Airway smooth muscle proliferation and inflammation in asthma
Bibliographic record
Abstract
In asthma, it is unclear if the airway smooth muscle cells proliferate more or are increased at the onset of asthma and remain stable. This study aimed to compare smooth muscle cell proliferation in individuals with and without asthma and correlate proliferation rates with cell size and number and with granulocytic airway inflammation. Postmortem airway sections were labeled with proliferating cell nuclear antigen (PCNA) and percent positive muscle cells calculated. On the same sections, smooth muscle cell size and number and the number of eosinophils and neutrophils were estimated and compared in cases of nonfatal ( n = 15) and fatal ( n = 15) asthma and control subjects ( n = 15). The %PCNA+ muscle cells was not significantly different in fatal (29.4 ± 7.7%, mean ± SD), nonfatal asthma (28.6 ± 8.3%), or control subjects (24.6 ± 6.7%) and not related to mean muscle cell size ( r = 0.09), number ( r = 0.36), thickness of the muscle layer ( r = 0.05), or eosinophil numbers ( r = 0.04) in the asthma cases. These data support the hypothesis that in asthma the increased thickness of the smooth muscle layer may be present before or at the onset of asthma and independent of concurrent granulocytic inflammation or exacerbation. NEW & NOTEWORTHY There is debate regarding the origins of the increased airway smooth muscle in asthma. It may be independent of inflammation or arise as a proliferative response to inflammation. The present study found no increase in the proportion of proliferating smooth muscle cells in asthma and no relation of proliferation to numbers of airway smooth muscle cells or inflammation. These results support a stable increase in smooth muscle in asthma that is independent of airway inflammation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".