Diagnosing Alzheimer's Disease from Circulating Blood Leukocytes Using a Fluorescent Amyloid Probe
Bibliographic record
Abstract
BACKGROUND: Toxic amyloid-β (Aβ) peptides aggregate into higher molecular weight assemblies and accumulate not only in the extracellular space, but also in the walls of blood vessels in the brain, increasing their permeability, and promoting immune cell migration and activation. Given the prominent role of the immune system, phagocytic blood cells may contact pathological brain materials. OBJECTIVE: To develop a novel method for early Alzheimer's disease (AD) detection, we used blood leukocytes, that could act as "sentinels" after trafficking through the brain microvasculature, to detect pathological amyloid by labelling with a conformationally-sensitive fluorescent amyloid probe and imaging with confocal spectral microscopy. METHODS: Formalin-fixed peripheral blood mononuclear cells (PBMCs) from cognitively healthy control (HC) subjects, mild cognitive impairment (MCI) and AD patients were stained with the fluorescent amyloid probe K114, and imaged. Results were validated against cerebrospinal fluid (CSF) biomarkers and clinical diagnosis. RESULTS: K114-labeled leukocytes exhibited distinctive fluorescent spectral signatures in MCI/AD subjects. Comparing subjects with single CSF biomarker-positive AD/MCI to negative controls, our technique yielded modest AUCs, which improved to the 0.90 range when only MCI subjects were included in order to measure performance in an early disease state. Combining CSF Aβ 42 and t-Tau metrics further improved the AUC to 0.93. CONCLUSION: Our method holds promise for sensitive detection of AD-related protein misfolding in circulating leukocytes, particularly in the early stages of disease.
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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 | 0.001 |
| 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 | 0.001 | 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".