Retinal dysfunction in <i>APOE4</i> knock‐in mouse model of Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: Late-onset Alzheimer's Disease (LOAD) is the predominant form of Alzheimer's disease (AD), and apolipoprotein E (APOE) ε4 is a strong genetic risk factor for LOAD. As an integral part of the central nervous system, the retina displays a variety of abnormalities in LOAD. Our study is focused on age-dependent retinal impairments in humanized APOE4-knock-in (KI) and APOE3-KI mice developed by the Model Organism Development and Evaluation for Late-Onset Alzheimer's Disease (MODEL-AD) consortium. METHODS: All the experiments were performed on 52- to 57-week-old mice. The retina was assessed by optical coherence tomography, fundoscopy, fluorescein angiography, electroretinography, optomotor response, gliosis, and neuroinflammation. mRNA sequencing was performed to find molecular pathways. RESULTS: APOE4-KI mice showed impaired retinal structure, vasculature, function, vision, increased gliosis and neuroinflammation, and downregulation of synaptogenesis. DISCUSSION: The APOE ε4 allele is associated with increased susceptibility to retinal degeneration compared to the APOE ε3 allele. HIGHLIGHTS: Apolipoprotein E (APOE)4 mice exhibit structural and functional deficits of the retina. The retinal defects in APOE4 mice are attributed to increased neuroinflammation. APOE4 mice show a unique retinal transcriptome, yet with key brain similarities. The retina offers a non-invasive biomarker for the detection and monitoring of Alzheimer's disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".