Search Results (984)

Background and Objectives: Ureteral access sheaths (UASs) are widely used in retrograde intrarenal surgery (RIRS) to facilitate irrigation and instrument access. Recently, flexible and navigable suction UASs (FANS-UASs) have been developed to enhance visibility and stone fragment evacuation; however, their comparative effectiveness remains uncertain. This study aimed to evaluate the clinical outcomes of FANS-UAS versus conventional UAS during RIRS for renal stones. Materials and Methods: A systematic review and meta-analysis were performed following PRISMA guidelines. PubMed, Embase, and the Cochrane Library were searched through May 2025 for comparative studies of FANS-UAS and conventional UAS. Study quality was assessed using the Scottish Intercollegiate Guidelines Network checklist. Primary outcomes included stone-free rate (SFR), operative time, complications, and hospital stay. Subgroup analyses were conducted according to stone size (≤2 cm vs. >2 cm). Results: Nine studies involving 1791 patients were included. FANS-UAS demonstrated a significantly higher SFR (OR = 5.99, 95% CI: 2.86–12.51; I² = 86.7%) and fewer complications (OR = 0.33, 95% CI: 0.23–0.45; I² = 0%). Operative time and hospital stay did not differ significantly between groups. Subgroup analysis showed no significant SFR difference for stones ≤2 cm, whereas for stones >2 cm, FANS-UAS tended to yield higher SFR—though based on limited evidence. Conclusions: FANS-UASs appear to improve stone clearance and reduce perioperative complications in RIRS without increasing operative burden. While further high-quality randomized trials are needed, current evidence supports the growing adoption of FANS-UAS in endourological practice. Full article

This systematic review and network meta-analysis aimed to compare the effects of dietary protein, creatine, and omega-3 fatty acid supplementation on muscle strength, endurance performance, and recovery outcomes in trained athletes. A comprehensive literature search across MEDLINE, Embase, Cochrane CENTRAL, Web of Science, SPORTDiscus, and Scopus identified randomized controlled trials evaluating these supplements in individuals engaged in structured training for a minimum of six months. Network meta-analysis employing a frequentist random-effects model synthesized direct and indirect evidence, with treatment rankings determined using Surface Under the Cumulative Ranking curve probabilities. The analysis incorporated 35 trials enrolling 1211 participants. Creatine supplementation demonstrated superior effects for muscle strength (SMD = 0.46, 95% CI: 0.29 to 0.63, SUCRA = 82.4%), protein supplementation proved most effective for endurance performance (SMD = 0.28, 95% CI: 0.08 to 0.48, SUCRA = 85.2%), and omega-3 supplementation yielded the greatest benefits for recovery outcomes (SMD = 0.40, 95% CI: 0.18 to 0.62, SUCRA = 88.7%). Network consistency assessment revealed no significant disagreement between direct and indirect evidence across all outcomes. These findings reveal an outcome-specific efficacy pattern supporting targeted supplementation strategies aligned with primary training objectives in athletic populations. Full article

