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. © Wolters Kluwer Health, Inc. All rights reserved.

[Consideration on the possible etiological mechanisms and countermeasures about severe acute hepatitis of unknown origin in children].

Since April 2022, severe acute hepatitis of unknown origin in children has spread to 35 countries and regions around the world, and more than 1 010 cases have been reported. Since the severe acute hepatitis of unknown origin involves a wide range of areas and has a high rate, it is critical to identify the etiology and establish effective preventive, diagnostic and therapeutic measures as soon as possible. This study discusses the possible mechanisms and countermeasures of the severe acute hepatitis of unknown origin in children. It speculates that the occurrence of the recent severe acute hepatitis might be related to adenovirus, adeno-associated virus infection, and the COVID-19 epidemic, while the difference in HLA polymorphism among different races might be related to the fact that reported cases were more common in Europe and the United States. Based on the currently available evidence, it can be preliminarily judged that the risk of large-scale outbreak of severe acute hepatitis of unknown origin in children would be low in China, but the persistent awareness and vigilance of the etiology is still needed.

Designing a novel nano-vaccine against SARS-CoV-2

The new coronavirus SARS-CoV-2 has become a global pandemic, which has had a huge impact on the lives of people around the world and has caused huge impacts and losses on global economic development. To now, there is still no effective drug or therapy against coronavirus. A large number of studies have shown that vaccines are the ultimate weapon to eliminate major infectious diseases. The development of new vaccines against new coronaviruses is the best way to prevent new coronavirus infections. In this study, we developed a new vaccine against the new coronavirus by combining our self-developed nano adjuvant loaded with carnosine graphene oxide adjuvant with loaded with CpG molecule and RBD protein antigen. Our results showed that this vaccine can produce high titer anti-SARS-CoV-2 RBD antibody neutralizing SARS-CoV-2 in mice within 2 weeks.

[Exploring the mechanism of liver enzyme abnormalities in patients with novel coronavirus-infected pneumonia].

Objective: To explore and analyze the possible mechanism of liver injury in patients with coronavirus disease 2019 (novel coronavirus pneumonia, NCP). Methods: The correlation between ALT, AST and other liver enzyme changes condition and NCP patients' disease status reported in the literature was comprehensively analyzed. ACE2 expression in liver tissue for novel coronavirus was analyzed based on single cell sequencing (GSE115469) data. RNA-Seq method was used to analyze Ace2 expression and transcription factors related to its expression in liver tissues at various time-points after hepatectomy in mouse model of acute liver injury with partial hepatectomy. t-test or Spearman rank correlation analysis was used for statistical analysis. Results: ALT and AST were abnormally elevated in some patients with novel coronavirus infection, and the rate and extent of ALT and AST elevation in severe NCP patients were higher than those in non-severe patients. Liver tissue results of single cell sequencing and immunohistochemistry showed that ACE2 was only expressed in bile duct epithelial cells of normal liver tissues, and very low in hepatocytes. In a mouse model of acute liver injury with partial hepatectomy, Ace2 expression was down-regulated on the first day, but it was elevated up to twice of the normal level on the third day, and returned to normal level on seventh day when the liver recovered and hepatocyte proliferation stopped. Whether this phenomenon suggests that the bile duct epithelial cells with positive expression of Ace2 participate in the process of liver regeneration after partial hepatectomy deserves further study. In RNA-Seq data, 77 transcription factors were positively correlated with the expression of Ace2 (r > 0.2, FDR < 0.05), which were mainly enriched in the development, differentiation, morphogenesis and cell proliferation of glandular epithelial cells. Conclusion: We assumed that in addition to the over activated inflammatory response in patients with NCP, the up-regulation of ACE2 expression in liver tissue caused by compensatory proliferation of hepatocytes derived from bile duct epithelial cells may also be the possible mechanism of liver tissue injury caused by 2019 novel coronavirus infection.

[Exploration of omics mechanism and drug prediction of coronavirus-induced heart failure based on clinical bioinformatics].

