Search Results (2,869)

Background/Objectives: This systematic review and meta-analysis of observational studies aimed to assess the association between fish consumption and the risk of general and pregnancy-related depression, with implications for public health promotion. Methods: We retrieved 5074 articles from PubMed and Embase through November 2023 and included 35 observational studies in the analysis. We synthesized effect estimates as relative risks (RRs) with corresponding 95% confidence intervals (CIs) using a random-effects model. Additional dose–response analyses and stratified subgroup analyses were performed. Results: A significant inverse association was found between fish consumption and depression risk (RR = 0.79, 95% CI: 0.73 to 0.86). A similar association was observed for pregnancy-related depression (RR = 0.78, 95% CI: 0.69–0.89). Stratified analyses showed that only studies with fish intake ≥68.4 g/day demonstrated a statistically significant reduction in depression risk (RR = 0.75, 95% CI: 0.67–0.84), whereas studies with lower intake (<68.4 g/day) showed no significant association (RR = 0.83, 95% CI: 0.69–1.01), suggesting a potential threshold effect. Dose–response analysis further supported a 6% reduction in depression risk per 15 g/day increase in fish intake. Conclusions: This meta-analysis supports fish consumption as a modifiable factor for depression prevention, with ≥68.4 g/day as a possible threshold, potentially informing dietary guidelines and public health strategies. Full article

Natural disasters pose significant risks to engineering projects, necessitating a systematic analysis of their risk factors. This study focuses on identifying and mapping these factors using a mixed-methods approach that integrates a qualitative literature review with scientometric analysis via Social Network Analysis (SNA). Through a meta-analysis of 81 peer-reviewed articles from Web of Science, Scopus, and ScienceDirect, the qualitative review establishes a comprehensive list and classification of 48 natural disaster risk factors, categorized into geological, climatic, hydrological, topographic, and biological groups, while providing a theoretical foundation. SNA complements this by quantifying co-occurrence frequencies, centrality metrics (degree, betweenness, and eigenvector), and network structures, revealing dynamic interactions, key influential factors, and research gaps—particularly in under-explored areas like hydrological hazards, extreme temperatures, lightning storms, and temperature variations—that qualitative methods alone might miss. This multi-perspective integration highlights discrepancies between theoretical discussions and practical applications, underscoring overlooked cascading effects. Findings emphasize the absence of an integrated model for all 48 factors, urging the development of a holistic predictive framework to bolster disaster resilience. Theoretically, the study offers a novel SNA-based quantification of factor importance and interrelations, addressing literature fragmentation. Practically, it guides project managers in prioritizing risks for optimized design, resource allocation, and prevention strategies. Future research should incorporate real-time data sources to refine this framework for enhanced risk management in engineering projects. Full article

Background: Abdominal wall hernias represent a significant global surgical burden, with over 20 million repairs performed annually. The convergence of rising obesity and diabetes rates with complex hernia management has necessitated innovative preoperative optimization strategies that address both metabolic dysfunction and mechanical challenges. Objectives: This comprehensive review synthesizes current evidence on emerging pharmacologic and procedural optimization strategies for patients undergoing abdominal wall hernia repair, with particular emphasis on glucagon-like peptide-1 (GLP-1) receptor agonists, botulinum toxin A (BTA) injections, progressive preoperative pneumoperitoneum (PPP) and biomechanical calculated repair. Methods: We conducted an extensive literature review incorporating recent clinical trials, observational studies, and meta-analyses, focusing on metabolic optimization with GLP-1 receptor agonists, mechanical preparation techniques, and their comparative effectiveness in reducing perioperative complications and hernia recurrence. Results: GLP-1 and GLP-1/GIP agonists demonstrate substantial metabolic benefits including weight reduction (10–20%), improved glycemic control, reduced systemic inflammation, and decreased postoperative complications in surgical populations. Recent evidence suggests reduced surgical site infection, thromboembolic events, and wound dehiscence in GLP-1 receptor agonists users. However, concerns regarding delayed gastric emptying and aspiration risk require careful perioperative management. BTA and PPP remain valuable techniques for mechanical optimization in loss-of-domain hernias, though modern biomechanically calculated repair (BCR) approaches using cyclic load analysis may reduce their necessity in many cases. The GRIP/CRIP concept demonstrates superior outcomes with 5–7% five-year recurrence rates compared to 15% with conventional approaches. Emerging evidence highlights collagen metabolism dysfunction as a fundamental determinant of hernia recurrence, prompting development of collagen-focused prehabilitation programs incorporating nutritional supplementation, aquatic exercise, and targeted physical conditioning. Conclusions: A paradigm shift toward integrated, personalized preoperative optimization is emerging, combining metabolic conditioning with mechanical preparation based on individual patient phenotypes and hernia complexity. Future research should focus on comparative effectiveness trials, optimal timing protocols, and multimodal strategies to maximize surgical outcomes while minimizing complications. Full article

