Autophagy Activation in Asthma Airways Remodeling
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Bibliographic record
Abstract
Abstract Current asthma therapies fail to target airway remodeling that correlates with asthma severity driving disease progression that ultimately leads to loss of lung function. Macroautophagy (hereinafter “autophagy”) is a fundamental cell-recycling mechanism in all eukaryotic cells; emerging evidence suggests that it is dysregulated in asthma. We investigated the interrelationship between autophagy and airway remodeling and assessed preclinical efficacy of a known autophagy inhibitor in murine models of asthma. Human asthmatic and nonasthmatic lung tissues were histologically evaluated and were immunostained for key autophagy markers. The percentage area of positive staining was quantified in the epithelium and airway smooth muscle bundles using ImageJ software. Furthermore, the autophagy inhibitor chloroquine was tested intranasally in prophylactic (3 wk) and treatment (5 wk) models of allergic asthma in mice. Human asthmatic tissues showed greater tissue inflammation and demonstrated hallmark features of airway remodeling, displaying thickened epithelium (P < 0.001) and reticular basement membrane (P < 0.0001), greater lamina propria depth (P < 0.005), and increased airway smooth muscle bundles (P < 0.001) with higher expression of Beclin-1 (P < 0.01) and ATG5 (autophagy-related gene 5) (P < 0.05) together with reduced p62 (P < 0.05) compared with nonasthmatic control tissues. Beclin-1 expression was significantly higher in asthmatic epithelium and ciliated cells (P < 0.05), suggesting a potential role of ciliophagy in asthma. Murine asthma models demonstrated effective preclinical efficacy (reduced key features of allergic asthma: airway inflammation, airway hyperresponsiveness, and airway remodeling) of the autophagy inhibitor chloroquine. Our data demonstrate cell context–dependent and selective activation of autophagy in structural cells in asthma. Furthermore, this pathway can be effectively targeted to ameliorate airway remodeling in 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) | 0.000 | 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 | 0.000 |
| 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