Lip, G. Y. H. | Search bioRxiv

bioRxiv2026

Integrated Transcriptomic Profiling Reveals MAPK14-Related Inflammatory Signalling Associated with Postoperative Atrial Fibrillation

Postoperative atrial fibrillation (POAF) affects up to half of patients after cardiac surgery and raises long-term risk of stroke and death. Why only some patients develop it, despite similar surgical exposure, remains poorly understood. We studied a pilot cohort of 20 patients undergoing cardiac surgery in Northwest England, comparing those who developed POAF within four days (n=10) with those who did not (n=10). Circulating plasma RNA was profiled using the NanoString nCounter Cardiovascular Disease panel, and differentially expressed genes were mapped onto protein-protein interaction networks using STRING and Cytoscape. Candidate genes were then validated by RT-qPCR in the same cohort, alongside serial ELISA measurement of plasma MAPK14 protein. Peripheral blood monocyte, neutrophil and total white blood cell counts were also compared between the two groups on postoperative Days 1, 2 and 4. Screening identified MAPK14, COX6C and COX7B as elevated in POAF, with PTPN12 reduced. These genes clustered within a connected inflammatory and stress-response network, and pathway enrichment pointed to immune activation. RT-qPCR confirmed higher MAPK14, COX6C and COX7B expression in POAF patients. Plasma MAPK14 rose after surgery in both groups, with the largest increase in POAF patients on day two, though this did not reach significance. Peripheral blood monocyte counts were significantly higher in POAF patients than in those without POAF at all three postoperative timepoints, whereas neutrophil and total white blood cell counts did not differ significantly between groups. These early findings identify a circulating plasma RNA signature linked to MAPK14-related inflammatory signalling as a candidate marker of POAF susceptibility, warranting confirmation in a larger, adequately powered cohort.

physiology↗

bioRxiv2026

Comparing Machine Learning Approaches for Predicting CFD-Derived Stroke Risk Indicators in Atrial Fibrillation Patients

Non-valvular atrial fibrillation (AF) is associated with a five-fold increased risk of stroke, mainly due to impaired contractility of the left atrium (LA) leading to blood stasis and subsequent thrombus formation within the left atrial appendage (LAA). Current AF stroke risk stratification schemes, such as the CHA2DS2-VASc/ CHA2DS2-VA score, use comorbidities and do not capture mechanistic factors like blood flow dynamics and hypercoagulability. To address this, we developed a multiphase computational fluid dynamics (CFD) model of the LA, incorporating patient-specific geometries; modelling of the coagulation cascade; and non-Newtonian blood behaviour within the LAA. Using 84 simulation cases generated via Latin Hypercube Sampling of physiological blood parameters and 21 patient-derived LA anatomies, we trained surrogate machine learning models, including Ridge regression, XGBoost, Gaussian Process Emulators (GPEs), and deep learning networks, to predict CFD outputs such as blood viscosity in and fibrin concentrations in the LAA. Deep learning achieved R{superscript 2} values up to 0.90, with the accuracy increasing when both physiological parameters and the raw CT image were included. Other models showed uneven performance with R2 values below 0.7, highlighting the role of nonlinearities between parameters. The study presents a novel CFD model that captures the transition from blood stasis to clot formation, representing the full thrombotic continuum underlying stroke risk in AF, and a deep learning approach to enable efficient prediction of mechanistic outputs of clinical value for stroke risk stratification in AF patients. Author SummaryAtrial fibrillation is a common heart rhythm disorder that greatly increases the risk of stroke. In many patients, blood can pool inside a small pouch of the heart called the left atrial appendage, where clots may form and later travel to the brain. Current clinical tools used to estimate stroke risk mainly rely on a patients medical history and do not directly assess the mechanistic processes that lead to clot formation. In this study, we developed a computer model that simulates how blood flows and clots inside the heart using patient-specific heart anatomies derived from medical imaging. Our model combines blood flow, blood biochemistry, and the changing physical properties of blood during clot formation. We then used machine learning methods to predict these complex simulation results more efficiently. Deep learning models performed best, particularly when both clinical parameters and heart imaging data were included. Our work provides a new way to study the full process linking abnormal blood flow to clot formation in atrial fibrillation. In the future, this approach could support more personalised and mechanistic assessment of stroke risk and help guide treatment decisions.

bioengineering↗