bias, correlation, coverage, and Continuous Ranked Probability Score (CRPS). Regional QRF achieves exceptional point forecast stability with minimal RMSE increase but suffers persistent under-coverage, rendering probabilistic bounds unreliable. BART attains near-nominal coverage at individual sites but experiences catastrophic calibration collapse under regional pooling, driven by fixed noise priors inadequate for spatially heterogeneous data. In contrast, GPR maintains robust probabilistic skill regionally despite larger point forecast RMSE penalties, achieving the lowest overall CRPS and near-nominal coverage through kernel-based variance inflation. Variable importance analysis identifies surface pressure and minimum temperature as dominant predictors (60–80%), with spatial covariates critical for regional differentiation. Operationally, regional QRF is prioritised for point accuracy, regional GPR for calibrated probabilistic forecasts in risk-sensitive applications, and at-site BART when local data suffice. These findings show that Bayesian machine learning methods can effectively navigate the trade-off between local specificity and regional pooling, a challenge common to wind forecasting in diverse terrain globally. The methodology and insights are transferable to other heterogeneous regions, providing guidance for probabilistic wind forecasting and renewable energy grid integration. Full article

Breast cancer remains a leading cause of mortality among women worldwide, motivating the development of transparent and reproducible risk models for clinical decision making. Using the open-access SEER Breast Cancer dataset (November 2017 release), we analyzed 4005 women diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma (ICD-O-3 8522/3). Thirty-one clinical and demographic variables were preprocessed with one-hot encoding and z-score standardization, and the lymph node ratio was derived to characterize metastatic burden. Two supervised models, L1-regularized logistic regression and a feedforward artificial neural network, were compared under identical preprocessing, fixed 60/20/20 data splits, and stratified five-fold cross-validation. To define clinically meaningful endpoints and handle censoring, we reformulated mortality prediction as fixed-horizon classification at 3 and 5 years, and evaluated discrimination, calibration, and operating thresholds. Logistic regression demonstrated consistently strong performance, achieving test ROC-AUC values of 0.78 at 3 years and 0.75 at 5 years, with substantially superior calibration (Brier score less than or equal to 0.12, ECE less than or equal to 0.03). A structured hyperparameter search with repeated-seed evaluation identified optimal neural network architectures for each horizon, yielding test ROC-AUC values of 0.74 at 3 years and 0.73 at 5 years, but with markedly poorer calibration (ECE 0.19 to 0.23). Bootstrap analysis showed no significant AUC difference between models at 3 years, but logistic regression exhibited greater stability across folds and lower sensitivity to feature pruning. Overall, L1-regularized logistic regression provides competitive discrimination (ROC-AUC 0.75 to 0.78), markedly superior probability calibration (ECE below 0.03 versus 0.19 to 0.23 for the neural network), and approximately 40% lower cross-validation variance, supporting its use for scalable screening, risk stratification, and triage workflows on structured registry data. Full article

