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2,489 results for “Sars-CoV-2”
Incidence of SARS-CoV-2 reinfection in a pediatric cohort in Kuwait
<p><strong><span>Objective: </span></strong><span>Subsequent protection from severe acute respiratory syndrome-related coronavirus 2 (SARS-COV-2) infection in pediatrics is not well reported in the literature. We aimed to describe the clinical characteristics and dynamics of SARS-CoV-2 PCR repositivity in children. </span></p> <p><strong><span>Design:</span></strong><span> This is a population-level retrospective cohort study</span></p> <p><strong><span>Setting: </span></strong><span>Patients were identified through multiple national-level electronic coronavirus disease 2019 (COVID-19) databases covering all Kuwait's primary, secondary and tertiary centers. </span></p> <p class="MsoNormal"><strong><span>Participants: The </span></strong><span>study included children 12 years and younger over an 11-month period between 2020 and 2021. SARS-CoV-2 reinfection was defined as having two or more positive SARS-CoV-2 PCR done on a respiratory sample, at least 45 days apart. Clinical data were obtained from the Pediatric COVID-19 Registry in Kuwait (PCR-Q8). </span></p> <p class="MsoNormal"><strong><span>Primary and secondary outcome measures:</span></strong><span> The primary measure is to estimate the SARS-CoV-2 PCR repositivity rate. The secondary objective was to establish average duration between first and subsequent SARS-CoV-2 infection.</span><span> </span><span>Descriptive statistics was used to present clinical data for each infection episode. Also, incidence-sensitivity analysis was performed to evaluate 60- and 90-day PCR repositivity intervals.</span></p> <p><strong><span>Results:</span></strong><span> Thirty pediatric COVID-19 patients had SARS-CoV-2 reinfection at an incidence of 1.02 (95% CI 0.71-1.45) infection per 100,000 person-days and a median time to reinfection of 83 days (IQR 62-128.75). <span>The incidence of reinfection decreased to 0.78 (95% CI 0.52-1.17) and 0.47 (95% CI 0.28-0.79) per person-days when the minimum interval between PCR repositivity was increased to 60 and 90 days, respectively. </span>The mean age of reinfected subjects was 8.5 years (IQR 3.7-10.3) and the majority (70%) were females. Most children (55.2%) had asymptomatic reinfection. Fever was the most common presentation in symptomatic patients. One immunocompromised experienced two reinfection episodes.</span></p> <p><strong><span>Conclusion: </span></strong><span>SARS-CoV-2 reinfection is uncommon in children. Previous confirmed COVID-19 in children seems to result in milder reinfection.</span></p>
Tomogram of SARS-CoV-2
<p>Tomogram of SARS-CoV-2 purified using Capto Core</p>
Predicting past and future SARS-CoV-2-related sick leave using discrete time Markov modelling
<p><strong>Background: </strong>Prediction of SARS-CoV-2-induced sick leave among healthcare workers (HCWs) is essential for being able to plan the healthcare response to the epidemic.</p> <p><strong>Methods: </strong>During first wave of the SARS-Cov-2 epidemic (April 23<sup>rd </sup>to June 24<sup>th</sup>, 2020), the HCWs in the greater Stockholm region in Sweden were invited to a study of past or present SARS-CoV-2 infection. We develop a discrete time Markov model using a cohort of 9449 healthcare workers (HCWs) who had complete data on SARS-CoV-2 RNA and antibodies as well as sick leave data for the calendar year 2020. The one-week and standardized longer term transition probabilities of sick leave and the ratios of the standardized probabilities for the baseline covariate distribution were compared with the referent period (an independent period when there were no SARS-CoV-2 infections) in relation to PCR results, serology results and gender.</p> <p><strong>Results:</strong> The one-week probabilities of transitioning from healthy to partial sick leave or full sick leave during the outbreak as compared to after the outbreak were highest for healthy HCWs testing positive for large amounts of virus (ratio: 3.69, (95% confidence interval, CI: 2.44-5.59) and 6.67 (95% CI: 1.58-28.13), respectively). The proportion of all sick leaves attributed to COVID-19 during outbreak was at most 55% (95% CI: 50%-59%).</p> <p><strong>Conclusions: </strong>A robust Markov model enabled use of simple SARS-CoV-2 testing data for quantifying past and future COVID-related sick leave among HCWs, which can serve as a basis for planning of healthcare during outbreaks.</p>
Supplementary figure: No evidence of any wild animal samples positive for SARS-CoV-2 RNA or antibodies were found in China.
