<scp>d</scp>-Aspartate in Low-Protein Diets Improves the Pork Quality by Regulating Energy and Lipid Metabolism via the Gut Microbes
Bibliographic record
Abstract
d -Aspartate is critical in maintaining hormone secretion and reproductive development in mammals. This study investigated the mechanism of different d -aspartate levels (0, 0.005, 0.05, and 0.5% d -aspartate) in low-protein diets on growth performance and meat quality by mediating the gut microbiota alteration in pigs. We found that adding 0.005% d -aspartate to a low-protein diet could dramatically improve the growth performance during the weaned and growing periods. Dietary d -aspartate with different levels markedly increased the back fat, and 0.5% d -aspartate significantly increased the redness in 24 h and reduced the shear force of the longissimus dorsi (LD) muscle. Moreover, d -aspartate treatments decreased the mRNA expression of MyHC II a and MyHC IIx in the LD muscle. The protein expression of MyH1, MyH7, TFAM, FOXO1, CAR, UCP2, and p-AMPK was upregulated by 0.005% d -aspartate. Additionally, the abundance of Alistipes, Akkermansia, and the [ Eubacterium ]_ coprostanoligenes _group in the intestinal chyme of pigs was significantly decreased by d -aspartate treatments at the genus level, which was also accompanied by a significant decrease in acetate content. These differential microorganisms were significantly correlated with meat quality characteristics. These results indicated that d -aspartate in low-protein diets could improve the growth performance and meat quality in pigs by regulating energy and lipid metabolism via the alteration of gut microbiota.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".