Search Results (223)

Background: Human papillomavirus (HPV)-associated oral and oropharyngeal squamous cell carcinomas have risen dramatically in incidence over recent decades. Yet, unlike cervical neoplasia, there is no established screening paradigm for HPV-driven oropharyngeal dysplasia, as precursor lesions are often occult and are not easily accessible for examination. This drives an urgent need for non-invasive biomarkers to enable early detection, risk stratification, and timely intervention. Objective of this review is to highlight advances in liquid biopsy modalities, specifically saliva- and blood-based biomarkers—in the context of HPV-driven oral carcinogenesis—and to evaluate their utility in early cancer detection, prognostic, post-treatment surveillance, and recurrence monitoring. Methods: We performed a narrative review of PubMed-indexed studies (2015–2025) focusing on HPV-positive oral and oropharyngeal squamous cell carcinomas. and liquid biopsy analytes. Key sources were high-impact original studies and meta-analyses from 2020–2025 examining circulating tumor DNA (ctDNA), viral nucleic acids, circulating tumor cells (CTCs), extracellular vesicles (EVs), and related biomarkers in saliva and blood. Reported data on assay performance, biases, and validation were reviewed to highlight how oral cancer findings align with trends seen in other solid tumors. Results: In reviewing recent studies (2015–2025), we found consistent evidence that saliva best captures locoregional tumor signals while plasma circulating tumor HPV DNA (ctHPV DNA) reflects systemic disease, and that using both matrices improves detection over either alone. Dual-fluid testing will potentially enable earlier identification of molecular residual disease with clinically meaningful lead time before radiographic recurrence, supporting risk-adapted surveillance. Overall, literature favors standardized pre-analytics and combined saliva plus plasma workflows to enhance early detection and follow-up in HPV-positive oral and oropharyngeal squamous cell carcinomas. Conclusions: Liquid biopsy approaches offer promising tools for the early, non-invasive detection and real-time monitoring of HPV-associated oral cancers. Realizing their full clinical potential will require robust prospective validation and standardization of pre-analytical protocols. Integrating salivary and blood biomarkers into tailored surveillance programs may further support earlier intervention and improved patient outcomes, while potentially reducing reliance on unnecessary invasive procedures. Full article

Network traffic analysis is crucial for understanding network behavior and identifying underlying applications, protocols, and service groups. The increasing complexity of network environments, driven by the evolution of the Internet, poses significant challenges to traditional analytical approaches. Graph Neural Networks (GNNs) have recently garnered considerable attention in network traffic analysis due to their ability to model complex relationships within network flows and between communicating entities. This scoping review systematically surveys major academic databases, employing predefined eligibility criteria to identify and synthesize key research in the field, following the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) methodology. We present a comprehensive overview of a generalized architecture for GNN-based traffic analysis and categorize recent methods into three primary types: node prediction, edge prediction, and graph prediction. We discuss challenges in network traffic analysis, summarize solutions from various methods, and provide practical recommendations for model selection. This review also compiles publicly available datasets and open-source code, serving as valuable resources for further research. Finally, we outline future research directions to advance this field. This work offers an updated understanding of GNN applications in network traffic analysis and provides practical guidance for researchers and practitioners. Full article

This paper investigates the short-term predictability of daily crude oil price movements by employing a multi-method analytical framework that incorporates both econometric and machine learning techniques. Utilizing a dataset of 21 financial and commodity time series spanning ten years of trading days (2015–2024), we explore the dynamics of oil price volatility and its key determinants. In the forecasting phase, we applied seven models. The meta-learner model, which consists of three base learners (Random Forest, gradient boosting, and support vector regression), achieved the highest R² value of 0.532, providing evidence that our complex model structure can successfully outperform existing approaches. This ensemble demonstrated that the most influential predictors of next-day oil prices are VIX, OVX, and MOVE (volatility indices for equities, oil, and bonds, respectively), and lagged oil returns. The results underscore the critical role of volatility spillovers and nonlinear dependencies in forecasting oil returns and suggest future directions for integrating macroeconomic signals and advanced volatility models. Moreover, we show that combining multiple machine learning procedures into a single meta-model yields superior predictive performance. Full article

