A new approach to COVID-19 data mining: A deep spatial-temporal prediction model based on tree structure for traffic revitalization index.

The outbreak of the COVID-19 epidemic has had a huge impact on a global scale and its impact has covered almost all human industries. The Chinese government enacted a series of policies to restrict the transportation industry in order to slow the spread of the COVID-19 virus in early 2020. With the gradual control of the COVID-19 epidemic and the reduction of confirmed cases, the Chinese transportation industry has gradually recovered. The traffic revitalization index is the main indicator for evaluating the degree of recovery of the urban transportation industry after being affected by the COVID-19 epidemic. The prediction research of traffic revitalization index can help the relevant government departments to know the state of urban traffic from the macro level and formulate relevant policies. Therefore, this study proposes a deep spatial-temporal prediction model based on tree structure for the traffic revitalization index. The model mainly includes spatial convolution module, temporal convolution module and matrix data fusion module. The spatial convolution module builds a tree convolution process based on the tree structure that can contain directional features and hierarchical features of urban nodes. The temporal convolution module constructs a deep network for capturing temporal dependent features of the data in the multi-layer residual structure. The matrix data fusion module can perform multi-scale fusion of COVID-19 epidemic data and traffic revitalization index data to further improve the prediction effect of the model. In this study, experimental comparisons between our model and multiple baseline models are conducted on real datasets. The experimental results show that our model has an average improvement of 21%, 18%, and 23% in MAE, RMSE and MAPE indicators, respectively.

5α-Reductase Inhibitors Are Associated with Reduced Risk of SARS-CoV-2 Infection: A Matched-Pair, Registry-Based Analysis.

PURPOSE: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has a disproportionately severe effect on men, suggesting that the androgen pathway plays a role in the disease. Studies on the effect of castration and androgen receptor blockade have been mixed, while 5α-reductase inhibitor (5ARI) use in men with COVID-19 (2019 novel coronavirus) have shown potential benefits. We assessed the association of 5ARI use on risk of community acquired SARS-CoV-2 infection. MATERIALS AND METHODS: A total of 60,474 males in a prospective registry of people tested for SARS-CoV-2 between March 8, 2020 and February 15, 2021 were included. Using a matched cohort design, men using 5ARIs were matched 1:1 to non5ARI users. Independent analysis using unconditional multivariable logistic regression on the entire unmatched data set was completed for validation. Primary outcome measures were the association of 5ARI use on rates of SARS-Cov-2 positivity and disease severity. RESULTS: Of the men 1,079 (1.8%) reported 5ARI use and 55,100 were available for matching. The final matched cohorts included 944 men each. Mean duration of use was 60.4 months (IQR 17-84 months). Absolute risk for infection was significantly lower in 5ARI users compared to nonusers, 42.3% (399/944) vs 47.2% (446/944), respectively (absolute risk reduction [ARR] 4.9%, OR 0.81, 95% CI 0.67-0.97, p=0.026). Unconditional multivariable logistic regression analysis of the entire study cohort of 55,100 men confirmed the protective association of 5ARI use (ARR 5.3%, OR=0.877, 95% CI 0.774-0.995, p=0.042). Use of 5ARIs was not associated with disease severity. CONCLUSIONS: Use of 5ARIs in men without prostate cancer was associated with a reduction in community acquired SARS-CoV-2 infection.

The high diversity of SARS-CoV-2-related coronaviruses in pangolins alerts potential ecological risks.

Understanding the zoonotic origin and evolution history of SARS-CoV-2 will provide critical insights for alerting and preventing future outbreaks. A significant gap remains for the possible role of pangolins as a reservoir of SARS-CoV-2 related coronaviruses (SC2r-CoVs). Here, we screened SC2r-CoVs in 172 samples from 163 pangolin individuals of four species, and detected positive signals in muscles of four Manis javanica and, for the first time, one M. pentadactyla. Phylogeographic analysis of pangolin mitochondrial DNA traced their origins from Southeast Asia. Using in-solution hybridization capture sequencing, we assembled a partial pangolin SC2r-CoV (pangolin-CoV) genome sequence of 22 895 bp (MP20) from the M. pentadactyla sample. Phylogenetic analyses revealed MP20 was very closely related to pangolin-CoVs that were identified in M. javanica seized by Guangxi Customs. A genetic contribution of bat coronavirus to pangolin-CoVs via recombination was indicated. Our analysis revealed that the genetic diversity of pangolin-CoVs is substantially higher than previously anticipated. Given the potential infectivity of pangolin-CoVs, the high genetic diversity of pangolin-CoVs alerts the ecological risk of zoonotic evolution and transmission of pathogenic SC2r-CoVs.

