Airway Epithelial Cells Drive Airway Smooth Muscle Cell Phenotype Switching to the Proliferative and Pro-inflammatory Phenotype
Bibliographic record
Abstract
The increased mass of airway smooth muscle (ASM) in the airways of asthmatic patients may contribute to the pathology of this disease by increasing the capacity for airway narrowing. Evidence for the airway epithelium as a participant in ASM remodeling is accruing. To investigate mechanisms by which airway epithelial cells induce ASM cell (ASMC) proliferation, we have employed a co-culture model to explore markers of ASMC proliferative phenotype. Co-culture with epithelial cells led to incorporation of bromodeoxyuridine into ASMCs, indicating augmented proliferation and an associated increase in mRNA of the pro-proliferative co-transcription factor Elk1. Although the mitogen heparin-binding epidermal growth factor (HB-EGF) was augmented in the co-culture supernatant, the ASMC epidermal growth factor receptor (EGFR), an effector of HB-EGF induced proliferation, did not mediate epithelial-induced proliferation. The co-culture increased the expression of ASMC mRNA for the pro-inflammatory cytokines IL-6 and IL-8 as well as the pro-proliferative microRNA miR-210. The transcriptional repressor Max-binding protein (Mnt), a putative target of miR-210, was transcriptionally repressed in co-cultured ASMCs. Together, these data indicate that the airway epithelium-induced proliferative phenotype of ASMCs is not driven by EGFR signaling, but rather may be dependent on miR210 targeting of tumor suppressor Mnt.
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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 | 0.001 |
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".