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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".