Search Results (942)

Dental caries remains a major global public health problem characterized by pronounced social inequalities. This study aimed to identify, critically appraise, and synthesize the most recent evidence on the relationship between socioeconomic indicators and dental caries among adults aged 19–60 years, providing an updated systematic review that builds upon our previous reviews from 2012 and 2018. Reported following the PRISMA 2020 guidelines, we conducted a systematic search of eight electronic databases for observational studies published between March 2017 and April 2024 (PROSPERO: CRD42017074434). Two independent reviewers performed study selection, data extraction, and risk of bias assessment using the Newcastle–Ottawa Scale. Due to substantial methodological heterogeneity across the 22 included studies, a narrative synthesis was undertaken. The findings demonstrated a strong inverse association between socioeconomic position and caries experience. Lower income, lower educational attainment, and unemployment or employment in manual/unskilled occupations were associated with a higher overall caries experience. Advanced analytical approaches in recent studies, including life-course, reinforced that education and income are key contributors of these oral health inequalities, with persistent social disadvantage conferring the greatest risk. In conclusion, dental caries in adults aged 19–60 years is a social condition reflecting the cumulative effects of socioeconomic inequality across the life course. Addressing adult dental caries requires integrated approaches that combine clinical prevention with social and public policies aimed at reducing structural inequalities. Full article

With the increasing complexity of cyber threats and the inefficiency of traditional vulnerability management, artificial intelligence has been increasingly integrated into cybersecurity. This review provides a comprehensive evaluation of AI-powered strategies including machine learning, deep learning, and large language models for identifying cybersecurity vulnerabilities and supporting automated patching. In this review, we conducted a synthesis and appraisal of 29 peer-reviewed studies published between 2019 and 2024. Our results indicate that AI methods substantially improve the precision of detection, scalability, and response speed compared with human-driven and rule-based approaches. We detail the transition from conventional ML categorization to using deep learning for source code analysis and dynamic network detection. Moreover, we identify advanced mitigation strategies such as AI-powered prioritization, neuro-symbolic AI, deep reinforcement learning and the generative abilities of LLMs which are used for automated patch suggestions. To strengthen methodological rigor, this review followed a registered protocol and PRISMA-based study selection, and it reports reproducible database searches (exact queries and search dates) and transparent screening decisions. We additionally assessed the quality and risk of bias of included studies using criteria tailored to AI-driven vulnerability research (dataset transparency, leakage control, evaluation rigor, reproducibility, and external validation), and we used these quality results to contextualize the synthesis. Our critical evaluation indicates that this area remains at an early stage and is characterized by significant gaps. The absence of standard benchmarks, limited generalizability of the models to various domains, and lack of adversarial testing are the obstacles that prevent adoption of these methods in real-world scenarios. Furthermore, the research suggests that the black-box nature of most models poses a serious problem in terms of trust. Thus, XAI is quite pertinent in this context. This paper serves as a thorough guide for the evolution of AI-driven vulnerability management and indicates that next-generation AI systems should not only be more accurate but also transparent, robust, and generalizable. Full article

Solar ultraviolet radiation (UVR) is classified as a Group 1 carcinogen and poses a significant occupational hazard to outdoor workers. Despite preventive guidelines, adherence to protective measures remains inconsistent. This systematic review identified the protective measures adopted by healthy outdoor workers and assessed their adherence to and the effectiveness of these measures. Following the PRISMA 2020 statement, the review searched Scopus, Web of Science, and PubMed for peer-reviewed studies published between 2015 and 2025. Eligible studies included at least 100 healthy participants and evaluated preventive or protective measures against solar UVR. Independent reviewers extracted data and assessed risk of bias using the McMaster Critical Review Form. From 17,756 records, 51 studies met the inclusion criteria after screening and a subsequent snowballing process. The identified protective strategies clustered into physical, behavioural, and organisational categories. Adherence ranged from low to moderate, with structured interventions and employer support improving compliance. Sunscreen use remained low due to perceived inconvenience and lack of provision. Overall, the evidence revealed substantial variability in implementation and effectiveness across occupations. Strengthened regulations and integrated interventions combining education, personal protective equipment, and organisational measures are essential. Future research should prioritise longitudinal designs and objective indicators such as biomarkers and dosimetry. Full article

