Evidence map›Paper›PMID 25691853›Full record

ArticleMicrofluidics and nanofluidics2015

Development of a low-volume, highly sensitive microimmunoassay using computational fluid dynamics-driven multiobjective optimization.

Mehdi Ghodbane, Anthony Kulesa, Henry H Yu, Tim J Maguire, Rene R Schloss, Rohit Ramachandran, Jeffrey D Zahn, Martin L Yarmush

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Microfluidics and nanofluidics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Mehdi GhodbaneDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, New Jersey 08854, USA.
Anthony KulesaDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, New Jersey 08854, USA.
Henry H YuDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, New Jersey 08854, USA.
Tim J MaguireDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, New Jersey 08854, USA.
Rene R SchlossDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, New Jersey 08854, USA.
Rohit RamachandranDepartment of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, 98 Brett Road, Piscataway, NJ 08854, USA.
Jeffrey D ZahnDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, New Jersey 08854, USA.
Martin L YarmushDepartment of Biomedical Engineering, Rutgers, The State University of New Jersey, 599 Taylor Road, Piscataway, New Jersey 08854, USA ; Center for Engineering in Medicine/Surgical Services, Massachusetts General Hospital, 51 Blossom Street, Boston, MA, 02114, USA.

Funding

Bio-MicroElectroMechanical Systems Resource CenterP41EB002503 · NIBIB · MASSACHUSETTS GENERAL HOSPITAL · PI TONER, MEHMET · 2004 to 2018
$17.4M
UMDNJ TRAINING GRANT IN BIOTECHNOLOGYT32GM008339 · NIGMS · RUTGERS THE ST UNIV OF NJ NEW BRUNSWICK · PI YARMUSH, MARTIN L · 1989 to 2019
$6.1M
Collaborations to Extend the Microphysiology Database for Multiple Organ Models,UH2TR000503 · NCATS · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI TAYLOR, D. LANSING, YARMUSH, MARTIN L · 2012 to 2013
$2.3M
NCATS NIH HHS UH2 TR000503NIBIB NIH HHS P41 EB002503NIGMS NIH HHS T32 GM008339
6 · The paper itself

Abstract

Immunoassays are one of the most versatile and widely performed biochemical assays and, given their selectivity and specificity, are used in both clinical and research settings. However, the high cost of reagents and relatively large sample volumes constrain the integration of immunoassays into many applications. Scaling the assay down within microfluidic devices can alleviate issues associated with reagent and sample consumption. However, in many cases a new device is designed and empirically optimized for each specific analyte, a costly and time consuming approach. In this paper, we report the development of a microfluidic bead-based immunoassay which, using antibody coated microbeads, can potentially detect any analyte or combination of analytes for which antibody coated microbeads can be generated. We also developed a computational reaction model and optimization algorithm that can be used to optimize the device for any analyte. We applied this technique to develop a low volume IL-6 immunoassay with high sensitivity (358 fM, 10 pg/mL) and a large dynamic range (4 orders of magnitude). This device design and optimization technique can be used to design assays for any protein with an available antibody and can be used with a large number of applications including biomarker discovery, temporal in vitro studies using a reduced number of cells and reagents, and analysis of scarce biological samples in animal studies and clinical research settings.

Indexed as

Computational Fluid DynamicsImmunoassayMicrofluidicMulti-Objective Optimization

Identifiers

PMID25691853
PMCPMC4327895

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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.