Airway and parenchymal tissue resistance and elastance in ex vivo sheep lungs: effects of bronchochallenge and deep inspiration
Bibliographic record
Abstract
Lung resistance ( RL) is determined by airway and parenchymal tissue resistance, as well as the degree of heterogeneity in airway constriction. Deep inspirations (DIs) are known to reverse experimentally induced increase in RL, but the mechanism is not entirely clear. The first step toward understanding the effect of DI is to determine how each of the resistance components is affected by DI. In the present study, we measured RL and apparent airway resistance ( RAW, which combines the effects of airway resistance and airway heterogeneity) simultaneously before and after a DI in acetylcholine (ACh)-challenged ex vivo sheep lungs. We found that at normal breathing frequency (0.25 Hz) ACh-challenge led to a doubling of RL, 80.3% of that increase was caused by an increase in RAW; the increase in apparent tissue resistance ( RT) was insignificant. 57.7% of the increase in RAW was abolished by a single DI. After subtracting RAW from RL, the remaining RT was mostly independent of ACh-challenge and its reduction after a DI came mostly from the change in the mechanical properties of lung parenchyma. We conclude that at normal breathing frequency, RL in an unchallenged lung is mostly composed of RT, and the increase in RL due to ACh-challenge stems mostly from the increase in RAW and that both RAW and RT can be greatly reduced by a DI, likely due to a reduction in true airway resistance and heterogeneity, as well as parenchymal tissue hysteresis post DI.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".