Whether the risk of gastric neoplasm is modified by newer glucose-lowering therapies—dipeptidyl peptidase-4 inhibitors (DPP4is), glucagon-like peptide-1 receptor agonists (GLP1RAs), and sodium–glucose cotransporter 2 inhibitors (SGLT2is)—remains uncertain. Given their global uptake and long-term use in populations already predisposed to malignancy, decision-grade comparative safety evidence is needed. We conducted a systematic review and network meta-analysis (NMA) of randomized controlled trials (RCTs) in adults without baseline gastric neoplasms. PubMed, Embase, Cochrane CENTRAL, Web of Science, ClinicalTrials.gov, ClinicalKey, ProQuest, and ScienceDirect were searched from inception to 10 January 2026, without language restrictions. The primary outcome was incident gastric neoplasms (benign or malignant). Random-effects frequentist NMA estimated risk ratios (RRs) with 95% confidence intervals (CIs); Bayesian NMA served as sensitivity analysis. Certainty of evidence was assessed using GRADE adapted for NMA (PROSPERO CRD420261282728). Fifty-two RCTs (171,165 participants; mean age 63.6 years; 36.9% women; mean follow-up 141.8 weeks) were included. At the class level, GLP1RAs were associated with lower gastric neoplasm risk versus controls (RR = 0.51, 95% CI = 0.28–0.92), whereas DPP4is were associated with higher risk (RR = 1.77, 95% CI = 1.09–2.85). These signals persisted in prespecified subgroup analyses among participants with diabetes mellitus, in trials with duration ≥52 weeks (GLP1RA: RR = 0.52, 95% CI = 0.28–0.95; DPP4i: RR = 2.05, 95% CI = 1.19–3.55), and in older populations (age ≥60 years; DPP4i: RR = 2.08, 95% CI = 1.15–3.77). No class showed a significant association in younger participants (<60 years) or shorter trials (<52 weeks). Across available RCT evidence, GLP1RA prescription generally had a relatively lower gastric neoplasm risk than controls. In contrast, among patients with diabetes mellitus receiving longer-term therapy, GLP1RAs may be the preferable option from the perspective of gastric neoplasm risk, while DPP4is warrant heightened vigilance and mechanistic clarification. These findings support improved neoplasms ascertainment in future trials rather than immediate prescribing changes. Full article

Background and Objectives: Genitourinary syndrome of menopause (GSM), previously known as vulvovaginal atrophy, is a chronic, progressive hypoestrogenic condition affecting vulvovaginal, urinary and sexual health in women. Common symptoms include vaginal dryness, itching, dyspareunia, urinary urgency and recurrent urinary tract infections (UTIs). Despite the high prevalence, GSM is underdiagnosed and undertreated, thereby negatively impacting women’s quality of life. To illustrate the practical aspects of GSM diagnosis and provide evidence-based management, we present a case-based narrative review synthesizing recently published, high-quality evidence. Materials and Methods: Evidence was drawn from multiple sources through targeted searches of databases, and included the 2025 AUA/SUFU/AUGS guideline (AUA), the 2024 NICE network meta-analyses (NICE), a 2025 systematic review/meta-analysis in breast-cancer survivors, the 2020 Menopause Society GSM Position Statement, the 2018 NAMS/ISSWSH breast cancer consensus, several primary source citations and other high quality peer-reviewed publications. Results: Five illustrative composite case vignettes of GSM are presented to highlight the evaluation strategy and evidence-supported treatment choices. Nonhormonal options are the first line treatments for mild GSM symptoms, either with or without the addition of vaginal estrogen therapy. For moderate to severe GSM, low-dose vaginal estrogen, vaginal DHEA, and ospemifene are all effective FDA-approved options. In breast cancer survivors, individualized decisions with oncology input are warranted. Maximal caution and a shared decision-making approach is required for women using Aromatase Inhibitors (AIs) for breast cancer risk reduction when choosing treatments for GSM. Conclusions: Treating GSM improves vaginal, sexual and urinary outcomes and quality of life of women. Clinicians need to proactively screen for GSM and offer evidence-based treatment options. The treatment decisions in breast cancer survivors are nuanced, requiring a shared-decision approach. Full article

Accurate river discharge estimation is fundamental for water resource management under increasingly variable hydrological conditions. While conventional in situ techniques remain hydrometric reference standards, their operational deployment is constrained by cost, accessibility, and limited spatial coverage. Advances in remote sensing and artificial intelligence (AI) have introduced non-contact discharge estimation frameworks based on image-derived observations. This systematic review, conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting guidelines, examines the evolution of river discharge measurement methods between 2004 and 2024 through a structured two-stage design. An initial search in Web of Science and Scopus identified 2809 records, of which 249 were retained for first-stage synthesis. A focused second-stage screening isolated seven studies that directly integrate image-based data with machine learning or deep learning architectures for discharge estimation. The analysis reveals a methodological transition from instrument-based hydrometry toward computationally assisted, image-driven approaches. The retained studies employ close-range and satellite imagery combined with Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and related models. Although reported validation metrics indicate strong predictive capability under specific conditions, performance remains dependent on site-specific calibration and reference discharge records. Broader operational deployment requires improved transferability, uncertainty integration, and cross-basin validation. Full article

