Field dependence of <i>T</i><sub>1</sub> for hyperpolarized [1‐<sup>13</sup>C]pyruvate
Bibliographic record
Abstract
In vivo metabolism of hyperpolarized pyruvate has been demonstrated to be an important probe of cellular glycolysis in diseases such as cancer. The usefulness of hyperpolarized (13)C imaging is dependent on the relaxation rates of the (13)C-enriched substrates, which in turn depend on chemical conformation and properties of the dissolution media such as buffer composition, solution pH, temperature and magnetic field. We have measured the magnetic field dependence of the spin-lattice relaxation time of hyperpolarized [1-(13)C]pyruvate using field-cycled relaxometry. [1-(13)C]pyruvate was hyperpolarized using dynamic nuclear polarization and then rapidly thawed and dissolved in a buffered solution to a concentration of 80 mmol l(-1) and a pH of ~7.8. The hyperpolarized liquid was transferred within 8 s to a fast field-cycling relaxometer with a probe tuned for detection of (13)C at a field strength of ~0.75 T. The magnetic field of the relaxometer was rapidly varied between relaxation and acquisition fields where the sample magnetization was periodically measured using a small flip angle. Data were recorded for relaxation fields varying between 0.237 mT and 0.705 T to map the T(1) dispersion of the C-1 of pyruvate. Using similar methods, we also determined the relaxivity of the triarylmethyl radical (OX063; used for dynamic nuclear polarization) on the C-1 of pyruvate at field strengths of 0.001, 0.01, 0.1 and 0.5 T using 0.075, 1.0 and 2.0 mmol l(-1) concentrations of OX063 in the hyperpolarized pyruvate solution.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".