ABSTRACT
Objective: The purpose of this survey was to explore the association of delivery mode with overweight and neurodevelopment of Chinese infants aged 1-5 months. Materials and methods: This study was based on a cross-sectional survey. Data for this study were obtained from the Children's Nutrition and Health System Survey in China which was conducted from 2019 to 2020. Characteristics of parents and children and the delivery mode were obtained using interview-administered questionnaires. Body mass index-for age z-score (BMI z) was calculated using World Health Organization (WHO) child growth standards. Children's neurodevelopment was assessed by a trained child health care physician using the Child Psychological Development Scale. The association of delivery mode with infant overweight was analyzed using a multivariable logistic regression model. We conducted a multivariable linear regression model to explore the relationship between delivery modes with neurodevelopment. Results: In total, the present analysis included 1,347 children aged 1-5 months, 35.61% were born via cesarean section, of which 15.21% were overweight. After adjustment for infant characteristics and parental factors, the cesarean section was significantly related with the likehood of being overweight [OR = 1.95; 95% confidence interval (CI): 1.27 to 2.98]. Children born via cesarean section had a 3.41-point decrease in gross motor development (ß = -3.41; 95% CI: -5.77 to -1.05), a 3.65-point decrease in fine motor development (ß = -3.65; 95% CI: -6.03 to -1.28), and a 2.96-point in language development (ß = -2.96; 95% CI: -5.20 to -0.73), a 1.65-point in total development (ß = -1.65; 95% CI: -3.17 to -0.14) compared with those who were vaginal birth. Conclusion: In our study population, cesarean section was associated with overweight and neurodevelopment outcomes. The cesarean section might increase the likehood of infant overweight, and might decrease the developmental scores of gross motor, fine motor and language. Further studies should be conducted to verify the associations and explore the possible mechanisms.
ABSTRACT
Based on the statistical features, short text messages published by different gender users are different in terms of the words and semantics used. In this paper, two new features are constructed after constructing a gender-specific thesaurus. A new classification model is constructed by combining the traditional statistical features and the improved text implicitness feature. The experimental evaluation performed on the Sina Weibo dataset demonstrated the effectiveness of gender-specific thesaurus-based features, and the improved text implicitness feature improved the accuracy of gender classification to 84.7%.
