#AI reads Urine# Urinary Peptidomic Profiling In Post-Acute Sequelae of SARS-CoV-2 Infection
Published 25 December, 2025
This study focuses on the post-acute sequelae of SARS-CoV-2 infection (PASC). To address the issues that PASC diagnosis relies on subjective symptoms and is easily confused with other diseases, it uses capillary electrophoresis-mass spectrometry to analyze urinary peptides from 50 PASC patients (10 months after COVID-19 infection) and 50 controls (including healthy individuals and patients with myalgic encephalomyelitis/chronic fatigue syndrome not associated with COVID-19). It identifies 195 characteristic peptides with significantly different abundances and constructs a diagnostic model. This model achieves an accuracy of 94.9% and 96.2% in distinguishing PASC patients from controls in the training set and independent validation set, respectively, and can also distinguish PASC from myalgic encephalomyelitis/chronic fatigue syndrome not associated with COVID-19. Meanwhile, based on the changes in characteristic peptides, it infers that PASC may be related to collagen metabolism disorders, persistent inflammation, and other factors. Through in silico simulation, it finds that exercise and two specific types of drugs may be effective for PASC patients. It also observes that common symptoms in patients, such as difficulty concentrating, are associated with the indicators of characteristic peptides. However, this diagnostic model fails to be validated in a cohort with a 3-year follow-up, so further large-scale studies are needed for improvement. Overall, the study provides a non-invasive and accurate diagnostic method for PASC, and also offers clues for revealing the pathogenesis of PASC and subsequent treatment.
Proteomics. 2025 Nov 21:e70074. doi: 10.1002/pmic.70074.
Youhe Gao
Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.
For earlier AI Reads Urine articles：