Correlation between the progression of diabetic retinopathy and inflammasome biomarkers in vitreous and serum – a systematic review
Bibliographic record
Abstract
Abstract Activation of the NOD-like receptor protein 3 (NLRP3) inflammasome pathway has been implicated in Diabetic retinopathy (DR) pathogenesis, but its impact on DR development and progression remains unclear. Therefore, the primary aim of this systematic literature review was to determine the role of the inflammasome in DR development. Furthermore, the secondary aim was to determine whether systemic inflammasome activity can be used to predict DR progression. Studies measuring vitreous and/or serum inflammasome biomarkers in DR patients with Type 2 Diabetes Mellitus (T2DM) were searched systematically using online databases EMBASE, PubMed and Web of Science with the last search conducted on 29 th of September 2021. The risk of bias was assessed using the Newcastle Ottawa Scale and 20 studies were eligible for narrative analysis. Limitations included the heterogeneity in detection assays used, the small and uneven sample size, a lack of vitreous data in earlier disease stages, and not accounting for patients with other systemic co-morbidities. Analysis showed that inflammasome biomarkers IL-1β and IL-18 increased significantly from non-proliferative DR to proliferative DR in both vitreous and serum, suggesting the inflammasome pathway is activated as DR progresses and that serum inflammasome levels could be explored as potential biomarkers for DR progression.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".