Identification of differentially expressed genes in fibroblasts derived from patients with Dupuytren's Contracture
Bibliographic record
Abstract
Dupuytren's contracture (DC) is the most common inherited connective tissue disease of humans and is hypothesized to be associated with aberrant wound healing of the palmar fascia. Fibroblasts and myofibroblasts are believed to play an important role in the genesis of DC and the fibroproliferation and contraction that are hallmarks of this disease. This study compares the gene expression profiles of fibroblasts isolated from DC patients and controls in an attempt to identify key genes whose regulation might be significantly altered in fibroblasts found within the palmar fascia of Dupuytren's patients. Total RNA isolated from diseased palmar fascia (DC) and normal palmar fascia (obtained during carpal tunnel release; 6 samples per group) was subjected to quantitative analyses using two different microarray platforms (GE Code Linktrade mark and Illuminatrade mark) to identify and validate differentially expressed genes. The data obtained was analyzed using The Significance Analysis of Microarrays (SAM) software through which we identified 69 and 40 differentially regulated gene transcripts using the CodeLinktrade mark and Illuminatrade mark platforms, respectively. The CodeLinktrade mark platform identified 18 upregulated and 51 downregulated genes. Using the Illuminatrade mark platform, 40 genes were identified as downregulated, eleven of which were identified by both platforms. Quantitative RT-PCR confirmed the downregulation of three high-interest candidate genes which are all components of the extracellular matrix: proteoglycan 4 (PRG4), fibulin-1 (FBLN-1) transcript variant D, and type XV collagen alpha 1 chain. Overall, our study has identified a variety of candidate genes that may be involved in the pathophysiology of Dupuytren's contracture and may ultimately serve as attractive molecular targets for alternative therapies.
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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 | 0.001 |
| 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".