Search Results (21,770)

Background: Migraine is a highly prevalent neurological disorder associated with substantial disability and socioeconomic burden. Although pharmacological therapies remain the mainstay of treatment, their effectiveness may be limited by incomplete response and adverse effects. Repetitive transcranial magnetic stimulation (rTMS) has emerged as a non-invasive neuromodulatory technique that may modulate cortical excitability and pain-processing networks involved in migraine pathophysiology. This systematic review aimed to evaluate the current evidence regarding the efficacy and safety of rTMS compared with sham stimulation in individuals with migraine. Methods: A systematic search was conducted in PubMed (MEDLINE), PsycNet, and Ovid (including MEDLINE and Embase) from database inception to December 2025 in accordance with PRISMA 2020 guidelines. Studies investigating rTMS in adults with migraine and including a sham comparator were eligible for inclusion. Data regarding study design, participant characteristics, rTMS parameters, outcomes, and adverse events were extracted using a predefined template. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Results: Seven studies comprising a total of 301 participants were included. Most trials evaluated high-frequency rTMS targeting the dorsolateral prefrontal cortex. Across studies, rTMS was generally associated with reductions in migraine frequency and severity compared with sham stimulation, although results varied depending on stimulation parameters and study design. Treatment was consistently well tolerated, with only mild and transient adverse effects reported. However, considerable heterogeneity was observed in diagnostic criteria, stimulation protocols, outcome measures, and follow-up duration. Conclusions: Preliminary evidence suggests that rTMS may represent a promising and well-tolerated neuromodulatory approach for migraine management. Nevertheless, methodological variability, limited sample sizes, and concerns regarding risk of bias restrict definitive conclusions. Larger randomized controlled trials with standardized protocols and longer follow-up periods are needed to clarify the clinical role of rTMS in migraine treatment. Full article

Background: Dual-energy computed tomography (DECT) with virtual non-calcium (VNCa) reconstruction is emerging as an alternative method for identifying bone marrow edema (BME) in cases of inflammatory sacroiliitis, especially when magnetic resonance imaging (MRI) is contraindicated or unavailable. Objectives: The aim of this systematic review and meta-analysis is to evaluate the diagnostic performance of DECT with VNCa reconstruction for detecting BME in inflammatory sacroiliitis, using MRI as the reference standard. Methods: Following PRISMA-DTA guidelines and a pre-registered PROSPERO protocol (CRD420251103652), we searched PubMed, Web of Science, and the Cochrane Library up to June 2025. Seven studies comprising 358 patients and 591 sacroiliac joints were included. Quality assessment was performed using QUADAS-2. Pooled sensitivity and specificity were calculated using a bivariate random-effects regression model. Heterogeneity was evaluated using the I² statistic. Subgroup analyses were performed for anatomical site and slice thickness, along with a meta-regression according to anatomical site. Publication bias was assessed using Deeks’ test. Results: Quality assessment revealed a moderate risk of bias, primarily related to patient selection. The pooled sensitivity and specificity of DECT VNCa were 78% (95% CI: 65–88%) and 83% (95% CI: 71–91%), respectively. The area under the SROC curve was 0.91 (95% CI: 0.88–0.93), indicating good diagnostic accuracy. Heterogeneity was moderate for sensitivity (I² ≈ 68%) and high for specificity (I² ≈ 88%), largely driven by one outlier study. Sensitivity was lower for sacral BME than for iliac BME (69% vs 79%), although this difference did not reach statistical significance (p ≈ 0.07). Specificity tended to be higher with slice thickness ≥1 mm than with <1 mm (90% vs 84%), although the difference was not statistically significant (p ≈ 0.06). Exclusion of one outlier study (Deppe et al.) increased pooled specificity to 92% and reduced heterogeneity. Deeks’ test did not reveal significant publication bias. Conclusions: DECT VNCa has been shown to be highly accurate in the diagnosis of inflammatory sacroiliac BME. Its high specificity makes it suitable for confirming inflammatory activity when an MRI scan is not feasible. However, its sensitivity is moderate and variable, with a trend towards lower values for sacral lesions. This precludes its use as a standalone test to rule out active inflammatory sacroiliitis, especially in young patients. Standardization of acquisition protocols and site-specific diagnostic thresholds is recommended for optimal clinical implementation. Clinical Impact: Clarifying the diagnostic performance of VNCa of DECT to define its role as a complementary tool to MRI for assessing BME in inflammatory sacroiliitis. These results could guide appropriate patient selection and highlight the need for standardized protocols. Full article

