Vitreous proteomics in rhegmatogenous retinal detachment and proliferative vitreoretinopathy
Bibliographic record
Abstract
Rhegmatogenous retinal detachment (RRD) is a serious ophthalmic condition that, if untreated, can result in significant vision loss. Proliferative vitreoretinopathy (PVR) often complicates RRD and is the leading cause of surgical failure. Proteomic analysis of the vitreous has emerged as a powerful tool for elucidating the molecular mechanisms underlying RRD and PVR. This article reviews proteomic findings related to these conditions. A comprehensive literature search on PubMed was conducted, focusing on studies of vitreous proteomics in RRD and PVR published between 1988 and August 2024. Relevant findings on protein expression, metabolic pathways, and therapeutic targets were synthesized. Proteomic studies reveal significant alterations in photoreceptor-specific proteins, such as rhodopsin and Monocyte Chemoattractant Protein-1 (MCP-1), associated with apoptosis and inflammation during RRD. Metabolic dysregulation is evidenced by changes in glycolytic enzymes and antioxidants, including downregulation of peroxiredoxin-2 and ascorbic acid, suggesting impaired energy production and oxidative stress. Elevated cytokines, complement proteins, and matrix metalloproteinases highlight the role of inflammation and extracellular matrix remodelling in disease progression. Cytokine expression in PVR demonstrates distinct temporal patterns, with early stages marked by T-cell activation and mTOR pathway-related cytokines, and advanced stages characterized by monocyte chemoattractants associated with chronic inflammation. Currently, the potential of pharmacologic interventions in RRD and PVR remains limited. In contrast, proteomics offers critical insights into molecular mechanisms, identifying potential biomarkers and therapeutic pathways. The adoption of single-molecule and top-down proteomics, along with the integration of advanced technologies with artificial intelligence and bioinformatics, holds promise for accelerating progress toward precision medicine. These developments represent a promising avenue for future research and clinical application.
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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".