Newt anterior gradient (nAG) protein, a salamander-derived protein, as an inhibitor of TGF-ß signaling and fibrotic responses
Bibliographic record
Abstract
Background: Salamanders have the amazing ability to regenerate their limbs within 30 days when amputated.One of the key proteins responsible for this regeneration is the newt anterior gradient (nAG) protein.A previous study demonstrated that local injection of a recombinant nAG protein reduces hypertrophic scarring in a rabbit ear model.Fibrotic disorders of the skin such as scleroderma, hypertrophic scarring and keloids are characterized by excessive TGF-b action, leading to an increase in deposition of collagen and other extracellular matrix (ECM) components, resulting in functional impairment which is often debilitating.To date, the treatment options remain limited.We hypothesized that nAG protein would inhibit the TGF-b signaling pathway and fibrotic responses in skin fibroblasts of scleroderma patients and may therefore have therapeutic potential for the treatment of scleroderma.The aim of this study is to examine the effectiveness of nAG protein to inhibit fibrotic responses in scleroderma skin fibroblasts and study its regulation of the TGF-b signaling pathway.Methods: Fibroblasts from lesional scleroderma patient skin were treated with nAG protein in doses of 0, 100 pM,1 nM and 10nM for 24 hrs and were then left untreated or treated with 20 pM of TGF-β.TGF-β-mediated pro-fibrotic responses were determined by measuring the levels of ECM (collagen III, Fibronectin), connective tissue growth factor (CTGF/CCN2) and alpha smooth muscle actin (α-SMA) protein production by Western blot and immunofluorescence.Also, gene expression at the mRNA level was determined by quantitative PCR.Activation of the TGF-β pathway was determined by measuring the TGF-β receptor 1 (ALK5) and phosphorylated Smad2/3 levels by using Western blot and immunofluorescence.
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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) | 0.001 | 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".