Background: Physical activity (PA), physical fitness (PF), and motor skills (MS) play crucial roles in overall health and well-being, particularly in early childhood, when habits that affect future health are formed. Methods: This study, involving 299 children (150 boys, 149 girls, mean age 6.9 ± 0.96 years), explored the variance explained by external factors such as socioeconomic status (SES), body composition (BC), sex, and geographical location on motor-related physical fitness (MRPF) and health-related physical fitness (HRPF) in children. Using a variety of assessments, including demographics, anthropometric data, BIA, ActiGraphs, the 20 m shuttle run, 10 and 20 m speed tests, and test items from the Körperkoordinations test für Kinder (KTK) and the TGMD-2, a multiple stepwise regression analysis using SPSS (v 28.0) identified the associated factors. Results: The variables tested show modest explained variance for HRPF, MRPF, and MS, with the largest cumulative explained variance of 26.4%. The explained variances for MRPF and MS were lower (medium to small) than the significant, medium-to-large, explained variances for HRPF. Body fat percentage (BF%), moderate-to-vigorous physical activity (MVPA), parental education and income, and BMI emerged as substantial contributors to HRPF, explaining 12.1% to 26.4% of the variance. Sex, BF%, and quintile status were the most influential associated factors for MRPF, and for MS, BMI and sex emerged as the strongest contributors. Conclusions: These findings underscore the importance of holistic approaches that consider individual factors, such as MVPA, body composition (BC), PA levels, sex, and broader social and economic contexts, to promote children’s well-being. The study emphasises the need for comprehensive strategies to address the multifaceted associations with children’s physical development. Full article

Background/Objectives: Brain tumors are highly lethal cancers, with gliomas representing the most complex subtype. Magnetic resonance imaging (MRI) is the main non-invasive imaging modality. This review evaluates deep learning (DL) and artificial intelligence methods for brain tumor segmentation and classification. Methods: In this systematic review, PubMed and Scopus were searched for articles published from 2022 to March 2025. Authors independently identified eligible studies based on predefined inclusion criteria and extracted data. The study quality and risk of bias were assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS) checklist. Results: Thirty-one studies met the inclusion criteria from 310 records, with eight addressing both segmentation and classification. Most segmentation studies used publicly available multiparametric MRI datasets. Performance varied by architecture and tumor region, with whole-tumor segmentation achieving the highest Dice Similarity Coefficient (DSC). Classical U-Nets reported DSC values ranging 80–87%, while models with residual or attention mechanisms exceeded 90%. Classification focused on tumor type and glioma grading, using features learned from multiparametric MRI. Reported accuracy ranged from 91.3% to 99.4%, with sensitivity and specificity often above 95%. However, variability across tumor subregions, limited external validation, reliance on public datasets, and heterogeneous preprocessing raise concerns about robustness and real-world generalizability. Evidence on the use of explainability methods for both tasks remains limited. Conclusions: DL models for glioma segmentation and classification demonstrate promising performance. However, standardized validation protocols, multi-center datasets, and the integration of explainable artificial intelligence techniques are needed to improve transparency, robustness, and clinical applicability. Full article

