Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank.

Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19). To reveal genetic determinants of susceptibility to COVID-19 related mortality, we train a deep learning model to identify groups of genetic variants and their interactions that contribute to the COVID-19 related mortality risk using the UK Biobank data (28,097 affected cases and 1656 deaths). We refer to such groups of variants as super variants. We identify 15 super variants with various levels of significance as susceptibility loci for COVID-19 mortality. Specifically, we identify a super variant (odds ratio [OR] = 1.594, p = 5.47 × 10-9 ) on Chromosome 7 that consists of the minor allele of rs76398985, rs6943608, rs2052130, 7:150989011_CT_C, rs118033050, and rs12540488. We also discover a super variant (OR = 1.353, p = 2.87 × 10-8 ) on Chromosome 5 that contains rs12517344, rs72733036, rs190052994, rs34723029, rs72734818, 5:9305797_GTA_G, and rs180899355.

Impact of COVID-19 Pandemic on the Clinical Activities in Obstetrics and Gynecology: A National Survey in China.

Objective: Few studies have quantified the influence of coronavirus disease 2019 (COVID-19) pandemic on medical providers. This is the first national study to investigate the impact of the pandemic on physicians practicing obstetrics and gynecology in China. Methods: A two-stage, stratified, cluster sampling method was performed based on the city categories (category 1, fewer than 10,000 beds; category 2, 10,000-30,000; and category 3, more than 30,000) and public hospital levels (primary, secondary, and tertiary). Physicians practicing obstetrics and gynecology reported the relevant changes in their general clinical activities and changes in the management of specific diseases or conditions occurring during the periods that they were most strongly affected. These changes were compared by municipal and hospital characteristics. Results: Questionnaires were collected from a representative sample of 11,806 physicians actively practicing obstetrics and gynecology in 779 hospitals from 157 cities of 31 provinces. Except emergency visits and online consultations, category 3 cities, tertiary hospitals and general hospitals had greater reductions in overall clinical activities than category 1 cities, primary hospitals and specialized hospitals (all adjusted p < 0.05), respectively. The differences also existed in the management of specific diseases and conditions, especially for less urgent conditions, including cervical cancer screening, instructions regarding contraception and miscarriage, and assisted reproduction (all p < 0.05). Conclusions: During the COVID-19 pandemic, the clinical obstetrics and gynecology activities in China markedly decreased, with significant differences across municipal and hospital characteristics. Trial Registration: This study was registered with ClinicalTrials.gov on July 27, 2020 (NCT04491201).

Willingness of Women with Endometriosis Planning to Undergo IVF to Participate in a Randomized Clinical Trial and the Effects of the COVID-19 Pandemic on Potential Participation.

The Pre-IVF Treatment with a GnRH Antagonist in Women with Endometriosis (PREGnant) Trial (clinicaltrials.gov no. NCT04173169) was designed to test the hypothesis that 60-day pre-treatment with an oral GnRH antagonist in women with documented endometriosis and planning an IVF cycle will result in a superior live birth rate to placebo. Eight hundred fourteen women are required from 4 national sites. To determine the feasibility of using an electronic medical record (EMR)-based strategy to recruit 204 participants at the Colorado site, we conducted a survey of women within the UCHealth system. Eligible women, identified using relevant ICD-10 codes, were invited to complete a 6-question survey to assess planned utilization of IVF, potential interest in participation, and whether delays in treatment due to COVID-19 would influence their decision to participate. Of 6354 age-eligible women with an endometriosis diagnosis, 421 had a concurrent infertility diagnosis. After eliminating duplicates, 212 were emailed a survey; 76 (36%) responded, 6 of whom reported no endometriosis diagnosis. Of the remaining 70, 29 (41%) were planning fertility treatment; only 19 planned IVF. All 19 expressed interest in participation. COVID-19 delays in treatment were not considered as a factor affecting participation by 8/19; the remaining 11 felt that it would "somewhat" affect their decision. None reported that they would not consider participation because of COVID-19. EMR-based recruitment for an endometriosis clinical trial is feasible although the overall yield of participants is low. Delays in treatment due to COVID-19 did not appear to overly influence potential recruitment.

Clinical characteristics of coronavirus disease 2019 (COVID-19) in patients out of Wuhan from China: a case control study.

