Search Results (23,840)

Objective: TNFSF13B, which encodes B-cell-activating factor (BAFF) and peptidylarginine deiminase 4 (PADI4), plays crucial roles in the pathogenesis of ANCA-associated vasculitis (AAV). This study investigated the associations of single-nucleotide polymorphisms (SNPs) in TNFSF13B/BAFF and PADI4 genes with AAV susceptibility, clinical phenotypes, and disease activity in a Guangxi Chinese population. Methods: A case–control study included 324 AAV patients and 324 healthy controls. After propensity score matching (201 pairs), genomic DNA was genotyped for TNFSF13B/BAFF rs3759467 (formerly rs386492354) and rs1041569, and PADI4 rs11203366 and rs874881 using multiplex PCR and high-throughput sequencing. Genetic associations were analyzed via logistic regression, subgroup, haplotype, and clinical correlation analyses. For each of the four SNPs separately, machine learning models (logistic regression, SVM, Random Forest, XGBoost) were built and evaluated via 5-fold cross-validation. No formal adjustment for multiple comparisons was applied due to the exploratory nature of this study. Results: For TNFSF13B/BAFF, the rs3759467 C allele was protective (dominant model OR = 0.60, p = 0.011; log-additive OR = 0.71, p = 0.020; CA haplotype OR = 0.71, p = 0.019), while the rs1041569 T allele was a risk factor (dominant model OR = 1.70, p = 0.016). Subgroup analysis revealed stronger protective effects of rs3759467 in females, Han ethnicity, and MPA patients, and stronger risk effects of rs1041569 in Han ethnicity and MPA patients. Haplotype CA was protective (OR = 0.71, p = 0.019), and TT was risk-associated (OR = 1.55, p = 0.017). Both TNFSF13B/BAFF SNPs were associated with rash and hemoptysis incidence (p < 0.05). rs1041569 was also associated with RBC (red blood cell) count and HB (hemoglobin) levels (p < 0.05). For PADI4, rs11203366 and rs874881 showed no association with AAV susceptibility (all p > 0.05). However, their genotypes were associated with disease activity (BVAS, Birmingham Vasculitis Activity Score), RBC count, and HB levels (p < 0.05). Although machine learning was applied to explore predictive patterns, its performance was suboptimal (AUC < 0.6), indicating limited clinical applicability. Accordingly, the primary findings rely on the genetic model analysis, and the machine learning results should not be overinterpreted as clinically actionable. SHAP analysis indicated that risk-associated genotypes contributed most to model predictions. Conclusions:TNFSF13B/BAFF gene polymorphisms rs3759467 and rs1041569 were associated with AAV susceptibility in this Guangxi cohort, influencing clinical manifestations like rash, hemoptysis, and anemia severity. PADI4 polymorphisms rs11203366 and rs874881 are not associated with susceptibility but may correlate with disease activity and hematological parameters. These findings highlight the ethnic and clinical subtype specificity of genetic influences in AAV. Due to the lack of external validation, these findings are exploratory and require replication. Full article

A multiphase high-strength steel austempered at 260 °C for 24 h was investigated by quasi-in-situ tensile characterization and EBSD-based crystal plasticity finite element modeling. The experimental observations reveal that local plastic deformation is strongly heterogeneous: von Mises strain concentrates preferentially near bainitic-ferrite packets, phase boundaries, and retained-austenite/martensite–austenite regions, whereas blocky retained austenite contributes to strain accommodation at the early deformation stage. To quantify the underlying stress–strain partitioning, a quasi-two-dimensional representative volume element was reconstructed from EBSD data and implemented in ABAQUS through a user-defined material subroutine. The model contained the real grain morphology, phase distribution, and crystal orientation information of the 24 h austempered specimen. A rate-dependent crystal plasticity constitutive framework with BCC matrix, FCC retained austenite, and transformed martensite branches was calibrated against the macroscopic tensile curve. The simulated tensile response agrees well with the experimental curve before macroscopic instability, and the predicted local fields are consistent with the quasi-in-situ strain maps. The results show that local plastic strain first accumulates in M/A-related regions and phase-boundary-neighboring zones, while high Mises stress migrates dynamically with slip activity and stress-induced martensitic transformation. Retained-austenite transformation increases the local load-bearing capacity, modifies interphase load transfer, and delays the direct linkage of strain-localization bands. The present work clarifies the coupling among retained-austenite stability, TRIP-assisted load redistribution, and microstructural strain partitioning in multiphase high-strength steel, providing a mesoscale basis for microstructure-guided strength–ductility optimization. Full article