<p>A compilation of results of wildlife sampling from the Huanan market and from China showing no evidence of positive animal for SARS-CoV-2 in China</p>
PanDDA files from a ligand screen against the NSP3 macrodomain of SARS-CoV-2 - ligands from fragment merging/linking and virtual screening
<p>This deposition contains the X-ray diffraction data used to run PanDDA in the ligand screen against the NSP3 macrodomain of SARS-CoV-2 described in Gahbauer et al. 2022 (doi: https://doi.org/10.1101/2022.06.27.497816).</p> <p>mac1_pandda.zip contains the structure factor intensities, PanDDA input/ouput and refined models/maps. A description of the files can be found in the README file. </p> <p>mac1_ligand-bound_states.zip contains the ligand-bound states extracted from the multi-state PDB files. </p>
Test data for RonaQC - mapped SARS-CoV-2 reads
<p>This dataset includes test data for <a href="https://ronaqc.netlify.app/">RonaQC</a></p> <p>RonaQC accepts mapped SARS-CoV-2 reads (BAM format), generated from the SARS-CoV-2 bioinformatic pipelines like ARTIC, and any control samples from the respective sequencing run (negative/positive) as input. It will then assess the levels of cross contamination and primer contamination in the samples, and determine if the samples are reliable for detecting SARS-CoV-2, phylogenetic analysis, and/or submission to public databases.</p> <p><br> The dataset includes SARS-CoV-2 sequenced reads compiled by <a href="https://github.com/CDCgov/datasets-sars-cov-2">CDCgov/datasets-sars-cov-2</a> [1]. </p> <p>These were reads were processed using the <a href="https://github.com/connor-lab/ncov2019-artic-nf">ncov2019-artic-nf pipelines</a>, which is a Nextflow pipeline for running the <a href="https://github.com/artic-network/fieldbioinformatics">ARTIC network's fieldbioinformatics tools</a>, with a focus on ncov2019. </p> <p><br> This dataset includes: </p> <ul> <li><strong>FailedQC </strong>- A cohort of 24 samples failed basic QC metrics, covering 8 possible failure scenarios, Illumina platform, amplicon-based approach </li> <li><strong>VOCRepresentatives </strong>- A cohort of 16 samples from 10 representative CDC defined VOI/VOC lineages as of 06/15/2021, Illumina platform, amplicon-based approach </li> <li><strong>Test </strong>- Smaller test samples, including sequenced negative controls of varying quality</li> </ul> <p>[1] Timme, Ruth E., et al. "Benchmark datasets for phylogenomic pipeline validation, applications for foodborne pathogen surveillance." PeerJ 5 (2017): e3893. </p>
Data for: Immunogenicity of SARS-CoV-2 spike antigens derived from Beta & Delta variants of concern
<p>Using our strongly immunogenic SmT1 SARS-CoV-2 spike antigen platform, we developed novel antigens based on the Beta & Delta variants of concern. These antigens elicited higher neutralizing antibody activity to the corresponding variant than comparable vaccine formulations based on the original reference strain, while a multivalent vaccine generated cross-neutralizing activity to all three variants. This suggests that while current vaccines may be effective at reducing severe disease to existing variants of concern, variant-specific antigens, whether in a mono- or multivalent vaccine, may be required to induce optimal immune responses and reduce infection against arising variants.</p>
Diet-induced obesity and NASH impair disease recovery in SARS-CoV-2-infected golden hamsters
<p>Obese patients with nonalcoholic steatohepatitis (NASH) are prone to severe forms of COVID-19. There is an urgent need for new treatments that lower the severity of COVID-19 in this vulnerable population. To better replicate the human context, we set up a diet-induced model of obesity associated with dyslipidemia and NASH in the golden hamster (known to be a relevant preclinical model of COVID-19). A 20-week, free-choice diet induces obesity, dyslipidemia and NASH (liver inflammation and fibrosis) in golden hamsters. Obese NASH hamsters have higher blood and pulmonary levels of inflammatory cytokines. In the early stages of a SARS-CoV-2 infection, the lung viral load and inflammation levels were similar in lean hamsters and obese NASH hamsters. However, obese NASH hamsters showed worse recovery (i.e. less resolution of lung inflammation 10 days