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Abstract Gut microbiota and their metabolic products might play important roles in regulating the pathogenesis of autism spectrum disorder (ASD). The purpose of this study was to characterize gut microbiota and serum amino acid metabolome profiles in children with ASD. A non-randomized controlled study was carried out to analyze the alterations in the intestinal microbiota and their metabolites in patients with ASD (n = 30) compared with neurotypical controls (NC) (n = 30) by metagenomic sequencing to define the gut microbiota community and liquid chromatography/mass spectrometry (LC/MS) analysis to characterize the metabolite profiles. Compared with children in the NC group, those in the ASD group showed lower richness, higher evenness, and an altered microbial community structure. At the class level, Deinococci and Holophagae were significantly lower in children with ASD compared with TD. At the phylum level, Deinococcus-Thermus was significantly lower in children with ASD compared with TD. In addition, the functional properties (such as galactose metabolism) displayed significant differences between the ASD and NC groups. Five dominant altered species were identified and analyzed (LDA score > 2.0, P

Abstract Background Children with autism spectrum disorders (ASD) usually experience difficulty regarding symbolic play. However, studies on whether symbolic play test (SPT) can differentiate between ASD and other developmental disorders are inconsistent, and evaluating the application value of the SPT in the identification of ASD without global developmental delay (GDD) and developmental language disorder (DLD) is necessary. Methods A total of 200 children were selected as the research participants. There were 100 cases of ASD without GDD and 100 cases of DLD. All children were tested by SPT and Children Neuropsychological and Behavioral Scale-Revision (CNBS-R2016). Binomial logistic regression was used for multivariate analysis. The receiver operating characteristic (ROC) curve was used to evaluate the value of SPT in identifying ASD without GDD and DLD. Results SPT equivalent age was lower than chronological age in the two groups, the difference between the ASD without GDD group was greater than that in the DLD group, and the proportion of SPT equivalent age retardation was higher than that in the DLD group; the differences were statistically significant. Logistic regression analysis showed that there was a difference in SPT equivalent age between DLD and ASD without GDD. When the cut-off value of the SPT was 8.5, the largest area under the ROC curve was 0.723, and the sensitivity and specificity for the diagnosis of ASD without GDD were 0.720 and 0.620 respectively. Conclusions Symbolic play ability in ASD children is worse than that of DLD children at comparable development levels. SPT may be helpful to distinguish ASD without GDD from children with DLD.

Abstract Objective To explore the prevalence and correlation between husbands and lifetime domestic violence (DV) among women in Wuhan, China. Methods A cross-sectional study was conducted in a community health center in Wuhan from June 2015 to December 2015. A total of 1015 women who came to the center for gynecological examination were selected through a random sampling. They were assessed using the WHO Violence Against Women Instrument to evaluate the prevalence of DV. The chi-square test, the Wilcoxon rank test, and unadjusted and adjusted logistic regression analyses were used to analyze the possible risk or protective factors for DV. Results The lifetime prevalence of DV was 29.36% (298/1015). The risk factors included heavy physical labor (OR 3.54, 95% CI 1.63–7.77), long-term drinking (OR 1.60, 95% CI 1.19–2.14), overweight or obesity (OR 1.36, 95% CI 1.01–1.88) and long-term smoking (OR 1.03, 95% CI 1.01–1.04). Higher education was a protective factor (OR 0.80, 95% CI 0.66–0.96). Conclusion Women whose husbands had lower education, performed heavy physical labor, were long-term alcohol consumers, had overweight or obesity, and were long-term smokers were vulnerable to lifetime DV.

Hybrid pairing of the corresponding silkworm species is a pivotal link in sericulture, ensuring egg quality and directly influencing silk quantity and quality. Considering the potential of image recognition and the impact of varying pupal postures, this study used machine learning and deep learning for global modeling to identify pupae species and sex separately or simultaneously. The performance of traditional feature-based approaches, deep learning feature-based approaches, and their fusion approaches were compared. First, 3600 images of the back, abdomen, and side postures of 5 species of male and female pupae were captured. Next, six traditional descriptors, including the histogram of oriented gradients (HOG), and six deep learning descriptors, including ConvNeXt-S, were utilized to extract significant species and sex features. Finally, classification models were constructed using the multilayer perceptron (MLP), support vector machine, and random forest. The results indicate that the {HOG + ConvNeXt-S + MLP} model excelled, achieving 99.09% accuracy for separate species and sex recognition and 98.40% for simultaneous recognition, with precision–recall and receiver operating characteristic curves ranging from 0.984 to 1.0 and 0.996 to 1.0, respectively. In conclusion, it can capture subtle distinctions between pupal species and sexes and shows promise for extensive application in sericulture.

BACKGROUND:Adverse childhood experiences (ACEs) have been associated with poor health status later in life. The objective of the present study was to examine the relationship between ACEs and health-related behaviors, chronic diseases, and mental health in adults. METHODS:A cross-sectional study was performed with 1501 residents of Macheng, China. The ACE International Questionnaire (ACE-IQ) was used to assess ACEs, including psychological, physical, and sexual forms of abuse, as well as household dysfunction. The main outcome variables were lifetime drinking status, lifetime smoking status, chronic diseases, depression, and posttraumatic stress disorder. Multiple logistic regression models were used to examine the associations between overall ACE score and individual ACE component scores and risk behaviors/comorbidities in adulthood after controlling for potential confounders. RESULTS:A total of 66.2% of participants reported at least one ACE, and 5.93% reported four or more ACEs. Increased ACE scores were associated with increased risks of drinking (adjusted odds ratio [AOR] = 1.09, 95% confidence intervals [CI]: 1.00-1.09), chronic disease (AOR = 1.17, 95% CI: 1.06-1.28), depression (AOR = 1.37, 95% CI: 1.27-1.48), and posttraumatic stress disorder (AOR = 1.32, 95% CI: 1.23-1.42) in adulthood. After adjusting for confounding factors, the individual ACE components had different impacts on risk behavior and health, particularly on poor mental health outcomes in adulthood. CONCLUSIONS:ACEs during childhood were significantly associated with risk behaviors and poor health outcomes in adulthood, and different ACE components had different long-term effects on health outcomes in adulthood.