Mild experimental ketosis increases brain uptake of<sup>11</sup>C-acetoacetate and<sup>18</sup>F-fluorodeoxyglucose: a dual-tracer PET imaging study in rats
Bibliographic record
Abstract
Brain glucose and ketone uptake was investigated in Fisher rats subjected to mild experimental ketonemia induced by a ketogenic diet (KD) or by 48 hours fasting (F). Two tracers were used, (11)C-acetoacetate ((11)C-AcAc) for ketones and (18)F-fluorodeoxyglucose for glucose, in a dual-tracer format for each animal. Thus, each animal was its own control, starting first on the normal diet, then undergoing 48 hours F, followed by 2 weeks on the KD. In separate rats on the same diet conditions, expression of the transporters of glucose and ketones (glucose transporter 1 (GLUT1) and monocarboxylic acid transporter (MCT1)) was measured in brain microvessel preparations. Compared to controls, uptake of (11)C-AcAc increased more than 2-fold while on the KD or after 48 hours F (P < 0.05). Similar trends were observed for (18)FDG uptake with a 1.9-2.6 times increase on the KD and F, respectively (P < 0.05). Compared to controls, MCT1 expression increased 2-fold on the KD (P < 0.05) but did not change during F. No significant difference was observed across groups for GLUT1 expression. Significant differences across the three groups were observed for plasma beta-hydroxybutyrate (beta-HB), AcAc, glucose, triglycerides, glycerol, and cholesterol (P < 0.05), but no significant differences were observed for free fatty acids, insulin, or lactate. Although the mechanism by which mild ketonemia increases brain glucose uptake remains unclear, the KD clearly increased both the blood-brain barrier expression of MCT1 and stimulated brain (11)C-AcAc uptake. The present dual-tracer positron emission tomography approach may be particularly interesting in neurodegenerative pathologies such as Alzheimer's disease where brain energy supply appears to decline critically.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".