Study on Analytical Method of Leachable Substances of Allogeneic Products / 中国医疗器械杂志

Products made from allogeneic tissue are largely used in clinical treatment due to its wide source compared with autologous tissue, causing less secondary trauma of patients and the good biocompatibility. Various organic solvents and other substances introduced in the production process of allogeneic products will leach down into the human through clinical treatment, thus bringing varying degrees of harm to patients. Therefore, it is very necessary to detect and control the leachables in such products. Based on the classification and summary of leachable substances existing in the allogeneic products, the preparation of extract and the establishment of the detection techniques for known and unknown leachable are briefly introduced in this study, in order to provide research method for the study of leachable substances of allogeneic products.

Review of Leachable Substances in Hemodialyzer / 中国医疗器械杂志

With the rapid development of my country's hemodialysis industry, the application of hemodialysis machines has become more and more extensive, but at the same time, the quality control technology of hemodialysis machines is not perfect. Especially for a wide range of leachable substances in dialyzers, there are few studies and detection methods. This study first briefly describes the development of hemodialyzers, and then expounds the common types of leachables, extraction methods, and chromatography and mass spectrometry conditions. It is summarized that the research plan of leachable substances is to determine the type first, then formulate the extraction plan, and then establish the detection method. Finally, we look forward to the research prospects of hemodialyzer leachables, and point out that with the deepening and extensive development of research, it can further promote the healthy development of the hemodialyzer industry.

Application of Machine Learning to Predict Macrosomia / 实用妇产科杂志

Objective:To improve the accuracy of prediction of macrosomia by application of machine learning.Methods:Ultrasound measurement data and fetal birth weight of macrosomia and normal birth weight neonates were collected during January 2015 to December 2016 in Mindong Hospital Affiliated to Fujian Medical University.Ultrasound built-in Hadlock formula,multiple linear regression,k-nearest neighbor,support vector machine,random forest were evaluated and compared to predict macrosomia using actual fetal birth weight as the gold standard.Results:The sensitivity of built-in Hadlock formula to predict macrosomia was 40.86％ and Youden index was 39.95％.The sensitivity of the multivariate linear regression was 60.22％ and the Youden index was 58.85％.The sensitivity of the k-nearest neighbor was 86.21％ and the Youden index was 75.10％.The sensitivity of the support vector machine was 86.21％ and the Youden index was 73.51％.The sensitivity of the random forest was 81.03％ and the Youden index was 71.51％.The Youden index of multivariate linear regression was significantly bigger than that of built-in Hadlock(u =3.64,P ＜0.001).The Youden index of k-nearest neighbor,support vector machine and random forest was significantly bigger and built-in Hadlock and multivariate linear regression (P＜0.001,P＜ 0.05).Conclusions:The machine learning is of high accuracy and great value of application.