Objective: This umbrella review aimed to synthesize evidence from meta-analyses on the efficacy of rebamipide in major gastrointestinal disorders and dyspeptic symptoms. Methods: This umbrella review followed Joanna Briggs Institute standards and was registered in PROSPERO (CRD420251185686). A comprehensive search of MEDLINE, EMBASE, Cochrane, and Scopus (1 January 1985, to 10 September 2025) was conducted to identify systematic reviews and meta-analyses assessing rebamipide therapy. Methodological quality was appraised using AMSTAR-2, ROBIS, and GRADE tools. Pooled data were analyzed using fixed- or random-effects models according to heterogeneity, as assessed using the I² statistic. Results: Eleven meta-analyses (88 primary studies) were included. Rebamipide significantly improved H. pylori eradication (OR = 1.76; 95% CI: 1.44–2.16), reduced NSAID-induced mucosal injury (OR = 2.72; 95% CI: 1.89–5.14), enhanced ulcer healing after endoscopic submucosal dissection (OR = 2.28; 95% CI: 1.42–3.65), and alleviated dyspeptic symptoms (OR = 2.95; 95% CI: 1.04–8.37). Overall evidence quality was moderate to high, with low to moderate risk of bias. Conclusions: Rebamipide demonstrates consistent therapeutic benefits across diverse gastrointestinal disorders, improving H. pylori eradication rates, mucosal protection, ulcer healing, and symptom relief. These findings support rebamipide as an effective and well-tolerated adjunctive agent for the prevention and management of upper gastrointestinal diseases. Full article

Root-knot nematodes (Meloidogyne spp., RKN) penetrate the roots of plants, blocking the flow of water and nutrients, preventing plant development, and causing losses of up to 68% in production. Its management is limited by the low availability of genetically resistant materials, the inefficient use of biological controllers, and the high risk of environmental contamination from the application of pesticides. The aim of this study was to contribute to the integrated management of (RKN) through the use of chitosan. A completely randomized experimental design was used in a factorial arrangement with two applications (foliar or edaphic), two cherry tomato genotypes (IAC1687 and LA2076), and eight treatments (three concentrations of chitosan (1.5–2.0–2.5 mg/mL), commercial controls and absolute controls). The yield and nematode population components were evaluated. The cherry tomato (IAC1687) obtained the greatest yield, with 33.517.1 kg/ha and an 85% reduction in the nematode population with the application of 2.5 mg/mL of chitosan to the soil. Chitosan improved the yield components of the evaluated cultivars and reduced nematode populations, suggesting that it can be a sustainable alternative in commercial production systems, as it can help reduce the use of chemical pesticides and improve health and crop productivity. As a limitation of this study, the use of acetic acid as a solvent for chitosan potentially interfered with the results associated with the nematode population, increasing bias and imprecision as there was no blockage due to light, temperature, or irrigation. Therefore, we suggest that future research explores alternative solvents to elucidate the mechanism of action or response of chitosan. Full article

In this paper, we propose FedENLC, an end-to-end noisy label correction model that performs model training and label correction simultaneously to fundamentally mitigate the label noise problem of federated learning (FL). FedENLC consists of two stages. In the first stage, the proposed model employs Symmetric Cross Entropy (SCE), a robust loss function for noisy labels, and label smoothing to prevent the model from being biased by incorrect information in noisy environments. Subsequently, a Bayesian Gaussian Mixture Model (BGMM) is utilized to detect noisy clients. BGMM mitigates extreme parameter bias through its prior distribution, enabling stable and reliable detection in FL environments where data heterogeneity and noisy labels coexist. In the second stage, only the top noisy clients with high noise ratios are selectively included in the label correction process. The selection of top noisy clients is determined dynamically by considering the number of classes, posterior probabilities, and the degree of data heterogeneity. Through this approach, the proposed model prevents performance degradation caused by incorrect detection, while improving both computational efficiency and training stability. Experimental results show that FedENLC achieves significantly improved performance over existing models on the CIFAR-10 and CIFAR-100 datasets under data heterogeneity settings along with four noise settings. Full article

