Notes

The role of computer-assisted systems with regard to upper-endoscopy high quality overseeing as well as examination of gastric lesions.
Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by the DPP9 locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated that DPP9 was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs with severe COVID-19 demonstrated colocalization of the GWAS signal of the ABO locus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN), pointing to a possible mechanism whereby glycosylation of CD209 by ABO may regulate COVID-19 disease severity. Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches.SARS-CoV-2 Spike protein is critical for virus infection via engagement of ACE2, and amino acid variation in Spike is increasingly appreciated. Isoxazole9 Given both vaccines and therapeutics are designed around Wuhan-1 Spike, this raises the theoretical possibility of virus escape, particularly in immunocompromised individuals where prolonged viral replication occurs. Here we report chronic SARS-CoV-2 with reduced sensitivity to neutralising antibodies in an immune suppressed individual treated with convalescent plasma, generating whole genome ultradeep sequences by both short and long read technologies over 23 time points spanning 101 days. Although little change was observed in the overall viral population structure following two courses of remdesivir over the first 57 days, N501Y in Spike was transiently detected at day 55 and V157L in RdRp emerged. However, following convalescent plasma we observed large, dynamic virus population shifts, with the emergence of a dominant viral strain bearing D796H in S2 and Δ H69/ Δociated with emergence of viral variants with reduced susceptibility to neutralising antibodies.
With more than 20 million residents, Mexico City Metropolitan Area (MCMA) has the largest number of Covid-19 cases in Mexico and is at risk of exceeding its hospital capacity in late December 2020.

We used SC-COSMO, a dynamic compartmental Covid-19 model, to evaluate scenarios considering combinations of increased contacts during the holiday season, intensification of social distancing, and school reopening. Model parameters were derived from primary data from MCMA, published literature, and calibrated to time-series of incident confirmed cases, deaths, and hospital occupancy. Outcomes included projected confirmed cases and deaths, hospital demand, and magnitude of hospital capacity exceedance.

Following high levels of holiday contacts even with no in-person schooling, we predict that MCMA will have 1·0 million (95% prediction interval 0·5 - 1·7) additional Covid-19 cases between December 7, 2020 and March 7, 2021 and that hospitalizations will peak at 35,000 (14,700 - 67,500) on January 27, 2021, with were maintained. Under all scenarios and policies, current hospital capacity appears insufficient, highlighting the need for rapid capacity expansion.Implications of all the available evidence MCMA officials should prioritize rapid hospital capacity expansion. MCMA's ability to reopen schools in mid-January 2021 depends on sustaining social distancing and that contacts during the end-of-year holiday were well controlled.
The start of 2021 will be marked by a global vaccination campaign against the novel coronavirus SARS-CoV-2. Formulating an optimal distribution strategy under social and economic constraints is challenging. Optimal distribution is additionally constrained by the potential emergence of vaccine resistance. Analogous to chronic low-dose antibiotic exposure, recently inoculated individuals who are not yet immune play an outsized role in the emergence of resistance. Classical epidemiological modelling is well suited to explore how the behavior of the inoculated population impacts the total number of infections over the entirety of an epidemic.

A deterministic model of epidemic evolution is analyzed, with 7 compartments defined by their relationship to the emergence of vaccine-resistant mutants and representing three susceptible populations, three infected populations, and one recovered population. This minimally computationally intensive design enables simulation of epidemics across a broad parameter space. Ththat optimization of the vaccination rate and limiting post-vaccination contacts can affect the course of an epidemic. Given the relatively short window between inoculation and the acquisition of immunity, these results might merit consideration for an immediate, practical public health response.Electronic Health Records (EHR) are not designed for population-based research, but they provide access to longitudinal health information for many individuals. Many statistical methods have been proposed to account for selection bias, missing data, phenotyping errors, or other problems that arise in EHR data analysis. However, addressing multiple sources of bias simultaneously is challenging. Recently, we developed a methodological framework (R package, SAMBA ) for jointly handling both selection bias and phenotype misclassification in the EHR setting that leverages external data sources. These methods assume factors related to selection and misclassification are fully observed, but these factors may be poorly understood and partially observed in practice. As a follow-up to the methodological work, we explore how these methods perform for three real-world case studies. In all three examples, we use individual patient-level data collected through the University of Michigan Health System and various external population-based data sources. In case study (a), we explore the impact of these methods on estimated associations between gender and cancer diagnosis. In case study (b), we compare corrected associations between previously identified genetic loci and age-related macular degeneration with gold standard external estimates. In case study (c), we evaluate these methods for modeling the association of COVID-19 outcomes and potential risk factors. These case studies illustrate how to utilize diverse auxiliary information to achieve less biased inference in EHR-based research.
To characterize the SARS-CoV-2 testing cascade and associated barriers in three US states.

