Decreased secretion of MMP by non-lesional late-stage scleroderma fibroblasts after selection via activation of the apoptotic fas-pathway
Bibliographic record
Abstract
Our hypothesis is that the development of lesional areas of skin in patients with systemic sclerosis (SSc) originates from the selection of profibrotic cell subpopulations within their non-lesional skin areas, due to their greater resistance to apoptosis. Sensitivity to apoptosis of early-stage or late-stage SSc fibroblasts as well as of healthy cells was compared using extrinsic or intrinsic apoptotic pathway-inducers. Subpopulations of non-lesional SSc cells and healthy cells obtained after repeated Fas-induced apoptosis were compared with respect to their fibrotic parameters such as collagen and MMP secretion. Only late-stage lesional SSc cells were more resistant to Fas-induced apoptosis than their non-lesional counterparts isolated from the same patient. This result correlated with an increase in the levels of the anti-apoptotic proteins cFLIPs and cIAP in lesional cells compared to non-lesional cells. Healthy and non-lesional cell populations could be selected to generate a subpopulation that was more resistant to apoptosis. However, only the late-stage non-lesional SSc fibroblast populations showed a significant decrease in MMP secretion, one of parameters of the fibrosis. Our results show that resistance to apoptosis is an important characteristic of the late-stage lesional SSc fibroblast phenotype. We thus hypothesized that a selection of specific fibroblast subpopulations from late-stage non-lesional SSc skin areas could be at the origin of lesional populations. These cells should become independent of any exogenous stimuli and can induce or maintain SSc skin lesions.
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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".