Electrochemical detection is widely used in environmental, health, and food analysis due to its portability, low cost, and high sensitivity. However, when analytes with similar redox potentials coexist, overlapping voltammetric signals often occur, which compromises detection accuracy and sensitivity. In this study, a simple second-derivative image sharpening (IS) algorithm is applied to the electrochemical detection of chlorophenol (CP) isomers with similar redox behaviors. Specifically, a graphene-modified electrode was employed for the electrochemical detection of two chlorophenol isomers: ortho-CP (o-CP) and meta-chlorophenol (m-CP) in the range from 1.0 to 10.0 μmol/L. After image-sharpening, the peak potential difference between o- and m-CP increased from 0.08 V to 0.12 V. The limits of detection (LOD) for o-CP and m-CP decreased from 0.6 to 0.9 μmol/L to 0.12 and 0.31 μmol/L, respectively. The corresponding sensitivities also improved from 0.92 to 1.35 A/(mol L⁻¹) to 4.11 and 3.71 A/(mol L⁻¹), respectively. Moreover, the sharpened voltammograms showed enhanced peak resolution, facilitating visual discrimination of the two isomers. These results demonstrate that image sharpening can significantly improve peak shape, peak separation, sensitivity, and detection limit in electrochemical analysis. The obtained algorithm is computationally efficient (<30 lines of C++ (Version 6.0)/OpenCV, executable in <1 ms on an ARM-M0 microcontroller) and easily adaptable to various programming environments, offering a promising approach for data processing in portable electrochemical sensing systems. Full article

Industrial engineering research has been reshaped by the integration of artificial intelligence (AI) within the framework of Industry 4.0, characterized by the interplay between cyber-physical systems (CPS), advanced automation, and the Industrial Internet of Things (IIoT). While this integration opens new opportunities, it also introduces biases that undermine the reliability and robustness of scientific and industrial outcomes. This article presents a systematic literature review (SLR), supported by natural language processing techniques, aimed at identifying and classifying biases in AI-driven research within industrial contexts. Based on this meta-research approach, a taxonomy is proposed that maps biases across the stages of the scientific method as well as the operational layers of intelligent production systems. Statistical analysis confirms that biases are unevenly distributed, with a higher incidence in hypothesis formulation and results dissemination. The study also identifies emergent AI-related biases specific to industrial applications such as predictive maintenance, quality control, and digital twin management. Practical implications include stronger reliability in predictive analytics for manufacturers, improved accuracy in monitoring and rescue operations through transparent AI pipelines, and enhanced reproducibility for researchers across stages. Mitigation strategies are then discussed to safeguard research integrity and support trustworthy, bias-aware decision-making in Industry 4.0. Full article

Background: Chronic pain affects nearly one in five adults worldwide and is increasingly recognized not only as a disease but as a potential risk factor for neurocognitive decline and dementia. While some evidence supports this association, existing systematic reviews are static and rapidly outdated, and none have leveraged advanced methods for continuous updating and robust uncertainty modeling. Objective: This protocol describes a living systematic review with dose–response Bayesian meta-analysis, enhanced by artificial intelligence (AI) tools, to synthesize and maintain up-to-date evidence on the prospective association between any type of chronic pain and subsequent cognitive decline. Methods: We will systematically search PubMed, Embase, Web of Science, and preprint servers for prospective cohort studies evaluating chronic pain as an exposure and cognitive decline as an outcome. Screening will be semi-automated using natural language processing models (ASReview), with human oversight for quality control. Bayesian hierarchical meta-analysis will estimate pooled effect sizes and accommodate between-study heterogeneity. Meta-regression will explore study-level moderators such as pain type, severity, and cognitive domain assessed. If data permit, a dose–response meta-analysis will be conducted. Living updates will occur biannually using AI-enhanced workflows, with results transparently disseminated through preprints and peer-reviewed updates. Results: This is a protocol; results will be disseminated in future reports. Conclusions: This living Bayesian systematic review aims to provide continuously updated, methodologically rigorous evidence on the link between chronic pain and cognitive decline. The approach integrates innovative AI tools and advanced meta-analytic methods, offering a template for future living evidence syntheses in neurology and pain research. Full article

