Fibrosis, gene expression and orbital inflammatory disease
Bibliographic record
Abstract
BACKGROUND/AIMS: To clarify the pathogenesis of fibrosis in inflammatory orbital diseases, we analysed the gene expression in orbital biopsies and compared our results with those reported for idiopathic pulmonary fibrosis. METHODS: We collected 140 biopsies from 138 patients (58 lacrimal glands; 82 orbital fat). Diagnoses included healthy controls (n=27), non-specific orbital inflammation (NSOI) (n=61), thyroid eye disease (TED) (n=29), sarcoidosis (n=14) and granulomatosis with polyangiitis (GPA) (n=7). Fibrosis was scored on a 0-3 scale by two experts, ophthalmic pathologists. Gene expression was quantified using Affymetrix U133 plus 2.0 microarray. RESULTS: Within orbital fat, fibrosis was greatest among subjects with GPA (2.75±0.46) and significantly increased in tissue from subjects with GPA, NSOI or sarcoidosis (p<0.01), but not for TED, compared with healthy controls (1.13±0.69). For lacrimal gland, the average score among controls (1.36±0.48) did not differ statistically from any of the four disease groups. Seventy-three probe sets identified transcripts correlating with fibrosis in orbital fat (false discovery rate <0.05) after accounting for batch effects, disease type, age and sex. Transcripts with increased expression included fibronectin, lumican, thrombospondin and collagen types I and VIII, each of which has been reported upregulated in pulmonary fibrosis. CONCLUSIONS: A pathologist's recognition of fibrosis in orbital tissue correlates well with increased expression of transcripts that are considered essential in fibrosis. Many transcripts implicated in orbital fibrosis have been previously implicated in pulmonary fibrosis. TED differs from other causes of orbital fat inflammation because fibrosis is not a major component. Marked fibrosis is less common in the lacrimal gland compared with orbital adipose tissue.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".