瘟疫与中医:我们为什么要关注?

Wen-Yi refers to an acute infectious disease that is highly contagious and can cause widespread epidemics.Also known as“Yi”or“Li”in the Chinese medicine(CM)literature,Wen-Yi diseases,such as corona virus disease 2019(COVID-19),seasonal influenza,malaria,and tuberculosis,have imposed a significant disease burden globally.

A Longitudinal Cohort Study Using a Modified Child-Pugh Score to Escalate Respiratory Support in COVID-19 Patients—Hubei Province,China,2020

Background:We explored a phenotype of liver dysfunction based on modified Child-Pugh(MCP)with coronavirus disease 2019(COVID-19)and evaluated its relationship with escalation of respiratory support and survival.Methods:This was a retrospective cohort study involving COVID-19 in-patients at the Wuhan Jinyintan Hospital.This study was performed between January 24,2020 and March 31,2020.Escalation of respiratory support and survival were evaluated.Furthermore,the trajectory of liver function was delineated considering the risk of escalation of respiratory support and survival using multilevel logistic regression.Results:A total of 298 patients were enrolled in this study.A higher proportion of patients with MCPB on admission exhibited an escalated respiratory support(26 of 55;47.3%)when compared to patients with MCP-A(9 of 62;14.5%),indicating that MCP-B was strongly associated with escalation of respiratory support[adjusted hazard ratio(HR):4.530;95%confidence interval(CI):2.060–9.970;P<0.001].Among the patients on escalated respiratory support,5(55.6%of 9)patients with MCP-A died compared to 10(38.5%of 26)of the patients with MCP-B.Patients with a history of liver disease had a higher mortality risk(adjusted HR:7.830;95%CI:1.260-48.420).Conclusion:MCP is efficient at stratifying liver dysfunction levels in COVID-19 patients and is strongly associated with escalation of respiratory support.

Comparing different machine learning techniques for predicting COVID-19 severity

Background Coronavirus disease 2019(COVID-19)is still ongoing spreading globally,machine learning techniques were used in disease diagnosis and to predict treatment outcomes,which showed favorable performance.The present study aims to predict COVID-19 severity at admission by different machine learning techniques including random forest(RF),support vector machine(SVM),and logistic regression(LR).Feature importance to COVID-19 severity were further identified.Methods A retrospective design was adopted in the JinYinTan Hospital from January 26 to March 28,2020,eighty-six demographic,clinical,and laboratory features were selected with LassoCV method,Spearman’s rank correlation,experts’opinions,and literature evaluation.RF,SVM,and LR were performed to predict severe COVID-19,the performance of the models was compared by the area under curve(AUC).Additionally,feature importance to COVID-19 severity were analyzed by the best performance model.Results A total of 287 patients were enrolled with 36.6%severe cases and 63.4%non-severe cases.The median age was 60.0 years(interquartile range:49.0–68.0 years).Three models were established using 23 features including 1 clinical,1 chest computed tomography(CT)and 21 laboratory features.Among three models,RF yielded better overall performance with the highest AUC of 0.970 than SVM of 0.948 and LR of 0.928,RF also achieved a favorable sensitivity of 96.7%,specificity of 69.5%,and accuracy of 84.5%.SVM had sensitivity of 93.9%,specificity of 79.0%,and accuracy of 88.5%.LR also achieved a favorable sensitivity of 92.3%,specificity of 72.3%,and accuracy of 85.2%.Additionally,chest-CT had highest importance to illness severity,and the following features were neutrophil to lymphocyte ratio,lactate dehydrogenase,and D-dimer,respectively.Conclusions Our results indicated that RF could be a useful predictive tool to identify patients with severe COVID-19,which may facilitate effective care and further optimize resources.

Infection with SARS-CoV-2 can cause pancreatic impairment

Evidence suggests associations between COVID-19 patients or vaccines and glycometabolic dysfunction and an even higher risk of the occurrence of diabetes.Herein,we retrospectively analyzed pancreatic lesions in autopsy tissues from 67 SARS-CoV-2 infected non-human primates(NHPs)models and 121 vaccinated and infected NHPs from 2020 to 2023 and COVID-19 patients.Multi-label immunofluorescence revealed direct infection of both exocrine and endocrine pancreatic cells by the virus in NHPs and humans.Minor and limited phenotypic and histopathological changes were observed in adult models.Systemic proteomics and metabolomics results indicated metabolic disorders,mainly enriched in insulin resistance pathways,in infected adult NHPs,along with elevated fasting C-peptide and C-peptide/glucose ratio levels.Furthermore,in elder COVID-19 NHPs,SARS-CoV-2 infection causes loss of beta(β)cells and lower expressed-insulin in situ characterized by islet amyloidosis and necrosis,activation ofα-SMA and aggravated fibrosis consisting of lower collagen in serum,an increase of pancreatic inflammation and stress markers,ICAM-1 and G3BP1,along with more severe glycometabolic dysfunction.In contrast,vaccination maintained glucose homeostasis by activating insulin receptorαand insulin receptorβ.Overall,the cumulative risk of diabetes post-COVID-19 is closely tied to age,suggesting more attention should be paid to blood sugar management in elderly COVID-19 patients.