Force Oscillations Simulating Breathing Maneuvers Do Not Prevent Force Adaptation
Bibliographic record
Abstract
Airway inflammation in patients with asthma exposes the airway smooth muscle (ASM) to a variety of spasmogens. These spasmogens increase ASM tone, which can lead to force adaptation. Length oscillations of ASM, which occur in vivo due to breathing maneuvers, can attenuate force adaptation. However, in the presence of tone, the force oscillations required to achieve these length oscillations may be unphysiologic (i.e., magnitude greater than the ones achieved due to the swings in transpulmonary pressure required for breathing). In the present study, we applied force oscillations simulating the tension oscillations experienced by the wall of a fourth-generation airway during tidal breathing with or without deep inspirations (DI) to ASM. The goal was to investigate whether force adaptation occurs in conditions mimicking breathing maneuvers. Tone was induced by carbachol (average, 20 nM), and the force-generating capacity of the ASM was assessed at 5-minute intervals before and after carbachol administration using electrical field stimulations (EFS). The results show that force oscillations applied before the introduction of tone had a small effect on the force produced by EFS (declined to 96.8% [P > 0.05] and 92.3% [P < 0.05] with and without DI, respectively). The tone induced by carbachol transiently decreased after a DI and declined significantly (P < 0.05) due to tidal breathing oscillations (25%). These force oscillations did not prevent force adaptation (gain of force of 11.2 ± 2.2 versus 13.5 ± 2.7 and 11.2 ± 3.0% in static versus dynamic conditions with or without DI, respectively). The lack of effect of simulated breathing maneuvers on force adaptation suggests that this gain in ASM force may occur in vivo and could contribute to the development of airway hyperresponsiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".