Acute Lung Injury in Cholinergic-Deficient Mice Supports Anti-Inflammatory Role of α7 Nicotinic Acetylcholine Receptor
Bibliographic record
Abstract
(1) Background: The lung cholinergic pathway is important for controlling pulmonary inflammation in acute lung injury, a condition that is characterized by a sudden onset and intense inflammation. This study investigated changes in the expression levels of nicotinic and muscarinic acetylcholine receptors (nAChR and mAChR) in the lung during acute lung injury. (2) Methods: acute lung injury (ALI) was induced in wild-type and cholinergic-deficient (VAChT-KDHOM) mice using intratracheal lipopolysaccharide (LPS) instillation with or without concurrent treatment with nicotinic ligands. Bronchoalveolar lavage fluid was collected to evaluate markers of inflammation, and then the lung was removed and processed for isolation of membrane fraction and determination of acetylcholine receptors level using radioligand binding assays. (3) Results: LPS-induced increase in lung inflammatory markers (e.g., neutrophils and IL-1β) was significantly higher in VAChT-KDHOM than wild-type mice. In contrast, LPS treatment resulted in a significant increase in lung’s α7 nicotinic receptor level in wild-type, but not in VAChT-KDHOM mice. However, treatment with PNU 282987, a selective α7 nicotinic receptor agonist, restored VAChT-KDHOM mice’s ability to increase α7 nicotinic receptor levels in response to LPS-induced acute lung injury and reduced lung inflammation. LPS also increased muscarinic receptors level in VAChT-KDHOM mice, and PNU 282987 treatment reduced this response. (4) Conclusions: Our data indicate that the anti-inflammatory effects of the lung cholinergic system involve an increase in the level of α7 nicotinic receptors. Pharmacological agents that increase the expression or the function of lung α7 nicotinic receptors have potential clinical uses for treating acute lung injury.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".