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Additional file 1: Fig. S1. Best parameter select for Meta-transcript linkage group cluster. Red represents all 50 species identified; gray represents more than 30, less than or equal to 49; less than 30 is not shown. Fig. S2. Enterovirus phylogenetic tree between mild & severe case using whole genome wide. A: the phylogenetic tree of the enterovirus 71. B: the phylogenetic tree of the coxsackievirus A4. Red color highlight in branch means mild case and black means severe case. The branch name started with “gi” as the reference sequences. Fig. S3. Proportion of DEGs in different age. Yellow, mild case, Red, severe case. Fig. S4. Gene marker identify algorithm (LOOCV) to find microorganisms gene marker index distinguishing Severe & Mild cases. A: minimum redundancy–maximum relevance (mRMR) feature selection method and leave-one-out cross-validation (LOOCV) steps. ‘x’ means any genes and ‘a’ means weight of the gene in the formula, it can be plus and minus. B: Find the optimum lowest error rate subset to build a linear discrimination classifier. Fig. S5. The rpkm value of all 20 gene markers for the mild and severe case distinguish index model. Yellow, mild case, Red, severe case. The annotation of gene markers were list left together with the weight of each gene in the mRMR selection method. The red box highlight the shared 2 genes in the model build from samples only EV71 positive. Fig. S6. EV71 positive samples index model for distinguish severe and mild case. A: leave-one-out cross-validation (LOOCV) to find the optimum subset to build a linear discrimination classifier (18 genes, Training: M:S = 31:35). B: For each individual, an index was calculated to evaluate the risk of severe HFMD disease. The histogram shows the distribution of indices for all individuals. Red means mild case and blue means severe case. C: new samples for test use the index model (Verify: M:S = 10:10). Lowest error rate: 16.67%. Mild case and severe case were distinguished by the index model (p

Jing Yuan,1,* Shanglong Kou,2,* Yanhua Liang,1 JianFeng Lan,1 Xiaohe Li,1 Lijiao Zeng,1 Rongrong Zou,1 Yingxia Liu,1 Lei Liu,1 Yanchao Pan1 1Diagnosis and Treatment of Infectious Disease Research Center, Shenzhen Third People’s Hospital, Shenzhen, Guangdong, People’s Republic of China; 2College of Chemistry and Chemical Engineering, South China University of Technology, Guangzhou, Guangdong, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yanchao PanDiagnosis and Treatment of Infectious Diseases Research Laboratory, Shenzhen Third People’s Hospital, Shenzhen 518112, People’s Republic of ChinaTel +86 755-61222333Fax +86 755-61238928Email panychao@126.comPurpose: Novel coronavirus disease has become such an escalating epidemic that the exponential growth of infected patients has overloaded the health-care systems in many countries. Determination of early assessments for patients with a risk of clinical deterioration would benefit the management of COVID-19 outbreaks.Patients and Methods: A total of 214 confirmed COVID-19 patients were enrolled from January 11th to February 11th 2020. Medical records including laboratory parameters, clinical outcomes and other characteristics of the admitted patients were analyzed retrospectively.Results: The critical patients experienced a significantly prolonged onset–admission interval and presented with lymphopenia (r=− 0.547, p=0.015) and lower albumin level (p< 0.001) 6 days after symptom onset. Early admission of critical patients significantly reduced the duration of hormone therapy. Starting from 9 days of hospital stay, the reduced lymphocyte counts exhibited linear growth. Furthermore, on days 9 and 12, significant correlations were demonstrated between immunological manifestations and duration of hormone therapy in critical patients, and length of hospital stay in severe patients. In addition, the virus negative conversion rate was more significantly correlated with increased lymphocytes in critical patients.Conclusion: Early intervention, within 6 days of symptom onset, benefited patients’ recovery from critical illness. The 9– 12 days of hospital care represented a valuable window during which to evaluate the therapeutic effects on physical recovery and virus clearance.Keywords: COVID-19, critical illness, potential window, length of hospital stay

Background An outbreak of coronavirus disease 2019 (COVID-19) is becoming a public health emergency. Data are limited on the duration and host factors related to viral shedding. Methods In this retrospective study, risk factors associated with severe acute respiratory coronavirus 2 (SARS-CoV-2) RNA shedding were evaluated in a cohort of 113 symptomatic patients from two hospitals outside Wuhan. Results The median duration of SARS-CoV-2 RNA detection was 17 days (Interquartile Range [IQR], 13–22 days) as measured from illness onset. When comparing patients with early (