谷歌浏览器插件
订阅小程序
在清言上使用

Detection Of Human Immunodeficiency Virus Type-1 Antibody From Oral Mucosal Transudate Using Gelatin Particles Aggregation Less-Sensitive Method

INTERNATIONAL JOURNAL OF CLINICAL AND EXPERIMENTAL PATHOLOGY(2016)

引用 0|浏览1
暂无评分
摘要
Health care workers in stomatology department are at a high risk of occupational HIV infection, and so a non-invasion detecting method with higher bio-safety is essential to be applied before therapy. The oral mucosal transudate (OMT) and venous blood specimens were collected in three groups, including HIV-1 antibody-positive group, high-risk group and general group. Simultaneously the OMT samples were detected by gelatin particles aggregation less-sensitive (PA-LS), and the serum samples were screened by ELISA and confirmed by Western blot (WB). According to the final results of ELISA/WB, it was to evaluate the sensitivity, specificity, omission diagnostic rate, mistake diagnostic rate, positive predictive value and negative predictive value of PA-LS method. For HIV-1 antibody-positive group, the sensitivity of PA-LS detecting HIV-1 antibody in OMT specimens was 100%, the omission diagnostic rate was 0. For high-risk group, the sensitivity of PA-LS detecting HIV-1 antibody on OMT samples was 100%, the specificity was 97.49%, the omission diagnostic rate was 0, the mistake diagnostic rate was 2.51%, the positive predictive value was 88.52% and the negative predictive value was 100%. Compared to ELISA detecting HIV-1 antibody in serum specimens, the consistency of PA-LS detecting HIV-1 antibody positive on OMT samples was excellent (Kappa > 0.8), and the statistical difference of PA-LS detecting HIV-1 antibody in OMT specimens was significant (P< 0.05). In conclusion, compared to HIV-1 antibody detection from serum specimens by ELISA/WB, PALS detecting HIV-1 antibody from OMT specimens was non-invasive and accurate.
更多
查看译文
关键词
Human immunodeficiency virus type-1 antibody, gelatin particles aggregation less-sensitive method, oral mucosal transudate
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要