Search Results (13,704)

Chest X-ray (CXR) imaging remains the most widely used radiological modality for assessing pulmonary and cardiothoracic disease, yet its interpretation is inherently constrained by tissue superposition, subtle radiographic findings and marked inter-observer variability. Recent advances in artificial intelligence (AI) have driven significant progress in automated CXR analysis, supported by large public datasets, evolving annotation strategies and increasingly expressive deep learning architectures. This review presents a comprehensive synthesis of approaches for pulmonary abnormality detection, encompassing convolutional neural networks, transformers, multimodal and vision–language models and self-supervised representation learning. We critically discuss their strengths, limitations and vulnerability to label noise, domain shift and shortcut learning. In parallel, we examine dataset properties, annotation practices, robustness challenges, explainability methods and the heterogeneity of evaluation protocols that hinder fair comparison and clinical translation. Building on these observations, the review identifies key future directions, including foundation models, multimodal integration, federated and domain-generalized training, longitudinal modeling, synthetic data generation and standardized clinical evaluation frameworks. By integrating methodological and clinical perspectives, this work offers an up-to-date reference for researchers and clinicians and outlines a roadmap toward reliable, interpretable and clinically deployable AI systems for chest radiography. Full article

Under the backdrop of digital subsurface and intelligent field development, together with sustainable development planning, reliable and continuous well-log measurements are increasingly essential for reservoir evaluation and geological interpretation. Density (DEN) logging is critical for reservoir evaluation and geological interpretation, providing fundamental constraints for lithology/porosity-related assessment and integrated subsurface characterization. However, the DEN curve often contains missing intervals or distortions caused by borehole conditions and tool/environmental interference. This study proposes an RF–Transformer framework for DEN reconstruction that couples (i) Random-Forest-based feature screening to suppress redundant or low-contribution channels and (ii) a Transformer encoder with mask-aware self-attention to capture both local fluctuations and long-range depth dependencies. Experiments were conducted on logging data from nine vertical wells in the Lianggaoshan Formation (Sichuan Basin, China) with a unified sampling step of 0.125 m. Under a well-wise split protocol, RF–Transformer achieved RMSE = 0.0126 g/cm³, MAE = 0.0079 g/cm³, R² = 0.9863, and r = 0.9932, outperforming Random Forest, Decision Tree, KNN, LightGBM, LightGBM–NN, and a base Transformer. The pass rate reached 92.86% under an error tolerance of ±0.02 g/cm³, demonstrating robust reconstruction in long missing sections and lithological transition zones. The proposed workflow provides an effective route for repairing density logs in complex reservoirs and for improving the continuity of multi-log interpretation. Full article

Background: A sedentary behavior and being overweight represent major public health issues associated with both physical and psychological risks. Based on self-determination theory (SDT), the psychoeducational intervention PsicoFIT—a component of the TIGREFIT program—aims to foster motivation toward physical activity, to promote healthy habits, and to reduce psychological ill-being in sedentary adults who are overweight and are fans of a football club. Methods: This protocol corresponds to a longitudinal comparative pragmatic clinical trial, designed in accordance with the recommendations of the SPIRIT Statement. The intervention, preceded by a training program for the coaches involved, will comprise 12 weekly modules delivered in two modalities: (1) face-to-face, through group sessions, and (2) semi face-to-face, through short video capsules hosted on a digital platform. Changes associated with the intervention will be evaluated using hierarchical multiple regression and pre-post comparisons, assessing baseline and post-intervention data within and between the intervention modalities. Primary outcomes will include changes in healthy lifestyle and burnout as indicators of well-being and ill-being, respectively. Secondary outcomes will assess basic psychological needs satisfaction and autonomous motivation as potential mediators of these effects, as well as the coach’s controlling interpersonal style as a possible contextual predictor. The modality of participation will be analyzed as a potential moderator of the observed changes. Finally, the acceptability and perceived contribution of the intervention will be explored through a focus group. Discussion: PsicoFIT will provide a methodological framework for designing interventions within multicomponent programs aimed at promoting healthy lifestyles and psychological well-being in sedentary adults who are overweight, considering the social context of football fandom and allowing for an exploration of the impact of the face-to-face and semi-face-to-face modalities. Future empirical application of the protocol will help verify its effectiveness, guide adaptations across contexts, and contribute to the development of evidence-based interventions. Conclusions: The implementation of PsicoFit will allow for the evaluation of its effectiveness, psychological mechanisms, and delivery modalities, thus guiding future evidence-based interventions in sport. Full article

