Ketogenic diet for infantile epileptic spasms
Bibliographic record
Abstract
Approximately half of all cases of Infantile Epileptic Spasms Syndrome (IESS) do not respond to vigabatrin and hormonal therapies. There is no clear consensus as to the second-line therapy for IESS. Ketogenic diet (KD) has emerged as an effective treatment for certain drug-resistant epilepsies and in many cases of IESS. Understanding the mechanism of action of the KD in IESS will allow for harnessing the power of the KD and discovering novel therapeutics for IESS. In this review, we will summarize the current state of knowledge of the action of the KD in IESS derived from animal models. We emphasize the importance of the KD in altering respiration to cause brain acidosis. In addition, we review recent data implicating altered gut microbiome and the tryptophan-serotonin-kynurenine pathway in KD animals with infantile epileptic spasms syndrome. PLAIN LANGUAGE SUMMARY: Infantile Epileptic Spasms Syndrome is a serious seizure condition in babies, often resistant to standard drugs, failing in half of cases. Animal studies helped unravel multiple mechanisms through which a high-fat, low-carb ketogenic diet can control seizures, including altering gut bacteria, reducing inflammation, balancing brain chemicals, boosting mitochondrial function, or adjusting breathing to slightly acidify the brain. These findings could lead to new, targeted therapies that are simpler to use and more accessible for families facing this challenging condition.
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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 | 0.001 |
| 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) | 0.002 | 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".