Apoptosis plays a key role in clonal selection of pathologic fibroblast sub‐populations in systemic sclerosis
Bibliographic record
Abstract
Systemic sclerosis or scleroderma (SSc), is an autoimmune disease with an incidence of 20 people per million and per year in USA. No cure for SSc is currently available so, after a debilitating phase, death is frequently the outcome of this disease with a higher risk of mortality among Afro‐Americans and women. Causes of SSc are unknown for now but various factors seem to be involved like genetics or exposure to chemical or virus. One of the features of this disease is extracellular matrix excessive deposition which affect not only skin but also internal organs. Despite heterogeneity of cellular and molecular responses of fibroblasts (Fb), a larger proportion of SSc Fb express higher level of collagen than in normal ones. So, many groups postulated that SSc lesions could result from clonal expansion of a Fb sub‐population with a higher capacity to proliferate and/or a better resistance to apoptosis. Dermal Fb populations from 17 French‐Canadian volunteers (4 healthy and 13 SSc affected) were tested for proliferation and apoptosis parameters. We have found that SSc Fb proliferate with a lower rate than the normal one, excluding proliferation as a clonal selection origin. Nevertheless lesional SSc Fb were less sensitive than normal Fb or non‐lesional SSc Fb to Fas pathway apoptosis allowing to hypothesize that clonal selection could be performed via apoptotic pathway. This work was supported by grants from CHIR and FRSQ.
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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".