Big Data Reality Check (BDRC) for public health: to what extent the environmental health and health services research did meet the 'V' criteria for big data? A study protocol.

INTRODUCTION: Big data technologies have been talked up in the fields of science and medicine. The V-criteria (volume, variety, velocity and veracity, etc) for defining big data have been well-known and even quoted in most research articles; however, big data research into public health is often misrepresented due to certain common misconceptions. Such misrepresentations and misconceptions would mislead study designs, research findings and healthcare decision-making. This study aims to identify the V-eligibility of big data studies and their technologies applied to environmental health and health services research that explicitly claim to be big data studies. METHODS AND ANALYSIS: Our protocol follows Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P). Scoping review and/or systematic review will be conducted. The results will be reported using PRISMA for Scoping Reviews (PRISMA-ScR), or PRISMA 2020 and Synthesis Without Meta-analysis guideline. Web of Science, PubMed, Medline and ProQuest Central will be searched for the articles from the database inception to 2021. Two reviewers will independently select eligible studies and extract specified data. The numeric data will be analysed with R statistical software. The text data will be analysed with NVivo wherever applicable. ETHICS AND DISSEMINATION: This study will review the literature of big data research related to both environmental health and health services. Ethics approval is not required as all data are publicly available and involves confidential personal data. We will disseminate our findings in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42021202306.

In silico identification of natural product inhibitors of JAK2.

Emodic acid (1) and 6-chloroemodic acid (2) have been identified from a natural product database as useful scaffolds for the future development of novel JAK2 inhibitors using structure-based high-throughput virtual screening. Low-energy binding conformations of 1 and 2 in the JAK2 PTK domain were generated by virtual ligand docking and were found to overlap considerably with the binding pose of CMP6, a known JAK2 inhibitor. Compounds 1 and 2 displayed low micromolar efficacies against JAK2 enzyme activity and JAK2 autophosphorylation in human erythroleukemia cells, and inhibited STAT3 DNA-binding activity in a human hepatocarcinoma cell line.