Objective:To explore the impact and underlying mechanisms of social support on illness uncertainty among parents of children with autism.Methods:A convenience sample of 312 parents of children with autism was recruited from the outpatient clinic of Peking University Sixth Hospital between September 2023 and January 2024. Data were collected using a general information questionnaire, the Chinese version of the Parent's Perception Uncertainty Scale (PPUS), the Social Support Scale for Families with Children with Autism, the Generalized Anxiety Disorder-7 (GAD-7), and the Questionnaire on Caregiving Issues and Service Needs of Parents of Children with Autism. Independent samples t-tests or one-way ANOVA were used to compare illness uncertainty scores across different characteristics. Pearson correlation analysis examined relationships among illness uncertainty, social support, caregiving issues and service needs, and anxiety. Chain mediation analysis was conducted using the SPSS macro PROCESS v4.1 to test the mediating roles of caregiving issues and service needs and anxiety. Results:The illness uncertainty score of the 307 valid respondents was (82.40±14.09). Mediation analysis indicated a direct effect of social support on illness uncertainty (effect value=-1.040), accounting for 72.27% of the total effect (-1.040/-1.439). A chain-mediated effect through caregiving issues and service needs and anxiety was also observed (effect value=-0.065), accounting for 4.50% of the total effect (-0.065/-1.439) .Conclusions:Parents of children with autism experience a relatively high level of illness uncertainty. Enhancing social support, addressing caregiving issues and service needs, alleviating parental anxiety may reduce their illness uncertainty.

Autism Spectrum Disorders (ASDs) are reported as a group of neurodevelopmental disorders. The structural changes of brain regions including the hippocampus were widely reported in autistic patients and mouse models with dysfunction of ASD risk genes, but the underlying mechanisms are not fully understood. Here, we report that deletion of Trio, a high-susceptibility gene of ASDs, causes a postnatal dentate gyrus (DG) hypoplasia with a zigzagged suprapyramidal blade, and the Trio-deficient mice display autism-like behaviors. The impaired morphogenesis of DG is mainly caused by disturbing the postnatal distribution of postmitotic granule cells (GCs), which further results in a migration deficit of neural progenitors. Furthermore, we reveal that Trio plays different roles in various excitatory neural cells by spatial transcriptomic sequencing, especially the role of regulating the migration of postmitotic GCs. In summary, our findings provide evidence of cellular mechanisms that Trio is involved in postnatal DG morphogenesis.
Animals ; Dentate Gyrus/metabolism* ; Mice ; Morphogenesis/physiology* ; Neurons/pathology* ; Cell Movement ; Mice, Inbred C57BL ; Autism Spectrum Disorder/pathology* ; Mice, Knockout ; Neural Stem Cells ; Male ; Neurogenesis ; Autistic Disorder/genetics*

Accurate prediction of drug-target interactions (DTIs) plays a pivotal role in drug discovery, facilitating optimization of lead compounds, drug repurposing and elucidation of drug side effects. However, traditional DTI prediction methods are often limited by incomplete biological data and insufficient representation of protein features. In this study, we proposed KG-CNNDTI, a novel knowledge graph-enhanced framework for DTI prediction, which integrates heterogeneous biological information to improve model generalizability and predictive performance. The proposed model utilized protein embeddings derived from a biomedical knowledge graph via the Node2Vec algorithm, which were further enriched with contextualized sequence representations obtained from ProteinBERT. For compound representation, multiple molecular fingerprint schemes alongside the Uni-Mol pre-trained model were evaluated. The fused representations served as inputs to both classical machine learning models and a convolutional neural network-based predictor. Experimental evaluations across benchmark datasets demonstrated that KG-CNNDTI achieved superior performance compared to state-of-the-art methods, particularly in terms of Precision, Recall, F1-Score and area under the precision-recall curve (AUPR). Ablation analysis highlighted the substantial contribution of knowledge graph-derived features. Moreover, KG-CNNDTI was employed for virtual screening of natural products against Alzheimer's disease, resulting in 40 candidate compounds. 5 were supported by literature evidence, among which 3 were further validated in vitro assays.
Alzheimer Disease/drug therapy* ; Biological Products/therapeutic use* ; Humans ; Neural Networks, Computer ; Machine Learning ; Drug Discovery/methods* ; Algorithms ; Drug Evaluation, Preclinical/methods*

The etiology of schizophrenia, bipolar disorder and major depressive disorder are complex, posing significant challenges to their diagnosis and treatment. Traditional symptom-based approaches often fall short in addressing these complexities. Precision medicine, through the integration of multi-dimensional data such as molecular genetics, peripheral biomarkers, and neuroimaging, offers promising new strategies for optimizing the diagnostic and therapeutic framework for schizophrenia, bipolar disorder and major depressive disorder. This approach allows us to achieve objective diagnosis and personalized treatment for individuals with these disorders.

