Glycolytic inhibition: A novel approach toward controlling neuronal excitability and seizures
Bibliographic record
Abstract
Conventional antiseizure medications reduce neuronal excitability through effects on ion channels or synaptic function. In recent years, it has become clear that metabolic factors also play a crucial role in the modulation of neuronal excitability. Indeed, metabolic regulation of neuronal excitability is pivotal in seizure pathogenesis and control. The clinical effectiveness of a variety of metabolism-based diets, especially for children with medication-refractory epilepsy, underscores the applicability of metabolic approaches to the control of seizures and epilepsy. Such diets include the ketogenic diet, the modified Atkins diet, and the low-glycemic index treatment (among others). A promising avenue to alter cellular metabolism, and hence excitability, is by partial inhibition of glycolysis, which has been shown to reduce seizure susceptibility in a variety of animal models as well as in cellular systems in vitro. One such glycolytic inhibitor, 2-deoxy-d-glucose (2DG), increases seizure threshold in vivo and reduces interictal and ictal epileptiform discharges in hippocampal slices. Here, we review the role of glucose metabolism and glycolysis on neuronal excitability, with specific reference to 2DG, and discuss the potential use of 2DG and similar agents in the clinical arena for seizure management.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".