Association between glucocorticoids treatment and viral clearance delay in patients with COVID-19: a systematic review and meta-analysis.

BACKGROUND: Evidence of glucocorticoids on viral clearance delay of COVID-19 patients is not clear. METHODS: In this systematic review and meta-analysis, we searched for studies on Medline, Embase, EBSCO, ScienceDirect, Web of Science, Cochrane Library, and ClinicalTrials.gov from 2019 to April 20, 2021. We mainly pooled the risk ratios (RRs) and mean difference (MD) for viral clearance delay and did subgroup analyses by the severity of illness and doses of glucocorticoids. RESULTS: 38 studies with a total of 9572 patients were identified. Glucocorticoids treatment was associated with delayed viral clearance in COVID-19 patients (adjusted RR 1.52, 95% CI 1.29 to 1.80, I2 = 52%), based on moderate-quality evidence. In subgroup analyses, risk of viral clearance delay was significant both for COVID-19 patients being mild or moderate ill (adjusted RR 1.86, 95% CI 1.35 to 2.57, I2 = 48%), and for patients of being severe or critical ill (adjusted RR 1.59, 95% CI 1.23 to 2.07, I2 = 0%); however, this risk significantly increased for patients taking high doses (unadjusted RR 1.85, 95% CI 1.08 to 3.18; MD 7.19, 95% CI 2.78 to 11.61) or medium doses (adjusted RR 1.86, 95% CI 0.96 to 3.62, I2 = 45%; MD 3.98, 95% CI 3.07 to 4.88, I2 = 4%), rather those taking low doses (adjusted RR 1.38, 95% CI 0.94 to 2.02, I2 = 59%; MD 1.46, 95% CI -0.79 to 3.70, I2 = 82%). CONCLUSIONS: Glucocorticoids treatment delayed viral clearance in COVID-19 patients of taking high doses or medium doses, rather in those of taking low doses of glucocorticoids.

Comparison of Associations Between Glucocorticoids Treatment and Mortality in COVID-19 Patients and SARS Patients: A Systematic Review and Meta-Analysis.

BACKGROUND: The response to glucocorticoids treatment may be different between coronavirus disease 2019 (Covid-19) and severe acute respiratory syndrome (SARS). METHODS: In this systematic review and meta-analysis, we searched studies on Medline, Embase, EBSCO, ScienceDirect, Web of Science, Cochrane Library, ClinicalTrials.gov, International Clinical Trials Registry Platform from 2002 to October 7, 2020. We used fixed-effects and random-effects models to compute the risk ratio of death in the group receiving glucocorticoids treatment and the control group for COVID-19 and SARS, respectively. RESULTS: Ten trials and 71 observational studies, with a total of 45,935 patients, were identified. Glucocorticoids treatment was associated with decreased all-cause mortality both in COVID-19 (risk ratio, 0.88; 95% confidence interval, 0.82-0.94; I2 = 26%) and SARS (0.48; 0.29-0.79; 10%), based on high-quality evidence, as well as decreased all-cause mortality-including composite outcome of COVID-19 (0.89; 0.82-0.98; 0%). In subgroup analyses, all-cause mortality was significantly lower among COVID-19 patients being accompanied by severe ARDS but not mild ARDS, taking low-dose or pulse glucocorticoids, being critically severe but not only severe, being of critical severity and old but not young, being of critical severity and men but not women, non-early taking glucocorticoids, taking dexamethasone or methylprednisolone, and with the increased inflammatory state; but for SARS, lower mortality was observed among those who were taking medium-high dose glucocorticoids, being severe or critically severe, early taking glucocorticoids, and taking methylprednisolone or prednisolone. CONCLUSIONS: Glucocorticoids treatment reduced mortality in COVID-19 and SARS patients of critical severity; however, different curative effects existed between the two diseases among subpopulations, mainly regarding sex- and age-specific effects, optimal doses, and use timing of glucocorticoids.

Deep learning in the COVID-19 epidemic: A deep model for urban traffic revitalization index.

