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Record W4415781242 · doi:10.1113/jp288819

<i>In silico</i> predictions of action potential propagation in doxorubicin cardiotoxicity: A parametric study using preclinical 3D magnetic resonance imaging‐based fibrotic left ventricle models

2025· article· en· W4415781242 on OpenAlexafffund
Javier Villar‐Valero, Jairo Rodríguez Padilla, Nicolas Cedilnik, Buntheng Ly, Juan F. Gómez, Maxime Sermesant, Mihaela Pop, Beatriz Trénor

Bibliographic record

VenueThe Journal of Physiology · 2025
Typearticle
Languageen
FieldMedicine
TopicChemotherapy-induced cardiotoxicity and mitigation
Canadian institutionsSunnybrook Hospital
FundersEuropean Social FundEuropean Regional Development FundBarcelona Supercomputing CenterAgencia Estatal de InvestigaciónAgence Nationale de la RechercheCanadian Institutes of Health ResearchEuropean Commission
KeywordsVentricleAction potentialParametric statisticsMagnetic resonance imagingCardiotoxicityDoxorubicinComputational modelNerve conduction velocity

Abstract

fetched live from OpenAlex

Doxorubicin (DOX) is a widely used chemotherapeutic agent, but its cardiotoxic effects, including diffuse myocardial fibrosis, increase the risk of dangerous arrhythmias. There is a critical need for non-invasive tools to predict DOX-related ventricular arrhythmias in early chronic stages following chemotherapy. A computational study was performed using experimental data from three pigs: one control and two at 9 weeks following DOX. Customized 3D left ventricular (LV) models were generated from late gadolinium-enhanced magnetic resonance imaging and electro-anatomical maps, integrating tissue structure, electrical properties (healthy/fibrosis) and fibre directions. Action potential (AP) wave propagation was simulated using a high-performance numerical solver. A virtual programmed stimulation protocol was applied in 96 simulations to assess arrhythmia inducibility, varying the parameters corresponding to excitability and conduction velocity in fibrotic zones. Arrhythmias were inducible only in DOX-treated cases. Reentrant wave genesis depended on: excitability, conduction velocity, fibrosis distribution and AP duration heterogeneity. In one scenario, AP heterogeneities and a ≥70% reduction in diffusion coefficient were required to induce reentry despite unchanged excitability in fibrosis. This study presents the first computational simulation of DOX-induced cardiotoxicity in a realistic 3D LV model using a highly efficient, automated Lattice-Boltzmann approach. Our findings provide insights into arrhythmogenic mechanisms and may aid in developing strategies to prevent and treat DOX-related cardiotoxicity. KEY POINTS: We developed a novel semi-automated computational framework to construct high-resolution 3D magnetic resonance imaging-based left ventricular models designed to study via simulations the electrical activity after chemotherapy using a GPU-optimized Lattice-Boltzmann method solver. Our digital heart twins were directly calibrated and validated using measurements of conduction velocity and action potential wave features obtained via catheter-based electro-anatomical mapping after chemotherapy in preclinical swine models. This specific virtual parametric study demonstrates that both electrophysiological and structural alterations induced by diffuse fibrosis substantially modulate ventricular arrhythmias in the sub-chronic phase following doxorubicin therapy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.328
Teacher spread0.297 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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