Objective To explore the influencing factors of aggressive behavior in patients with bipolar disorder and to construct a nomogram prediction model.Method Eighty patients with bipolar disorder who were admitted to our hospital from March 2021 to April 2023 were selected as the research subjects.They were divided into non-aggressive and aggressive groups.Univariate analysis was performed on the data of the two groups,and factors with statistical significance were subjected to logistic regression analysis.A nomogram was drawn to determine the influencing factors of aggressive behavior in patients with bipolar disorder.Result A total of 80 patients were included,of which 28 were in the aggressive group(35.0%)and 52 were in the non-aggressive group(65.0%).The proportion of patients who lived alone for a long time,the total hospitalization time,and the proportion of patients with a history of suicidal tendencies were higher in the aggressive group than in the non-aggressive group.Moreover,the scores of ITAQ and SSRS were lower in the aggressive group(P<0.05).Multivariate logistic regres-sion analysis showed that living alone for a long time and having a history of suicidal tendencies were risk factors for aggressive behavior in patients with bipolar disorder,while high scores on ITAQ and SSRS were protective factors(P<0.05).A nomogram was constructed,which has good predictive value.Conclusion Long-term solitary living and a history of suicidal tendencies may increase the risk of aggressive behavior in patients with bipolar disorder.

Objective:The aim of this study was to explore the molecular mechanisms of sulforaphane in the treatment of autism spectrum disorders (ASD), identify metabolomic biomarkers associated with efficacy and construct efficacy prediction models.Methods:Forty children with ASD who were treated in Second Xiangya Hospital of Central South University and Guangzhou Huiai Hospital were recruited from August 2016 to May 2019. The patients were randomly allocated into sulforaphane treatment group ( n=26) and placebo group ( n=14). The OSU Autism Rating Scale-DSM-Ⅳ (OARS-4) was used to assess the change in clinical symptoms of children with ASD at baseline, week 4, week 8 and week 12 of treatment. A generalized linear mixed model was used to compare the differences in OARS-4 scale scores between groups and time. Plasma samples were collected from patients before and after treatment for untargeted metabolomic detection using ultra performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS). Differential metabolites were screened using ANOVA-component analysis, and metabolic pathway analysis was performed. Then, spearman correlation analysis was performed to find differential metabolites significantly associated with the efficacy of sulforaphane treatment, and finally Fisher′s discriminant analysis was used to screen for efficacy predictors. Result:After 12 weeks of treatment, the clinical symptoms improvement was significantly better in the sulforaphane group than in the placebo group ( F=14.11, P<0.001). There were differences in a total of 201 metabolites between the two groups, which were mainly significantly enriched in glycerophospholipid metabolism and primary bile acid biosynthesis pathways. Spearman′s correlation analysis showed that taurine, phosphatidylserine and lysophosphatidylserine were significantly positively associated with symptom changes in patients with ASD ( r=0.643, 0.401, 0.414, P<0.05 or 0.001), while lysophosphatidylethanolamine, sphingomyelin and triglyceride metabolites were significantly negatively associated with symptom changes ( r=-0.481--0.392, all P<0.05). Among them, sphingomyelin (d35∶1) and taurine entered the Fisher′s discriminant analysis model, which the accuracy of efficacy prediction was 84.6%(22/26). Conclusions:The molecular mechanism of sulforaphane in improving ASD related clinical symptoms may be related to cell membrane phospholipid metabolism. Sphingomyelin (d35∶1) and taurine may be possible predictors on the efficacy of sulforaphane in the treatment of ASD.

