CC and CXC Chemokines Induce Airway Smooth Muscle Proliferation and Survival
Bibliographic record
Abstract
The increase in airway smooth muscle (ASM) mass is a major structural change in asthma. This increase has been attributed to ASM cell (ASMC) hyperplasia and hypertrophy. The distance between ASMC and the epithelium is reduced, suggesting migration of smooth muscle cells toward the epithelium. Recent studies have suggested a role of chemokines in ASMC migration toward the epithelium; however, chemokines have other biological effects. The objective of the current study is to test the hypothesis that chemokines (eotaxin, RANTES, IL-8, and MIP-1α) can directly influence ASMC mass by increasing the rate of proliferation or enhancing the survival of these cells. Human ASMCs were exposed to different concentrations of eotaxin, RANTES, IL-8, or MIP-1α. To test for proliferation, matched control and stimulated ASMC were pulsed with [(3)H]thymidine, or ASMCs were stained with BrdU and then analyzed with flow cytometry. Apoptosis was measured using Annexin V staining and flow cytometry. Expression of phosphorylated p42/p44 and MAPKs was assessed by Western blot. In a concentration-dependent manner, chemokines including eotaxin, RANTES, IL-8, and MIP-1α increased ASMC's [(3)H]thymidine incorporation and DNA synthesis. IL-8, eotaxin, and MIP-1α decreased the rate of apoptosis of ASMCs compared with the matched controls. A significant increase in phosphorylated p42/p44 MAPKs was seen after treating ASMCs with RANTES and eotaxin. Moreover, inhibition of p42/p44 MAPK phosphorylation reduced the level of chemokine-induced ASM proliferation. We conclude that chemokines might contribute to airway remodeling seen in asthma by enhancing the number and survival of ASMCs.
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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) | 0.002 | 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".