Bibliographic record
Abstract
Scleroderma, an autoimmune disorder characterized by skin and organ fibrosis, has no treatment. Although over the past decade valuable insights into the molecular mechanisms underlying scleroderma have been generated, results in clinical trials have been disappointing. This issue is likely to arise due to the heterogeneity of scleroderma. Molecular insights into the heterogeneity of this disease have been provided by genome-wide expression profiling. In a recent paper, Bhattacharyya and colleagues (PLOS One 6:e23082, 2011b) to show that the overexpression of a range of "fibroproliferative" genes in diffuse cutaneous scleroderma patients are likely to be caused by the overexpression of transcription factor Early growth response (Egr)-1. Only a minority of Egr-1-regulated genes were also found to be regulated by TGF-ß. Moreover, Greenblatt and colleagues (Am J Pathol., 2012) have shown that the overexpression of "inflammatory" genes overexpressed in "localized" scleroderma and a small subset of limited and diffuse scleroderma patients is likely to be due to the activity of interleukin-13 (IL-13). Intriguingly, at a gene expression level, murine sclerodermatous graft-versus-host disease (sclGVHD) approximates this inflammatory subset of scleroderma. These data suggest that targeting Egr-1 expression/activity might be a novel therapeutic strategy to control fibrosis in a subset of diffuse scleroderma patients, and further emphasize that notion that elevated canonical TGFβ signaling is insufficient to explain the fibrosis observed in scleroderma. Moreover, targeting IL-13 expression/activity might be a novel therapeutic strategy to target the inflammation leading to "localized" scleroderma.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".