Distribution of phenotypically disparate myocyte subpopulations in airway smooth muscle
Bibliographic record
Abstract
Phenotype and functional heterogeneity of airway smooth muscle (ASM) cells in vitro is well known, but there is limited understanding of these features in vivo. We tested whether ASM is composed of myocyte subsets differing in contractile phenotype marker expression. We used flow cytometry to compare smooth muscle myosin heavy chain (smMHC) and smooth muscle-alpha-actin (sm-alpha-actin) abundance in myocytes dispersed from canine trachealis. Based on immunofluorescent intensity and light scatter characteristics (forward and 90 degrees side scatter), 2 subgroups were identified and isolated. Immunoblotting confirmed smMHC and sm-alpha-actin were 10- and 5-fold greater, respectively, in large, elongate myocytes that comprised approximately 60% of total cells. Immunohistochemistry revealed similar phenotype heterogeneity in human bronchial smooth muscle. Canine tracheal myocyte subpopulations isolated by flow cytometry were used to seed primary subcultures. Proliferation of subcultures established with myocytes exhibiting low levels of smMHC and sm-alpha-actin was approximately 2 x faster than subcultures established with ASM cells with a high marker protein content. These studies demonstrate broad phenotypic heterogeneity of myocytes in normal ASM tissue that is maintained in cell culture, as demonstrated by divergent proliferative capacity. The distinct roles of these subgroups could be a key determinant of normal and pathological lung development and biology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".