Neuroprotective effects of a ketogenic diet in combination with exogenous ketone salts following acute spinal cord injury
Bibliographic record
Abstract
We have previously shown that induction of ketosis by ketogenic diet (KD) conveyed neuroprotection following spinal cord injury in rodent models, however, clinical translation may be limited by the slow raise of ketone levels when applying KD in the acute post-injury period. Thus we investigated the use of exogenous ketone supplementation (ketone sodium, KS) combined with ketogenic diet as a means rapidly inducing a metabolic state of ketosis following spinal cord injury in adult rats. In uninjured rats, ketone levels increased more rapidly than those in rats with KD alone and peaked at higher levels than we previously demonstrated for the KD in models of spinal cord injury. However, ketone levels in KD + KS treated rats with SCI did not exceed the previously observed levels in rats treated with KD alone. We still demonstrated neuroprotective effects of KD + KS treatment that extend our previous neuroprotective observations with KD only. The results showed increased neuronal and axonal sparing in the dorsal corticospinal tract. Also, better performance of forelimb motor abilities were observed on the Montoya staircase (for testing food pellets reaching) at 4 and 6 weeks post-injury and rearing in a cylinder (for testing forelimb usage) at 6 and 8 weeks post-injury. Taken together, the findings of this study add to the growing body of work demonstrating the potential benefits of inducing ketosis following neurotrauma. Ketone salt combined with a ketogenic diet gavage in rats with acute spinal cord injury can rapidly increase ketone body levels in the blood and promote motor function recovery. This study was approved by the Animal Care Committee of the University of British Columbia (protocol No. A14-350) on August 31, 2015.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 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".