A robust protocol for regional evaluation of methacholine challenge in mouse models of allergic asthma using hyperpolarized <sup>3</sup>He MRI
Bibliographic record
Abstract
Hyperpolarized (HP) (3)He magnetic resonance imaging has been recently used to produce high-resolution images of pulmonary ventilation after methacholine (MCh) challenge in mouse models of allergic inflammation. This capability presents an opportunity to gain new insights about these models and to more sensitively evaluate new drug treatments in the pre-clinical setting. In the current study, we present our initial experience using two-dimensional (2D), time-resolved (3)He MRI of MCh challenge-induced airways hyperreactivity (AHR) to compare ovalbumin-sensitized and challenged (N = 8) mice to controls (N = 8). Imaging demonstrated that ovalbumin-sensitized and challenged animals exhibited many large ventilation defects even prior to MCh challenge (four out of eight) compared to no defects in the control animals. Additionally, the ovalbumin-sensitized and challenged animals experienced a greater number of ventilation defects (4.5 +/- 0.4) following MCh infusion than did controls (3.3 +/- 0.6). However, due to variability in MCh delivery that was specific to the small animal MRI environment, the difference in mean defect number was not statistically significant. These findings are reviewed in detail and a comprehensive solution to the variability problem is presented that has greatly enhanced the magnitude and reproducibility of the MCh response. This has permitted us to develop a new imaging protocol consisting of a baseline 3D image, a time-resolved 2D series during MCh challenge, and a post-MCh 3D image that reveals persistent ventilation defects.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".