Parental care shapes anxiety-like behavior, oxytocin, social interaction, and ethanol sensitivity in adolescent C57BL/6J mice.

Assessing vision and autism spectrum disorder-relevant social interaction phenotypes in Dscam mice.

The relation of depressive symptoms to social-emotional expertise in adults.

Neurofibromatosis type 1 (NF1) is a genetic condition presenting with variable symptomatology, however most individuals will demonstrate cognitive and behavioural difficulties, including autism. Using a heterozygous germline knockout mouse model of NF1 (Nf1 +/-), we performed in-depth behavioural evaluations encompassing learning and memory, stereotypy, social interaction, anxiety- and depression-like behaviour. Anatomical and functional studies of the brain and gastrointestinal tract were followed by the first investigation of gut microbiota composition (via full-length 16S rRNA sequencing) in a Nf1 +/- mouse model. The cognitive and autism-like behavioural phenotype seen in Nf1 +/- mice was accompanied by a striking increase in relative brain size which is highly relevant to clinical NF1. Furthermore, brain size was correlated with behaviour, supporting a potential mechanistic link. Nf1 +/- mice showed significant alterations in gut microbiota composition vs. Nf1 +/+ wild-type controls, with males additionally showing significant changes to species abundance of the Clostridium and Blautia genera, and the Lachnospiraceae family, findings which partially overlap with those in preclinical and clinical autism. Composition of associated functional pathways was not globally altered, however +/- mice showed significant changes in a pyrimidine deoxynucleotide biosynthesis pathway. In male Nf1 +/- mice, we also identified a genotype-specific host-microbial signature, pointing towards a mechanistic link between gut microbiome composition and brain size. These findings significantly expand our understanding of brain and behavioural abnormalities in this preclinical model of NF1 and, importantly, have uncovered the gut microbiome as a highly promising new area of research and a potential therapeutic target for these symptom clusters.

Stress can cause or exacerbate psychiatric illness, and effects on the transcription factor CREB within the nucleus accumbens (NAc) are critically involved. In rodents, stress-induced activation of NAc CREB produces elevations in dynorphin (DYN), an endogenous opioid expressed in dopamine D1-receptor (D1R)-expressing medium spiny neurons (MSNs). In turn, elevated DYN signaling produces features of mood and anxiety disorders via actions at kappa-opioid receptors (KORs). Although individual differences in stress sensitivity have been described-with some appearing susceptible and others resilient-the contribution of NAc DYN to these phenotypes is unclear. Here we examined relationships between social behavior and DYN in D1R-expressing MSNs in mice exposed to chronic social defeat stress (CSDS). We used quantitative (q)RNAscope to assess co-expression of genes encoding CREB ( Creb1 ), D1Rs ( Drd1 ), and DYN ( Pdyn ) within the NAc. To leverage individual variability, we performed regression analyses across all mice, revealing negative correlations between social interaction behavior and expression of Drd1 and Pdyn , linking higher social avoidance with higher expression of these genes. There was no correlation with Creb1 , suggesting stress-induced elevations in Pdyn depend on CREB activation (phosphorylation). These findings suggest that stress-induced elevations in D1R-associated DYN signaling within the NAc is a biomarker of susceptibility.

Autism spectrum disorder (ASD) is a neurodevelopmental disorder distinguished by an extensive range of symptoms, including reduced social interaction, communication difficulties and tiresome behaviors. Early detection of ASD is important because it allows for timely intervention, which significantly improves developmental, behavioral, and communicative outcomes in children. Early diagnosis of autism spectrum disorder (ASD) is critical for effective intervention, and this system aims to provide a non-invasive, objective screening method. The proposed project presents an intelligent and automated system for early autism detection using facial image analysis, addressing the growing need for accessible and accurate diagnostic support tools. Traditional diagnostic methods rely heavily on behavioral assessments, which are time-consuming, subjective, and require expert intervention, highlighting the necessity for a reliable AI-based solution. In this work, a transfer learning approach is employed using Residual Neural Network with 50 layers (ResNet50) to extract deep and discriminative facial features from input images. These features are then utilized to train multiple machine learning classifiers, including Gaussian Naïve Bayes (GNB), Decision Tree Classifier (DTC), and the proposed hybrid model DeepRes-FusionRFC, which combines ResNet50-based feature extraction with Random Forest Classifier (RFC) for enhanced performance. The system follows a structured pipeline comprising dataset acquisition, preprocessing, feature extraction, model training, evaluation, and real-time prediction within a secure graphical user interface (GUI) environment with role-based access control. Experimental results demonstrate that while GNB and DTC provide moderate classification performance, the DeepRes-FusionRFC model significantly outperforms them in terms of accuracy, precision, recall, and F1-score, indicating its robustness and effectiveness in capturing complex facial patterns associated with autism. The integration of deep learning with ensemble machine learning not only improves prediction accuracy but also ensures scalability and efficiency. The proposed system offers a reliable, user-friendly, and high-performance solution for early autism detection, contributing to the advancement of AI-driven healthcare diagnostics.

