Quercetin and cancer: new insights into its therapeutic effects on ovarian cancer cells
Bibliographic record
Abstract
Ovarian cancer is known as a serious malignancy that affects women's reproductive tract and can considerably threat their health. A wide range of molecular mechanisms and genetic modifications have been involved in ovarian cancer pathogenesis making it difficult to develop effective therapeutic platforms. Hence, discovery and developing new therapeutic approaches are required. Medicinal plants, as a new source of drugs, could potentially be used alone or in combination with other medicines in the treatment of various cancers such as ovarian cancer. Among various natural compounds, quercetin has shown great anti-cancer and anti-inflammatory properties. In vitro and in vivo experiments have revealed that quercetin possesses a cytotoxic impact on ovarian cancer cells. Despite obtaining good results both in vitro and in vivo, few clinical studies have assessed the anti-cancer effects of quercetin particularly in the ovarian cancer. Therefore, it seems that further clinical studies may introduce quercetin as therapeutic agent alone or in combination with other chemotherapy drugs to the clinical setting. Here, we not only summarize the anti-cancer effects of quercetin but also highlight the therapeutic effects of quercetin in the ovarian cancer.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".