Constitutive and inducible thymic stromal lymphopoietin expression in human airway smooth muscle cells: role in chronic obstructive pulmonary disease
Bibliographic record
Abstract
Thymic stromal lymphopoietin (TSLP) is a novel cytokine that triggers dendritic cell-mediated T helper (Th)-2 inflammatory responses. Previous studies have demonstrated that human airway smooth muscle cells (HASMC) play a critical role in initiating or perpetuating airway inflammation by producing chemokines and cytokines. In this study, we first evaluated the expression of TSLP in primary HASMC and investigated how proinflammatory cytokines (TNF-alpha and IL-1beta) and Th-2 cytokines (IL-4, IL-9) regulate TSLP production from HASMC. TSLP mRNA and protein were assessed by real-time RT-PCR, ELISA, and immunofluorescence from primary HASMC cultures. Primary HASMC express constitutive level of TSLP. Incubation of HASMC with IL-1 or TNF-alpha resulted in a significant increase of TSLP mRNA and protein release from HASMC. Furthermore, combination of IL-1beta and TNF-alpha has an additive effect on TSLP release by HASMC. Primary HASMC pretreated with inhibitors of p38 or p42/p44 ERK MAPK, but not phosphatidylinositol 3-kinase, showed a significant decrease in TSLP release on IL-1beta and TNF-alpha treatment. Furthermore, TSLP immunoreactivity was present in ASM bundle from chronic obstructive pulmonary disease (COPD) and to lesser degree in normal subjects. Taken together, our data provide the first evidence of IL-1beta- and TNF-alpha-induced TSLP expression in HASMC via (p38, p42/p44) MAPK signaling pathways. Our results raise the possibility that HASMC may play a role in COPD airway inflammation via TSLP-dependent pathway.
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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 | 0.000 |
| 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 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".