This study investigates the impact of family environments on the psychological well-being of children and adolescents in rural China, focusing on loneliness, depression, and internet addiction. Using a cross-sectional design, data were collected from 652 families in rural Hunan province to examine the role of family communication, caregiver psychological states, and parenting styles. Structural equation modeling revealed that family economic conditions influenced children’s mental health through primary caregivers’ emotional well-being, hostile parenting styles, and family communication. Notably, hostile parenting and poor family communication were the strongest mediators across all three psychological outcomes, consistently exhibiting significant associations with loneliness, depression, and internet addiction. The findings highlight the crucial role of parenting styles and poor family communication in shaping children’s psychological health, emphasizing the need for interventions that enhance family communication and promote supportive parenting. Addressing caregiver emotional well-being and adjusting parental expectations may serve as effective strategies for improving mental health outcomes among rural youth.

The global prevalence and burden of anxiety disorders (ADs) are increasing. However, findings on the acute effects of air pollution on ADs remain inconclusive. We evaluated the effects of short-term exposure to ambient air pollutants, including fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), nitrogen dioxide (NO₂), carbon monoxide (CO), sulfur dioxide (SO₂), and ozone (O₃), on daily hospital visits for ADs. A generalized additive model was used to perform a time-series analysis on data from a Southern China city’s medical insurance system between 1 March 2021, and 31 July 2023. Although the daily levels of most pollutants (PM₁₀, SO₂, CO, NO₂ and O₃) were consistently below China and WHO’s Ambient Air-Quality Standards, significant associations were observed between daily hospital visits for ADs and all six air pollutants. Each interquartile range increase in concentrations resulted in the largest odds ratios of 1.08 (95% CI: 1.01, 1.16) at lag1 for PM_2.5, 1.19 (95% CI: 1.05, 1.34) at lag07 for NO₂, 1.14 (95% CI: 1.05, 1.23) at lag02 for CO, 1.12 (95% CI: 1.01, 1.25) at lag07 for PM₁₀, 1.06 (95% CI: 1.01, 1.12) at lag7 for SO₂ and 1.08 (95% CI: 1.01, 1.15) at lag7 for O₃, respectively. The effects of NO₂ and CO remained robust across subgroup analyses and sensitivity analyses. Females and middle-aged individuals showed stronger associations than other subgroups. The findings underscore the necessity for public health efforts to alleviate the impact of air pollution on mental health, even in low-concentration settings.

Multi-omics molecular subtyping is complicated by small cohorts, heterogeneous data layers, and sensitivity to data splitting and feature selection. We developed multi-channel attention-based omics fusion (MCOF), an end-to-end framework that combines omics-specific projection, one-dimensional convolution, and integrated gradients for candidate biomarker prioritization. MCOF was evaluated against six baselines using five repeated stratified holdouts across four multi-omics datasets. In the restricted four-class breast invasive carcinoma task, MCOF achieved the highest observed mean for all five metrics, including an accuracy of 0.900 ± 0.022 and a macro-averaged area under the receiver operating characteristic curve (macro-AUC) of 0.982 ± 0.003. In the restricted three-class stomach adenocarcinoma task, it achieved the highest observed macro-F1 (0.786 ± 0.035), macro-AUC (0.934 ± 0.020), and weighted-AUC (0.930 ± 0.016), whereas support vector machines had a slightly higher accuracy and weighted F1. It also achieved the highest or near-highest AUC on the two non-cancer datasets. Compared with MOGONET, MCOF used 30.6% fewer trainable parameters on both cancer tasks. Sensitivity analyses indicated that the contribution of multi-omics fusion varied by dataset and metric. Excluding the 28 prediction analysis of microarray 50 (PAM50) genes present in the retained BRCA RNA panel produced only a modest reduction in the macro-AUC, from 0.982 ± 0.003 to 0.978 ± 0.004. Implementation tests across 20 trained checkpoints detected no batch-order effect or cross-sample gradient, while the IG rankings were moderately stable across repeated holdouts and numerically consistent across class-weighting schemes and 25–100 integration steps. MCOF provides a practical framework for multi-omics classification and candidate prioritization.