Protective effects of <i>Melissa officinalis</i> ethanolic extract on doxorubicin-induced cardiotoxicity in a rat model
Bibliographic record
Abstract
Despite its proven efficacy in cancer treatment, doxorubicin's therapeutic potential is limited by cumulative, dose-dependent cardiotoxicity, primarily associated with oxidative stress. Given the well-documented antioxidant properties of Melissa officinalis L., this study aimed to assess the cardioprotective potential of its ethanolic extract (MOE) against doxorubicin-induced cardiotoxicity (DIC). Twenty-one female Wistar albino rats were randomly divided into three groups: CTRL (healthy, untreated), DOX (doxorubicin-treated), and DOX-MO (treated with both doxorubicin and MOE). Doxorubicin (15 mg/kg, i.p.) was administered on Day 7, while MOE (200 mg/kg, orally) was given daily for 10 days. Cardiac function was evaluated using echocardiography and Langendorff-perfused hearts, followed by analysis of oxidative stress markers and morphometric analysis. MOE improved cardiac function and partially preserved myocardial architecture following DIC, significantly reducing fibrosis compared to the DOX group. Nitrite levels were significantly elevated in MOE-treated rats, suggesting preserved endothelial function and enhanced nitric oxide-mediated vasodilation. These results suggest that MOE may mitigate DIC through antioxidative and vasodilatory mechanisms, as well as by preserving endothelial and myocardial integrity. Histological findings further indicate a possible reduction in inflammatory changes, supporting a modest anti-inflammatory effect. These findings suggest that MOE may have cardioprotective potential against DIC, warranting further investigation in preclinical and clinical settings.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".