Background: Stenotrophomonas maltophilia is a multidrug-resistant pathogen with limited therapeutic options that predominantly affects critically ill and immunocompromised patients. Trimethoprim–sulfamethoxazole (TMP/SMX) remains the conventional first-line therapy; however, emerging resistance and toxicity concerns necessitate alternative regimens. This study represents, to our knowledge, the first network meta-analysis (NMA) comparing the efficacy and safety of clearly defined monotherapy and combination antibiotic regimens for S. maltophilia infections. Materials and methods: A systematic search of PubMed, Cochrane Library, Web of Science, and ClinicalTrials.gov (inception to January 2026) identified eligible randomized-controlled studies and retrospective studies. Data were analyzed using a frequentist random-effects NMA with TMP/SMX as the reference. Evaluated regimens included TMP/SMX, fluoroquinolone (FQ), minocycline (MIN), TMP/SMX + FQ, TMP/SMX + MIN, FQ + MIN and FQ + other. Primary and secondary outcomes were all-cause mortality, clinical cure, and adverse effects. Results: Thirteen retrospective studies encompassing 2980 patients were included. Using TMP/SMX as the reference, network meta-analysis demonstrated heterogeneity in all-cause mortality across antimicrobial regimens. FQ and MIN monotherapies were associated with lower odds of mortality (effect sizes: 0.65, 95% CI: 0.49–0.85 and 0.50, 95% CI: 0.28–0.90), whereas combination therapy with TMP/SMX plus FQ was associated with higher mortality (effect size: 2.93, 95% CI: 1.18–7.31). Treatment ranking based on effect sizes suggested more favorable mortality profiles for MIN and FQ regimsens. No significant differences were observed in clinical cure, while FQ was associated with a lower incidence of adverse effects compared with TMP/SMX. Conclusions: This network meta-analysis suggests that FQ and MIN monotherapies may be associated with more favorable survival and tolerability compared with TMP/SMX monotherapy. No clear differences were observed for combination therapy relative to other active monotherapy options. Prospective randomized studies are required to validate these observations and to better inform the management of S. maltophilia infections. Full article

Though deep learning has brought transformative advances to the field of modulation recognition, conventional approaches typically rely on a large amount of labeled data, which is often difficult to obtain in real-world communication scenarios. Few-shot modulation recognition (FSMR), which aims to identify modulation formats with extremely limited training samples, serves as a key enabler for next-generation cognitive radio, intelligent spectrum management, and non-cooperative communications. However, existing neural network models are not inherently designed for few-shot learning (FSL) and cannot be directly applied to FSMR tasks. To address this gap, this paper proposes a spatio-temporal prototypical network (STPN) trained within a meta-learning framework. Through a lightweight multi-module design that sequentially captures spatial patterns and temporal dependencies, STPN effectively integrates hybrid feature extraction with prototype-based classification. In contrast to existing approaches, STPN features a streamlined architecture free from intricate operations that could compromise generalization. This advantage is especially crucial when the model is trained on numerous meta-tasks with only a few samples. Comprehensive experiments on public benchmarks show that STPN achieves superior classification accuracy over several baseline models, while also offering advantages in parameter efficiency and computational cost. Further analysis investigates the key parameters influencing model performance, and ablation studies confirm the individual contribution of each module. This work not only deepens the theoretical understanding of prototype-based FSL techniques but also establishes a practical framework applicable to other signal processing tasks that demand robust performance under limited labeled data. Full article

