MicroRNA-146a and microRNA-146b expression and anti-inflammatory function in human airway smooth muscle
Why this work is in the frame
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Bibliographic record
Abstract
MicroRNA (miR)-146a and miR-146b are negative regulators of inflammatory gene expression in lung fibroblasts, epithelial cells, monocytes, and endothelial cells. The abundance of cyclooxygenase-2 (COX-2) and IL-1β is negatively regulated by the miR-146 family, suggesting miR-146a and/or miR-146b might modulate inflammatory mediator expression in airway smooth muscle thereby contributing to pathogenesis of asthma. To test this idea we compared miR-146a and miR-146b expression in human airway smooth muscle cells (hASMCs) from nonasthmatic and asthmatic subjects treated with cytomix (IL-1β, TNF-α, and IFNγ) and examined the miRNAs' effects on COX-2 and IL-1β expression. We found that cytomix treatment elevated miR-146a and miR-146b abundance. Induction with cytomix was greater than induction with individual cytokines, and asthmatic cells exhibited higher levels of miR-146a expression following cytomix treatment than nonasthmatic cells. Transfection of miR-146a or miR-146b mimics reduced COX-2 and IL-1β expression. A miR-146a inhibitor increased COX-2 and IL-1β expression, but a miR-146b inhibitor was ineffective. Repression of COX-2 and IL-1β expression by miR-146a correlated with reduced abundance of the RNA-binding protein human antigen R. These results demonstrate that miR-146a and miR-146b expression is inducible in hASMCs by proinflammatory cytokines and that miR-146a expression is greater in asthmatic cells. Both miR-146a and miR-146b can negatively regulate COX-2 and IL-1β expression at pharmacological levels, but loss-of-function studies showed that only miR-146a is an endogenous negative regulator in hASMCs. The results suggest miR-146 mimics may be an attractive candidate for further preclinical studies as an anti-inflammatory treatment of asthma.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it