Ning Hao | PubMed Health

PubMed2026

Family communication patterns and adolescent depressive symptoms: sequential mediating effects of hedonic capacity and anger expression.

OBJECTIVE: Depression is increasingly prevalent among adolescents, highlighting the need to explore its underlying developmental mechanisms to inform effective prevention. This study examined how different family communication patterns longitudinally impact adolescent depressive symptoms, with a focus on the mediating roles of diminished hedonic capacity and anger expression. METHOD: A total of 729 Chinese adolescents from five high schools participated in this two-wave longitudinal study over a six-month period. Participants completed the Family Communication Patterns Scale (FCPS), the Children's Depression Inventory (CDI), the Anhedonia Scale for Adolescents (ASA), and the Children's Inventory of Anger (ChIA). Mediation analyses were conducted using Hayes' PROCESS macro, alongside cross-lagged panel analyses to explore longitudinal effect. RESULTS: Cross-sectional analyses indicated that diminished hedonic capacity and anger experience sequentially mediated the relationship between family communication patterns and adolescent depressive symptoms. Specifically, this mediating effect existed for conversation orientation in junior high school students and female senior high school students, as well as for conformity orientation among female junior and senior high school students. In addition, longitudinal analyses revealed that diminished hedonic capacity mediated the associations at Wave 2. CONCLUSION: Our findings suggest that enhanced hedonic capacity and reduced anger expression may play a key role in the relationship between family communication patterns and adolescent depression, and hedonic capacity should be prioritized in practical interventions. These results support the potential value of interventions that promote positive family communication patterns as a means of mitigating adolescent depression, particularly for families with daughters.

anger expression↗

PubMed2025

Integrating RNA sequencing with deep learning-based metabolic toxicity prediction: A new perspective on screening prioritized liquid crystal monomers.

Nearly 99 % of liquid crystal monomers (LCMs) toxicological data remains gaps, especially to aquatic organisms. Herein, this study proposes a rapid and high-throughput screening method for identifying priority LCMs in natural water. Using six fluorinated LCMs (LCMsF) with significant enrichment characteristics in zebrafish as examples, RNA sequencing revealed that LCMsF-induced metabolic disturbances are predominant, including 28 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway abnormalities attributed to 498 differentially expressed genes. Notably, the intricate sequencing process resulted in the inability to rapid identify additional 857 LCMsF that may induce metabolic disturbances. To address this, LCMsT-MTP, a predictive deep learning model based on RNA sequencing, was developed. This model integrates a comprehensive representation of LCMsF structures and metabolic toxicity target sequences. LCMsT-MTP improves upon traditional methods that are limited to single targets and mechanisms by facilitating the simultaneous identification of 21 metabolic toxicities induced by LCMsF. In addition, the LCMsT-MTP model was further applied to non-fluorinated LCMs (LCMsNone F) that satisfy the applicability domains test. Accordingly, a metabolic toxicity priority list of LCMs was proposed, with ∼95 % of LCMs classified as high or medium risk. Priority list validation by molecular dynamics confirmed that the interactions of LCMsF/LCMsNone F and metabolic toxicity targets in representative KEGG pathways were distinct.

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