Background and Objectives: Hand and wrist scars alter physical appearance and can result in functional impairments and psychosocial difficulties. Although these effects are clinically important, rehabilitation services in Saudi Arabia lack consistent and standardised scar assessment protocols. The limited use of validated outcome measures hinders both clinical practice and research. Standardised scar assessment is essential for evidence synthesis, developing new scar care interventions and promoting best outcomes. We aim to investigate healthcare professionals’ perspectives on key scar outcome domains for evaluating hand and wrist scars and identify gaps in current practice and training needs to support the development of evidence-based guidelines. The study design is a cross-sectional descriptive study. Materials and Methods: The Saudi Commission for Health Specialties distributed a survey to 5000 randomly selected licensed healthcare professionals. The adapted questionnaire obtained sociodemographic data, professional experience, and ratings of scar outcome domains using a five-point Likert scale. Descriptive statistics were used for the analyses. Results: The analysis included 74 completed responses (response rate, 41.5%). Nurses (32.4%) and occupational therapists (29.7%) represented the largest groups. Only 37.8% of the participants reported receiving specialised training in scar assessment. Furthermore, the use of outcome measures remained limited, with 41.3% utilising clinician-reported outcome measures (CROMs) and 54.05% using patient-reported outcome measures (PROMs). The Vancouver Scar Scale and Patient and Observer Scar Assessment Scale were the most frequently used assessment tools. Clinicians primarily evaluated physical symptoms, including hypersensitivity (69.8%) and pain (67.6%), as well as scar characteristics such as colour (62.2%), adhesion (65.8%), and thickness (64.9%). Psychological factors were also considered important, particularly self-confidence (59.5%), acceptance of the scar (60.3%), and satisfaction with the scar (60.8%). Conclusions: Healthcare professionals in Saudi Arabia recognise the complex effects of hand and wrist scarring; however, they show limited integration of validated assessment tools, especially patient-reported outcome measures, in clinical practice. This gap suggests the need for targeted training, interdisciplinary educational initiatives, and efforts to strengthen standardised approaches to scar assessment. Exploring the development of future national guidance and engaging in international efforts to develop a core outcome measurement set may support evidence-based evaluation and improved long-term patient outcomes. Full article

Background/Objectives: Driving performance and competence represent a complex functional domain that may be affected in some individuals with schizophrenia. This scoping review aimed to map existing evidence characterizing driving-related functioning by identifying the cognitive, pharmacological and functional factors that influence driving ability and by synthesizing findings from experimental, neurocognitive and population-based studies. Methods: A structured search of the PubMed, Scopus and ScienceDirect databases was performed in accordance with PRISMA-ScR guidelines to identify studies published between 2015 and 2025 that examined cognitive, pharmacological and functional dimensions of driving in individuals with schizophrenia. Extracted data were narratively and thematically synthesized. Eleven studies met the inclusion criteria. Results: Findings clustered into three domains: cognitive, including attention, executive function, reaction time and visuospatial processing; pharmacological, encompassing drug comparisons, dosage, side effects and treatment stability; and functional, covering license status, driving participation, driving cessation, avoidance behaviors and self-regulation. Conclusions: This review integrates current evidence within a multidimensional and conditional framework, highlighting interactions between cognitive functioning, pharmacological factors, and compensatory self-regulation in individuals with schizophrenia. Understanding these interrelations may inform individualized fitness-to-drive evaluations and contribute to structured, context-sensitive interpretation of driving-related evidence in clinical and regulatory settings. Full article

Four-dimensional (4D) flow MRI enables the comprehensive assessment of cardiovascular hemodynamics. To compensate for respiratory motion, self-gating strategies are typically used and perform reliably at clinical field strengths. With the recent push towards field strengths below 1 Tesla, these strategies need to be re-evaluated given the reduced signal-to-noise ratio (SNR). Camera-based, contactless respiratory monitoring offers an attractive alternative to self-gating, as it is unaffected by imaging. This study compared respiratory self-gating (SG) and camera-based (VE) binning for phase-contrast gradient-echo (PC-GRE) 4D flow MRI at 0.6 T. Data were acquired from twenty healthy subjects (age: 32.8 ± 12.6 years) using a pseudo-spiral undersampled Cartesian four-point velocity encoding scheme. Reconstructions were performed with FlowMRI-Net for the end-expiratory state using either SG or VE binning. SG and VE showed strong agreement, with cross-correlation coefficients of ~0.87, accuracies of ~0.87, and F1-scores of ~0.9. Velocity analysis revealed high concordance (R² = 0.99; RMSE = 3.9 cm/s), with mean differences in peak velocity of 1.25 ± 2.36 cm/s. In this feasibility study, respiratory self-gating and camera-based binning yielded similar hemodynamic parameters from PC-GRE 4D flow MRI at 0.6 T, with the camera-based approach being independent of MR image SNR. Full article

