New hypothesis concerning pathogenesis of systemic sclerosis using a tissue engineering method
Bibliographic record
Abstract
Scleroderma is a connective tissue disease characterized by skin and internal organs fibrosis, vasculopathy of small arteries and activation of the immune system. The objective of this project is to study the capacity of scleroderma fibroblasts to induce fibrosis using a model of tissue engineered dermis. Fibroblasts were isolated from skin biopsies of patient with scleroderma for less than one year (early stage) or for more than ten years (late stage). For each patient, two biopsies were taken, one from affected area and the other one from a non‐affected area. Control fibroblasts were obtained from healthy donors. Only fibroblasts isolated from the skin biopsies from affected area of patient at late stage of scleroderma were able to reconstruct dermis thicker than that obtained with control fibroblasts. All of the other fibroblasts tested formed dermis thinner than the one obtained with control fibroblasts. These observations are not the consequence of a change in the amount of fibroblast present, but due to a different turnover of extracellular matrix (matrix metalloproteinase‐1/collagen I) according to the stage and the area. These results suggest the necessary presence of exogenous factor(s) to induce fibrosis in the early stage of the disease, while in late stage these factor(s) would not be essential. This study was supported by CIHR, by the “Fondation de l’Hôpital St‐Sacrement” and a scholarship from FRSQ (VM).
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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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".