Machine Learning-Assisted Characterization of Cardiovascular Long COVID

Introduction: We aim to identify molecular mediators for vascular post-acute sequelae of SARS-CoV-2 (PASC).

Objective: We will compare clinical information of vascular PASC patients with transcriptomics of PBMCs and plasma proteomics.

Methods: We aim to identify clinical and biological parameters that characterize vascular PASC. Using subsets of vascular PASC patients and vascular non-COVID-19 patients in the RECOVER post-acute cohort, we will characterize these populations by mass spec-powered plasma proteomics and single cell RNA-sequencing (scRNA-seq) of circulating monocytes to identify key molecules that represent vascular PASC. We will also examine whether some of these molecules are more abundant in vascular tissues from vascular PASC autopsy cases.

Results: Pending.

Conclusion/Discussion: Pending.

Key Topics:

• Collaborative and systems biology approaches
• Linking autopsy findings with pathobiological mechanisms of Long COVID to guide targeted interventions
• Studies of vascular injury, thrombosis, and other potential mechanisms of Long COVID