Introduction: Atrial fibrillation (AF) is common in patients with Brugada syndrome (BrS). The impact and significance of AF in this patient population needs to be further clarified. Method: We performed a systematic review and meta-analysis of studies comparing the risks of developing major arrhythmic events (MAEs) in patients with BrS with and without AF. Databases including MEDLINE, Embase, and Cochrane CENTRAL were searched from inception to July 2024, using appropriate search and MeSH terms. Data were sought on the comparison of patients with BrS with and without AF. The protocol was specified prior to the searches being performed, and standard meta-analytic techniques were used. Results: Thirteen observational studies were included (a total of 5413 patients). A significant increase in MAEs was observed in patients with both BrS and AF (20.6% vs. 7.8%; OR 2.81, 95% CI 1.82–4.34; p < 0.0001; I² = 46%). Significantly higher rates of syncope (33.3% vs. 23.4%; OR 1.97, 95% CI 1.04–3.76; p = 0.04, I² = 59%) and a significant increase in all-cause mortality (11.3% vs. 3.7%; OR 4.21, 95% CI 1.69–10.45; p = 0.002, I² = 0%) and sodium channel mutations (43.1% vs. 29.9%; OR 1.87, 95% CI 1.07–3.29; p = 0.028, I² = 0%) were observed for patients with BrS and AF. Conclusions: Patients with both BrS and AF seem to have a more severe disease phenotype. More research into the added role of AF in risk stratification of asymptomatic BrS patients is needed, but the prognostic implications of AF may need to be considered when developing future personalised medicine approaches in the BrS population. Full article

This meta-analysis explored the relationship of palm kernel cake inclusion level (PKC_Inclusion) with nutrient utilization and performance in cattle, goats and sheep under confinement. For this purpose, a dataset with 51 studies was constructed by using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) Protocol. Relationships of PKC_Inclusion with dry matter (DM), crude protein (CP), EE (ether extract), NDF (neutral detergent fiber) and TDN (total digestible nutrients) intake and digestibility, as well as with ADG (average daily gain) and FE (feed efficiency), were explored under a mixed model approach, considering the species and study as fixed and random effects, respectively. Data revealed independent (p < 0.075) relationships of EE and TDN intake and digestibility with PKC_Inclusion for cattle, goats and sheep. However, the relationship of CP intake and digestibility with PKC_Inclusion did not differ (p > 0.114) between ruminant species. Goats and sheep demonstrated similar quantitative patterns for DM and NDF intake but different quantitative patterns (p < 0.037) from those observed for cattle with the increase in PKC_Inclusion in the diet. Regarding performance, FE responses were similar between cattle and sheep but differed (p < 0.001) from those observed for goats; however, ADG demonstrated similar (p = 0.243) decreasing rates among ruminant species in function of dietary PKC_Inclusion. In conclusion, PKC_Inclusion has differential effects on the intake and digestibility of DM and most of the nutritional components in confined cattle, goats and sheep, except for CP. The data reported herein could be used in future nutritional models to allow for the better use of alternative feedstuffs, such as PKC in productive ruminants under confinement. Full article

Quantitative proteomics relies on robust statistical methods for differential expression, critically impacting downstream functional enrichment. This meta-analysis systematically investigated how statistical hypothesis testing approaches and criteria for defining biological relevance influence functional enrichment concordance. We reanalyzed five independent label-free quantitative proteomics datasets using diverse frequentist (t-test, Limma, DEqMS, MSstats) and Bayesian (rstanarm) approaches. Concordance of Gene Ontology (GO) and KEGG pathways was assessed using Jaccard indices and correlation metrics, grouping comparisons by statistical test and biological relevance consistency. The results demonstrated highly significant differences in similarity distributions among the comparison groups. Comparisons varying only hypothesis testing methods (with constant relevance criteria, FC or Bayesian) showed the highest consistency. Conversely, comparisons with differing biological relevance criteria (or varied methodological choices) yielded significantly lower consistency, highlighting this definition’s critical impact on GO term overlaps. KEGG pathways displayed more uniform, method-insensitive concordance. Sensitivity analysis confirmed the findings’ robustness, underscoring that methodological choices profoundly influence functional enrichment outcomes. This work emphasizes the critical need for transparency and careful consideration of analytical decisions in proteomics research to ensure reproducible and biologically sound interpretations. Full article

Background/Objectives: Dementia is a major public health challenge, with age as its primary non-modifiable risk factor. Several modifiable conditions, such as hypertension, diabetes, and depression, have been identified as potential targets for prevention. The aim is to describe the methodology and preliminary results of a study that will be conducted within the Italian National Health Service (INHS), designed to assess the impact of hypertension, diabetes, depression, and their interactions on the onset of dementia. Methods: This population-based cohort study, part of the PREV-ITA-DEM project, was conducted using a Common Data Model (CDM) approach across five Italian regions and cities participating in the NeuroEpiNet network. Individuals aged ≥ 50 years without prior diagnoses of dementia, depression, diabetes, or hypertension were followed from cohort entry (2011–2013) until dementia diagnosis, death, emigration, or study end (2019–2022). Exposures were time-dependent and defined using validated algorithms applied to Healthcare Utilization Databases (HUDs). Associations between chronic conditions and dementia risk will be estimated using competing risks regression models adjusted for confounders. Results: The final cohort comprised more than 3 million individuals, with a mean baseline age of 63–65 years and a female proportion of 52–55%. On 1 January 2011, the prevalence of individuals aged ≥ 50 years with dementia ranged from 8.7 to 14.7 per 1000 population. A harmonized methodological framework based on a CDM was developed and implemented across all sites, incorporating a shared protocol, standardized local databases, and uniform analytic scripts, and the results will be pooled using meta-analytic techniques. Conclusions: Preliminary findings confirm the feasibility of a standardized, multi-regional CDM approach and the potential for HUDs to support large-scale dementia prevention studies in real-world settings. Full article

