Hyperspectral Analysis of Sweat Metabolite Biometrics for Real-Time Detection of COVID-19

Overview

Background: The COVID-19 pandemic has challenged the health systems worldwide. Many tools have been developed in response to the pandemic, but there is no current way to quickly screen multiple people for the disease. Research has shown that people with COVID-19 have higher levels of some proteins involved in the immune response and inflammation. These proteins can be detected in sweat using a special camera. Researchers want to see if analysis of sweat from fingerprints could be used to detect COVID-19 infection in people. Objective: To test a new technology to detect COVID-19 infection based on an analysis of sweat from fingerprints. Eligibility: Adults ages 18 and older who tested positive or negative for COVID-19 within the last 7 days. Design: Participants will visit the NIH Clinical Center for one day within 7 days from COVID-19 testing. The visit will last for 3 to 4 hours. Participants who show symptoms for COVID-19 with a positive test will give blood samples to correlate with the sweat markers. About 1/2 tablespoon of blood will be drawn. For sweat markers, 10 fingers will be imaged by a camera using a touchless system. This will be repeated 3 times. It will take about 15 minutes. Participants will use the device. They will get instructions and watch a short video on how to use the device.

Background The Coronavirus Disease 19 (COVID19) pandemic has challenged healthcare systems worldwide. Massive testing, contact tracing and social distancing proved to be the most effective tools to fight the pandemic prior to the development of vaccines. Despite the effort to develop rapid diagnostic testing, we still don t have an available large population screening modality. Analysis of sweat metabolites from hyperspectral images of fingertips has the potential to be a valid clinic strategy to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)infected individuals. COVID19 has shown higher levels of inflammatory proteins like IL6, LDH, CRP, and d-dimer which have been implicated with severe COVID-19 induced pneumonitis and coagulopathy. These molecules can be detected as sweat metabolites and used as a biomarker for viral infection detection. Objective Identify a pattern classifier to distinguish between SARS-CoV-2 positive and SARS-CoV-2 negative human subjects by analysis of sweat metabolites from hyperspectral images of fingertips. Eligibility Individuals must all be \>=18 years old Must have standard of care molecular testing (either antigen or PCR) for SARS-CoV-2 within 7 days from study enrollment. Those individuals who tested positive will be enrolled in cohort 1 and those who tested negative will be enrolled in cohort 2 Study Design This is an exploratory multisite study to evaluate the use of biometric analysis of sweat metabolites from hyperspectral images of fingertips to detect SARS-CoV-2 infection. Center for Cancer research (CCR), NCI will be the coordinating center. All adult subjects that have available testing for SARS-CoV-2 completed within 7 days from the study enrollment are eligible for this study. The study will have two cohorts, cohort 1 (SARS-CoV-2 positive), and cohort 2 (SARS-CoV-2 negative). Fifty participants will be enrolled in each cohort to have hyperspectral imaging of the fingertips. Every participant will have the right and left index fingers imaged by the camera with a touchless system. The imaging will be repeated three times. This imaging will take about 10 minutes. The data obtained by the digital analysis will be compared to the result of the standard SARS-CoV-2 tests in use at the enrolling sites.