Multi-omics analysis reveals genomic, clinical and immunological features of SARS-CoV-2 virus target genes in pan-cancer.

The SARS-CoV-2 virus, also known as the severe acute respiratory syndrome coronavirus 2, has raised great threats to humans. The connection between the SARS-CoV-2 virus and cancer is currently unclear. In this study, we thus evaluated the multi-omics data from the Cancer Genome Atlas (TCGA) database utilizing genomic and transcriptomic techniques to fully identify the SARS-CoV-2 target genes (STGs) in tumor samples from 33 types of cancers. The expression of STGs was substantially linked with the immune infiltration and may be used to predict survival in cancer patients. STGs were also substantially associated with immunological infiltration, immune cells, and associated immune pathways. At the molecular level, the genomic changes of STGs were frequently related with carcinogenesis and patient survival. In addition, pathway analysis revealed that STGs were involved in the control of signaling pathways associated with cancer. The prognostic features and nomogram of clinical factors of STGs in cancers have been developed. Lastly, by mining the cancer drug sensitivity genomics database, a list of potential STG-targeting medicines was compiled. Collectively, this work demonstrated comprehensively the genomic alterations and clinical characteristics of STGs, which may offer new clues to explore the mechanisms on a molecular level between SARS-CoV-2 virus and cancers as well as provide new clinical guidance for cancer patients who are threatened by the COVID-19 epidemic.

Quantitative analysis of the impact of various urban socioeconomic indicators on search-engine-based estimation of COVID-19 prevalence.

Numerous studies have proposed search engine-based estimation of COVID-19 prevalence during the COVID-19 pandemic; however, their estimation models do not consider the impact of various urban socioeconomic indicators (USIs). This study quantitatively analysed the impact of various USIs on search engine-based estimation of COVID-19 prevalence using 15 USIs (including total population, gross regional product (GRP), and population density) from 369 cities in China. The results suggested that 13 USIs affected either the correlation (SC-corr) or time lag (SC-lag) between search engine query volume and new COVID-19 cases ( p <0.05). Total population and GRP impacted SC-corr considerably, with their correlation coefficients r for SC-corr being 0.65 and 0.59, respectively. Total population, GRP per capita, and proportion of the population with a high school diploma or higher had simultaneous positive impacts on SC-corr and SC-lag ( p <0.05); these three indicators explained 37-50% of the total variation in SC-corr and SC-lag. Estimations for different urban agglomerations revealed that the goodness of fit, R 2 , for search engine-based estimation was more than 0.6 only when total urban population, GRP per capita, and proportion of the population with a high school diploma or higher exceeded 11.08 million, 120,700, and 38.13%, respectively. A greater urban size indicated higher accuracy of search engine-based estimation of COVID-19 prevalence. Therefore, the accuracy and time lag for search engine-based estimation of infectious disease prevalence can be improved only when the total urban population, GRP per capita, and proportion of the population with a high school diploma or higher are greater than the aforementioned thresholds.

Reconstruction of the Transmission Chain of COVID-19 Outbreak in Beijing's Xinfadi Market, China.

OBJECTIVES: The aim of the study was to reconstruct the complete transmission chain of the COVID-19 outbreak in Beijing's Xinfadi Market using data from epidemiological investigations, which contributes to reflecting transmission dynamics and transmission risk factors. METHODS: We set up a transmission model, and the model parameters are estimated from the survey data via Markov chain Monte Carlo sampling. Bayesian data augmentation approaches are used to account for uncertainty in the source of infection, unobserved onset, and infection dates. RESULTS: The rate of transmission of COVID-19 within households is 9.2%. Older people are more susceptible to infection. The accuracy of our reconstructed transmission chain was 67.26%. In the gathering place of this outbreak, the Beef and Mutton Trading Hall of Xinfadi market, most of the transmission occurs within 20 m, only 19.61% of the transmission occurs over a wider area (>20 m), with an overall average transmission distance of 13.00 m. The deepest transmission generation is 9. In this outbreak, there were 2 abnormally high transmission events. CONCLUSIONS: The statistical method of reconstruction of transmission trees from incomplete epidemic data provides a valuable tool to help understand the complex transmission factors and provides a practical guideline for investigating the characteristics of the development of epidemics and the formulation of control measures.