The increasing global incidence of thyroid cancer highlights the importance of accurately assessing risk factors, particularly those related to family history. Although having affected family members is widely recognized as a risk factor for thyroid cancer, the exact degree of risk and its variation across types of familial relationships, parental gender, and geographic regions remain unclear. This systematic review and meta-analysis aimed to clarify the association between family history and thyroid cancer risk. We conducted a comprehensive literature search of PubMed, Web of Science, and Embase following PRISMA guidelines, identifying 13 studies from 503 initially screened. Statistical analyses were performed using random-effects models to estimate pooled odds ratios and risk ratios, with subgroup analyses to assess variations across population and relationship types. Our findings showed an approximately 4.5-fold higher risk of thyroid cancer in individuals with affected family members. Individuals with affected siblings were more likely to develop thyroid cancer while the risks associated with maternal and paternal family history were comparable in magnitude, with no statistical difference between them. Socioeconomic, educational, and lifestyle differences did not significantly influence risk, and geographic variations in familial risk could not be statistically confirmed by the subgroup analysis, in the context of high between-study heterogeneity. These results suggest that family history is a substantial risk factor for thyroid cancer, reinforcing the need for enhanced surveillance and screening strategies for those with a familial predisposition. Full article

Background and Objectives: IgA nephropathy represents the most prevalent form of primary glomerulonephritis around the world, with significant heterogeneity in management strategies and outcomes. We conducted a systematic review and meta-analysis to evaluate the efficacy and safety of pharmacological interventions for IgA nephropathy. Materials and Methods: We searched multiple databases through June 2025, identifying randomized controlled trials and observational studies evaluating pharmacological treatments in biopsy-proven IgA nephropathy. Primary outcomes included proteinuria reduction and estimated glomerular filtration ration (eGFR) preservation. Secondary outcomes included hard kidney endpoints and safety parameters. Random-effects meta-analyses were performed with comprehensive risk–benefit assessments. Results: Twenty-five studies were included. B-cell/plasma-cell-targeted therapies showed significant proteinuria reduction (−34.0% [95% CI: −45.7, −22.3%]), complement pathway inhibitors demonstrated superior eGFR preservation (+5.8 mL/min/1.73 m²/year [95% CI: 2.4, 9.2]). Systemic corticosteroids showed observed hard outcome benefits (HR 0.37 [95% CI: 0.26, 0.52]) but highest adverse event risk (RR 3.28 [95% CI: 2.11, 5.09]). Novel agents showed projected favorable effects (B-cell: HR 0.38; complement: HR 0.42) pending validation. Conclusions: Novel targeted therapies, especially B-cell/plasma-cell-targeted agents and complement pathway inhibitors, show promising risk–benefit profiles. However, longer-term data and standardized eGFR slope reporting are needed to confirm these findings compared to other immunosuppressive agents. Full article

