Amyloid-β Deposits Lead to Retinal Degeneration in a Mouse Model of Alzheimer Disease
Bibliographic record
Abstract
PURPOSE: To compare the temporal and spatial expression patterns of amyloid precursor protein (APP), amyloid-beta deposits, inflammatory chemokines, and apoptosis in the retina of a mouse model of Alzheimer disease (AD). METHODS: Retinas of transgenic mice harboring a mutant presenilin (PS1) and a mutant APP gene were processed for TUNEL and immunohistochemistry with antibodies against APP, amyloid-beta, monocyte chemotactic protein (MCP)-1, and F4/80. Comparisons were made between age groups and between transgenic and wild-type congeners. RESULTS: The neuroretina demonstrated age-dependent increases in APP in the ganglion cells (RGCs) and in neurons of the inner nuclear layer (INL). Amyloid-beta demonstrated significant age-dependent deposition in the nerve fiber layer (NFL). TUNEL-positive RGC increased in an age-dependent fashion and in transgenic compared with wild-type congeners. Concomitant overexpression of MCP-1 and intense immunoreactivity for F4/80 suggested that RGCs upregulate MCP-1 in response to amyloid-beta. Activated microglia proliferated in response to MCP-1. In the outer retina, retinal pigment epithelium (RPE) demonstrated moderate age-dependent APP immunoreactivity, but nearby drusenlike deposits were not present. Amyloid-beta was observed in the choriocapillaris of the older animals. CONCLUSIONS: Amyloid-beta deposits accumulate with age in the retina of a transgenic mouse model of AD. The amyloid-beta loads are accompanied by increased immunoreactivity for MCP-1, F4/80, and TUNEL-positive profiles in the RGC layer. The results suggest that amyloid-beta causes neurodegeneration in the retina of the doubly mutant transgenic mouse model of AD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".