Machine Learning-Based Scoring System for Early Prognosis Evaluation of Patients with Coronavirus Disease 2019

Background The global spread of coronavirus disease 2019 (COVID-19) continues to threaten human health security, exerting considerable pressure on healthcare systems worldwide. While prognostic models for COVID-19 hospitalized or intensive care patients are currently available, prognostic models developed for large cohorts of thousands of individuals are still lacking. Methods Between February 4 and April 16, 2020, we enrolled 3,974 patients admitted with COVID-19 disease in the Wuhan Huo-Shen-Shan Hospital and the Maternal and Child Hospital, Hubei Province, China. (1) Screening of key prognostic factors: A univariate Cox regression analysis was performed on 2,649 patients in the training set, and factors affecting prognosis were initially screened. Subsequently, a random survival forest model was established through machine analysis to further screen for factors that are important for prognosis. Finally, multivariate Cox regression analysis was used to determine the synergy among various factors related to prognosis. (2) Establishment of a scoring system: The nomogram algorithm established a COVID-19 patient death risk assessment scoring system for the nine selected key prognostic factors, calculated the C index, drew calibration curves and drew training set patient survival curves. (3) Verification of the scoring system: The scoring system assessed 1,325 patients in the test set, splitting them into high- and low-risk groups, calculated the C-index, and drew calibration and survival curves. Results The cross-sectional study found that age, clinical classification, sex, pulmonary insufficiency, hypoproteinemia, and four other factors (underlying diseases: blood diseases, malignant tumor;complications: digestive tract bleeding, heart dysfunction) have important significance for the prognosis of the enrolled patients with COVID-19. Herein, we report the discovery of the effects of hypoproteinemia and hematological diseases on the prognosis of COVID-19. Meanwhile, the scoring system established here can effectively evaluate objective scores for the early prognoses of patients with COVID-19 and can divide them into high- and low-risk groups (using a scoring threshold of 117.77, a score below which is considered low risk). The efficacy of the system was better than that of clinical classification using the current COVID-19 guidelines (C indexes, 0.95 vs. 0.89). Conclusions Age, clinical typing, sex, pulmonary insufficiency, hypoproteinemia, and four other factors were important for COVID-19 survival. Compared with general statistical methods, this method can quickly and accurately screen out the relevant factors affecting prognosis, provide an order of importance, and establish a scoring system based on the nomogram model, which is of great clinical significance.

FBXO11 amplifies type I interferon signaling to exert antiviral effects by facilitating the assemble of TRAF3-TBK1-IRF3 complex.

As the key component of host innate antiviral immunity, type I interferons (IFN-Is) exert multiple antiviral effects by inducing hundreds of IFN-stimulated genes. However, the precise mechanism involved in host sensing of IFN-I signaling priming is particularly complex and remains incompletely resolved. This research identified F-box protein 11 (FBXO11), a component of the E3-ubiquitin ligase SKP/Cullin/F-box complex, acted as an important regulator of IFN-I signaling priming and antiviral process against several RNA/DNA viruses. FBXO11 functioned as an essential enhancer of IFN-I signaling by promoting the phosphorylation of TBK1 and IRF3. Mechanistically, FBXO11 facilitated the assembly of TRAF3-TBK1-IRF3 complex by mediating the K63 ubiquitination of TRAF3 in a NEDD8-dependent manner to amplify the activation of IFN-I signaling. Consistently, the NEDD8-activating enzyme inhibitor MLN4921 could act as a blocker for FBXO11-TRAF3-IFN-I axis of signaling. More significantly, examination of clinical samples of chronic hepatitis B virus (HBV) infection and public transcriptome database of severe acute respiratory syndrome coronavirus-2-, HBV-, and hepatitis C virus-infected human samples revealed that FBXO11 expression was positively correlated with the stage of disease course. Taken together, these findings suggest that FBXO11 is an amplifier of antiviral immune responses and might serve as a potential therapeutic target for a number of different viral diseases.

Advances in mRNA vaccines.

mRNA vaccines have been increasingly recognized as a powerful vaccine platform since the FDA approval of two COVID-19 mRNA vaccines, which demonstrated outstanding prevention efficacy as well as great safety profile. Notably, nucleoside modification and lipid nanoparticle-facilitated delivery has greatly improved the immunogenicity, stability, and translation efficiency of mRNA molecule. Here we review the recent progress in mRNA vaccine development, including nucleoside modification, in vitro synthesis and product purification, and lipid nanoparticle vectors for in vivo delivery and efficient translation. We also briefly introduce the clinical application of mRNA vaccine in preventing infectious diseases and treating inflammatory diseases including cancer.

[Bioinformatics Analysis of the Influence of Coronavirus Infection on Hematopoietic System and Potential Intervention Drugs and Their Significance for COVID-19].

OBJECTIVE: To analyze and predict the effect of coronavirus infection on hematopoietic system and potential intervention drugs, and explore their significance for coronavirus disease 2019 (COVID-19). METHODS: The gene expression omnibus (GEO) database was used to screen the whole genome expression data related with coronavirus infection. The R language package was used for differential expression analysis and KEGG/GO enrichment analysis. The core genes were screened by PPI network analysis using STRING online analysis website. Then the self-developed apparent precision therapy prediction platform (EpiMed) was used to analyze diseases, drugs and related target genes. RESULTS: A database in accordance with the criteria was found, which was derived from SARS coronavirus. A total of 3606 differential genes were screened, including 2148 expression up-regulated genes and 1458 expression down-regulated genes. GO enrichment mainly related with viral infection, hematopoietic regulation, cell chemotaxis, platelet granule content secretion, immune activation, acute inflammation, etc. KEGG enrichment mainly related with hematopoietic function, coagulation cascade reaction, acute inflammation, immune reaction, etc. Ten core genes such as PTPRC, ICAM1, TIMP1, CXCR5, IL-1B, MYC, CR2, FSTL1, SOX1 and COL3A1 were screened by protein interaction network analysis. Ten drugs with potential intervention effects, including glucocorticoid, TNF-α inhibitor, salvia miltiorrhiza, sirolimus, licorice, red peony, famciclovir, cyclosporine A, houttuynia cordata, fluvastatin, etc. were screened by EpiMed plotform. CONCLUSION: SARS coronavirus infection can affect the hematopoietic system by changing the expression of a series of genes. The potential intervention drugs screened on these grounds are of useful reference significance for the basic and clinical research of COVID-19.