MR Imaging and Electrophysiological Features of Doxorubicin-Induced Fibrosis: Protocol Development in a Small Preclinical Pig Study with Histological Validation
Bibliographic record
Abstract
A critical chemotherapeutic complication is cardiotoxicity, often leading, in time, to heart failure. In this work, we developed a novel animal protocol using magnetic resonance (MR) imaging and electrophysiology (EP) tests, designed to detect subtle structural and functional changes associated with myocardial damage in sub-chronic phases post-chemotherapy. A weekly dose of doxorubicin (DOX) was injected in four juvenile swine throughout a four-week plan, using an intravenous approach that mimics the treatment in cancer patients. We performed cardiac MR imaging as follows: in all four pigs pre-DOX; at 1 and 5 weeks post-DOX in a group of two pigs; and, at 1 and 9 weeks post-DOX in the other two pigs, using Cine imaging to assess ejection fraction (EF) and late gadolinium enhancement to quantify collagen density in the left ventricle. Additionally, X-ray-guided voltage mapping and arrhythmia tests were conducted in the group at 9 weeks post-DOX and in a healthy pig. Tissue samples were collected for histology. The results showed that EF decreased from ~46% pre-DOX to ~34% within the first 9 weeks post-DOX. This decline in LV function was explained by a gradual increase in collagen density, especially noticeable at week 9 post-DOX as derived from MRI analysis. Furthermore, ventricular fibrillation was induced via rapid pacing at 9 weeks post-DOX, most likely caused by fibrotic patches identified in voltage maps, as confirmed by MRI and collagen-sensitive histological stains. Overall, our novel preclinical protocol was able to reveal key signs of potentially-irreversible tissue changes, along with electrical remodeling and arrhythmia risk in the early months following DOX therapy. Future work will include more datasets to statistically power the study, and will use the protocol to test cardioprotective strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".