Regional ventilation mapping of the rat lung using hyperpolarized <sup>129</sup>Xe magnetic resonance imaging
Bibliographic record
Abstract
Lung ventilation was mapped in seven healthy male Sprague-Dawley rats (433 ± 24 g) using hyperpolarized ¹²⁹Xe magnetic resonance imaging (MRI) at 3.0 T, and validated with hyperpolarized ³He MRI under similar ventilator conditions. Ventilation maps were obtained using flip angle variation for offset of RF and relaxation (FAVOR) which is a multiple breath imaging technique that extracts the fractional ventilation parameter, r, on a pixel-by-pixel basis from the dynamic signal enhancement. r is defined as the fractional refreshment of gas per breath. Under the ventilator conditions used in this work, whole-lung measurements of fractional ventilation obtained using hyperpolarized ¹²⁹Xe were not significantly different from those obtained using hyperpolarized ³He (p = 0.8125 by a Wilcoxon matched pairs test). Fractional ventilation gradients calculated in the superior/inferior (S/I) and anterior/posterior (A/P) directions obtained using hyperpolarized ¹²⁹Xe were not significantly different from those obtained using hyperpolarized ³He (p = 0.9375 and p = 0.1563, for the S/I and A/P directions, respectively). Following baseline fractional ventilation measurements, one representative rat was challenged with methacholine and fractional ventilation measurements were performed over a time course of 10 min. A reduction and subsequent recovery in whole-lung r values were detected using the FAVOR method.
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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) | 0.000 | 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 | 0.000 |
| 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".