Background/Objectives: Effective glycemic management from the time of diagnosis is essential in the care of children and adolescents with type 1 diabetes (T1D), as early glycemic patterns can influence long-term health outcomes. Methods: This exploratory study evaluated a one-month interactive, group- and play-based education program designed to enhance food and carbohydrate counting skills among families of children and adolescents with newly diagnosed (ND) T1D (<1 year since diagnosis) or suboptimal glycemic control (SGC) (hemoglobin A1c (HbA1c) > 7.5% (58 mmol/mol)). The intervention included hands-on learning activities in food and carbohydrate counting, and peer interaction to support development of diabetes self-management skills. Data were collected at baseline, post-intervention, and at six-months follow-up through medical records, glucose sensor data, and a questionnaire assessing diabetes self-management skills, dietary practices, and carbohydrate counting. Results: Between September 2022 and April 2024, 55 children and adolescents were enrolled in the ND group and 22 in the SGC group. Post-intervention, carbohydrate counting skills improved, particularly in the ND group. Participants reported greater confidence and independence in carbohydrate counting and insulin dosing, with parents noting sustained benefits at six-months follow-up. No significant changes were observed in glycemic control, including time-in-range and postprandial glucose profiles. Conclusions: In this exploratory study, early interactive and play-based group education was associated with improvements in carbohydrate counting skills and self-care confidence in children and adolescents with newly diagnosed T1D. These improvements were not accompanied by changes in glycemic outcomes. The findings occurred during a complex and transitional phase following diagnosis. Further research is needed to examine sustainability and long-term clinical impact. Full article

Standard Retrieval-Augmented Generation (RAG) models are limited by their “one-shot” nature, failing to assess or improve answer quality dynamically. To address this, we introduce SCIM (Self-Correcting Iterative Mechanism), a framework featuring multi-dimensional evaluation and adaptive retrieval. A key distinction of SCIM is its efficiency: it operates on a lightweight Flan-T5-base model (250M parameters) and requires no fine-tuning, challenging the industry’s reliance on 7B+ parameter models. Experimental results across four major benchmarks show that SCIM yields a 17.2% improvement over standard RAG (p $<0.001$). Notably, SCIM achieves parity with state-of-the-art models like ITER-RETGEN while reducing retrieval overhead by 31%, with 35% of queries converging within just 1–2 iterations. With high human correlation (Spearman $\rho$$=0.842$), SCIM demonstrates that robust, self-correcting RAG performance is attainable without the computational costs of large-scale LLMs. Full article

WIB-801CE, a standardized Cordyceps militaris extract containing 7.0% cordycepin, suppresses platelet activation induced by thrombin, collagen, and Adenosine diphosphate (ADP). As these agonists generate thromboxane A₂ (TXA₂), which amplifies platelet activation via a self-propagating feedback loop, blockade of TXA₂-mediated signaling offers strong antithrombotic potential. TXA₂-antagonistic effects were evaluated using U46619, a stable TXA₂ analog. Platelet activation was assessed by fibrinogen binding to integrin αIIbβ₃, aggregation, and phosphorylation of platelet-activating proteins—PI3K (Tyr458), Akt (Ser473), p38 MAPK (Thr180/Tyr182), ERK1 (Thr202/Tyr204), JNK1 (Thr183/Tyr185)—and inhibitory proteins—VASP (Ser157) and IP₃RI (Ser1756)—via immunoblotting. Thrombin-induced fibrin clot retraction, cytotoxicity, coagulation parameters, and antioxidant capacity were also examined. WIB-801CE significantly inhibited U46619-induced fibrinogen binding to integrin αIIbβ₃ and platelet aggregation, without inducing cytotoxicity or impairing hemostatic function. It also significantly downregulated the phosphorylation of platelet-activating proteins and upregulated the phosphorylation of platelet-inhibiting proteins. Additionally, WIB-801CE abolished thrombin-induced fibrin clot retraction and demonstrated antioxidant capacity. WIB-801CE disrupts TXA₂-driven platelet activation and thrombus stabilization by selectively modulating phosphorylation of key signaling proteins at defined regulatory sites. These properties highlight its promise as a therapeutic candidate for thrombotic disorders with platelet hyperreactivity. Full article

