Nanoscale flow cytometry‐based quantification of blood‐based extracellular vesicle biomarkers distinguishes MCI and Alzheimer's disease
Bibliographic record
Abstract
INTRODUCTION: Accurate testing for Alzheimer's disease (AD) represents a crucial step for therapeutic advancement. Currently, tests are expensive and require invasive sampling or radiation exposure. METHODS: We developed a nanoscale flow cytometry (nFC)-based assay of extracellular vesicles (EVs) to screen biomarkers in plasma from mild cognitive impairment (MCI), AD, or controls. RESULTS: Circulating amyloid beta (Aβ), tau, phosphorylated tau (p-tau)181, p-tau231, p-tau217, p-tauS235, ubiquitin, and lysosomal-associated membrane protein 1-positive EVs distinguished AD samples. p-tau181, p-tau217, p-tauS235, and ubiquitin-positive EVs distinguished MCI samples. The most sensitive marker for AD distinction was p-tau231, with an area under the receiver operating characteristic curve (AUC) of 0.96 (sensitivity 0.95/specificity 1.0) improving to an AUC of 0.989 when combined with p-tauS235. DISCUSSION: This nFC-based assay accurately distinguishes MCI and AD plasma without EV isolation, offering a rapid approach requiring minute sample volumes. Incorporating nFC-based measurements in larger populations and comparison to "gold standard" biomarkers is an exciting next step for developing AD diagnostic tools. HIGHLIGHTS: Extracellular vesicles represent promising biomarkers of Alzheimer's disease (AD) that can be measured in the peripheral circulation. This study demonstrates the utility of nanoscale flow cytometry for the measurement of circulating extracellular vesicles (EVs) in AD blood samples. Multiple markers including amyloid beta, tau, phosphorylated tau (p-tau)181, p-tau231, p-tau217, and p-tauS235 accurately distinguished AD samples from healthy controls. Future studies should expand blood and cerebrospinal fluid-based EV biomarker development using nanoflow cytometry approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| 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".