Therapeutic Effects of Glutamic Acid in Piglets Challenged with Deoxynivalenol
Bibliographic record
Abstract
The mycotoxin deoxynivalenol (DON), one of the most common food contaminants, primarily targets the gastrointestinal tract to affect animal and human health. This study was conducted to examine the protective function of glutamic acid on intestinal injury and oxidative stress caused by DON in piglets. Twenty-eight piglets were assigned randomly into 4 dietary treatments (7 pigs/treatment): 1) uncontaminated control diet (NC), 2) NC+DON at 4 mg/kg (DON), 3) NC+2% glutamic acid (GLU), and 4) NC+2% glutamic acid + DON at 4 mg/kg (DG). At day 15, 30 and 37, blood samples were collected to determine serum concentrations of CAT (catalase), T-AOC (total antioxidant capacity), H2O2 (hydrogen peroxide), NO (nitric oxide), MDA (maleic dialdehyde), DAO (diamine oxidase) and D-lactate. Intestinal morphology, and the activation of Akt/mTOR/4EBP1 signal pathway, as well as the concentrations of H2O2, MDA, and DAO in kidney, liver and small intestine, were analyzed at day 37. Results showed that DON significantly (P<0.05) induced oxidative stress in piglets, while this stress was remarkably reduced with glutamic acid supplementation according to the change of oxidative parameters in blood and tissues. Meanwhile, DON caused obvious intestinal injury from microscopic observations and permeability indicators, which was alleviated by glutamic acid supplementation. Moreover, the inhibition of DON on Akt/mTOR/4EBP1 signal pathway was reduced by glutamic acid supplementation. Collectively, these data suggest that glutamic acid may be a useful nutritional regulator for DON-induced damage manifested as oxidative stress, intestinal injury and signaling inhibition.
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) | 0.001 | 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 | 0.001 | 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".