Search Results (17,271)

Teachers’ well-being continues to attract global attention due to rising workload demands and emotional exhaustion in educational settings. In secondary education, stress and burnout remain critical issues that impact both teaching quality and teacher retention. This study explored gender differences in how workload demands and motivation influence teachers’ stress and burnout, using a multigroup structural equation modelling (SEM) approach based on the Job Demands–Resources (JD–R) model, Self-Determination Theory (SDT), and the Transactional Model of Stress and Coping. Data were collected from 353 Nigerian secondary school science, mathematics, and technology teachers using validated questionnaires measuring workload demands, motivation, attitudes toward work, stress, and burnout. Results indicated that workload demands significantly predict stress and burnout across genders, with a stronger relationship among female teachers. Motivation positively affected attitudes towards work but showed mixed effects on stress and burnout depending on gender. Additionally, attitude towards work predicts stress and burnout, while stress also strongly predict burnout in both groups. These findings suggest that burnout is driven by both demands and motivation, with gender moderating teachers’ responses to occupational pressures. The study concludes that interventions aimed at improving teacher well-being must be gender-sensitive, focusing on reducing excessive workload, fostering intrinsic motivation, and strengthening professional support networks. These insights offer valuable guidance for policymakers and educational administrators seeking to enhance teacher resilience and performance through targeted professional development initiatives. Full article

Background/Objectives: Food allergy (FA) is a substantial health burden in children. FA is often associated with malnutrition and malabsorption, due to restrictive food avoidance diets, which can significantly impair the patient’s and their family’s quality of life. To this date, population-based data combining sensitization and clinical allergy remain limited. This study aimed to assess the patterns of sensitization rates to food and food allergy prevalence rates in Croatian children and to evaluate differences according to age, sex, and region of origin. Materials and Methods: In this cross-sectional study, 1948 preschool and school-aged children from three Croatian regions (Zagreb, Dalmatia, and Slavonia) were included. Participants underwent skin prick testing to common food and inhalant allergens. Data on personal and family medical history were collected using questionnaires and medical records. FA prevalence was evaluated using self-reported data in school-aged children and physician-diagnosed FA data in preschool children. Results: Overall, 41% of participants were sensitized to at least one allergen, while 13% were sensitized to at least one food allergen. Tree nuts—particularly hazelnut—were the most common food-derived sensitizers, followed by hen’s egg, cow’s milk, and fish. Boys exhibited higher total sensitization rates than girls (44.2% vs. 37.5%; p = 0.001), higher food allergen sensitization rates (14.7% vs. 11.4%; p = 0.037), and higher total polysensitization rates (30.7% vs. 22.6%; p < 0.001). School-aged children showed higher total sensitization (44.8% vs. 33.4%; p < 0.001) and polysensitization rates (29.8% vs. 20.5%; p < 0.001) than preschool children, while sensitization to food allergens did not differ between age groups. Food allergen sensitization rates differed by region, with higher prevalence in Zagreb compared with Dalmatia and Slavonia (p = 0.0055), whereas total sensitization rates did not differ regionally. The agreement between sensitization and self-reported FA among school-aged children was low (κ = 0.22; p < 0.001), as was the agreement between sensitization and physician-diagnosed FA in preschool children (κ = 0.13; p < 0.001), despite high specificity in both analyses (95% and 99%%, respectively). Conclusions: Allergic sensitization is common among Croatian children, but it poorly predicts clinically relevant food allergy. These findings highlight the multifactorial nature of allergen sensitization in children and emphasize the need for improvements in diagnostic pathways, targeted prevention strategies, and continued surveillance to optimize allergy prevention and management in children. Full article

This study details the synthesis, characterization, molecular docking and preliminary biological evaluation of a new heterocyclic compound, 2-((4-morpholino-1,2,5-thiadiazol-3-yl)oxy)benzaldehyde. This molecule was designed using an artificial intelligence (AI)-based molecular generative model. It was synthesized through a nucleophilic substitution between 3-chloro-4-morpholino-1,2,5-thiadiazole and 2-hydroxybenzaldehyde. Structural elucidation was performed using ¹H NMR, ¹³C NMR, Elemental Analysis, and Single Crystal X-ray diffraction. AI-guided in silico predictions suggested promising pharmacophoric features and potential biological activity. Preliminary biological evaluation, primarily through anticancer assays, demonstrated moderate to significant activity, supporting further investigation. The findings therefore suggest that this AI-generated molecule could serve as a lead scaffold for developing drugs targeting cancer and other infectious diseases. Full article

