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458 results for “Long Covid”
Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO2 Emissions and Thermosteric Sea Level Rise Projections - Supplementary Materials
<p>Supplementary materials for Gonzalez, A. R., & Lin, T. (2022). Translated Emission Pathways (TEPs): Long-Term Simulations of COVID-19 CO<sub>2</sub> Emissions and Thermosteric Sea Level Rise Projections. <em>Earth's Future</em>. In Press.</p> <p><strong>Summary: This study introduces climate science to a broader audience by presenting an accessible research framework and environmental data related to the ongoing COVID-19 pandemic. A series of translated emission pathways (TEPs) were constructed based on the CO<sub>2</sub> emission patterns from the various phases of COVID-19 response. In addition to resembling the forcing scenarios used within climate research, a thermosteric sea level rise analysis was incorporated to further emphasize the environmental benefits that can be obtained from long-term sustainability. As a promising start for including the general public in climate change discussion, this research promotes collective environmental action that mirrors the recommendations of the scientific community.</strong></p>
Short and long term impacts of Covid-19 on Older childreN's healTh-Related behAviours, learning and wellbeing STudy (CONTRAST) dataset
<p>The CONTRAST study explored how the Covid-19 (lockdown) restrictions affected lives of older children in the UK, particularly how they have influenced learning, eating, physical and other activities and wellbeing.</p>
Long COVID IRIS Study Olink Proteomics Dataset
<p>Plasma samples collected from Stanford University's “Infection Recovery in SARS-CoV-2” (IRIS) study participants during acute SARS-CoV-2 infection, approximately 3 months post infection, and approximately 12 months post infection were analyzed using the Olink® Target 96 Inflammation panel and Olink® Target 96 Immune Response panel. </p> <p>Proteomic data were obtained from the Olink biomarker platform and presented as "NPX" (i.e. Normalized Protein eXpression) for each protein assay. The dataset features de-identified patient metadata, including participant study number (i.e. IRIS_number), sex, long COVID status (0 = recovered; 1 = Long COVID), and timepoint of the plasma sample (i.e. acute infection sample, approximately 3 months post infection, or approximately 12 months post infection). </p> <p>Notes:</p> <ul> <li>Consistent with the World Health Organization (WHO) definition, long COVID was defined in this study as the continuation or development of symptoms three months after SARS-CoV-2 infection, which were not readily attributable to other etiologies.</li> <li>For acute infection samples, long COVID status (0 = recovered; 1 = Long COVID) refers to whether the patient will have fully recovered or will have long COVID at 3 months post infection. </li> <li>For 3 month samples, long COVID status (0 = recovered; 1 = Long COVID) refers to whether the patient has fully recovered or has long COVID at 3 months post infection.</li> <li>For 12 month samples, long COVID status (0 = recovered; 1 = Long COVID) refers to whether the patient has fully recovered or has ongoing long COVID at 12 months post infection, but these patients all had long COVID at 3 months post infection.</li> </ul>
Long Covid collection
<p>A significant percentage of COVID-19 survivors experience ongoing multisystemic symptoms that often affect daily living, a condition known as Long Covid or post-acute-sequelae of SARS-CoV-2 infection. However, identifying scientific articles relevant to Long Covid is challenging since there is no standardized or consensus terminology. We developed an iterative human-in-the-loop machine learning framework combining data programming with active learning into a robust ensemble model, demonstrating higher specificity and considerably higher sensitivity than other methods. This dataset contains current and historical releases of the Long Covid collection, which is updated weekly. The current release is searchable online at the LitCovid portal: <a href="https://www.ncbi.nlm.nih.gov/research/coronavirus/docsum?filters=e_condition.LongCovid">https://www.ncbi.nlm.nih.gov/research/coronavirus/docsum?filters=e_condition.LongCovid</a></p>
Loss of socioemotional and occupational roles in people with Long COVID according to sociodemographic and clinical factors: Secondary data from Randomized Clinical Trial.