We propose a terahertz (THz) antenna-coupled wire-channel field-effect transistor—modified EdgeFET (m-EdgeFET), formed by combining single-gate FinFET and dual-side-gate EdgeFET concepts, which is used for THz detection. The proposed hybrid design was implemented on AlGaN/GaN high-electron-mobility transistor (HEMT) structures, demonstrating distinct response characteristics under 150 GHz and 300 GHz radiation at room temperature. The responsivity dependence on the channel length was determined, revealing that the peak responsivity reached up to 6.5 V/W at a gate voltage of −3 V, i.e., at a gate bias that is an order lower in magnitude than that required for EdgeFET to reach the maximum response. Meanwhile, the gate leakage current decreased by an order of magnitude (to about 1 nA) compared to a FinFET with similar geometry. The proposed geometry was shown to operate in two regimes: source-drain coupling (SD) and gate coupling (GG) of THz radiation with the transistor wire channel. The results confirm that the m-EdgeFET design is suitable for electrically controlled and fast THz detection. Full article

Background/Objectives: Pediatric HIV case-finding and monitoring remain constrained by delayed early infant diagnosis (EID), loss to follow-up, and limited viral load (VL) testing—challenges particularly consequential in the operationally diverse Asia–Pacific region. We systematically reviewed innovations in point-of-care (POC) and near-patient HIV diagnostics and digital monitoring relevant to children and adolescents. Methods: Following a registered protocol (INPLASY2025110058) and PRISMA 2020 guidance, we searched PubMed, EMBASE, Cochrane Library, and WHO Global Index Medicus for studies on POC/near-patient EID and VL testing, dried blood spot (DBS) workflows, and digital monitoring tools. Risk of bias was assessed using RoB 2, QUADAS-2, and MMAT. Results: Fifty-three primary studies were included (39 sub-Saharan Africa, 12 Asia–Pacific, 1 multi-country/global, 1 Americas/Caribbean). Patient selection and flow/timing were common limitations in diagnostic accuracy studies; sample representativeness and nonresponse bias were frequent concerns in implementation studies. The most consistent benefits of POC EID and near-patient VL testing were shorter turnaround times and improved cascade completion when paired with quality assurance and connectivity. Conclusions: POC diagnostics and digital monitoring can help close pediatric HIV cascade gaps, though evidence derives predominantly from sub-Saharan Africa. Impact depends on implementation design. Asia–Pacific programs should prioritize generating context-specific evidence alongside the adaptation of established lessons. Full article

The emergence of pangenome graphs represents a paradigm shift in genomics, moving beyond linear reference genomes to embrace the full spectrum of genetic diversity within and across species. These graph-based models provide a unified framework for representing alternative haplotypes, structural variants, and complex genomic rearrangements that are often missed by traditional approaches. This paper reviews the latest developments in pangenome graph construction, data structures, alignment algorithms, and variant inference. We explore recent human, plant, and microbial genomics applications, highlighting the advantages of graph representations in capturing population diversity and improving read mapping accuracy. We briefly discuss emerging directions such as machine learning-assisted graph analysis, although current applications remain limited and exploratory. Furthermore, we examine the emerging field of clinical genomics, where pangenome references have demonstrated measurable improvements in diagnostic yield—specifically increasing variant calling sensitivity for complex structural variants by up to 10–40% compared to linear models. We conclude by addressing ongoing challenges in graph scalability and standardization, aiming to guide future research and implementation in this rapidly evolving field. Full article

Accurate estimation of river discharge is fundamental for water resources management, flood forecasting, and drought monitoring in the Amazon River Basin. Rating curves, which relate water level (stage) to discharge, are the primary tool for streamflow estimation. This study evaluates traditional curve-fitting methods and machine learning algorithms for modeling rating curves at the two largest gauging stations in the Amazon River: Itacoatiara and Óbidos. The analysis is based on 70 stage–discharge measurements at Itacoatiara (2008–2023) and 176 measurements at Óbidos (1968–2023). Five modeling approaches were compared: Power Law, Linear Regression, Decision Tree, Random Forest, XGBoost, and Multi-Layer Perceptron (MLP). Model performance was assessed against official baseline rating curves maintained by Brazil’s National Water Agency (ANA) and the Geological Survey of Brazil (SGB/CPRM) using Root Mean Square Error (RMSE), coefficient of determination (r²), Mean Bias Error (MBE), Nash–Sutcliffe Efficiency (NSE) and Kling–Gupta Efficiency (KGE). Results indicate that ensemble-based machine learning methods, particularly XGBoost (RMSE = 7463 m³/s, NSE = 0.973 at Itacoatiara; RMSE = 18,378 m³/s, NSE = 0.872 at Óbidos), outperformed traditional methods. However, the Decision Tree exhibited overfitting that could not be resolved through pruning, depth limitation, or other strategies given the sample size. Traditional methods such as the optimized Power Law remain practical and transparent alternatives for operational use. The findings suggest that machine learning can complement traditional approaches for improving rating curve accuracy in large tropical rivers, with K-fold cross-validation used to assess variability and performance. Full article

