Chemokines in diabetic eye disease
Bibliographic record
Abstract
BACKGROUND: Diabetic eye disease is a common micro-vascular complication of diabetes and a leading cause of decreased vision and blindness in people of working age worldwide.Although previous studies have shown that chemokines system may be a player in pathogenesis of diabetic eye disease, it is unclear which chemokines play the most important role.To date, there is no meta-analysis which has investigated the role of chemokines in diabetic eye disease.We hope this study will contribute to a better understanding of both the signaling pathways of the chemokines in the pathophysiological process, and more reliable therapeutic targets for diabetic eye disease. METHODS: Embase, PubMed, Web of Science and Cochrane Library systematically searched for relevant studies from inception to Sep 1, 2023. A random-effect model was used and standardized mean differences (SMDs) and 95% confidence intervals (CIs) were calculated to summarize the associated measure between chemokines concentrations and diabetic eye disease. Network meta-analysis to rank chemokines-effect values according to ranked probabilities. RESULTS: A total of 33 different chemokines involving 11,465 subjects (6559 cases and 4906 controls) were included in the meta-analysis. Results of the meta-analysis showed that concentrations of CC and CXC chemokines in the diabetic eye disease patients were significantly higher than those in the controls. Moreover, network meta-analysis showed that the effect of CCL8, CCL2, CXCL8 and CXCL10 were ranked highest in terms of probabilities. Concentrations of CCL8, CCL2, CXCL8 and CXCL10 may be associated with diabetic eye disease, especially in diabetic retinopathy and diabetic macular edema. CONCLUSION: Our study suggests that CCL2 and CXCL8 may play key roles in pathogenesis of diabetic eye disease. Future research should explore putative mechanisms underlying these links, with the commitment to develop novel prophylactic and therapeutic for diabetic eye disease.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".