Serum Amyloid A released by lung fibroblasts as novel biomarker of exacerbation of lung fibrosis
Bibliographic record
Abstract
Idiopathic pulmonary fibrosis (IPF) is a severe diffuse parenchymal lung disease associated with high mortality and poor prognosis. Patients with IPF may experience an acute decline of their disease (acute exacerbation, AE-IPF) often triggered by bacterial or viral infections. Fibroblasts play a key role in collagen deposition during IPF, but their inflammatory function during acute exacerbation remains unclear. Here we found that PDGFRα-positive fibroblasts isolated from lungs of mice with S. pneumoniae (Spn) induced exacerbation of AdTGF-β1 driven lung fibrosis developed an early inflammatory phenotype upon infection. This phenotype was mainly characterized by production of Serum Amyloid A (SAA) as judged by global and single-cell RNA sequencing and secretome profiling of sorted fibroblasts. Additionally, SAA levels were significantly increased in the bronchoalveolar lavage (BAL) fluid and plasma of mice with Spn-induced exacerbation of AdTGF-β1- or bleomycin-induced lung fibrosis. Treatment with ceftriaxone significantly reduced SAA levels in BAL fluid and plasma, indicating the use of SAA levels as an indicator of the efficacy of therapeutic interventions in AE-IPF. Furthermore, SAA levels were dramatically elevated in BAL fluid and plasma of patients with acute exacerbation of pulmonary fibrosis, but not in patients without exacerbation. Finally, SAA deficient mice displayed significant bacterial outgrowth and increased mortality following Spn-induced fibrosis exacerbation. Taken together, SAA plasma levels may serve as a novel marker for the identification and tracking of patients with pulmonary fibrosis who are more likely to progress towards an acute exacerbation of IPF.
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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 | 0.001 | 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) | 0.000 | 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".