Food-trade-associated COVID-19 outbreak from a contaminated wholesale food supermarket in Beijing.

The re-emerging outbreak of COVID-19 in Beijing, China, in the summer of 2020 originated from a SARS-CoV-2-infested wholesale food supermarket. We postulated that the Xinfadi market outbreak has links with food-trade activities. Our Susceptible to the disease, Infectious, and Recovered coupled Agent Based Modelling (SIR-ABM) analysis for studying the diffusion of SARS-CoV-2 particles suggested that the trade-distancing strategy effectively reduces the reproduction number (R0). The retail shop closure strategy reduced the number of visitors to the market by nearly half. In addition, the buy-local policy option reduced the infection by more than 70% in total. Therefore, retail closures and buy-local policies could serve as significantly effective strategies that have the potential to reduce the size of the outbreak and prevent probable outbreaks in the future.

Application of time series model and deep learning method in measuring the impact of COVID-19 on agriculture in Hubei, China

Taking the agricultural situation of Hubei Province as the research object, this paper uses the time series ARIMA model, ARMA model and deep learning LSTM neural network model to explore the impact of COVID-19 on the agriculture of Hubei Province. Three main indicators are screened out to measure the development of agricultural economy, that is, gross regional product, gross output value of agriculture, forestry, animal husbandry and fishery, agricultural product production price index. Based on the quarterly data of indicators from 2001 to 2019 from the National Bureau of Statistics, three indicators in Hubei Province in the first quarter and the second quarter of 2020 are predicted by using the deep learning-based time series ARIMA model, ARMA model and LSTM neural network model. By comparing the predicted data with the real data, the impact of COVID-19 is measured on the agricultural situation of Hubei Province. It was found that COVID-19 had a great impact on the agricultural situation of Hubei Province in both of the first and second quarters of 2020, with the impact in the first quarter being greater than that in the second quarter. At the same time, the prediction accuracy of the two methods is compared to find that the time series model is more effective and reliable in predicting the agricultural product price index. The LSTM neural network model with a long and short term memory has a good prediction effect on the regional gross product and the total output value of agriculture, forestry, animal husbandry and fishery.

JAK-inhibitors for coronavirus disease-2019 (COVID-19): a meta-analysis.

We analyzed reports on safety and efficacy of JAK-inhibitors in patients with coronavirus infectious disease-2019 (COVID-19) published between January 1st and March 6th 2021 using the Newcastle-Ottawa and Jadad scales for quality assessment. We used disease severity as a proxy for time when JAK-inhibitor therapy was started. We identified 6 cohort studies and 5 clinical trials involving 2367 subjects treated with ruxolitinib (N = 3) or baricitinib 45 (N = 8). Use of JAK-inhibitors decreased use of invasive mechanical ventilation (RR = 0.63; [95% Confidence Interval (CI), 0.47, 0.84]; P = 0.002) and had borderline impact on rates of intensive care unit (ICU) admission (RR = 0.24 [0.06, 1.02]; P = 0.05) and acute respiratory distress syndrome (ARDS; RR = 0.50 [0.19, 1.33]; P = 0.16). JAK-inhibitors did not decrease length of hospitalization (mean difference (MD) -0.18 [-4.54, 4.18]; P = 0.94). Relative risks of death for both drugs were 0.42 [0.30, 0.59] (P < 0.001), for ruxolitinib, RR = 0.33 (0.13, 0.88; P = 0.03) and for baricitinib RR = 0.44 (0.31, 0.63; P < 0.001). Timing of JAK-inhibitor treatment during the course of COVID-19 treatment may be important in determining impact on outcome. However, these data are not consistently reported.

Comparative effectiveness of Lopinavir/Ritonavir-based regimens in COVID-19.

