首页> 外文期刊>Clinical chemistry and laboratory medicine: CCLM >New serum biomarkers for detection of tuberculosis using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry.
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New serum biomarkers for detection of tuberculosis using surface-enhanced laser desorption/ionization time-of-flight mass spectrometry.

机译:使用表面增强的激光解吸/电离飞行时间质谱技术检测结核病的新型血清生物标志物。

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BACKGROUND: New technologies for the early detection of tuberculosis (TB) are urgently needed. Pathological changes within an organ might be reflected in proteomic patterns in serum. The aim of the present study was to screen for the potential protein biomarkers in serum for the diagnosis of TB using proteomic fingerprint technology. METHODS: Proteomic fingerprint technology combining protein chips with surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was used to profile the serum proteins from 50 patients with TB, 25 patients with lung disease other than TB, and 25 healthy volunteers. The protein fingerprint expression of all the serum samples and the resulting profiles between TB and control groups were analyzed with the Biomarker Wizard system. RESULTS: A total of 30 discriminating m/z peaks were detected that were related to TB (p<0.01). The model of biomarkers constructed by the Biomarker Patterns Software based on the three biomarkers (2024, 8007, and 8598 Da) generated excellent separation between the TB and control groups. The sensitivity was 84.0% and the specificity was 86.0%. Blind test data indicated a sensitivity of 80.0% and a specificity of 84.2%. CONCLUSIONS: The data suggested a potential application of SELDI-TOF MS as an effective technology to profile serum proteome, and with pattern analysis, a diagnostic model comprising three potential biomarkers was indicated to differentiate people with TB and healthy controls rapidly and precisely.
机译:背景:迫切需要用于早期发现结核病(TB)的新技术。器官内的病理变化可能反映在血清中的蛋白质组学模式中。本研究的目的是使用蛋白质组指纹技术筛选血清中可用于诊断结核病的蛋白质生物标志物。方法:结合蛋白质芯片和表面增强激光解吸/电离飞行时间质谱(SELDI-TOF MS)的蛋白质组指纹技术,对50例结核病患者,25例肺结核以外的肺部疾病患者的血清蛋白进行了分析,以及25名健康志愿者。使用Biomarker Wizard系统分析了所有血清样品的蛋白质指纹表达以及结核病组与对照组之间的蛋白质谱。结果:共检测到30个与结核病相关的m / z峰(p <0.01)。由生物标志物模式软件基于三种生物标志物(2024、8007和8598 Da)构建的生物标志物模型在TB和对照组之间产生了极好的分离。敏感性为84.0%,特异性为86.0%。盲测数据表明灵敏度为80.0%,特异性为84.2%。结论:数据表明SELDI-TOF MS作为一种有效的技术用于分析血清蛋白质组的潜在技术,并且通过模式分析,表明包含三种潜在生物标志物的诊断模型可快速准确地区分结核病患者和健康对照者。

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