The research of traffic revitalization index can provide support for the formulation and adjustment of policies related to urban management, epidemic prevention and resumption of work and production. This paper proposes a deep model for the prediction of urban Traffic Revitalization Index (DeepTRI). The DeepTRI builds model for the data of COVID-19 epidemic and traffic revitalization index for major cities in China. The location information of 29 cities forms the topological structure of graph. The Spatial Convolution Layer proposed in this paper captures the spatial correlation features of the graph structure. The special Graph Data Fusion module distributes and fuses the two kinds of data according to different proportions to increase the trend of spatial correlation of the data. In order to reduce the complexity of the computational process, the Temporal Convolution Layer replaces the gated recursive mechanism of the traditional recurrent neural network with a multi-level residual structure. It uses the dilated convolution whose dilation factor changes according to convex function to control the dynamic change of the receptive field and uses causal convolution to fully mine the historical information of the data to optimize the ability of long-term prediction. The comparative experiments among DeepTRI and three baselines (traditional recurrent neural network, ordinary spatial-temporal model and graph spatial-temporal model) show the advantages of DeepTRI in the evaluation index and resolving two under-fitting problems (under-fitting of edge values and under-fitting of local peaks).

A graph spatial-temporal model for predicting population density of key areas.

Predicting the population density of key areas of the city is crucial. It helps reduce the spread risk of Covid-19 and predict individuals' travel needs. Although current researches focus on using the method of clustering to predict the population density, there is almost no discussion about using spatial-temporal models to predict the population density of key areas in a city without using actual regional images. We abstract 997 key areas and their regional connections into a graph structure and propose a model called Word Embedded Spatial-temporal Graph Convolutional Network (WE-STGCN). WE-STGCN is mainly composed of the Spatial Convolution Layer, the Temporal Convolution Layer, and the Feature Component. Based on the data set provided by the DataFountain platform, we evaluate the model and compare it with some typical models. Experimental results show that WE-STGCN has 53.97% improved to baselines on average and can commendably predicting the population density of key areas.

[Annual progress in critical care medicine in 2020].

The epidemic of coronavirus disease 2019 (COVID-19) puts higher demands on critical care medicine. Lots of studies have been conducted to solve COVID-19-related problems. Therefore, we reviewed the annual progress for COVID-19-related issues including antivirals threapies, respiratory support and immunomodulatory therapies and other critical issues, including the effect of antibiotic on mitochondrial damage and its relationship with sepsis, the goal and direction of antimicrobial de-escalation, drug prophylaxis of constipation, bleeding in gastrointestinal disorders and management of critical illness in the informalization era and so on. We hope to provide reference for clinical and scientific research work of the intensivists.

Androgen Deprivation Therapy in Men with Prostate Cancer Does Not Affect Risk of Infection with SARS-CoV-2.

PURPOSE: TMPRSS2 is a host co-receptor for cell entry of SARS-CoV-2. A prior report suggested that use of androgen deprivation therapy, which downregulates TMPRSS2, may protect men with prostate cancer from infection. MATERIALS AND METHODS: This is a cohort study of a prospective registry of all patients tested for SARS-CoV-2 between March 12 and June 10, 2020 with complete followup until disease recovery or death. The main exposure examined was the use of androgen deprivation therapy, and the outcome measures were the rate of SARS-CoV-2 positivity and disease severity as a function of androgen deprivation therapy use. RESULTS: The study cohort consisted of 1,779 men with prostate cancer from a total tested population of 74,787, of whom 4,885 (6.5%) were positive for SARS-CoV-2. Of those with prostate cancer 102 (5.7%) were SARS-CoV-2 positive and 304 (17.1%) were on androgen deprivation therapy. Among those on androgen deprivation therapy 5.6% were positive as compared to 5.8% not on androgen deprivation therapy. Men on androgen deprivation therapy were slightly older (75.5 vs 73.8 years, p=0.009), more likely to have smoked (68.1% vs 59.3%, p=0.005) and more likely to report taking steroids (43.8% vs 23.3%, p <0.001). Other factors known to increase risk of infection and disease severity were equally distributed (asthma, diabetes mellitus, hypertension, coronary artery disease, heart failure and immune suppressive disease). Multivariable analysis did not indicate a difference in infection risk for those with prostate cancer on androgen deprivation therapy (OR 0.93, 95% CI 0.54-1.61, p=0.8). CONCLUSIONS: Androgen deprivation therapy does not appear to be protective against SARS-CoV-2 infection.