Deep learning methods have been extensively used for drug–drug interaction (DDI) prediction, aiding the development of effective and safe combination therapies. Most studies focus on either the internal molecular structure or external contextual information of individual drugs to improve feature diversity and validity. However, the latent similarities between drug pairs, which are essential for accurate predictions, have largely been overlooked. Therefore, we propose an interpretable predictive approach for graph embedding called PINGE, which relies solely on the interaction network of drugs. Specifically, we constrain the joint features of drug pairs to their interactions, allowing those with similar types to achieve cosine similarity. This similarity in direction helps the joint features converge to the same class during prediction. Additionally, each known drug can link to multiple others, enhancing its diversity. Extensive experiments demonstrate that PINGE outperforms current advanced prediction methods on both KEGG and Drugbank datasets, achieving improvements of 0.7% and 2.4% in ACC while providing network structure-based explanations for predictions. Furthermore, PINGE surpasses advanced baselines by 1% and 1.1% in AUC on the human drug–target dataset and HuRI protein–protein interaction dataset, showcasing excellent versatility. Full article

Human metapneumovirus genotype B (HMPV-B) is an important respiratory pathogen, requiring detailed elucidation of the evolutionary and antigenic features of its fusion (F) gene. Using 500 sequences collected between 1982 and 2024, we investigated the molecular evolution, phylodynamics, and structural epitope landscape of the HMPV-B F gene. Time-scaled phylogeny dated the divergence of sublineages B1 and B2 to around 1937, and Bayesian Skyline Plot analysis showed that these sublineages exhibited distinct demographic trajectories over time. The F gene evolved at a rate of 1.01 × 10⁻³ substitutions/site/year; however, amino acid variation remained limited, consistent with pervasive purifying selection, with 39% of codons under strong negative selection and little consensus evidence for positive selection. Conformational B-cell epitope prediction demonstrated a high degree of conservation across neutralizing antibody binding regions (sites Ø and I–V), and amino acid substitutions occurring within these sites were not predicted to substantially alter epitope architecture. Together, these findings indicate that the HMPV-B F gene evolves under strong evolutionary constraint while maintaining stable antigenic features, supporting the potential for antibody-based strategies that target neutralizing antibody binding regions of the F protein. Full article

Wildfire susceptibility mapping supports proactive forest management, and estimated predictive performance may vary with spatial dependence and the control-point sampling strategy. We developed an interpretable random-forest framework to map wildfire occurrence probability across Hubei Province, China, by integrating multi-source environmental (meteorological, topographic, and vegetation) and socio-economic predictors. To enhance methodological robustness and address high-dimensional data complexity, the Boruta algorithm was employed for rigorous feature selection, identifying the most significant drivers while filtering out random noise. The model showed strong discrimination on held-out data (AUC = 0.942, accuracy = 87.9%), and variable importance highlighted sunshine duration, elevation, relative humidity, and maximum temperature as dominant predictors. Predicted wildfire probability exhibited a clear east–west gradient; high and very high susceptibility classes covered 22% of forested land while containing 82% of historical fires, indicating priority zones for targeted prevention and resource allocation. These results demonstrate that combining multi-source predictors with machine-learning interpretability can produce actionable susceptibility maps for regional fire-risk management. Full article

The increasing complexity of disease mechanisms challenges accurate diagnosis, prevention, and early risk stratification. Beyond genetic predisposition, epigenetic regulation—particularly DNA methylation—represents a dynamic molecular interface linking environmental exposures, metabolic imbalance, inflammation, and disease development. DNA methylation is the most extensively studied epigenetic mechanism and plays a central role in controlling gene expression across physiological and pathological conditions. In this review, we provide an integrated overview of DNA methylation biology and its involvement in inflammatory, metabolic, and oncological diseases, with a specific focus on pathways related to chronic inflammation and oxidative stress. We summarize evidence demonstrating how aberrant methylation patterns contribute to disease initiation and progression, highlighting recurrent epigenetic signatures affecting key regulatory genes. In parallel, we discuss current and emerging technologies for DNA methylation analysis, ranging from targeted methylation-specific assays to next-generation sequencing-based approaches, including nanopore adaptive sampling. Finally, we explore the translational potential of DNA methylation-based tests as predictive and preventive tools, emphasizing their ability to identify disease-associated molecular alterations before clinical onset. Overall, this evidence supports the integration of epigenetic profiling into future precision medicine strategies aimed at early risk assessment, prognosis refinement, and personalized prevention. Full article

