摘要
目的探讨以细胞表面分子CD161为标识的流式检测技术的诊断价值,建立能区分痰涂片阴性肺结核、痰涂片阴性IGRA阳性/阴性的肺结核和肺炎患者的深度学习网络方法。方法通过流式细胞术检测血液中淋巴细胞、单核细胞和CD161阳性淋巴细胞比例,利用深度学习网络建立分类模型。结果深度学习网络测试结果显示,三群细胞比例均能很好区分痰涂片阴性肺结核与肺炎患者、痰涂片阴性IGRA阳性肺结核与肺炎患者、痰涂片阴性IGRA阴性肺结核与肺炎患者。结论以CD161为标识的流式检测技术可作为辅助诊断技术,对痰涂片阴性、痰涂片阴性IGRA阳性/阴性的肺结核和肺炎患者进行初步区分,提高痰涂片阴性病人检出率,指导临床提前用药。
Objective To explore the diagnostic value of cell surface molecule CD161 by flow cytometry technology, and to establish deep learning networks that can distinguish sputum smear-negative pulmonary tuberculosis, sputum smear-negative IGRA positive/negative pulmonary tuberculosis and pneumonia patients. Methods The proportions of lymphocytes, monocytes and CD161-positive lymphocytes were detected by flow cytometry,and used to construct classification model using deep learning networks. Results The tests on the deep learning networks showed that the ratios of three cell populations were able to distinguish sputum smear-negative tuberculosis, sputum smear-negative IGRA-positive tuberculosis, and sputum smear-negative IGRA-positive tuberculosis from pneumonia patients. Conclusion Based on CD161-flow cytometry technique might be used as an auxiliary diagnostic method to make a preliminary distinction between sputum smear-negative, sputum smear-negative IGRA-positive/negative tuberculosis and pneumonia patients, to improve detection rate of sputum smear-negative tuberculosis patients and guide clinical treatment in advance.
作者
张惠华
陈骑
杨倩婷
张明霞
代友超
蔡毅
温志华
陈文斌
谭耀驹
关平
邓国防
陈心春
Zhang Huihua;Chen Qi;Yang Qianting;Zhang Mingxia;Dai Youchao;Cai Yi;Wen Zhihua;Chen Wenbin;Tan Yaoju;Guan Ping;Deng Guofang;Chen Xinchun(Guangdong Provincial Key Laboratory of Regional Immunity and Disease,Department of Pathogen Biology,School of Medicine,Shenzhen University,Shenzhen 518052,Guangdong,China;Shenzhen Third People's Hospital,Shenzhen 518112,Guangdong,China;Laboratory of Shenzhen University-Yuebei Second People's Hospital,Shaoguan 512000,Guangdong,China;Guangzhou Chest Hospital,Guangzhou 510095,Guangdong,China)
出处
《生物医学转化》
2021年第2期91-98,共8页
Biomedical transformation
基金
国家十三五科技重大专项“艾滋病和病毒性肝炎等重大传染病防治”专题课题(2017ZX10201301)
国家自然科学基金面上项目(81772145)
深圳市学科布局项目(JCYJ20170412151620658)
广东省科技专项(Sqybey01)。
关键词
活动性肺结核
肺炎
CD161
痰涂片
深度学习网络
Active pulmonary tuberculosis
Pneumonia
CD161
Sputum smear
Deep learning network