Background/Objectives: Patients with myasthenia gravis (MG) are at increased risk of osteoporotic fractures due to long-term oral corticosteroid use and disease-related muscle weakness. FRAX^® estimates 10-year fracture probability but does not incorporate falls or MG-specific functional impairment. To explore heterogeneity of fracture occurrence within MG patients classified as high risk by FRAX major osteoporotic fracture (MOF) probability. Methods: In a single-center retrospective cohort of 68 MG patients assessed in 2012, FRAX MOF with femoral neck BMD was calculable in 54 patients; the 29 patients with FRAX MOF ≥ 9.0% (the median of these 54 patients) comprised the high-FRAX cohort. Patients were stratified by the cohort medians of age (67 years) and MG-ADL (2 points) into four strata (HH, HL, LH, LL). This median-based stratification was exploratory and not intended as a clinically meaningful threshold. The primary outcome was time to first MOF (up to 10 years). We compared fracture occurrence using both proportions and Kaplan–Meier analyses (log-rank test) and performed exploratory univariable Cox models for selected predictors. No multivariable confounder adjustment was performed. Results: Eight of twenty-nine patients (27.6%) experienced an MOF. The proportions with MOF were HH 25.0%, HL 40.0%, LH 57.1%, and LL 0.0% (global p = 0.068). Kaplan–Meier curves differed across strata (log-rank p = 0.03), with separation most evident between LH and LL. For univariable Cox analyses, age was associated with shorter time to MOF (hazard ratio [HR] 1.13 per year, p = 0.041), and baseline difficulty rising from a chair (MG-ADL item) was associated with higher hazard rates (HR 3.45, p = 0.048). Conclusions: In this small, selected high-FRAX MG cohort, fracture events appeared to cluster in patients with impaired ADL and fall-related MG-ADL abnormalities, whereas FRAX values remained strongly age-driven. These findings are exploratory and hypothesis-generating and should not be interpreted as evidence of FRAX miscalibration; confirmation in larger, prospectively followed cohorts is needed. Full article

Knee osteoarthritis prognostic tools often target structural progression or surgery and require imaging or biomarker inputs that are not routinely available. Using Osteoarthritis Initiative data, we developed a fully clinical nomogram to estimate both the probability of long-term pain non-resilience (clinically important worsening) and, by complement, maintenance of acceptable pain in radiographic knee osteoarthritis. We included participants with radiographic knee osteoarthritis and complete worst-knee WOMAC pain scores at baseline, 24 and 48 months; non-resilience was defined as a ≥9-point increase on the 0–100 WOMAC pain scale over 4 years. A six-predictor Firth logistic regression model (age, body mass index, Kellgren–Lawrence grade, baseline pain, 0–24-month pain change and Center for Epidemiologic Studies Depression Scale score) was fitted and translated into a point-based nomogram. Among 2365 eligible participants, 527 (22.3%) were non-resilient. The model showed good performance, with optimism-corrected AUC 0.74 and Brier score 0.15, and decision-curve analysis indicated positive net benefit versus treat-none across 1–15% thresholds and small gains versus treat-all. Early pain worsening and higher depressive symptoms were the strongest predictors of non-resilience. This six-variable, clinic-ready nomogram provides a simple, well-calibrated tool for prognostic counseling and risk stratification in radiographic knee osteoarthritis and requires external validation before wider clinical use. Full article

To identify and evaluate the failure issues in the livestock manure-to-biomethane supply chain, this study employs a Bayesian network approach with three inference analysis methods: diagnostic analysis, sensitivity analysis, and maximum causal chain inference. First, the main hazard categories affecting the failure of the supply chain are identified, establishing risk indicators for feedstock collection, pretreatment, anaerobic digestion, purification and upgrading, transportation, and biomethane end-use. Then, the half-interval method and possibility superiority comparison are used to calculate and rank the severity of related accidents, obtaining the severity ranking of secondary indicators as well as the severity ranking of work items and risk items. Finally, Bayesian forward inference is applied to investigate the failure probability of the supply chain, combined with backward inference to identify the risk factors most likely to cause supply chain failures and trace the formation of failure hazards. The Bayesian sensitivity analysis method is ultimately applied to determine the key hazards affecting supply chain failures and the correlations between accident hazards, followed by validation. The results show that the failure probability of the supply chain through causal inference is approximately 54.76%, indicating relatively high failure risk. The three factors with the highest posterior probabilities are mechanical stirring failure C₃ (88.11%), corrosion-induced ammonia leakage poisoning D6, and equipment explosion caused by excessive pressure due to overheating during dehumidification heating D₉, which are the hazards most likely to cause failures in the supply chain. Improper operations and the toxicity of related chemicals are key hazards leading to supply chain failures, with the correlation between accident hazards presented as a hazard chain by integrating severity and accident probability, and the key risk points in the supply chain are identified. Full article