logo
ResearchBunny Logo
Abstract
This cross-sectional study enrolled 275 individuals with or without long COVID to identify biological features associated with long COVID using multidimensional immune phenotyping and machine learning. Significant differences were found in circulating myeloid and lymphocyte populations between long COVID patients and controls, along with exaggerated humoral responses to SARS-CoV-2 and other viruses, especially Epstein-Barr virus. Cortisol levels were lower in long COVID patients. Machine learning models identified key features strongly associated with long COVID status, potentially aiding future studies and biomarker development.
Publisher
Nature
Published On
Sep 25, 2023
Authors
Jon Klein, Jamie Wood, Jillian R. Jaycox, Rahul M. Dhodapkar, Peiwen Lu, Jeff R. Gehlhausen, Alexandra Tabachnikova, Kerrie Greene, Laura Tabacof, Amyn A. Malik, Valter Silva Monteiro, Julio Silva, Kathy Kamath, Minlu Zhang, Abhilash Dhal, Isabel M. Ott, Gabrielee Valle, Mario Peña-Hernández, Tianyang Mao, Bornali Bhattacharjee, Takehiro Takahashi, Carolina Lucas, Eric Song, Dayna McCarthy, Erica Breyman, Jenna Tosto-Mancuso, Yile Dai, Emily Perotti, Koray Akduman, Tiffany J. Tzeng, Lan Xu, Anna C. Geraghty, Michelle Monje, Inci Yildirim, John Shon, Ruslan Medzhitov, Denyse Lutchmansingh, Jennifer D. Possick, Naftali Kaminski, Saad B. Omer, Harlan M. Krumholz, Leying Guan, Charles S. Dela Cruz, David van Dijk, Aaron M. Ring, David Putrino, Akiko Iwasaki
Tags
long COVID
immune phenotyping
machine learning
biological features
SARS-CoV-2
humoral responses
biomarkers
Listen, Learn & Level Up
Over 10,000 hours of research content in 25+ fields, available in 12+ languages.
No more digging through PDFs—just hit play and absorb the world's latest research in your language, on your time.
listen to research audio papers with researchbunny