Objective: Present study investigated the mechanism of heart failure associated with coronavirus infection and predicted potential effective therapeutic drugs against heart failure associated with coronavirus infection. Methods: Coronavirus and heart failure were searched in the Gene Expression Omnibus (GEO) and omics data were selected to meet experimental requirements. Differentially expressed genes were analyzed using the Limma package in R language to screen for differentially expressed genes. The two sets of differential genes were introduced into the R language cluster Profiler package for gene ontology (GO) and Kyoto gene and genome encyclopedia (KEGG) pathway enrichment analysis. Two sets of intersections were taken. A protein interaction network was constructed for all differentially expressed genes using STRING database and core genes were screened. Finally, the apparently accurate treatment prediction platform (EpiMed) independently developed by the team was used to predict the therapeutic drug. Results: The GSE59185 coronavirus data set was searched and screened in the GEO database, and divided into wt group, ΔE group, Δ3 group, Δ5 group according to different subtypes, and compared with control group. After the difference analysis, 191 up-regulated genes and 18 down-regulated genes were defined. The GEO126062 heart failure data set was retrieved and screened from the GEO database. A total of 495 differentially expressed genes were screened, of which 165 were up-regulated and 330 were down-regulated. Correlation analysis of differentially expressed genes between coronavirus and heart failure was performed. After cross processing, there were 20 GO entries, which were mainly enriched in virus response, virus defense response, type Ⅰ interferon response, γ interferon regulation, innate immune response regulation, negative regulation of virus life cycle, replication regulation of viral genome, etc. There were 5 KEGG pathways, mainly interacting with tumor necrosis factor (TNF) signaling pathway, interleukin (IL)-17 signaling pathway, cytokine and receptor interaction, Toll-like receptor signaling pathway, human giant cells viral infection related. All differentially expressed genes were introduced into the STRING online analysis website for protein interaction network analysis, and core genes such as signal transducer and activator of transcription 3, IL-10, IL17, TNF, interferon regulatory factor 9, 2'-5'-oligoadenylate synthetase 1, mitogen-activated protein kinase 3, radical s-adenosyl methionine domain containing 2, c-x-c motif chemokine ligand 10, caspase 3 and other genes were screened. The drugs predicted by EpiMed's apparent precision treatment prediction platform for disease-drug association analysis were mainly TNF-α inhibitors, resveratrol, ritonavir, paeony, retinoic acid, forsythia, and houttuynia cordata. Conclusions: The abnormal activation of multiple inflammatory pathways may be the cause of heart failure in patients after coronavirus infection. Resveratrol, ritonavir, retinoic acid, amaranth, forsythia, houttuynia may have therapeutic effects. Future basic and clinical research is warranted to validate present results and hypothesis.

Epidemiologic and clinical characteristics of 91 hospitalized patients with COVID-19 in Zhejiang, China: a retrospective, multi-centre case series.

BACKGROUND: Recent studies have focused on initial clinical and epidemiological characteristics of the coronavirus disease 2019 (COVID-19), which is the mainly revealing situation in Wuhan, Hubei. AIM: This study aims to reveal more data on the epidemiological and clinical characteristics of COVID-19 patients outside of Wuhan, Zhejiang, China. DESIGN: This study was a retrospective case series. METHODS: Eighty-eight cases of laboratory-confirmed and three cases of clinically confirmed COVID-19 were admitted to five hospitals in Zhejiang province, China. Data were collected from 20 January 2020 to 11 February 2020. RESULTS AND DISCUSSION: Of all 91 patients, 88 (96.70%) were laboratory-confirmed COVID-19 with throat swab samples that tested positive for SARS-Cov-2, three (3.30%) cases were clinically diagnosed. The median age of the patients was 50 (36.5-57) years, and female accounted for 59.34%. In this sample, 40 (43.96%) patients had contracted the disease from local cases, 31 (34.07%) patients had been to Wuhan/Hubei, eight (8.79%) patients had contacted with people from Wuhan, and 11 (12.09%) patients were diagnosed after having flown together in the same flight with no passenger that could later be identified as the source of infection. In particular within the city of Ningbo, 60.52% cases can be traced back to an event held in a temple. The most common symptoms were fever (71.43%), cough (60.44%) and fatigue (43.96%). The median of incubation period was 6 (interquartile range 3-8) days and the median time from the first visit to a doctor to the confirmed diagnosis was 1 (1-2) days. According to the chest computed tomography scans, 67.03% cases had bilateral pneumonia. CONCLUSIONS: Social activity cluster, family cluster and flying alongside with persons already infected with COVID-19 were how people got infected with COVID-19 in Zhejiang.