Human Disease Ontology (DO) Project release files. The DO project actively collaborates with biomedical and clinical researchers and data repositories to coordinate a standardized classification of human disease. The DO's GitHub repository includes the monthly releases (https://tinyurl.com/DiseaseOntologyReleases), of OWL, OBO, JSON and XML formatted files along with DO subsets (ontology slims) for the Alliance of Genome Resouces, FlyBase, Mouse Genome Informatics Database, the Immune Epitope Database, DO_Cancer_slim (for cancer variant resources), and zoonotic infectious diseases.The DO SPARQL GitHub directory provides a suite (https://tinyurl.com/DiseaseOntologySrcSparql) of data reporting and QC queries for the community to explore DO's data. File formats include OWL, OBO, JSON, and XML. User requests for disease term review or inclusion of new disease terms may be submitted via the DO GitHub tracker: https://github.com/DiseaseOntology/HumanDiseaseOntology/issues.

Through a systematic review, this study collected published and unpublished data on human parainfluenza virus (hPIV) burden to estimate the global and regional number of hPIV-associated acute lower respiratory infections (ALRI) cases, hospitalizations, and mortality by children under five years (stratified 0–5 months, 6–11 months, and 12–59 months) for 2018. The datasets include incidence rates of hPIV-associated ALRI, hospitalization rates of hPIV-associated ALRI, hospitalization rates of hPIV with hypoxemia, in-hospital case fatality ratios of hPIV-associated ALRI, and proportion positives of hPIV in hospitalized ALRI. The data was collected to help guide health investment priorities and resource allocation to accelerate the development of targeted prevention and treatment interventions.

This study uses a computing platform to analyze and estimate the correlation between human mobility and COVID-19 spreading. The infection data are collected from various county, state and national sources and mobile device location data are procured from multiple third-party data providers. To capture the time-varying relationship between the number of infection and mobility inflow, the authors developed a simultaneous equations model with time-varying coefficients. Mobility data can be found in the University of Maryland COVID-19 Impact Analysis Platform. The analysis platform displays the data in a user-friendly interface utilizing a map of the United States and charts that show, under Mobility and Social Distancing, the % of in and out-of-county trips, % of in and out- of-state trips, and others. The information on the map can also be adjusted to show the mobility per state. Codebase, data of infection cases and computed metrics are shared on GitHub. The statistic modeling including data processing, prediction and visualization is written in Python and R.

For two decades the standard drug therapy for patients with Crohn’s disease (CD) and ulcerative colitis (UC) has been anti-tumor necrosis factor agents (anti-TNFs) such as adalimumab (Humira), certolizumab pegol (Cimzia), infliximab (Remicade) and several others. More recently, vedolizumab (Entyvio) has entered the market as the first gut-selective integrin blocker that offers an alternative treatment for inflammatory bowel disease (IBD). Using the FDA Adverse Event Reporting System (FAERS) database, this study compared adverse event reports (AE) for the two types of therapies. The search retrieved 499 reports for vedolizumab and 119,620 for anti-TNFs for the years 1998 through 2015. Using the proportional reporting ratio (PPR) and the empirical Bayesian geometric mean (EBGM) algorithms, AE data were reviewed for signals of disproportionate reporting for the two types of drugs. This dataset includes statistical summary tables of characteristics (demographic) and a variety of adverse event comparisons of vedolizumab, anti-TNFs, and all other medications. Cleaned FAERS data was provided with permission by DrugLogic, Inc. Interested researchers may contact DrugLogic Inc. (Reston, VA) by calling 800-393-1313 or emailing swordham@druglogic.com

Randomised, placebo-controlled clinical trials are considered the gold standard for evaluating new vaccines. To assess its efficacy and safety, the manufacturer of quadrivalent human papillomavirus (qHPV) vaccine conducted multiple clinical trials involving approximately 30,000 volunteers. The trials of the qHPV vaccine are reported as ‘placebo-controlled.’ However, participants in the ‘placebo’ arms received an injection-containing amorphous aluminium hydroxyphosphate sulfate (AAHS), a proprietary adjuvant. AAHS is used in the qHPV vaccine to boost immune response, but the rationale for adding it to the ‘placebo’ is not reported in publications of these trials and is contrary to the advice of the public health bodies and regulators. Standard recommendations for control recipients in trials testing an unlicensed, experimental vaccine include using either an inert substance or an approved efficacious vaccine. However, several pivotal trial publications incompletely reported important methodological details and inaccurately described the formulation that the control arms received. Under the Restoring Invisible and Abandoned Trials Initiative (RIAT), the primary objective of this study was to characterize the reporting of the methodology with respect to the rationale for the choice of standalone aluminum-containing adjuvanted controls. Clinical study reports (CSRs) from five randomized controlled trials described as placebo-controlled were obtained from the European Medicines Agency (EMA). Content and rationale for the choice of control used in each trial was extracted across six data sources: trial publications, register records, CSR synopses, CSR main bodies, protocols and informed consent forms. For each source within each trial, the following was recorded: (1) the phrases used to describe the comparator to qHPV vaccine; (2) the rationale for using aluminum-containing adjuvant as a control, if present and (3) all listed contents (ingredients) of formulation received by intervention and control arms. This dataset includes data extraction sheets and RIAT protocol documentation publicly accessible via the Open Science Framework with the CSRs available upon request.