■新的冠状病毒病,COVID-19提出了复杂的挑战,这些挑战由几个因素加剧,其中呼吸道组织病变明显。因此,迫切需要鉴定可以指示疾病严重程度的信息生物学标记。一些研究强调了蛋白质的参与,如APOA1,XPNPEP2,ORP150,CUBN,HCII,和CREB3L3在这些呼吸道组织病变中。然而,缺乏有关人体中这些蛋白质抗体的信息,这可能是COVID-19的有价值的诊断标志物。同时,选择无需侵入性程序即可获得的生物流体是相关的。尿液就是这样一种液体,但是由于缺乏对其成分的研究,其对临床实验室分析的影响尚未完全了解。
■本研究使用的方法如下:血清总蛋白分析;中重度COVID-19患者血清和尿液的ELISA;生物信息学方法:ROC分析,PCA,SVM。
■抗APOA1,抗XPNPEP2,抗ORP150,抗CUBN的水平,AntiHCII,和抗CREB3L3在COVID-19患者的血清和尿液中表现出从中度到重度的逐渐波动。然而,个体抗蛋白抗体的诊断价值较低,在血清和尿液中。相反,主成分分析(PCA)和支持向量机(SVM)的结果表明,在患者血清中联合检测这些抗体可显著提高诊断价值.非线性回归模型的精度为0.833。此外,PCA有助于鉴定对患者群体歧视影响最大的血清蛋白标志物。该研究表明,血清是描述蛋白质定量的优良分析物,由于其一致的组成和缺乏有机盐和药物残留,否则会影响蛋白质的稳定性。
UNASSIGNED: The new coronavirus disease, COVID-19, poses complex challenges exacerbated by several factors, with respiratory tissue lesions being notably significant among them. Consequently, there is a pressing need to identify informative biological markers that can indicate the severity of the disease. Several studies have highlighted the involvement of proteins such as APOA1, XPNPEP2, ORP150, CUBN, HCII, and CREB3L3 in these respiratory tissue lesions. However, there is a lack of information regarding antibodies to these proteins in the human body, which could potentially serve as valuable diagnostic markers for COVID-19. Simultaneously, it is relevant to select biological fluids that can be obtained without invasive procedures. Urine is one such fluid, but its effect on clinical laboratory analysis is not yet fully understood due to lack of study on its composition.
UNASSIGNED: Methods used in this study are as follows: total serum protein analysis; ELISA on moderate and severe COVID-19 patients\' serum and urine; bioinformatic methods: ROC analysis, PCA, SVM.
UNASSIGNED: The levels of antiAPOA1, antiXPNPEP2, antiORP150, antiCUBN, antiHCII, and antiCREB3L3 exhibit gradual fluctuations ranging from moderate to severe in both the serum and urine of COVID-19 patients. However, the diagnostic value of individual anti-protein antibodies is low, in both blood serum and urine. On the contrary, joint detection of these antibodies in patients\' serum significantly increases the diagnostic value as demonstrated by the results of principal component analysis (PCA) and support vector machine (SVM). The non-linear regression model achieved an accuracy of 0.833. Furthermore, PCA aided in identifying serum protein markers that have the greatest impact on patient group discrimination. The study revealed that serum serves as a superior analyte for describing protein quantification due to its consistent composition and lack of organic salts and drug residues, which can otherwise affect protein stability.