Extracellular Matrix Proteins in Epiretinal Membranes and in Diabetic Retinopathy
Bibliographic record
Abstract
PURPOSE: Non-vascular epiretinal membranes (ERM) and neovascular membrane in proliferative diabetic retinopathy (PDR) are recognized causes of visual impairment. Both ERMs and neovascular membranes in PDR consist of cellular components and extracellular matrix (ECM) proteins such as fibronectin (FN) and collagen. Transforming growth factor-beta (TGF-beta) and endothelin-1 (ET-1) regulate ECM protein production. In this study, we investigated ECM proteins and their regulators in ERMs and vitreous from PDR subjects and non-diabetic subjects undergoing vitrectomy. METHODS: ERMs from non-diabetic subjects undergoing membrane peeling were collected. Vitreous samples from non-diabetic and PDR subjects undergoing vitrectomy were also collected and separated into solid pellets consisting of fibrovascular tissue and vitreous fluid. Real-time PCR was done for estimating mRNA levels of extracellular matrix proteins like collagen, FN, its splice variant extra-domain B containing FN (EDBFN), and their regulators, TGF-beta and ET-1. ELISA was done to detect the EDBFN level in blood and vitreous from non-diabetic and PDR subjects undergoing vitrectomy. RESULTS: ECM proteins, including FN, its splice variant EDBFN, and collagen were significantly upregulated in the ERMs and PDR compared to vitreous from both other two group. The levels were, however, higher in the ERM. ECM protein regulators like TGF-beta and ET-1 were also elevated. FN and EDBFN show significant correlation with TGF-beta in vitreous but not in ERMs. Plasma and vitreous EDBFN were elevated in the PDR subjects compared to non-diabetic subjects. CONCLUSIONS: Data from these studies show that ECM proteins such as EDBFN and collagen are upregulated in ERM and PDR, and are regulated by TGF-beta. Elevated serum EDBFN in the PDR may potentially be further explored as a possible molecular marker for the early detection of diabetic end organ damages.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".