Monosodium <scp>l</scp>-glutamate and dietary fat exert opposite effects on the proximal and distal intestinal health in growing pigs
Bibliographic record
Abstract
The Chinese population has undergone rapid transition to a high-fat diet. Furthermore, monosodium L-glutamate (MSG) is widely used as a flavour enhancer in China. Previous studies have reported that high-fat diet modifies intestinal metabolism and physiology. However, little information is available on the effects of oral MSG on intestine, and no study focus on the interaction of dietary fat and MSG for intestinal health. The aim of the present study was to evaluate the effects of MSG and dietary fat on intestinal health in growing pigs, and to try to identify possible interactions between these 2 nutrients for such effects. A total of 32 growing pigs were used and fed with 4 isonitrogenous and isocaloric diets (basal diet, high-fat diet, basal diet with 3% MSG and high fat diet with 3% MSG). Parameters related to reactive oxygen species metabolism, epithelial morphology, pro-inflammation factors and tight junction protein expression and several species of intestinal microbe were measured. Overall, dietary fat and MSG had detrimental effects on several of the physiological and inflammatory parameters measured in the proximal intestine, while exerting beneficial effects on the distal intestine in growing pigs, with generally antagonistic effects. These results may be of particular relevance for nutritional concerns in patients with intestinal diseases.
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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 | 0.001 |
| 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".