Hyperpolarized ketone body metabolism in the rat heart
Bibliographic record
Abstract
The aim of this work was to investigate the use of 13C‐labelled acetoacetate and β‐hydroxybutyrate as novel hyperpolarized substrates in the study of cardiac metabolism. [1‐13C]Acetoacetate was synthesized by catalysed hydrolysis, and both it and [1‐13C]β‐hydroxybutyrate were hyperpolarized by dissolution dynamic nuclear polarization (DNP). Their metabolism was studied in isolated, perfused rat hearts. Hyperpolarized [1‐13C]acetoacetate metabolism was also studied in the in vivo rat heart in the fed and fasted states. Hyperpolarization of [1‐13C]acetoacetate and [1‐13C]β‐hydroxybutyrate provided liquid state polarizations of 8 ± 2% and 3 ± 1%, respectively. The hyperpolarized T1 values for the two substrates were 28 ± 3 s (acetoacetate) and 20 ± 1 s (β‐hydroxybutyrate). Multiple downstream metabolites were observed within the perfused heart, including acetylcarnitine, citrate and glutamate. In the in vivo heart, an increase in acetylcarnitine production from acetoacetate was observed in the fed state, as well as a potential reduction in glutamate. In this work, methods for the generation of hyperpolarized [1‐13C]acetoacetate and [1‐13C]β‐hydroxybutyrate were investigated, and their metabolism was assessed in both isolated, perfused rat hearts and in the in vivo rat heart. These preliminary investigations show that DNP can be used as an effective in vivo probe of ketone body metabolism in the heart.
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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".