347 Prolactin, prolactin receptor, and adiponutrin messenger RNA abundances in mammary extraparenchymal tissue of gilts are affected by body condition
Bibliographic record
Abstract
Study objectives were 1) to determine if different body conditions in late gestation, which were maintained from mating to d 110 of gestation, affect the mRNA abundance of adipokines and their receptors in mammary extraparenchymal tissue and 2) to look for associations between mammary gland composition variables and the gene expression of selected adipokines in extraparenchymal tissue. A total of 45 gilts were selected at mating according to their backfat thickness: low (LBF; 12–15 mm; n = 14), medium (MBF; 17–19 mm; n = 15), and high (HBF; 22–26 mm; n = 16) backfat. Throughout the gestation period, LBF, MBF, and HBF gilts received different amounts of a conventional diet to maintain similar backfat thicknesses from mating until the end of gestation. Gilts were slaughtered on d 110 of gestation. One side of the udder was dissected to evaluate mammary gland composition. Extraparenchymal tissue (mammary fat) was collected from the fourth teat of the other side to measure mRNA abundance of adipokines (ADIPOQ, LEP, PNPLA3, and PRL) and their receptors (ADIPOR1, ADIPOR2, LEPR-LF, and PRLR-LF) using real-time PCR amplifications. Statistical analyses were performed with the mixed procedure of SAS using a univariate model (3 levels), and means were compared with a Tukey test. PROC CORR of SAS was used for correlation analyses. In the extraparenchymal tissue, there was a greater PRL mRNA abundance in HBF gilts than in LBF and MBF gilts (P < 0.05). The PNPLA3 mRNA abundance was lower for HBF gilts than for MBF gilts (P < 0.05), and lower PRLR-LF mRNA abundance was found in LBF gilts than in HBF gilts (P < 0.05). In the overall gilt population, there was a negative correlation between the PNPLA3 mRNA abundance in extraparenchymal tissue and the percentage of parenchymal tissue fat (r = −0.30, P < 0.05) and a positive correlation with the percentage of protein (r = 0.32, P < 0.05). The PRL mRNA abundance in the extraparenchyma positively correlated with percent parenchymal DM (r = 0.42, P < 0.01) and percent fat (r = 0.37, P < 0.05). A negative correlation was observed between PRL mRNA abundance and the percent protein in parenchymal tissue (r = −0.31, P < 0.05). Maintaining different backfat thicknesses from mating to the end of gestation affected PNPLA3, PRL, and PRLR-LF gene expression in mammary extraparenchymal tissue. Correlation analyses show a relationship between PNPAL3 and PRL gene expression in extraparenchymal tissue and mammary gland composition at the end of gestation.
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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) | 0.002 | 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".