Paternal high molecular weight poly I:C administration alters the sperm small non-coding RNA profile and offspring brain development and behavioural phenotype.

The rapid development of digital technology has significantly increased gadget usage among early childhood children, particularly following the COVID-19 pandemic. This phenomenon requires active parental involvement to ensure that digital exposure does not negatively affect child development. This study aims to analyze parental roles in controlling gadget usage and its impact on early childhood development at PAUD Al-Kamal Jampangtengah, Sukabumi Regency. A descriptive qualitative approach was employed, with data collected through in-depth interviews, observations, and documentation. Participants consisted of parents and teachers selected through purposive sampling. Data were analyzed using the interactive model of Miles and Huberman, including data reduction, data display, and conclusion drawing, with validity ensured through source and technique triangulation. The findings indicate that parental control strategies include time limitation, active mediation, content selection, and total restriction. Active mediation and selective educational content were found to positively influence children's social, emotional, language, cognitive, and behavioral development. In contrast, uncontrolled usage or restrictive prohibition without communication tends to result in negative responses, including reduced social interaction and dependency behaviors. The study concludes that the effectiveness of gadget control is not solely determined by screen time duration but largely depends on the quality of parental mediation and interaction during digital engagement.

ABSTRACT Adaptability is frequently discussed in terms of adjusting to changing environments or situations; however, the capacity to adapt one's social interaction behavior, known as behavioral adaptability (BA), in response to different partners may be equally critical for success in social, organizational, and leadership contexts. In two online experiments (aggregated N = 662), participants were randomly assigned to low‐, equal‐, or high‐power conditions and interacted with targets who spoke either quickly or slowly. We measured speech tempo (articulation rate) convergence as a proxy for BA. Mixed‐effects modeling revealed no main effect of power on BA. However, a significant interaction was found, indicating that low‐power participants adapted more toward fast‐speaking targets compared to those in equal or high‐power positions. A post‐hoc study showed that fast speech signals higher status and rank, potentially explaining the selective adaptability of individuals with low power. Our findings show the role of power dynamics, rank, and nonverbal cues in social interactions, advancing research on interpersonal communication behavior in hierarchical settings.

Paternal anabolic-androgenic steroid exposure promotes autism-like behavior in adult mouse offspring of both sexes.