Background/Objective: Playing-related musculoskeletal disorders are highly prevalent among bowed string musicians and may impair performance and career longevity. This study aimed to evaluate the effects of motor control-based interventions on pain, functional outcomes, range of motion, and neuromuscular parameters in musicians playing bowed string instruments. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines. PubMed, Scopus, Web of Science Core Collection, and Cochrane CENTRAL were searched from inception to October 2025, and the search was updated before resubmission to identify any newly published eligible studies. Eligibility screening, full-text assessment, data extraction, and risk-of-bias assessment were independently verified by a second reviewer. Risk of bias was assessed according to study design using RoB 2 for the randomized controlled trial and ROBINS-I for non-randomized interventional studies. Results: Four interventional studies met the inclusion criteria. Three studies reported improvements in pain-related outcomes or PRMD severity, and three reported improvements in functional outcomes. One study demonstrated improved cervical range of motion, whereas one study reported altered shoulder girdle muscle activation patterns with reduced playing comfort. Overall, the certainty of the available evidence was limited by small sample sizes, non-randomized designs, and risk of bias. Conclusions: Limited evidence suggests that motor control-based interventions may be associated with improvements in pain and playing-related function in bowed string musicians; however, the evidence base remains small and methodologically heterogeneous, and conclusions should be interpreted with caution. Full article

Objectives: This systematic review aims to evaluate the effects of Edible Bird’s Nest (EBN) extract on cognitive function and neuroprotection in animal models and systematically review the relevant research evidence. Methods: A systematic search was conducted in the databases of PubMed, Scopus, Web of Science, EMBASE, Taylor Francis, Wiley, and Cochrane Library for relevant research published up to October 2025. Search terms included “Edible Bird’s Nest”, “Bird’s Nest Extract”, “EBN”, “Swiftlet nest”, “Collocalia”, “Cognitive”, “Memory”, “Learning”, “Neuroprotection”, “Brain”, “Neural”, “Neurotrophic”, “Animal”, “Mice”, “Mouse”, “Rat”, “Rats”, “In vivo”, and “Model”. Two researchers independently screened all the relevant articles, extracted relevant data, and assessed the quality of included studies using the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) risk of bias assessment tool. Results: This systematic review included 11 animal studies, primarily focused on rodent models. Preclinical evidence suggests that Edible Bird’s Nest extract (EBN) may improve performance in several cognitive function tests. Animals treated with EBN appeared to show enhanced spatial memory and learning abilities in experimental settings. At the molecular level, the EBN treatment group showed improved antioxidant capacity and reduced neuroinflammation. Additionally, EBN promoted the expression of neuroprotective factors and enhanced synaptic plasticity. Research suggests that appropriate doses of EBN may have beneficial effects on cognitive enhancement and can alleviate cognitive dysfunction and neuropathological changes. Conclusions: Preliminary evidence from this systematic review suggests that EBN appears to show protective and potentially enhancing effects on cognitive function in animal models. EBN works through multiple mechanisms, including antioxidant and anti-inflammatory effects, as well as promoting the expression of neurotrophic factors and synaptic plasticity. These findings provide initial support for further investigation of EBN as a potential neuroprotective agent and cognitive enhancer. However, there is heterogeneity and methodological limitations in the research, and more standardized studies and preclinical translational research are needed to further validate the application potential of EBN in neuroprotection. These results provide an important reference for developing EBN-based functional foods and supplements for the prevention and adjuvant treatment of cognitive impairment and neurodegenerative diseases. Full article

