Emerging Evidence of Noncoding RNAs in Bleb Scarring after Glaucoma Filtration Surgery
Bibliographic record
Abstract
PURPOSE: To conduct a narrative review of research articles on the potential anti- and pro-fibrotic mechanisms of noncoding RNAs following glaucoma filtration surgery. METHODS: Keyword searches of PubMed, and Medline databases were conducted for articles discussing post-glaucoma filtration surgeries and noncoding RNA. Additional manual searches of reference lists of primary articles were performed. RESULTS: Fifteen primary research articles were identified. Four of the included papers used microarrays and qRT-PCR to identify up- or down-regulated microRNA (miRNA, miR) profiles and direct further study, with the remainder focusing on miRNAs or long noncoding RNAs (lncRNAs) based on previous work in other organs or disease processes. The results of the reviewed papers identified miR-26a, -29b, -139, -155, and -200a as having anti-fibrotic effects. In contrast, miRs-200b and -216b may play pro-fibrotic roles in filtration surgery fibrosis. lncRNAs including H19, NR003923, and 00028 have demonstrated pro-fibrotic effects. CONCLUSIONS: Noncoding RNAs including miRNAs and lncRNAs are emerging and promising therapeutic targets in the prevention of post-glaucoma filtration surgery fibrosis.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".