Correlation of hyperpolarized <sup>13</sup>C‐MRI data with tissue extract measurements
Bibliographic record
Abstract
Hyperpolarized (HP) MRI provides the means to monitor lactate metabolism noninvasively in tumours. Since ‐lactate signal levels obtained from HP imaging depend on multiple factors, such as the rate of substrate delivery via the vasculature, the expression level of monocarboxylate transporters (MCTs) and lactate dehydrogenase (LDH), and the local lactate pool size, the interpretation of HP metabolic images remains challenging. In this study, ex vivo tissue extract measurements (i.e., NMR isotopomer analysis, western blot analysis) derived from an MDA‐MB‐231 xenograft model in nude rats were used to test for correlations between the in vivo data and the ex vivo measures. The lactate‐to‐pyruvate ratio from HP MRI was strongly correlated with [1‐ ]lactate concentration measured from the extracts using NMR (R = 0.69, p 0.05), as well as negatively correlated with tumour wet weight (R = 0.60, p 0.05). In this tumour model, both MCT1 and MCT4 expressions were positively correlated with wet weight ( = 0.78 and 0.93, respectively, p 0.01). Lactate pool size and the lactate‐to‐pyruvate ratio were not significantly correlated.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".