Background/Objectives: Negative emotional states such as depression, anxiety, and stress have been proposed as psychological correlates of injury occurrence, yet evidence regarding their independent and combined effects remains inconsistent, particularly with respect to sex differences. The present investigation focused on whether the relationship between adverse emotional states and injury occurrence differs between men and women among physically active young adults. Methods: The study was conducted with a cross-sectional design and included 418 university students (199 men and 219 women; mean age: men 20.73 ± 0.85 years; women 20.56 ± 0.74 years). Participants’ anthropometric characteristics included body height (men, 182.19 ± 7.10 cm; women, 168.17 ± 6.01 cm) and body weight (men, 79.63 ± 9.87 kg; women, 60.86 ± 9.05 kg). Symptoms of depression, anxiety, and stress were measured using the Depression Anxiety Stress Scale (DASS-21), and injury history within the previous 12 months was obtained via a structured self-report injury questionnaire. Logistic regression models were used to evaluate associations between emotional states and injury occurrence, including assessment of linear, non-linear, and interaction effects. Analyses were stratified by sex and adjusted for training weekly load and training experience. Complementary profile analysis was conducted to assess emotional state configurations by injury occurrence. Results: Linear models provided the most parsimonious representation of the associations between emotional states and injury occurrence, with no support for non-linear or interaction effects. In sex-stratified multivariable models, anxiety was modestly associated with injury occurrence in males (OR = 1.05; 95% CI: 1.00–1.11), whereas depression and stress were not significant correlates. No significant associations were observed in females. Profile analysis revealed distinct emotional dimensions but showed no differences in overall profile level or shape between injured and non-injured participants. Conclusions: Negative emotional states demonstrated limited and predominantly additive associations with injury occurrence. Anxiety showed a small, sex-specific association in males, while overall emotional state measures exhibited limited explanatory value for injury occurrence. Full article

Machine translation with large language models has recently attracted growing attention due to its flexibility and strong zero-shot and few-shot capabilities. However, most prompt-based LLM translation methods rely on linear generation or shallow self-refinement, implicitly committing to a single reasoning path. Such designs are brittle when translating long and syntactically complex sources, where reliable translation often requires structured planning and hypothesis exploration. In this paper, we propose TransGoT, a novel machine translation framework inspired by the graph-of-thoughts paradigm, which formulates translation as a structured, multi-stage reasoning process over a graph of intermediate thoughts. TransGoT explicitly decomposes translation into constraint identification, draft generation, and culture- and style-aware refinement, enabling systematic exploration and aggregation of alternative translation hypotheses. To better adapt graph-based reasoning to translation, we design two key mechanisms: (1) Uncertainty-driven thought transformation. Unlike general reasoning tasks, translation uncertainty is often localized and unevenly distributed across tokens, making holistic regeneration inefficient. We therefore design uncertainty-driven thought transformation, which leverages model-internal confidence signals to guide targeted token-level revision; (2) Dispersion-adaptive thought scoring. It emphasizes evaluation criteria with stronger inter-candidate variance to enable robust multi-criteria thought selection. We evaluate TransGoT on the WMT22 benchmarks and experimental results demonstrate that TransGoT consistently outperforms strong LLM-based translation baselines, validating the effectiveness of structured graph-based reasoning for machine translation. Full article

Vegetable grafting is increasingly adopted to stabilize tomato production under Mediterranean conditions, where water scarcity and soil-borne pressures limit crop performance. A factorial rootstock × scion trial was conducted during an organic cold greenhouse cycle in Sicily (2022–2023). Three experimental rootstocks (two interspecific and one intraspecific, developed within the H2020 BRESOV framework) were compared with the commercial rootstock Optifort, along with self-grafted and non-grafted controls. Three commercial F₁ scions (Barbarela, Cherry, Vittorio) were evaluated for vegetative growth, root traits, flowering dynamics, yield components, and fruit quality. Grafting generally enhanced plant vigor compared with self- and non-grafted plants, and significant rootstock × scion interactions were observed for several traits, indicating that performance depended on partner compatibility. Root biomass and yield varied widely among combinations, while fruit soluble solids ranged from 3.63 to 7.10 °Brix, with consistently higher values in Cherry and Vittorio scions. Multivariate analyses highlighted a predominant scion effect on fruit-related traits, whereas rootstocks mainly influenced vegetative growth and root system development. Tomato performance under Mediterranean organic greenhouse conditions strongly depends on rootstock–scion compatibility, confirming grafting as an effective strategy to improve yield stability and fruit quality in sustainable production systems. Full article

