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Population-based Age-stratified Seroprevalence Investigation on SARS-CoV-2 Virus in Selected States of High and Low Burden of Disease in Nigeria

Results of population-based age stratified seroepidemiological investigation in Nigeria

opencc-by-4.0Dec 2020View details →

SARS-CoV-2 exposure in Malawian blood donors: an analysis of seroprevalence and variant dynamics between January 2020 and July 2021

Results of population-based age stratified seroepidemiological investigation in Malawi.

High prevalence of SARS-CoV-2 antibodies in pregnant women in the inner city of Johannesburg, Gauteng Province, South Africa

Results of population-based age stratified seroepidemiological investigation in South Africa.

Seroprevalence of SARS-CoV-2 in 10 Regional Capitals of Cameroon

Results of population-based age stratified seroepidemiological investigation in Cameroon

Age-stratified sero-surveillance for SARS-CoV-2 infections in four cities in Thailand

Results of population-based age stratified seroepidemiological investigation in Thailand

opencc-zeroNov 2021View details →

dryad32/100

Spatiotemporal interactions of a novel mesocarnivore community in an urban environment before and during SARS‐CoV‐2 lockdown

1. Studying species interactions and niche segregation under human pressure provides important insights into species adaptation, community functioning and ecosystem stability. Due to their high plasticity in behaviour and diet, urban mesocarnivores are ideal species for studying community assembly in novel communities. 2. We analysed the spatial and temporal species interactions of an urban mesocarnivore community composed of the red fox (Vulpes vulpes) and the marten (Martes sp.) as native species, the raccoon (Procyon lotor) as invasive species, and the cat (Felis catus) as a domestic species in combination with human disturbance modulated by the SARS-CoV-2 lockdown effect that happened while the study was conducted. 3. We analysed camera-trap data and applied a joint species distribution model to understand not only the environmental variables influencing the detection of mesocarnivores and their use intensity of environmental features but also the species' co-occurrences while accounting for environmental variables. We then assessed whether they displayed temporal niche partitioning based on activity analyses, and finally analysed at a smaller temporal scale the time of delay after the detection of another focal species. 4. We found that species were more often detected and displayed a higher use intensity in gardens during the SARS-CoV-2 lockdown period, while showing a shorter temporal delay during the same period, meaning a high human-induced spatio-temporal overlap. All three wild species spatially co-occurred within the urban area, with a positive response of raccoons to cats in detection and use intensity, whereas foxes showed a negative trend towards cats. When assessing the temporal partitioning, we found that all wild species showed overlapping nocturnal activities. All species displayed temporal segregation based on temporal delay. According to the temporal delay analyses, cats were the species avoided the most by all wild species. To conclude, we found that although the wild species were positively associated in space, the avoidance occurred at a smaller temporal scale, and human pressure in addition led to high spatio-temporal overlap. 5. Our study sheds light to the complex patterns underlying the interactions in a mesocarnivore community both spatially and temporally, and the exacerbated effect of human pressure on community dynamics.

Effectiveness of Inactivated SARS-CoV-2 Vaccine (CoronaVac) on Survival at Intensive Care Unit: A Cross-sectional Study

Background: This study compared the course of COVID-19 in vaccinated and unvaccinated patients admitted to an intensive care unit (ICU) and evaluated the effect of vaccination with CoronaVac on admission to ICU. Methods: Patients admitted to ICU due to COVID-19 between 1 April 2021 and 15 May 2021 were enrolled to the study. Clinical, laboratory, radiological parameters, hospital and intensive care unit mortality were compared between vaccinated patients and eligible but unvaccinated patients. Patients over 65 years old were the target population of the study due to the national vaccination schedule. Results: Data from 90 patients were evaluated. Of these, 36 (40.0%) were vaccinated. All patients had the CoronaVac vaccine. Lactate dehydrogenase and ferritin levels were higher in unvaccinated group than vaccinated group (p=0.021 and 0.008, respectively). SpO2 from the first arterial blood gas at ICU was 83.71±19.50 % in vaccinated, 92.36±6.59 % in unvaccinated patients (p=0.003). Length of ICU and hospital stay were not different (p=0.204, 0.092, respectively).  ICU and hospital mortality were similar between groups (p=0.11 and 0.70, respectively). Conclusions: CoronaVac vaccine had no effect on survival from COVID-19. CoronaVac’s protective effect, especially on new genetic variants, should be investigated further.