Obesity is a chronic disease caused by the accumulation of cholesterol, which often requires long-term management strategies, such as dietary changes, increased physical activity, and psychological support. Obesity associated neurobehavioral disorders are a growing global health concern, emphasizing the need for innovative therapeutic strategies. Our study evaluates the therapeutic efficacy of (Z)-1-(furan-2-yl)-N-(4-(2-nitrophenyl)-6-(p-tolyl)pyrimidin-2-yl)methanimine referred as BN3 derivative, in treating high-fat diet-induced metabolic and behavioral dysfunctions in a zebrafish model. The research focused on reducing oxidative stress, lipid accumulation, and neurobehavioral deficits, which are closely linked to obesity-related metabolic stress. In this study, zebrafish were divided into five separate experimental groups: control group, model of obesity caused by high-fat diets, BN3 (50µM and 100µM), and Positive Control (PC) Group treated with Lovastatin 100µM. Initially, fish were fed a high-fat diet for 14days and followed by 30days of exercise and simultaneously administering BN3treatments via oral gavage. Assessment of biochemical, histopathology, gene expression, and behavioral were carried out. The results indicated that BN3 treatment significantly decreased oxidative stress levels by enhancing the activity of four antioxidant enzymes (Superoxide Dismutase, Catalase, Glutathione Transferase and Glutathione Peroxidase). BN3 also decreased lipid accumulation as evidenced through histological staining analysis, and total cholesterol estimation. BN3 enhanced locomotion, social interaction, and exploratory behaviors, and reduced anxiety, with the 100µM treatment group exhibiting the same results as the PC. Gene expression analysis indicates that BN3 is modulating pparγ, fas, pik3cd, src-3, and bdnf pathways (metabolic and neuroinflammation pathways). BN3 impacted these multiple metabolic and neurobehavioral impairments associated with obesity through a multisite treatment approach. BN3 demonstrates significant therapeutic potential, assuring further studies to explore its long-term safety, pharmacokinetics, and translational application in managing obesity and related disorders.

The interplay between cAMP/PKA and neuroplasticity involves intricate signaling pathways that are crucial for brain function and are frequently disrupted in schizophrenia. This study sought to examine the potential neuroprotective effects of formoterol on ketamine-induced schizophrenia-like behaviors with emphasis on several PKA downstream crucial targets for schizophrenia management. Male mice were injected with ketamine (20 mg/kg/day, i.p) for 14 days. From day 8, animals were treated with formoterol (100 μg/kg) with or without, the PKA inhibitor, H89 (0.05 mg/kg). Behavioral endpoints were assessed with n = 15 per group, and following sacrifice animals were stratified into subsets for downstream analyses: histopathology (n = 3), biochemical/neurochemical assays (n = 6), and molecular profiling including western blotting (n = 3) and qRT‑PCR (n = 6). Formoterol improved ketamine- induced anxiety, impaired social interaction and anhedonic behavior. It also restored neurochemical balance and enhanced learning and memory functions. Formoterol attenuated neuro-inflammation and oxidative stress, and modulated synaptic plasticity. Formoterol-induced neuroprotection could be attributed to its boosting action on cAMP/PKA/BDNF signaling to promote RIM-1α and PEA-3 gene expression. Consequently, it upregulated glutamate NMDA receptor subunits namely; GluN2A, and GluN2B and augmented expression of vital synaptic plasticity regulators; kalirin-7, PSD-95, synaptophysin and synapsin-2. Accordingly, formoterol is a promising candidate against schizophrenia-associated synaptic dysfunction and neurotransmitters imbalance. Formoterol neuroprotective effects were abolished upon administration of PKA inhibitor confirming that the cAMP/PKA cascade is a vital key-player in the drug favorable effects.Supplementary InformationThe online version contains supplementary material available at 10.1007/s44446-026-00079-x.

Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that significantly impacts children's physical and mental health, yet effective pharmacological treatments remain limited. The primary objective of this study was to investigate the therapeutic effects of human umbilical cord blood mesenchymal stem cells (hUC-MSCs) on ASD, evaluate the safety profile of hUC-MSCs, and elucidate their underlying mechanisms and functional roles. In this study, we utilized the offspring of pregnant mice exposed to valproic acid (VPA) as an animal model of ASD. At the beginning of 5 weeks of age, 5 × 105 hUC-MSCs were administered into the lateral ventricles to evaluate their safety profile and elucidate their potential roles and underlying mechanisms. Specifically, we first monitored the growth and overall health status of the mice following hUC-MSC treatment and assessed potential toxic effects by performing H&E staining on major organs. Second, behavioral analyses were conducted to examine changes in social interaction, repetitive and stereotyped behaviors, and anxiety-like behaviors in young mice before and after hUC-MSC intervention. Finally, the mechanisms underlying the therapeutic effects of hUC-MSCs in ASD were explored using techniques such as RT-PCR, Western blot analysis, brain tissue staining, and neuron culture experiments. Here, we demonstrate that hUC-MSCs effectively mitigate behavioral abnormalities in a VPA-induced mouse model of autism without notable adverse effects. Mechanistically, hUC-MSC treatment promotes cortical neuronal dendritic development and restores the phosphorylation levels of insulin-like growth factor 1 receptor (IGF-1R) and protein kinase B (Akt). Furthermore, mRNA expression of synaptic plasticity-associated genes GAP-43 and SYP, as well as the anti-inflammatory cytokine IL-10, was significantly upregulated, while the expression of proapoptotic genes Bax and Caspase-3, along with pro-inflammatory cytokines IL-6 and IL-1β, was markedly suppressed. These findings suggest that hUC-MSCs may exert neuroprotective effects by modulating the IGF-1/Akt signaling pathway, promoting neuronal development, reducing neuroinflammation, and inhibiting apoptosis, ultimately alleviating core ASD-like symptoms. The therapeutic benefits may stem from paracrine factors secreted by hUC-MSCs or their ability to regulate gene expression linked to neuronal development. Our study provides new insights into ASD pathogenesis and highlights the potential of hUC-MSCs as a novel stem cell-based therapy for ASD.