Abiotic stresses such as drought and heat severely constrain the growth and productivity of Solanaceae crops, including potato (Solanum tuberosum L.) and tomato (Solanum lycopersicum L.), yet the conserved regulatory mechanisms underlying their stress adaptation remain incompletely understood. Here, we performed an integrative meta-analysis of publicly available transcriptomic datasets, complemented by comparative and evolutionary analyses across the Solanum genus. Functional annotation revealed coordinated transcriptional reprogramming characterized by induction of protective processes, including molecular chaperone activity, oxidative stress responses, and immune signaling, accompanied by repression of photosynthetic and primary metabolic pathways, reflecting energy reallocation under stress conditions. Promoter motif and transcription factor enrichment analyses implicated the bZIP, bHLH, DOF, and BBR/BPC families as central regulators of drought- and heat-induced transcriptional programs. Orthogroup inference and $K_a/K_s$ analysis across representative Solanum species demonstrated a predominance of purifying selection, indicating evolutionary conservation of regulatory network architecture. Integration of motif occurrence, co-expression profiles, and protein–protein interaction data enabled reconstruction of regulatory networks and identification of conserved hub transcription factors coordinating stress responses. Comparative analysis revealed distinct but conserved transcriptional signatures for heat and drought shared between potato and tomato, indicative of conserved abiotic stress strategies across Solanaceae. Full article

Background: Chronic total occlusion (CTO) percutaneous coronary intervention (PCI) remains among the most complex procedures in interventional cardiology, with variable technical success and heterogeneous long-term outcomes. Conventional angiographic scores such as J-CTO and PROGRESS-CTO provide only modest predictive accuracy and neglect critical patient and operator-related factors. Artificial intelligence (AI) and machine learning (ML) have emerged as transformative tools, capable of integrating multimodal data and offering enhanced diagnostic, procedural, and prognostic insights. Methods: We performed a structured narrative review of the literature between January 2010 and September 2025 using PubMed, Scopus, and Web of Science. Eligible studies were peer-reviewed original research, reviews, or meta-analyses addressing AI/ML applications in CTO PCI across imaging, procedural planning, and prognostic modeling. A total of 330 records were screened, and 33 studies met the inclusion criteria for qualitative synthesis. Results: AI applications in diagnostic imaging achieved high accuracy, with deep learning on coronary CT angiography yielding AUCs up to 0.87 for CTO detection, and IVUS/OCT segmentation demonstrating reproducibility > 95% compared with expert analysis. In procedural prediction, ML algorithms (XGBoost, LightGBM, CatBoost) outperformed traditional scores, achieving AUCs of 0.73–0.82 versus 0.62–0.70 for J-CTO/PROGRESS-CTO. Prognostic models, particularly CatBoost and neural networks, achieved AUCs of 0.83–0.84 for 5-year mortality in large registries (n ≈ 3200), surpassing regression-based methods. Importantly, comorbidities and functional status emerged as stronger predictors than procedural strategy. Future Directions: AI integration holds promise for real-time guidance in the catheterization laboratory, robotics-assisted PCI, federated learning to overcome data privacy barriers, and multimodality fusion incorporating imaging, clinical, and patient-reported outcomes. However, clinical adoption requires prospective multicenter validation, harmonization of endpoints, bias mitigation, and regulatory oversight. Conclusions: AI represents a paradigm shift in CTO PCI, providing superior accuracy over conventional risk models and enabling patient-centered risk prediction. With continued advances in federated learning, multimodality integration, and explainable AI, translation from research to routine practice appears within reach. Full article

Financial time-series labeling remains fundamentally limited by three critical deficiencies: temporal rigidity (fixed horizons regardless of market conditions), scale blindness (single-resolution analysis), and correlation-causation conflation. These limitations cause systematic failure during regime shifts. We introduce Adaptive Event-Driven Labeling (AEDL), integrating three core innovations: (1) multi-scale temporal analysis capturing hierarchical market patterns across five time resolutions, (2) causal inference using Granger causality and transfer entropy to filter spurious correlations, and (3) model-agnostic meta-learning (MAML) for adaptive parameter optimization. The framework outputs calibrated probability distributions enabling uncertainty-aware trading strategies. Evaluation on 16 assets spanning 25 years (2000–2025) with rigorous out-of-sample validation demonstrates substantial improvements: AEDL achieves average Sharpe ratio of 0.48 (across all models and assets) while baseline methods average near-zero or negative (Fixed Horizon: −0.29, Triple Barrier: −0.03, Trend Scanning: 0.00). Systematic ablation experiments on a 12-asset subset reveal that selective innovation deployment outperforms both minimal baselines and maximal integration: removing causal inference improves performance to 0.65 Sharpe while maintaining full asset coverage (12/12), whereas adding attention mechanisms reduces applicability to 2/12 assets due to compound filtering effects. These findings demonstrate that judicious component selection outperforms kitchen-sink approaches, with peak individual asset performance exceeding 3.0 Sharpe. Wilcoxon tests confirm statistically significant improvements over Fixed Horizon baseline (p = 0.0024). Full article

