Gene expression of leptin, leptin receptor, prolactin receptor and whey acidic protein in mammary glands of late-pregnant gilts from two breeds
Bibliographic record
Abstract
In order to identify genes which are essential for pig mammary gland development, mRNA levels of prolactin receptor (PRL-R), leptin, leptin receptor and whey acidic protein (WAP) were measured in parenchymal tissue of 110-d-pregnant gilts. Thirteen Upton-Meishan (UM) and 14 Large White (LW) pregnant gilts and 5 non-pregnant control gilts (2 LW and 3UM) were used. PRL-R and WAP mRNA levels were higher in pregnant than in non-pregnant gilts (P < 0.05). Leptin mRNA levels were higher in UM than in LW gilts (P < 0.05), but this breed effect was not seen when leptin mRNA levels were corrected for percent fat in parenchyma. Correlations were found between concentrations of IGF-I in plasma and PRL-R (P < 0.01) and WAP (P < 0.05) mRNA levels in UM gilts. Serum prolactin (PRL) was correlated with leptin mRNA levels in the overall (P < 0.05) and LW (P < 0.05) populations of gilts, while estradiol was associated with leptin receptor mRNA in UM gilts (P < 0.05). The mRNA levels of all studied genes were positively correlated with mammary parenchymal and extra parenchymal weights in UM gilts, whereas these variables were only correlated with PRL-R and WAP gene expression in LW gilts. The presence of leptin and leptin receptor mRNA in parenchymal tissue suggests a paracrine role for leptin in mammary tissue of late-pregnant gilts. These results also suggest that the PRL signalling pathway is fully active at the transcriptional level in the mammary gland of gilts at 110 d of pregnancy. Key words: Genetics, pig, mammary glands, Meishan, mRNA
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".