Zinc increased the sensitivity of HepG2 and AML12 cells towards retinoic acid‐mediated growth inhibition
Bibliographic record
Abstract
Retinoic acid has been shown to reduce cell growth by interrupting cell cycle. This effect of retinoic acid involves retinoic acid receptor and retinoid X receptor, two zinc‐finger proteins. We hypothesized that the sensitivity of cells towards retinoic acid‐mediated growth inhibition can be modulated by zinc status. To test this hypothesis, hepatoma HepG2 cells were cultured in a low‐zinc media supplemented with 0 (low‐zinc group), 5 (adequate‐zinc group), and 10 (zinc‐supplemented group) μM of zinc followed by treating the cells with retinoic acid at 0 and 35 μM. Culturing the cells in the low‐zinc media depleted the labile intracellular pool of zinc by 86% while the total cellular zinc concentration was reduced by 28%. Treating the cells with retinoic acid reduced cell proliferation in the low‐zinc, adequate‐zinc, and zinc‐supplemented group by 36, 49, and 55%, respectively. Cell cycle analysis showed that retinoic acid treatment reduced the number of cells in the S‐phase in the adequate‐zinc and zinc‐supplemented groups by 27% compared to the low‐zinc group. Similar effects were obtained in normal hepatocyte AML12 cells using the same treatment regime. Collectively, these results suggested that zinc supplementation sensitized HepG2 and AML12 cells towards retinoic acid‐mediated growth inhibition. Supported by the Vitamin Research Fund .
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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) | 0.002 | 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".