Antiproliferative activity of long chain acylated esters of quercetin-3- <i>O</i> -glucoside in hepatocellular carcinoma HepG2 cells
Bibliographic record
Abstract
Despite their strong role in human health, poor bioavailability of flavonoids limits their biological effects in vivo. Enzymatically catalyzed acylation of fatty acids to flavonoids is one of the approaches of increasing cellular permeability and hence, biological activities. In this study, six long chain fatty acid esters of quercetin-3-O-glucoside (Q3G) acylated enzymatically and were used for determining their antiproliferative action in hepatocellular carcinoma cells (HepG2) in comparison to precursor compounds and two chemotherapy drugs (Sorafenib and Cisplatin). Fatty acid esters of Q3G showed significant inhibition of HepG2 cell proliferation by 85 to 90% after 6 h and 24 h of treatment, respectively. The cell death due to these novel compounds was associated with cell-cycle arrest in S-phase and apoptosis observed by DNA fragmentation, fluorescent microscopy and elevated caspase-3 activity and strong DNA topoisomerase II inhibition. Interestingly, Q3G esters showed significantly low toxicity to normal liver cells than Sorafenib (P < 0.05), a chemotherapy drug for hepatocellular carcinoma. Among all, oleic acid ester of Q3G displayed the greatest antiproliferation action and a high potential as an anti-cancer therapeutic. Overall, the results of the study suggest strong antiproliferative action of these novel food-derived compounds in treatment of cancer.
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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".