Public attitudes to a human challenge study with SARS-CoV-2: a mixed-methods study

Human challenge studies involve the deliberate exposure of healthy volunteers to an infectious micro-organism in a highly controlled and monitored way. They are used to understand infectious diseases and have contributed to the development of vaccines. In early 2020, the UK started exploring the feasibility of establishing a human challenge study with SARS-CoV-2. Given the significant public interest and the complexity of the potential risks and benefits, it is vital that public views are considered in the design and approval of any such study and that investigators and ethics boards remain accountable to the public. We conducted a mixed methods study comprising online surveys conducted with 2,441 UK adults and in-depth virtual focus groups with 57 UK adults during October 2020 to explore the public’s attitudes to a human challenge study with SARS-CoV-2 taking place in the UK. This depositary includes: <ul> <li>Documentation relating to the online surveys; <ul> <li>Survey</li> <li>Animation that survey participants were asked to view</li> <li>Survey wrappers (invitations, participant information sheet and consent, end of survey messages)</li> <li>Survey data (demographic information not included)</li> </ul> </li> <li>Documentation relating to focus groups: <ul> <li>Participant Information Sheet</li> <li>Focus Group Discussion guide</li> </ul> </li> </ul>

Simulations from 'Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples'

Simulations used in "Estimating the relative proportions of SARS-CoV-2 strains from wastewater samples"

Virtual screening of the Janssen compound collection against SARS-CoV-2 Mpro

This short report describes the most relevant results of virtually screening the Janssen Pharmaceutica compound collection for potential activity against SARS-CoV-2 Mpro and confirmation of potential hits in a VeroE6 cell-based anti-SARS-CoV-2 assay.

Virtual screening of the Enamine REAL database against SARS-CoV-2 RdRP

This short report describes the most relevant results of virtually screening the Enamine REAL database for potential activity against SARS-CoV-2 RdRP and confirmation of potential hits in a VeroE6 cell-based anti-SARS-CoV-2 assay.

Virtual screening of 2629 Janssen compounds against SARS-CoV-2 RdRP

This short report describes the most relevant results of virtually screening the Janssen Pharmaceutica compound collection for potential activity against SARS-CoV-2 RdRP and confirmation of potential hits in a VeroE6 cell-based anti-SARS-CoV-2 assay.

Screening of 67 compounds with predicted biosignature profile similar to remdesivir for anti-SARS-CoV-2 activity

This short report describes the most relevant results of screening compounds with a similar predicted biosignature as remdesivir in a VeroE6 cell-based anti-SARS-CoV-2 assay.

Screening of ~7000 coronavirus-specific compounds for anti-SARS-CoV-2 activity

This short report describes the most relevant results of screening selected compounds with potential activity against SARS-CoV-2 in a VeroE6 cell-based anti-SARS-CoV-2 assay.