Background: Falls are a leading cause of morbidity and loss of independence among older adults, and occupational therapy (OT) offers a unique, multidimensional approach to fall prevention. This systematic review evaluates the effectiveness of OT-based interventions for improving balance, mobility, functional performance, and psychological outcomes related to fall risk in older adults. Methods: This review followed PRISMA (2020) guidelines. A comprehensive search of PubMed, Scopus, Dialnet, and OTseeker was conducted from March to May 2025. The inclusion criteria targeted studies involving non-pharmacological, OT-led interventions in adults aged ≥65. Seventeen studies were selected, including randomized controlled trials, pilot studies, and quasi-experimental designs. The data extraction and quality appraisal were performed independently by two reviewers. Results: The included interventions varied among exercise-based programs (e.g., Tai Chi, Pilates), virtual reality training, home safety modifications, cognitive–behavioral therapy, and wearable technologies. Most of the studies reported significant improvements in postural balance, fear of falling, and functional independence. Environmental adaptations and educational strategies also yielded positive outcomes. However, a real-world fall incidence reduction was inconsistently reported, and the methodological heterogeneity limited the meta-analytic synthesis. Conclusions: Occupational therapy contributes significantly to fall prevention through multimodal, person-centered strategies that integrate physical, cognitive, and environmental components. Future research should aim to standardize the outcome measures, include high-risk populations, and assess the long-term efficacy and cost-effectiveness of OT-led programs. Full article

Optimized water-saving irrigation (WSI) practices are critical for enhancing resource use efficiency and ensuring sustainable wheat production in water-scarce regions. This meta-analysis quantitatively assessed the effects of various WSI methods on wheat yield, water use efficiency (WUE), and partial factor productivity of nitrogen (PFPN) across China’s wheat regions. The results showed that optimized irrigation, particularly drip and micro-sprinkler systems, significantly reduced irrigation water and nitrogen inputs by 35.1% and 7.2%, respectively, without yield penalties. Drip and micro-sprinkler irrigation, which together accounted for over 97% of observations, improved WUE by 18.7% and 10.1%, respectively, and increased PFPN by 6.8% and 5.5%, highlighting their dominant role in current WSI practices. Moderate deficit irrigation (60–100% of full irrigation) optimized WUE and PFPN while maintaining stable yields, whereas severe deficit irrigation (<40%) caused substantial yield losses. Soil texture and bulk density strongly modulated WSI effectiveness. Climatic factors, particularly growing season precipitation, negatively correlated with WSI benefits, highlighting enhanced efficiency gains under drier conditions. These findings emphasize the need to prioritize drip and micro-sprinkler irrigation in national water-saving strategies and advocate for integrated approaches combining WSI with soil health management and site-specific irrigation scheduling to promote sustainable wheat intensification under variable agroecological conditions. Full article

This article presents a systematic review of optimization methods applied to enhance the performance of photovoltaic (PV) systems, with a focus on critical challenges such as system design and spatial layout, maximum power point tracking (MPPT), energy forecasting, fault diagnosis, and energy management. The emphasis is on the integration of classical and algorithmic approaches. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines (PRISMA) methodology, 314 relevant publications from 2020 to 2025 were analyzed to identify current trends, methodological advances, and practical applications in the optimization of PV performance. The principal novelty of this review lies in its integrative critical analysis, which systematically contrasts the applicability, performance, and limitations of deterministic classical methods with emerging stochastic metaheuristic and data-driven artificial intelligence (AI) techniques, highlighting the growing dominance of hybrid models that synergize their strengths. Traditional techniques such as analytical modeling, numerical simulation, linear and dynamic programming, and gradient-based methods are examined in terms of their efficiency and scope. In parallel, the study evaluates the growing adoption of metaheuristic algorithms, including particle swarm optimization, genetic algorithms, and ant colony optimization, as well as machine learning (ML) and deep learning (DL) models applied to tasks such as MPPT, spatial layout optimization, energy forecasting, and fault diagnosis. A key contribution of this review is the identification of hybrid methodologies that combine metaheuristics with ML/DL models, demonstrating superior results in energy yield, robustness, and adaptability under dynamic conditions. The analysis highlights both the strengths and limitations of each paradigm, emphasizing challenges related to data availability, computational cost, and model interpretability. Finally, the study proposes future research directions focused on explainable AI, real-time control via edge computing, and the development of standardized benchmarks for performance evaluation. The findings contribute to a deeper understanding of current capabilities and opportunities in PV system optimization, offering a strategic framework for advancing intelligent and sustainable solar energy technologies. Full article

