Acute Tyrosine Depletion and Alcohol Ingestion in Healthy Women
Bibliographic record
Abstract
BACKGROUND: Recently we reported that, in vervet monkeys, ingestion of an amino acid mixture deficient in the catecholamine precursors, phenylalanine and tyrosine, produced a decrease in alcohol self-administration. We now report the results of a similar study in humans. METHODS: Three groups of healthy female social drinkers were administered a nutritionally balanced amino acid mixture (B, n = 13), a mixture deficient in the serotonin precursor, tryptophan (Trp-free, n = 14), or a phenylalanine/tyrosine deficient mixture (Phe/Tyr-free, n = 12). Six hours after administration of the amino acid mixture, alcohol ingestion was measured during a free-choice "Taste Test." RESULTS: Compared to the B mixture, Phe/Tyr-free, but not Trp-free, significantly decreased the ingestion of alcohol [p < 0.02]. Neither Phe/Tyr-free nor Trp-free significantly decreased orange juice ingestion or the self-reported "Liking" of either substance. Some subjects experienced transient nausea and/or regurgitated the amino acid mixtures, but excluding these subjects did not change the results. CONCLUSIONS: The results suggest that (a) Phe/Tyr-free may be a suitable method for investigating the role of catecholamines in the self-administration and subjective effects of alcohol, (b) acutely decreased catecholamine neurotransmission might disrupt mechanisms mediating alcohol self-administration, and (c) acutely decreased serotonin neurotransmission seems not to alter alcohol self-administration.
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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 | 0.001 | 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) | 0.002 | 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".