Mechanism of HanShi ZuFei formula in treatment of COVID-19 based on network pharmacology and molecular docking technology

Objective: To explore the mechanism of Hanshi Zufei Formula (HSZFF) treating coronavirus disease 2019 (COVID-19) by the data mining analysis of network pharmacology and the molecular docking.

Analysis of clinical characteristics of 118 patients with severe COVID-19

Objective: To analyse the relevant clinical predictors of patients who died of severe novel coronavirus disease 2019(COVID-19).

Novel serological biomarkers for inflammation in predicting disease severity in patients with COVID-19.

BACKGROUND: Patients with severe coronavirus disease 2019 (COVID-19) develop acute respiratory distress and multi-system organ failure and are associated with poor prognosis and high mortality. Thus, there is an urgent need to identify early diagnostic and prognostic biomarkers to determine the risk of developing serious illness. METHODS: We retrospectively analyzed 114 patients with COVID-19 at the Jinyintan Hospital, Wuhan based on their clinical and laboratory data. Patients were categorized into severe and mild to moderate disease groups. We analyzed the potential of serological inflammation indicators in predicting the severity of COVID-19 in patients using univariate and multivariate logistic regression, receiver operating characteristic curves, and nomogram analysis. The Spearman method was used to understand the correlation between the serological biomarkers and duration of hospital stay. RESULTS: Patients with severe disease had reduced neutrophils and lymphocytes; severe coagulation dysfunction; altered content of biochemical factors (such as urea, lactate dehydrogenase); elevated high sensitivity C-reactive protein levels, neutrophil-lymphocyte, platelet-lymphocyte, and derived neutrophil-lymphocyte ratios, high sensitivity C-reactive protein-prealbumin ratio (HsCPAR), systemic immune-inflammation index, and high sensitivity C-reactive protein-albumin ratio (HsCAR); and low lymphocyte-monocyte ratio, prognostic nutritional index (PNI), and albumin-to-fibrinogen ratio. PNI, HsCAR, and HsCPAR correlated with the risk of severe disease. The nomogram combining the three parameters showed good discrimination with a C-index of 0.873 and reliable calibration. Moreover, HsCAR and HsCPAR correlated with duration of hospital stay. CONCLUSION: Taken together, PNI, HsCAR, and HsCPAR may serve as accurate biomarkers for the prediction of disease severity in patients with COVID-19 upon admission/hospitalization.