Publications by authors named "M V Coignet"
Article Synopsis
- COVID-19 and influenza are respiratory illnesses caused by different viruses but share some symptoms and clinical risk factors, yet their genetic connections remain poorly understood.
- A study involving over 18,000 influenza cases and nearly 276,000 control subjects found no common genetic risk factors between COVID-19 and influenza, revealing specific gene variants linked only to influenza.
- The research highlights the potential for targeting cell surface receptors involved in viral entry, showing that manipulating specific genes could lead to treatments that prevent both COVID-19 and influenza infections.
To help determine the unmet need for improved diagnostic tools to evaluate patients with nonspecific signs and/or symptoms (NSSS) and suspicion of cancer, we examined patient characteristics, diagnostic journey, and cancer incidence of patients with NSSS within The US Oncology Network (The Network), a secondary care community oncology setting. This retrospective, observational cohort study included patients aged ≥40 years with ≥1 NSSS in their problem list at their first visit within The Network (the index date) between 1 January 2016 and 31 December 2020. Patients were followed longitudinally with electronic health record data for initial cancer diagnosis, new noncancer diagnosis, death, end of study observation period, or 12 months, whichever occurred first.
View Article and Find Full Text PDFPurpose: A multi-cancer detection test using a targeted methylation assay and machine learning classifiers was validated and optimized for screening in prospective, case-controlled Circulating Cell-free Genome Atlas (ClinicalTrials.gov identifier: NCT02889978) substudy 3. Here, we report test performance in a subgroup of participants with symptoms suspicious for cancer to assess the test's ability to potentially facilitate efficient diagnostic evaluation in symptomatic individuals.
View Article and Find Full Text PDFObjectives: The enormous toll of the COVID-19 pandemic has heightened the urgency of collecting and analysing population-scale datasets in real time to monitor and better understand the evolving pandemic. The objectives of this study were to examine the relationship of risk factors to COVID-19 susceptibility and severity and to develop risk models to accurately predict COVID-19 outcomes using rapidly obtained self-reported data.
Design: A cross-sectional study.