Statins Protect Against Early Stages of Doxorubicin-induced Cardiotoxicity Through the Regulation of Akt Signaling and SERCA2
Bibliographic record
Abstract
Background: Doxorubicin-induced cardiomyopathy (DICM) is one of the complications that can limit treatment for a significant number of cancer patients. In animal models, the administration of statins can prevent the development of DICM. Therefore, the use of statins with anthracyclines potentially could enable cancer patients to complete their chemotherapy without added cardiotoxicity. The precise mechanism mediating the cardioprotection is not well understood. The purpose of this study is to determine the molecular mechanism by which rosuvastatin confers cardioprotection in a mouse model of DICM. Methods: Rosuvastatin was intraperitoneally administered into adult male mice at 100 μg/kg daily for 7 days, followed by a single intraperitoneal doxorubicin injection at 10 mg/kg. Animals continued to receive rosuvastatin daily for an additional 14 days. Cardiac function was assessed by echocardiography. Optical calcium mapping was performed on retrograde Langendorff perfused isolated hearts. Ventricular tissue samples were analyzed by immunofluorescence microscopy, Western blotting, and quantitative polymerase chain reaction. Results: reuptake. These effects occurred independently of perturbations in ryanodine receptor 2 function. Conclusions: Rosuvastatin counteracts the cardiotoxic effects of doxorubicin by directly targeting sarcoplasmic calcium cycling.
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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".