COVID-19 or treatment associated immunosuppression may trigger hepatitis B virus reactivation: A case report.

BACKGROUND: Since the initial recognition of coronavirus disease 2019 (COVID-19) in Wuhan, this infectious disease has spread to most areas of the world. The pathogenesis of COVID-19 is yet unclear. Hepatitis B virus (HBV) reactivation occurring in COVID-19 patients has not yet been reported. CASE SUMMARY: A 45-year-old hepatitis B man with long-term use of adefovir dipivoxil and entecavir for antiviral therapy had HBV reactivation after being treated with methylprednisolone for COVID-19 for 6 d. CONCLUSION: COVID-19 or treatment associated immunosuppression may trigger HBV reactivation.

Multicenter analysis and a rapid screening model to predict early novel coronavirus pneumonia using a random forest algorithm.

ABSTRACT: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.

Characterising contact in disease outbreaks via a network model of spatial-temporal proximity (preprint)

Contact tracing is a key tool in epidemiology to identify and control outbreaks of infectious diseases. Existing contact tracing methodologies produce contact maps of individuals based on a binary definition of contact which can be hampered by missing data and indirect contacts. Here, we present our Spatial-temporal Epidemiological Proximity (StEP) model to recover contact maps in disease outbreaks based on movement data. The StEP model accounts for imperfect data by considering probabilistic contacts between individuals based on spatial-temporal proximity of their movement trajectories, creating a robust movement network despite possible missing data and unseen transmission routes. We showcase the potential of StEP for contact tracing with outbreaks of multidrug-resistant bacteria and COVID-19 in a large hospital group in London, UK. In addition to the core structure of contacts that can be recovered using traditional methods of contact tracing, the StEP model reveals missing contacts that connect seemingly separate outbreaks. Comparison with genomic data further confirmed that these additional contacts indeed improve characterisation of disease transmission and so highlights how the StEP framework can inform effective strategies of infection control and prevention.

A rapid screening model for early predicting novel coronavirus pneumonia in Zhejiang Province of China: a multicenter study.

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

Investigation on mechanism and active components of Shufeng Jiedu Capsule in treatment of COVID-19 based on network pharmacology and molecular docking

Objective: To explore the novel coronavirus disease 2019 (COVID-19) treatment mechanism and active ingredients of Shufeng Jiedu Capsule by network pharmacology and molecular docking. Methods: TCMSP databases were used to search the chemical composition and target of Shufeng Jiedu Capsule, which was composed of Isatidis Radix, Polygonum cuspidatum, Forsythia suspensa, Phragmitis Rhizoma, Patrinia, Verbena officinalis, Bupleurum chinense, and Glycyrrhiza uralensis. The Swiss target prediction database was used to remove the target with possibility of 0. The corresponding targets of the disease were searched in the GeneCards and OMIM databases with the key words of "coronavirus", "pneumonia", "cough", and "fever". Through the UniProt databases to correct the name of the target point, take the intersection of Shufeng Jiedu Capsule and the disease target point, then use the software of Cytoscape 3.7.2 to build the network of traditional Chinese medicine-compound-target for visualization, through DAVID databases to carry out the GO function enrichment analysis and KEGG pathway enrichment analysis, predict the interaction mechanism of the target, and draw the column and bubble chart for visualization. The novel coronavirus (SARS-CoV-2) 3CL hydrolase was then docking with all compounds and the first five compounds with the least binding energy were selected for docking with angiotensin-converting enzyme II (ACE2). Results: The traditional Chinese medicine-compound-target compound target network contains eight kinds of traditional Chinese medicine-compound-target, 157 compounds and 260 corresponding targets. The key targets were PTGS2, ESR1, AR, etc. There were 393 items in GO functional enrichment analysis (P < 0.05), and 139 signaling pathways in KEGG pathway enrichment analysis. Molecular docking results showed that SARS-CoV-2 3CL hydrolase and ACE2 binding energy of the five core compounds, including 6-(3-oxoindolin-2-ylidene) indolo [2,1-b] quinazolin-12-one, bicuculline, physciondiglucoside, dihydroverticillatine, and licoisoflavanone, was smaller than that of recommended chemical drugs, and the binding energy to ACE2 was similar to that of the recommended chemical drug. Conclusion: The compounds in Shufeng Jiedu Capsule can regulate the signaling pathway of human cytomegalovirus infection, Kaposi's sarcoma associated herpesvirus infection, IL-17 signaling pathway, small cell lung cancer, etc. to treat COVID-19 by binding with SARS-CoV-2 3CL hydrolase and ACE2.