Focal adhesion kinase and reactive oxygen species contribute to the persistent fibrotic phenotype of lesional scleroderma fibroblasts
Bibliographic record
Abstract
OBJECTIVE: Fibrotic diseases such as SSc (systemic sclerosis, scleroderma) are characterized by the abnormal presence of the myofibroblast, a specialized type of fibroblast that overexpresses the highly contractile protein α-smooth muscle actin. Myofibroblasts display excessive adhesive properties and hence exert a potent mechanical force. We aim to identify the precise contribution of adhesive signalling, which requires integrin-mediated activation of focal adhesion kinase (FAK)/src, to fibrogenic gene expression in normal and fibrotic SSc fibroblasts. METHODS: We subject either FAK wild-type and knockout fibroblasts or normal and SSc fibroblasts treated with FAK/src inhibitors to real-time polymerase chain, western blot, cell migration and collagen gel contraction analyses. RESULTS: FAK operates downstream of both integrin β1 and reactive oxygen species (ROS) to promote the expression of genes involved in matrix production and remodelling, including CCN2, α-smooth muscle actin and type I collagen. Blocking either FAK/src with PP2 or ROS with N-acetyl cysteine alleviates the elevated contractile and migratory capability of lesional SSc dermal fibroblasts. CONCLUSIONS: Excessive adhesive signalling is intimately involved with the fibrotic phenotype of lesional SSc fibroblasts; blocking adhesive signalling or ROS generation may be beneficial in controlling the fibrosis observed in 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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".