Venous thromboembolism (VTE) significantly impacts lung adenocarcinoma outcomes, yet current predictive tools lack precision. We investigated plasma extracellular vesicle (EV) mRNA as a liquid biopsy source to identify a pro-thrombotic molecular profile in VTE patients. Within a prospective cohort of 260 patients, we performed a retrospective nested case–control study, matching 10 VTE cases with 11 thrombosis-free controls. Plasma EV-RNA was analyzed via high-throughput sequencing. Differentially expressed genes (DEGs) were integrated with functional enrichment and explored across public non-cancer VTE datasets, buffy coat samples, and cell lines. RNA-seq identified 483 DEGs within the VTE patient EV compartment, predominantly linked to neutrophil degranulation (NETosis), inflammation, and coagulation. We identified a set of EV-associated candidate genes (SELP, ELANE, MYL9, DNASE1L3) distinguishing cancer-associated thrombosis from non-malignant VTE, along with transcripts (TFPI, FCGR2A) selectively enriched within the EV compartment relative to circulating blood cells. P-selectin (SELP) was the only significantly increased marker, providing the strongest complementary support at the protein level. This molecular state was detectable prior to the occurrence of VTE. Plasma EVs capture a multicellular mRNA profile, reflecting the systemic immunothrombotic activation in lung adenocarcinoma. Despite sample size limitations, these findings should be considered exploratory and hypothesis-generating, but they suggest the EV-derived mRNA in combination with circulating markers such as SELP may provide a framework for future studies aimed at improving risk stratification. Full article

Soil salinity is a major constraint on global wheat productivity, yet the genetic and molecular determinants of root system architecture (RSA) adaptation under salt stress remain poorly characterized. We integrated a genome-wide association study (GWAS) of 566 wheat accessions with comparative RNA-seq transcriptomics to identify the genetic and transcriptional determinants of RSA adaptation under 200 mM NaCl. GWAS identified a candidate locus on chromosome 7B harboring TaIAO, which encodes a protein with predicted aldehyde oxidase-like activity consistent with a role in tryptophan-dependent auxin biosynthesis. Accessions carrying the favorable CC allele exhibited significantly greater root volume retention than those carrying the GG genotype (p < 0.001). Comparative RNA-seq revealed that the salt-tolerant Sarajevo 1 exhibited coordinated transcriptional repression of three distinct modules—cell wall expansion (TaExpansin), auxin redistribution (TaPIN-like), and stress-associated ROS defense (TaPOD1)—whereas the sensitive genotype CI 17260 aberrantly induced or incompletely repressed these modules under stress. ELISA-based IAA quantification, ROS imaging, and qRT-PCR analysis provided independent physiological and transcriptional support for these patterns. These findings support a two-tier adaptive model in which constitutive genetic variation at the TaIAO locus may contribute to a developmental baseline, coupled with coordinated stress-responsive transcriptional repression of energy-consuming modules, providing promising targets for marker-assisted breeding of salt-tolerant wheat. Full article

Pediatric drug therapy remains fundamentally challenged by profound interindividual variability driven by dynamic development, genetic, and environmental factors. Although dosing strategies based on age, body weight, or body surface area remain important starting points in pediatric pharmacotherapy, they may not fully capture ontogeny-dependent variability in drug disposition and response. Consequently, clinically relevant differences in efficacy and toxicity may still occur among children receiving similar weight-adjusted doses. Pharmacogenomics offers a promising framework for individualized therapy; however, its clinical translation in pediatrics is limited by developmental variability in gene expression and enzyme activity. Emerging evidence highlights the pivotal role of epigenetic regulation, including DNA methylation, histone modifications, and microRNAs, in modulating pharmacogenetic expression across developmental stages, thereby reshaping drug response trajectories. Concurrently, advances in artificial intelligence and next-generation sequencing enable integration of multidimensional datasets, facilitating predictive modeling of drug efficacy and toxicity. This narrative review provides a comprehensive synthesis of developmental pharmacology, pharmacogenomics, and epigenetic mechanisms, while critically evaluating current translational gaps and implementation challenges. Importantly, it proposes an integrative precision framework that incorporates genetic, epigenetic, and computational insights to optimize pediatric pharmacotherapy. By bridging mechanistic biology with emerging digital health technologies, this work advances a paradigm shift from empirical prescribing toward predictive, adaptive, and individualized therapeutic strategies. The proposed approach holds significant potential to enhance clinical outcomes, minimize adverse effects, and accelerate the realization of precision medicine in pediatric populations. Full article