Sarcopenia and physical frailty are interconnected geriatric syndromes that frequently coexist in older adults, sharing common pathophysiological pathways. However, their early detection in community settings is limited by resource constraints and by the lack of simplified, scalable diagnostic tools. This cross-sectional study aimed to estimate the prevalence and overlap of sarcopenia and frailty in a real-world public health screening programme and to evaluate the diagnostic performance of a pragmatic two-step algorithm. In September 2025, a total of 256 consecutive community-dwelling adults aged ≥65 years underwent standardized assessment using the SARC-F questionnaire, handgrip strength dynamometry, and selective bioelectrical impedance analysis (BIA). Sarcopenia was defined according to 2019 EWGSOP2 criteria, and frailty according to the Fried phenotype. Confirmed sarcopenia was identified in 37 participants (14.5%, 95% CI 10.7–19.1%) and frailty in 31 (12.1%, 95% CI 8.6–16.7%), with substantial overlap (77.4% of frail individuals also had sarcopenia; Cohen’s κ = 0.62). The two-step algorithm (Step 1: SARC-F ≥ 4; Step 2: handgrip strength and BIA only in screen-positive participants) demonstrated excellent accuracy for confirmed sarcopenia (AUC 0.913, 95% CI 0.871–0.955), with sensitivity 91.9%, specificity 81.3%, and a 53.9% reduction in BIA use. Factors independently associated with confirmed sarcopenia included older age, BMI < 22 kg/m², physical inactivity, and higher SARC-F score. A simple, function-centered two-step approach enables efficient and scalable identification of sarcopenia and frailty in community settings, supporting early preventive strategies to preserve physical function. Full article

Surface deformation induced by the extraction of natural resources constitutes a non-stationary spatiotemporal process. Modeling surface deformation time series obtained through Interferometric Synthetic Aperture Radar (InSAR) technology using deep learning methods is crucial for disaster prevention and mitigation. However, the complexity of model hyperparameter configuration and the lack of interpretability in the resulting predictions constrain its engineering applications. To enhance the reliability of model outputs and their decision-making value for engineering applications, this study presents a workflow that combines a Tree-structured Parzen Estimator (TPE)-based Bayesian optimization approach with ensemble inference. Using the Rhineland coalfield in Germany as a case study, we systematically evaluated six deep learning architectures in conjunction with various spatiotemporal coding strategies. Pairwise comparisons were conducted using a Welch t-test to evaluate the performance differences across each architecture under two parameter-tuning approaches. The Benjamini–Hochberg method was applied to control the false discovery rate (FDR) at 0.05 for multiple comparisons. The results indicate that TPE-optimized models demonstrate significantly improved performance compared to their manually tuned counterparts, with the ResNet+Transformer architecture yielding the most favorable outcomes. A comprehensive analysis of the spatial residuals further revealed that TPE optimization not only enhances average accuracy, but also mitigates the model’s prediction bias in fault zones and mineralize areas by improving the spatial distribution structure of errors. Based on this optimal architecture, we combined the ten highest-performing models from the optimization stage to generate a quantile-based susceptibility map, using the ensemble median as the central predictor. Uncertainty was quantified from three complementary perspectives: ensemble spread, class ambiguity, and classification confidence. Our analysis revealed spatial collinearity between physical uncertainty and absolute residuals. This suggests that uncertainty is more closely related to the physical complexity of geological discontinuities and human-disturbed zones, rather than statistical noise. In the analysis of super-threshold probability, the threshold sensitivity exhibited by the mining area reflects the widespread yet moderate impact of mining activities. By contrast, the fault zone continues to exhibit distinct high-probability zones, even under extreme thresholds. It suggests that fault-controlled deformation is more physically intense and poses a greater risk of disaster than mining activities. Finally, we propose an engineering decision strategy that combines uncertainty and residual spatial patterns. This approach transforms statistical diagnostics into actionable, tiered control measures, thereby increasing the practical value of susceptibility mapping in the planning of natural resource extraction. Full article

