Lipid peroxidation is associated with the inhibitory action of all-trans-retinoic acid on mammary cell transformation.
Bibliographic record
Abstract
BACKGROUND: Retinoids are effective in reducing transformation of various cell types; however the role of lipid peroxidation has not been studied in this regard. MATERIALS AND METHODS: Retinoic acid (RA) and retinol were tested on several SV-40 large-T-antigen transformed bovine mammary fibroblast (MFB) lines that form foci on plastic with long-term culture. RESULTS: Dose response studies revealed that RA at 10(-6) M was the most potent dose in delaying the onset of foci formation and reducing total foci number. RA was always more effective than retinol. Addition of RA (10(-6) M) to MFB cells increased lipid peroxide (LPO) concentrations by approximately three-fold relative to untreated MFB cells or to RA or control treated normal mammary fibroblasts. The lipoxygenase inhibitor, nordihydroguaiaretic acid (NDGA), acted synergistically with RA to increase LPO and cell death in MFB cells. The combination of the cyclooxygenase inhibitor, indomethacin, at 10(-4) M with 10(-6) M RA lowered MFB fibroblast cell numbers when compared to fibroblasts cultured singly with either RA or indomethacin. CONCLUSIONS: These data indicate that an increase in lipid peroxidation occurs specifically in tumor cells treated with RA and this may play a role in RA-mediated suppression of cellular transformation in the mammary gland. Additionally, eicosanoid inhibitors may have an additive or synergistic effect with RA on the inhibition of mammary tumor cell transformation and proliferation.
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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) | 0.001 | 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 | 0.001 |
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".