Impaired wound healing arises from interacting biological and material challenges, including persistent infection, biofilm formation, oxidative stress, unresolved inflammation, impaired angiogenesis, defective epithelialization, hemorrhage, and insufficient real-time assessment of wound status. Quantum dot (QD) and nanodot nanosystems have emerged as a versatile class of bioactive wound interfaces capable of addressing these barriers through functions that extend beyond passive coverage. This review synthesizes the design rationale, material composition, validation strategies, functional outcomes, mechanistic interpretation, and translational relevance of QD-enabled platforms for precision wound repair. Across the reviewed literature, carbon dots, graphene QDs, black phosphorus QDs, metal and metal oxide QDs, transition-metal nanodots, and hybrid nanocomposites were incorporated into hydrogels, films, sponges, nanofibers, microneedles, scaffolds, membranes, sprays, and injectable matrices. Their major precision-enabling attributes include localized antimicrobial and antibiofilm activity, redox-adaptive behavior, photothermal and photodynamic activation, inflammatory and macrophage modulation, hemostasis, controlled therapeutic delivery, angiogenic and epithelial support, and fluorescence-based monitoring. The strongest conceptual advance is the transition from static wound dressings toward adaptive biointerfaces that can sense, respond to, or compensate for local wound state abnormalities. Nevertheless, the field remains largely preclinical, with important gaps in long-term safety, standardized characterization, clinically predictive models, manufacturing reproducibility, regulatory alignment, and human validation. Future progress will depend on rationally simplified multifunctional platforms, rigorous comparative testing, wound state-specific evaluation frameworks, and translation-oriented safety and usability studies. QD nanosystems therefore represent a promising foundation for precision wound repair, provided that their multifunctionality is matched by equally rigorous evidence of safety, reproducibility, and clinical relevance. Full article

Air Traffic Control Officers (ATCOs) are responsible for controlling air traffic and ensuring the safety of the aircraft. Capacity, understood as the maximum number of aircraft that can be safely managed for one hour, is calculated based on the workload of ATCOs. This calculation normally is based on a manual and tedious data collection process that demands a high consumption of human resources. To improve and relieve human re-sources, automation tools that automatically generate a preliminary annotation of Air Traffic Control (ATC) activity have been developed. This paper focuses on the feasibility of employing data-driven approaches using neural networks to classify ATC events, as well as if it is possible to improve the performance of these ATC-activity tools. Particularly, this approach seeks to infer ATC intent for separation actions, which are the most critical in terms of ATC workload. A modular methodology has been developed to include information from different sources: flight plans, radar trajectories, trajectory prediction, conflict detection and rule-based knowledge. Different experiments are evaluated based on the different input’s combination, as well as three neural networks (Multilayer Perceptron, Convolutional Neural Network and TabNet). Results show that TabNet is the best neural network option, reaching a similar performance in task classification than current ATC tools and improving classification metrics around 4% by employing the outputs of ATC tool metrics as inputs. Full article

