microRNA profiling in pulmonary fibroblasts in COPD
Bibliographic record
Abstract
COPD is characterized by emphysema with loss of extracellular matrix (ECM), and (small) airways disease with increased ECM deposition and airway wall thickening. How both processes can occur in close proximity in one lung is unknown and needs more attention. Fibroblasts are the principal cells involved in ECM production in the lung. MicroRNAs are small RNAs that can cause downregulation of target protein expression. We hypothesize that microRNA-mediated differences between COPD and healthy fibroblasts, and additionally airway and parenchymal fibroblasts, contribute to the airway and parenchymal changes in COPD. We profiled microRNA expression in pulmonary fibroblasts from severe COPD patients and controls to investigate effects of COPD, smoking (ex-smokers vs current smokers) and fibroblast type (airway vs parenchymal fibroblasts). Using linear models we found 42 microRNAs differentially expressed in COPD patients, 25 between ex-smokers and current smokers and 45 between airway and parenchymal fibroblasts in COPD (p<0.01). Interestingly some of the microRNAs differentially expressed in COPD fibroblasts, i.e. mir-181d and mir-296-5p, are also differentially expressed in lung tissue in relation to emphysema severity (Christenson, S.A. et al. Am J Resp Crit Care Med 2010;181:A2024) . COPD and smoking had similar effects on several microRNAs, including mir-181d, mir-296-5p, mir-29b1*, mir-23* and mir-202, suggesting a possible mechanism for the link between smoking and COPD development. Furthermore, mir-155 expression was decreased in COPD and within COPD in airway fibroblasts. Given the role of mir-155 in airway remodeling (Rodriguez, A. et al. Science 2007; 316:608-11) this microRNA could be important in the airway changes in COPD.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".