Influence of antiretroviral prophylaxis on growth of HIV-exposed uninfected infants / 中华流行病学杂志

Objective@#To evaluate the influence of antiretroviral prophylaxis on the growth and development of HIV-exposed uninfected infants in Guangzhou.@*Methods@#Data were from the national information system for prevention of mother-to-child transmission of HIV infection, syphilis and hepatitis B. After excluding death and perinatal HIV infection cases, 564 HIV-exposed uninfected infants were included. The infants were divided into three groups, nevirapine (NVP) group, zidovudine (AZT) group and untreated group. The influences of antiretroviral prophylaxis on the body weight and height of the HIV-exposed uninfected infants were analyzed by using generalized estimating equations.@*Results@#The HIV-exposed uninfected infants at 1-month old had lower Z scores of body weight-for-age and body height-for-age than the World Health Organization’s reference standard. The prevalence of wasting in AZT group (17.5%) was higher than that in NVP group (6.2%) for 1-month old infants. Taking NVP or AZT was a protective factor for Z score of body length-for-age (P<0.05). Intrauterine exposure to triple antiviral drugs was a risk factor for the Z scores of body weight-for-age and body length-for-age (P<0.05).@*Conclusion@#The physical growth and development of HIV-exposed uninfected infants at 1-month old was not well, and HIV-exposed uninfected infants who taking AZT had a higher incidence of wasting. Attention should be paid to these infants.

Analysis of influencing factors of hospitalization expenses based on support vector machine / 中华医院管理杂志

Objective To analyze main influencing factors of hospitalization expenses by support vector machine modeling,and explore effective influence factors analysis methods of medical expenses. Methods Random selection of six hospitals in Zhejiang province.Using hospital electronic medical record system of the hospitals and selecting three kinds of typical diseases of internal medicine and surgery,to build the support vector machine model,BP neural network model,and multiple linear regression model for comparison of analysis results.The SVM model is used to analyze three various diseases.Results The support vector machine model based on radial basis kernel function scored the highest prediction accuracy on the hospitalization expenses,up to 96.07%.In a mixed analysis of different diseases,analysis results of all three models pointed the main influence factors of hospitalization expense as days of stay,disease types,and hospital coding for the surgery.In the analysis by diseases individually,the influencing factors, though varying with diseases, key factors remain the same. Conclusion The support vector machine in the influence factor analysis is feasible in hospitalization expenses.According to the analysis results,the single disease payment system can be made rationally, which can effectively control excessive growth of medical expenses.

The association between prostaglandin E2 and tumor / 中华疾病控制杂志

Epidemiological,experimental and clinical studies had demonstrated the affinity between the development of tumor and prostaglandin E2 (PGE2).The level of PGE2 was elevated in tumor tissues and the relation was detected between PGE2 and the tumor size,tumor stage,metastasis,prognosis as well as reoccurrence.Thus,using inhibitors and detecting level of PGE2 play a vital role in the prevention and clinical treatment of tumors.