Tag: News Approved

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Effects of maternal autoantibody exposure at the cellular level: implications for the developing brain

Maternal autoimmunity during pregnancy can have significant impacts on fetal development. In the case of maternal autoantibody-related autism (MARA), autoantibodies (aABs) reactive to fetal brain proteins in the circulation of pregnant mothers have been strongly associated with an offspring autism diagnosis. Clinical studies have identified the protein targets for the MARA aABs as well as maternal aAB patterns that are more commonly observed in mothers of children with autism, but not in mothers of neurotypical children. Additionally, rodent studies show that gestational exposure to MARA aABs alters offspring brain development and behavior. How MARA aABs cause changes at the cellular level is an area of active investigation. In this review, we discuss the potential mechanisms by which MARA aABs interact with key neuronal and non-neuronal cells as well as their target autoantigens in the developing brain. Additionally, we highlight the potential impacts of these interactions on the developmental trajectories of targeted cells, resulting in an autism diagnosis.
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Psychometric properties of the Chinese version of the school refusal assessment scale–revised in a clinical sample of adolescents and their caregivers with depressive disorders

School refusal behavior (SRB) is a prevalent and functionally heterogeneous problem among children and adolescents that can lead to serious academic, social, and psychological consequences. The School Refusal Assessment Scale–Revised (SRAS-R) is the most widely used instrument for identifying the functional motivations underlying school refusal, yet its psychometric properties have not been examined in Chinese clinical populations. The present study aimed to translate and culturally adapt the SRAS-R into Chinese and to evaluate its psychometric properties in a clinical sample of adolescents with depressive disorders. A total of 171 adolescent outpatients (age range 12–19 years; M = 15.5, SD = 1.90; 67.3% female) diagnosed with DSM-5 depressive disorders and meeting criteria for school refusal behavior completed both child and parent versions of the Chinese SRAS-R. Confirmatory factor analysis (CFA) using diagonally weighted least squares estimation was conducted. After removing Items 20 and 24, the four-factor model yielded strong CFI and RMSEA values for both parent (CFI = 0.99, RMSEA = 0.021, SRMR = 0.094) and child reports (CFI = 0.98, RMSEA = 0.028, SRMR = 0.097), although SRMR values were marginally above the prespecified threshold. Internal consistency reliability ranged from marginal to good across subscales (Cronbach’s α: parent = 0.664–0.815; child = 0.700–0.864), with parent-reported Factor 4 showing the weakest reliability. Factor 1 (avoidance of aversive school situations) obtained the highest mean scores for both informants, consistent with the depression-related negative affectivity characteristic of this clinical sample. Cross-informant discrepancy analyses revealed that children reported significantly higher scores than parents on Factor 2 (escape from social/evaluative situations; p = .017, d = 0.18) and Factor 4 (pursuit of tangible reinforcement; p <.001, d = 0.27), suggesting that parents may underestimate internally driven motivations. Intraclass correlation coefficients indicated fair to good parent–child agreement (ICC = 0.45–0.62), with the highest agreement for Factor 1 and the lowest for Factor 4. The findings provide initial internal-structure and reliability evidence for the Chinese SRAS-R in this single-site clinical sample and underscore the need for multi-informant assessment, while future studies should examine convergent, discriminant, criterion-related, and predictive validity in more diverse samples.
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Path and Bayesian network analyses in the complex design of a well-being survey via New Zealand’s Integrated Data Infrastructure

IntroductionIn mental health research involved with sensitive features and privacy issues, using integrated data offers an efficient alternative to traditional approaches such as interviews or in−person data collection. These conventional methods frequently face logistical barriers, including low response rates among study populations. Using integrated data from multiple existing administrative and survey sources provides protection for participants and economic savings for researchers, despite being constrained by the limitations of the original data sources. Our research questions were: 1) Can we conduct path and network analyses of school absenteeism, psychosocial factors, and mental health outcomes using the integrated survey data from New Zealand’s Integrated Data Infrastructure (IDI)? and 2) How can we account for the complex design of the General Social Survey (GSS) to ensure representative inference?Materials and methodsThe study population was New Zealand youth enrolled in school, aged 15 years and older in 2018, who participated in the 2018 GSS. The study analyzed the Ministry of Education data integrated with the 2018 GSS survey data from New Zealand’s IDI. To explore the relationship between outcomes of including the WHO-5, “perceived life worthwhileness”, “perceived general health”, and predictors including school absenteeism, psychosocial factors, and family factors, quantile mixed−effects regression, path analysis, and Bayesian network (BN) analysis were used. The complex survey design was calibrated using replicated weights from the GSS, a design-based method for complex sampling, in path and regression analyses, and bootstrap resampling, in BN analysis.ResultsIn descending order from the weighted path model, overall life satisfaction (standardized path coefficient: 0.36), perceived health condition (0.34), ease in accepting cultural identity (0.12), and trust in the education system (0.07) were all significantly (positively) related to students’ mental health well-being (WHO-5). Perceived life worthwhileness correlated with the WHO-5 but was only connected to overall life satisfaction. Similar factors were identified for perceived health, with additional attributing factors, such as fear of crime in the area and the family’s overall well-being.ConclusionThe integration of Bayesian networks and path analysis offers rigorous methods for detecting complex interrelationships among integrated mental health outcomes from population data and variables from a complex survey design.
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Integrated care pathway in individuals with Long COVID: STIMULATE-ICP, a cluster-randomized, phase 3 trial

Nature Medicine, Published online: 28 July 2026; doi:10.1038/s41591-026-04552-x

This cluster-randomized phase 3 trial examining the effectiveness of integrated care pathways comparing usual care with the addition of MRI imaging (Coverscan), or a digitally enabled rehabilitation platform app, or both (Coverscan + app), or neither, found specialist Long COVID care improved fatigue response in all arms.