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Eco-friendly nanocomposite SA/Al2O3/Ag2Mo2O7 microbeads for fast and sustainable photo-induced adsorption of methylene blue dye from industrial wastewater
Abstract The increasing release of hazardous synthetic dye waste poses a serious threat to the environmental and human health. In this study, a novel ternary nanocomposite was designed as an innovative photo-responsive adsorbent by embedding poly silver molybdate (Ag 2 Mo 2 O 7 ) and alumina nanoparticles (Al 2 O 3 ) into a sodium alginate (SA) polymeric matrix. Designed to efficiently eliminate methylene blue (MB) dye from contaminated water and overcome the separation bottleneck of traditional powdered materials. The SA/Al 2 O 3 /Ag 2 Mo 2 O 7 microbeads were synthesized via a facile and eco-friendly ionotropic gelation method. Structural and morphological characterization of the prepared nanocomposite was authenticated via X-ray diffraction (XRD) and transmission electron microscopy (TEM), confirmed the successful formation of the ternary nanocomposite with particle size ranging from 10 to 12 nm. The incorporation of Ag 2 Mo 2 O 7 and Al 2 O 3 within the alginate polymer matrix significantly enhanced the photo-induced adsorption performance compared to bulk sodium alginate (SA) microbeads. Notably, 1.2 g/L of the microbeads achieved a remarkable 91.78% removal efficiency for methylene blue (MB) dye (77.5 mg/g) within just 40 min under visible light irradiation. In comparison, the nanocomposite demonstrates a significantly higher elimination compared to individual Ag 2 Mo 2 O 7 and Al 2 O 3 , which exhibited only 60.69% and 56.89%, respectively. Kinetic studies revealed that the removal process obeyed the pseudo-second-order model, while the equilibrium data closely followed the Freundlich isotherm. Thermodynamic parameters confirmed that the photo-induced adsorption process is spontaneous and endothermic. Furthermore, the reusability of the SA/Al 2 O 3 /Ag 2 Mo 2 O 7 microbeads was tested through five adsorption–desorption cycles, consistently removing more than 73.89% of MB dye. These results demonstrate the practical application of this photo-responsive nanocomposite as an easily recoverable and efficient solution for eliminating toxic cationic dyes from industrial wastewater.
Single-cell RNA sequencing of hematopoietic and non-hematopoietic cells defines a distinct signature in atopic and NRG1 mouse lung
Transcriptomic meta-analysis identifies dysregulated pathways and potential therapeutic targets in Vestibular Schwannoma
Vestibular schwannoma (VS) is a benign Schwann cell–derived tumor that frequently causes progressive hearing loss and vestibulocochlear dysfunction, substantially impacting quality of life. The molecular mechanisms underlying VS pathobiology remain poorly defined, and reliable biomarkers or targeted therapies are lacking. This study aimed to delineate the molecular landscape of VS through a transcriptome-wide meta-analysis. We performed a genome-wide random-effects meta-analysis of four independent Affymetrix microarray datasets from the Gene Expression Omnibus (GEO) database. Differential expression analyses were conducted with and without covariate adjustment. Gene Ontology enrichment and DrugBank-based drug–gene interaction analyses were subsequently applied to characterize biological pathways and assess translational potential. Across the meta-analysis, more than 3,200 differentially expressed genes were identified in the covariate-free model. After applying a more stringent threshold (|metaLFC| > 1 and FDR < 0.05), 1,095 genes remained differentially expressed, with high concordance between the covariate-free and covariate-adjusted models. Downregulated genes included extracellular matrix and stromal components ( MFAP5 , FABP4 , DCN ), and sensory- and synapse-related transcripts ( SLC22A3 , LGI1 ). Upregulated genes included immune- and inflammation-associated genes ( TREM2 , CCL3 , CCL4 , L1CAM ) and proliferative regulators ( CCND1 , RAB31 , MOXD1 ). Functional enrichment highlighted extracellular matrix remodeling, immune modulation, sensory signaling, and cell cycle pathways. Notably, many of the most strongly dysregulated genes have not previously been associated with VS. Drug–gene interaction analysis identified multiple dysregulated genes with known pharmacological targets, suggesting potential translational relevance. This transcriptome-wide meta-analysis provides a comprehensive overview of gene expression patterns in VS, highlighting alterations related to extracellular matrix organization, sensory and synaptic processes, immune-associated signaling, and cell cycle–related pathways. The study highlights novel disease-associated genes and pathways and may help prioritize candidates for further investigation, including those with potential relevance for therapeutic targeting.
