Ketones Prevent Oxidative Impairment of Hippocampal Synaptic Integrity through KATP Channels
Bibliographic record
Abstract
Dietary and metabolic therapies are increasingly being considered for a variety of neurological disorders, based in part on growing evidence for the neuroprotective properties of the ketogenic diet (KD) and ketones. Earlier, we demonstrated that ketones afford hippocampal synaptic protection against exogenous oxidative stress, but the mechanisms underlying these actions remain unclear. Recent studies have shown that ketones may modulate neuronal firing through interactions with ATP-sensitive potassium (KATP) channels. Here, we used a combination of electrophysiological, pharmacological, and biochemical assays to determine whether hippocampal synaptic protection by ketones is a consequence of KATP channel activation. Ketones dose-dependently reversed oxidative impairment of hippocampal synaptic integrity, neuronal viability, and bioenergetic capacity, and this action was mirrored by the KATP channel activator diazoxide. Inhibition of KATP channels reversed ketone-evoked hippocampal protection, and genetic ablation of the inwardly rectifying K+ channel subunit Kir6.2, a critical component of KATP channels, partially negated the synaptic protection afforded by ketones. This partial protection was completely reversed by co-application of the KATP blocker, 5-hydoxydecanoate (5HD). We conclude that, under conditions of oxidative injury, ketones induce synaptic protection in part through activation of KATP channels.
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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) | 0.000 | 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 | 0.001 |
| 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".