COVID-19: Vaccine, prevention, diagnosis & treatment datasets

Early in the pandemic, we designed a SARS-CoV-2 peptide vaccine containing epitope regions optimized for concurrent B cell, CD4+ T cell, and CD8+ T cell stimulation. The rationale for this design was to drive both humoral and cellular immunity with high specificity while avoiding undesired effects such as antibody-dependent enhancement (ADE). In this study, we combine computational prediction of T cell epitopes, recently published B cell epitope mapping studies, and epitope accessibility to select candidate peptide vaccines for SARS-CoV-2. We begin with an exploration of the space of possible T cell epitopes in SARS-CoV-2 with interrogation of predicted HLA-I and HLA-II ligands, overlap between predicted ligands, protein source, as well as concurrent human/murine coverage. Beyond MHC affinity, T cell vaccine candidates were further refined by predicted immunogenicity, viral source protein abundance, sequence conservation, coverage of high frequency HLA alleles and co-localization of CD4+ and CD8+ T cell epitopes. B cell epitope regions were chosen from linear epitope mapping studies of convalescent patient serum, followed by filtering to select regions with surface accessibility, high sequence conservation, spatial localization near functional domains of the spike glycoprotein, and avoidance of glycosylation sites. From 58 initial candidates, three B cell epitope regions were identified. By combining these B cell and T cell analyses, as well as a manufacturability heuristic, we propose a set of SARS-CoV-2 vaccine peptides for use in subsequent murine studies. The immunogenicity of the selected peptides was validated using ELISpot and ELISA following murine vaccination. We also curated a dataset of almost a thousand observed T-cell epitopes from convalescent COVID-19 patients across eight studies. Our vaccine selection process appears to be effective at predicting recurrent T-cell epitopes and strong T-cell responses were observed in mice following vaccination. Humoral responses were deficient, likely due to the unrestricted conformational space inhabited by linear vaccine peptides. Overall, we find our selection process and vaccine formulation to be appropriate for identifying T-cell epitopes and eliciting T-cell responses against those epitopes.

Inhibiting the main proteasome of severe acute respiratory syndrome–coronavirus 2 (SARS-CoV-2) may serve as a treatment option for patients suffering from COVID-19. Inhibition of the main proteasome (SARS-CoV-2 Mpro) may improve patient outcomes and recovery through blocking viral replication and assembly. A literature review of potential drug treatments for COVID-19 included Nelfinavir, an HIV antiviral, and Epirubicin, an anthracycline and topoisomerase inhibitor. The mechanism of action for both drugs includes binding to SARS-CoV-2 Mpro. These data highlight in-silico binding pose energy predictions of SARS-CoV-2 Mpro (receptor) each of the two drug targets (ligands) using a Generic Evolutionary Method for Molecular Docking (iGEMDOCK). In-silico screening provides highly accurate, reproducible complex-ligand binding affinity prediction data. These data are derived from a population size of 200 and 70 docking generation trials. The 3-D Protein Data Bank (PDB) structure of the main proteasome (SARS-CoV-2 Mpro) for this investigation was derived from the RCSB Protein Data Bank (PDB ID: 6LU7). The 3-D structures of Nelfinavir and Epirubicin were derived from the PubChem database (PubChem CIDs 64143, 41867 respectively). The 3-D structures were converted from 3D conformer SDF files to Protein Data Bank (PDB) formatting through OpenBabble. The data show Nelfinavir as outcompeting Epirubicin in binding to the main proteasome (SARS-CoV-2 Mpro). The complex formation with Nelfinavir was more energetically favorable than that with Epirubicin. The data include relevant binding site residues and energy values, in units of kcal/mol. A composite of van der Waals forces, hydrogen bonding, and electrostatic charge provide the energy values.