Background: Deep convolutional neural networks (DCNNs) are increasingly used in computer-aided dental diagnostics. However, the relative diagnostic performance of commonly applied architectures, particularly Faster R-CNN and Mask R-CNN, has not been systematically synthesized across imaging modalities. This systematic review and meta-analysis compared the diagnostic accuracy of Faster R-CNN and Mask R-CNN for dental caries detection using radiographic and photographic images. Methods: PubMed (MEDLINE), EMBASE, Web of Science, and Scopus were systematically searched for studies published up to 15 June 2025. Studies applying Faster R-CNN and/or Mask R-CNN to dental caries detection were included. Binary diagnostic data were extracted, and pooled sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) were estimated using a bivariate random-effects model. Study quality was assessed with QUADAS-AI, and radiomics-based radiographic studies were additionally evaluated using the Radiomics Quality Score (RQS). The protocol was registered in PROSPERO (CRD420251074443). Results: Seventeen studies met the inclusion criteria. Across all imaging modalities, Mask R-CNN showed significantly higher pooled sensitivity (85.6% vs. 71.7%, p = 0.0244), specificity (94.2% vs. 81.4%, p = 0.00089), and AUC (0.95 vs. 0.84, p = 0.0053) than Faster R-CNN. In radiographic images, Mask R-CNN consistently outperformed Faster R-CNN in sensitivity (86.3% vs. 67.2%, p = 0.0497), specificity (96.5% vs. 85.0%, p = 0.00105), and AUC (0.97 vs. 0.86, p = 0.0067). In photographic images, Mask R-CNN achieved a higher AUC (0.91 vs. 0.83, p = 0.048), whereas differences in pooled sensitivity (83.5% vs. 77.3%, p = 0.435) and specificity (86.0% vs. 75.1%, p = 0.156) were not statistically significant. Conclusions: Faster R-CNN and Mask R-CNN both show potential for dental caries detection, but current evidence is limited by substantial heterogeneity, predominantly retrospective designs, and variability in imaging and labeling. Across the included studies, Mask R-CNN showed higher pooled performance estimates than Faster R-CNN, with the clearest differences in radiographic applications; however, this comparison is indirect and should be considered suggestive rather than definitive given study-level heterogeneity and uncertainty in the reference standard in a sizable proportion of studies. Prospective, multi-center studies with standardized imaging protocols, rigorous annotation, and independent external validation are required to support reliable clinical implementation. Full article

As network communication technologies rapidly advance, ransomware has emerged as a significant cybersecurity threat that organizations cannot ignore. Static analysis enables rapid identification of ransomware by examining file structure and code characteristics before execution. However, existing classifiers are predominantly designed under the closed-set assumption, causing them to misclassify novel variants into known families. Furthermore, ransomware datasets typically exhibit long-tailed distributions with emerging families having very few available samples, making it difficult for models to learn discriminative features. To address these challenges, we propose Few-Shot Open-Set Ransomware Detection through Meta-learning and Energy-based Modeling (MEM), a unified open-set recognition framework based on static analysis of Portable Executable features. By integrating Model-agnostic Meta-learning (MAML), the model rapidly adapts to new families with limited samples. The Energy Function quantifies the confidence of predictions in distinguishing between known samples and unknown ones, while Focal Loss dynamically adjusts sample weights to reduce bias introduced by imbalanced distributions. The experimental results demonstrate that MEM achieves higher classification accuracy and better rejection performance of unknown samples than existing open-set recognition methods. Full article

