Evidence map›Paper›PMID 42199027›Full record

ArticleNanoscale horizons2026

Catch-and-display immunoassay for digital biomarker detection.

Yuxuan Liu, Samuel N Walker, Michael E Klaczko, Ahmet Gurcan, Benjamin H Singer, Michel Godin, Vincent Tabard-Cossa, Jonathan D Flax, James L McGrath

Abstract read
In one paragraph

Article in Nanoscale horizons, 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

9 authors.

Yuxuan LiuMaterials Science, University of Rochester, Rochester, NY 14627, USA.
Samuel N WalkerDepartment of Biomedical Engineering, University of Rochester, Rochester, NY 14627, USA. james.mcgrath@rochester.edu.
Michael E KlaczkoDepartment of Chemistry, University of Rochester, Rochester, NY 14627, USA.ORCID http://orcid.org/0000-0003-0478-0561
Ahmet GurcanDepartment of Biomedical Engineering, University of Rochester, Rochester, NY 14627, USA. james.mcgrath@rochester.edu.
Benjamin H SingerDepartment of Internal Medicine, University of Michigan, Ann Arbor, MI 48109, USA.
Michel GodinDepartment of Physics, University of Ottawa, Ottawa, ON K1N6N5, Canada.ORCID http://orcid.org/0000-0002-9360-0675
Vincent Tabard-CossaDepartment of Physics, University of Ottawa, Ottawa, ON K1N6N5, Canada.
Jonathan D FlaxDepartment of Biomedical Engineering, University of Rochester, Rochester, NY 14627, USA. james.mcgrath@rochester.edu.
James L McGrathDepartment of Biomedical Engineering, University of Rochester, Rochester, NY 14627, USA. james.mcgrath@rochester.edu.ORCID http://orcid.org/0000-0003-2017-8335

Funding

Solid-state nanopores and silicon nanomembranes for ultrasensitive protein biomarker detectionR01EB031581 · NIBIB · UNIVERSITY OF ROCHESTER · PI FLAX, JONATHAN D, GODIN, MICHEL · 2021 to 2024
$1.7M
NIBIB NIH HHS R01 EB031581
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 in 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), a cost-effective and laboratory-friendly digital immunoassay for common research 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 (microfluidic device featuring an ultrathin silicon membrane for diagnostics) platform, which is manually assembled from mass produced, cost-effective components. Using the traumatic brain injury (TBI) biomarker S100B as a model, we demonstrate that CAD-IA provides consistent digital outputs and linear quantification over a dynamic range of at least two orders of magnitude when digital and analog analyses are combined on the same image sets. We further demonstrate that the assay maintains linearity in serum matrices and achieves suitable sensitivity (LoD = 0.02 µg mL

Indexed as

S100 Calcium Binding Protein beta SubunitBiomarkersBiosensing TechniquesHumansImmunoassayLab-On-A-Chip DevicesNanoparticlesSilicon CompoundsBiomarkersS100B protein, humanS100 Calcium Binding Protein beta SubunitSilicon Compounds

Identifiers

PMID42199027
PMCPMC13563515

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.