Objective To investigate the correlation between vitamin A and surfactant protein (SP)-B, SP-C in human body,and to explore the effects on lung development and pulmonary function of neonates. Methods We collected the blood samples of 170 pregnant women and umbilical cord serum of their neonatal babies. The levels of vitamin A in pregnant women and their neonatal babies,and the levels of SP-B and SP-C in neonatal umbilical cord serum were detected by ELISA. We conducted a follow-up by standard telephone questionnaire,which we concerned was the number of respiratory tract infection within six months,in order to assess the neonatal pulmonary functions. Results (1) There was a positive correlation between the vitamin A levels in neonatal umbilical cord blood and in the blood of pregnant women(r=0. 866,P<0. 05). (2) There was a positive correlation between the vitamin A levels in neonatal umbilical cord blood and the levels of SP-B,SP-C in the blood(r=0. 817,P<0. 05). (3)In the follow-up of 170 cases of infants within six months,three cases with pneumonia hospitalized more than once,but no respiratory distress syndrome hap-pened. Conclusion Vitamin A can be used as an important biological marker to evaluate the neonatal pul-monary maturity. If we detect the vitamin A levels of pregnant women,increase the intake of vitamin A,we can improve the content of SP-B,SP-C,improve the development of neonatal lung function in growth.

Objective:A machine learning algorithm was used to develop a predictive model of self-injury among college students and to explore the high-risk factors for self-injury among college students.Methods:From November to December 2022, a convenience sample of 791 college students from a university in Hebei Province was selected.Whether the self-injurious behavior occurred or not was regarded as an outcome variable.The basic demographics data were collected for statistical analysis.The adolescent self-harm questionnaire, the acquired helplessness scale, the Chinese version of the interpersonal needs questionnaire, the adolescent life events scale, and the childhood traumatic experiences questionnaire were used for assessment.The predictor variables were statistically analyzed by SPSS 26.0 software, and the performance of the model was evaluated by random forest, support vector machine and logistic regression so as to predict the self-injury behavior of college students.The model performance was evaluated by the accuracy, F1 score, sensitivity, specificity, and AUC value of the model, and the optimal model was selected.Finally, the optimal model was used to analyze the high-risk factors of college students' self-injury behaviors.Results:(1) The results of one-way ANOVA showed that the detection rate of self-injury behavior among college students was 42.4%(335/791), and the detection rate of male students was significantly higher than that of female students ( χ2=14.139, P<0.05). Individuals with lower-middle monthly household income(RMB 3 000-5 999) had a significantly higher detection rate of self-injury behavior than those with other monthly household income( P<0.05). (2) The accuracy of random forest, support vector machine, and logistic regression models were 85.53%, 85.96%, and 68.86%, F1 scores were 0.853, 0.864, and 0.676, and sensitivities were 83.91%, 89.04%, and 64.91%, respectively.The AUCs of support vector machine, logistic regression models and random forest were 0.89, 0.73 and 0.92.(3) The top ten characteristic variables of high risk factors for college students' self-injury behaviors based on the random forest algorithm with better predictive efficacy were emotional abuse, frustration of belonging, helplessness, interpersonal relationship factor, despair, emotional neglect, academic stress factor, monthly family income, perception of tiredness, and health adaptation factor, in that order. Conclusions:Random forest is optimal for predicting self-injury behavior among college students compared to support vector machine and logistic regression.Factors influencing self-injury behavior among college students originate from environmental factors, individual factors and interpersonal factors.