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Shared reading is associated with fewer emotional/behavioral problems and better prosocial behavior in preschool children: a cross-sectional study in western China

BackgroundThe home literacy environment, particularly shared reading, plays a critical role in preschool children’s cognitive and socioemotional development. However, its associations with emotional and behavioral problems remain underexplored in large-scale studies. This study examined the relationship between shared reading and emotional/behavioral problems as well as prosocial behavior in preschool children.MethodsA cross-sectional study was conducted using stratified cluster sampling across 189 kindergartens in a major city in western China. A total of 21,366 parent-child pairs were included. Shared reading was assessed with the reading subscale of the StimQ-P (score range 0–22), which evaluates quantity, diversity of concepts and content, and interactivity quality. Emotional and behavioral problems were measured using the parent-reported Strengths and Difficulties Questionnaire (SDQ). Multivariate logistic regression and generalized additive models were employed to examine associations, adjusting for child age, gender, parental socioeconomic factors, lifestyle variables, and parental mental health (CES-D).ResultsHigher shared reading scores were significantly associated with lower odds of emotional/behavioral problems (adjusted OR = 0.96 per point increase, 95% CI: 0.95–0.97, P < 0.0001) and higher odds of adequate prosocial behavior (adjusted OR = 1.09, 95% CI: 1.08–1.10, P < 0.0001) in fully adjusted models. All four dimensions of shared reading showed independent associations. Nonlinear analyses revealed threshold effects, with associations becoming stronger above approximately 18 points for total difficulties and 15 points for prosocial behavior. These associations were largely consistent across subgroups after correction for multiple testing.ConclusionIn this large cross-sectional study conducted in western China, higher levels of shared reading were associated with lower odds of emotional/behavioral problems and higher odds of prosocial behaviors among preschool children. The results suggest possible threshold patterns in these associations. However, given the cross-sectional nature of the study, causality cannot be established.
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Dynamic changes of gut microbiota during progression of three Alzheimer’s disease mice models

IntroductionAlzheimer’s disease (AD) is an age-related and progressive neurodegenerative disorder characterized by cognitive impairment and irreversible neuronal degeneration, affecting approximately 55 million individuals worldwide. Despite extensive research efforts, the underlying pathogenic mechanisms of AD remain incompletely understood, and effective therapeutic strategies for preventing or delaying disease progression are still lacking. Increasing evidence suggests that the microbiota-gut-brain axis plays an important role in neurodegenerative diseases, including AD. However, the dynamic alterations of gut microbiota during AD progression across different transgenic mouse models remain poorly characterized.MethodsIn the present study, we investigated age-dependent changes in gut microbiota composition in three commonly used AD mouse models, including APP/PS1, 3xTg, and 5xFAD mice, using 16S rRNA gene sequencing. Fecal samples were collected longitudinally at 2, 4, 6, and 8 months of age to evaluate microbial diversity, community structure, and differential bacterial taxa during aging and disease progression.ResultsOur results demonstrated distinct and model-dependent alterations in gut microbiota composition across different stages of AD progression. Significant changes in microbial diversity and bacterial community structure were observed among the three AD mouse models and wild-type controls. In particular, dynamic alterations in Verrucomicrobiota, Proteobacteria, and Actinobacteriota were consistently identified during aging in AD mice. In addition, β-diversity, Linear discriminant analysis effect size (LEfSe), and correlation network analyses further revealed differential microbial signatures associated with different AD mouse models and age stages.DiscussionOverall, our findings provide additional evidence that gut microbiota composition undergoes dynamic alterations during aging in multiple AD mouse models and may be associated with AD-related progression. This study may contribute to a better understanding of microbiota-associated changes during AD development and provide a basis for future mechanistic studies targeting the microbiota-gutbrain axis in AD.
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Applications of machine learning algorithms to detect digital addiction: a meta-analysis

Digital addiction (DA) has emerged as a significant global concern, yet traditional diagnostic methods relying on self-report questionnaires face subjective bias and threshold inconsistencies. Recent advances in machine learning (ML) offer promising alternatives for automated DA detection. This study conducted a systematic meta-analysis of 64 eligible studies (75 independent datasets; N = 165,624), employing both single-group proportion and bivariate diagnostic test accuracy (DTA) models. The pooled classification accuracy was 0.87 (95% CI [0.85, 0.90]), and the DTA framework yielded a robust AUC of 0.92, with balanced sensitivity and specificity (both 0.86). Subgroup analyses showed high accuracy across subtypes, particularly for internet (0.90) and social media addiction (0.86). Accuracy was comparable between survey-based and physiological data, though physiological markers demonstrated superior specificity (0.90). These findings underscore the potential of ML-driven tools as scalable screening instruments while emphasizing the need for representative sampling and standardized diagnostic criteria to advance digital mental health practice.
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Gender-specific symptom outcomes on cariprazine treatment: a 12-month naturalistic longitudinal follow-up study in schizophrenia