Background and Objectives: Emergence agitation (EA) is a common neurobehavioral disturbance during recovery from sevoflurane anesthesia in pediatric patients, particularly after ophthalmic surgery. Clinically deployable and rigorously validated risk stratification approaches remain limited. We aimed to develop and internally validate an explainable machine learning model to estimate individualized EA risk after pediatric ophthalmic surgery. Materials and Methods: This retrospective cohort study included 1029 children aged 3–7 years who underwent ophthalmic surgery under sevoflurane anesthesia between 2016 and 2025. EA was defined as clinically significant agitation requiring active management in the post-anesthesia care unit. Four machine learning algorithms (regularized logistic regression, random forest, XGBoost, and CatBoost) were developed using stratified patient-level 5-fold cross-validation. Performance was evaluated using pooled out-of-fold predictions. Discrimination, calibration, and classification metrics at the optimal Youden threshold were assessed. SHAP analysis was applied for interpretability. Results: EA occurred in 543 patients (52.8%). XGBoost showed comparable discrimination with slightly higher AUPRC (0.827) and sensitivity (0.796) compared with other models, while maintaining acceptable specificity (0.728). Calibration demonstrated good agreement between predicted and observed risk. SHAP identified airway management and anesthetic-related variables as key contributors. Conclusions: ML-based analysis identified clinically relevant perioperative factors associated with emergence agitation and may provide preliminary insight into perioperative risk stratification pending external validation. External validation is required before clinical implementation. Full article

Characterizing the human health risk posed by constituents in drinking water is often challenging due to a lack of published toxicity values. The PIANO (Paraffin, Isoparaffin, Aromatic, Naphthene, and Olefin) analytical method measures nearly 300 compounds in JP-5 jet fuel, 43 of which have published oral reference doses (RfDs). The remaining compounds are typically assigned surrogate toxicity values. We predict RfDs for 290 PIANO compounds using Quantitative Structure–Activity Relationship (QSAR) models based on stepwise linear regression of 2-dimensional molecular descriptors (MDs) and published toxicity values. Five training groups, created by randomly selecting 80% of the non-PIANO compounds and 50% of the 43 PIANO compounds that have RfDs within a master dataset of 1113 compounds, were used to develop five QSAR models. We used the geometric means of four QSAR model results of sufficient quality to predict RfDs for compounds lacking toxicological information. For compounds with known RfDs, 884 (79%) were within 8-fold of published RfDs, well within the acknowledged uncertainty inherent in published RfDs. Our approach has applicability beyond PIANO compounds and represents a new alternative methodology (NAM) that may be used to reduce uncertainty in human health risk assessment and guide regulatory decisions. Full article

Background and Objectives: Chronic rhinosinusitis with nasal polyps (CRSwNP) is a heterogeneous type 2 inflammatory disease for which biologic therapies have expanded treatment options; however, biomarkers capable of guiding biologic selection remain poorly defined. This systematic review aimed to evaluate the available evidence regarding predictive and prognostic biomarkers associated with currently available biologic agents for CRSwNP (omalizumab, dupilumab, mepolizumab, benralizumab, reslizumab, and tezepelumab). Materials and Methods: A systematic search of PubMed/MEDLINE, Embase, Google Scholar, and the Cochrane Library identified studies published between January 2006 and September 2025. Results: Twenty-five eligible studies, including 12 randomized controlled trials, 12 systematic reviews/meta-analyses, and one indirect treatment comparison study, were analyzed. Multiple biomarkers, including blood eosinophils, total IgE, periostin, eotaxins, eosinophil cationic protein, IL-5, TARC, PARC, and urinary leukotriene E4, were evaluated across biologics targeting IgE, IL-4/IL-13, and IL-5 pathways. Conclusions: Although several biomarkers reflected the modulation of type 2 inflammation and disease activity, no validated biomarker has reliably predicted the superiority of one biologic over another. Nasal IL-5 showed potential for predicting the response to anti-IL-5 therapy but requires further validation. Current evidence supports biomarker use primarily for confirming type 2 inflammation rather than guiding biologic selection. Prospective biomarker-driven and head-to-head comparative studies are needed to enable precision medicine approaches in CRSwNP. Full article