This dataset presents a schematic representation of some of the currently acknowledged solutions from the open-source projects of ventilators, made to challenge the COVID-19 pandemic. The framework is composed by two graphical tools: the Problem-Solution-Network (PSN) and the chart of structural solutions. Red boxes in the PSN represents the rows of the chart. More specifically, it has been hypothesized a framework capable to abstractly model existing solutions, in order to represent them without focusing on mere structural details that can somehow induce bias in designers that will consult them. Indeed, complete information about available open-source ventilators can be found, in terms of electrical scheme, detailed CAD models, videos and photos. However, many of them share the same working principles and differ only in terms of manufacturing-related choices and/or forms. Differently, other solutions implement completely different principles, but are sometimes implemented at a very rough level. It is acknowledged in literature that the way in which a prototype is presented (otherwise said “Fidelity” level) can actually influence the opinion of the audience (in this case, of the stakeholders involved in the development of new ventilators). Therefore, poorly implemented original ideas often risk to be discarded because “not convincing”, if compared to other (maybe old) ideas developed in higher detail. In order to avoid this problem, it has been chosen to model the compressing unit of the ventilators in two ways, i.e. abstractly with schemas, and graphically with generalized CAD models. For the abstract schematization, it has been chosen to apply the Problem-Solution-Network (PSN) approach. In particular, its last version has been taken as a reference, where different abstraction levels are considered to formulate both design problems and solutions. Then, a chart to collect the generalized CAD models of key mechanisms has been used, and directly linked to the PSN. The following paragraphs explain both the PSN and the chart. This particular set of graphical tools allows to represent ventilators by decomposing them into the main design problems and the related solutions used to implement them. Besides the generation of brand new solutions for each problem, the proposal allows to explore different combinations between already explored solutions (not to be confounded with a mere recombination of parts fom acknowledged ventilators). Please note that the file type "graphml" can be open and edited with the software yEd (available at www.yworks.com/products/yed).

This COVID-19 dataset consists of Non-COVID and COVID cases of both X-ray and CT images. The associated dataset is augmented with different augmentation techniques to generate about 17099 X-ray and CT images. The dataset contains two main folders, one for the X-ray images, which includes two separate sub-folders of 5500 Non-COVID images and 4044 COVID images. The other folder contains the CT images. It includes two separate sub-folders of 2628 Non-COVID images and 5427 COVID images.

Raw data for main figures and supplemaentary figures.

Dataset name Normal COVID-19 Pneumonia Total MOMA- Dataset 234 221 148 603 MOMA Dataset are collected from three resources

Dataset used in the "Effect of an E-Learning Module on Personal Protective Equipment Apprehension by Prehospital Personnel" study.

Objectives: The present systematic review aimed to evaluate the diagnostic accuracy of saliva in detecting novel coronavirus in symptomatic or asymptomatic people at risk of exposure to COVID-19. Materials and methods: The comprehensive searches were made using four electronic databases [MEDLINE (via PubMed), EMBASE, Google Scholar, and The Cochrane Central Register of Controlled Trials] till 31st April 2020 and clinical trials meeting the predefined inclusion criteria were included. The Real-time polymerase chain reaction (RT-PCR) test analyzing the salivary samples was considered as an index test and analyzing the nasopharyngeal/Oropharyngeal/nasal/throat swabs was considered as a reference standard test. The diagnostic accuracy was measured using sensitivity and specificity, positive and negative likelihood ratios, diagnostic odds ratio, and summary receiver operating curves (SROC). The risk of bias was analyzed using the revised tool for the quality assessment of diagnostic accuracy studies (QUADAS -2). The random effect models were used to pool sensitivity and specificity and the extracted data were analyzed and interpreted using MetaDisc software. Results: From the initial search of 160 studies, 10 studies were included for qualitative analysis, and 4 studies were included for quantitative analysis. The salivary diagnostic showed good sensitivity [0.89 (95% CI: 0.81-0.95; p=0.000; I2 = 92.5%)] and specificity [0.95 (95% CI: 0.92-0.98; p=0.000; I2 = 94.7%)] with “High” quality evidence. The area under the SROC curve was 0.97, inferring an ‘excellent’ diagnostic potential of saliva in detecting COVID-19 patients. Conclusion: The accuracy of salivary samples in detecting the 2019-nCoV in symptomatic and asymptomatic people at risk of COVID-19 exposure indicates a high sensitivity (0.89) and high specificity (0.95) with 'high' certainty of evidence.

Raw data of the JAAD Research Letter: Androgenetic alopecia present in the majority of patients hospitalized with COVID-19 - The "Gabrin Sign". (10.1016/j.jaad.2020.05.079). Data of patients admitted due to severe COVID-19 in three Madrid hospitals. The study took place from March 23, 2020 to April 12, 2020. Dermatologists recorded the age, gender and alopecia diagnosis. Alopecia severity was evaluated using the Hamilton–Norwood scale (HNS) for males and the Ludwig scale (LS) for females.

Table in excel format with the data and the averages of the work