Retinal blood oxygen saturation and aqueous humour biomarkers in early diabetic retinopathy
Bibliographic record
Abstract
PURPOSE: ) and specific aqueous humour (AH) concentrations of proangiogenic biomarkers in diabetic patients with nonproliferative diabetic retinopathy (NPDR) and to compare them with those of matched control subjects. METHODS: The sample comprised 14 participants with mild-to-moderate NPDR (69.1 ± 6.6 years) and 17 age-matched healthy controls (69.7 ± 6.3 years); all participants were previously scheduled for routine cataract extraction with intraocular lens implantation. Multiplex cytokine analyses of specific biomarkers, including vascular endothelial growth factor A (VEGF-A), angiopoietin2 (Ang2), epidermal growth factor (EGF), hepatocyte growth factor (HGF) and interleukin-8 (IL-8) were performed by BioPlex 200 system. Six non-invasive hyperspectral retinal images were acquired. RESULTS: was significantly higher in both arterioles (94.4 ± 1.9 versus 93.0 ± 1.6) and venules (64.4 ± 5.6 versus 55.9 ± 4.8) of NPDR than in the healthy controls (p < 0.001). AH levels of HGF (p = 0.018), Ang2 (p = 0.005) and IL-8 (p = 0.034) were significantly higher, and EGF (p = 0.030) was significantly lower in NPDR subjects. The study demonstrated a correlation between venular retinal blood oxygen saturation and proangiogenic factors HGF (r = 0.558, p = 0.038), Ang2 (r = 0.556, p = 0.039) and EGF (r = -0.554, p = 0.040), but did not find any correlation for IL-8 (r = 0.330, p = 0.249) even though this biomarker was significantly higher in the diabetic group. CONCLUSION: and AH concentrations of protein biomarkers in diabetic retinopathy. The biomarkers of interest have been shown to participate in cell death, which may explain higher oxygen saturation in NPDR.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".