Retinal Manifestations of Alzheimer’s Disease: MicroRNAs and Microglial Phenotypes
Bibliographic record
Abstract
Abstract Background As part of the CNS, retina is a promising site for studying Alzheimer’s disease (AD) and developing potential biomarkers for accessible screening and early diagnosis. Our group has previously investigated retinal amyloid beta (Aβ) deposition, glial cell degeneration, drusen aggregation, microRNA (miRNA) analysis in AD mouse models, and in vivo fluorescence in the retinas from AD donor eyes and AD animal models. We report on our updated work on miRNA expression in mouse models and microglia morphology in the human AD retina. Method Ten candidate miRNAs were investigated in the brain and eye tissues and tear fluids of transgenic APP‐PS1 mice, non‐carrier siblings, and wildtype (WT, C57BL/6J) controls at young (∼3months) and old (∼10months) ages. 3D microglia morphology was quantitatively and qualitatively analysed in Z‐stacks of human donor retinal tissues stained for microglia (IBA1), activated microglia (CD68) and retinal vessels (UEA‐lectin). Result Relative miRNA expression levels were similar in APP‐PS1 mice and non‐carrier siblings compared with WT, suggesting the miRNAs were mostly intergenic. Relative miRNA expression was lower in neocortex‐hippocampus, eye tissues, and tear fluids compared to other regions in young transgenic mice, but the same regions showed higher relative miRNA expression in old transgenic mice. miRNAs associated with Aβ misfolding (miRNA‐101a, ‐15a and ‐342) and proinflammation (miRNA‐125b, ‐146a, and ‐34a) showed significant upregulation in tear fluids with disease progression tracked by cortical Aβ load and reactive astrogliosis. Custom software analysis suggest distinct populations of microglia in the AD retina vs. age‐matched control human retina based on 1) morphology (e.g., ramified to amoeboid), 2) their activation state and 3) retinal vessel proximity. Conclusion Our results demonstrate the non‐invasive potential for analysis of miRNAs from tear fluids and the in vivo imaging of the retinal microglial for tracking AD progression.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".