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Record W4390192228 · doi:10.1002/alz.082324

Hyperspectral analysis of amyloid beta (Aβ) evolutional changes in preclinical to late‐stage Alzheimer’s disease using matched brain and retinal tissue

2023· article· en· W4390192228 on OpenAlexaff
Margaret E. Flanagan, B Danner, Carlos Zamudio, Callen Spencer, Jaclyn Lilek, Arleen Matos, Meghan Morris, Kaouther Ajroud, Wei Xie, Catherine Bornbaum, Jared Westreich, Swati S. More

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsOsteoporosis Canada
Fundersnot available
KeywordsNeuropathologyRetinalPathologyRetinaAlzheimer's diseaseRetinal DisorderDementiaAmyloid betaMedicineBiologyNeuroscienceOphthalmologyDisease

Abstract

fetched live from OpenAlex

Abstract Background Alzheimer’s disease (AD) is a progressive, degenerative brain disorder that leads to cognitive impairment and eventually death. It is marked by buildup of abnormal amyloid aggregates (Aβ) in the brain. This study aimed to elucidate the neuropathologic sequence and corresponding [RetiSpec] hyperspectral signals of Aβ evolutional changes in preclinical to late‐stage AD using retinal and brain samples. Method We used brain‐ and retina‐matched human tissue samples from the NIH Brain Bank at Johns Hopkins. Study groups included: No AD neuropathologic change with negative AD neuropathology (n = 15); Low‐level AD neuropathologic change with positive AD neuropathology (n = 15); High level AD neuropathologic change with positive AD neuropathology (n = 15). To validate retinal hyperspectral imaging (rHSI) detection of Aβ in these retinal samples, we quantified Aβ oligomers (AβOs) in human retinal tissue. Immunohistochemistry was performed. We used humanized, affinity‐matured, IgG2 mAβ selective biotinylated antibody (ACU‐193) to detect soluble AβOs in retinal and brain tissues followed by objective quantification of ACU193 retinal staining by performing annotations and data extraction using QuPath software. One‐way ANOVA and t‐tests were performed to compare between controls and AD stages. Result When assessing ACU193 staining present from retinal tissue samples, we observed 29‐53% retinal tissue area positivity in controls and 15‐72% retinal tissue area positivity in the AD group. Level of AD pathology demonstrated an effect on the rHSI spectra (450‐600nm) in regions near the optic disc and the ventral periphery: intermediate AD pathology showed the strongest rHSI signature (decrease in optical transmittance) followed by high, then low AD pathology samples. Correlation of the Braak neurofibrillary tangle stage and ΔOD offered a Pearson r score of 0.79 (p = 0.004). Conclusion This study was the first to assess and quantify AβOs in clinically, neuropathologically, and hyperspectrally characterized postmortem matched brain and retinal tissues. Progression of well‐established neuropathologically classified AD stages (e.g Braak) may correlate with retinal AβO levels measured using rHSI. Certain retina regions that share nervous tissue with the brain (optic disc, periphery) are likely more sensitive to rHSI‐mediated detection of AD pathology. Larger samples are needed to comprehensively assess and quantify retinal AβOs.

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 score0.003

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.084
GPT teacher head0.388
Teacher spread0.304 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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