Increased Alveolar Concentration of Nitric Oxide Is Related to Serum-induced Lung Fibroblast Proliferation in Patients with Systemic Sclerosis
Bibliographic record
Abstract
Objective. Lung inflammation is present in patients with systemic sclerosis (SSc) and interstitial lung disease (ILD), but the mechanisms linking inflammatory and fibrotic processes in ILD are unknown. Our aim was to investigate whether alveolar inflammation, reflected by increased alveolar concentration of exhaled nitric oxide (CANO), is related to the ability of serum from patients with SSc to induce pulmonary fibroblast proliferation (PFP) and myofibroblast conversion. Methods. CANO was measured in all subjects (37 patients with SSc and 10 healthy controls) whose sera were used to stimulate PFP (assessed by BrdU labeling index) and myofibroblast conversion (detected by α-smooth muscle actin expression). The PFP index in patients with SSc was compared to control values, and between patients with SSc who had elevated (> 4.3 ppb) and normal (≤ 4.3 ppb) CANO values. Results. Both CANO and the PFP index were significantly greater in patients with SSc compared to controls. In patients with SSc, the PFP index was directly related to CANO levels (r = 0.48; p = 0.002). The median PFP index was significantly higher in patients with SSc who had elevated CANO (> 4.3 ppb; n = 25, median 1.1, range 0.98–1.23) than in patients with SSc who had normal CANO (≤ 4.3 ppb; n = 12, median 0.93, range 0.82–1.08; p = 0.01). Similarly, myofibroblast conversion induced by SSc serum was significantly greater in patients with CANO > 4.3 ppb than in patients whose CANO was ≤ 4.3 ppb (p < 0.001) and controls (p < 0.001). Conclusion. Alveolar inflammation reflected by increased nitric oxide production was related to serum-induced PFP and myofibroblast conversion, linking the active alveolitis process to cell proliferation and lung fibrosis in patients with SSc.
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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 | 0.002 |
| 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".