Construction and evaluation of risk prediction model for non suicidal self injury of middle school students / 中国学校卫生

Objective@#To construct a nonsuicidal selfinjury (NSSI) risk prediction model for middle school students using different machine learning algorithms and evaluate the models effectiveness, so as to provide guidance for the prevention and control of NSSI in campus.@*Methods@#In March 2023, a total of 3 372 middle and high school students from schools in Nanchang, Fuzhou and Shangrao cities in Jiangxi Province were selected by combining stratified random cluster sampling and convenient sampling methods. Questionnaire surveys were conducted using various instruments including general information questionnaire, Selfesteem Scale, Ottawa Selfinjury Scale, Social Support Assessment Scale, Chinese Version of the Olweus Bullying Questionnaire, Event Attribution Style Scale, Adolescent Resilience Scale, and Adolescent Life Events Scale. Data were divided into training set (n=2 361) and test set (n=1 011) at a ratio of 7∶3, and variables were selected based on univariate and LASSO regression results. Four machine learning algorithms including namely random forest, support vector machine, Logistic regression and XGBoost, were used to construct NSSI risk prediction models, and the models performance was evaluated and compared using metrics including area under curve (AUC), sensitivity, specificity, positive predictive value, negative predictive value and F1 score.@*Results@#The detection rate of NSSI among middle school students was 34.4%. Univariate analysis showed that there were statistically significant differences in NSSI detection rates among middle school students of different grades, genders, registered residence locations, whether they were class cadres and four types of bullying (physical, verbal, relational bullying and cyberbullying) (χ2=27.17, 15.81, 11.54, 4.63；68.22, 140.63, 77.81, 13.95, P<0.05). NSSI was included as the dependent variable in the LASSO regression model for variable screening, and the results regression identified 10 predictive variables including grade level, selfesteem, subjective support, support utilization, verbal bullying, emotional control, interpersonal relationships, punishment, loss of relatives and property, and health and adaptation issues. The AUC values of random forest, support vector machine, Logistic regression, and XGBoost algorithms were 0.76, 0.76, 0.76 and 0.77, respectively, with no statistically significant differences between pairwise comparisons (Z=-0.59-0.82, P>0.05). Sensitivity values were 0.62, 0.61, 0.62 and 0.61, respectively. Specificity values were 0.74, 0.78, 0.78 and 0.78, respectively. Positive predictive values were 0.56, 0.59, 0.60 and 0.59, respectively. Negative predictive values were 0.79, 0.79, 0.80 and 0.79, respectively. F1 scores were 0.59, 0.60, 0.61 and 0.60, respectively.@*Conclusions@#All four nonsuicidal selfinjury risk prediction models perform well, with the Logistic regression model slightly outperforming the others. Schools and parents should pay attention to the predictive factors corresponding to NSSI, so as to reduce the occurrence of NSSI among middle school students.

Effects of different intestinal cleaning methods on bowel preparation for laparoscopic peritoneal vaginoplasty / 现代临床护理

Objective To investigate the effect of different intestinal cleaning methods on bowel preparation for laparoscopic peritoneal vaginoplasty.Methods One hundred patients undergoing laparoscopic peritoneal vaginoplasty were divided into experiment group and control group with 50 cases in each group .For bowel preparation,the experiment group was managed with oral sodium phosphate salt solution of 45 mL and the control group with oral senna leaf of 30 g in boiled water.The two groups were compared in terms of intestinal cleaning degree,adverse reactions and postoperative exhaust time.Results The incidence of adverse reactions in the former group was significantly lower than the control group.Degree of bowel cleaning satisfaction was significantly better than that of the control group and the postoperative exhaust was significantly earlier than that in the control group(P<0.05).Conclusion The method of oral sodium phosphate salt solution for bowel preparation for laparoscopic peritoneal vaginoplasty can improves the intestinal cleanliness,reduce incidence of intestinal adverse reaction and promote postoperative exhaust so that it can improve postoperative intestinal restoration of kinetic energy.

Design for the course of nursing English based on the investigation of needs for nursing English in high-end medical institutions in China / 中国实用护理杂志

Objective To analyze and consider the design for nursing English course through the investigation of demands for nursing English in 6 domestic high-end medical institutions.Methods Firstly,a face-to-face discussion was held with 10 experts in nursing management from above-mentioned institutions,field notes were made and themes were refined afterwards.Then,111 nurses were investigated in the 6 hospitals using purposive sampling method by filling questionnaire.Results In the teaching content,77 kinds of diseases were involved,including 27 most common ones; on the aspect of teaching methods and skills,90.1％ of the nurses recommended situational dialogue,72.1％ in favor of case teaching,92.8％ believed that nursing English course should cultivate students' communication skills; Its teaching emphasis in turn included vocabulary,communication capability,nursing knowledge (89.2％,88.3％,73.0％).Conclusions The objective of nursing English course should focus on cultivating nurse students' applied capability including listening,reading,speaking and written English.The textbook of nursing English should be written based on the characteristics of common diseases in the hospital stressing on nursing knowledge,vocabulary and communication skills.Teaching method of nursing English should consider the situational teaching model in hospital nursing English.

PULMONARY VESSEL EXTRACTION BASED ON COHERENCE DIFFUSION AND FAST MARCHING / 药物分析学报

Objective To accurately extract pulmonary vessels on medical images. Methods An efficient vessel segmentation framework is presented, which includes a smoothing method and a extraction algorithm. The smoothing method is based on an improved coherence diffusion approach that integrates the second-order directional differential information. It can analyze weak edges such as narrow peak or ridge-like structures. Meanwhile, an improved extraction algorithm is proposed. It is based on a fast marching algorithm where a sorted sequence array and multi-initialization technique are applied. Results The improved coherence diffusion approach can precisely preserve important oriented patterns and remove noises on the images. Experimental results on several images show that the proposed method can effectively find the location of pulmonary vessels. Conclusion The segmentation method is accurate and fast that can be a useful tool for medical imaging applications.