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Record W4406222894 · doi:10.1002/alz.094671

Validation of the plasma phosphorylated tau quantified using NUcleic acid Linked Immuno‐Sandwich Assay (NULISA) for the detection of Alzheimer’s disease pathology

2024· article· en· W4406222894 on OpenAlexaff
Yi‐Ting Wang, Nicholas J. Ashton, Joseph Therriault, Andréa L. Benedet, Arthur C. Macedo, Ilaria Pola, Étienne Aumont, Guglielmo Di Molfetta, Jaime Fernández Arias, Kübra Tan, Nesrine Rahmouni, Stijn Servaes, Richard Isaacson, Tevy Chan, Seyyed Ali Hosseini, Cécile Tissot, Sulantha Mathotaarachchi, Jenna Stevenson, Firoza Z Lussier, Tharick A. Pascoal, Serge Gauthier, Kaj Blennow, Henrik Zetterberg, Pedro Rosa‐Neto

Bibliographic record

VenueAlzheimer s & Dementia · 2024
Typearticle
Languageen
FieldMedicine
TopicAlzheimer's disease research and treatments
Canadian institutionsMcGill University
Fundersnot available
KeywordsNucleic acidDiseasePhosphorylationPathologyAlzheimer's diseaseChemistryBiologyComputational biologyBiochemistryMedicine

Abstract

fetched live from OpenAlex

Abstract Background Blood‐based biomarkers have been revolutionizing the detection, diagnosis and screening of Alzheimer’s disease (AD). Antibody‐based immunoassays are powerful tools to investigate pathological changes indicated by blood‐based biomarkers and have been studied extensively in AD research. A novel proteomic technology ‐ NUcleic acid Linked Immuno‐Sandwich Assay (NULISA) – was developed to improve the sensitivity of traditional proximity ligation assays and offer a comprehensive outlook for protein biomarkers in neurodegenerative diseases. Due to the relative novelty of the NULISA technology in quantifying AD plasma biomarkers, validation through comparisons with more established methods is required. Method In this present study, we assessed 397 participants from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort where participants had plasma measurements of p‐tau181, p‐tau217 and p‐tau231 from both NULISA and other established immunoassays. Participants also underwent neuroimaging assessments including MRI, amyloid and tau positron emission tomography (PET). Result Our findings suggest an excellent agreement between plasma p‐tau variants quantified using different immunoassays and strong associations with PET signals in the brain (Fig. 1). As shown in Figure 2, similar to p‐tau217 quantified using Janssen and ALZpath immunoassays, plasma p‐tau217 NULISA shows excellent discriminative accuracy for abnormal amyloid‐PET (AUC = 0.918, 95%CI: 0.883 to 0.953, P < 0.0001) and abnormal tau‐PET status (AUC = 0.939; 95%CI: 0.909 to 0.969, P < 0.0001). It also presents the capability for differentiating tau‐PET staging (Table 1). Conclusion Validation of the NULISA CNS panel adds to the current analytical methods for AD diagnosis, screening, and staging, and could potentially expedite the development of a blood‐based biomarker panel.

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)0.006
metaresearch head score (Gemma)0.007
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.052
GPT teacher head0.325
Teacher spread0.273 · 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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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