Machine learning-based systems are increasingly deployed in high-stakes domains, such as healthcare, finance, law, and e-commerce, where their predictions directly influence critical decisions. Although these systems offer powerful data-driven support, they also introduce serious concerns related to fairness, bias, and discrimination. As a result, detecting and addressing unfairness in machine learning software has become a central research challenge. This study presents a systematic mapping of research on software unfairness detection in machine learning systems, with the aim of consolidating existing fairness definitions, identifying major problem types, examining testing approaches, reviewing commonly used datasets, and highlighting open research gaps. A structured search was conducted across five major digital libraries and additional sources, covering publications from 2010 to 2025. From 1805 initially identified records, 67 primary studies met the inclusion and quality assessment criteria. The findings show that research activity has grown significantly since 2019, reaching a peak in 2022. Most studies were published in conference proceedings, accounting for 52% of the primary studies, followed by journals and workshop proceedings, which accounted for 42% and 6% of the primary studies. The literature encompasses multiple research themes, with 36% of the primary studies focusing on the analysis of existing fairness methods, 22% addressing bias mitigation strategies, 30% investigating testing techniques, and 12% proposing or evaluating evaluation frameworks. Fairness testing was conducted across multiple testing levels, including unit, integration, and system testing. Integration-level testing was the most prevalent, accounting for approximately 37.9% of the studies, followed by system-level testing at 27.3% and unit-level testing at 12.1%. Additionally, 22.7% of the studies applied fairness testing across more than one testing level. Frequently used datasets included COMPAS, Adult Census Income, and German Credit. Widely adopted tools, such as IBM AI Fairness 360, Themis, and Aequitas, were also identified. Overall, the systematic mapping study (SMS) highlights the progress made in fairness research while emphasizing the need for stronger integration of fairness into practical machine learning development. Full article

Chest radiography remains a cornerstone in the diagnosis of thoracic diseases. However, differences in image acquisition—particularly projection type—may influence the apparent prevalence and detectability of radiographic findings. Such differences may represent a potential source of bias in large imaging datasets used for clinical research and artificial intelligence. Importantly, projection type is closely associated with the patient’s condition and may therefore reflect both technical imaging factors and underlying clinical characteristics, including disease severity. A total of 120,120 chest radiographs were available in the dataset. After applying inclusion criteria, 112,104 images were included in the primary analysis. Multivariable logistic regression models were used to assess the association between projection type and the presence of radiographic findings, adjusted for age and sex. Subgroup and interaction analyses were performed to evaluate effect modification by demographic factors. Given the large sample size, emphasis was placed on effect sizes and confidence intervals rather than statistical significance alone. Compared with posteroanterior projection, anteroposterior projection was associated with higher odds of detecting consolidation (aOR 3.27; 95% CI 3.07–3.48), infiltration (aOR 1.90; 95% CI 1.84–1.96), pleural effusion (aOR 1.66; 95% CI 1.60–1.72), atelectasis (aOR 1.63; 95% CI 1.57–1.70), and cardiomegaly (aOR 1.19; 95% CI 1.10–1.28). These associations were consistent across age and sex strata. A significant interaction between projection type and sex was observed for infiltration (p = 0.01). Projection type is associated with substantial differences in the detection of thoracic abnormalities on chest radiographs. These associations should be interpreted with caution, as they likely reflect a combination of technical imaging effects and residual confounding related to patient severity and clinical context. Projection may therefore act as a marker of dataset heterogeneity rather than a purely causal factor. Accounting for projection metadata is therefore essential to improve clinical interpretation and to ensure the robust development and validation of artificial intelligence models. Full article

