Modulation of airway inflammation and resistance in mice by a nicotinic receptor agonist
Bibliographic record
Abstract
Nicotinic agonists, including 1,1-dimethyl-4-phenylpiperazinium (DMPP), have anti-inflammatory properties and in some instances smooth muscle relaxing effects. Since inflammation and airway smooth muscle contraction are two major components of asthma, the present authors investigated the effects of DMPP on airway inflammation and airway resistance in a mouse model of asthma. Mice were sensitised and challenged with ovalbumin (OVA) and treated either intraperitoneally or intranasally with DMPP. The effect of DMPP was tested on airway inflammation, airway resistance and on the increase of intracellular calcium in bronchial smooth muscle cells. DMPP given either during sensitisation, OVA challenges or throughout the protocol prevented lung inflammation and decreased the serum level of OVA specific immunoglobulin E. DMPP administration reduced the number of total cells, lymphocytes and eosinophils in the bronchoalveolar lavage (BAL) fluid. Intranasal DMPP administration was as effective as dexamethasone (DEXA) in reducing total cell count and eosinophil counts in BAL fluid. DMPP, but not DEXA, reduced tissue inflammation. Intranasal DMPP, given 10 min before the test, reduced airway responsiveness to metacholine. DMPP also reduced the increase in intracellular calcium in response to bradykinin. In conclusion, these results show that 1,1-dimethyl-4-phenylpiperazinium reduces lung inflammation and prevents airway hyperresponsiveness in the mouse model of asthma.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".