Body condition of late pregnant gilts affects the expression of selected adipokines and their receptors in mammary fat and backfat tissues1
Bibliographic record
Abstract
The impact of body condition in late gestating gilts on gene expression of selected adipokines and their receptors in backfat and mammary fat tissues was studied. The presence of associations between mammary gland composition variables and the mRNA abundance of selected genes and serum concentrations of adiponectin and leptin was also investigated. A total of 45 gilts were selected at mating based on their backfat depth and were allocated to three groups: (1) low backfat (LBF; 12-15 mm; n = 14), (2) medium backfat (MBF; 17-19 mm; n = 15), and (3) high backfat (HBF; 22-26 mm; n = 16). Gilts were fed different amounts of a conventional diet to maintain differences in backfat depth throughout the gestation period. Blood samples were collected at day 109 of gestation to measure adiponectin and leptin serum concentrations. Gilts were slaughtered on day 110 of gestation, and mammary glands were collected to determine mammary composition. Mammary fat and backfat tissues were also sampled to measure the mRNA abundance of selected genes. In mammary fat tissue, there was an effect of body condition on the prolactin (PRL; P = 0.01), adiponutrin (PNPLA3; P < 0.10), and prolactin receptor long form (PRLR-LF; P < 0.10) genes. There was a greater PRL mRNA abundance in mammary fat tissue from HBF than LBF or MBF gilts (P < 0.05). The PNPLA3 mRNA abundance was lower in HBF than in MBF gilts (P < 0.05), and that of PRLR-LF was lower in LBF than in HBF gilts (P < 0.05). In backfat, body condition affected the mRNA abundance of leptin (P < 0.05) and PNPLA3 (P < 0.01), with the greatest expression levels being observed in HBF gilts for both genes. Association analyses suggest a detrimental effect of high circulating leptin concentrations on gilts mammary development, as reflected by the negative correlations between serum leptin and protein percent (r = -0.66, P < 0.01), and concentrations of DNA (r = -0.62, P < 0.01) and RNA (r = -0.60, P < 0.01) in mammary parenchyma. Current results show that body condition of gilts at the end of gestation can affect the expression of adipokines in mammary fat and backfat tissues, with a different regulation of transcript abundance being observed in these two fat depots. Results also suggest that circulating leptin is strongly associated with mammary gland composition of late pregnant gilts, whereas locally synthesized leptin from mammary fat tissue is not.
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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) | 0.002 | 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".