Airway smooth muscle inflammation is regulated by micro<scp>RNA</scp>‐145 in <scp>COPD</scp>
Bibliographic record
Abstract
Chronic obstructive pulmonary disease (COPD) is a common, highly debilitating disease of the airways, primarily caused by smoking. Chronic inflammation and structural remodelling are key pathological features of this disease, in part caused by the aberrant function of airway smooth muscle (ASM) cells under the regulation of transforming growth factor (TGF)-β. miRNA are short, noncoding gene transcripts involved in the negative regulation of specific target genes, through their interactions with mRNA. Previous studies have proposed that mRNA-145 (miR-145) may interact with SMAD3, an important downstream signalling molecule of the TGF-β pathway. TGF-β was used to stimulate primary human ASM cells isolated from healthy nonsmokers, healthy smokers and COPD patients. This resulted in a TGF-β-dependent increase in CXCL8 and IL-6 release, most notably in the cells from COPD patients. TGF-β stimulation increased SMAD3 expression, only in cells from COPD patients, with a concurrent increased miR-145 expression. Regulation of miR-145 was found to be negatively controlled by pathways involving the MAP kinases, MEK-1/2 and p38 MAPK. Subsequent, overexpression of miR-145 (using synthetic mimics) in ASM cells from patients with COPD suppressed IL-6 and CXCL8 release, to levels comparable to the nonsmoker controls. Therefore, this study suggests that miR-145 negatively regulates pro-inflammatory cytokine release from ASM cells in COPD by targeting SMAD3.
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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) | 0.002 | 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".