Preventive and therapeutic treatment with a7 nicotinic receptor agonist reduced lung inflammation and collagen deposition in mice with acute lung injury
Bibliographic record
Abstract
Although cholinergic activity is involved in airway tone control, particularly by stimulation of muscarinic receptors, there is evidence that cholinergic anti-inflammatory pathway modulates immune systemic inflammatory responses in different models. Aim: We evaluated whether a7-nicotinic acetylcholine receptor (a7nAChR)stimulation reverted lung inflammation observed in an experimental model of acute lung injury (ALI). Methods: Male C57/BL6 mice received PNU-282987 (specificα7nAChRagonist) 30 min before (10 mg/kg, ip) or 6 hours after (2.5; 5 or 10 mg/kg) LPS administration (5 mg/kg, intratraqueal). We evaluated pulmonary mechanics, bronchoalveolar lavage fluid (BALF), collagen fibers and activation of NF-kBby immunohistochemistry and by Western Blotafter 24 hours from LPS. Results: After six hours, LPS treated animals presented increase in neutrophils and macrophages in BALF. Both pre and pos-treatment with PNU (10mg/Kg) reduced the total cells, macrophagesand neutrophilsin BALF (p<0.001 for all comparisons) as well as the NF-kB protein expression and positive cells (p<0.05) compared to LPS animals treated with vehicle. In addition, the PNU pretreatment attenuated the volume proportion of collagen fibers in lung tissue induced by LPS instillation (p<0.01) Conclusion: We concluded that a7 nAChRstimulation prevents and revertslung inflammation in a model of ALI probably by controlling NF-kB activation. Our data reinforced the idea that a7 nAChRis an important pathway to be better explored in the treatment of acute respiratory distress syndrome.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".