Anti-connective tissue growth factor (CTGF/CCN2) monoclonal antibody attenuates skin fibrosis in mice models of systemic sclerosis
Bibliographic record
Abstract
BACKGROUND: Systemic sclerosis (SSc) is characterized by fibrosis of the skin and internal organs. Although the involvement of connective tissue growth factor (CTGF/CCN2) has been well-documented in SSc fibrosis, the therapeutic potential of targeting CTGF in SSc has not been fully investigated. Our aim was to examine the therapeutic potential of CTGF blockade in a preclinical model of SSc using two approaches: smooth muscle cell fibroblast-specific deletion of CTGF (CTGF knockout (KO)) or a human anti-CTGF monoclonal antibody, FG-3019. METHODS: Angiotensin II (Ang II) was administered for 14 days by subcutaneous osmotic pump to CTGF KO or C57BL/6 J mice. FG-3019 was administered intraperitoneally three times per week for 2 weeks. Skin fibrosis was evaluated by histology and hydroxyproline assay. Immunohistochemistry staining was used for alpha smooth muscle actin (αSMA), platelet-derived growth factor receptor β (PDGFRβ), pSmad2, CD45, von Willebrand factor (vWF), and immunofluorescence staining was utilized for procollagen and Fsp1. RESULTS: Ang II-induced skin fibrosis was mitigated in both CTGF KO and FG-3019-treated mice. The blockade of CTGF reduced the number of cells expressing PDGFRβ, procollagen, αSMA, pSmad2, CD45, and Fsp1 in the dermis. In addition, inhibition of CTGF attenuated vascular injury as measured by the presence of vWF-positive cells. CONCLUSIONS: Our data indicate that inhibition of CTGF signaling presents an attractive therapeutic approach 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".