Chronic <i>in vitro</i> ketosis is neuroprotective but not anti‐convulsant
Bibliographic record
Abstract
The ketogenic diet (KD), used successfully to treat a variety of epilepsy syndromes in humans and to attenuate seizures in different animal models, also provides powerful neuroprotection in various CNS injury models. Yet, a direct role for ketone bodies in limiting seizure and neuronal damage remains poorly understood. Using organotypic hippocampal slice cultures, we established an in vitro model of chronic ketosis for parallel studies of its neuroprotective and anti-convulsant effects. Chronic in vitro treatment with a ketone body, D-beta-hydroxybutyrate, protected the cultures against chronic hypoglycemia, oxygen-glucose deprivation, and NMDA-induced excitotoxicity, but failed to suppress intrinsic and induced seizure-like activity, indicating improved neuroprotection is not directly translated into seizure control. However, chronic in vitro ketosis abolished hippocampal network hyperexcitability following a metabolic insult, hypoxia, demonstrating for the first time a direct link between metabolic resistance and better control of excessive, synchronous, abnormal electrical activity. These findings suggest that the KD and, possibly, exogenous ketone administration, can be more beneficial for the treatment of seizures associated with metabolic stress or underlying metabolic abnormalities, and can potentially be used to optimize clinical applications of the traditional KD or its variants.
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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 | 0.001 |
| 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".