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Record W7116929019 · doi:10.1002/alz70861_108703

Saliva Sampling and Testing Strategies for Screening AD Biomarkers Using a Portable Antibody Functionalized Bioanalyzer Platform

2025· article· en· W7116929019 on OpenAlexaffabout
S. Johri, Dylan Layton‐Matthews, Andrew Frank, Amit Arora, Evgueni Doukhanine, Rafal Iwasiow, Ravi Prakash, Organic Sensors and Devices Lab (OSDL)

Bibliographic record

VenueAlzheimer s & Dementia · 2025
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsBruyèreCarleton University
Fundersnot available
KeywordsSalivaSampling (signal processing)AntibodyClinical trialAmyloid (mycology)

Abstract

fetched live from OpenAlex

BACKGROUND: Saliva collection has been explored for non-invasive AD biomarker testing in several studies (Kirmess et al.,2021, De Meyer et al.,2020), however, challenges persist in maintaining consistent sampling and reliable benchmarking for the low salivary Aß42 concentration in healthy controls (0.6-66.1pg/ml; Agnello et al.,2024, Sabbagh et al.,2018). We share preliminary outcomes on using novel saliva collection kit prototypes from DNA Genotek Inc.(Ottawa) for clinical trial deployment of our patent-pending bioanalyzer platform. METHOD: Saliva was collected from healthy control (established using brain MRI at the IMHR, Royal Hospital, Ottawa) using two collection kit prototypes (Drool Saliva Collection(DSC) and Sponge-tip Saliva Collection(SSC)). Collected samples were spiked to 10 µg/ml of Aß42 and a dilution series (100ng/mL-100fg/mL) was created using the supernatant. Figure 1(A) shows the portable bioanalyzer (Ab-OEGFET; Johri et al.,2024, Johri et al.,2025) used for saliva testing. RESULT: The Ab-OEGFET sensor(bioanalyzer) predicts Aß42 concentration through an inversely correlated output current(ID-SAT). The output (Figure 1B) shows current modulation with gate bias (VSG), and Figure 1(C) shows the current modulation for different concentrations of Aß42(VSG=2.4V). The human saliva supernatant(HSN) collected via DSC kit prototype was tested first without Aß42 spiking, and then by spiking with 10 µg/mL Aß42, followed by serial dilution. The non-spiked HSN showed a predictable drop in current with increase in overall protein baseline concentration of saliva, whereas Aß42 spiked HSN showed a clear inflection ∼1ng/ml spiking level (Figure 2). In another experiment (Figure 3), HSN collected from DSC and SSC prototypes were spiked with 10 µg/ml Aß42. The DSC prototype represented inflection in sensing range ∼1ng/ml of spiked protein and the SSC prototype appeared to have inflection point ∼10 ng/ml of spiked protein, showing variation in overall protein baselines from the two collection strategies. As evident, DSC prototype samples had higher background protein concentration whereas samples from the SSC prototype had comparatively higher concentration of dilution buffer. CONCLUSION: The saliva sampling prototype testing with bioanalyzer platform was conducted to verify the protein stability and platform capability prior to deployment in an AD dementia clinical trial in Ottawa. The team will now proceed with testing saliva samples from patients with different stages of AD pathology as established using amyloid PET scans (IMHR, Ottawa).

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.640
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.101
GPT teacher head0.353
Teacher spread0.252 · 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 teacher head, 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

Citations0
Published2025
Admission routes2
Has abstractyes

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