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51 results for “COVID-19 diagnosis”
Dataset:Biomarker-based diagnosis of Post COVID-19 Condition
<p>The persistence or development of new symptoms three months after the initial acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection is referred to as post-coronavirus disease (COVID) condition (PCC). The identification of new biomarkers specific for the occurrence of PCC is vital to proceed in the future towards prediction of its evolution.</p> <p>This study was registered with ISRCTN Registry during recruitment (ISRCTN27312680), and it was conducted comparing two parallel groups: individuals diagnosed with PCC versus individuals who completely recovered within 3 months after acute COVID-19.</p> <p>All participants were enrolled between the first semester of 2022 in Primary Health Care Centers (PHCCs) of Zaragoza (Spain). The two parallel groups were matched by age, gender, and date of acute COVID-19 diagnosis. The diagnosis of PCC was determined by a general practitioner, following the WHO criteria [7], before or at the time of inclusion in the study. Recovered individuals were required to have passed acute COVID-19, confirmed by RT-qPCR, antigen test, or SARS-CoV-2 serology.<strong> </strong></p>
Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning
<p>COVID-19 plasma samples spectrometry datasets for machine learning input. Used in the work of article Covid-19 automated diagnosis and risk assessment through Metabolomics and Machine Learning, currently under submittion.</p> <p>Abstract:</p> <p>COVID-19 is still placing a heavy health and financial burden worldwide. Impairments in patient screening and risk management play a fundamental role on how governments and authorities are directing resources, planning reopening, as well as sanitary countermeasures, especially in regions where poverty is a major component in the equation. An efficient diagnostic method must be highly accurate, while having a cost-effective profile. We combined a machine learning-based algorithm with mass spectrometry to create an expeditious platform that discriminate COVID-19 in plasma samples within minutes, while also providing tools for risk assessment, to assist healthcare professionals in patient management and decision-making. A cross-sectional study with 815 patients (442 COVID-19, 350 controls and 23 COVID-19 suspicious) was enrolled from three Brazilian epicenters from April to July 2020. We were able to elect and identify 19 molecules that are related to the disease’s pathophysiology and several discriminating features to patient’s health-related outcomes. The method applied for COVID-19 diagnosis showed specificity >96% and sensitivity >83%, and specificity >80% and sensitivity >85% during risk assessment, both from blinded data. Our method introduced a new approach for COVID-19 screening, providing the indirect detection of infection through metabolites and contextualizing the findings the disease’s pathophysiology. The pairwise analysis of biomarkers brought robustness to the model developed using Machine Learning algorithms, transforming this screening approach in a tool with great potential for real-world application. </p>
Comparing plasma and skin imprint metabolic profiles in COVID-19 diagnosis and severity assessment_dataset
<p>As SARS-CoV-2 continues to produce new variants, the demand for diagnostics and a better understanding of COVID-19 remain key topics in healthcare. Skin manifestations have been widely reported in cases of COVID-19, but the mechanisms and markers of these symptoms are poorly described. In this cross-sectional study, 101 patients (64 COVID-19 positive patients and 37 controls) were enrolled between April and June 2020, during the first wave of COVID-19, in São Paulo, Brazil. Enrolled patients had skin imprints sampled non-invasively using silica plates; plasma samples were also collected. Samples were used for untargeted lipidomics/metabolomics through high-resolution mass spectrometry. We identified 558 molecular ions, with lipids comprising most of them. We found 245 plasma ions that were significant for COVID-19 diagnosis, compared to 61 from the skin imprints. Plasma samples outperformed skin imprints in distinguishing patients with COVID-19 from controls, with F1-scores of 91.9% and 84.3%, respectively. Skin imprints were excellent for assessing disease severity, exhibiting an F1-score of 93.5% when discriminating between patient hospitalization and home care statuses. Specifically, oleamide and linoleamide were the most discriminative biomarkers for identifying hospitalized patients through skin imprinting, and palmitic amides and N-acylethanolamine 18:0 were also identified as significant biomarkers. These observations underscore the importance of primary fatty acid amides and N-acylethanolamines in immunomodulatory processes and metabolic disorders. These findings confirm the potential utility of skin imprinting as a valuable non-invasive sampling method for COVID-19 screening; a method that may also be applied in the evaluation of other medical conditions.</p><p>Journal of Molecular Medicine https://doi.org/10.1007/s00109-023-02396-3</p>
Artificial Intelligence and COVID-19 using chest CT scan and chest X-ray images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment
<p>We uploaded the Table of included articles in the systematic review "Artificial Intelligence and COVID-19 using chest CT scan and chest X-ray images: Machine Learning and Deep Learning Approaches for Diagnosis and Treatment"</p>
NeuroCovid Rehab and Recovery Related to COVID-19 Diagnosis
ClinicalTrials.gov study NCT04638673. IPD Sharing: NO. Countries: 1. Publications: 2.
