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A new study by Westlake University sheds light on the serum of COVID-19 patients
en.hangzhou.com.cn  2020/04/09 14:39  Zhejiang News

The proteome big data lab led by Guo Tiannan, PI of School of Life Sciences, Westlake University, made an important discovery in the research of novel coronavirus in the past few days.

Upon systematic tests of the protein and metabolite molecules from the blood of COVID-19 patients, they, along with their cooperation team, found a variety of unique molecular changes in the critically ill patients’ serum and also a series of biomarkers, which is expected to provide guidance for predicting the progression of mild cases to severe cases.

The related research results have been put online on the preprint platform medRxiv at 0:15 on April 8, Beijing time.

COVID-19 has spread rapidly worldwide, with more than one million people infected. However, we know little about the disease changes at the microscopic molecular level, seeing only some clinical symptoms and imaging features. We still know neither the impacts of the novel coronavirus infection on patients nor why some mild cases quickly evolve into severe cases in clinical treatment.

Compared to the control (healthy people) group, the ordinary influenza group and the mild case group, 93 specific protein expressions and 204 metabolic molecules with characteristic changes have been found in the samples from critically ill patients with COVID-19. Among them, 50 proteins are related to the macrophages, the complement system, and the platelet degranulation in the patients. The research team also found significant reductions in more than 100 amino acids and more than 100 lipids in critically ill patients infected with the novel coronavirus. This may be the consumption caused by the rapid expansion of the virus and can provide a certain reference for clinicians to monitor the disease and make or adjust treatment plans.

In addition, Guo Tiannan's team further screened 22 proteins and 7 metabolites that are unique in severe patients by using the machine learning method based on the mass spectrometry analysis data. Patients with a serum sample composition that matches this combination are likely to be severely ill or have a high likelihood of developing severe cases. This finding is expected to be used in the prediction of severe patients, promoting the rational allocation of medical resources, and providing some guidance on drug selection for severe patients. Of course, the results still need to be verified in more independent clinical cohort studies.

In the next step, the laboratory will continue to conduct in-depth research on the novel coronavirus infection by using interdisciplinary and proteomic technologies, hoping to obtain more discoveries that can help to understand the disease progression and assist in existing detection and diagnosis means to achieve more accurate and efficient treatment effects.

Author: Editor:Wang Yueyun
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