We recruited participants from Florida, Illinois, and Maryland (∼1000/state) for an online survey September 16 - October 15, 2020. The survey covered demographics, COVID-19 symptoms, and experiences around SARS-CoV-2 PCR testing in the prior 2 weeks. Logistic regression was used to analyze associations with outcomes of interest.

Overall, 316 (10%) of 3,058 respondents wanted/needed a test in the two weeks prior to the survey. link2 Of these, 166 (53%) were able to get tested and 156 (94%) received results; 53% waited ≥ 8 days to get results from when they wanted/needed a test. There were no significant differences by state. Among those wanting/needing a test, getting tested was significantly less common among men (aOR 0.46) and those reporting black race (aOR 0.53) and more common in those reporting recent travel (aOR 3.35).

There is an urgent need for a national communication strategy on who should get tested and where one can get tested. Additionally, measures need to be taken to improve access and reduce turn-around-time.
There is an urgent need for a national communication strategy on who should get tested and where one can get tested. Additionally, measures need to be taken to improve access and reduce turn-around-time.
The COVID-19 pandemic has highlighted the importance of rapid dissemination of scientific and medical discovery. Social media (SoMe) has become an invaluable platform in science and medicine. This study analyzed activity of SoMe (Twitter), preprints, and publications related to COVID-19 and gastroenterology (GI) during the COVID-19 pandemic.

Data from Twitter, preprint servers and PubMed was collected and analyzed from December 2019 through May 2020. Global and regional geographic and gastrointestinal organ specific social media trends were compared to preprint and publication activity; any associations were identified.

Over the 6-month period, there were 73,079 tweets from 44,609 users, 7,164 publications, and 4,702 preprints. Twitter activity peaked during March while preprints and publications peaked in April 2020. Strong correlations were identified between Twitter and both preprints and publications activity (p<0.001 for both). While COVID-19 data across the 3 platforms concentrated on pulmonology/critical care, the majority of GI tweets pertained to pancreatology, most publications focused on hepatology, and most preprints covered hepatology and luminal GI (LGI). There were significant associations between Twitter activity and research for all GI subfields (p=0.009 for LGI, p=0.006 for hepatology and IBD, p=0.007 for endoscopy), except pancreatology (p=0.2). Twitter activity was highest in the US (7,331 tweets) whereas PubMed activity was highest in China (1,768 publications).

The COVID-19 pandemic has highlighted the utility of SoMe as a vehicle for disseminating scientific information during a public health crisis. Scientists and clinicians should consider the use of SoMe in augmenting public awareness of their scholarly pursuits.
The COVID-19 pandemic has highlighted the utility of SoMe as a vehicle for disseminating scientific information during a public health crisis. Scientists and clinicians should consider the use of SoMe in augmenting public awareness of their scholarly pursuits.
Given the limited supply of two COVID-19 vaccines, it will be important to choose which risk groups to prioritize for vaccination in order to get the most health benefits from that supply.

In order to help decide how to get the maximum health yield from this limited supply, we implemented a logistic regression model to predict COVID-19 death risk by age, race, and sex and did the same to predict COVID-19 case risk.

Our predictive model ranked all demographic groups by COVID-19 death risk. It was highly concentrated in some demographic groups, e.g. 85+ year old Black, Non-Hispanic patients suffered 1,953 deaths per 100,000. If we vaccinated the 17 demographic groups at highest COVID-19 death ranked by our logistic model, it would require only 3.7% of the vaccine supply needed to vaccinate all the United States, and yet prevent 47% of COVID-19 deaths. Nursing home residents had a higher COVID-19 death risk at 5,200 deaths/100,000, more than our highest demographic risk group. Risk of prison residents and les.A recent report found that rare predicted loss-of-function (pLOF) variants across 13 candidate genes in TLR3- and IRF7-dependent type I IFN pathways explain up to 3.5% of severe COVID-19 cases. We performed whole-exome or whole-genome sequencing of 1,934 COVID-19 cases (713 with severe and 1,221 with mild disease) and 15,251 ancestry-matched population controls across four independent COVID-19 biobanks. link3 We then tested if rare pLOF variants in these 13 genes were associated with severe COVID-19. We identified only one rare pLOF mutation across these genes amongst 713 cases with severe COVID-19 and observed no enrichment of pLOFs in severe cases compared to population controls or mild COVID-19 cases. We find no evidence of association of rare loss-of-function variants in the proposed 13 candidate genes with severe COVID-19 outcomes.
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