IntroductionA growing body of literature is focusing on third-generation antipsychotics and their unique characteristics, but few studies have examined gender as a crucial factor in response profiles. The present study aims to address this gap by analyzing the outcomes of 12-month naturalistic treatment with cariprazine to elucidate changes in specific psychopathological domains between men and women.MethodsThe present 12-month longitudinal naturalistic study involved a sample of individuals diagnosed with schizophrenia according to the DSM-5-TR treated with cariprazine at the outpatients’ psychiatric services of a major university and community hospitals in Italy. The assessments conducted included sociodemographic data, the Structured Clinical Interview for the DSM-5 (SCID-5), and the Positive and Negative Symptom Scale (PANSS) Total and Subscale scores, as well as the PANSS-derived Marder factors. The PANSS was administered at three time points: before starting the treatment with cariprazine (T0), after 6 months (T1), and after 12 months (T2).ResultsFifteen male and 17 female subjects were assessed at the three time points. The mean dose of cariprazine was 4.2 ± 1.3 mg for men and 4.0 ± 1.5 mg for women. Both genders exhibited improvements in all PANSS subscale symptoms after 6 and 12 months of cariprazine treatment compared to the baseline, with the only exception of the Uncontrolled hostility/excitement Marder factor among men. Progressive improvements through time points in symptom subscales were found in both sexes, reaching numerical differences in every PANSS subscale in both sexes at T2. Gender specifc response profiles emerged after 6 and 12 months of treatment in the PANSS subscales and items in men and women.DiscussionCariprazine exhibited significant efficacy in both sexes, with no significant differences between men and women despite a gender specific response profile emerged. Additional studies are needed to further investigate the efficacy profile and long-term outcome of cariprazine treatment by gender.
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Long‑Range Gene Networks Uncover 641 New Schizophrenia‑Associated Genes

Schizophrenia’s genetic landscape just expanded dramatically. A new study in Nature Genetics identifies 641 previously unrecognized genes associated with schizophrenia, thanks to a modeling framework that captures how distant genetic variants regulate gene expression through co‑expression networks. The work reframes schizophrenia not as a collection of isolated genetic hits, but as a disorder shaped by long‑range regulatory relationships across the brain. The study is titled, “Co‑expression‑based models improve eQTL predictions for transcriptome‑wide association studies and highlight new schizophrenia‑associated genes.”

The research team, led by Giulio Pergola, PhD, at the Lieber Institute for Brain Development (LIBD), developed two trans‑aware predictive models—INGENE and MODULE—that quantify how variants far from a gene influence its expression through co‑regulated partners. Traditional transcriptome‑wide association studies (TWAS) focus almost exclusively on cis‑expression quantitative trait loci (cis–eQTLs), variants within ±1 Mb of a gene. But as the paper noted, “Most transcriptome‑wide association approaches primarily model local (cis) genetic effects, leaving much of gene regulation unexplained.” By contrast, the new models incorporate distal (trans) regulatory effects, capturing regulatory relationships that behave more like social networks than neighborhood blocks.

Using RNA‑seq data from six human post‑mortem brain regions and genetic data from more than 102,000 individuals, the team integrated cis‑based predictors (CIS, EpiXcan) with their new trans‑based frameworks. The combined approach improved gene‑expression prediction for 18,744 genes, and when applied to Psychiatric Genomics Consortium (PGC3) datasets, it identified 766 schizophrenia‑associated genes, including 641 not previously detected by TWAS.

Pergola said the field has been “looking for the light under the lamppost, focusing only on genes close to disease‑associated DNA variants.” By illuminating long‑range interactions, he explained, “we’ve essentially turned on lights across the entire neighborhood, revealing how distant genetic variants coordinate to build the genetic basis of schizophrenia.”

The findings converge on pathways involved in glutamate signaling, neuronal communication, immune processes, and neurodevelopment—biological systems repeatedly implicated in psychiatric risk. MODULE‑derived trans‑single nucleotide polymorphisms (SNPs) showed particularly strong enrichment for schizophrenia‑associated variants, and many overlapped with cis‑eQTLs for transcription factors such as GATAD2A, RERE, IRF3, and SP4, all previously prioritized in schizophrenia GWAS.

Daniel Weinberger, MD, CEO and director of LIBD, emphasized the shift in perspective: “Schizophrenia risk isn’t just about individual genes acting one after another—it’s about how networks of genes work together. Understanding these coordinated genetic programs brings us closer to precision psychiatry.”

By demonstrating that trans‑regulatory architecture is both detectable and biologically meaningful, the study provides a roadmap for expanding TWAS beyond local effects. It also underscores the importance of integrating multi‑region brain transcriptomics with large‑scale genetic cohorts to reveal disease‑relevant regulatory relationships.

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