Evaluation of onconutraceutical potential and chemical characterization of vegetable smoothies
Bibliographic record
Abstract
The research towards onconutraceutical products not only for cancer prevention but also as a valid support to the pharmacological therapies is of growing interest. Doxorubicin is one of the most potent and widely used chemotherapeutic agents for various tumors, as breast cancer. However, doxorubicin clinical application is limited by cumulative and dose-related cardiotoxicity, which may lead to congestive heart failure [ 1 ]. Thus, this study aims to identify possible nutraceutical matrices able to reduce the doxorubicin toxicity without modifying its antineoplastic activity on breast cancer cells. We evaluated the onconutraceutical potential of smoothies polyphenolic extracts from orange ( Citrus sinensis ) and red grape ( Vitis vinifera ), and 3 different mixes, composed by different ratios of the two matrices, on embryonic rat heart-derived cells (H9c2) and human breast adenocarcinoma cells (MCF-7), also in presence of doxorubicin. The tested extracts, as well as the relative mixes, don’t exhibit a significant antiproliferative activity on H9c2 and MCF-7. In doxorubicin-treated cells, the orange and the grapes extracts, and the 3 mixes, reduce the antiproliferative activity induced by doxorubicin on H9c2. Interestingly, the doxorubicin antiproliferative activity on MCF-7 cells was unaltered, in presence of the tested extracts. In particular, the more effective smoties’ mix, 1:1 ratio, was able to reduce the doxorubicin-induced reactive oxygen species release, and to increase the expression of antioxidant cytoprotective enzymes in cardiomyocytes. Our results indicate that the smoothies polyphenolic extracts could be usefull as onconutraceutics, in order to reduce the some doxorubicin-induced side effects.
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) | 0.000 | 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".