Transmission characteristic and dynamic analysis of COVID-19 on contact network with Tianjin city in China.

The outbreak of 2019 novel coronavirus pneumonia (COVID-19) has had a profound impact on people's lives around the world, and the spread of COVID-19 between individuals were mainly caused by contact transmission of the social networks. In order to analyze the network transmission of COVID-19, we constructed a case contact network using available contact data of 136 early diagnosed cases in Tianjin. Based on the constructed case contact network, the structural characteristics of the network were first analyzed, and then the centrality of the nodes was analyzed to find the key nodes. In addition, since the constructed network may contain missing edges and false edges, link prediction algorithms were used to reconstruct the network. Finally, to understand the spread of COVID-19 in the network, an individual-based susceptible-latent-exposed-infected-recover (SLEIR) model is established and simulated in the network. The results showed that the disease peak scale caused by the node with the highest centrality is larger, and reducing the contact infection rate of the infected person during the incubation period has a greater impact on the peak disease scale.

Machine Learning Models for Classifying High- and Low-Grade Gliomas: A Systematic Review and Quality of Reporting Analysis.

Objectives: To systematically review, assess the reporting quality of, and discuss improvement opportunities for studies describing machine learning (ML) models for glioma grade prediction. Methods: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy (PRISMA-DTA) statement. A systematic search was performed in September 2020, and repeated in January 2021, on four databases: Embase, Medline, CENTRAL, and Web of Science Core Collection. Publications were screened in Covidence, and reporting quality was measured against the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement. Descriptive statistics were calculated using GraphPad Prism 9. Results: The search identified 11,727 candidate articles with 1,135 articles undergoing full text review and 85 included in analysis. 67 (79%) articles were published between 2018-2021. The mean prediction accuracy of the best performing model in each study was 0.89 ± 0.09. The most common algorithm for conventional machine learning studies was Support Vector Machine (mean accuracy: 0.90 ± 0.07) and for deep learning studies was Convolutional Neural Network (mean accuracy: 0.91 ± 0.10). Only one study used both a large training dataset (n>200) and external validation (accuracy: 0.72) for their model. The mean adherence rate to TRIPOD was 44.5% ± 11.1%, with poor reporting adherence for model performance (0%), abstracts (0%), and titles (0%). Conclusions: The application of ML to glioma grade prediction has grown substantially, with ML model studies reporting high predictive accuracies but lacking essential metrics and characteristics for assessing model performance. Several domains, including generalizability and reproducibility, warrant further attention to enable translation into clinical practice. Systematic Review Registration: PROSPERO, identifier CRD42020209938.

Azvudine is a thymus-homing anti-SARS-CoV-2 drug effective in treating COVID-19 patients.

Azvudine (FNC) is a nucleoside analog that inhibits HIV-1 RNA-dependent RNA polymerase (RdRp). Recently, we discovered FNC an agent against SARS-CoV-2, and have taken it into Phase III trial for COVID-19 patients. FNC monophosphate analog inhibited SARS-CoV-2 and HCoV-OC43 coronavirus with an EC50 between 1.2 and 4.3 µM, depending on viruses or cells, and selective index (SI) in 15-83 range. Oral administration of FNC in rats revealed a substantial thymus-homing feature, with FNC triphosphate (the active form) concentrated in the thymus and peripheral blood mononuclear cells (PBMC). Treating SARS-CoV-2 infected rhesus macaques with FNC (0.07 mg/kg, qd, orally) reduced viral load, recuperated the thymus, improved lymphocyte profiles, alleviated inflammation and organ damage, and lessened ground-glass opacities in chest X-ray. Single-cell sequencing suggested the promotion of thymus function by FNC. A randomized, single-arm clinical trial of FNC on compassionate use (n = 31) showed that oral FNC (5 mg, qd) cured all COVID-19 patients, with 100% viral ribonucleic acid negative conversion in 3.29 ± 2.22 days (range: 1-9 days) and 100% hospital discharge rate in 9.00 ± 4.93 days (range: 2-25 days). The side-effect of FNC is minor and transient dizziness and nausea in 16.12% (5/31) patients. Thus, FNC might cure COVID-19 through its anti-SARS-CoV-2 activity concentrated in the thymus, followed by promoted immunity.

Orthogonal genome-wide screens of bat cells identify MTHFD1 as a target of broad antiviral therapy.

Bats are responsible for the zoonotic transmission of several major viral diseases, including those leading to the 2003 SARS outbreak and likely the ongoing COVID-19 pandemic. While comparative genomics studies have revealed characteristic adaptations of the bat innate immune system, functional genomic studies are urgently needed to provide a foundation for the molecular dissection of the viral tolerance in bats. Here we report the establishment of genome-wide RNA interference (RNAi) and CRISPR libraries for the screening of the model megabat, Pteropus alecto. We used the complementary RNAi and CRISPR libraries to interrogate P. alecto cells for infection with two different viruses: mumps virus and influenza A virus, respectively. Independent screening results converged on the endocytosis pathway and the protein secretory pathway as required for both viral infections. Additionally, we revealed a general dependence of the C1-tetrahydrofolate synthase gene, MTHFD1, for viral replication in bat cells and human cells. The MTHFD1 inhibitor, carolacton, potently blocked replication of several RNA viruses, including SARS-CoV-2. We also discovered that bats have lower expression levels of MTHFD1 than humans. Our studies provide a resource for systematic inquiry into the genetic underpinnings of bat biology and a potential target for developing broad-spectrum antiviral therapy.

Using Machine Learning Algorithms to Predict Efficiency of US’s States Mask Policy During COVID-19

Over the past year, the COVID-19 outbreak deeply and thoroughly changed the way the world is. However, the control policy’s efficiency is still in dispute. Through the way of machine learning, now we are able to find and to probe into the data about corona-virus spreading patterns in a short period of time, suiting the remedy to the case, to launch targeted prevention policies, and minimize the economic loss under the premise of control the spread of the virus on a large scale. We directly use the LES algorithm and K-means clustering to make a comparison about the data feature. Therefore, the results are much more convincing than using any other recursive analyzing method alone. It is precise because of the ID3 algorithm, which we use for further analysis, to find the reason why those policies work.

Transmission analysis of COVID-19 with discrete time imported cases: Tianjin and Chongqing as cases.

In 2020, an unexpectedly large outbreak of the coronavirus disease 2019 (COVID-19) epidemic was reported in mainland China. As we known, the epidemic was caused by imported cases in other provinces of China except for Hubei in 2020. In this paper, we developed a differential equation model with tracing isolation strategy with close contacts of newly confirmed cases and discrete time imported cases, to perform assessment and risk analysis for COVID-19 outbreaks in Tianjin and Chongqing city. Firstly, the model behavior without imported cases was given. Then, the real-time regeneration number in Tianjin and Chongqing city revealed a trend of rapidly rising, and then falling fast. Finally, sensitivity analysis demonstrates that the earlier with Wuhan lock-down, the fewer cases in these two cities. One can obtain that the tracing isolation of close contacts of newly confirmed cases could effectively control the spread of the disease. But it is not sensitive for the more contact tracing isolation days on confirmed cases, the fewer cases. Our investigation model could be potentially helpful to provide model building technology for the transmission of COVID-19.