Coronavirus Disease 2019 (COVID-19) has been spreading like a wildfire everywhere in the globe. It has been challenging the global health care system ever since the end of 2019, with its virulence and pathogenicity. Recent studies have shown the association between ABO blood group, Rhesus blood type and susceptibility to COVID-19 infection. Various studies and few meta-analyses have been done and some might be inconsistent; therefore, this meta-analysis was done to assess the relationship between different ABO and Rhesus blood types on the susceptibility to COVID-19 infections. This meta-analysis assessed the odds ratio of COVID-19 infection of different ABO and Rhesus blood types. Subgroup analyses according to (1) age and gender matched; (2) different blood group antigens; (3) Rhesus positive and negative of each blood group were carried out. Publication bias and Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) were also done to assess the risk of bias in these publications. It was found that blood group A showed significant difference in odds ratio of COVID-19 infection (OR, 1.16; 95% CI, 1.08-1.24). Blood group AB showed significant difference in odds ratio when studies with lower QUADAS-2 score were removed. This means that populations with blood group A and AB are more likely to be infected with COVID-19. As there is a higher tendency that blood group A and AB to be infected with COVID19, precautious care should be taken by these populations.

To address the increasing need for detecting and validating protein biomarkers in clinical specimens, mass spectrometry (MS)-based targeted proteomic techniques, including the selected reaction monitoring (SRM), parallel reaction monitoring (PRM), and massively parallel data-independent acquisition (DIA), have been developed. For optimal performance, they require the fragment ion spectra of targeted peptides as prior knowledge. In this report, we describe a MS pipe-line and spectral resource to support targeted proteomics studies for human tissue samples. To build the spectral resource, we integrated common open-source MS computational tools to assemble a freely accessible computational workflow based on Docker. We then applied the workflow to gen-erate DPHL, a comprehensive DIA pan-human library, from 1096 data-dependent acquisition (DDA) MS raw files for 16 types of cancer samples. This extensive spectral resource was then applied to a proteomic study of 17 prostate cancer (PCa) patients. Thereafter, PRM validation was applied to a larger study of 57 PCa patients and the differential expression of three proteins in prostate tumor was validated. As a second application, the DPHL spectral resource was applied to a study consisting of plasma samples from 19 diffuse large B cell lymphoma (DLBCL) patients and 18 healthy control subjects. Differentially expressed proteins between DLBCL patients and healthy control subjects were detected by DIA-MS and confirmed by PRM. These data demonstrate that the DPHL supports DIA and PRM MS pipelines for robust protein biomarker discovery. DPHL is freely accessible at https://www.iprox.org/page/project.html?id=IPX0001400000.