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Record W7117150342 · doi:10.1002/alz70855_104480

Impaired Aβ Clearance Mechanisms at the Inner Blood‐Retina Barrier in Alzheimer's Disease: Insights from Ex Vivo Retinal Imaging

2025· article· en· W7117150342 on OpenAlexaff
Printha Wijesinghe, Amir Hosseini, Matthew Campbell, Shivani Tejpal, Justin R. Haynes, Jeanne Xi, Ian R. Mackenzie, Veronica Hirsch‐Reinshagen, Ging‐Yuek Robin Hsiung, Benjamin W. Spiller, Brian E. Wadzinski, Wellington Pham, Joanne A. Matsubara

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsVancouver Coastal HealthUniversity of British Columbia
Fundersnot available
KeywordsEx vivoRetinalPathogenesisRetinaIn vivoPhagocytosisPeripheral

Abstract

fetched live from OpenAlex

BACKGROUND: The imbalance between amyloid-beta (Aβ) production and clearance is a key factor in the pathogenesis of sporadic Alzheimer's disease (AD). This study investigates impaired clearance mechanisms by examining interactions among neuronal and glial cells, retinal vasculature, and blood-derived macrophages, particularly at the inner blood-retina barrier (iBRB), a functional analog of the blood-brain barrier (BBB), using wholemount neuroretinas and three-dimensional ex vivo imaging. These interactions cannot be similarly studied in brain tissues due to its highly complex structure. METHODS: Wholemount neuroretinas from human AD donors (N = 10, mean age ± SD: 76.8 ± 9.9 years; 6 males) and controls (N = 10, mean age: 72.5 ± 2.2 years; 5 males), as well as eye and brain cross-sections from APP-PS1 mice and controls (3 and 9 months, all females, N = 4 per group), were analyzed using three- and two-dimensional ex vivo retinal imaging. Immunolabeling markers included Aβ1-42 peptides (12F4), soluble Aβ1-42 oligomers (SAβOs, NIR-E3 nanobody), macroglia (GFAP, GS), microglia/macrophages (IBA1), water channels (AQP4), and retinal blood vessel endothelium (UEA-1). RESULTS: disease-associated microglia/macrophages (p = 0.0036), accompanied by reduced levels of macroglial markers GFAP (p = 0.0025), GS (p = 0.0015), and AQP4 (p = 0.0121), which are essential for glymphatic drainage, compared to age-matched controls. Clearance of SAβOs by peripheral macrophage-like monocytes through retinal blood vessels was also significantly diminished in AD retinas (p < 0.0001). In transgenic mouse retinal cross-sections, increased GFAP, AQP4, and IBA1 levels, along with elevated APP/Aβ peptides, indicated gliosis and AQP4 dysregulation, contributing to impaired clearance systems compared to sibling controls. CONCLUSION: The imaging plane (wholemount vs. cross-section) may influence outcomes in AD pathogenesis studies. Wholemount analysis revealed glymphatic clearance and microglial phagocytosis as compensatory mechanisms for mitigating Aβ accumulation, with peripheral macrophage-like monocytes contributing to SAβO clearance in control neuroretinas. These mechanisms were largely disrupted in AD donors. Ex vivo 3D retinal imaging, applied here for the first time to study clearance at the iBRB, provides novel insights into retinal and BBB analog processes in AD.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.276
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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