Ketone Esters Partially and Selectively Rescue Mitochondrial Bioenergetics After Acute Cervical Spinal Cord Injury in Rats: A Time-Course
Bibliographic record
Abstract
Spinal cord injury (SCI) pathology and pathophysiology can be attributed to both primary physical injury and secondary injury cascades. Secondary injury cascades involve dysregulated metabolism and energetic deficits directly linked to compromised mitochondrial bioenergetics. Rescuing mitochondrial function and reducing oxidative stress are associated with neuroprotection. In this regard, ketosis after traumatic brain injury (TBI), or after SCI, improves secondary neuropathology by decreasing oxidative stress, increasing antioxidants, reducing inflammation, and improving mitochondrial bioenergetics. Here, we follow up on our previous study and have used an exogenous ketone monoester, (R)-3-hydroxybutyl (R)-3-hydroxybutyrate (KE), as an alternative to a ketogenic diet, focusing on mitochondrial function between 1 and 14 days after injury. Starting 3 h following a cervical level 5 (C5) hemi-contusion injury, animals were fed either a standard control diet (SD) or a ketone ester diet (KED) combined with KE administered orally (OKE). We found that mitochondrial function was reduced after SCI at all times post-SCI, accompanied by reduced expression of most of the components of the electron transport chain (ETC). The KE rescued some of the bioenergetic parameters 1 day after SCI when D-β-Hydroxybutyrate (BHB) concentrations were ~2 mM. Still, most of the beneficial effects were observed 14 days after injury, with BHB concentrations reaching values of 4-6 mM. To our knowledge, this is the first report to show the beneficial effects of KE in rescuing mitochondrial function after SCI and demonstrates the suitability of KE in ameliorating the metabolic dysregulation that occurs after traumatic SCI without requiring a restrictive dietary regime.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".