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Record W4416779641 · doi:10.1002/smll.202506622

Magnetically Retrievable Platinum Nanoreporters for Efficient Lateral Flow Immunoassay in Complex Bio‐Samples

2025· article· en· W4416779641 on OpenAlexafffund
Yuxi Cheng, Luca Panariello, Adam Creamer, Chris Sadler, André Shamsabadi, Kathleen Lupien, Ali Vaughan, Thomas Gervais

Bibliographic record

VenueSmall · 2025
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsPolytechnique Montréal
FundersNIHR Oxford Biomedical Research CentreNatural Sciences and Engineering Research Council of CanadaEngineering and Physical Sciences Research CouncilMitacsImperial College LondonHORIZON EUROPE Marie Sklodowska-Curie ActionsRosetrees TrustWellcome TrustRoyal Academy of EngineeringCancer Research UK
KeywordsNitrocellulosePlatinumDetection limitImmunoassayMagnetic separationSIGNAL (programming language)Flow (mathematics)Magnetic nanoparticles

Abstract

fetched live from OpenAlex

Abstract Lateral flow immunoassays (LFIAs) are widely used for point‐of‐care diagnostics, but their development is challenged by the complexity and variability of patient samples. In particular, LFIAs often exhibit reduced sensitivity and specificity when used with patient samples, compared to their performance with analyte‐spiked idealized matrices. Patient samples are inherently complex, with variations in physical and biochemical properties between patients. This complexity has consequences for the performance of LFIAs, and can result in non‐specific binding on the test line, discoloration of the nitrocellulose membrane, and incomplete sample flow along the test strip. To address these challenges, a magnetically retrievable platinum nanoreporter (termed Pt@Fe 3 O 4 ) is developed for LFIAs. Leveraging the magnetic properties of the Fe 3 O 4 core, magnetic separation is utilized to enable the purification and concentration of target antigens from complex human matrices, including serum, saliva, and even stool samples. This also eliminates assay inconsistencies caused by inter‐sample variability. Further, the suitability of Pt@Fe 3 O 4 nanoreporters has been explored for use as detection probes in LFIAs. Signal enhancement is demonstrated by the utilization of the magnetic and enzyme‐mimicking activity of the nanoreporter, resulting in a marked improvement in sensitivity, as evidenced by a 2‐ to 4‐fold decrease in the visual limit of detection.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.668
Threshold uncertainty score0.425

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 head0.016
GPT teacher head0.218
Teacher spread0.202 · 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 designSimulation or modeling
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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