Metformin has been demonstrated to attenuate hyperglycaemia by modulating the gut microbiota. However, the mechanisms through which the microbiome mediates metformin monotherapy failure (MMF) are unclear. Herein, in a prospective clinical cohort study of newly diagnosed type 2 diabetes mellitus (T2DM) patients treated with metformin monotherapy, metagenomic sequencing of faecal samples revealed that Phocaeicola vulgatus abundance was approximately 12 times higher in nonresponders than in responders. P. vulgatus rapidly hydrolysed taurine-conjugated bile acids, leading to ceramide accumulation and reversing the improvements in glucose intolerance conferred by metformin in high-fat diet-fed mice. Interestingly, C22:0 ceramide bound to mitochondrial fission factor to induce mitochondrial fragmentation and impair hepatic oxidative phosphorylation in P. vulgatus-colonized hyperglycaemic mice, which could be exacerbated by metformin. This work suggests that metformin may be unsuitable for P. vulgatus-rich T2DM patients and that clinicians should be aware of metformin toxicity to mitochondria. Suppressing P. vulgatus growth with cefaclor or improving mitochondrial function using adenosylcobalamin may represent simple, safe, effective therapeutic strategies for addressing MMF.

Objective:To compare the consistency of microarray chemiluminescent protein chip and immunoblotting in the autoantibody spectrum of patients and the diagnostic efficacy of systemic lupus erythematosus(SLE), and to explore the correlation between the detection results of protein microarray and clinical indicators and lymphocyte subsets.Methods:Serum autoantibodies in 649 samples collected between December 2023 and December 2024 in Nanjing Drum Tower Hospital were analyzed using the microarray chemiluminescent protein chip method, with 401 samples simultaneously tested by immunoblotting. Kappa coefficient assessed inter-method consistency. Diagnostic performance was compared via ROC curves. Spearman correlation analysis evaluated relationships between autoantibody levels and SLEDAI-2000 scores, clinical parameters, and lymphocyte subsets.Results:The two methods demonstrated good consistency across 14 antinuclear antibodies, with optimal agreement for anti-SSA/Ro ( Kappa=0.845, P<0.001), anti-SSB ( Kappa=0.825, P<0.001), and anti-CENP B ( Kappa=0.851, P<0.001). The protein chip method significantly improved SLE diagnostic efficacy, particularly for anti-dsDNA (AUC difference=0.291, P<0.001) and anti-Sm antibodies (AUC difference=0.295, P<0.001). Combined detection of anti-SSA/Ro and anti-nRNP/Sm antibodies achieved superior diagnostic performance (AUC=0.927). Anti-dsDNA, anti-histone, and anti-nucleosome antibodies positively correlated with SLEDAI-2000 ( r=0.408, 0410, 0.384, all P<0.001), complement ( P<0.001), and 24-hour urinary protein ( r=0.374, 0.387, 0.301, all P<0.001). Immunological analysis showed that the proportion of NK cells was generally negatively correlated with antinuclear antibodies such as anti-dsDNA ( r=-0.352, P<0.001) and anti-Sm ( r=-0.328, P<0.001) antibodies. Meanwhile, the proportion of CD8 + T cells was positively correlated with anti-nRNP/Sm ( r=0.229, P=0.002) and anti-Sm antibodies ( r=0.211, P=0.005). The proportion of CD4 + T cells was negatively correlated with anti-SSA/Ro ( r=-0.239, P<0.001), while the proportion of B cells was positively correlated with anti-dSDNA antibody ( r=0.300, P<0.001). Conclusion:The protein chip method showed strong consistency with immunoblotting for detecting 14 autoantibodies but demonstrated superior SLE diagnostic efficacy. The combined use of multiple detection methods can enhance the reliability of clinical diagnosis.

