Measuring cerebral enzymatic activity, brain pH and extracranial muscle metabolism with hyperpolarized <sup>13</sup>C‐pyruvate
Bibliographic record
Abstract
Abstract Hyperpolarized carbon‐13 ( 13 C) magnetic resonance imaging (MRI) has shown promise for non‐invasive assessment of the cerebral metabolism of [1‐ 13 C]pyruvate in both healthy volunteers and patients. The exchange of pyruvate to lactate catalysed by lactate dehydrogenase (LDH) and that of pyruvate flux to bicarbonate through pyruvate dehydrogenase (PDH) are the most widely studied reactions in vivo. Here we show the potential of the technique to probe additional enzymatic activity within the brain. Approximately 50 s after intravenous injection of hyperpolarized pyruvate, high‐flip‐angle pulses were used to detect cerebral 13 C‐labelled carbon dioxide ( 13 CO 2 ), in addition to the 13 C‐bicarbonate (H 13 CO 3 − ) subsequently formed by carbonic anhydrase (CA). Brain pH measurements, which were weighted towards the extracellular compartment, were calculated from the ratio of H 13 CO 3 − to 13 CO 2 in seven volunteers using the Henderson–Hasselbalch equation, demonstrating an average pH ± SD of 7.40 ± 0.02, with inter‐observer reproducibility of 0.04. In addition, hyperpolarized [1‐ 13 C]aspartate was also detected, demonstrating irreversible pyruvate carboxylation to oxaloacetate by pyruvate carboxylase (PC) and subsequent transamination by aspartate aminotransferase (AST), with the average flux being on average 11% ± 3% of that through PDH. A hyperpolarized [1‐ 13 C]alanine signal was also detected, but this was localized to extracranial muscle tissue in keeping with skeletal alanine aminotransferase (ALT) activity. The results demonstrate the potential of hyperpolarized 13 C‐MRI to assess cerebral and extracerebral [1‐ 13 C]pyruvate metabolism in addition to LDH and PDH activity. Non‐invasive measurements of brain pH could be particularly important in assessing cerebral pathology given the wide range of disease processes that alter acid–base balance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".