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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.
Characterization of accumulated space charge and charge-caused AC gas discharge characteristics in enclosed air spaces with novel electrostatic induction method
Preferences for accessing sexual and reproductive health information and services among adolescent girls and young women in higher learning institutions in Tanzania: A qualitative study
Introduction Adolescence is a critical developmental stage characterized by significant physical, emotional, and cognitive changes, including sexual maturation. However, this population group faces challenges such as early sexual initiation, limited contraceptive access, high sexually transmitted infection risks, including HIV (with 43% of new HIV infections in Tanzania occurring among youth, with higher rates found among young women), and unintended pregnancies, negative outcomes of limited access to sexual and reproductive health (SRH) information and services. Social, cultural, and policy constraints restrict access to SRH services, highlighting the need to understand adolescents’ preferences to improve accessibility and health outcomes. Aim The study aimed to explore the preferences for accessing sexual and reproductive health information and services among adolescent girls and young women (AGYW) in higher learning institutions in Tanzania. Methods This qualitative descriptive study was conducted at two universities in Dodoma, Tanzania. Purposive sampling recruited 13 sexually active students aged 19–24. Data were collected through in-depth interviews conducted in Swahili, audio-recorded, and transcribed verbatim. Data saturation was reached at the 13th interview. Results Young women reported different preferences for accessing sexual and reproductive health (SRH) information and services. They accessed SRH information through community, media, and healthcare-based sources. Family members,(particularly sisters)and friends, were key information sources, though parental reluctance limited access. Social media and search engines provided privacy but raised concerns about misinformation. Healthcare facilities were perceived as reliable yet unwelcoming, leading many participants to prefer over-the-counter services. Participants advocated for reliable online platforms to enhance the accessibility and accuracy of SRH information. Conclusion This study highlights AGYW’s preferences for accessing SRH information, emphasizing digital platforms and peer education. Strengthening these approaches while improving the youth-friendliness of healthcare settings is essential for improving SRH outcomes among this population.
An interpretable multimodal framework using compact biomarkers and Kolmogorov–Arnold networks improves the early diagnosis of Alzheimer’s disease
Abstract Early diagnosis of Alzheimer’s disease (AD), especially accurate identification at the mild cognitive impairment (MCI) stage, is crucial for slowing disease progression. Although deep learning has achieved promising performance in AD diagnosis, existing multimodal models often operate as “black boxes,” lacking the transparency required for clinical practice and failing to explicitly model deep interactions between imaging and clinical features. To address these limitations, this study proposes an interpretable multimodal framework, namely the Compact Biomarker Kolmogorov–Arnold Network (CBKAN). Specifically, we introduce EHCTNet with disease-specific attention to extract features from 3D MRI data, and innovatively constrain the encoder to output a set of compact biomarkers instead of traditional high-dimensional abstract vectors, mimicking the diagnostic logic of clinicians (e.g., judging brain atrophy). In addition, a hybrid feature Transformer is used to fuse these imaging biomarkers with clinical and genetic data, explicitly capturing complementary relationships across modalities. Finally, the Kolmogorov–Arnold Network (KAN) is adopted as the classifier to effectively model the highly nonlinear characteristics of AD progression. Experiments on 800 subjects from the ADNI dataset show that CBKAN achieves 91.1% accuracy and an F1-score of 0.910 in the AD/MCI/CN classification task, significantly outperforming existing mainstream methods. Statistical analyses validate the effectiveness of each component of the model. The proposed model provides a potentially interpretable and high-performing decision-support framework for early Alzheimer’s disease diagnosis, although its cross-cohort generalizability and real-world clinical utility require further validation in independent external datasets.
