生物标志物可以预测COVID-19疾病

Utkarsh Sharma, Shikha Sharma
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引用次数: 0

摘要

2019冠状病毒病(COVID-19)的持续流行给卫生专业人员带来了一系列挑战。为了尽可能多地挽救生命,必须及时诊断和入院,并对风险进行分层,有效利用重症监护服务,选择适当的治疗方法,对患者进行监测,并确保及时出院。实验室标记物,也称为生物标记物,可以提供客观的附加信息,并有可能显著影响患者护理的各个方面。临床评估是必要的,但实验室标记可以提供这一信息。COVID-19病毒不是引起呼吸系统的感染;相反,它是一种多系统疾病,由弥漫性全系统过程引起,涉及免疫、神经和内分泌系统在炎症和凝血级联反应中的复杂相互作用。由于对病毒对人体的影响以及人体如何对其作出反应有了更好的了解,各种潜在的生物标志物已经被发现。在这里,病理生理学和当前数据被检查与各种生物标志物,如免疫和炎症生物标志物,凝血和血液学生物标志物,以及心脏,生化和其他生物标志物。这篇综述全面分析了生物标志物与临床特征、病毒载量、治疗效果之间的关系,以及这些知识如何最有用地为患者护理做出贡献。
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Biomarkers can Predict COVID-19 Disease
abstract: Health professionals have been confronted with a series of challenges because of the ongoing pandemic of coronavirus disease 2019 (COVID-19). To save the greatest number of lives possible, it is essential to make a prompt diagnosis and admission to the hospital, as well as to stratify risks, make efficient use of intensive care services, choose appropriate treatments, monitor patients, and ensure a prompt discharge. Laboratory markers, also known as biomarkers, can provide additional information that is objective and has the potential to significantly influence various aspects of patient care. Clinical assessment is necessary, but laboratory markers can provide this information. The COVID-19 virus is not an infection that causes the respiratory system; rather, it is a multisystem disease that is caused by a diffuse system-wide process that involves a complex interplay of the immune, nervous, and endocrine systems in inflammatory and coagulative cascades. A wide variety of potential biomarkers have been uncovered because of a better understanding of the virus's effects on the body and how the body responds to them. Here, the pathophysiology and current data are examined in relation to various kinds of biomarkers, such as immunological and inflammation biomarkers, coagulation and hematological biomarkers, as well as cardiac, biochemical, and other biomarkers. This review provides a comprehensive analysis of the research on the association between biomarkers and clinical characteristics, viral load, treatment efficacy, and how this knowledge might most usefully contribute to patient care.
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