Objective:The aim of this study was to explore the molecular mechanisms of sulforaphane in the treatment of autism spectrum disorders (ASD), identify metabolomic biomarkers associated with efficacy and construct efficacy prediction models.Methods:Forty children with ASD who were treated in Second Xiangya Hospital of Central South University and Guangzhou Huiai Hospital were recruited from August 2016 to May 2019. The patients were randomly allocated into sulforaphane treatment group ( n=26) and placebo group ( n=14). The OSU Autism Rating Scale-DSM-Ⅳ (OARS-4) was used to assess the change in clinical symptoms of children with ASD at baseline, week 4, week 8 and week 12 of treatment. A generalized linear mixed model was used to compare the differences in OARS-4 scale scores between groups and time. Plasma samples were collected from patients before and after treatment for untargeted metabolomic detection using ultra performance liquid chromatography-high resolution mass spectrometry (UPLC-HRMS). Differential metabolites were screened using ANOVA-component analysis, and metabolic pathway analysis was performed. Then, spearman correlation analysis was performed to find differential metabolites significantly associated with the efficacy of sulforaphane treatment, and finally Fisher′s discriminant analysis was used to screen for efficacy predictors. Result:After 12 weeks of treatment, the clinical symptoms improvement was significantly better in the sulforaphane group than in the placebo group ( F=14.11, P<0.001). There were differences in a total of 201 metabolites between the two groups, which were mainly significantly enriched in glycerophospholipid metabolism and primary bile acid biosynthesis pathways. Spearman′s correlation analysis showed that taurine, phosphatidylserine and lysophosphatidylserine were significantly positively associated with symptom changes in patients with ASD ( r=0.643, 0.401, 0.414, P<0.05 or 0.001), while lysophosphatidylethanolamine, sphingomyelin and triglyceride metabolites were significantly negatively associated with symptom changes ( r=-0.481--0.392, all P<0.05). Among them, sphingomyelin (d35∶1) and taurine entered the Fisher′s discriminant analysis model, which the accuracy of efficacy prediction was 84.6%(22/26). Conclusions:The molecular mechanism of sulforaphane in improving ASD related clinical symptoms may be related to cell membrane phospholipid metabolism. Sphingomyelin (d35∶1) and taurine may be possible predictors on the efficacy of sulforaphane in the treatment of ASD.

ObjectiveTo explore the influence of the core symptoms of attention deficit hyperactivity disorder （ADHD） on behavioral problems of children with ADHD propensity， so as to provide references for early identification and targeted intervention for children with ADHD propensity. MethodsFrom July to August 2021， 25 children with ADHD propensity were screened as the ADHD propensity group， and 25 children matched for age， gender and grade were included as the normal group in an elementary school in Guangzhou. ADHD core symptoms were assessed by the Chinese version of the Swanson Nolan and Pelham， version IV-parent form for ADHD （SNAP-IV）， and behavioral problems were assessed by Questionnaire-Children with Difficulties （QCD） and Conners Parental Symptom Questionnaire （PSQ）. Spearman correlation analysis was used to examine the correlation between ADHD core symptoms and QCD and PSQ scores， and hierarchical linear regression analysis was used to explore the effect of ADHD core symptoms on behavioral problems. Results① The differences between the groups showed that both attention deficit and hyperactivity-impulsivity factor scores were higher in the ADHD propensity group than those in the normal group （t=7.771， 6.726， P<0.01）. ② Correlation analysis showed that the attention deficit factor score was negatively correlated with QCD total score （r=-0.440， P<0.05）， and positively correlated with the learning problem factor score of PSQ （r=0.457， P<0.05）. The score of hyperactivity-impulsivity was negatively correlated with score of anxiety factor in PSQ （r=-0.457， P<0.05）， and positively correlated with impulse-hyperactivity factor score （r=0.552， P<0.01）. ③ Hierarchical linear regression analysis showed that the attention deficit factor score negatively predicted the total score of QCD （B=-0.682， P<0.05， R²=0.468）. The hyperactivity-impulsivity factor score had a negative predictive effect on the anxiety factor score of PSQ （B=-0.048， P<0.05， R²=0.367）， and had a positive predictive effect on the impulsivity-hyperactivity factor score （B=0.077， P<0.01， R²=0.424）. ConclusionChildren with ADHD propensity have significant attention deficit symptoms， hyperactivity-impulsivity symptoms and behavioral problems， and the attention deficit may be the main cause of their daily behavioral problems， while hyperactivity-impulsivity may be the main cause of their impulsive-hyperactivity problems.