Alteration of epidermal lipid metabolism by 2-linoleoylglycerol in a 3D skin model
AI-driven diagnosis of mpox using deep learning models
Mpox lesions can resemble other dermatological conditions, motivating image-based screening, yet published studies remain difficult to compare owing to differences in dataset construction, augmentation policy, and evaluation design. This study provides a leakage-aware benchmark for binary mpox classification using a unified dataset assembled from MSLD v1.0 and v2.0. Seven pretrained backbones and a weighted ensemble were compared under group-stratified five-fold cross-validation with original-only test evaluation, validation-based threshold selection, and temperature scaling. The weighted ensemble achieved mean accuracy 0.8729, F1-score 0.8334, and AUC 0.9388; ConvNeXt-Tiny was the strongest single model (F1 0.8159, AUC 0.9284). These grouped original-only results are intentionally conservative relative to augmentation-heavy or single-split designs and should be interpreted as deflated but more trustworthy reference values. Post hoc calibration analysis, content-level near-duplicate auditing, and a test-time augmentation ablation are provided to substantiate the methodological claims. The contribution is methodological: a transparent benchmark emphasizing reproducible dataset curation, grouped evaluation, and calibrated comparison, while highlighting the limitations of current public skin-image data. Accordingly, these results should be interpreted as a reproducible reference benchmark rather than a clinically validated diagnostic tool, and external clinical validation remains necessary before deployment.
Cross-scenario evaluation of explainable machine learning for non-invasive summer occupancy detection across five building scenarios
Abstract Accurate identification of indoor occupant presence is crucial for intelligent building energy management. Traditional monitoring methods are often invasive and lack standardized quantitative indicators. Therefore, this study proposes a non-invasive occupant presence state prediction method based on machine learning. Six machine learning algorithms—Logistic Regression, Decision Tree, k-Nearest Neighbor (KNN), Random Forest, CatBoost, and XGBoost—were evaluated across five building scenarios (hospitals, classrooms, dormitories, offices, and dwelling houses) using core environmental features including air temperature, relative humidity, sound pressure level, illuminance, CO 2 concentration, formaldehyde (HCHO), PM 2.5 , PM 1.0 , and PM 10 . A temporally ordered walk-forward evaluation framework was adopted to prevent data leakage, with VIF screening and RFECV for feature selection. The best-performing models achieved mean accuracies ranging from 0.61 (dwelling) to 0.88 (office), with the optimal algorithm varying by scenario. SHAP-based interpretability analysis identified CO 2 concentration, illuminance as the most consistently influential predictors, with PM 2.5 , temperature, and sound contributing in scenario-specific patterns. A leave-one-feature-out sensitivity analysis showed that removing CO 2 caused the largest performance drop in the hospital scenario. This study provides a comparative analysis of explainable machine-learning predictors across five single-room building scenarios under a temporally aligned protocol, providing comparative observations that may inform subsequent multi-room and closed-loop studies of smart building management.
Enhancing perovskite solar cells efficiency via dual surface passivation
Effective defect passivation is essential for achieving high performance in perovskite solar cells (PSCs). Dimensional engineering provides a powerful strategy to suppress non-radiative recombination in both the bulk and surface regions of PSCs. In this work, we present a novel interfacial passivation approach for the perovskite/hole transport layer interface using a dual-cation passivation layer composed of guanidinium bromide (GuaBr) and n-phenylethylammonium bromide (n-PEABr). This dual-cation strategy delivers an open-circuit voltage of 1.23 V and a power conversion efficiency (PCE) of 25.11%, significantly outperforming devices based on single-cation passivation. The combined cations induce the formation of a mixed 1D/2D perovskite structure, resulting in a more uniform and hydrophobic surface compared with unpassivated films. Moreover, stability tests conducted under ambient conditions (80% relative humidity) and continuous light-soaking reveal markedly enhanced device stability. The results demonstrate the superior passivation effectiveness of phenylethylammonium compared with previously reported methods. In particular, this approach surpasses the 23% PCE achieved using octylammonium passivation, achieving efficiencies exceeding 25%. Overall, the excellent defect passivation and favorable optical and electrical properties of phenylethylammonium play a key role in significantly improving both the efficiency and stability of PSCs.
