Melissa officinalis Protects against Doxorubicin-Induced Cardiotoxicity in Rats and Potentiates Its Anticancer Activity on MCF-7 Cells
Bibliographic record
Abstract
Cardiotoxicity is a limiting factor of doxorubicin (DOX)-based anticancer therapy. Due to its beneficial effects, we investigated whether standardized extract of Melissa officinalis (MO) can attenuate doxorubicin-induced cardiotoxicity and can potentiate the efficacy of DOX against human breast cancer cells. MO was administered orally to male albino rats once daily for 10 consecutive days at doses of 250, 500 and 750 mg/kg b.wt. DOX (15 mg/kg b.wt. i.p.) was administered on the 8th day. MO protected against DOX-induced leakage of cardiac enzymes and histopathological changes. MO ameliorated DOX-induced oxidative stress as evidenced by decreasing lipid peroxidation, protein oxidation and total oxidant capacity depletion and by increasing antioxidant capacity. Additionally, MO pretreatment inhibited inflammatory responses to DOX by decreasing the expressions of nuclear factor kappa-B, tumor necrosis factor-alpha and cyclooxygenase-2 and the activity of myeloperoxidase. MO ameliorated DOX-induced apoptotic tissue damage in heart of rats. In vitro study showed that MO augmented the anticancer efficacy of DOX in human breast cancer cells (MCF-7) and potentiated oxidative damage and apoptosis. Thus, combination of DOX and MO may prove future cancer treatment protocols safer and more efficient.
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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 | 0.000 |
| 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".