Background: Influenza A virus is an acute respiratory virus that spreads quickly, affects a broad range of populations, and can lead to many complications and mortality. Artemisia L. species are widely used in traditional medicine, but their antiviral potential against H1N1 remains uncertain. Methodology: Network pharmacology and molecular docking were used to computationally explore their potential function in this domain, and to investigate how their invasion mechanisms and adsorption occur. UPLC-MS/MS analysis identified the main components of the extracts. The anti-H1N1 mechanism of Artemisia L. extracts was studied in vitro. Results: Network pharmacology identified 95 key targets between Artemisia L. and IAV, with quercetin and luteolin as core active compounds. Molecular docking predicted strong binding affinities between these compounds and influenza virus proteins. UPLC-MS/MS analysis identified 75, 100, and 64 chemical components in ACBE, AALE, and ACTE, respectively, mainly flavonoids and terpenoids. Artemisia L. extracts exhibited both preventive and therapeutic effects against H1N1, reducing progeny virus NP mRNA and protein levels. In vitro experiments showed that higher concentrations of the extracts prevent virus attachment to MDCK cells by denaturing the HA protein. NA plays an essential role in progeny virus release. We found that a high concentration of ACTE can inhibit NA up to 85%, and ACBE showed a low inhibitory effect on NA. Conclusions: In terms of therapeutic effects, Artemisia L. extracts can regulate intracellular inflammatory factors via the TLR4/NF-κB/MyD88 signaling pathways and reduce the expression of IL-1β, IL-6, TNF-α, TLR4, NF-κB, p65, and MyD88 at the mRNA level, thereby inhibiting H1N1 virus replication. These results suggest that bioactive components in Artemisia L. extracts may inhibit H1N1, potentially leading to the development of natural-product-based anti-influenza agents. Full article

Background: Insufficient physical exercise is a growing public health concern in China, where only 30.3% of adults exercise regularly. Exploring the key factors associated with physical exercise participation is essential for promoting healthier lifestyles. Method: This study utilized data from the 2021 China General Social Survey (CGSS) to apply a progressive framework of dimensionality reduction, machine learning prediction, and SHAP-based interpretability analysis. A total of 19 potential factors were considered, with LassoCV used for feature selection and multiple models constructed for comparison. Results: The SVM model showed the best predictive performance. SHAP analysis revealed that watching sports events, household registration, educational attainment, subjective well-being, smoking, age, sleep quality, social activities, and residence suitability for physical exercise are the most important factors influencing participation. Higher education, greater subjective well-being, urban residency, frequent sports viewing, and residence suitability for physical exercise were positively associated with participation, while smoking and poor sleep quality were negatively associated with it. Conclusion: This study highlights the value of combining machine learning with interpretability methods to uncover the key predictors of physical exercise. The findings provide new evidence on the social, psychological, and environmental factors associated with Chinese adults’ exercise behavior, offering insights for targeted health promotion strategies. Full article

