Amyloid Beta Immunoreactivity in the Retinal Ganglion Cell Layer of the Alzheimer’s Eye
Bibliographic record
Abstract
Alzheimer’s disease (AD) is the most prevalent form of dementia, accounting for 60-70% of all dementias. AD is often under-diagnosed and recognized only at a later, more advanced stage, and this delay in diagnosis has been suggested as a contributing factor in the numerous unsuccessful AD treatment trials. Although there is no known cure for AD, early diagnosis is important for disease management and care. A hallmark of AD is the deposition of amyloid-β (Aβ)-containing senile neuritic plaques and neurofibrillary tangles composed of hyperphosporylated tau in the brain. However, current in vivo methods to quantify Aβ in the brain are invasive, requiring radioactive tracers and positron emission tomography. Towards development of alternative methods to assess AD progression, we focus on the retinal manifestation of AD pathology. The retina is an extension of the central nervous system uniquely accessible to light-based, non-invasive ophthalmic imaging. However, earlier studies in human retina indicate that the literature is divided on the presence of Aβ in the AD retina. To help resolve this disparity, this study assessed retinal tissues from neuropathologically confirmed AD cases to determine the regional distribution of Aβ in retinal wholemounts and to inform on future retinal image studies targeting Aβ. Concurrent post-mortem brain tissues were also collected. Neuropathological cortical assessments including neuritic plaque (NP) scores and cerebral amyloid angiopathy (CAA) were correlated with retinal Aβ using immunohistochemistry, confocal microscopy, and quantitative image analysis. Aβ load was compared between AD and control (non-AD) eyes. Our results indicate that levels of intracellular and extracellular Aβ retinal deposits were significantly higher in AD than controls. Mid-peripheral Aβ levels were greater than central retina in both AD and control eyes. In AD retina, higher intracellular Aβ was associated with lower NP score, while higher extracellular Aβ was associated with higher CAA score. Our data support the feasibility of using the retinal tissue to assess ocular Aβ as a surrogate measure of Aβ in the brain of individuals with AD. Specifically, mid-peripheral retina possesses more Aβ deposition than central retina, and thus may be the optimal location for future in vivo ocular imaging.
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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 | 0.001 | 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".