Neurochemical effects of the monoamine oxidase inhibitor phenelzine on brain GABA and alanine: A comparison with vigabatrin
Bibliographic record
Abstract
PURPOSE: To compare phenelzine (PLZ), an antidepressant drug with anxiolytic properties which inhibits monoamine oxidase (MAO) but also elevates rat brain levels of the amino acids ?-aminobutyric acid (GABA) and alanine (ALA), with vigabatrin (VIG), an anticonvulsant which elevates brain GABA by inhibition of GABA transaminase (GABA-T), with regard to their actions on brain levels of GABA and ALA and on activities of MAO, GABA-T and ALA transaminase (ALA-T). METHODS: Male rats were administered PLZ (10 mg/kg) or VIG (1,000 mg/kg) i.p., and the rats were euthanized 4 hours later and the brains removed for analysis of levels of GABA and ALA (by electron capture gas chromatography after derivatization) and activities of MAO, GABA-T and ALA-T (radiochemical assays). RESULTS: Both PLZ and VIG inhibited GABA-T and elevated GABA levels. Only PLZ inhibited MAO and ALA-T and elevated ALA levels. The effects of PLZ on both amino acids and their transaminases were blocked by pre-treatment with the MAO inhibitor tranylcypromine. This pretreament had no effect on the inhibition of GABA-T or the elevation of brain GABA levels produced by VIG. CONCLUSIONS: At the doses studied, PLZ was as effective as VIG at elevating brain GABA levels, but, unlike VIG, also inhibited MAO and ALA-T (and increased brain ALA levels). Pretreatment of rats with the MAO inhibitor tranylcypromine prevented the increase in brain GABA and ALA levels with PLZ, but did not block the effect of VIG on GABA. These observations with tranylcypromine and PLZ support the hypothesis that an active metabolite of PLZ produced by the actions of MAO on this drug plays a major role in its GABA- and ALA-elevating actions.
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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 | 0.000 |
| 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".