Effect of long-term corticosteroid treatment on microRNA and gene-expression profiles in COPD
Bibliographic record
Abstract
The aim was to investigate whether microRNA (miRNA) expression is modulated by inhaled corticosteroid (ICS) treatment We performed genome-wide miRNA analysis on bronchial biopsies of 69 moderate/severe chronic obstructive pulmonary disease (COPD) patients at baseline and after 6- and 30-month treatment with the ICS fluticasone propionate or placebo. The effect of ICS on miRNA expression was validated in differentiated primary bronchial epithelial cultures, and functional studies were conducted in BEAS-2B cells. MiRNAs affected by ICS and their predicted targets were compared to an independent miRNA dataset of bronchial brushings from COPD patients and healthy controls. Treatment with ICS for both 6 and 30 months significantly altered the expression of four miRNAs, including miR-320d, which was increased during ICS treatment compared with placebo. The ICS-induced increase of miR-320d was confirmed in primary airway epithelial cells. MiR-320d negatively correlated targets were enriched for pro-inflammatory genes and were increased in the bronchial brushes of patients with lower lung function in the independent dataset. Overexpression of miR-320d in BEAS-2B cells dampened cigarette smoke extract-induced pro-inflammatory activityviainhibition of nuclear factor-κB. Collectively, we identified miR-320d as a novel mediator of ICS, regulating the pro-inflammatory response of the airway epithelium.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".