The use of machine learning (ML) and deep learning (DL) in lung cancer detection and classification offers great promise for improving early diagnosis and reducing death rates. Despite major advances in research, there is still a significant gap between successful model development and clinical use. This review identifies the main obstacles preventing ML/DL tools from being adopted in real healthcare settings and suggests practical advice to tackle them. Using PRISMA guidelines, we examined over 100 studies published between 2022 and 2024, focusing on technical accuracy, clinical relevance, and ethical aspects. Most of the reviewed studies rely on computed tomography (CT) imaging, reflecting its dominant role in current lung cancer screening workflows. While many models achieve high performance on public datasets (e.g., >95% sensitivity on LUNA16), they often perform poorly on real clinical data due to issues like domain shift and bias, especially toward underrepresented groups. Promising solutions include federated learning for data privacy, synthetic data to support rare subtypes, and explainable AI to build trust. We also present a checklist to guide the development of clinically applicable tools, emphasizing generalizability, transparency, and workflow integration. The study recommends early collaboration between developers, clinicians, and policymakers to ensure practical adoption. Ultimately, for ML/DL solutions to gain clinical acceptance, they must be designed with healthcare professionals from the beginning. Full article

Artificial intelligence (AI) is becoming more and more integrated into manufacturing processes, revolutionizing conventional production, like CNC (Computer Numerical Control) machining. This study analyzes how large language models (LLMs), exemplified by ChatGPT, behave when tasked with G-code optimization for improving surface quality and productivity of automotive metal parts, with emphasis on systematically documenting failure modes and limitations that emerge when general-purpose AI encounters specialized manufacturing domains. Even if software programming remains essential for highly regulated sectors, free AI tools will be increasingly used due to advantages like cost-effectiveness, adaptability, and continuous innovation. The condition is that there is sufficient technical expertise available in-house. The experiment carried out involved milling three identical parts using a Haas VF-3 SS CNC machine. The G-code was generated by SolidCam and was optimized using ChatGPT considering user-specified criteria. The aim was to improve the quality of the part’s surface, as well as increase productivity. The measurements were performed using an ISR C-300 Portable Surface Roughness Tester and Axiom Too 3D measuring equipment. The experiment revealed that while AI-generated code achieved a 37% reduction in cycle time (from 2.39 to 1.45 min) and significantly improved surface roughness (Ra—arithmetic mean deviation of the evaluated profile—decreased from 0.68 µm to 0.11 µm—an 84% improvement), it critically eliminated the pocket-milling operation, resulting in a non-conforming part. The AI optimization also removed essential safety features including tool length compensation (G43/H codes) and return-to-safe-position commands (G28), which required manual intervention to prevent tool breakage and part damage. Critical analysis revealed that ChatGPT failures stemmed from three factors: (1) token-minimization bias in LLM training leading to removal of the longest code block (31% of total code), (2) lack of semantic understanding of machining geometry, and (3) absence of manufacturing safety constraints in the AI model. This study demonstrates that current free AI tools like ChatGPT can identify optimization opportunities but lack the contextual understanding and manufacturing safety protocols necessary for autonomous CNC programming in production environments, highlighting both the potential, but also the limitation, of free AI software for CNC programming. Full article