Blood test dynamics in hospitalized COVID-19 patients: potential utility of D-dimer for pulmonary embolism diagnosis
<p>SPSS dataset with metadata of the published article in PlosOne and MedRxIv</p>
Dataset & Code related to article 'Bilateral Adaptive Graph Convolutional Network on CT based COVID-19 Diagnosis with Uncertainty-Aware Consensus-Assisted Multiple Instance Learning'
<p>This record contains the 7768 lung masks <strong>manual annotations, implementation code, and pre-trained models</strong> related to the article 'Bilateral Adaptive Graph Convolutional Network on CT based COVID-19 Diagnosis with Uncertainty-Aware Consensus-Assisted Multiple Instance Learning'</p> <p>Also we include the visualised, selected top D reliable CT slices for all COVID-19 patients in the test dataset for better understanding. </p> <p>For the detailed usage of the data and code, please refer to https://github.com/smallmax00/BAGCN-Covid19</p> <p> </p>
Analysis of symptoms and quality of life of people with prolonged diagnosis of COVID-19, and the efficacy of an intervention in primary care using ICT
<p>This is a ranzomized clinical study called: “Analysis of symptoms and quality of life of people with prolonged diagnosis of COVID-19, and the efficacy of an intervention in primary care using ICT”, approved in mid-2020, with reference number ISRCTN91104012. The main objetive is to analyse the overall effectiveness and cost-efficiency of a mobile application (APP) as a community health asset (HA) with recommendations and recovery exercises created bearing in mind the main symptoms presented by Long COVID patients in order to improve their quality of life, as well as other secondary variables, such as the number and severity of ongoing symptoms, physical and cognitive functions, affective state, and sleep quality. The first step was to design and develop the technologic community resource, the APP, following the steps involved in the process of recommending health assets (RHA). After this, a protocol of a randomised clinical trial for analysing its effectiveness and cost-efficiency as a HA was developed. The participants will be assigned to: (1st) usual treatment by the primary care practitioner (TAU), as a control group; and (2nd) TAU + use of the APP as a HA and adjuvant treatment in their recovery + three motivational interviews (MI), as an interventional group. An evaluation will be carried out at baseline with further assessments three and six months following the end of the intervention. </p>
Presepsin Biomarker for Ventilator-associated Pneumonia Diagnosis in COVID-19 Patients
ClinicalTrials.gov study NCT04840940. IPD Sharing: NO. Countries: 1. Publications: 22.
Decision Support System Algorithm for COVID-19 Diagnosis
ClinicalTrials.gov study NCT04479319. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
Interferon Lambda for Immediate Antiviral Therapy at Diagnosis in COVID-19
ClinicalTrials.gov study NCT04354259. IPD Sharing: NO. Countries: 2. Publications: 3.
Evaluation of the AudibleHealth Dx AI/ML-Based Dx SaMD Using FCV-SDS in the Diagnosis of COVID-19 Illness
ClinicalTrials.gov study NCT05175690. IPD Sharing: NO. Countries: 1. Publications: 17.
Computed Tomography for COVID-19 Diagnosis
ClinicalTrials.gov study NCT04355507. IPD Sharing: NO. Countries: 1. Publications: 4.
The CEDiD Study (COVID-19 Early Diagnosis in Doctors and Healthcare Workers)
ClinicalTrials.gov study NCT04363489. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Patients Reactions Towards Their Diagnosis as Having COVID-19
ClinicalTrials.gov study NCT04581928. IPD Sharing: NO. Countries: 1. Publications: 1.
Feasibility and Analytic Performance of TestNPass (IVDMD) for CoViD-19 Diagnosis on Saliva Sample
ClinicalTrials.gov study NCT04654442. IPD Sharing: UNDECIDED. Countries: 1. Publications: 5.
Therapeutic Plasmapheresis in Critically Ill Adult Patients With COVID-19 Confirmed Diagnosis
ClinicalTrials.gov study NCT04480632. IPD Sharing: NO. Countries: 1. Publications: 8.
Evaluation of the AudibleHealth Dx AI/ML-Based Dx SaMD Using FCV-SDS in the Diagnosis of COVID-19 Illness: Clinical Validation
ClinicalTrials.gov study NCT05364268. IPD Sharing: NO. Countries: 1. Publications: 17.
Does Coronavirus Disease 2019 (COVID-19) Pandemic Cause a Delay in the Diagnosis of Gastric Cancer Patients?
ClinicalTrials.gov study NCT04797624. IPD Sharing: NO. Countries: 1. Publications: 3.
Study Investigating the Effects of UVC Beam and Laser Beam Therapy According to Standard Therapy in Patients With Covid-19 Diagnosis
ClinicalTrials.gov study NCT04642326. IPD Sharing: UNDECIDED. Countries: 1. Publications: 9.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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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.