Respiratory syncytial virus (RSV) resurged in many regions after the relaxation of stringent non-pharmaceutical interventions (NPIs) implemented during the COVID-19 pandemic. Here, we characterized the epidemiological patterns and molecular evolution of RSV among pediatric inpatients with acute respiratory tract infections (ARTIs) on tropical Hainan Island, China. We retrospectively analyzed 32,329 children (≤18 years) hospitalized at Hainan Women and Children’s Medical Center from January 2021 to December 2024. RSV positivity was determined using targeted next-generation sequencing. In total, 4483/32,329 (13.86%) patients were RSV-positive, with a high positivity in 2021 (20.27%, 957/4721), marked suppression in 2022 (2.03%, 106/5227) during intensive NPIs, and a rebound in 2023–2024 (15.31%, 1490/9732; 15.26%, 1930/12,649). RSV positivity was higher in boys than girls (14.42% vs. 13.00%). Seasonality shifted from a summer–autumn peak in 2021 to a spring–summer predominance in 2023–2024. Among 56 sequenced RSV-positive specimens (29 RSV-A; 27 RSV-B), all RSV-A strains belonged to genotype ON1 (lineages A.D.3 and A.D.5.2), and all RSV-B strains belonged to genotype BA9 (lineages B.D.4.1.1, B.D.E.1, and B.D.E.2). Subtype dominance transitioned from RSV-A (2021–2023; mainly A.D.3) to RSV-B in 2024 (all B.D.E.1). Lineage-specific amino-acid and predicted N-glycosylation changes were observed, including loss of the N179 site in A.D.5.2 and acquisition of N258 in B.D.E.1. These findings indicate that RSV circulation on tropical Hainan was strongly suppressed during intensive NPIs and re-established after policy relaxation, accompanied by earlier seasonal activity and clear lineage replacement, underscoring the need for sustained genomic surveillance to inform locally tailored clinical preparedness and immunization strategies. Full article

Metal–organic frameworks (MOFs) are among the most structurally diverse classes of crystalline nanomaterials, offering exceptional tunability, porosity, and chemical modularity. These characteristics have positioned MOFs as promising platforms for nanomedicine, bioimaging, and integrated nanotheranostic applications. However, the rational design of MOFs that satisfy stringent biomedical requirements, including high drug loading capacity, controlled and stimuli responsive release, selective targeting, physiological stability, biodegradability, and multimodal imaging capability, remains challenging due to the vast combinatorial design space and the complex interplay between physicochemical properties and biological responses. The objective of this review is to critically examine recent advances in artificial intelligence approaches based on Transformer architectures for the design and optimization of MOFs aimed at next-generation nanotheranostics. In contrast to prior reviews that broadly survey machine learning methods for MOF research, this article focuses specifically on Transformer-based models and their ability to capture long-range, hierarchical, and multiscale relationships governing MOF structure, chemistry, and functional behavior. We review state-of-the-art models, including MOFormer, MOFNet, MOFTransformer, and Uni MOF, and discuss graph-based and sequence-based representations used to encode MOF topology and composition. This review highlights how Transformer-based models enable predictive assessment of properties directly relevant to nanotheranostic performance, such as adsorption energetics, framework stability, diffusion pathways, pore accessibility, and surface functionality. By explicitly linking these predictive capabilities to drug delivery efficiency, imaging performance, targeted therapeutic action, and combined diagnostic and therapeutic applications, this work delineates the specific contribution of Transformer-based artificial intelligence to biomedical translation. Finally, we discuss emerging opportunities and remaining challenges, including generative Transformer models for inverse MOF design, self-supervised learning on hybrid experimental and computational datasets, and integration with autonomous synthesis and screening workflows. By defining the scope, novelty, and contribution of Transformer-based design strategies, this review provides a focused roadmap for accelerating the development of MOF-based platforms for next-generation nanotheranostics. Full article

The global challenge of antimicrobial resistance (AMR) has been framed primarily in terms of genetic resistance mechanisms. Nevertheless, bacteria can also survive antimicrobial stress through phenotypic plasticity, resulting in transient, non-genetic states such as tolerance, persistence, and population-level resilience. These phenotypic states complicate diagnostic efforts, diminish antibiotic efficacy, and contribute to the chronic nature of infections even in the absence of heritable resistance. This review evaluates phenotypic plasticity as a significant yet underrecognized factor in AMR, with a focus on responses to oxidative and photodynamic stress. Key manifestations of plasticity are discussed, including morphological and metabolic remodeling such as filamentation, small-colony variants, and metabolic rewiring, as well as envelope- and biofilm-associated heterogeneity and regulatory flexibility mediated by gene networks and horizontal regulatory transfer. The review highlights plastic responses elicited by reactive oxygen species-mediated stress and antimicrobial photodynamic inactivation, where single-cell heterogeneity, biofilm and mucus barriers, and light-dependent cues influence bacterial survival. Case studies are presented to demonstrate how photodynamic strategies can induce transient protective states and act synergistically with antibiotics, revealing mechanisms of action that extend beyond conventional single-target therapeutic models. Drawing on evidence from single-cell analyses, biofilm ecology, and experimental evolution, this review establishes phenotypic plasticity as a central element in the chemical biology of AMR. Enhanced understanding of plasticity is essential for advancing diagnostics, informing the development of adjuvant therapies, and predicting bacterial responses to novel antimicrobial interventions. Full article

