Ketogenic diet protects myelin and axons in diffuse axonal injury
Bibliographic record
Abstract
Background Ketogenic diet (KD) has been identified as a potential therapy to enhance recovery after traumatic brain injury (TBI). Diffuse axonal injury (DAI) is a common type of traumatic brain injury that is characterized by delayed axonal disconnection. Previous studies showed that demyelination resulting from oligodendrocyte damage contributes to axonal degeneration in DAI.Aim The present study tests a hypothesis that ketone bodies from the ketogenic diet confers protection for myelin and attenuates degeneration of demyelinated axon in DAI.Methods A modified Marmarou’s model of DAI was induced in adult rats. The DAI rats were fed with KD and analyzed with western blot, transmission electron microscope, ELISA test and immunohistochemistry. Meanwhile, a co-culture of primary oligodendrocytes and neurons was treated with ketone body β-hydroxybutryate (βHB) to test for its effects on the myelin-axon unit.Results Here we report that rats fed with KD showed an increased fatty acid metabolism and ketonemia. This dietary intervention significantly reduced demyelination and attenuated axonal damage in rats following DAI, likely through inhibition of DAI-induced excessive mitochondrial fission and promoting mitochondrial fusion. In an in vitro model of myelination, the ketone body βHB increased myelination significantly and reduced axonal degeneration induced by glucose deprivation (GD). βHB robustly increased cell viability, inhibited GD-induced collapse of mitochondrial membrane potential and attenuated death of oligodendrocytes.Conclusion Ketone bodies protect myelin-forming oligodendrocytes and reduce axonal damage. Ketogenic diet maybe a promising therapy for DAI.
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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 | 0.000 | 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) | 0.001 | 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".