Glutamate ingestion and its effects at rest and during exercise in humans
Bibliographic record
Abstract
Glutamate is central to several transamination reactions that affect the production of ammonia, alanine, glutamine, as well as TCA cycle intermediates during exercise. To further study glutamate metabolism, we administered 150 mg/kg body wt of monosodium glutamate (MSG) and placebo to seven male subjects who then either rested or exercised (15-min cycling at approximately 85% maximal oxygen consumption). MSG ingestion resulted in elevated plasma glutamate, aspartate, and taurine, both at rest and during exercise (P < 0.05), whereas most other amino acids were unchanged. Neither plasma alanine nor ammonia was altered at rest. During exercise and after glutamate ingestion, alanine was increased (P < 0.05) and ammonia was attenuated (P < 0.05). Glutamine was also elevated after glutamate ingestion during rest and exercise trials. MSG administration also resulted in elevated insulin levels (P < 0.05), which were parallel to the trend in C-peptide levels. Thus MSG can successfully elevate plasma glutamate, both at rest and during exercise. The plasma amino acid responses suggest that increased glutamate availability during exercise alters its distribution in transamination reactions within active muscle, which results in elevated alanine and decreased ammonia levels.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".