β<sub>2</sub>-Agonists block tussive responses in guinea pigs<i>via</i>an atypical cAMP-dependent pathway
Bibliographic record
Abstract
beta(2)-Adrenoceptor agonists are the most effective bronchodilators currently available, and are used for symptom management in asthmatics. However, whether beta(2)-agonists are also antitussive is controversial. Identifying an antitussive role for beta(2)-agonists and dissecting the possible mechanism of action may help to explain the inconsistencies in the clinical literature and lead to the development of novel therapeutic agents. The aim of the present study was to determine whether or not beta(2)-agonists attenuate the tussive response in guinea pig and human models, and, if so, to identify the mechanism(s) involved. Depolarisation of vagal sensory nerves (human and guinea pig) was assessed as an indicator of sensory nerve activity. Cough was measured in a conscious guinea pig model. A beta(2)-agonist, terbutaline, dose-dependently inhibited the cough response to tussive agents in conscious guinea pigs. Terbutaline and another beta(2)-agonist, fenoterol, blocked sensory nerve activation in vitro. Using these mechanistic models, it was established that beta(2)-agonists suppress the tussive response via a nonclassical cyclic adenosine monosphosphate-dependent pathway that involves the activation of protein kinase G and, subsequently, the opening of large-conductance calcium-activated potassium channels. In conclusion, beta(2)-adrenoceptor agonists are antitussive, and this property occurs due to a direct inhibition of sensory nerve activation. These findings may help to explain the confusion that exists in the clinical literature, and could be exploited to identify novel therapies for the treatment of cough, which is a significant unmet medical need.
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 | 0.000 |
| 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) | 0.002 | 0.001 |
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".