Cyclin-dependent kinase 4/6 inhibitors (CDK4/6i; palbociclib, ribociclib, abemaciclib, dalpiciclib) combined with endocrine therapy (ET) were a major advance in the treatment of hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) metastatic breast cancer (MBC) worldwide. Notably, clinical activity has also been observed in HR+HER2-positive (HER2+) MBC, with significant progression-free survival (PFS) benefits. Cyclin-dependent kinases 4/6 (CDK4/6) are downstream of HER2 and pathways driving resistance to HER2-targeted therapies. However, clinical development of CDK4/6i in HER2+ MBC slowed, given the advent of highly effective tyrosine-kinase inhibitors (TKIs) (i.e., tucatinib) and antibody–drug conjugates (ADCs) (i.e., trastuzumab deruxtecan), which currently dominate the treatment armamentarium. The observation that luminal disease defined by a predictive analysis of microarray 50 (PAM50) was independently associated with a significantly longer PFS versus nonluminal disease was important, with researchers inferring that intrinsic molecular subtypes could be used to identify patients most suitable for ET + CDK4/6i + HER2-targeted treatment. Subsequently, the phase III PATINA trial (which included patients with 1L HR+HER2+ MBC, treated with palbociclib vs. placebo with maintenance ET+ H[P]) noted a striking PFS improvement of >15 months in the palbociclib arm, renewing interest in CDK4/6i-based treatments for HR+HER2+ MBC. Herein, we review the development of CDK4/6i in HER2+ BC, discussing current challenges and potential future directions. Full article

Immune checkpoint inhibitors (ICIs) targeting PD-1/PD-L1 and CTLA-4 are widely used in the treatment of several cancers and have significantly improved survival outcomes in responsive patients. However, a substantial proportion of patients fail to benefit from these therapies, underscoring the urgent need for accurate prediction of ICI response. We propose a deep learning framework, ICIsc, to accurately predict ICI response by integrating single-cell RNA sequencing (scRNA-seq) data with protein large language models. Specifically, patient representations are constructed using transcriptomic profiles and immune-related gene set scores as latent embedding features, while drug representations are derived from amino acid sequences of ICI encoded by the Evolutionary Scale Modeling 2 (ESM2). For bulk data, ICIsc employs a bilinear attention module to fuse patient and drug embeddings for response prediction. For scRNA-seq data, ICIsc infers cell–cell interactions using a single-sample network (SSN) approach and applies GATv2 to model immune microenvironment heterogeneity at the single-cell level. Benchmark evaluations and independent validation demonstrate that ICIsc consistently outperforms baseline models and exhibits robust generalization performance. SHAP-based interpretability analysis further identifies key genes (e.g., GAPDH) associated with immunotherapy response and patient prognosis. Overall, ICIsc provides an accurate and interpretable framework for predicting immunotherapy outcomes and elucidating underlying mechanisms. Full article

The prediction of chiller performance parameters is crucial for optimal control and fault diagnosis. Numerous efficient and accurate data-driven models have been developed and implemented. These models are normally trained on historical operational data of chiller units. However, the distribution of operational data may shift due to accumulated operating hours or changes in control strategies. Under new operating conditions, models trained on historical data often generalize poorly, leading to prediction deviations. To address this issue, this study integrates a one-dimensional convolutional neural network with a domain adaptation method that extracts features from both the source and target domains and aligns their inverse Gram matrices in terms of angle and scale. A predictive model applicable to multiple chiller performance parameters is established using limited historical data, enhancing the model’s generalization ability. Compared to the baseline model (MLP), the proposed method achieves an average reduction of 74.3% in mean absolute error (MAE) and 76.1% in root mean square error (RMSE), while the R² values exceed 0.96 (for certain scenarios). Additionally, this paper analyzes the data distribution between the source and target domains, investigates key factors affecting the model’s generalization capability, and provides insights for evaluating the quality of modeling data. Full article