post-infection (dpi), and lower body weight recovery on dpi 25). Obese NASH hamsters also exhibited higher levels of pulmonary fibrosis on dpi 25. Unlike lean animals, obese NASH hamsters infected with SARS-CoV-2 presented long-lasting dyslipidemia and systemic inflammation. Relative to lean controls, obese NASH hamsters had lower serum levels of angiotensin-converting enzyme 2 activity and higher serum levels of angiotensin II - a component known to favor inflammation and fibrosis. Even though the SARS-CoV-2 infection resulted in early weight loss and incomplete body weight recovery, obese NASH hamsters showed sustained liver steatosis, inflammation, hepatocyte ballooning, and marked liver fibrosis on dpi 25.<strong> </strong>We conclude that diet-induced obesity and NASH impair disease recovery in SARS-CoV-2-infected hamsters. This model might be of value in characterizing the pathophysiologic mechanisms of COVID-19 and in evaluating the efficacy of treatments for the severe forms of COVID-19 observed in obese patients with NASH.</p>
PredictION: A predictive model to establish the performance of Oxford sequencing reads of SARS-CoV-2
<p>Dataset (1) that included 1461 samples and a dataset (2) with 471 samples that was a subset of dataset 1 that included Number of sequenced reads per genome, CT (Cycle threshold) value [N2 target gene], mean coverage depth, coverage genome (percentage), and quantification cDNA (ng/µl).</p>
Summary of sequence variations in strains of Sars-CoV-2
<p>This entry collects JSON files specifying sequence variations of different strains (aka variants) of SARS-CoV-2 based on data retrieved from <a href="https://outbreak.info/">outbreak.info</a> and/or <a href="https://covariants.org/variants/">covariants.org</a>. These JSON files are formatted to match the <a href="https://docs.google.com/document/d/1wFJjdyl1OASnsBNkUzUx4ME8YhVybhIWCTr3Z1fBEWQ/pub">Feature API</a> of <a href="http://aquaria.ws/covid">aquaria.ws</a>. File names match the <a href="https://www.who.int/en/activities/tracking-SARS-CoV-2-variants/">linage names</a> of VOCs and VOIs. In the case of <em>omicron</em> we have added a version (omicron_charge) that highlights charge changing residues in magenta, as well as an extra file (omicron_BA2) for the <a href="https://cov-lineages.org/lineage.html?lineage=BA.2">BA.2</a>/21K sister clade and a file (omicron_BA1_BA2_diff) highlighting the differences between the lineages (coloring variations in only BA.1 red, only BA.2 blue, in both magenta). <a href="https://zenodo.org/record/5792281/files/CovidVariants.pdf">CovidVariants.pdf</a> lists SARS-CoV-2 proteins together with links that show how to map variations onto the structures.</p>
Sars-escape network for escape prediction of SARS-COV-2
<p>This dataset belongs to a paper Sars-escape network for escape prediction of SARS-COV-2</p> <p><a>Prem Singh Bist</a>, <a>Hilal Tayara</a>, <a>Kil To Chong</a></p> <p><em>Briefings in Bioinformatics</em>, Volume 24, Issue 3, May 2023, bbad140, <a href="https://doi.org/10.1093/bib/bbad140">https://doi.org/10.1093/bib/bbad140</a></p> <p>Abstract</p> <p><strong>Motivation: </strong>Viruses have coevolved with their hosts for over millions of years and learned to escape the host's immune system. Although not all genetic changes in viruses are deleterious, some significant mutations lead to the escape of neutralizing antibodies and weaken the immune system, which increases infectivity and transmissibility, thereby impeding the development of antiviral drugs or vaccines. Accurate and reliable identification of viral escape mutational sequences could be a good indicator for therapeutic design. We developed a computational model that recognizes significant mutational sequences based on escape feature identification using natural language processing along with prior knowledge of experimentally validated escape mutants.</p> <p><strong>Results: </strong>Our machine learning-based computational approach can recognize the significant spike protein sequences of severe acute respiratory syndrome coronavirus 2 using sequence data alone. This modelling approach can be applied to other viruses, such as influenza, monkeypox and HIV using knowledge of escape mutants and relevant protein sequence datasets.</p> <p><strong>Availability: </strong>Complete source code and pre-trained models for escape prediction of severe acute respiratory syndrome coronavirus 2 protein sequences are available on Github at https://github.com/PremSinghBist/Sars-CoV-2-Escape-Model.git. </p> <p><strong>Contact: </strong>premsing212@jbnu.ac.kr.</p> <p><strong>Keywords: </strong>SARS-CoV-2; mutation; sequence analysis; viral escape prediction.</p>
Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy – data set 07
<p>We developed a sedimentation method using desktop ultracentrifugation (see description below) to visualize SARS-CoV-2 particles in suspensions from oro- and/or nasopharyngeal swabs by thin section electron microscopy. A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 07 comprises three stitched image montages recorded from an area of a thin section through the sediment obtained from a swab sample which was negative by quantitative PCR (control). Ciliated cells and extracellular material, such as vesicles and needle-like crystals, are visible, but no coronavirus particles.</p> <p>Related publication: Laue M, Hoffmann T, Michel J, Nitsche A. Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy. Virol J. 2023 Feb 6;20(1):21. doi: 10.1186/s12985-023-01981-9. PMID: 36747188; PMCID: PMC9901382.</p>
Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy – data set 06
<p>We developed a sedimentation method using desktop ultracentrifugation (see description below) to visualize SARS-CoV-2 particles in suspensions from oro- and/or nasopharyngeal swabs by thin section electron microscopy. A detailed description of the methods and the data set is provided in the download container.</p> <p>Data set 06 comprises three stitched image montages recorded from an area of a thin section through the sediment obtained from a swab sample which was negative by quantitative PCR (control). Ciliated cells and extracellular material, such as vesicles and needle-like crystals, are visible, but no coronavirus particles.</p> <p>Related publication: Laue M, Hoffmann T, Michel J, Nitsche A. Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy. Virol J. 2023 Feb 6;20(1):21. doi: 10.1186/s12985-023-01981-9. PMID: 36747188; PMCID: PMC9901382.</p>
SARS-CoV-2 variants
<p>This dataset is a temporary upload and will be removed after double-blind peer review process is finished.</p>
Data and scripts from Epidemiological and clinical insights from SARS-CoV-2 RT-PCR crossing threshold values, France, January to November 2020
<p>Raw data and scripts used in the publication "<em>Epidemiological and clinical insights from SARS-CoV-2 RT-PCR crossing threshold values, France, January to November 2020 separator commenting unavailable</em>" in Eurosurveillance in 2022.</p> <p> </p> <p>https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2022.27.6.2100406</p>
Automated and manual pooled sample testing with panther fusion and aptima SARS-CoV-2 assays
<p>Combining diagnostic specimens into pools has been considered as a strategy to augment throughput, decrease turnaround time, and leverage resources. This study utilized a multi-parametric approach to assess optimum pool size, impact of automation, and effect of nucleic acid amplification chemistries on the detection of SARS-CoV-2 RNA in pooled samples for surveillance testing on the Hologic Panther Fusion® System. Dorfman pooled testing was conducted with previously tested SARS-CoV-2 nasopharyngeal samples using Hologic's Aptima® and Panther Fusion® SARS-CoV-2 Emergency Use Authorization assays. A manual workflow was used to generate pool sizes of 5:1 (five samples: one positive, four negative) and 10:1. An automated workflow was used to generate pool sizes of 3:1, 4:1, 5:1, 8:1 and 10:1. The impact of pool size, pooling method, and assay chemistry on sensitivity, specificity, and lower limit of detection (LLOD) was evaluated. Both the Hologic Aptima® and Panther Fusion® SARS-CoV-2 assays demonstrated >85% positive percent agreement between neat testing and pool sizes ≤5:1, satisfying FDA recommendation. Discordant results between neat and pooled testing were more frequent for positive samples with CT>35. Fusion® CT (cycle threshold) values for pooled samples increased as expected for pool sizes of 5:1 (CT increase of 1.92 - 2.41) and 10:1 (CT