Sci-Fri AM: Imaging - 06: Hyperpolarized <sup>13</sup>C Magnetic Resonance Imaging and Dynamic Spectroscopy for Detection of Radiation-Induced Lung Injury
Bibliographic record
Abstract
Carbon-13 (13C) can be hyperpolarized by Dynamic Nuclear Polarization (DNP) to increase the signal by 10,000 times. By hyperpolarizing 13C in the pyruvate molecule, radiation—induced lung injury (RILI) may be studied with Magnetic Resonance Imaging. In-vivo, pyruvate breaks down into lactate, alanine, bicarbonate and other metabolites in a time-dependent manner. The early stages of RILI lead to higher concentration of lactate in tissues. Therefore the ratio of pyruvate to lactate measured in rat lungs, following irradiation may provide an early indication of RILI. In the present study, dynamic MR spectroscopic techniques are developed to ensure proper quantification of these metabolites. [1-13C] labelled pyruvate (CIL, Cambridge, MA) was hyperpolarized by a commercially-available DNP system (Hypersense, Oxford Instruments). The DNP system utilizes low temperature (1.4 °K), high magnetic field strength (3.35 T) and irradiation with a microwave source (94.15 GHz) to polarize the pyruvate. Data were obtained following bolus injection of 3 ml of 0.8 mM [1-13C] pyruvic acid in the tail vein of healthy Sprague Dawley rats (400–450 g) following an animal care protocol approved by the Animal Use Subcommittee of the University Council on Animal Care at the University of Western Ontario. Pyruvate and lactate signals were identified based on their chemical shift from the obtained in-vivo dynamic spectra. The relative signal strength from these metabolites was quantified. This study demonstrated the capability of quantification of hyperpolarized [1-13C] labeled pyruvate and lactate from in-vivo dynamic spectra in rats which represents an important step toward studies of RILI.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.133 | 0.042 |
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".