<p>This is a cross-sectional study was carried out with the participation of 100 patients diagnosed with Long-COVID, over 18 years of age and attended by Primary Health Care in the Autonomous Community of Aragon. The purpose of this study is to analyse the loss of socioemotional and occupational roles that people with Long COVID have suffered in their lives as a consequence of the disease. As a secondary objective, it was proposed to analyze the sociodemographic and clinical factors associated with this loss of roles. The main study variable was the loss of significant socioemotional and occupational roles of the participants. Sociodemographic and clinical data were also collected through a structured interview.</p>
Prevalence and characteristics of long COVID-19 in Jordan: A cross sectional survey
<p>Early in the pandemic, the spread of the emerging virus SARS-CoV-2 was causing mild illness lasting less than two weeks for most people, with a small proportion of people developing serious illness or death. However, as the pandemic progressed, many people reported suffering from symptoms for weeks or months after their initial infection. Persistence of COVID-19 symptoms beyond one month, or what is known as long COVID-19, is recognized as a risk of acute infection. Up to date, information on long COVID-19 among Jordanian patients has not been reported. Therefore, we sought to conduct this cross-sectional study utilizing a self-administered survey. The survey asks a series of questions regarding participant demographics, long COVID-19 symptoms, information about pre-existing medical history, supplements, vaccination history, and symptoms recorded after vaccination. Chi square analysis was conducted on 990 responders, and the results showed a significant correlation (P<0.05) between long COVID-19 syndrome and age, obesity, chronic illness, vitamin D intake, number of times infected by COVID-19, number of COVID-19 symptoms and whether the infection was pre or post vaccination. The long-term symptoms most enriched in those with long COVID-19 were tinnitus (73.4%), concentration problems (68.6%) and muscle and joint ache (68.3%). A binomial logistic regression analysis was done to explore the predictors of long COVID-19 and found that age 18-45, marital status, vitamin D, number of COVID-19 symptoms and signs after vaccination are positive predictors of long COVID-19, while zinc intake is a negative predictor. Although further studies on long-term persistence of symptoms are needed, the present study provides a baseline that allows us to understand the frequency and nature of long COVID-19 among Jordanians.</p>
Questionnaire and interview guidelines related to Süsser et al. (2024) on how the COVID-19 pandemic changed stakeholder engagement processes in sustainability research in the long-term
<p>The survey questionnaire and the interview guidelines have been designed in the framework of the EU H2020 projects SENTINEL to study the impact of the coronavirus disease 2019 (COVID-19) on the stakeholder engagement processes in susatinability research in the longterm. We conducted interviews with researchers and engaged stakeholders, using a semi-structured interview guideline. We designed the survey as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “LimeSurvey”. The survey was reworked based on our previous survey. The study was a follow-up study on our own work (<em>Süsser, D., Ceglarz, A., Stavrakas, V., & Lilliestam, J. COVID-19 vs. stakeholder engagement: the impact of coronavirus containment measures on stakeholder involvement in European energy research projects [version 1; peer review: awaiting peer review]. Open Research Europe 2021,1:57. doi: <a href="https://doi.org/10.12688/openreseurope.13683.1">https://doi.org/10.12688/openreseurope.13683.1</a>). </em></p> <p>If you use this questionnaire or the interview guideline in an academic publication, please cite the following article:</p> <p><em>Süsser, D., Schibline, A. Ceglarz, A., Lilliestam, J., Stavrakas, V., & Schweizer, P.J. (2024), How the COVID-19 pandemic changed stakeholder engagement processes in sustainability research in the long-term, F1000 Research (to be published).</em></p> <p><em> </em></p>
Olink data for 'Identification of soluble biomarkers that associate with distinct manifestations of long COVID'
