GABA, ?-hydroxybutyric acid, and neurological disease
Bibliographic record
Abstract
gamma-Aminobutyric acid (GABA) is the primary inhibitory neurotransmitter in the central nervous system. GABA is converted from glutamic acid by the action of glutamic acid decarboxylase (GAD) of which two isoforms exist GAD65 and GAD67. GABA then is broken down, both within the cell and in the synaptic cleft by GABA transaminase to form succinic semialdehyde. In turn, succinic semialdehyde is converted either to succinic acid by succinic semialdehyde dehydrogenase or into gamma-hydroxybutyric acid (GHB) by succinic semialdehyde reductase. Because GABA modulates the majority of inhibition that is ongoing in the brain, perturbations in GABAergic inhibition have the potential to result in seizures. Therefore, the most common disorder in which GABA is targeted as a treatment is epilepsy. However, other disorders such as psychiatric disease, spasticity, and stiff-person syndrome all have been related to disorders of GABAergic function in the brain. This review covers the roles of GABAergic neurotransmission in epilepsy, anxiety disorders, schizophrenia, stiff-person syndrome, and premenstrual dysphoric disorder. In the final section of this review, the GABA metabolite GHB is discussed in terms of its physiological significance and its role in epilepsy, sleep disorders, drug and alcohol addiction, and an inborn error of GABA metabolism, succinic semialdehyde dehydrogenase deficiency.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".