Measurement of xenon diffusing capacity in the rat lung by hyperpolarized <sup>129</sup>Xe MRI and dynamic spectroscopy in a single breath‐hold
Bibliographic record
Abstract
We used the dual capability of hyperpolarized 129Xe for spectroscopy and imaging to develop new measures of xenon diffusing capacity in the rat lung that (analogously to the diffusing capacity of carbon monoxide or DLCO) are calculated as a product of total lung volume and gas transfer rate constants divided by the pressure gradient. Under conditions of known constant pressure breath-hold, the volume is measured by hyperpolarized 129Xe MRI, and the transfer rate is measured by dynamic spectroscopy. The new quantities (xenon diffusing capacity in lung parenchyma (DLXeLP)), xenon diffusing capacity in RBCs (DLXeRBC), and total lung xenon diffusing capacity (DLXe)) were measured in six normal rats and six rats with lung inflammation induced by instillation of fungal spores of Stachybotrys chartarum. DLXeLP, DLXeRBC, and DLXe were 56 +/- 10 ml/min/mmHg, 64 +/- 35 ml/min/mmHg, and 29 +/- 9 ml/min/mmHg, respectively, for normal rats, and 27 +/- 9 ml/min/mmHg, 42 +/- 27 ml/min/mmHg, and 16 +/- 7 ml/min/mmHg, respectively, for diseased rats. Lung volumes and gas transfer times for LP (TtrLP) were 16 +/- 2 ml and 22 +/- 3 ms, respectively, for normal rats and 12 +/- 2 ml and 35 +/- 8 ms, respectively, for diseased rats. Xenon diffusing capacities may be useful for measuring changes in gas exchange associated with inflammation and other lung diseases.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".