Structural dynamics of SARS-CoV-2 nucleocapsid protein induced by RNA binding

This dataset contains files of the molecular dynamics simulations performed in "Structural dynamics of SARS-CoV-2 nucleocapsid protein induced by RNA binding" study. Further information in presented in README.md file and the abstract of the study is presented below: "The nucleocapsid (N) protein of the SARS-CoV-2 virus, the causal agent of COVID-19, is a multifunction phosphoprotein that plays critical roles in the virus life cycle, including transcription and packaging of the viral RNA. To play such diverse roles, the N protein has two globular RNA-binding modules, the N- (NTD) and C-terminal (CTD) domains, which are connected by an intrinsically disordered region. Despite the wealth of structural data available for the isolated NTD and CTD, how these domains are arranged in the full-length protein and how the oligomerization of N influences its RNA-binding activity remains largely unclear. Herein, using experimental data from electron microscopy and biochemical/biophysical techniques combined with molecular modeling and molecular dynamics simulations, we showed that, in the absence of RNA, the N protein formed structurally dynamic dimers, with the NTD and CTD arranged in extended conformations. However, in the presence of RNA, the N protein assumed a more compact conformation where the NTD and CTD are packed together. We also provided an octameric model for the full-length N bound to RNA that was consistent with electron microscopy images of the N protein in the presence of RNA. Together, our results shed new light on the dynamics and higher-order oligomeric structure of this versatile protein."

SARS-CoV-2 Seroprevalence in a Rural and Urban Household Cohort, South Africa, July 2020–September 2021

Early results of the PHIRST-C study seroprevalence results in South Africa

opencc-by-4.0Feb 2022View details →

Serial seroprevalence study: follow-up of immunity to SARS-Cov-2 infection and monitoring of effective vaccination coverage, in three Chilean cities

Population-based serosurvey in Santiago, Talca, and Coquimbo–La Serena

opencc-by-4.0Dec 2021View details →

Signature of long-lived memory CD8+ T cells in acute SARS-CoV-2 infection

The datasets uploaded in this Zenodo entry were generated in the single cell RNA sequencing part of the project. For the scRNAseq analysis, cells from ten patients and the same time point were pooled together, generating four individual sample sets in total: (1) patients CoV2_T001- CoV2_T010, acute; (2) patients CoV2_T001- CoV2_T010, six months post-infection; (3) patients CoV2_T011- CoV2_T020, acute; (4) patients CoV2_T011- CoV2_T011-20, six months post-infection. We additionally generated two more sample sets: using 5000 unsorted PBMCs from each patient’s sample: (5) patients CoV2_T001- CoV2_T010, six months post-infection unsorted; (6) patients CoV2_T011- CoV2_T020, six months post-infection unsorted. The cells in these two sample sets were hashed. Finally, using PBMCs from four healthy donors, we generated sample set (7) by sorting and pooling 2000 CD8+ T cells from each healthy donor sample. We are here providing the pre-processed sets for each sample set (1-7), i.e. : - "filtered feature bc matrix" files, as output from the ‘cellranger multi’ pipeline (Cell Ranger version 5.0.0), containing cell-RNA count matrices and cell-ADT matrices. ADTs comprise counts for TotalSeq antibodies and dCODE Dextramers. - "filtered_contig_annotations.csv" files, as output from the ‘cellranger multi’ pipeline (Cell Ranger version 5.0.0), containing High-level annotations of each high-confidence, cellular contig for TCR clonal analysis. This file is not present for sets 5 and 6, because we did not perform TCR profiling for these samples. - "clusters.tsv" files, as output from the souporcell SNP analysis (version 2). To cluster cells based on their patient specific genetic variants, we merged sample sets 1, 2  and 5 (comprising sorted cells from both time points of patients CoV2_T001- CoV2_T010 and unsorted cells of the same patients) and sets 3, 4 and 6 (comprising cells from both time points of patients CoV2_T011- CoV2_T020 and unsorted cells of the same patients). Then, we executed the souporcell pipeline with option k=10 (number of clusters to be determined) for each of the two merged sample sets. Together, these files allow to reproduce the analysis as reported in the paper. Additionally, we provide the Seurat Objects "Integrated.h5seurat"  and "Integrated_NA_filtered.h5seurat" which can be used to skip the pre-processing steps of the data analysis. See the code provided on https://github.com/Moors-Code/SARS-CoV-2-Tcell-Boyman-collaboration for details.