Inhibition of HDAC increases the senescence induced by natural polyphenols in glioma cells
Bibliographic record
Abstract
Cellular senescence is an irreversible block of cellular division, and induction of senescence is being considered for treatment of many cancer types, mainly those resistant to classical pro-apoptotic therapies. Resveratrol (Rsv) and quercetin (Quer), two natural polyphenols, are able to induce senescence in different cancer models, including gliomas, the most common and aggressive primary brain tumor. These polyphenols modulate the activity of several proteins involved in cell growth and death in cancer cells, including histone deacetylases (HDAC), but the role of HDAC in senescence induced by Rsv and Quer is unclear. The HDAC inhibitor sodium butyrate (NaB) potentiated the pro-senescent effect of Rsv and Quer in human and rat glioma cell lines but not in normal rat astrocytes. Furthermore, the increment of Quer-induced senescence by NaB was accompanied by an increase of reactive oxygen species levels and an increment of the number of cells with nuclear abnormalities. Altogether, these data support a positive role of HDAC inhibition on the senescence induced by these polyphenols, and therefore co-treatment of HDAC inhibitors and polyphenols emerges as a potential alternative for gliomas.
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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 | 0.001 |
| 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".