BACKGROUND: A large-scale global outbreak of coronavirus disease-19 (COVID-19) out of Wuhan, from China, occurred in January 2020. To examine the clinical characteristics of COVID-19 in infected patients out of Wuhan, from China. METHODS: Thirteen patients were confirmed to be infected with novel coronavirus-2019 (2019-nCoV) between January 27 and February 8, 2020, in Baoji city, Shannxi, northwestern China. Epidemiological and clinical information, and computed to morphology imaging data from all COVID-19 patients were collected; cases were divided into two groups according to the severity of infection (mild or severe). RESULTS: Nine (9/13) COVID-19 patients exhibited mild disease severity, and defined as second-generation human-to-human transmission cases. Most patients (11/13) had a history of travel to or from Wuhan. There were no differences in sex and age between the mild and severe cases (all P > 0.05). A moderate degree of fever (11/13), cough (13/13), and fatigue (8/13) were common symptoms; however, there was no statistical difference between mild and severe cases in this regard (all P > 0.05). Oxyhemoglobin saturation and oxygenation index decreased, and C-reactive protein (CRP) and serum amyloid A (SAA) levels were elevated in all patients with COVID-19 infection, with statistically significant differences between those with severe disease and mild infection (all P < 0.05). Twelve of 13 COVID-19 patients exhibited changes in chest CT imaging features, and time course changes were different between mild and severe cases (all P < 0.05). CONCLUSION: Most cases of COVID-19 infection were second-generation human-to-human transmissions from Wuhan and were mild in severity. The clinical characteristics of COVID-19 varied. Oxyhemoglobin saturation, oxygenation index, CRP and SAA levels, and CT features were reliable parameters to evaluate the severity of COVID-19 infection. However, a few patients with mild COVID-19 disease lacked typical characteristics such as fever and changes in CT imaging features.

Genetic variants are identified to increase risk of COVID-19 related mortality from UK Biobank data.

BACKGROUND: The severity of coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly heterogeneous. Studies have reported that males and some ethnic groups are at increased risk of death from COVID-19, which implies that individual risk of death might be influenced by host genetic factors. METHODS: In this project, we consider the mortality as the trait of interest and perform a genome-wide association study (GWAS) of data for 1778 infected cases (445 deaths, 25.03%) distributed by the UK Biobank. Traditional GWAS fails to identify any genome-wide significant genetic variants from this dataset. To enhance the power of GWAS and account for possible multi-loci interactions, we adopt the concept of super variant for the detection of genetic factors. A discovery-validation procedure is used for verifying the potential associations. RESULTS: We find 8 super variants that are consistently identified across multiple replications as susceptibility loci for COVID-19 mortality. The identified risk factors on chromosomes 2, 6, 7, 8, 10, 16, and 17 contain genetic variants and genes related to cilia dysfunctions (DNAH7 and CLUAP1), cardiovascular diseases (DES and SPEG), thromboembolic disease (STXBP5), mitochondrial dysfunctions (TOMM7), and innate immune system (WSB1). It is noteworthy that DNAH7 has been reported recently as the most downregulated gene after infecting human bronchial epithelial cells with SARS-CoV-2. CONCLUSIONS: Eight genetic variants are identified to significantly increase the risk of COVID-19 mortality among the patients with white British ancestry. These findings may provide timely clues and potential directions for better understanding the molecular pathogenesis of COVID-19 and the genetic basis of heterogeneous susceptibility, with potential impact on new therapeutic options.

Risk factors associated with mortality of COVID-19 in 3125 counties of the United States.

BACKGROUND: The number of cumulative confirmed cases of COVID-19 in the United States has risen sharply since March 2020. A county health ranking and roadmaps program has been established to identify factors associated with disparity in mobility and mortality of COVID-19 in all counties in the United States. The risk factors associated with county-level mortality of COVID-19 with various levels of prevalence are not well understood. METHODS: Using the data obtained from the County Health Rankings and Roadmaps program, this study applied a negative binomial design to the county-level mortality counts of COVID-19 as of August 27, 2020 in the United States. In this design, the infected counties were categorized into three levels of infections using clustering analysis based on time-varying cumulative confirmed cases from March 1 to August 27, 2020. COVID-19 patients were not analyzed individually but were aggregated at the county-level, where the county-level deaths of COVID-19 confirmed by the local health agencies. Clustering analysis and Kruskal-Wallis tests were used in our statistical analysis. RESULTS: A total of 3125 infected counties were assigned into three classes corresponding to low, median, and high prevalence levels of infection. Several risk factors were significantly associated with the mortality counts of COVID-19, where higher level of air pollution (0.153, P < 0.001) increased the mortality in the low prevalence counties and elder individuals were more vulnerable in both the median (0.049, P < 0.001) and high (0.114, P < 0.001) prevalence counties. The segregation between non-Whites and Whites (low: 0.015, P < 0.001; median:0.025, P < 0.001; high: 0.019, P = 0.005) and higher Hispanic population (low and median: 0.020, P < 0.001; high: 0.014, P = 0.009) had higher likelihood of risk of the deaths in all infected counties. CONCLUSIONS: The mortality of COVID-19 depended on sex, race/ethnicity, and outdoor environment. The increasing awareness of the impact of these significant factors may help decision makers, the public health officials, and the general public better control the risk of pandemic, particularly in the reduction in the mortality of COVID-19.