Endogenous β-Adrenergic Receptors Inhibit Lipopolysaccharide-Induced Pulmonary Cytokine Release and Coagulation
Bibliographic record
Abstract
Beta2-adrenergic receptors are expressed on different cell types in the lung, including respiratory epithelial cells, smooth muscle cells, and macrophages. The aim of the current study was to determine the role of beta-adrenergic receptors in the regulation of lung inflammation induced by instillation via the airways of lipopolysaccharide (LPS) (a constituent of the gram-negative bacterial cell wall) or lipoteichoic acid (LTA) (a component of the gram-positive bacterial cell wall). Mice inhaled the beta-adrenergic antagonist propranolol or saline 30 minutes before and 3 hours after intranasal LPS or LTA administration. LPS and LTA induced a profound inflammatory response in the lungs as reflected by an influx of neutrophils and the release of proinflammatory cytokines and chemokines into bronchoalveolar lavage fluid (BALF). Propranolol inhalation resulted in enhanced LPS-induced lung inflammation, which was reflected by a stronger secretion of TNF-alpha, IL-6, and monocyte chemoattractant protein-1 into BALF and by enhanced coagulation activation (thrombin-antithrombin complexes). In LTA-induced lung inflammation, propranolol did not influence cytokine release but potentiated activation of coagulation. Propranolol did not alter neutrophil recruitment in either model. This study suggests that beta-adrenergic receptors, which are widely expressed in the lungs, serve as negative regulators of pulmonary cytokine release and coagulation induced by LPS and less so during LTA-induced pulmonary inflammation.
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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) | 0.000 | 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) | 0.001 | 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 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".