The construction industry accounts for approximately 13% of global GDP but suffers from chronic productivity stagnation. Although artificial intelligence (AI) offers transformative potential, its adoption is constrained by three key barriers: data integrity issues (H1), socio-technical challenges (H2), and system integration problems (H3). This study investigates whether academic research attention aligns with these practitioner-identified barriers through a bibliometric analysis of 4668 publications from OpenAlex (1990–2025), applying a five-pillar analytical framework synthesized into composite scores (0–100 scale) via min-max normalization, weighted summation, and bootstrap validation. H3 achieved a nominal 15.9% prevalence rate (adjusted to ~13.0% after correcting for an 18.2% false positive rate in keyword classification), robust growth (R² = 0.654), significant overrepresentation in top-cited works (risk ratio = 1.31, p = 0.003), and received a composite score of 62/100 (confirmed). H1 (2.7%, score: 17/100) and H2 (4.6%, score: 13/100) were both rejected. The rank ordering by prevalence (H3 > H2 > H1) remains robust under all adjustment scenarios. These findings contrast notably with the RICS Global Construction Monitor (2025, n = 2200+), where practitioners most frequently reported socio-technical barriers (46%), followed by system integration (37%) and data quality (30%), yielding practitioner-to-publication ratios of 4.7:1, 5.2:1, and 1.1:1, respectively. This apparent research–practice paradox appears primarily volume-driven rather than clearly quality-driven: H1/H2 publications receive citation attention broadly comparable to the baseline, though this comparison is limited by control group heterogeneity. We call for rebalanced research agendas addressing data governance frameworks, competency development, and organizational change management. Full article

Science universities strive to offer efficient lab training to their students and at the same time secure their safety and minimize the damages to the lab equipment. Thus, the development of distance learning tools for students to be trained virtually and safely in using the various lab instruments and performing experiments is necessary. Since the students are evaluated for their performance at the on-site labs, the assessment at the virtual labs is also needed and consequently, an embedded assessment mechanism for the evaluation of the user’s performance in the virtual lab is a necessary feature. For the assessment mechanism to be reliable and devoid of the designer’s bias, though, it may need calibration with Machine Learning. Hellenic Open University has developed its own virtual biology laboratory, Onlabs, which simulates its on-site one for its students to be trained and evaluated at. Considering the evaluation of the user’s performance in Onlabs, it is made with respect to particular experiments and is based on an embedded scoring algorithm. The latter is two-fold, measuring the extent to which the necessary steps have been made and the extent to which those steps were made in the correct order. Within the context of the experimental procedure of microscoping, the scoring algorithm has been recalibrated with the use of various Machine Learning techniques. In this paper, we propose the design of a Reinforcement Learning variant and recalibration of the scoring measure concerning the steps order. The results suggest that under specific parameters and Reinforcement Learning methods, a more efficient scoring mechanism may be achieved. Full article

Speed is a key determinant of crash risk and injury severity, particularly on urban and secondary roads with frequent interactions between vulnerable road users. Traffic calming measures (TCMs) encompass physical, regulatory, perceptual, and technological interventions and aim to reduce operating speeds and improve safety and liveability. This study systematically evaluates the effectiveness of TCMs in reducing speed and improving safety outcomes on urban roads, following PRISMA 2020 guidelines. It encompasses the identification, screening, and synthesis of articles from the Scopus, ScienceDirect, and SpringerLink databases, published between January 2020 and February 2026. Risk of bias in the included studies was assessed qualitatively by the co-authors. The assessment was conducted independently, with discrepancies resolved through discussion. A total of 91 studies were included in the review. Evidence from field studies, driving simulator experiments, and analytical, simulation, and computation-based evaluations is reviewed and structured within a three-cluster taxonomy comprising physical and geometrical measures, regulatory and perceptual interventions, and digital and technological approaches. The synthesis indicates that physically self-enforcing measures yield the most consistent reductions in speed. At the same time, regulatory and digital interventions can deliver meaningful safety benefits when implemented at scale with credible governance. Perceptual and advisory measures show more varying and context-dependent effects. The evidence base is limited by heterogeneity in study designs, short-term evaluations, and inconsistent reporting across studies. Full article