The association of toll-like receptor 4 gene polymorphisms with primary open angle glaucoma susceptibility: a meta-analysis
Bibliographic record
Abstract
Primary open angle glaucoma (POAG) and normal tension glaucoma (NTG) cause irreversible blindness while current medications cannot completely inhibit disease progression. An understanding of immunopathogenesis is thus a keystone to develop novel drug targets and genetic markers are still required for early diagnosis. Toll-like receptor 4 (TLR4) is an essential player in inflammation in various diseases. However, the TLR4 polymorphisms have not been completely elucidated in both types of glaucoma. The aim of the present study was to identify the association between TLR4 polymorphism and glaucoma (POAG and NTG) via the use of a comprehensive review and meta-analysis. The relevant studies were collected from PubMed, Excerpta Medica Database (EMBASE), and Web of Science to identify eight included articles, assessed for quality by a modified Newcastle-Ottawa Scale (NOS) for gene association study. A meta-analysis was applied to calculate the pooled odds-ratio and 95% confidence intervals (CIs) to evaluate the association between TLR4 polymorphism and glaucoma. The results revealed that TLR4 rs1927911 A/G, rs12377632 C/T, and rs2149356 G/T significantly decrease the risk of POAG and NTG in allele contrast models 0.71-, 0.71-, and 0.67-fold, respectively. Moreover, rs4986790 A/G and rs4986791 C/T showed a stringent association with POAG in allele contrast, heterozygous, recessive, and overdominant models. In conclusion, this meta-analysis represented a significant correlation between TLR4 polymorphisms and both types of glaucoma suggesting that TLR4 might be involved in the pathogenesis of glaucoma and may be applied as a genetic marker for disease screening.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.041 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".