Background: In this systematic review with meta-analysis, we aimed to compare the kinematic and kinetic variables of the involved limb with the contralateral limb in individuals who had undergone an anterior cruciate ligament reconstruction (ACLR) recorded during walking from short-term (<6 months) to mid-term (6–<12 months) and long-term (≥12 months) periods after surgery. Methods: Five electronic databases (Scopus, PubMed, EMBASE, PEDro, CENTRAL) were systematically searched for articles potentially eligible for inclusion from inception until November 2025. Biomechanical gait patterns were assessed short-term (<6 months), mid-term (6–<12 months), and long-term (≥12 months) post-surgery. Gait biomechanics were extracted from the included articles. Comparisons were made between the affected limb and the contralateral limb. Standardized mean differences (SMDs) with 95% confidence intervals (CI) were computed using a random-effects model. Results: The systematic search revealed 3522 hits, and according to a priori defined in-/exclusion criteria, 32 studies with male and female individuals aged 18–55 years involving 1026 participants were included. Meta-analysis indicated that the peak knee flexion angle was significantly lower in the ACLR compared to the contralateral limb (19 studies: small SMDs = −0.39, 95% CI −0.58 to −0.19, p < 0.0001, I² = 66%). More specifically, the peak knee flexion angle was 2.63° (95% CI −3.81 to −1.44) lower in the ACLR compared to the contralateral limb. The analysis of time post-surgery revealed significant differences in the short-term (four studies: large SMDs = −1.14, 95% CI −1.61 to −0.67, p < 0.00001, I² = 56%) and mid-term (five studies: small SMDs = −0.52, 95% CI −0.74 to −0.29, p < 0.0001, I² = 0%) periods after surgery but not for the long-term follow-up (10 studies: small SMDs = −0.10, 95% CI −0.27 to 0.07, p = 0.26, I² = 32%). Meta-analysis indicated that the peak knee flexion moment was significantly lower in the ACLR compared to the contralateral limb (11 studies: small SMDs = −0.37, 95% CI −0.59 to −0.14, p = 0.0001, I² = 46%). A lower peak knee flexion moment was observed in the ACLR limb for both less than 12 months (three studies: moderate SMDs = −0.76, 95% CI −1.44 to −0.07, p = 0.03, I² = 66%) and over 12 months (eight studies: small SMDs = −0.25, 95% CI −0.43 to −0.07, p = 0.01, I² = 46%) after surgery time points compared to the contralateral limb. Conclusion: These findings suggest a time-dependent compensatory mechanism, where protective adaptations (e.g., reduced flexion/extension moments) may initially offload the reconstructed limb, with some asymmetries resolving over time. Clinically, these results underscore the need for rehabilitation strategies tailored to address phase-specific deficits, promoting symmetrical loading and functional recovery. Full article

