Essential Role of Excessive Tryptophan and its Neurometabolites in Fatigue
Bibliographic record
Abstract
PURPOSE: Serotonin, a neurotransmitter synthesized from tryptophan, has been proposed to play a key role in central fatigue. In this study, we examined whether tryptophan itself and/or its two metabolites, kyneurenic acid (KYNA) and quinolinic acid (QUIN), are involved in central fatigue. MATERIALS AND METHODS: Experiments were conducted using Sprague-Dawley rats (SDR) and Nagase analbuminemic rats (NAR). Central fatigue was assessed by treadmill running and a Morris water maze test. Microdialysis was used to collect samples for measurement of extracellular concentration of tryptophan, serotonin and 5-hydroxyindoleacetic acid (5-HIAA) and to infuse test agents. To examine the kinetics of release, synaptosomes in the striatum were prepared in vitro to measure intra- and extrasynaptosomal concentration of tryptophan, serotonin and 5-HIAA. RESULTS: The concentration of tryptophan secreted into the extracellular space of the striatum was higher during fatigue only, and quickly returned to basal levels with recovery from fatigue. Running time to exhaustion was reduced by activation of tryptophan receptors. Time to exhaustion was shorter in NAR, which maintain a higher extracellular level of striatum tryptophan than SDR. Impaired memory performance in a water maze task after tryptophan treatment was attributable to high levels of KYNA and QUIN in the hippocampus acting synergistically on N-methyl-D-aspartic acid receptors. When branched-chain amino acids were administered, tryptophan transport to the extracellular space of the striatum was drastically inhibited. CONCLUSION: Our findings demonstrate that the increase in fatigue which occurs because of excessively elevated brain tryptophan can be further amplified by the use of synthetic KYNA and QUIN.
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) | 0.000 | 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 | 0.000 |
| 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".