Elevated Serum Insulin-like Growth Factor (IGF-1) and IGF Binding Protein-3 Levels in Patients with Systemic Sclerosis: Possible Role in Development of Fibrosis
Bibliographic record
Abstract
OBJECTIVE: To examine serum concentrations of insulin-like growth factor (IGF-1) and IGF binding protein (IGFBP-3), a major carrier protein for IGF-1, in patients with systemic sclerosis (SSc); and to relate the results to clinical features in SSc. METHODS: Serum IGF-1 and IGFBP-3 levels in 92 Japanese patients with SSc were measured by ELISA. Expression of IGF-1 and IGFBP-3 messenger RNA (mRNA) in the skin was quantified by real-time reverse transcription-polymerase chain reaction. RESULTS: Serum IGF-1 and IGFBP-3 levels were significantly elevated in patients with SSc compared with patients with systemic lupus erythematosus or healthy controls. IGF-1 levels were higher in patients with diffuse cutaneous SSc (dcSSc) than in patients with limited cutaneous SSc (lcSSc). Patients with increased IGF-1 levels had more severe skin involvement and pulmonary fibrosis. IGF-1 mRNA was upregulated in the affected skin of patients with SSc. There were no significant differences in serum IGFBP-3 levels between dcSSc and lcSSc. IGFBP-3 levels were not associated with skin thickness and pulmonary fibrosis. Patients with increased IGF-1 or IGFBP-3 had lower frequency of telangiectasia than patients with normal levels. CONCLUSION: These results suggest that both IGF-1 and IGFBP-3 are involved in the development of SSc. The role of IGF-1 appears to be different from that of IGFBP-3.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".