High expression of VISTA on M-MDSCs is associated with immunosuppression and predicts poor prognosis in acute myeloid leukemia
Research on the protection and restoration technology of glazed components in ancient architecture
In traditional Chinese architecture, glazed tile components are a significant decorative element that are frequently utilized on walls and roofs. However, glazed tile components typically show variable degrees of deterioration and damage owing to long-term exposure to the natural environment. This study examines and evaluates the preservation quality and primary forms of deterioration of historic architectural glazed tile components. The damage development process of glazed tile components under the combined influences of temperature, humidity fluctuations, and acidic environments was discovered through characterization of material morphological changes and corrosion products. Tetraethyl orthosilicate (TEOS) and hydroxyl-terminated polydimethylsiloxane (PDMS−OH) were chosen as reinforcement materials for common problems including pulverization of the glazed tile body. The glazed tile body was reinforced using ethanol as a solvent. The best material formulation for glazed tile body reinforcement was chosen by evaluating the performance of reinforcement systems with various ratios using a thorough index analysis. Concurrently, scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS) were used to examine the reinforcing process. This work suggests modifying the matrix material’s particle size to improve mechanical characteristics and structural stability for glazed tile components that are extensively damaged and challenging to repair with reinforcement. The study’s findings can serve as a theoretical foundation and technical guide for the scientific preservation and repair of glazed tile elements found in historic buildings.
Experimental study on accumulated deformation characteristics of compacted gravelly soil under dynamic loading
Physical activity and quality of life in children: Findings from the Health Oriented Pedagogical Project (HOPP)
Purpose The purpose of this study was to examine the associations between a 5-year school-based physical activity (PA) intervention and quality of life (QoL) in children aged 6–12 years. Methods Data were collected through the Health Oriented Pedagogical Project (HOPP), a longitudinal study conducted in Norway. HOPP involved children and their parents from nine elementary schools (n = 2,140 children and 1,639 parents completed the QoL-questionnaire). Seven schools received the intervention (an additional 225 minutes of physical activity per week), while two schools served as controls following the standard curriculum. QoL was measured using the Inventory of Life Quality in Children and Adolescents (ILC), and physical activity (PA) was assessed using accelerometers. Covariates included father’s education level as a proxy for socioeconomic status (SES) and children’s waist-to-height ratio (WHtR). Results The analysis revealed a significant positive association between MVPA and QoL (β = 0.008, p < 0.001), although the effect size was small. SES was significantly associated with QoL in intervention schools (β = 0.249, p < 0.001), while WHtR was negatively associated with QoL in both groups, with a stronger association in control schools (β = −4.344, p = 0.010). The control schools exhibited higher QoL scores than the intervention schools, with an average 0.5-point advantage (β = 0.458, p < 0.001), likely reflecting underlying SES differences. Conclusion This study highlights the complex interplay between MVPA, SES, WHtR, and QoL in children. While MVPA was associated with better QoL, these associations varied according to factors such as age, SES, and WHtR, with no significant association observed for sex. The findings suggest that sustained and varied physical engagement in school settings may be relevant for children’s QoL. Moreover, school-based initiatives should consider multiple individual and environmental factors, particularly SES and physical health metrics, when interpreting or targeting QoL-related outcomes. Trial registration The study is registered at ClinicalTrials.gov (Identifier: NCT02495714). The trial was retrospectively registered on June 20, 2015. Baseline data collection was initiated in mid-January 2015.
