View clinical trials related to Coronavirus.
Filter by:COVID-19 has a variety of symptoms from asymptomatic respiratory dysfunction to death. Considering the pathophysiology of SARS-CoV-2 and its relationship with the neuroimmune system, response, autonomic balance, musculoskeletal and respiratory and neuropsychiatric symptoms presented by patients, the investigators highlight the potential use of non-invasive neuromodulation methods to assess the effectiveness of treating patients with COVID-19, as these techniques can be useful in the management of important clinical aspects in the functional recovery of individuals affected by the disease. The investigators intend to evaluate the effects of HD- tDCS to promote ventilatory weaning in patients admitted to the Intensive Care Unit (ICU) and to improve the respiratory performance of those hospitalized in nursing beds for treatment of COVID - 19.
Background: Respiratory viruses circulate throughout the year and around the globe. Wearable and sensor devices, like smartwatches, may be able to help monitor infectious diseases. Researchers want to use them to learn how respiratory viruses affect people in different ways. Objective: To use digital devices to collect data from participants in challenge studies that could indicate subtle changes in health during an infection that might otherwise go unnoticed. Eligibility: Healthy adults who have enrolled in a challenge study. Design: Participants will stay at NIH for at least 9 days and then they will have outpatient visits. While at NIH, participants will wear a smartwatch at all times. It will record data like temperature, heart rate, breathing rate, and movements. Participants will have 2 smartphones. One will be recording at all times to listen for coughing. Participants will use the other smartphone to check their vital signs. They will collect data like heart rate, temperature, and the level of oxygen in the blood every 4 hours during the daytime. Participants will perform tasks every 4 hours during the daytime. They will record themselves coughing, breathing in deeply, and reading aloud. They will take pictures and videos of their face. A bedside sensor will record participants while they sleep. It will record heart rate and breathing rate. It will also look at sleep activity, such as movements participants make during sleep and how deeply they sleep. Participants sharing the same room will be exposed to the same challenge virus. For outpatient visits, participants will use one smartphone and the smartwatch to complete the above tasks. Participation will last from 10 weeks to 1 year.
A series of microbiota were correlated inversely with the disease severity and virus load. Gut microbiota could play a role in modulating host immune response and potentially influence disease severity and outcomes.
The purpose of this study is to examine how patients with multiple myeloma (MM) have been impacted by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. The study will use a questionnaire to further understand how patients are being affected and gather information in order to track the long-term effects of the coronavirus. The scope of the questionnaire will include, COVID-19 diagnosis and treatment, changes in myeloma treatment and care, clinical trial familiarity, health and fitness, and quality of life. This questionnaire is a follow-on to the "MM and COVID-19" questionnaire.
Best Practices to Prevent COVID-19 Illness in Staff and People With Serious Mental Illness and Developmental Disabilities in Congregate Living Settings is a research study aimed at developing, implementing, and evaluating a package of interventions specifically designed to reduce COVID-19 and other infectious-disease incidence, hospitalizations, and mortality among staff and adults with Serious Mental Illness and Intellectual and Developmental Disabilities in congregate-living settings.
The investigators propose a prospective, randomized, double-blind, placebo-controlled study, conducted in two phases. The purpose of the study is to evaluate the safety and efficacy of methotrexate in a cholesterol-rich non-protein nanoparticle (MTX -LDE) in adults diagnosed with mild Coronavirus-19(COVID-19) disease. A total of 100 patients will be randomized to receive MTX-LDE or placebo each 7 days, up to 3 times, during in hospital treatment.
The Multi-arm trial of Inflammatory Signal Inhibitors for COVID-19 (MATIS) study is a two-stage, open-label, randomised controlled trial assessing the efficacy of ruxolitinib (RUX) and fostamatinib (FOS) individually, compared to standard of care in the treatment of COVID-19 pneumonia. The primary outcome is the proportion of hospitalised patients progressing from mild or moderate to severe COVID-19 pneumonia. Patients are treated for 14 days and will receive follow-up assessment at 7, 14 and 28 days after the first study dose. Patients with mild or moderate COVID-19 pneumonia will be recruited. Initially, n=171 (57 per arm) patients will be recruited in Stage 1. Following interim analysis to assess the efficacy and safety of the treatments, approximately n=285 (95 per arm) will be recruited during Stage 2.
Establish a COVID-19 biorepository to aid in developing our knowledge of the disease.
A Randomized, Controlled, Phase III Study to Determine the Safety, Efficacy, and Immunogenicity of the Non-Replicating ChAdOx1 nCoV-19 Vaccine.
Coronavirus Disease 2019 (COVID-19) has been widespread worldwide since December 2019. It is highly contagious, and severe cases can lead to acute respiratory distress or multiple organ failure. On 11 March 2020, the WHO made the assessment that COVID-19 can be characterised as a pandemic. With the development of machine learning, deep learning based artificial intelligence (AI) technology has demonstrated tremendous success in the field of medical data analysis due to its capacity of extracting rich features from imaging and complex clinical datasets. In this study, we aim to use clinical data collected as part of routine clinical care (heart tracings, X-rays and CT scans) to train artificial intelligence and machine learning algorithms, to accurately predict the course of disease in patients with Covid-19 infection, using these datasets.