Cancer Treatment-Induced Cardiotoxicity: a Cardiac Stem Cell Disease?
Bibliographic record
Abstract
Cardiovascular diseases and cancer represent respectively the first and second cause of death in industrialized countries. These two conditions may become synergistic when cardiovascular complications of anti-cancer therapy are considered. More than 70% of childhood and 50% of adult cancer patients can be cured, however this important success obtained by the biological and medical research is obfuscated by emerging findings of early and late morbidity due to cardiovascular events. Although anthracyclines are effective drugs against cancer a dose-dependent cardiotoxic effects whose mechanism has not been elucidated resulting in failure of therapeutic interventions limit their use. Unexpectedly, tyrosine/kinase inhibitors (TKIs) aimed at molecularly interfering with oncogenic pathways, have been implicated in cardiac side effects. Possible explanations of this phenomenon have been ambiguous, further strengthening the need to deepen our understanding on the mechanism of cardiotoxicity. In addition to a detailed description of anthracyclines and TKIs-related cardiovascular effects, the present review highlights recent observations supporting the hypothesis that the cellular target of anthracyclines and TKIs may include myocardial compartments other than parenchymal cells. The demonstration that the adult mammalian heart possesses a cell turnover regulated by primitive cells suggests that this cell population may be implicated in the onset and development of cardiovascular effects of anti-cancer strategies. The possibility of preventing cardiotoxicity by preservation and/or expansion of the resident stem cell pool responsible for cardiac repair may open new therapeutic options to unravel an unsolved clinical issue.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".