Molecular identification of trichomonads in captive sugar gliders and tortoises with diarrhea: a case study
Machine learning-based analysis of drug resistance mutations in Mycobacterium tuberculosis
Tuberculosis is a deadly airborne disease caused by Mycobacterium tuberculosis . Drug-resistant tuberculosis presents significant challenges for treatment and control of the disease. Resistant strains of Mycobacterium tuberculosis arise from specific mutations in the bacterium. Identification and characterization of resistance-associated mutations are crucial for effective treatment strategies because the first- and second-line drugs for the disease target distinct genes in the bacterium and serve different purposes. Our study developed a machine learning prediction model to analyze mutations across multiple drug-resistance types. The proposed framework predicts drug-resistance mutations across four drug-resistance types, including Rifampicin Resistance, Isoniazid Resistance, Multidrug Resistance, and Pre-extensively Drug-Resistant tuberculosis. The NIAID-NIH TB portal is a publicly available dataset of tuberculosis patients, including drug-resistance information. Our study analyzed 3,065 cases of drug-resistant TB. Eight supervised ML algorithms were implemented for the study. A Random Forest classifier with 10-fold cross-validation shows higher predictive performance than the other seven algorithms considered for further analysis. Significant drug resistance mutations were identified using SHapley Additive exPlanations feature importance. The World Health Organisation mutation catalogues, considered the gold standard for drug-resistant mutations, were used to evaluate prediction results. Mutations not reported in the WHO catalogues were identified during the post-prediction comparative analysis stage, as they may represent potential resistance-conferring markers warranting further investigation, including structural and functional validation or experimental validation. The mutations are rpoB- I480T, rpoC -G332R, L527V, gyrA -D94V, KatG -G99E, A106V, W191R, W328C, T380I, and M420T. The study further checks the stability and pathogenicity of the mutations using computational tools, including I-Mutant 2.0 and PredictSNP. The findings added more clarity and further evidence for the significance of the mutation, based on its contribution to drug resistance.
Occupational injuries and personal protective equipment use in Ethiopia: findings from the 2021 national labour force and migration survey
Abstract Occupational injuries represent a serious global public health challenge with direct consequences for workforce productivity and economic stability. Using secondary data from the 2021 Ethiopian National Labour Force and Migration Survey (NLFMS) a nationally representative cross-sectional survey conducted by the Central Statistical Agency (CSA) of Ethiopia. This study provides national baseline estimates of occupational injury burden and personal protective equipment (PPE) utilization relevant to Sustainable Development Goal monitoring and progress toward the Sustainable Development Goals by 2030 especially SDG 8 -Decent Work and Economic Growth. A national injury rate of 4.96% was identified, corresponding to over 2 million affected workers. Males faced higher overall injury rates (5.40%), while females bore a disproportionate burden of health-related work inactivity (18.18%). General illnesses (24.76%) and back problems (22.82%) were the leading injury categories. PPE utilization remained critically low at 5.04% nationally concentrated in urban centers such as Addis Ababa (15.80%) and largely limited to masks and helmets leaving ergonomic and noise-related hazards (earplug use: 0.37%) almost entirely unaddressed. This gap suggests that injury reduction in Ethiopia depends less on PPE availability than on enforcement, employer provision, and rural outreach; national OHS policy should prioritize decentralized enforcement, gender-sensitive ergonomic interventions in agriculture, and mandatory employer-backed PPE provision to close this gap.
Topical sterosomes-based nanocarrier of miconazole for the management of cutaneous candidiasis
Background Miconazole (MN) is widely used to treat superficial fungal infections; however, limited skin penetration and short residence time restrict its therapeutic efficacy. This study aimed to develop and statistically optimize MN-loaded sterosomes (STEs) to enhance topical antifungal activity. Methods A central composite rotatable design (CCRD) was applied using Design-Expert® software to study the effects of cholesterol amount (mg) and sonication time (min) on vesicle size (VS), zeta potential (ZP), and entrapment efficiency (EE%). Vesicle morphology was characterized by transmission electron microscopy (TEM), and drug entrapment was confirmed using X-ray diffraction (XRD). The optimized formulation was incorporated into a hydroxypropyl methylcellulose (HPMC) gel and evaluated for in vitro release and in-vivo antifungal efficacy in a Wistar albino rats cutaneous candidiasis model (n = 6) following topical administration of optimized MN-loaded sterosome gel 1% w/w for ten days. Results The optimized formulation showed a desirability of 0.63 and consisted of 140.86 mg cholesterol and 8.99 min sonication time. It demonstrated vesicle size: 498.54 ± 6.12 nm, zeta potential: 40.82 ± 1.24 mV, entrapment efficiency: 77.41 ± 1.43%. MN release from STEs was significantly higher than the drug suspension. TEM images showed spherical non-aggregated vesicles. XRD patterns indicated successful MN entrapment. In-vivo , MN-STE gel produced significantly greater antifungal activity than commercial Daktarin® cream at a lower dose, which was consistent with histopathological improvement. Conclusion MN-loaded sterosomes enhanced drug entrapment, release, and antifungal efficacy while enabling dose reduction, representing a promising carrier for topical miconazole delivery.
