Impact of ellagic acid application on doxorubicin-induced cardiovascular toxicity model
Bibliographic record
Abstract
Doxorubicin is an anticancer agent that is commonly used to treat a number of tumors and is associated with acute and chronic changes of the cardiovascular system. Ellagic acid has strong free radical scavenging capacity, neuroprotective and hepatoprotective effects, and is known to protect against changes occurring due to diabetes, cardiovascular diseases, and cancer. Twenty-four Wistar rats were divided in four groups: control group received saline, doxorubicin group received doxorubicin in a single dose of 20 mg/kg, ellagic acid group received ellagic acid in a dose of 4 mg/kg, and doxorubicin + ellagic acid group received doxorubicin and ellagic acid in same doses as in previous groups. The effect of ellagic acid treatment, alone or in combination with doxorubicin, was studied on isolated heart frequency and strength of the contraction, and on thoracic aorta contractile responses. Application of ellagic acid to rats pre-treated with doxorubicin significantly prevented functional changes occurring in the heart, but not in the thoracic aorta tissue. Ellagic acid statistically significantly (p < 0.001) prevented doxorubicin-induced increase in heart rate, while at the same time increased single contraction force (p < 0.001) and attenuated morphological changes on heart tissue induced by doxorubicin. We can conclude that ellagic acid has potential to prevent doxorubicin-induced changes of the cardiovascular system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.002 | 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".