Background/Objectives: This meta-analytical review assesses the relationship between effect size and replication success in genetic studies of athletic performance, focusing on the ACTN3 and ACE polymorphisms across power- and endurance-based sports. The analysis revealed substantial heterogeneity in reported effect sizes (overall I² = 72.3%), indicating considerable variability between studies, likely influenced by differences in population genetics, study design, and sample size. Methods: For ACTN3, the pooled effect sizes were 1.40 (95% CI: 1.18–1.65) for power sports and 1.35 (95% CI: 1.12–1.58) for endurance sports. Although the difference between these estimates is small, it reached statistical significance (p = 0.0237), reflecting the large sample size, but it remains of limited practical and clinical significance. For the ACE polymorphism, effect sizes were similar in both endurance (ES = 1.22, 95% CI: 1.05–1.41) and power sports (ES = 1.20, 95% CI: 1.03–1.43), with overlapping confidence intervals, indicating no meaningful difference in association strength between sport types. Effect sizes were calculated as odds ratios (OR) with 95% confidence intervals for case–control designs, with standardized conversion protocols applied for alternative study designs reporting standardized mean differences or regression coefficients. Results: Publication bias was detected, particularly in smaller studies on ACTN3 and power sports (Egger’s test p = 0.007). The pooled effect of ACTN3 in power sports (OR 1.40, 95% CI: 1.18–1.65, 95% PI: 0.89–2.20) was adjusted to OR 1.32 (95% CI: 1.15–1.51) following trim-and-fill publication bias correction. The high degree of heterogeneity (I² = 72.3%) cautions against overgeneralization of the pooled results and highlights the need for careful interpretation, robust replication studies, and standardized methodologies. Conclusions: The findings emphasize that, while genetic markers such as ACTN3 and ACE are statistically associated with athletic performance, the magnitude of these associations is modest and should be interpreted conservatively. Methodological differences and publication bias continue to limit the reliability of the evidence. Future research should prioritize large, well-powered, and methodologically consistent studies—ideally genome-wide approaches—to better account for the polygenic and multifactorial nature of elite athletic ability. Full article

Background/Objectives: Human papillomavirus (HPV) is a known etiologic agent in oropharyngeal cancers, but its role in oral cavity squamous cell carcinoma (OCSCC) remains unclear. This systematic review and meta-analysis aimed to estimate the global prevalence of HPV in OCSCC and explore variation by clinicodemographic and tumor characteristics. Methods: We systematically searched multiple databases for studies reporting HPV prevalence in OCSCC. Pooled prevalence estimates were calculated, and subgroup analyses examined differences by age, gender, cancer stage, anatomical site, histologic subtype, region, and HPV type (HPV-16 and HPV-18). Heterogeneity and publication bias were assessed using standard meta-analytic techniques. Results: A total of 122 studies involving 16,311 patients were included. The pooled HPV prevalence in OCSCC was 25.8% (95% CI: 20.4–31.2), with HPV-16 and HPV-18 detected in 52.4% and 30.3% of positive cases, respectively. Prevalence varied geographically, from 73% in Singapore to 7.7% in South Korea. Younger patients (<40 years) had higher HPV positivity (29.7%) than older patients (>70 years, 23.8%). Early-stage cancers (stage I) showed higher HPV prevalence (41.8%) than advanced-stage cancers (stage IV, 10.4%). Verrucous carcinoma had the highest HPV positivity (34.1%), and moderately differentiated tumors the lowest (23.4%). HPV prevalence was highest in the lower alveolus (29.5%) and lips (25%), and lowest in the upper gingiva (3.9%). Conclusions: HPV prevalence in OCSCC demonstrates significant heterogeneity across regions and clinical subgroups. These findings emphasize the need for standardized diagnostic approaches and further research into the role of HPV in OCSCC pathogenesis and treatment. Full article