Evidence map›Paper›PMID 39550778›Full record

ArticleBiosensors & bioelectronics2025

A label-free optical biosensor-based point-of-care test for the rapid detection of Monkeypox virus.

Mete Aslan, Elif Seymour, Howard Brickner, Alex E Clark, Iris Celebi, Michael B Townsend, Panayampalli S Satheshkumar, Megan Riley, Aaron F Carlin, M Selim Ünlü and 1 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biosensors & bioelectronics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing 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

9 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Mete AslanDepartment of Electrical and Computer Engineering, Boston University, Boston, MA, 02215, USA.
Elif SeymouriRiS Kinetics, Boston University, Business Incubation Center, Boston, MA, 02215, USA.
Howard BricknerDepartment of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, CA, 92093, USA.
Alex E ClarkDepartment of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, CA, 92093, USA.
Iris CelebiDepartment of Electrical and Computer Engineering, Boston University, Boston, MA, 02215, USA.
Michael B TownsendPoxvirus and Rabies Branch, Centers for Disease Control and Prevention, Atlanta, GA, 30329, USA.
Panayampalli S SatheshkumarPoxvirus and Rabies Branch, Centers for Disease Control and Prevention, Atlanta, GA, 30329, USA.
Megan RileyaxiVEND, Winter Garden, FL, 34787, USA.
Aaron F CarlinDepartment of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, CA, 92093, USA; Department of Pathology, University of California, San Diego, CA, 92093, USA.
M Selim ÜnlüDepartment of Electrical and Computer Engineering, Boston University, Boston, MA, 02215, USA; Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA. Electronic address: selim@bu.edu.
Partha RayDepartment of Medicine, Division of Infectious Diseases and Global Public Health, University of California, San Diego, CA, 92093, USA. Electronic address: pray@health.ucsd.edu.

Funding

VirologyP30AI036214 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI SUSAN JANET LITTLE · 1994 to 2026
$78.4M
NIAID NIH HHS P30 AI036214
6 · The paper itself

Abstract

Diagnostic approaches that combine the high sensitivity and specificity of laboratory-based digital detection with the ease of use and affordability of point-of-care (POC) technologies could revolutionize disease diagnostics. This is especially true in infectious disease diagnostics, where rapid and accurate pathogen detection is critical to curbing the spread of disease. We have pioneered an innovative label-free digital detection platform that utilizes Interferometric Reflectance Imaging Sensor (IRIS) technology. IRIS leverages light interference from an optically transparent thin film, eliminating the need for complex optical resonances to enhance the signal by harnessing light interference and the power of signal averaging in shot-noise-limited operation In our latest work, we have further improved our previous 'Single-Particle' IRIS (SP-IRIS) technology by allowing the construction of the optical signature of target nanoparticles (whole virus) from a single image. This new platform, 'Pixel-Diversity' IRIS (PD-IRIS), eliminated the need for z-scan acquisition, required in SP-IRIS, a time-consuming and expensive process, and made our technology more applicable to POC settings. Using PD-IRIS, we quantitatively detected the Monkeypox virus (MPXV), the etiological agent for Monkeypox (Mpox) infection. MPXV was captured by anti-A29 monoclonal antibody (mAb 69-126-3) on Protein G spots on the sensor chips and were detected at a limit-of-detection (LOD) - of 200 PFU/mL (∼3.3 aM). PD-IRIS was superior to the laboratory-based ELISA (LOD - 1800 PFU/mL) used as a comparator. The specificity of PD-IRIS in MPXV detection was demonstrated using Herpes simplex virus, type 1 (HSV-1), and Cowpox virus (CPXV). This work establishes the effectiveness of PD-IRIS and opens possibilities for its advancement in clinical diagnostics of Mpox at POC. Moreover, PD-IRIS is a modular technology that can be adapted for the multiplex detection of pathogens for which high-affinity ligands are available that can bind their surface antigens to capture them on the sensor surface.

Indexed as

Biosensing TechniquesMonkeypox virusAnimalsEquipment DesignHumansInterferometryLimit of DetectionPoint-of-Care SystemsPoint-of-Care TestingIntact virus detectionLabel-free biosensorMonkeypox (mpox)Pixel diversity interferometric reflectance imaging sensor (PD-IRIS)Point of care (POC) diagnostics

Identifiers

PMID39550778
PMCPMC12962341

What OpenQuestion holds

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LicenceCC BY
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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.