Resveratrol inhibits reproductive toxicity induced by deoxynivalenol
Bibliographic record
Abstract
The aim of this in vitro study was to examine the release of progesterone by porcine ovarian granulosa cells (GCs) after exposure to toxic concentrations of deoxynivalenol (DON), resveratrol (RSV), and their combination (DON with RSV). Ovarian granulosa cells were incubated without (control) or with treatments of natural substances at various doses for 24 h: RSV (10, 30 and 50 μg/mL) / DON (2000, 3000 and 5000 ng/mL), and their combination (10 μg/mL of RSV with 2000 ng/mL of DON; 30 μg/mL of RSV with 3000 ng/mL of DON; 50 μg/mL of RSV with 5000 ng/mL of DON). Progesterone was determined by radioimmunoassay (RIA). Progesterone release was significantly (P < 0.05) stimulated by RSV at the doses 50 μg/mL but not at 30 and 10 μg/mL and by DON treatment at all used doses (2000, 3000 and 5000 ng/mL). RSV in combination with DON stimulated significantly (P < 0.05) the progesterone release by GCs at the highest doses (50 μg/mL of RSV with 5000 ng/mL of DON). On the other hand, the stimulatory effect of RSV in combination with DON was significantly (P < 0.05) lower in comparison with alone DON effect. In conclusion, our results indicate, (1) the dose-depended stimulatory effects of RSV, DON and combination of RSV with DON on release of steroid hormone progesterone and (2) reduction of the stimulatory effect of DON by RSV. Our in vitro results suggest that reproductive toxicity of animals induced by a mycotoxin - deoxynivalenol can be inhibited by a protective natural substance - resveratrol.
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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".