Sunnybrook Health Science Centre

Abstract Background Pulsed Field Ablation (PFA) is a new ablation method rapidly being adopted for the treatment of cardiac arrhythmias. Scar homogenization is a commonly used ablation strategy for scar-related ventricular tachycardia (VT). This strategy involves multiple PFA applications in border zone regions to eliminate re-entry circuits. However, quantifying the tissue-specific extent of these lesions in vivo is challenging, and assessment is largely limited to histology. Purpose Predict and evaluate the ablated volume after PFA scar homogenization in the infarcted swine left ventricle (LV) using numerical modeling. Methods MRI-guided structural scar homogenization with PFA was performed in five infarcted swine ventricles (4–6 weeks post-infarct) using an 8 Fr, 5 mm tip focal catheter in the LV and a return catheter in the inferior vena cava (IVC). Between 21 and 27 applications per animal were delivered, each consisting of eight R-wave–gated biphasic pulse trains at 1500 V. Pre-ablation 3D late gadolinium enhancement magnetic resonance images (LGE MRI) were processed to obtain animal-specific ventricular geometry and fibrotic tissue distribution (scar map). In the scar map, normalized pixel intensity thresholds of 40% and 60% differentiated healthy tissue (<40%), border zone (40%-60%), and dense scar (>60%). The numerical model incorporated the animal-specific geometry (LV, RV, and IVC) and scar map, which was used to assign distinct, tissue-specific electrical properties (Figure 1, Step 1). The electric field distribution was calculated for each PFA application with the ablation catheter positioned according to the ablation tags from the cardiac mapping system (Figure 1, Step 2). The ablated volume was determined by combining regions of tissue in the infarcted LV where the electric field exceeded the lethal threshold of 456 V/cm (as determined in the previous study [1]) for each PFA application, accounting for overlaps (Figure 1, Step 3). Results The modeling framework provided detailed, animal-specific predictions of the ablated volume and its spatial distribution relative to the fibrotic tissue distribution (Figure 2, panels A and B). Across the five animals, the models predicted a mean total ablated volume of 2.8±0.3 cm³, consisting of 1±0.4 cm³ healthy tissue, 1.2±0.2 cm³ border zone, and 0.6±0.4 cm³ dense scar (Figure 2, panel C). These data suggest a new mean dense scar volume of 2.2±0.2 cm³, assuming conversion of the initially ablated healthy tissue and border zone. Conclusion The presented in silico framework provides a method to quantify and visualize the predicted tissue-specific impact of PFA scar homogenization, demonstrating potential for valuable insights into treatment outcomes.Numerical modeling workflowNumerical modeling results

Background Vascular mild cognitive impairment (vaMCI) is frequently accompanied by neuropsychiatric symptoms (NPS), particularly apathy, which are associated with white matter hyperintensities (WMH). N-acetylcysteine (NAC) combined with exercise may provide neuroprotective benefits. Objective We hypothesized that baseline frontal and global WMH burden would moderate the neuropsychiatric response to NAC plus exercise. We further hypothesized that the strongest domain-specific effects would be observed for apathy and affective dysregulation. Methods Participants with vaMCI were randomized to NAC (2400 mg daily) or placebo plus exercise for 6 months. WMH volumes were quantified using structural MRI. NPS were assessed using the Mild Behavioral Impairment Checklist (MBI-C) at baseline, 3 months, and 6 months. Negative binomial generalized linear mixed models examined WMH × time × treatment interactions. False discovery rate (FDR) correction was applied. Results Fifty-one participants were included. Lower baseline frontal ( β = 0.229, SE = 0.070, FDR-adjusted p = 0.007) and global ( β = 0.258, SE = 0.080, FDR-adjusted p = 0.007) WMH volume was associated with greater reductions in NPS over 6 months in the NAC plus exercise group. This effect was driven by improvements in apathy (frontal: β = 0.186, SE = 0.076, FDR-adjusted p = 0.048; global: β = 0.207, SE = 0.089, FDR-adjusted p = 0.049). Conclusions Baseline WMH burden moderates neuropsychiatric treatment response in vaMCI. These findings support the importance of optimizing care for cerebrovascular risk factors in this vulnerable population.

Abstract Motivation Cell-free DNA methylation provides a minimally invasive signal for early cancer detection and tissue-of-origin prediction. Most methods represent methylation measurements as independent fixed-window features and therefore do not explicitly model relationships among genomic regions. Results We developed PANGEM (Pan-cancer Graph-based Cancer Detection Using the Cell-free DNA Methylome), a graph-learning framework that represents genomic bins as nodes and integrates CpG context, genomic proximity, and sample-specific methylation similarity in the graph topology. Across five repeated stratified train-test splits, PANGEM achieved the highest mean performance among evaluated methods, with an AUROC/AUPR of 0.997±0.003/1.000±0.000 for binary cancer detection and macro-AUROC/AUPR of 0.977±0.027/0.870±0.086 for multiclass tissue-of-origin prediction. In the independent INSPIRE cohort, 72 of 78 cancer cases (92.3%) exceeded the binary classification threshold, and PANGEM correctly classified 9 of 17 head and neck cancer cases (52.9%), the highest accuracy among evaluated methods. Subnetwork analysis further identified recurrent, graph-connected methylation patterns, including a 111-DMR subnetwork with increased methylation in cancer samples. Availability and Implementation Source code for this study is available at https://github.com/GenTensor/PANGEM . Contact: ping.luo@algomau.ca Supplementary information Supplementary data are available at Bioinformatics online.

The computational efficiency of contemporary multi-scale digital twins used to investigate cardiac arrhythmia is often hindered by the equation system complexity, while the accuracy of predictions depends on the calibration of model parameters and their intricate non-linear relation. Here we employed a robust pipeline for fast simulations of scar-related ventricular tachycardia (VT) inducibility using an efficient Lattice-Boltzmann Method with GPU-based accelerated code. We tested the pipeline on 3D digital twins built from MR images acquired in n = 8 swine with chronic infarction, by performing a subject-specific personalization of each model per tissue type (i.e., scar, border zone, and healthy) and tuning the key parameters (e.g., action potential duration, excitability, and wave speed) from recorded endocardial bipolar voltage maps and intracardiac electrograms. We first validated the VT simulation outcome by precisely replicating the stimulation protocol and pacing site used in the animal studies. Our results demonstrated very good agreement between the experiment and simulated VT outcome for personalized model parameters using subject-specific values, compared to a poor outcome when parameters were tuned from average values from all cases. Second, we performed a comprehensive in silico study where the stimulation was delivered from 10 000 virtual endocardial locations and found a strong dependence of VT (non)/-inducibility per case on the stimulation site. Simulating 10 s of sustained VT induced on a 3D model comprising 1.2 million cubic voxels (0.7 mm edge size) took 30 min on a laptop using GPU, underlying the computational tractability of our pipeline and its potential clinical translation to predict infarct-related VT risk.