Anthropogenic pressures in the Amazon Basin are reshaping human–animal–environment interactions and increasing zoonotic disease risk. Within this One Health context, domestic dogs and cats are underrecognized contributors to pathogen circulation at the human–wildlife interface. We conducted a PRISMA-compliant systematic review of zoonotic pathogens reported in companion animals across Amazonian territories in nine countries, including literature published between 2000 and 2025 in four languages. Zoonotic pathogens showed a heterogeneous yet widespread distribution, with parasitic infections, particularly Leishmania spp., Toxoplasma gondii, and vector-borne protozoa, being the most frequently reported. A pronounced geographic bias was evident, with studies concentrated in Brazil and selected areas of the western Amazon, while large portions of the Basin remain understudied. Methodological limitations included reliance on cross-sectional designs and heterogeneous diagnostic approaches, often based solely on serology. These findings highlight the need to strengthen One Health-oriented governance frameworks that integrate animal health surveillance into environmental and public health policies. Priority actions include expanding surveillance to underrepresented regions, harmonizing diagnostic protocols, investing in regional laboratory capacity, and promoting community-based monitoring. Strengthened cross-sectoral and transboundary coordination is essential to reduce zoonotic risk and support evidence-based disease prevention in Amazonian ecosystems. Full article

Botulinum toxin type A (BoNT-A) is an established preventive therapy for chronic migraines; however, uncertainty remains regarding its comparative efficacy and safety. Thus, we aimed to summarize current evidence from high-quality systematic reviews of the therapeutic effects of BoNT-A in migraine management. An umbrella review was conducted following PRISMA guidelines and registered in PROSPERO. High-quality systematic reviews with meta-analysis evaluating BoNT-A efficacy were identified through five databases up to August 2024. Primary outcomes included monthly headache frequency and severity. Methodological quality and risk of bias were assessed using the umbrella review checklist. Fourteen articles were included. Overall, quantitative evidence indicated favorable effects of BoNT-A compared with placebo for chronic migraines, across headache frequency, headache severity, and acute medication use, but less efficacy than topiramate and the CGRP monoclonal antibodies (CGRPmAbs) galcanezumab and fremanezumab. Though the adverse events were frequent, BoNT-A was generally well-tolerated. Comparative data suggested superior tolerability versus topiramate and a safety profile like CGRPmAbs. Although botulinum toxin type A is widely used as a preventive treatment for chronic migraines, the available evidence supports its efficacy at a moderate level. Further head-to-head and long-term analyses are needed to clarify its comparative role alongside newer biologic treatments. Full article

Background/Objectives: Gambling disorder (GD) is characterized by a high prevalence of co-occurring psychiatric disorders, including personality disorders (PDs), which may negatively influence clinical presentation, treatment outcomes, and relapse rates. The aim of this systematic review was to synthesize recent evidence regarding the association between GD and formally diagnosed PD and/or diagnostically anchored PD symptomatology, and to describe the main personality dimension most frequently reported in affected individuals. Methods: A systematic search was conducted in the PubMed and Dialnet databases for articles published between 30 November 2015 and 30 November 2025, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines. PubMed was selected as the primary database because it is the most comprehensive source for peer-reviewed biomedical and psychiatric research, while Dialnet was included to complement PubMed by ensuring coverage of peer-reviewed psychiatric and psychological research published in other Romance-language journals, which are often underrepresented in international databases. The methodological quality and risk of bias of the included studies were evaluated using the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for cross-sectional studies and the Newcastle–Ottawa Scale (NOS) for observational studies. Data extraction and synthesis were performed manually by two independent reviewers. Eight studies, predominantly cross-sectional in nature, assessing exclusively formally diagnosed personality disorders in adult individuals (≥18 years) diagnosed with GD were included. Results: Eight studies met the inclusion criteria, including a total of 4607 patients with GD. Across studies, personality pathology was highly prevalent among individuals with GD, with antisocial and borderline personality disorders most consistently reported. Elevated levels of impulsivity, emotional dysregulation, and narcissistic traits were frequently observed and were additionally associated with greater gambling severity, earlier onset, and poorer clinical outcomes. Antisocial personality symptoms were strongly linked to high-risk gambling subtypes, while obsessive–compulsive personality traits showed a more heterogeneous relationship with gambling severity. Conclusions: These results underscore the importance of personality assessment in individuals with GD and highlight the need for longitudinal studies using standardized diagnostic frameworks to inform tailored prevention and treatment strategies. Full article

