Loss of endothelial cell-specific autophagy-related protein 7 exacerbates doxorubicin-induced cardiotoxicity
Bibliographic record
Abstract
Doxorubicin (DOX) is an effective, broad-spectrum antineoplastic agent with serious cardiotoxic side effects, which may lead to the development of heart failure. Current strategies to diagnose, prevent, and treat DOX-induced cardiotoxicity (DIC) are inadequate. Recent evidence has linked the dysregulation and destruction of the vascular endothelium to the development of DIC. Autophagy is a conserved pro-survival mechanism that recycles and removes damaged sub-cellular components. Autophagy-related protein 7 (ATG7) catalyzes autophagosome formation, a critical step in autophagy. In this study, we used endothelial cell-specific Atg7 knockout ( EC-Atg7 −/− ) mice to characterize the role of endothelial cell-specific autophagy in DIC. DOX-treated EC-Atg7 −/− mice showed reduced survival and a greater decline in cardiac function compared to wild-type controls. Histological assessments revealed increased cardiac fibrosis in DOX-treated EC-Atg7 −/− mice. Furthermore, DOX-treated EC-Atg7 −/− mice had elevated serum levels of creatine kinase-myocardial band, a biomarker for cardiac damage. Thus, the lack of EC-specific autophagy exacerbated DIC. Future studies on the relationship between EC-specific autophagy and DIC could establish the importance of endothelium protection in preventing DIC.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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".