Application of ImmunoScore Model for the Differentiation between Active Tuberculosis and Latent Tuberculosis Infection as Well as Monitoring Anti-tuberculosis Therapy.
Application of ImmunoScore Model for the Differentiation between Active Tuberculosis and Latent Tuberculosis Infection as Well as Monitoring Anti-tuberculosis Therapy.
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应用免疫评分模型区分活动性结核病和潜伏性结核感染并监测抗结核治疗
DOI：
10.3389/fcimb.2017.00457
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发表时间：
2017
影响因子：
5.7
通讯作者：
Sun ZY
中科院分区：
文献类型：
--
作者：
Zhou Y;Du J;Hou HY;Lu YF;Yu J;Mao LY;Wang F;Sun ZY
结核病（TB）是一个主要的全球公共卫生问题。为实现结核病终结战略，迫切需要用于结核病诊断和治疗监测的非侵入性标志物，特别是在中国等高流行国家。γ-干扰素释放试验（IGRAs）和结核菌素皮肤试验（TST）是结核病检测中常用的免疫学方法，但它们本质上不能区分活动性结核病（ATB）和潜伏性结核病感染（LTBI）。因此，这些方法在诊断ATB中的特异性取决于LTBI的局部患病率。抗酸染色、培养等病原体检测方法在临床应用中均存在局限性。免疫评分（ImmunoScore，IS）是一种新的肿瘤预后评估工具。然而，宿主免疫在结核病发病机制中的重要性也得到了证实，这意味着将IS模型用于ATB诊断和治疗监测的可能性。在本研究中，我们集中在IS模型在ATB和LTBI之间的区分和TB疾病的治疗监测中的性能。我们共筛选了5个免疫学标志物（4个非特异性标志物和1个结核特异性标志物），并通过Lasso logistic回归分析成功建立了IS模型。如预期，IS模型可以有效区分ATB和LTBI（灵敏度为95.7%，特异性为92.1%），并且在TB疾病的治疗监测中也具有潜在价值。
Tuberculosis (TB) is a leading global public health problem. To achieve the end TB strategy, non-invasive markers for diagnosis and treatment monitoring of TB disease are urgently needed, especially in high-endemic countries such as China. Interferon-gamma release assays (IGRAs) and tuberculin skin test (TST), frequently used immunological methods for TB detection, are intrinsically unable to discriminate active tuberculosis (ATB) from latent tuberculosis infection (LTBI). Thus, the specificity of these methods in the diagnosis of ATB is dependent upon the local prevalence of LTBI. The pathogen-detecting methods such as acid-fast staining and culture, all have limitations in clinical application. ImmunoScore (IS) is a new promising prognostic tool which was commonly used in tumor. However, the importance of host immunity has also been demonstrated in TB pathogenesis, which implies the possibility of using IS model for ATB diagnosis and therapy monitoring. In the present study, we focused on the performance of IS model in the differentiation between ATB and LTBI and in treatment monitoring of TB disease. We have totally screened five immunological markers (four non-specific markers and one TB-specific marker) and successfully established IS model by using Lasso logistic regression analysis. As expected, the IS model can effectively distinguish ATB from LTBI (with a sensitivity of 95.7% and a specificity of 92.1%) and also has potential value in the treatment monitoring of TB disease.
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影响因子：
7.4
作者：
Galon J;Fox BA;Bifulco CB;Masucci G;Rau T;Botti G;Marincola FM;Ciliberto G;Pages F;Ascierto PA;Capone M
通讯作者：
Capone M
DOI：
10.4049/jimmunol.1101122
发表时间：
2011-09-01
期刊：
Journal of immunology (Baltimore, Md. : 1950)
影响因子：
--
作者：
Day CL;Abrahams DA;Lerumo L;Janse van Rensburg E;Stone L;O'rie T;Pienaar B;de Kock M;Kaplan G;Mahomed H;Dheda K;Hanekom WA
通讯作者：
Hanekom WA
影响因子：
2.5
作者：
Cantini, Fabrizio;Lubrano, Ennio;Spadaro, Antonio
通讯作者：
Spadaro, Antonio
影响因子：
2.2
作者：
Della Bella, Silvia;Giannelli, Stefania;Villa, Maria Luisa
通讯作者：
Villa, Maria Luisa
TBAg/PHA 比值在免疫功能低下患者活动性结核病诊断中的表现
影响因子：
8.4
作者：
Bosco, Munyemana Jean;Hou, Hongyan;Wang, Feng
通讯作者：
Wang, Feng