Mechanisms of Action of Antiseizure Drugs and the Ketogenic Diet
Bibliographic record
Abstract
Antiseizure drugs (ASDs), also termed antiepileptic drugs, are the main form of symptomatic treatment for people with epilepsy, but not all patients become free of seizures. The ketogenic diet is one treatment option for drug-resistant patients. Both types of therapy exert their clinical effects through interactions with one or more of a diverse set of molecular targets in the brain. ASDs act by modulation of voltage-gated ion channels, including sodium, calcium, and potassium channels; by enhancement of γ-aminobutyric acid (GABA)-mediated inhibition through effects on GABAA receptors, the GABA transporter 1 (GAT1) GABA uptake transporter, or GABA transaminase; through interactions with elements of the synaptic release machinery, including synaptic vesicle 2A (SV2A) and α2δ; or by blockade of ionotropic glutamate receptors, including α-amino-3-hydroxy-5-methyl-4-isoxazole-propionate (AMPA) receptors. The ketogenic diet leads to increases in circulating ketones, which may contribute to the efficacy in treating pharmacoresistant seizures. Production in the brain of inhibitory mediators, such as adenosine, or ion channel modulators, such as polyunsaturated fatty acids, may also play a role. Metabolic effects, including diversion from glycolysis, are a further postulated mechanism. For some ASDs and the ketogenic diet, effects on multiple targets may contribute to activity. Better understanding of the ketogenic diet will inform the development of improved drug therapies to treat refractory seizures.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".