<p>Olink analysis output from plasma samples from healthy donors and donors with post-acute sequelae of SARS-CoV-2 infection. Data sourced from two cohorts - UK and Sweden based. UK patients with long covid start with CA. UK healthy controls start with CO. Swedish donors only include patients with long covid and start with KLIMP. Assays from the following panels were tested: Explore 384 Cardiometabolic, Explore 384 Cardiometabolic II, Explore 384 Inflammation, Explore 384 Inflammation II, Explore 384 Neurology, Explore 384 Neurology II, Explore 384 Oncology, Explore 384 Oncology II. </p> <p>Data relates to the manuscript titled 'Identification of soluble biomarkers that associate with distinct manifestations of long COVID'.</p>
Search strategies for an international review of the epidemiology of Long Covid
<p>The dataset includes the complete, reproducible search strategies for all literature databases searched during this project.</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>
URInary peptidomic patterns of Long-COVID syndrome "UriCoV"
<p>Post acute sequelae of SARS-CoV-2 infection (PASC), also referred to as long COVID, is the most frequent, yet poorly characterized sequelae of COVID-19. It has tremendous, unpredictable consequences on personal health and socioeconomic status of affected individuals, and on global economic issues. UriCov is a multidisciplinary, comprehensive project with the aim to investigate in depth the molecular phenotype(s) of individual patients previously infected by SARS-CoV-2 and identify patients at risk of PASC. This will be achieved through multi-disciplinary research based on omics and clinical data in a bioinformatics framework, based on the hypothesis that endothelial damage is a key event in PASC. The developed molecular tools may allow patient stratification to initiate personalized treatment for prevention of PASC prior to symptoms and to decrease the PASC incidence. UriCoV will also provide missing fundamental knowledge on the molecular pathophysiology of PASC.</p>
Persistent serum protein signatures define an inflammatory subcategory of long COVID
<p><strong>Readme:</strong><br> This repository consists of the input data, figure source data and R-code to perform analysis and generate figures of the manuscript</p> <p> </p> <p><strong>Abstract:</strong><br> Long COVID or post-acute sequelae of SARS-CoV-2 (PASC) is a clinical syndrome featuring diverse symptoms that can persist for months following acute SARS-CoV-2 infection. The aetiologies may include persistent inflammation, unresolved tissue damage or delayed clearance of viral protein or RNA, but the biological differences they represent are not fully understood. Here we evaluate the serum proteome in samples, longitudinally collected from 55 PASC individuals with symptoms lasting ≥60 days after onset of acute infection, in comparison to samples from symptomatically recovered SARS-CoV-2 infected and uninfected individuals. Our analysis indicates heterogeneity in PASC and identified subsets with distinct signatures of persistent inflammation. Type II interferon signaling and canonical NF-κB signaling (particularly associated with TNF), appear to be the most differentially enriched signaling pathways, distinguishing a group of patients characterized also by a persistent neutrophil activation signature. These findings help to clarify biological diversity within PASC, identify participants with molecular evidence of persistent inflammation, and highlight dominant pathways that may have diagnostic or therapeutic relevance, including a protein panel that we propose as diagnostic utility for differentiating inflammatory and non-inflammatory PASC.</p>
Evaluating the Neuromodulatory Effect of Ketamine in Long COVID-19
ClinicalTrials.gov study NCT06821087. IPD Sharing: YES. Countries: 1. Publications: 37.
CONFIDENT: Supporting Long-term Care Workers During COVID-19
ClinicalTrials.gov study NCT05168800. IPD Sharing: YES. Countries: 1. Publications: 2.
Telerehabilitation in People With Long COVID
ClinicalTrials.gov study NCT05205460. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Chronic-disease Self-management Program in Patients Living With Long-COVID in Puerto Rico
ClinicalTrials.gov study NCT06208696. IPD Sharing: NO. Countries: 1. Publications: 2.
Anti-SARS-CoV-2 Monoclonal Antibodies for Long COVID (COVID-19)
ClinicalTrials.gov study NCT05877508. IPD Sharing: NO. Countries: 1. Publications: 1.
Effect of Lithium Therapy on Long COVID Symptoms
ClinicalTrials.gov study NCT05618587. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effect of Pulmonary Rehabilitation and Physical Activity on Long COVID (PuReCOVID)
ClinicalTrials.gov study NCT07046442. IPD Sharing: YES. Countries: 1. Publications: 19.
Effects of Sodium Pyruvate Nasal Spray in COVID-19 Long Haulers.
ClinicalTrials.gov study NCT04871815. IPD Sharing: NO. Countries: 1. Publications: 1.
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.