The utility of cardiac biomarkers and echocardiography for the early detection of bevacizumab- and sunitinib-mediated cardiotoxicity
Bibliographic record
Abstract
The recent introduction of novel anticancer therapies, including bevacizumab (BVZ) and sunitinib (SNT), is associated with an increased risk of cardiotoxicity. However, early identification of left ventricular (LV) systolic dysfunction may facilitate dose modification and avoid the development of advanced heart failure. Using a murine model of BVZ- and SNT-mediated cardiotoxicity, we investigated whether cardiac biomarkers and/or tissue velocity imaging (TVI) using echocardiography can detect early changes in cardiac function, before a decrease in LV ejection fraction is identified. A total of 75 wild-type C57Bl/6 male mice were treated with either 0.9% saline, BVZ, or SNT. Serial monitoring of blood pressure, high-sensitivity troponin I, and echocardiographic indexes were performed over a 14-day study period, after which the mice were euthanized for histological and biochemical analyses. Mice treated with either BVZ or SNT developed systemic hypertension as early as day 7, which increased by day 14. Cardiac biomarkers, specifically high-sensitivity troponin I, were not predictive of early LV systolic dysfunction. Although conventional LV ejection fraction values decreased at day 13 in mice treated with either BVZ or SNT, TVI confirmed early LV systolic dysfunction at day 8. Histological and biochemical analysis demonstrated loss of cellular integrity, increased oxidative stress, and increased cardiac apoptosis in mice treated with BVZ or SNT therapy at day 14. In a murine model of BVZ- or SNT-mediated cardiomyopathy, noninvasive assessment by TVI detected early LV systolic dysfunction before alterations in conventional echocardiographic indexes.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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".