ABSTRACT Background Children’s cognitive development includes key skills such as creativity, verbal fluency and executive functions, essential for academic performance, social interaction and adaptive behaviour. Educational interventions that integrate cognitive and physical stimulation have shown potential in supporting these developmental processes. Purpose This study adopted a randomised controlled trial (RCT) design to examine the effects of a combined abacus training and physical exercise intervention on 82 children aged six to 12 in Andalusia, Spain. The intervention lasted 12 weeks and consisted of twice weekly sessions integrating physical activity and abacus-based cognitive tasks. The study hypothesised that: Children who participated in the combined intervention would demonstrate significantly greater improvements in creativity, executive functioning, and verbal fluency compared with children in the control group. Method Participants were randomly assigned either to receive the intervention (experimental group), or to a control group. The three outcomes of interest were evaluated as follows: Creativity – through the Creative Intelligence Test (CREA); executive functions – through the Behaviour Rating Inventory of Executive Function (BRIEF); and verbal fluency – through phonological and semantic subtests of the Wechsler Intelligence Scale for Children – Fifth Edition (WISC-V). Findings Children in the experimental group showed statistically significant improvements compared with their baseline performance and, in several outcomes, compared with the control group. Improvements were observed in creativity; executive functioning domains such as behavioural regulation, planning, and working memory; and phonological and semantic verbal fluency. Conclusions These findings provide promising evidence that combining physical exercise with abacus-based cognitive training may support the development of key cognitive skills in children. Further research involving larger samples, longer interventions, and longitudinal follow-up is recommended to confirm and extend these findings.

Children with Autism Spectrum Disorder (ASD) frequently experience difficulties in recognizing, expressing, and regulating their emotions, which significantly affects their social interaction and communication skills. This study aims to examine the importance of understanding appropriate emotional regulation strategies to support children with ASD in their socio-emotional development. The method employed in this research is a systematic literature review of relevant empirical studies and scholarly articles. The findings indicate that appropriate emotional regulation interventions contribute substantially to improving communication skills, social interaction, and adaptive behavior among children with ASD. Strategies such as visual schedules, positive reinforcement, occupational therapy, social stories, and sensory-based interventions have been shown to assist children in identifying and expressing emotions in a constructive manner. Furthermore, consistent emotional support from parents, educators, and caregivers plays a critical role in fostering optimal socio-emotional development. The study highlights that emotional regulation constitutes a foundational life skill that enhances social competence, academic engagement, independence, and overall quality of life for children with ASD.

Religiosity and empathy have been identified as two key variables that may significantly influence an individual's social justice attitude and behavior. Despite their significance, studies addressing the relationships between these variables are rare. Thus, the present study aims to explore the associations between interpersonal empathy, instrumental religiosity, and social justice values using the conceptual framework of motivated information processing theory. Structural equation modeling (SEM) was employed to assess the hypothetical relationships between these variables. The findings indicate that personal instrumental religiosity, social interaction, and cognitive behavior are positively associated with the level of adherence to both instrumental and social terminal values of social justice. In contrast, social instrumental religiosity exerts only a direct influence on the instrumental values of social justice. This study also revealed the role of social interaction and cognitive behavior as mediators between personal instrumental religiosity and the instrumental and social terminal values of social justice. The findings underscore the imperative for researchers to devise educational programs that acknowledge and promote the significance of religion and empathy in fostering a more equitable and compassionate society.