increase of 3.03 - 3.29). The Fusion® assay demonstrated lower LLOD than the Aptima® assay for pooled testing (956 vs 1503 cp/mL, pool size of 5:1). Lowering the cut-off threshold of the Aptima® assay from 560 kRLU (manufacturer's setting) to 350 kRLU improved the assay sensitivity to that of the Fusion® assay for pooled testing. Both Hologic's SARS-CoV-2 assays met the FDA recommended guidelines for percent positive agreement (>85%) for pool sizes ≤5:1. Automated pooling increased test throughput and enabled automated sample tracking while requiring less labor. The Fusion® SARS-CoV-2 assay, which demonstrated a lower LLOD, may be more appropriate for surveillance testing.</p>
Etiology of upper respiratory tract infection in outpatients before and during the SARS-CoV-2 pandemic
<p>Illumina MiSeq Viral reads after quality passing and detection in zipped FASTQ format. Files are named by Patient's codes and whether DNA or RNA workflow is used for sample preparation.</p>
SARS-CoV-2 RBD data along with ESM embeddings
<p>This data is published along with the paper "Biophysical principles predict fitness of SARS-CoV-2 variants" and the <a href="https://github.com/Dianzhuo-Wang/COVID19-Biophysical-Model">code</a>. </p> <p>Description:</p> <p><strong>rbd_df.csv</strong> : RBD sequences filtered from GISAID data, along with occurence time, up untill May 2023.</p> <p><strong>unique_mutant_sequence_emb_esm1v_650m.pkl</strong> : esm1v embeddings for unqiue RBDs in rbd_df.csv</p> <p><strong>df_Desai_15loci_complete.csv</strong>: esm1v embeddings for the Desai combinatoric dataset</p> <p>If you use the code or predictions please consider citing:</p> <pre><code>@article{ doi:10.1073/pnas.2314518121, author = {Dianzhuo Wang and Marian Huot and Vaibhav Mohanty and Eugene I. Shakhnovich }, title = {Biophysical principles predict fitness of SARS-CoV-2 variants}, journal = {Proceedings of the National Academy of Sciences}, volume = {121}, number = {23}, pages = {e2314518121}, year = {2024}, doi = {10.1073/pnas.2314518121}, URL = {https://www.pnas.org/doi/abs/10.1073/pnas.2314518121}, eprint = {https://www.pnas.org/doi/pdf/10.1073/pnas.2314518121}, }</code></pre>
Supplemental Figures - Detection of SARS-CoV-2-Specific Secretory IgA and Neutralizing Antibodies in the Nasal Secretions of Exposed Seronegative Individuals
<p>Figure S1: Flow diagram of exposed seronegative cohort.</p> <p>Figure S2: SARS-CoV-2-specific neutralization activity at Days 1 and 8 relative to enrollment in exposed seronegative nasal SIgA positive and infected participants. NPS SARS-CoV-2-specific neutralization activity is shown for exposed seronegative and infected participants with normalized OD490 at Day 1 and Day 8, respectively. Sample sizes (N) are indicated in parentheses. Wilcoxon signed-rank tests were used to determine if the median SARS-CoV-2 nasal SIgA neutralization activity differed significantly. A two-tailed p < 0.05 was considered significant.</p>
Long-term wastewater monitoring of SARS-CoV-2 viral loads and variants at the major international passenger hub Amsterdam Schiphol Airport: a valuable addition to COVID-19 surveillance
<p>Datasets used for the manuscript: <em>Long-term wastewater monitoring of SARS-CoV-2 viral loads and variants at the major international passenger hub Amsterdam Schiphol Airport: a valuable addition to COVID-19 surveillance</em></p> <p><em>pandemic_daily_passenger_counts.tsv</em>: An overview of daily passenger arrival counts at Amsterdam Schiphol Airport per continent of origin during the study period 16-02-2020 - 04-09-2022</p> <p><em>pre-pandemic_daily_passenger_averages.tsv: </em>An overview of mean daily passenger arrival counts at Amsterdam Schiphol Airport in the pre-pandemic period 2017-2019.</p> <p><em>viral_load_data.tsv: </em>Sample metadata (sample identifier, sampling date, flow, average # particles per ml, and flow-corrected viral-load) for samples taken at the wastewater treatment plant of Amsterdam Schiphol Airport.</p> <p><em>wastewater_variant_frequencies.tsv: </em>SARS-CoV-2 lineage estimates in samples taken at the wastewater treatment plant of Amsterdam Schiphol Airport, analyzed using whole-genome tiled amplicon sequencing.</p> <p> </p> <p> </p> <p> </p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.