The coronavirus disease 2019 (COVID-19) is an epidemic disease caused by the Severe Acute Respiratory Syndrome Corona Virus 2 (SARS-CoV-2) and spreading throughout the world rapidly. Here we evaluated the efficacy of the Lopinavir/Ritonavir (LPV/r) and its combination with other drugs in the treatment of COVID-19. We included 170 confirmed COVID-19 patients who had been cured and discharged. Their antiviral therapies were LPV/r alone or combinations with interferon (IFN), Novaferon and Arbidol. We evaluated the medication efficacy by comparing the time of the negative nucleic acid conversion and the length of hospitalization mainly. The LPV/r + Novaferon [6.00 (4.00-8.00) and 7.50 (5.00-10.00) days] had shorter time of the negative nucleic acid conversion (P = .0036) and shorter time of hospitalization (P < .001) compared with LPV/r alone [9.00 (5.00-12.00) and 12.00 (11.00-15.00) days] and LPV/r + IFN [9.00 (7.25-11.00) and 12.00 (10.00-13.50) days]. On the contrary, LPV/r + IFN [9.00 (7.25-11.00) and 12.00 (10.00-13.50) days] had shorter time of the negative nucleic acid conversion (P = .031) and shorter time of hospitalization (P < .001) compared with LPV/r + IFN +Novaferon [10.00 (8.00-11.25) and 13.50 (11.50-17.00) days] and LPV/r + IFN +Arbidol [14.00 (9.75-19.00) and 19.50 (13.25-24.00) days]. In conclusion, the combination of LPV/r and Novaferon may have better efficacy against COVID-19. However, adding IFN based on LPV/r + Novaferon or adding Arbidol based on LPV/r + IFN may not improve the efficacy.

Impact of corticosteroid therapy on outcomes of persons with SARS-CoV-2, SARS-CoV, or MERS-CoV infection: a systematic review and meta-analysis.

We performed a meta-analysis to determine safety and efficacy of corticosteroids in SARS-CoV-2, SARS-CoV, and MERS-CoV infections. We searched PubMed, Web of Science, Medline, WanFang Chinese database, and ZhiWang Chinese database using Boolean operators and search terms covering SARS-CoV-2, SARS-CoV, OR MERS-CoV AND corticosteroids to find appropriate studies. Review Manager 5.3 was used to analyze results of meta-analysis. Observational studies were analyzed for quality using the modified Newcastle-Ottawa scale and randomized clinical trials, using the Jadad scale. Subjects were divided into those with severe-only and other (severe and not severe) cohorts based on published criteria. Efficacy endpoints studied included mortality, hospitalization duration, rates of intensive care unit (ICU) admission, use of mechanical ventilation, and a composite endpoint (death, ICU admission, or mechanical ventilation). We included 11 reports including 10 cohort studies and 1 randomized clinical trial involving 5249 subjects (2003-2020). Two discussed the association of corticosteroids and virus clearing and 10 explored how corticosteroids impacted mortality, hospitalization duration, use of mechanical ventilation, and a composite endpoint. Corticosteroid use was associated with delayed virus clearing with a mean difference (MD) = 3.78 days (95% confidence Interval [CI] = 1.16, 6.41 days; I2 = 0%). There was no significant reduction in deaths with relative Risk Ratio (RR) = 1.07 (90% CI = 0.81; 1.42; I2 = 80%). Hospitalization duration was prolonged and use of mechanical ventilation increased. In conclusion, corticosteroid use in subjects with SARS-CoV-2, SARS-CoV, and MERS-CoV infections delayed virus clearing and did not convincingly improve survival, reduce hospitalization duration or ICU admission rate and/or use of mechanical ventilation. There were several adverse effects. Because of a preponderance of observational studies in the dataset and selection and publication biases our conclusions, especially regarding SARS-CoV-2, need confirmation in a randomized clinical trial. In the interim we suggest caution using corticosteroids in persons with COVID-19.