Leptin Gene Expression in Rabbits During Pregnancy and Fetal Life
Bibliographic record
Abstract
Leptin may act as the critical link between adipose tissue and reproduction. Although considerable progress has been achieved in understanding the reproductive actions of leptin, much work is needed for understanding its physiological role. Till now, no data has been published about the distribution and expression levels of leptin in the rabbit maternal adipose tissue, placenta, and various fetal rabbit tissues during pregnancy and postpartum. Our results indicated that circulating leptin levels in rabbit serum during pregnancy were significantly higher than in postpartum and non pregnant rabbits. Furthermore, leptin showed positive correlations with body weight and estrogen and negative correlation with progesterone in pregnant and postpartum rabbits. RT-PCR verified the presence of RNAs encoding leptin in the rabbit maternal perirenal adipose tissue, placenta, and several fetal tissues; including brain, liver, adipose tissue, and bone. The relative abundance of leptin RNA in rabbit maternal adipose was significantly higher at 20th day of pregnancy than that of non pregnant rabbits, while it was significantly decreased at 2nd day after parturition. No significant changes in the placental leptin RNA levels were noticed in pregnant rabbits at 10th, 20th, and 30th day of pregnancy. The relative abundance of fetal leptin transcripts at day 30th of pregnancy was in the order of liver> bone ? adipose tissue > brain. The present study provides new evidence for the distribution and expression levels of leptin in the rabbit maternal and fetal tissues during pregnancy and supports the importance of leptin in reproductive physiology and fetal development.
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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 | 0.001 | 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".