Allium sativum is widely consumed and studied plant for its potential health-promoting effects. Despite its widespread use, the impact of different extraction methods on the biological efficacy and specific phytochemical composition of garlic has not yet been fully elucidated. This study investigated the phytochemical profile, antibacterial, antioxidant, and anti-inflammatory properties of ethanolic and aqueous extracts of Moroccan-grown A. sativum using in vitro assays and in silico analyses. Total phenolic and flavonoid contents were determined by colorimetric methods, while phenolic aglycones were identified by HPLC. Antibacterial activity was evaluated by disc diffusion and determined the minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) values, antioxidant capacity by DPPH, TAC, and FRAP assays, and anti-inflammatory activity through protein denaturation inhibition. ADMET profiling was performed to predict pharmacokinetic and toxicological properties of the identified compounds. The ethanolic extract exhibited higher flavonoid and phenolic contents, reaching 13.27 ± 0.01 mg quercetin/_gextract and 1.57 ± 0.02 mg GAE/_gextract, respectively. HPLC analysis identified syringic, caffeic, ferulic, p-coumaric, and chlorogenic acids, as well as kaempferol and quercetin, whereas apigenin was detected only in the ethanolic extract under the present extraction and analytical conditions. Both extracts inhibited MRSA and E. coli but showed no activity against Pseudomonas aeruginosa. Docking analyses suggested favorable interactions between the identified compounds and bacterial target proteins. The ethanolic extract displayed stronger antioxidant activity, with DPPH IC₅₀ and TAC EC₅₀ values of 1.134 and 2.527 mg/mL, respectively. No ferric reducing activity was detected under the tested conditions. Protein denaturation inhibition ranged from 30.68% to 90.37%, with the aqueous extract showing significantly greater activity (p < 0.003). Overall, extraction-dependent differences in phenolic composition appear to influence the biological properties of A. sativum extracts, warranting further mechanistic and in vivo investigations. Full article

Dipeptidyl peptidase-4 (DPP-4) is a key therapeutic target for type 2 diabetes (T2D). Several synthetic anti-DPP-4 drugs are currently available for the treatment of T2D; however, the need for safe and effective therapies remains unmet due to the side effects associated with existing DPP-4 inhibitors. This study aimed to integrate structure-based and machine learning (ML)-based virtual high-throughput screening to identify natural DPP-4 inhibitors. Random forest, logistic regression, support vector machine (SVM), and multilayer perceptron (MLP) models were trained on DPP-4 IC₅₀ datasets. Among these, the SVM and MLP models achieved high predictive performance, with areas under the curve of 0.928 and 0.923, respectively. Screening of a natural compound database identified 107 compounds for further analysis. Subsequent structure-based screening, using sitagliptin as a positive control, identified sennidin B and doxorubicin hydrochloride as promising candidates with strong binding affinity for DPP-4. Molecular dynamics simulations (200 ns) and MM-PBSA calculations confirmed stable interactions with DPP-4. Further, sennidin B and doxorubicin hydrochloride inhibited DPP-4 activity in a concentration-dependent manner, with estimated IC₅₀ values of 39.39 and 19.78 μM, respectively. Sennidin B also reduced DPP-4 mRNA and protein expression levels in Caco-2 cells. Overall, sennidin B shows promise as a natural DPP-4 inhibitor and warrants further investigation as a potential antidiabetic agent. Full article

Estimation of rice crop yield at the village level is essential because village is the Insurance Unit (IU) for rice crop in many regions in India, and timely and accurate yield information at this scale supports timely and transparent claim settlements for farmers and supports local agricultural planning. To achieve this, a multi-source satellite data-based machine learning approach was used to estimate rice yield at the village level using optical and SAR data, climatic data and land surface model-derived parameters in Kakinada of Andhra Pradesh, India. The predictor dataset included seasonal cumulative rainfall, seasonal Normalized Difference Vegetation Index (NDVI)-Max, seasonal NDVI-Mean, seasonal Land Surface Water Index (LSWI)-Max, seasonal LSWI-Mean, season total Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) and season total Root Zone Soil Moisture (RZSM), and season total backscatter of the Sentinel-1 VH polarization were used to represent crop greenness, moisture status, photosynthetic activity, soil water availability, canopy structure, and seasonal water supply. For model development and validation, village-level rice yield data from 2017 to 2023 was used, which was collected through Crop Cutting Experiment (CCE) at the maturity stage of Kharif season. In this study, four machine learning models such as Random Forest (RF), Support Vector Regression (SVR), Extreme Gradient Boosting (XGBoost), and Gradient Boosting (GB) were evaluated. The multi-source satellite data and yield data for the period 2017–2021 were used to train the models, which were independently tested on 2022 data and then applied to predict the rice yield in 2023. Leave-One-Year-Out (LOYO) cross-validation was also conducted on the 2017–2022 data to assess temporal robustness and generalization capability across years. Among the evaluated models, Random Forest exhibited the best overall performance. For the independent test year 2022, RF achieved an R² of 0.465, RMSE of 415.34 kg ha⁻¹, MAE of 322.22 kg ha⁻¹, and MAPE of 10.36%. For the prediction year 2023, RF achieved improved accuracy with an R² of 0.838, RMSE of 325.75 kg ha⁻¹, MAE of 262.21 kg ha⁻¹, and MAPE of 7.68%. Further, LOYO cross-validation also showed the robustness of RF, achieving the highest mean R² of 0.702 and mean RMSE of 384.73 kg ha⁻¹. The results illustrate that multi-source satellite data combined with machine learning can be a reliable and operationally useful tool in predicting village-level rice yield, which can be used for crop insurance claim settlement. Full article