Exploring the knowledge, attitudes, and perceptions of young men towards fertility in the UK: A cross-sectional survey
Introduction Across many countries, paternal age at childbirth is rising, increasing exposure to age-related fertility decline. This study evaluated fertility-related knowledge, attitudes, and perceptions among young men. Methods An anonymous cross-sectional survey was distributed via social media between 13/02/2025 and 29/09/2025. UK adult males completed 54 close-ended questions covering participant characteristics, fertility knowledge, reproductive aspirations, and personal/socioeconomic influences. Results A total of 408 men participated in the survey, of whom 214 (52.5%) completed all components, with a median age of 34 years (interquartile range 31–38). Most were married (61.5%; N = 176), had degree-level education or higher (78.4%; N = 226), and were in full-time employment (85.4%; N = 246). Over half had no children (55.6%; N = 160). Most wished to have their first child between 30–35 years (64.5%; N = 71), and last between 36–40 years (64.3%; N = 119). More than half of the participants recognised that paternal age affects fertility (58.6%; N = 133). However, few were aware of the contribution of male-factor infertility (16.9%; N = 38), or the age at which semen quality declines (17.7%; N = 40). The majority understood that modifiable lifestyle factors influence semen quality (88.9%; N = 200), and either agreed or strongly agreed that smoking (99.1%; N = 211), recreational drug use (95.3%; N = 203), and radiation/chemical exposure (98.1%; N = 209) impacted male fertility. Many men (43.2%; N = 92) had never sought fertility information; among those who did, online medical websites were most common (32.9%; N = 70), while social media was rarely used (1.4%; N = 3). Key considerations influencing reproductive plans included biological parenthood (76.5%; N = 160), partner suitability (94.7%; N = 196), financial security (95.2%; N = 197), and suitable housing (72.4%; N = 150). Conclusion Young men demonstrated limited awareness of age-related fertility decline despite recognising lifestyle effects. Routine healthcare offers an opportunity for counselling to improve awareness, alongside consideration of socioeconomic factors. Findings are limited by a small, highly educated sample and possible self-selection bias. Trial registration number Imperial College Research Ethics Committee number: 700608.
Collective dynamics and self-assembly of calcium-alginate motors of varying sizes
Abstract Self-propelling calcium-alginate motors exhibit complex interactions driven by attractive capillary and repulsive Marangoni forces, yet how motor size influences their collective dynamics remains unclear. Here we experimentally investigate the size-dependent oscillatory collisions, synchronization, and self-assembly of calcium-alginate motors ranging from two to fifteen units. Using high-speed imaging and a particle-based model incorporating pairwise interactions, we reveal that smaller motors sustain longer oscillations, while larger motors coordinate the motion of smaller ones, leading to vibrational-like dynamics. Assemblies of eight or more motors form clusters characterized topologically by Betti numbers, reflecting evolving connectivity and voids. Our findings elucidate how size-dependent forces govern the emergent spatiotemporal patterns and self-organization of active polymer motors, providing insights for designing intelligent collective systems with potential applications in micro-actuation and soft robotics.
Topical medium-length PDRN enhances dermal extracellular matrix repair in photodamaged skin via PI3K–Akt/TGF-β–regulated pathways
Chronic ultraviolet (UV) exposure disrupts dermal fibroblast function and extracellular matrix (ECM) homeostasis, driving the structural features of photoaged skin. Polydeoxyribonucleotide (PDRN) has shown regenerative effects in injectable applications, but the mechanism and delivery of topical PDRN remain insufficiently defined. This study investigated the mechanism, skin delivery, and efficacy of a medium-length PDRN preparation (PDRN-850K) in photodamaged skin. Pathway activation was examined in basal human dermal fibroblasts using western blotting, gene expression analysis, and pharmacological inhibition. Skin delivery was assessed by confocal Raman spectroscopy in a reconstructed epidermal model and by complementary ex vivo and in vivo penetration studies. Tissue-level effects were assessed in a UV-irradiated human ex vivo skin model. Clinical efficacy was evaluated in a randomized, double-blind, split-face study comparing 0.1% PDRN-850K eye cream with 0.1% retinol over 28 days. PDRN-850K activated PI3K–Akt, TGF-β/Smad, and autophagy-related signaling in basal fibroblasts, and inhibition of these pathways reduced ECM gene induction. Raman analysis demonstrated time-dependent distribution of PDRN-associated signal into viable epidermal regions, with additional support from FITC-labeled porcine skin and pilot in vivo human Raman studies. In UV-irradiated ex vivo skin, PDRN-850K increased viable epidermal thickness and upregulated multiple collagens, elastic fiber–associated proteins, and YAP. Clinically, PDRN-850K achieved approximately two-fold greater improvements in periocular wrinkles, dermal thickness, density, and eye bag parameters compared with retinol, with good tolerability. These findings support PDRN-850K as a topical pro-repair approach for photodamaged skin.