Wearable video cameras may offer a feasible approach to assess the contexts of screen use (eg, screen content and co-use) among preschool-aged children. The objective of this study was to assess the contexts of screen use among preschool-aged children using wearable video cameras. Children aged 2 to 5 years from Melbourne, Australia, wore a video camera for 1 day in the home environment during May 2023. One researcher manually coded video footage second by second; 15% was double coded for reliability. Coding included device type, screen activity, screen content classified using Common Sense Media ratings, streaming service, setting, social interaction, screen multitasking, and concurrent behaviors. A total of 37,944 seconds (10.5 hours) of video camera footage from 8 children were identified and coded as screen use, equating to 21.8% (37,944/174,290) of total camera wear time (range 0.3%-74.0%). Screen use was predominately characterized by program viewing (n=37,461, 98.7% seconds) on televisions (n=34,192, 90.1% seconds) in the lounge room (n=33,710, 88.8% seconds). Programs scored low for educational value (mean 1.7, SD 1.4 of 5 stars), and approximately one-third (3/9, 33.3%) of programs were classified as appropriate for an age older than that of the children in this sample. Screen multitasking was rare (n=46, 0.1% of seconds), and coviewing occurred in approximately one-third of all screen use (n=11,010, 29%). Contexts considered beneficial for development (eg, educational and age-appropriate content) were infrequently observed. This suggests that interventions to equip parents with practical strategies to identify genuine educational content and recognize and avoid age-inappropriate content are warranted. However, our small sample size limits generalizability.

This current article examines the relationship between Autism Spectrum Disorder (ASD) and school avoidance, focusing on the theoretical and practical challenges involved. ASD, a neurodevelopmental condition, presents significant challenges in social interaction, communication, and restricted and repetitive behaviors, which can hinder educational engagement. School avoidance, characterized by elevated absenteeism and emotional distress, emerges as a critical issue for many students with ASD. By exploring historical foundations, diagnostic classification systems, and the core characteristics and challenges of ASD, this analysis highlights factors that contribute to school avoidance. The distinction between school avoidance and truancy is emphasized, underscoring the importance of tailored interventions that address sensory sensitivities, emotional wellbeing, and the need for structured and predictable environments. Practical strategies for inclusion, teacher-student relationships, school-home collaboration, and interdisciplinary cooperation are also discussed, offering a framework for creating supportive educational settings. Ultimately, this work connects theoretical insights with actionable practices to promote engagement, reduce stress, and support the development of students with ASD.

Abstract Background In recent years, mental health problems among college students have shown multi-factor characteristics, including concealment and dynamics, with a continuous rise in risks such as anxiety, depression, and academic burnout. Traditional monitoring methods relying on self-assessment scales and regular interviews are insufficient in terms of timeliness and objectivity, making it difficult to identify high-risk individuals in a timely manner. However, with the development of artificial intelligence and behavioral computing technologies, psychological state analysis based on multi-source behavioral data has gradually become a research hotspot. Existing research shows that learning, social interaction, daily routines, and internet usage behaviors are closely related to mental health, but these studies mostly remain at the level of single data sources or static assessments, lacking a practical integrated early warning and intervention model. Therefore, this study proposes an AI-based behavioral analysis-based early warning and tiered intervention model for college students' mental health. Through multi-source behavioral data fusion and intelligent modeling, it aims to achieve early identification and precise intervention of psychological risks. Methods This study used 1268 students from a comprehensive university as subjects. For 12 consecutive months, logs from the learning management system, campus card consumption and access records, dormitory daily routines, and anonymized internet usage characteristics were collected. Standard psychological scales were also obtained as a reference. Behavioral feature vectors were constructed using feature engineering and temporal windows. A multimodal neural network incorporating attention mechanisms was introduced to predict psychological risk levels, and the contribution of key behavioral features was analyzed using the SHapley Additive exPlanations (SHAP). Based on this, a three-tiered early warning system of "low-medium-high risk" was established, and a differentiated intervention process was designed. Results The results showed that the research model performed stably in psychological risk identification, with a prediction accuracy of 86.7% and an AUC of 0.912, outperforming logistic regression and single-feature models (p<.01). The recall rate for high-risk individuals was 0.84, and the false positive rate was 12.5%. Feature analysis showed that disrupted nighttime sleep patterns, fluctuating learning behavior, and decreased social interaction were the most discriminative indicators. Longitudinal results showed that the proportion of high-risk students whose scores deteriorated within 3 months was 41.3%, significantly higher than the 8.6% in the low-risk group. Intervention assessment results indicated that stratified intervention reduced the average score of medium- and high-risk students by 21.4% within 6 months, and the intervention effectiveness increased by approximately 18%. Discussion In summary, the AI-based behavioral analysis-based early warning model can achieve early identification and dynamic tracking of psychological risks without increasing the burden on students, demonstrating high practical value. The tiered intervention model helps improve the efficiency of psychological service resource allocation and provides data-driven support for university mental health management. In the future, wearable physiological signals and multi-center data can be combined to further enhance the model's generalization ability, and human-machine collaborative intelligent intervention models can be explored within the framework of privacy and ethics.

