Evidence map›Paper›PMID 41659652›Full record

ArticlebioRxiv : the preprint server for biology2026

Catch-and-Display Immunoassay as an Accessible Platform for Digital Biomarker Detection.

Yuxuan Liu, Sam Walker, Michael Klaczko, Benjamin Singer, Michel Godin, Vincent Tabard-Cossa, Jon Flax, James McGrath

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Yuxuan LiuMaterials Science, University of Rochester, Rochester, NY 14627, United States.ORCID 0009-0001-6678-5408
Sam WalkerDepartment of Biomedical Engineering, University of Rochester, Rochester, NY 14627, United States.
Michael KlaczkoDepartment of Chemistry, University of Rochester, Rochester, NY 14627, United States.ORCID 0000-0003-0478-0561
Benjamin SingerDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, United States.ORCID 0000-0002-4721-6920
Michel GodinDepartment of Physics, University of Ottawa, Ottawa, ON K1N6N5, Canada.ORCID 0000-0002-9360-0675
Vincent Tabard-CossaDepartment of Physics, University of Ottawa, Ottawa, ON K1N6N5, Canada.ORCID 0000-0003-4375-717X
Jon FlaxDepartment of Biomedical Engineering, University of Rochester, Rochester, NY 14627, United States.
James McGrathDepartment of Biomedical Engineering, University of Rochester, Rochester, NY 14627, United States.ORCID 0000-0003-2017-8335

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital immunoassays provide exceptional analytical sensitivity for detecting low-abundance biomarkers, but their broad adoption is limited by practical barriers. Commercial platforms are prohibitively expensive for routine use by individual laboratories, and laboratory-scale concepts typically describe specialized biosensors and sophisticated workflows. Here, we introduce a nanomembrane-based Catch-and-Display Immunoassay (CAD-IA) as an accessible digital immunoassay for common laboratory settings. In CAD-IA, fluorescent nanoparticles are "captured" by the nanoscale pores of ultrathin silicon nitride membranes through a pipette powered filtration. The captured nanoparticles serve as optically isolated 'hotspots' for fluorescent immunocomplex formation when target antigen is present. Co-localization of the fluorescent particles and fluorescent immunocomplexes are then "displayed" and quantified by standard confocal microscopy to generate digital signals. CAD-IA is implemented using the μSiM-DX (

Identifiers

PMID41659652
PMCPMC12873968

What OpenQuestion holds

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LicenceCC BY-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.