Impact of arginine supplementation on serum prolactin and mRNA abundance of amino acid transporter genes in mammary tissue of lactating sows
Bibliographic record
Abstract
This study was conducted to test the hypothesis that supplemental dietary Arg to late-pregnant and lactating sows increases serum prolactin concentrations and mRNA abundance of SLC7A1, SLC7A2, and SLC6A14 in mammary parenchymal tissue. From day 108 of gestation and until day 21 of lactation, sows were fed a diet either supplemented with 0.10 g of l-Arg/kg body weight (BW) per day (n = 10, ARG) or 0.34 g of l-Glu/kg BW per day (n = 10, control). Litters were standardized to 10 piglets on day 1 of lactation and piglets were weighed on days 1, 7, 14, and 21 of lactation. Sow BW was recorded on day 108 of gestation and days 1, 10, and 21 of lactation. Lactation sow feed intake was recorded daily. Mammary parenchymal tissue was biopsied on day 5 of lactation to measure mRNA abundance SLC7A1, SLC7A2, and SLC6A14. On days 4 and 18 of lactation, blood samples were collected from sows at 2, 4, and 6 hr postfeeding to measure serum prolactin concentrations. Milk samples were collected on days 4, 10, and 18 of lactation to measure fat, lactose, urea N, and true protein concentrations. Sow BW, backfat, and feed intake over all sampling days did not differ between treatments. Piglet BW on d 1 tended to be greater for the ARG treatment than the control treatment (P = 0.12). Sow milk yield and composition (fat, protein, lactose, and urea N) and mammary mRNA abundance of candidate genes did not differ between the ARG and the control group. Compared to controls, serum prolactin concentrations tended to be greater (P = 0.08) in ARG sows on day 4 of lactation, and did not differ on day 18. Current findings show a potential beneficial effect of dietary supplementation with Arg to late-pregnant multiparous sows on BW of their piglets on day 1. Dietary Arg supplementation at a rate of 0.10 g/kg BW during late pregnancy and lactation tended to increase serum prolactin concentrations with no increase in mammary transcript abundance of SLC7A1, SLC7A2, and SLC6A14 in early lactation.
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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 | 0.001 |
| 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".