Fibroblast growth factor-2 is a sputum remodeling biomarker of severe asthma
Bibliographic record
Abstract
OBJECTIVE: Given the large phenotypic diversity of asthma, our aim was to characterize molecular profiles related to asthma severity using selected remodeling biomarkers in induced sputum. METHODS: Induced sputum from healthy controls, patients with mild to moderate asthma and severe asthma were collected. Twelve selected biomarkers previously associated to airway remodeling such as connective tissue growth factor (CTGF), fibroblast growth factor (FGF)-2, matrix metalloproteinase (MMP)-1, MMP-2, MMP-3, MMP-7, MMP-8, MMP-9, MMP-12, MMP-13, procollagen type 1 and tissue inhibitor of metalloproteinase (TIMP)-1 were measured in sputum samples using ELISA or Luminex technology. FGF-2 level was also evaluated in bronchial biopsies using immunohistochemistry. RESULTS: Sputum of severe asthma was characterized by reduced percentage of macrophages and increased percentage of neutrophils and eosinophils. FGF-2, MMP-1 and TIMP-1 levels increased with asthma severity. Interestingly, only FGF-2 level inversely correlated with FEV1/FVC ratio. Although percentage of eosinophils correlated with asthma severity, it did not correlate with FGF-2 levels. Increased levels of FGF-2 with asthma severity were confirmed in bronchial biopsies by immunohistochemistry. CONCLUSIONS: Level of FGF-2 in induced sputum represents a relevant remodeling biomarker of asthma severity and significantly correlates with pulmonary function. FGF-2 sputum biomarker is proposed to reveal the phenotype of asthma characterized by fixed airflow obstruction.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".