The fibrogenic actions of lung fibroblast-derived urokinase: a potential drug target in IPF
Bibliographic record
Abstract
The role of urokinase plasminogen activator (uPA) in idiopathic pulmonary fibrosis (IPF) remains unclear. uPA-generated plasmin has potent fibrogenic actions involving protease activated receptor-1 (PAR-1) and interleukin-6 (IL-6). Here we characterize uPA distribution or levels in lung tissue and sera from IPF patients to establish the mechanism of its fibrogenic actions on lung fibroblasts (LFs). uPA immunoreactivity was detected in regions of fibrosis including fibroblasts of lung tissue from IPF patients (n = 7). Serum uPA levels and activity were also higher in IPF patients (n = 18) than controls (n = 18) (P < 0.05), being negatively correlated with lung function as measured by forced vital capacity (FVC) %predicted (P < 0.05). The culture supernatants of LFs from IPF patients, as compared to controls, showed an increase in plasmin activity after plasminogen incubation (5-15 μg/mL), corresponding with increased levels of uPA and IL-6 (n = 5-6, P < 0.05). Plasminogen-induced increases in plasmin activity and IL-6 levels were attenuated by reducing uPA and/or PAR-1 expression by RNAi. Plasmin(ogen)-induced mitogenesis was also attenuated by targeting uPA, PAR-1 or IL-6. Our data shows uPA is formed in active regions of fibrosis in IPF lung and contributes to LF plasmin generation, IL-6 production and proliferation. Urokinase is a potential target for the treatment of lung fibrosis.
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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 | 0.000 | 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 | 0.001 |
| 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".