Artificial intelligence platform for promoting learning skills of people with autism spectrum disorder
Semi-analytical hierarchical Bayesian inference of nonlinear model structure in stochastic dynamics: Applied to compartmental models of infectious diseases
A Bayesian computational framework for parsimonious inference in stochastic nonlinear dynamical systems is presented. This framework enables the concurrent estimation of system states, time-varying parameters, time-invariant parameters, and the optimal sparsity structure of the model parameters. Because differential equation-based models are often simplified mechanistic or phenomenological representations, robust inference from noisy measurement data requires explicit treatment of model error and uncertainty. Model error and time-varying parameters can be represented as random processes, enabling inference while making minimal assumptions about the underlying sources of discrepancy and variability. Adopting stochastic differential equation representations affords the model significant flexibility, but can also render it susceptible to overfitting during statistical inversion, where the inferred model may track noise rather than the underlying signal. To alleviate the effects of overfitting and to enable the discovery of the optimal sparse representation of the time-invariant parameters, a Bayesian sparse learning algorithm is embedded within the framework. This sparse learning framework adopts an approximate hierarchical Bayesian setting defined by a series of semi-analytical expressions. The model structure inference framework is validated using a stochastic compartmental model for tracking and forecasting active cases of an infectious disease. Compartmental models describe population-level infectious disease dynamics through interactions among population fractions grouped by disease state. Mathematically, such models consist of a system of coupled ordinary differential equations. This example adopts an expressive compartmental model that includes multiple possible interactions between disease states, motivated by early uncertainty surrounding COVID-19 reinfection dynamics and their implications for long-term epidemic forecasting. The sparse learning exercise permits the inference of a priori unknown epidemiological dynamics from simulated public health data, discovering the nested compartmental model that optimizes the trade-off between average data-fit and model complexity. It is shown that inducing sparsity among the model parameters eliminates redundant interactions between compartments, equivalently revealing the optimal coupling structure between differential equations.
Internally validated machine learning models identify asthma and its phenotypes using multicenter real-world data
Real‑world safety evaluation of tranexamic acid: Signal detection from FAERS and VigiAccess databases
Background Tranexamic acid (TXA) is an antifibrinolytic agent commonly used to mitigate blood loss across various medical indications. Despite its widespread use, comprehensive data on its safety profile remain limited. This study aimed to systematically evaluate adverse events (AEs) associated with TXA. Methods Adverse event reports were extracted from the U.S. Food and Drug Administration’s Adverse Event Reporting System (FAERS) and the VigiAccess databases. Disproportionality analyses were conducted using reporting odds ratio (ROR), proportional reporting ratio (PRR), the Medicines and Healthcare products Regulatory Agency (MHRA) method, Bayesian confidence propagation neural network (BCPNN), and multi-item gamma Poisson shrinker (MGPS). Results A total of 17,787 TXA-related AE reports were identified. A higher proportion of reports involved females, with older adults (≥ 65 years) accounting for the largest proportion in FAERS and younger individuals (18–44 years) in VigiAccess. Overlapping PTs, including seizures, pulmonary embolism and anaphylactic reactions, were identified. Significant differences for TXA-related AEs were found by gender, age and death outcomes. Most AEs occurred within the first month, with an early failure pattern. Conclusion This study provides evidence to weigh risks and benefits of TXA by comprehensive assessment of safety profile for TXA. These findings provide valuable references for future pharmacovigilance research on TXA.