The Effects of Ascorbic Acid on the Estrogen/Progesteron Levels in the Isolated Rabbit Uterine Muscle
Bibliographic record
Abstract
Background: The study was carried out to examine the effect of Ascorbic Acid (AsA) on estrogen/progesterone levels in the isolated non-pregnant rabbit uterine muscle. Methods: Twelve non-pregnant rabbits were randomized into two groups as the rabbits pretreated with AsA (n = 6) and the rabbits not pretreated with AsA (n = 6). After 30 mg/kg AsA was administered intraperitoneally, serum and tissue (uterine smooth muscle) levels of estrogen and progesterone were measured. Results: AsA at the above mentioned concentration caused a significant increase in tissue estrogen/progesterone ratio (P < 0.01), while did not induce any change in the hormone ratio in the serum. The increase in the tissue estrogen/progesterone ratio was resulted from both the decrease of the progesterone levels and the increase of estrogen levels (P < 0.001). Conclusion: In this context, the fact that the tissue levels of hormones and autacoids is more important than the serum concentrations should be concidered. The findings of this study indicate that in comparison to the control group, AsA causes decrease in the non-pregnant isolated uterine smooth muscle tissue levels of progesterone and increase in the non-pregnant isolated uterine smooth muscle tissue levels of estrogen while the serum levels keep constant. doi: http://dx.doi.org/10.4021/jcgo47w
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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".