Abstract Background Social media has become an important scene for modern people's daily interaction, and the correlation between its human-computer interaction behavior characteristics and users' psychological health, especially loneliness, has received widespread attention. Previous cross-sectional studies have shown that passive browsing and social comparison may exacerbate feelings of loneliness, while active and supportive interaction may alleviate loneliness. However, there is still a lack of long-term tracking evidence on how these behavioral characteristics change over time and are dynamically associated with feelings of loneliness. The study adopts a longitudinal design to systematically examine the temporal relationship between the duration of social media use, content interaction behavior, language style, and changes in loneliness, providing longitudinal evidence for a deeper understanding of the psychological impact of social media use. Methods The study adopted a one-year longitudinal tracking design with three waves, including T1, T2, T3 (with a 2-month interval between each group), and four groups with a 6-month interval. A total of 320 active social media users aged 18-35 were recruited through online platforms, and 298 valid samples were ultimately included, with an average age of 26.4 years and 58% being females. Obtain objective behavioral data through backend authorization, including daily average usage duration, frequency of interaction types, and language style extraction of post texts voluntarily provided by users through language analysis tools (Linguistic Inquiry and Word Count, LIWC). Loneliness was measured using the UCLA Loneliness Scale. Statistical analysis was conducted using SPSS 26.0 and Mplus 8.3, including correlation analysis, cross lagged model testing of temporal predictive relationships between variables, and control for covariates such as gender, age, and offline social support. Results Descriptive statistics show that the average daily social media usage time of users is 2.8 hours (SD = 1.4), with likes being the most frequent interactive behavior. Cross lag model analysis revealed. (1) The difference in usage duration between T1 and T2 significantly positively predicts loneliness (β = 0.18, p<.01), but T2 loneliness cannot predict changes in T3 usage duration. (2) Those with higher frequency of content interaction T1 comments and private message interactions have significantly lower T2 loneliness (β = -0.15, p<.05), and lower T2 loneliness further predicts higher T3 active interaction behavior (β = 0.12, p<.05), showing a bidirectional protective association. (3) The higher frequency of first person singular words in language style T1 post texts significantly positively predicts T2 loneliness (β = 0.22, p<.01), while the proportion of positive emotion words negatively predicts (β = -0.14, p<.05). The proportion of negative emotion words is correlated with loneliness in the cross section, but no longitudinal predictive effect was shown. All models controlled for covariates and had good fit indicators. Discussion Research has shown that there is a dynamic and differentiated correlation between social media human-computer interaction behavior characteristics and feelings of loneliness. Long term passive use and high self attention expression styles may be risk factors for loneliness, while active, interpersonal oriented interactions may buffer loneliness and form a positive cycle. This suggests that simply reducing usage time may not be the best intervention direction, but should guide users to shift from passive browsing to meaningful active interaction, and pay attention to their self attention tendencies in online expression. Future research can combine experimental interventions to train users to adjust their interaction patterns and language styles. Funding No. 2023ZWY005.