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Record W4390194741 · doi:10.1002/alz.080244

Plasma based assessment of extracellular vesicles biomarkers in Alzheimer’s Disease

2023· article· en· W4390194741 on OpenAlexaff
Thamara Dayarathna, Austyn D. Roseborough, Janice Gomes, Reza Khazaee, Shawn N. Whitehead, Hon S. Leong, Stephen Pasternak

Bibliographic record

VenueAlzheimer s & Dementia · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicExtracellular vesicles in disease
Canadian institutionsRobarts Clinical TrialsParkwood InstituteSunnybrook HospitalWestern University
Fundersnot available
KeywordsBiomarkerAntibodyPathologyAmyloid (mycology)NeurodegenerationAmyloid betaBiologySynaptophysinMolecular biologyDiseaseMedicineImmunologyImmunohistochemistryBiochemistry

Abstract

fetched live from OpenAlex

Abstract Background Currently there are no widely accepted diagnostic tests for Alzheimer’s disease (AD). Although there is much excitement in the field of biomarker development, most proposed tests for AD are expensive, require highly specialized sample handling, invasive approaches or radiation exposure. The goal of this project was to develop non‐invasive AD biomarker assessment using nanoflow cytometry to examine proteins on circulating brain‐derived extracellular vesicles in plasma. Method Plasma samples were collected from a population of individuals clinically diagnosed with MCI and mild, moderate or severe Alzheimer’s disease and cognitively intact control subjects. Plasma was incubated with fluorescently‐conjugated antibodies against candidate biomarkers including oligomeric amyloid, fibrillar amyloid, amyloidβ‐42, pTau‐T181, pTau‐T217, pTau‐S235, Ubiquitin, Neurofilament, α‐synuclein and synaptophysin. After incubation, labelled events were quantified using the Apogee Microplus nanoflow cytometer. Using Receiver‐Operator Curve (ROC) analysis, the ability of individual markers and various combinations to distinguish diagnostic groups was determined. Result Nanoflow cytometry quantification of pTau‐T181, pTau‐T217, pTau‐S235, pTau‐T231, Aβ‐42 (among others) successfully distinguished MCI and AD plasma from healthy controls. Using ROC analysis, our most sensitive antibodies (pTau‐ T231) separate controls from AD patients with an AUC of 0.96 (Sens: 0.95/Spec: 1.0). Other prominent antibodies include pTau 181 with an AUC of 0.93 (Sens: 0.92/Spec: 0.92). The most promising combination of antibodies combines beta‐amyloid42 and pTau‐181 to separate AD patients from controls with an AUC of 0.960 (Sens: 0.93, Spec: 0.92). Isolation of EVs from plasma and validation of marker labeling was performed with Western Blot, ELISA and transmission electron microscopy. Conclusion Here we demonstrate the use of nanoflow cytometry to directly quantitate proteins on circulating extracellular vesicles in plasma from MCI and AD patients. The assays are rapid, inexpensive, directly quantitative, and require mini sample volume and manipulation. Future studies are required involving a larger population of patients who have had ‘gold standard’ biomarkers (Amyloid and tau CSF measurements), longitudinal follow‐up and/or autopsy confirmed diagnosis of 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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.024
GPT teacher head0.292
Teacher spread0.268 · 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 designObservational
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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