Background: Digital twins and patient-specific computational models are emerging technologies in healthcare, enabling predictive, personalized, and adaptive interventions. Their integration with artificial intelligence (AI) facilitates the simulation of clinical scenarios, optimization of treatment strategies, and advancement of precision medicine. Despite growing interest, the evidence base is still evolving, highlighting the need for a comprehensive synthesis to identify current trends, applications, and gaps. Methods: A narrative review was conducted using PubMed, Web of Science, and Scopus to identify relevant literature on digital twins in healthcare. Priority was given to systematic reviews and meta-analyses in the selection process. From this process, 28 studies were selected for in-depth analysis, and their findings were complemented by primary research and conceptual, and synthesized evidence to capture emerging trends and real-world applications. Results and Discussion: The analysis revealed that digital twins are increasingly applied for patient-specific monitoring, predictive simulations, and adaptive interventions. Integration with AI enhances their ability to model complex clinical scenarios and support precision medicine. While the selected systematic reviews provide consolidated evidence of established applications, the complementary analysis indicates that these studies actively contribute to stabilizing clinical evidence, consolidating knowledge, and enabling the development of more robust patient-specific strategies. Conclusions: Digital twins are progressively shaping patient-centered healthcare by combining AI-driven simulations with clinical insights. Current research is not only consolidating existing evidence but also exploring novel applications, underscoring the potential of digital twins to enhance precision medicine. Further studies are required to fully integrate these technologies into routine clinical practice. Full article

Web service classification plays an important role in software reuse, service discovery, and automatic metadata organization. Although recent deep learning approaches have improved classification performance by using service names and natural-language descriptions, most existing methods still operate as black-box models and offer limited insight into how different metadata sources influence classification decisions. This lack of transparency reduces their practical usefulness for developers who need to verify predicted categories, analyze incorrect classifications, and improve service metadata quality. A well-trained interpretable model can not only help developers choose more appropriate and reliable categories for each web service, but also help write a more reasonable service name and description. In this paper, we present xServeNet, an explainability-oriented extension of ServeNet for transparent web service classification. xServeNet preserves the BERT-based representation and CNN–BiLSTM feature extractor of ServeNet and introduces (i) an instance-wise dynamic source-fusion mechanism that adaptively combines service-name and service-description features according to their semantic contribution, and (ii) model-internal importance indicators at both the source and word levels that support inspection of classification decisions without introducing additional trainable parameters. We benchmark xServeNet against eleven machine learning baselines on two real-world ProgrammableWeb datasets of 10,943 and 14,086 services covering 50 categories. xServeNet reaches 71.08% Top-1/91.35% Top-5 accuracy on the original dataset and 74.10% Top-1/92.95% Top-5 accuracy on the updated dataset, consistently improving Top-1 accuracy over ServeNet while remaining competitive on Top-5, and achieving the lowest per-category Top-5 standard deviation among all twelve compared methods. In practice, the importance indicators support three concrete activities at the service registry: helping developers verify predicted categories at registration time, iterating on description wording when the predicted category looks wrong, and supporting registry curators in flagging likely mislabelled services for review. Full article