The COVID-19 pandemic has demonstrated that its effects go far beyond the initial respiratory illness, with many survivors experiencing lasting neurological problems. Some patients develop a condition known as Long COVID, or post-acute sequelae of SARS-CoV-2 infection (PASC), which includes current issues such as reduced cognitive function, chronic headaches, depression, neuropathic pain, and sensory disturbances. These symptoms can severely disrupt daily life and overall well-being. In this narrative review, we provide an overview of current understanding regarding the neurological effects of COVID-19, with a focus on Long COVID. We discuss possible underlying mechanisms, including direct viral invasion of the nervous system, immune-related damage, and vascular complications. We also summarize findings from cohort studies and meta-analyses that explore the causes, symptom patterns, and frequency of these neurological issues. Approximately one-third of people who have had COVID-19 report neurological symptoms, especially those who experienced severe illness or were infected with pre-Omicron variants. Emerging research has identified potential biomarkers such as neurofilament light chain (NFL) and glial fibrillary acidic protein (GFAP) that may help in diagnosis. Treatment approaches under investigation include antiviral medications, nutraceuticals, and comprehensive rehabilitation programs. Factors like older age, existing health conditions, and genetic differences in ACE2 and TMPRSS2 genes may affect an individual’s risk. To effectively address these challenges, current research is essential to improve diagnostic methods, develop targeted treatments, and enhance rehabilitation strategies. Ultimately, a coordinated, multidisciplinary effort is crucial to reduce the neurological impact of Long COVID and support better recovery for patients. Full article

Autistic children often experience eating difficulties due to sensory processing, food selectivity, and other eating behaviors. As a result, the feeding process can be particularly challenging for caregivers and professionals in healthcare and educational settings. This scoping review describes interventions that address feeding difficulties, focused on improving food acceptance and reducing challenging eating-related behavior in autistic children under 6 years. The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-ScR) guidelines. We searched the PubMed, Web of Science (WOS), and PsycINFO databases, as well as manually examined reference lists, to identify relevant articles. Nineteen studies were selected by two independent reviewers for inclusion in the review. Among the selected studies, a variety of effective feeding strategies were categorized into three groups: applied behavior analysis (ABA)-based interventions, a combination of ABA-based strategies with others, and emerging strategies beyond ABA. These interventions have been reported to increase the acceptance of foods and reduce challenging mealtime behaviors of autistic children. Future research should focus on developing comprehensive interventions to improve the quality of life of autistic children, their families, and their communities. Full article

Percutaneous coronary intervention (PCI) has evolved significantly over the past two decades, yet challenges in achieving optimal stent deployment and long-term outcomes persist, particularly in complex coronary anatomy. Intravascular imaging (IVI) modalities such as intravascular ultrasound (IVUS), optical coherence tomography (OCT), and near-infrared spectroscopy (NIRS) have transformed the precision of PCI by providing detailed cross-sectional visualization of vessel architecture, plaque morphology, and stent apposition. Compared to angiography-guided PCI, imaging-guided PCI enables more accurate lesion assessment, appropriate stent sizing, and detection of suboptimal results including under-expansion, malapposition, and edge dissections, factors strongly linked to restenosis and stent thrombosis. Large-scale randomized trials (e.g., ULTIMATE, ILUMIEN) and meta-analyses have demonstrated that imaging-guided PCI reduces major adverse cardiovascular events (MACE) and improves long-term stent patency, particularly in left main, bifurcation, and calcified lesions. Despite these benefits, adoption remains variable due to cost, procedural complexity, and training gaps. Emerging advances, including artificial intelligence-enhanced imaging, hybrid devices, and fusion of imaging with physiologic assessments, promise to integrate imaging more seamlessly into routine practice. This review summarizes current evidence, practical applications, and future directions of IVI-guided PCI, underscoring its growing role in contemporary interventional cardiology and its potential to personalize and optimize coronary revascularization strategies. Full article

The review has explored the application of Geographic Information Systems (GIS) in evaluating road traffic crashes, stressing its role in identifying crash spatial patterns and hotspots. GIS offers a framework for integrating spatial and non-spatial data, allowing scholars and planners to visualize crash-prone areas and understand their distribution. A total of 77 research articles from the publication period of 2010–2025 were included for final reviews. A Systematic Reviews and Meta-Analyses (PRISMA) approach is followed to provide well-structured, transparent, and standardized information on articles. The intention is to assess how different GIS techniques contribute to road safety analysis and to the development of effective intervention strategies. The review focused particularly on four key GIS-based spatial analysis methods: Kernel Density Estimation (KDE), Network KDE, Moran’s I (Global and Local), and Getis-Ord Gi*. Among these, KDE and Moran’s I were the most frequently adopted techniques, covering about 31.17% and 23.38% of reviewed articles, respectively. These techniques are essential for identifying statistically significant clusters and crash concentration. Despite their promising results, the studies also reveal limitations, including inconsistent data quality, high computational demands, and limited use of variables such as road geometry characteristics. Although GIS is an effective tool for planning and analyzing road safety, these deficiencies might be addressed by future studies that advance the use of real-time spatial analytics and incorporate more diversified information. Overall, the review has reinforced the critical role of GIS in improving traffic safety through real-time data-driven interventions. Full article

