Evaluating the quantity and spatial density of macrophage-like cells in patients with retinal vascular disease and healthy subjects via non-invasive retinal imaging
Bibliographic record
Abstract
OBJECTIVE: Here we investigate the spatial density and distribution of macrophage-like cells (MLCs) in ischemic retinal diseases, including branch retinal vein occlusion (BRVO), central retinal vein occlusion (CRVO), and proliferative diabetic retinopathy (PDR), compared to healthy controls. METHODS: In this pilot investigation with prospective cross-sectional design, OCT angiography (OCTA) images were obtained from 20 eyes across four groups (BRVO, CRVO, PDR, controls). Using a standardized semi-automated image processing protocol, MLCs were identified and quantified over a 6 × 6 mm area temporal to the macula. Additionally, perfusion densities were measured, and analyses were performed to assess differences in MLC counts and their correlations with perfusion. RESULTS: Patients with BRVO, CRVO, and PDR exhibited significantly higher MLC counts compared to controls (p = 0.002 for all comparisons). Median MLC counts were lowest in controls (132; 95% CI, 63–180), followed by BRVO (382; 95% CI, 290–446), CRVO (688; 95% CI, 507–716), and highest in PDR patients (973; 95% CI, 805–999). A moderate-to-strong negative correlation was found between perfusion density and MLC count, suggesting increased MLC accumulation in areas of reduced perfusion in the superior vascular complex (p = 0.04), deep vascular complex (p < 0.01), as well as combined vascular complexes (p = 0.01). CONCLUSION: Here, we demonstrate that MLC density is significantly elevated in BRVO, CRVO, and PDR compared to healthy eyes and is inversely correlated with retinal perfusion. By applying consistent imaging and analysis across a wider peripheral retinal field, these findings highlight MLC quantification as a potential biomarker for disease severity and progression in ischemic retinopathies. Future investigations should explore whether modulating MLC responses could offer new therapeutic strategies to improve outcomes in these conditions.
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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 | 0.001 | 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".