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Development and evaluation of a data-adaptive alerting algorithm for univariate temporal biosurveillance data

机译:单变量时间生物监视数据的数据自适应警报算法的开发和评估

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摘要

Acute respiratory distress syndrome (ARDS) is a severe form of acute lung injury. It is a response to various diseases of variable etiology, including SARS-CoV infection. To date, a comprehensive study of the genomic physiopathology of ARDS (and SARS) is lacking, primarily due to the difficulty of finding suitable materials to study the disease process at a tissue level (instead of blood, sputa or swaps). Hereby we attempt to provide such study by analyzing autopsy lung samples from patient who died of SARS and showed different degrees of severity of the pulmonary involvement. We performed real-time quantitative PCR analysis of 107 genes with functional roles in inflammation, coagulation, fibrosis and apoptosis; some key genes were confirmed at a protein expression level by imrnunohistochemistry and correlated to the degree of morphological severity present in the individual samples analyzed. Significant expression levels were identified for ANPEP (a receptor for CoV), as well as inhibition of the STAT1 pathway, IFNs production and CXCL10 (a T-cell recruiter). Other genes unassociated to date with ARDS/SARS include C1Qb, C5R1, CASP3, CASP9, CD14, CD68, FGF7, HLA-DRA, IGF1, IRF3, MALAT-1, MSR1, NFIL3, SLPI, USP33, CLC, GBP1 and TAC1. As a result, we proposed to therapeutically target some of these genes with compounds such as ANPEP inhibitors, SLPI and dexamethasone. Ultimately, this study may serve as a model for future, tissue-based analyses of fibroinflammatory conditions affecting the lung.
机译:急性呼吸窘迫综合征(ARDS)是急性肺损伤的一种严重形式。它是对包括SARS-CoV感染在内的多种病因变化的疾病的反应。迄今为止,缺乏对ARDS(和SARS)的基因组生理病理学的全面研究,这主要是由于难以找到合适的材料来研究组织水平的疾病过程(而不是血液,痰液或交换蛋白)。因此,我们尝试通过分析死于SARS并显示出不同程度的肺部受累程度的患者的尸检肺样本来提供此类研究。我们对107个在炎症,凝血,纤维化和细胞凋亡中具有功能的基因进行了实时定量PCR分析;通过免疫组化在蛋白质表达水平上确认了一些关键基因,这些关键基因与所分析的单个样品中存在的形态学严重程度相关。确定了ANPEP(CoV受体)的显着表达水平,以及对STAT1途径,IFN产生和CXCL10(T细胞募集者)的抑制作用。迄今未与ARDS / SARS相关的其他基因包括C1Qb,C5R1,CASP3,CASP9,CD14,CD68,FGF7,HLA-DRA,IGF1,IRF3,MALAT-1,MSR1,NFIL3,SLPI,USP33,CLC,GBP1和TAC1。结果,我们提议用化合物如ANPEP抑制剂,SLPI和地塞米松治疗性靶向这些基因中的一些。最终,这项研究可以作为将来基于组织的影响肺的纤维炎性疾病分析的模型。

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