Feng Tienan, Cheng Yan, Yu Suwen, Jiang Feng, Su Min, Chen Jin
Hongqiao International Institute of Medicine, Shanghai Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200335, Shanghai, China.
Clinical Research Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200025, Shanghai, China.
Comput Math Methods Med. 2019 Mar 25;2019:9872425. doi: 10.1155/2019/9872425. eCollection 2019.
The gold standard for diagnosing pulmonary (TB) is the detection of tubercle bacillus in patient sputum samples. However, current methods either require long waiting times to culture the bacteria or have a risk of getting false-positive results due to cross-contamination. In this study, a method to detect tubercle bacillus based on the molecular typing technique is presented. This method can detect genetic units, variable number of tandem repeat (VNTR), which are the characteristic of tuberculosis (TB), and performs quality control using a mathematical model, ensuring the reliability of the results. Compared to other methods, the proposed method was able to process and diagnose a large volume of samples in a run time of six hours, with high sensitivity and specificity. Our method is also in the pipeline for implementation in clinical testing. Reliable and confirmed results are stored into a database, and these data are used to further refine the model. As the volume of data processed from reliable samples increases, the diagnostic power of the model improves. In addition to improving the quality control scheme, the collected data can be also used to support other TB research, such as that regarding the evolution of the tubercle bacillus.
诊断肺结核（TB）的金标准是在患者痰液样本中检测结核杆菌。然而，目前的方法要么需要很长时间来培养细菌，要么存在因交叉污染而出现假阳性结果的风险。在本研究中，提出了一种基于分子分型技术检测结核杆菌的方法。该方法可以检测作为结核病（TB）特征的遗传单位——可变数目串联重复序列（VNTR），并使用数学模型进行质量控制，确保结果的可靠性。与其他方法相比，所提出的方法能够在6小时的运行时间内处理和诊断大量样本，具有高灵敏度和特异性。我们的方法也正在准备用于临床检测。可靠且经确认的结果存储在数据库中，这些数据用于进一步优化模型。随着从可靠样本处理的数据量增加，模型的诊断能力会提高。除了改进质量控制方案外，收集的数据还可用于支持其他结核病研究，例如关于结核杆菌进化的研究。
