In vivo Retinal Fluorescence Imaging With Curcumin in an Alzheimer Mouse Model
Bibliographic record
Abstract
Alzheimer’s disease (AD) is characterized by amyloid beta (Aβ) plaques in the brain detectable by highly invasive in vivo brain imaging or in post-mortem tissues. A non-invasive and inexpensive screening method is needed for early diagnosis of asymptomatic AD patients. The shared developmental origin and similarities with the brain make the retina a suitable surrogate tissue to assess Aβ load in AD. Using curcumin, a fluoroprobe that binds to Aβ, we labeled and measured the retinal fluorescence in vivo and compared with the immunohistochemical measurements of the brain and retinal Aβ load in the APP/PS1 mouse model. In vivo retinal images were acquired every 2 months using custom fluorescence scanning laser ophthalmoscopy (fSLO) after tail vein injections of curcumin in individual mice followed longitudinally from ages 5 to 19 months. At the same time points, 1-2 mice from the same cohort were sacrificed and immunohistochemistry was performed on their brain and retinal tissues. Results demonstrated cortical and retinal Aβ immunoreactivity were significantly greater in Tg than WT groups. Age-related increase in retinal Aβ immunoreactivity was greater in Tg than WT groups. Retinal Aβ immunoreactivity was present in the inner retinal layers and consisted of small speck-like extracellular deposits and intracellular labeling in the cytoplasm of a subset of retinal ganglion cells. In vivo retinal fluorescence with curcumin injection was significantly greater in older mice (11-19 months) than younger mice (5-9 months) in both Tg and WT groups. In vivo retinal fluorescence with curcumin injection was significantly greater in Tg than WT in older mice (ages 11-19 months). Finally, and most importantly, the correlation between in vivo retinal fluorescence with curcumin injection and Aβ immunoreactivity in the cortex was stronger in Tg compared to WT groups. Our data reveal that retina and brain of APP/PS1 Tg mice increasingly express Aβ with age. In vivo retinal fluorescence with curcumin correlated strongly with cortical Aβ immunohistochemistry in Tg mice. These findings suggest that using in vivo fSLO imaging of AD-susceptible retina may be a useful, non-invasive method of detecting Aβ in the retina as a surrogate indicator of Aβ load in the brain.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".