Background/Objectives: This network meta-analysis aimed to evaluate and compare the risks of lower eyelid complications—ectropion, entropion, scleral show, and postoperative scarring—associated with four surgical approaches (subciliary, subtarsal, infraorbital, and transconjunctival) for orbital and zygomaticomaxillary fracture repair. Methods: A systematic search of PubMed, Embase, and Cochrane databases identified relevant studies published between 1 January 1990 and 10 January 2026. Twenty-seven eligible studies involving 2790 patients were included. Direct pairwise meta-analyses and network meta-analyses were conducted to compare complication risks among the approaches. Sensitivity analyses were performed to assess the influence of individual studies, and inconsistency tests were applied to evaluate model robustness. Results: The subciliary approach was associated with the highest risk of ectropion and scleral show. The transconjunctival approach had the lowest risk of ectropion and scarring but the highest risk of entropion. The subtarsal approach had the lowest risk of scleral show, while the infraorbital approach had the highest risk of postoperative scarring. Sensitivity analyses confirmed consistent rankings, and no significant inconsistency was detected. Conclusions: This study provides updated, comprehensive evidence to guide the choice of surgical approach for orbital and zygomaticomaxillary fracture repair. Surgeons should balance operative exposure, cosmetic outcomes, and complication risk, and communicate these trade-offs clearly with patients to optimize decision-making. Full article

The Background: monoclonal antibody therapies represent high-efficacy treatment options for relapsing forms of multiple sclerosis (MS). However, the absence of direct head-to-head randomized trials and the use of heterogeneous comparators across pivotal studies complicate comparative effectiveness assessments. While network meta-analysis (NMA) offers a framework to integrate evidence, the fragmented structure of the available evidence base precludes a conventional NMA with global indirect comparisons and treatment ranking. Methods: A systematic review with qualitative assessment of treatment effects of randomized controlled trials evaluating monoclonal antibody therapies in relapsing forms of multiple sclerosis was conducted. Annualized relapse rate (ARR) was analyzed as the primary outcome, and six-month confirmed disability progression (CDP) as the key secondary outcome. Network geometry and connectivity were explicitly assessed for each outcome prior to quantitative synthesis. Analyses were restricted to comparator-defined connected components of the evidence base, and indirect comparisons across disconnected components were not performed. Sensitivity analyses, including descriptive analyses in progressive multiple sclerosis, were conducted where appropriate. Results: nine randomized controlled trials involving 6762 patients were included. For ARR, the evidence network was fragmented into three disconnected components defined by placebo-, interferon beta-1a-, and teriflunomide-controlled trials. Within connected sub-networks, monoclonal antibody therapies consistently demonstrated substantial reductions in ARR relative to their respective comparators, with overlapping confidence intervals suggesting broadly comparable relapse suppression among high-efficacy agents. For CDP, network connectivity was more limited, and treatment effects were more heterogeneous. Significant reductions in disability progression were observed for some agents within comparator-specific networks, while uncertainty remained for others. Due to network disconnection, no global treatment ranking was performed. Conclusions: this study provides a transparent synthesis of randomized evidence on monoclonal antibody therapies in relapsing MS. By explicitly accounting for network connectivity and comparator heterogeneity, the analysis avoids unsupported indirect comparisons and global treatment hierarchies. The findings support robust relapse suppression across monoclonal antibody therapies within comparable trial frameworks, while highlighting heterogeneity in disability outcomes. These results illustrate the importance of contextual interpretation in comparative effectiveness research in MS. Full article

Reliability is a fundamental aspect of modern structural engineering due to the inherent randomness of materials, loads, and environmental conditions. However, as system complexity increases, a substantial computational cost is typically required to evaluate the failure probability, often involving 10⁵–10⁶ limit state function evaluations in a conventional Monte Carlo simulation. To address this challenge, this study presents an active-learning-driven deep neural network (ALDNN) meta model algorithm to improve both efficiency and accuracy in reliability analysis. To substantially reduce the computational costs, a multi-phase active learning framework incorporating weighted sampling and adaptive threshold-based candidate filtering is implemented by iteratively selecting more important points and adaptively training deep neural networks. Thresholds for candidate sample points and training datasets are gradually adjusted based on feedback from estimated responses. The proposed method reduces the number of true limit state evaluations to the order of 10² in the benchmark problems considered, while maintaining high accuracy. Its performance is assessed using widely referenced benchmark problems, and finite-element-method-based implicit examples for frame structures are further employed to verify applicability. The results demonstrate the high efficiency, accuracy, and scalability of the ALDNN meta model as system complexity increases. Full article

