A Potential Contribution of <scp>S100A11</scp> to Skin Fibrosis and Pulmonary Involvement in Systemic Sclerosis
Bibliographic record
Abstract
Systemic sclerosis (SSc) is characterised by immune dysregulation, vasculopathy and fibrosis, driven by genetic and environmental factors. S100 proteins, which constitute a unique class of calcium-binding proteins, have been shown to be critically implicated in various inflammatory and fibrotic conditions. In this study, we investigated the possible involvement of S100A11 in SSc by examining its cutaneous expression and systemic serum levels, correlating them with key clinical parameters. First, we performed immunohistochemical (IHC) staining to examine S100A11 localisation in skin specimens from SSc patients and controls, and found that S100A11 was robustly expressed in SSc dermal fibroblasts. Analysis on the publicly available single-cell RNA-sequencing (scRNA-seq) data of SSc skin samples further confirmed that S100A11 was highly expressed in SSc dermal fibroblasts along with several key genes associated with cellular senescence. Finally, we evaluated serum levels of S100A11 in SSc patients and HCs using enzyme-linked immunosorbent assay (ELISA), and found that serum S100A11 levels were significantly elevated in diffuse cutaneous SSc (dcSSc) patients compared to controls. S100A11 serum levels in SSc patients were significantly correlated with modified Rodnan total skin thickness score and key parameters of SSc-related interstitial lung disease. Our data collectively suggested a potential pathophysiological role of S100A11 in the cutaneous and lung fibrosis associated with SSc, warranting further investigation into its functional roles in this disease.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".