HostSeq: a Canadian whole genome sequencing and clinical data resource.

HostSeq was launched in April 2020 as a national initiative to integrate whole genome sequencing data from 10,000 Canadians infected with SARS-CoV-2 with clinical information related to their disease experience. The mandate of HostSeq is to support the Canadian and international research communities in their efforts to understand the risk factors for disease and associated health outcomes and support the development of interventions such as vaccines and therapeutics. HostSeq is a collaboration among 13 independent epidemiological studies of SARS-CoV-2 across five provinces in Canada. Aggregated data collected by HostSeq are made available to the public through two data portals: a phenotype portal showing summaries of major variables and their distributions, and a variant search portal enabling queries in a genomic region. Individual-level data is available to the global research community for health research through a Data Access Agreement and Data Access Compliance Office approval. Here we provide an overview of the collective project design along with summary level information for HostSeq. We highlight several statistical considerations for researchers using the HostSeq platform regarding data aggregation, sampling mechanism, covariate adjustment, and X chromosome analysis. In addition to serving as a rich data source, the diversity of study designs, sample sizes, and research objectives among the participating studies provides unique opportunities for the research community.

Fusion in Cryptocurrency Price Prediction: A Decade Survey on Recent Advancements, Architecture, and Potential Future Directions

Cryptographic forms of money are distributed peer-to-peer (P2P) computerized exchange mediums, where the exchanges or records are secured through a protected hash set of secure hash algorithm-256 (SHA-256) and message digest 5 (MD5) calculations. Since their initiation, the prices seem highly volatile and came to their amazing cutoff points during the COVID-19 pandemic. This factor makes them a popular choice for investors with an aim to get higher returns over a short span of time. The colossal high points and low points in digital forms of money costs have drawn in analysts from the scholarly community as well as ventures to foresee their costs. A few machines and deep learning algorithms like gated recurrent unit (GRU), long short-term memory (LSTM), autoregressive integrated moving average with explanatory variable (ARIMAX), and a lot more have been utilized to exactly predict and investigate the elements influencing cryptocurrency prices. The current literature is totally centered around the forecast of digital money costs disregarding its reliance on other cryptographic forms of money. However, Dash coin is an individual cryptocurrency, but it is derived from Bitcoin and Litecoin. The change in Bitcoin and Litecoin prices affects the Dash coin price. Motivated from these, we present a cryptocurrency price prediction framework in this paper. It acknowledges different cryptographic forms of money (which are subject to one another) as information and yields higher accuracy. To illustrate this concept, we have considered a price prediction of Dash coin through the past days’prices of Dash, Litecoin, and Bitcoin as they have hierarchical dependency among them at the protocol level. We can portray the outcomes that the proposed scheme predicts the prices with low misfortune and high precision. The model can be applied to different digital money cost expectations. Author

Video based Modular Training on Occupational Health risks in Informal Work for Primary Care Physicians in India

Introduction: Mission Basic Occupational Health Services of the Indian Association of Occupational Health has been working to accentuate Occupational Health competency of primary health care personnel. Limitations of organizing contact training programs with scant support from formal services compelled the mission to leverage technology to create industry-specific learning resources as video-based modules for online learning followed by a mobile application. This modular program has been utilized during COVID-19 pandemic that had profoundly affected contact training due to movement restrictions during lock-downs. Materials & Methods: BOHS for Informal Industry: Manual for Primary Care Providers was used to create videos defining epidemiological profiles of twenty-two informal trades. These videos were released on the website and YouTube platform for free access. Mobile application IAOH BOHS was launched in 2019. Game-based assessment to determine effectiveness of video-based training was undertaken in a group of 100 doctors at a tertiary care institute in India. Results: Video modules were formally recognized as training resource. Video-assisted learning was appreciated for trade-specific content and showed selective improvement in knowledge and competence enhancement. With over 3000 views among diverse audience, 300 contact & online training sessions were conducted until 2020. Conclusions: Video-based learning through virtual platforms offered accessible engaging learning opportunities for primary care professionals. Restrictive circumstances demand workable alternatives to facilitate learning with effective outcomes.

The Effects Of Comorbidities On Covid-19 Outcomes Among Patients With Inflammatory Bowel Disease

Background: Comorbidities Increase The Risk Covid-19 Morbidity And Mortality. As Comorbidities Are Common In Patients With Inflammatory Bowel Diseases (Ibd), We Sought To Evaluate The Effect Of Comorbidities On Covid-19 Outcomes Among Ibd Patients. Methods: Data Were Obtained From Surveillance Epidemiology Of Coronavirus Under Research Exclusion For Inflammatory Bowel Disease (Secure-Ibd), An International Registry To Determine Characteristics And Outcomes Of Covid-19 In Ibd Patients. We Used Multivariable Regression To Analyze Associations Between Eleven Non-Ibd Comorbidities And Covid-19-Related Hospitalization Or Death. We First Modeled Each Comorbidity Individually, Adjusting Potential Confounders Such As Age, Gender, Race, Ethnicity And Medication Use. Then, To Determine The Independent Effects Of Each Comorbidity, We Fit A Model Including All Comorbidities As Covariates. Results: 2,035 Patients From 58 Countries Were Included (Mean Age Was 42.7 Years, 50.6% Male). A Total Of 538 Patients (26.4%) Experienced Covid-19-Related Hospitalization Or Death. Of Eleven Comorbidities Analyzed, All But A History Of Stroke And Obesity Were Associated With Hospitalization Or Death In Our Initial Analysis, With Adjusted Odds Ratio (Aor) Ranging From 1.9 (Asthma And Cardiovascular Disease) To 3.7 (Chronic Kidney Disease). After Adjusting For Age, Sex, Medications, And Comorbidites Found To Significantly Influence Severe Covid-19 In The Initial Analysis, The Independent Associations For Most Comorbidities Remained Significant And Were Strongest For Chronic Kidney Disease (Aor 3.02, 95% Ci 1.45-6.31) And Chronic Obstructive Pulmonary Disease (Copd) (Aor 2.92, 95% Ci 1.32-6.48) (Table 1). Conclusion: Comorbidities Are Associated With Covid-19 Hospitalization And Death Among Ibd Patients. These Data Can Be Used To Risk-Stratify Ibd Patients And Guide Treatment And Lifestyle Decisions During The Ongoing Pandemic. (Table Presented) Independent Effects Of Individual Comorbidities On The Risk Of Hospitalization Or Death From Covid-19 In Patients With Inflammatory Bowel Diseases