MEK modulates force-fluctuation-induced relengthening of canine tracheal smooth muscle
Bibliographic record
Abstract
Tidal breathing, and especially deep breathing, is known to antagonise bronchoconstriction caused by airway smooth muscle (ASM) contraction; however, this bronchoprotective effect of breathing is impaired in asthma. Force fluctuations applied to contracted ASM in vitro cause it to relengthen, force-fluctuation-induced relengthening (FFIR). Given that breathing generates similar force fluctuations in ASM, FFIR represents a likely mechanism by which breathing antagonises bronchoconstriction. Thus it is of considerable interest to understand what modulates FFIR, and how ASM might be manipulated to exploit this phenomenon. It was demonstrated previously that p38 mitogen-activated protein kinase (MAPK) signalling regulates FFIR in ASM strips. Here, it was hypothesised that the MAPK kinase (MEK) signalling pathway also modulates FFIR. In order to test this hypothesis, changes in FFIR were measured in ASM treated with the MEK inhibitor, U0126 (1,4-diamino-2,3-dicyano-1,4-bis[2-aminophenylthio]butadiene). Increasing concentrations of U0126 caused greater FFIR. U0126 reduced extracellular signal-regulated kinase 1/2 phosphorylation without affecting isotonic shortening or 20-kDa myosin light chain and p38 MAPK phosphorylation. However, increasing concentrations of U0126 progressively blunted phosphorylation of high-molecular-weight caldesmon (h-caldesmon), a downstream target of MEK. Thus changes in FFIR exhibited significant negative correlation with h-caldesmon phosphorylation. The present data demonstrate that FFIR is regulated through MEK signalling, and suggest that the role of MEK is mediated, in part, through caldesmon.
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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 | 0.000 |
| 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".