Objectives: Early and accurate identification of frailty is essential for preventing adverse outcomes in older adults. However, existing frailty prediction models often lack reliability, interpretability, and generalizability. Methods: Participants aged 60 years and older between 2011 and 2015 (n = 3419) from the CHARLS were used to develop models, and participants from the CLHLS-HF between 2014 and 2018 (n = 1017) were used for external validation. The frailty was assessed 4 years after baseline in both cohorts by Fried’s Frailty Phenotype (FFP). Six machine learning models were applied to develop prediction models. The SHapley Additive exPlanations (SHAP) method was utilized to explain the final model. Clinical outcomes were evaluated between participants predicted as frail and non-frail by the final model. Results: The XGBoost (AUC = 0.934, 95% CI: 0.921–0.948; F1 = 0.712, 95% CI: 0.686–0.736 in internal validation; AUC = 0.792, 95% CI: 0.750–0.830; F1 = 0.702, 95% CI: 0.652–0.753 in external validation) performed best among six models. Key predictors included lifestyle factors (e.g., instrumental daily living activities, BMI, and self-rated health) and psychological traits (e.g., depression). Participants predicted as frail had significantly elevated risks of falls (OR = 2.11), hospitalization (OR = 1.75), and disability (OR = 1.42). Conclusions: The proposed model provided a robust and interpretable digital tool for predicting frailty among older adults and associated adverse outcomes. Full article

Lightweight artificial aggregates (LWAs) are key materials for sustainable construction, offering reduced structural self-weight, improved thermal performance, and enhanced resource efficiency. However, their production remains geographically concentrated and largely dependent on virgin raw materials, while significant volumes of industrial waste continue to be landfilled. This study addresses these challenges by developing regional LWAs through the incorporation of high levels of porcelain polishing residue (PPR) into red clay matrices, promoting waste valorisation within a circular economy framework. Four mixtures were produced with 20, 40, 60, and 80 wt.% PPR replacing red clay and sintered at 1220 °C and 1240 °C. Raw materials were characterized by laser granulometry, X-ray fluorescence, and X-ray diffraction, while the produced aggregates were evaluated in terms of bloating index, mass loss, bulk density, water absorption, modulus of deformation, crushing strength, and visual morphology. A full factorial experimental design coupled with analysis of variance (ANOVA) was applied to quantify the effects of mixture composition, firing temperature, and aggregate size. All formulations exhibited significant bloating (>35%), with expansion intensifying as PPR content and firing temperature increased, reaching up to 140.6% for mixtures with 80% PPR at 1240 °C. Bulk density values ranged from 0.53 to 1.14 g/cm³, and water absorption remained below 20% for all compositions, confirming their classification as lightweight aggregates. Mechanical performance was strongly dependent on the balance between expansion and matrix densification. The mixture containing 40% red clay and 60% PPR sintered at 1220 °C showed the most favourable performance, achieving crushing strengths of approximately 5.00 MPa while maintaining low density, outperforming commercial reference aggregates. Statistical analysis identified mixture composition and firing temperature as the dominant factors governing expansion and density. The results demonstrate that porcelain polishing residue is a technically viable and sustainable raw material for high-performance LWA production, enabling regional manufacturing routes with reduced environmental impact and strong potential for structural and non-structural construction applications. Full article

Background: Existing XR simulators for enteral feeding rely mainly on self-reported learning outcomes and procedural checklists. As a result, they offer limited opportunities to capture objective behavioral data or to present dynamic patient reactions. This two-stage pilot study evaluated an XR-based gastrostomy tube-feeding simulator (ESTE-TF) that integrates sensor-derived performance metrics and two biological-reaction presentation modalities (projection mapping and tablet display). Methods: In Experiment 1, nursing students completed pre- and post-experience questionnaires assessing perceived learning across seven domains, alongside sensor-based measurements of feeding-start timing, dosing-rate characteristics, and total procedure time. Experiment 2 employed a tablet-based version with four learning items assessed for students and post-experience evaluations collected from registered nurses. Participants also compared the two XR presentation methods. Results: Students demonstrated perceived learning gains of small-to-large magnitude across both experiments (Experiment 1: d = 0.455–0.974; Experiment 2: d = 0.014–0.886), with wide 95% confidence intervals reflecting the exploratory nature of this pilot work. Sensor-derived data showed greater dosing-rate variability and longer procedure times among students than nurses. Participants reported that projection mapping offered a more embodied experience, whereas tablet displays provided clearer visibility. Conclusions: These findings indicate the feasibility and preliminary educational potential of integrating sensing technologies with XR-based biological-reaction presentation for gastrostomy-feeding training. Given the small samples and non-validated measures, results should be interpreted as exploratory. Future research will refine sensor accuracy, establish standardized performance metrics, and evaluate learning outcomes using validated instruments and controlled study designs. Full article