Ketogenic diets, mitochondria, and neurological diseases
Bibliographic record
Abstract
The ketogenic diet (KD) is a broad-spectrum therapy for medically intractable epilepsy and is receiving growing attention as a potential treatment for neurological disorders arising in part from bioenergetic dysregulation. The high-fat/low-carbohydrate "classic KD", as well as dietary variations such as the medium-chain triglyceride diet, the modified Atkins diet, the low-glycemic index treatment, and caloric restriction, enhance cellular metabolic and mitochondrial function. Hence, the broad neuroprotective properties of such therapies may stem from improved cellular metabolism. Data from clinical and preclinical studies indicate that these diets restrict glycolysis and increase fatty acid oxidation, actions which result in ketosis, replenishment of the TCA cycle (i.e., anaplerosis), restoration of neurotransmitter and ion channel function, and enhanced mitochondrial respiration. Further, there is mounting evidence that the KD and its variants can impact key signaling pathways that evolved to sense the energetic state of the cell, and that help maintain cellular homeostasis. These pathways, which include PPARs, AMP-activated kinase, mammalian target of rapamycin, and the sirtuins, have all been recently implicated in the neuroprotective effects of the KD. Further research in this area may lead to future therapeutic strategies aimed at mimicking the pleiotropic neuroprotective effects of the KD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".