This systematic review summarizes the application of artificial intelligence (AI) in classifying upper-limb motion using Electroencephalogram (EEG) and Electromyogram (EMG) signals, focusing on the field’s progression from Traditional Machine Learning (TML) to Deep Learning (DL) architectures. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a search of PubMed, IEEEXplore, and Web of Science yielded 301 eligible studies published up to June 2025. The results indicate a change from classical classifiers like Linear Discriminant Analysis (LDA) and Support Vector Machines (SVMs) toward DL approaches. While Convolutional Neural Networks (CNNs) remain the most frequently implemented, emerging architectures, including Long Short-Term Memory (LSTM) networks and Transformers, have demonstrated remarkable performance. Despite the rise of DL, classical models remain highly relevant due to their robustness and efficiency. This review also identifies a heavy reliance on EEG-only modalities (60%), with only 7% of studies utilizing hybrid EEG-EMG systems, representing a potential missed opportunity for signal fusion. Full article

Hydrogen is a potential energy carrier for the decarbonization of the heating sector; however, its long-term role remains highly debated. This meta-analysis (2024–early 2025) assesses hydrogen’s potential for domestic heating regarding consumption, costs, and environmental impacts. Current scientific evidence distinguishes between hydrogen use for direct residential heating and its role in integrated energy systems. For residential decarbonization, the literature does not support hydrogen as a primary solution: electrification, especially through heat pumps, remains the most efficient and cost-effective long-term pathway. Direct hydrogen heating faces major thermodynamic and economic barriers, including low conversion efficiency, high Levelized Costs of Energy (LCOE), infrastructure limitations, and challenges in achieving broad social acceptance. Hydrogen’s more strategic value emerges at the system level. Hybrid configurations that combine heat pumps with hydrogen storage show strong potential by using heat pumps to efficiently meet thermal demand while reserving hydrogen for flexible backup and storage. In particular, hydrogen is well suited for long-term seasonal energy storage and grid balancing, enhancing system flexibility and reliability. Its main contribution therefore lies not in direct end-use heating, but in strengthening grid resilience and supporting energy autarky in net-zero scenarios. Hydrogen blending into existing gas networks is widely viewed as a transitional measure to stimulate the hydrogen economy and deliver limited short-term emission reductions, rather than a definitive net-zero solution. Overall, hydrogen’s residential role remains niche, requiring targeted research, development, and large-scale pilot projects to validate competitive applications. Full article

High-density lipoprotein-binding protein (HDLBP), also called Vigilin, is a multifunctional RNA-binding protein with established roles in RNA transport and regulation, chromosome segregation, lipid homeostasis, and translational regulation. Frequently detected to be perturbed in phosphoproteome analysis, phosphorylation is indicated as a major mechanism in the regulation of HDLBP functions; however, its phosphorylation landscape remains unexplored. We performed a meta-phosphoproteome analysis of HDLBP to map site-specific functional and regulatory roles of its two most frequently detected phosphosites, S31 and S944. Co-occurrence analysis across multiple datasets indicated that they can be phosphorylated together, suggesting potential co-ordinated regulation. Site-specific co-regulation analysis revealed distinct phospho-regulatory networks, with upstream kinases identified exclusively for S944. Functional enrichment of co-regulated protein phosphosites (CPPs) highlighted its role in RNA metabolism, chromosome organization, and nucleoplasmic transport, while functional annotation of site-specific phosphorylation of CPPs indicates its involvement in cell cycle regulation, apoptosis, and carcinogenesis. Additionally, the potential role of CPPs in the lipid homeostasis network was explored. Furthermore, the differential expression of HDLBP phosphosites across multiple cancers was observed using UALCAN, suggesting a potential role for phospho-regulation of HDLBP in tumor-associated pathways. Together, these findings provide the first integrated view of HDLBP phosphorylation and could serve as a valuable framework for future targeted studies to elucidate the mechanistic roles of site-specific HDLBP phosphorylation in cellular and pathophysiological processes. Full article