Proteomic Investigation of Dermal Fibroblasts Isolated from Affected and Unaffected Skin Samples from Patients with Limited Cutaneous Systemic Sclerosis: 2 Distinct Entities?
Bibliographic record
Abstract
OBJECTIVE: To identify using proteomic analysis the proteins of altered abundance in the affected and unaffected limited cutaneous systemic sclerosis (lcSSc) skin fibroblasts. METHODS: Excision biopsies (3 mm) were obtained from the affected and unaffected skin of 5 patients with lcSSc. Dermal fibroblasts were isolated enzymatically. Two-dimensional gel electrophoresis was used to separate and define proteins in affected and unaffected fibroblast lysates. Proteins of altered abundance were identified by mass spectrometry. Differences among skin samples were confirmed also by immunohistochemistry (IHC) and by quantitative real-time PCR (qRT-PCR) for type I collagen (Col-1) and vimentin (VIM). RESULTS: Proteomic analysis revealed different expressions of proteins involved in cytoskeleton organization (27%), extracellular matrix remodeling (11%), response to oxidative stress (22%), energy metabolism (19%), protein metabolism (5%), cellular homeostasis (5%), signal transduction (3%), and protein transcription, synthesis, and turnover (8%). IHC analysis showed that SSc-affected epidermis is thickened and the dermis is strongly reactive to Col-1 and VIM (typical markers of activated myofibroblasts) compared to SSc-unaffected skin, whose stainings are comparable to those of control healthy skin. Overexpression of Col-1 and VIM mRNA levels in affected lcSSc fibroblasts compared to unaffected lcSSc ones was confirmed by qRT-PCR. CONCLUSION: Consistent with previous studies, these findings are important for 2 reasons: first, because they reveal the opposite behavior of dermal fibroblasts in the unaffected and affected skin areas of the same patient with lcSSc; second, because they demonstrate the histological/histochemical similarities between unaffected skin from patients with lcSSc and healthy control skin.
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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 | 0.001 | 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".