Aqueous humour concentrations of TGF‐<i>β</i>, PLGF and FGF‐1 and total retinal blood flow in patients with early non‐proliferative diabetic retinopathy
Bibliographic record
Abstract
Abstract Purpose To correlate angiogenic cytokines in the aqueous humour with total retinal blood flow in subjects with type 2 diabetes with non‐proliferative diabetic retinopathy (NPDR). Methods A total of 17 controls and 16 NPDR patients were recruited into the study. Aqueous humour was collected at the start of cataract surgery to assess the concentration of 14 angiogenic cytokines. Aqueous humour was analysed using the suspension array method. Six images were acquired to assess total retinal blood flow (TRBF) using the prototype RTVue™ Doppler Fourier domain optical coherence tomography (Doppler FD‐OCT) (Optovue, Inc., Fremont, CA) using a double circular scan protocol, 1 month postsurgery. At the same visit, forearm blood was collected to determine glycosylated haemoglobin (A1c). Results Transforming growth factor beta (TGF‐β1, TGF‐β2) and PLGF were increased while FGF‐1 was reduced in NPDR compared to controls (Bonferroni corrected, p < 0.003 for all). Total retinal blood flow (TRBF) was significantly reduced in the NPDR group compared to controls (33.1 ± 9.9 versus 43.3 ± 5.3 μl/min, p = 0.002). Aqueous FGF‐1 significantly correlated with TRBF in the NPDR group (r = 0.71, p = 0.01; r2 = 0.51). In a multiple regression analysis, A1c was found to be a significant predictor of aqueous TGF‐β1 and FGF‐1 (p = 0.018 and p = 0.020, respectively). Conclusion Aqueous angiogenic cytokines (TGF‐β1, TGF‐β2 and PLGF) were elevated in conjunction with a reduction in TRBF in patients with NPDR compared to controls. Non‐invasive measurement of TRBF may be useful for predicting aqueous FGF‐1 levels and severity of vasculopathy in DR.
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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 | 0.001 |
| 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".