Capsaicin‐induced mucus secretion in rat airways assessed<i>in vivo</i>and non‐invasively by magnetic resonance imaging
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: An up-regulation of the sensory neural pathways in the lung has been implicated in asthma and chronic obstructive pulmonary disease (COPD) and is thought to contribute to mucus hypersecretion, an essential feature of both diseases. The aim of this study was to assess non-invasively the acute effects (up to 60 min) of sensory nerve stimulation by capsaicin in the lung, using magnetic resonance imaging (MRI). EXPERIMENTAL APPROACH: Male Brown Norway rats were imaged prior to and 10, 30 and 60 min after intra-tracheal challenge with capsaicin (30 microg kg(-1)) or vehicle (0.5% ethanol solution). In subsequent studies, pre-treatment with the transient receptor potential vanilloid (TRPV)-1 antagonist, capsazepine; the dual neurokinin (NK) 1 and NK2 receptor antagonist, DNK333 and the mast cell stabilizer, di-sodium cromoglycate (DSCG) was used to modulate the effects of capsaicin. KEY RESULTS: Diffuse fluid signals were detected by MRI in the lung as early as 10 min after capsaicin, remaining constant 30 and 60 min after treatment. Broncho-alveolar lavage (BAL) fluid analysis performed 60 min after capsaicin revealed increased mucin concentration. Capsazepine (3.5 mg kg(-1)), DNK333 (10 mg kg(-1)) but not DSCG (10 mg kg(-1)) administered prophylactically were able to block the effect of capsaicin in the airways. CONCLUSIONS AND IMPLICATIONS: These observations suggest that the fluid signals detected by MRI after capsaicin administration reflected predominantly the release of mucus following activation of sensory nerves. They point to the opportunity of non-invasively assessing with MRI the influence of neuronal mechanisms in animal models of asthma and COPD.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 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) | 0.000 | 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 teacher head, 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".