Objective:To explore the impact and underlying mechanisms of social support on illness uncertainty among parents of children with autism.Methods:A convenience sample of 312 parents of children with autism was recruited from the outpatient clinic of Peking University Sixth Hospital between September 2023 and January 2024. Data were collected using a general information questionnaire, the Chinese version of the Parent's Perception Uncertainty Scale (PPUS), the Social Support Scale for Families with Children with Autism, the Generalized Anxiety Disorder-7 (GAD-7), and the Questionnaire on Caregiving Issues and Service Needs of Parents of Children with Autism. Independent samples t-tests or one-way ANOVA were used to compare illness uncertainty scores across different characteristics. Pearson correlation analysis examined relationships among illness uncertainty, social support, caregiving issues and service needs, and anxiety. Chain mediation analysis was conducted using the SPSS macro PROCESS v4.1 to test the mediating roles of caregiving issues and service needs and anxiety. Results:The illness uncertainty score of the 307 valid respondents was (82.40±14.09). Mediation analysis indicated a direct effect of social support on illness uncertainty (effect value=-1.040), accounting for 72.27% of the total effect (-1.040/-1.439). A chain-mediated effect through caregiving issues and service needs and anxiety was also observed (effect value=-0.065), accounting for 4.50% of the total effect (-0.065/-1.439) .Conclusions:Parents of children with autism experience a relatively high level of illness uncertainty. Enhancing social support, addressing caregiving issues and service needs, alleviating parental anxiety may reduce their illness uncertainty.

The etiology of schizophrenia, bipolar disorder and major depressive disorder are complex, posing significant challenges to their diagnosis and treatment. Traditional symptom-based approaches often fall short in addressing these complexities. Precision medicine, through the integration of multi-dimensional data such as molecular genetics, peripheral biomarkers, and neuroimaging, offers promising new strategies for optimizing the diagnostic and therapeutic framework for schizophrenia, bipolar disorder and major depressive disorder. This approach allows us to achieve objective diagnosis and personalized treatment for individuals with these disorders.

Objective:To compare the consistency of microarray chemiluminescent protein chip and immunoblotting in the autoantibody spectrum of patients and the diagnostic efficacy of systemic lupus erythematosus(SLE), and to explore the correlation between the detection results of protein microarray and clinical indicators and lymphocyte subsets.Methods:Serum autoantibodies in 649 samples collected between December 2023 and December 2024 in Nanjing Drum Tower Hospital were analyzed using the microarray chemiluminescent protein chip method, with 401 samples simultaneously tested by immunoblotting. Kappa coefficient assessed inter-method consistency. Diagnostic performance was compared via ROC curves. Spearman correlation analysis evaluated relationships between autoantibody levels and SLEDAI-2000 scores, clinical parameters, and lymphocyte subsets.Results:The two methods demonstrated good consistency across 14 antinuclear antibodies, with optimal agreement for anti-SSA/Ro ( Kappa=0.845, P<0.001), anti-SSB ( Kappa=0.825, P<0.001), and anti-CENP B ( Kappa=0.851, P<0.001). The protein chip method significantly improved SLE diagnostic efficacy, particularly for anti-dsDNA (AUC difference=0.291, P<0.001) and anti-Sm antibodies (AUC difference=0.295, P<0.001). Combined detection of anti-SSA/Ro and anti-nRNP/Sm antibodies achieved superior diagnostic performance (AUC=0.927). Anti-dsDNA, anti-histone, and anti-nucleosome antibodies positively correlated with SLEDAI-2000 ( r=0.408, 0410, 0.384, all P<0.001), complement ( P<0.001), and 24-hour urinary protein ( r=0.374, 0.387, 0.301, all P<0.001). Immunological analysis showed that the proportion of NK cells was generally negatively correlated with antinuclear antibodies such as anti-dsDNA ( r=-0.352, P<0.001) and anti-Sm ( r=-0.328, P<0.001) antibodies. Meanwhile, the proportion of CD8 + T cells was positively correlated with anti-nRNP/Sm ( r=0.229, P=0.002) and anti-Sm antibodies ( r=0.211, P=0.005). The proportion of CD4 + T cells was negatively correlated with anti-SSA/Ro ( r=-0.239, P<0.001), while the proportion of B cells was positively correlated with anti-dSDNA antibody ( r=0.300, P<0.001). Conclusion:The protein chip method showed strong consistency with immunoblotting for detecting 14 autoantibodies but demonstrated superior SLE diagnostic efficacy. The combined use of multiple detection methods can enhance the reliability of clinical diagnosis.