Background: Resin infiltration has emerged as a micro-invasive strategy for managing enamel porosities, offering both therapeutic and aesthetic benefits. ICON^® (DMG, Hamburg, Germany) is the most widely used system; however, evidence on its penetration behavior in sound enamel remains limited. Objectives: This in vitro study aimed to evaluate the penetration depth and morphological pattern of ICON resin infiltration in sound human enamel, using quantitative morphometric analysis and scanning electron microscopy (SEM). Methods: Fourteen freshly extracted, caries-free anterior teeth were sectioned longitudinally. ICON^® resin infiltrate was applied to the buccal enamel surfaces according to the manufacturer’s protocol, while the lingual/palatal surfaces served as internal controls. Penetration depth was measured quantitatively on both mesial (surface A) and distal (surface B) halves, and SEM was used to assess resin–enamel interface morphology. Statistical analysis included the Shapiro–Wilk test, paired t-test, Pearson correlation, and percentage difference calculation. Results: The mean difference in penetration depth between surfaces A and B was −21.29 µm (p = 0.525), indicating no statistically significant variation. A strong positive correlation was observed between surfaces (r = 0.783, p = 0.001). The mean percentage difference was −3.57% (SD = 18.61%), suggesting minimal directional bias. SEM images confirmed continuous and homogeneous resin infiltration within enamel prisms. Post-hoc power analysis indicated 15.2% power, reflecting the impact of the limited sample size typical for SEM-based exploratory studies. Conclusions: Within the limitations of this in vitro investigation, ICON resin infiltration demonstrated uniform and consistent penetration in sound enamel, supported by both quantitative and SEM analyses. These findings validate its potential as a reliable preventive and micro-invasive biomaterial in dental practice, particularly for protecting enamel surfaces prior to orthodontic bracket bonding. Further clinical research with larger cohorts is recommended to confirm its long-term stability and prophylactic performance. Full article

Background and Objectives: Obesity, heart failure (HF), and atherosclerosis have common pathways, including chronic inflammation, immune cells activation, and metabolic disturbances. These pathways often coexist and overlap, increasing cardiometabolic risk. Growth differentiation factor 15 (GDF-15) is an emerging cytokine linked to inflammation, oxidative stress, and metabolic dysregulation, which are common pathways between heart failure, obesity and atherosclerosis. Beyond its established prognostic value in cardiovascular diseases (CVD) and HF, recent evidence suggests that GDF-15 may also reflect subclinical atherosclerosis, potentially improving early risk stratification in obese and HF populations. The aim of this review is to synthesize current evidence on the association between GDF-15 and markers of subclinical atherosclerosis, and to evaluate whether GDF-15 may serve as an integrative biomarker reflecting shared cardiometabolic pathways. Materials and Methods: We conducted a systematic review following PRISMA recommendations registered by CRD420251267457 number on PROSPERO. PubMed, Embase, Scopus, and Web of Science were searched for human studies evaluating the correlation between markers of subclinical atherosclerosis and GDF-15 concentration. We excluded the studies not published in English, not involving human participants, and not meeting the inclusion criteria. We assessed the risk of bias using the Joanna Briggs Institute appraisal tool. Due to the heterogeneity of studies, a narrative synthesis was performed. Result: The review included 18 studies, which evaluated the association between GDF-15 and subclinical atherosclerosis markers, such as intima media thickness, coronary artery calcium score, ankle-brachial index, and atherosclerotic plaques. Studies included patients with metabolic disorders, chronic inflammatory diseases, HIV cohorts, and general population samples. Most of the studies reported that GDF-15 levels were associated with greater atherosclerotic burden; however, results were frequently influenced by confounders. Methodological limitations, such as limited or highly specified samples, cross-sectional designs, variability in atherosclerotic-imaging technique, and inconsistent adjustment for confounders, restrict generalization of the results. Conclusions: Current evidence supports GDF-15 as a biomarker integrating inflammatory and metabolic stress signals, indirectly linking obesity, HF and subclinical atherosclerosis. While current data supports its prognostic relevance, further studies are needed to confirm its clinical utility in routine assessment and preventive cardiovascular care. Full article