Precision agriculture technologies are widely recognized as a key pathway to achieving agricultural sustainable intensification. However, empirical research on their farm-level economic benefits and environmental gains has yielded inconclusive and hotly debated results. This study employs a meta-analysis to systematically integrate 85 empirical studies from around the world, comprising 1472 independent farm observations. This approach aims to quantify the average effects of precision agriculture technologies (PATs) and explore the sources of heterogeneity. The results indicate that: (1) Overall, the adoption of precision agriculture technologies generates significant economic benefits, increasing the average return on investment by 22.3% and net profit by 18.5%; (2) Environmentally, technology adoption significantly improves nitrogen use efficiency (average increase of 15.1%), reduces pesticide application (average reduction of 12.8%), and decreases greenhouse gas emissions (average reduction of 9.4%); (3) Moderating effect analysis reveals that technology type, farm size, region, and development level are key factors causing effect heterogeneity. Variable rate technology and auto-guidance systems show the most pronounced benefits in large-scale grain farms, whereas benefits are relatively weaker and less stable in small-scale farms and developing countries. The findings of this study emphasize that the realization of precision agriculture’s benefits is highly context-dependent. Therefore, policy formulation and technology promotion should abandon the “one-size-fits-all” model and adopt differentiated strategies. These strategies should focus on lowering application barriers for smallholders and developing low-cost, locally adapted technical solutions. This approach is essential to maximize the sustainability potential of the technologies. Full article

Background: Delirium is a common complication in patients receiving extracorporeal membrane oxygenation (ECMO), but its prevalence and determinants remain uncertain due to variable clinical practices. We conducted a systematic review and meta-analysis to estimate the pooled prevalence of delirium in ECMO-supported patients, evaluate the influence of diagnostic tools and ECMO modality, and synthesize evidence on associated risk factors and outcomes. Methods: We systematically searched PubMed, Embase, and Web of Science up to May 2025 for studies reporting delirium prevalence in adult ECMO patients. Subgroup analyses were stratified by ECMO modality and delirium assessment tool. Meta-regression was performed to explore potential moderators. Publication bias was assessed visually using funnel plots and statistically using Egger’s and Begg’s tests via the metabias command. Results: Thirteen studies involving 8679 adult ECMO patients were included. The pooled prevalence of delirium was 40.54% (95% CI: 23.01–58.06%), with substantial heterogeneity (I² = 98%; τ² = 0.29; p < 0.01). Subgroup prevalence was 56.83% (95% CI: 41.52–72.14%) for VA-ECMO, 32.84% (95% CI: 3.39–62.29%) for VV-ECMO, and 37.24% (95% CI: 13.71–60.77%) for mixed cohorts; differences were not statistically significant. Delirium prevalence varied by assessment tools, ranging from 57% with NuDESC to 7% with ICD-10 coding. Meta-regression showed a negative but non-significant association between sample size and delirium prevalence (β = −0.000049; p = 0.088). Sensitivity analyses confirmed the robustness of pooled estimates. Conclusions: Delirium affects a substantial proportion of ECMO-supported patients and is linked to considerable clinical and neurocognitive morbidity. The marked heterogeneity in prevalence reflects differences in diagnostic tools and clinical management practices, including sedation strategies. These findings underscore the urgent need for standardized, ECMO-specific delirium assessment protocols and proactive prevention strategies. Well-designed prospective studies with uniform methodologies and long-term follow-up are essential to clarify the trajectory and impact of delirium in this high-risk population. Full article