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2,562 results for “SARS CoV 2”
Quantification of SARS-CoV-2 RNA in Wastewater Treatment Plants Mirrors the Pandemic Trend in Hong Kong
<p>The dataset included the SARS-CoV-2 and PMMoV virus concentration of WWTPs from December 24, 2020 to June 30, 2021 in Hong Kong, China.</p>
Length-dependent motions of SARS-CoV-2 frameshifting RNA pseudoknot and alternative conformations suggest avenues for frameshifting suppression
<p>Supplementary dataset for manuscript "Length-dependent motions of SARS-CoV-2 frameshifting RNA pseudoknot and alternative conformations suggest avenues for frameshifting suppression"</p>
Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas
<p>Networks used in the paper <em>Quantifying the importance and location of SARS-CoV-2 transmission events in large metropolitan areas</em>.</p>
Visualization of SARS-CoV-2 particles in naso/oropharyngeal swabs by thin section electron microscopy – data set 03
<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 03 is a stitched image montage recorded from an area of a thin section through the sediment obtained from a swab sample which was positive by quantitative PCR (delta variant). One infected ciliated cell is visible in the center of the recorded area. Virus particles are visible within membrane-bound compartments of the cytoplasm. Spike visibility is poor and some virus particles appear compressed.</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 04
<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 04 is a stitched image montage recorded from an area of a thin section through the sediment obtained from a swab sample which was negative by quantitative PCR (negative control). The recorded area shows the profiles of four keratinocytes which are surrounded by heterogenous material (e.g. membrane lamella, needle-like crystals, round profiles with a fine-fibrous matrix). Virus partricles are not visible.</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 01
<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 01 is a stitched image montage recorded from an area of a thin section through the sediment obtained from a swab sample which was positive by quantitative PCR (delta variant). Two, more or less, intact ciliated cells are visible and surrounded by other cells or cellular debris. The ciliated cell in the upper right corner is infected with SARS-CoV-2. Virus particles are visible within membrane-bound compartments of the cytoplasm. Several double-membrane vesicles, which are typical compartments of the coronavirus replication machinery, are also detectable.</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 02
<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 02 is a stitched image montage recorded from an area of a thin section through the sediment obtained from a swab sample which was positive by quantitative PCR (delta variant). One ciliated cell is visible and surrounded by cellular debris. The ciliated cell is infected with SARS-CoV-2. Few virus particles are visible within membrane-bound compartments of the cytoplasm. Numerous virus particles are located at the cell surface intermingled between the cilia. The virus particles of this cell appear deformed and deviate from the oval/circular profile which is usually present.</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>
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>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.