ArticleAnalytical chemistry2007
Concentration gradient immunoassay. 2. Computational modeling for analysis and optimization.
Article in Analytical chemistry, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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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.
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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.
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Who cites it
10 citing papers in PubMed, 37 citations in OpenAlex.
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- A continuous flow microfluidic calorimeter: 3-D numerical modeling with aqueous reactants.Thermochimica acta · 2015Article
- Development of novel anti-Kv 11.1 antibody-conjugated PEG-TiOJournal of nanoparticle research : an interdisciplinary forum for nanoscale science and technology · 2013Article
- Highly efficient binding of paramagnetic beads bioconjugated with 100,000 or more antibodies to protein-coated surfaces.Analytical chemistry · 2012Article
- Albumin pre-coating enhances intracellular siRNA delivery of multifunctional amphiphile/siRNA nanoparticles.International journal of nanomedicine · 2012Article
- The assembly state between magnetic nanosensors and their targets orchestrates their magnetic relaxation response.Journal of the American Chemical Society · 2011Article
- Small molecule immunosensing using surface plasmon resonance.Sensors (Basel, Switzerland) · 2010Review
- Finite-element model of interaction between fungal polysaccharide and monoclonal antibody in the capsule of Cryptococcus neoformans.The journal of physical chemistry. B · 2008Article
- Concentration gradient immunoassay. 1. An immunoassay based on interdiffusion and surface binding in a microchannel.Analytical chemistry · 2007Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
A novel microfluidic surface-based competition immunoassay, termed the concentration gradient immunoassay (described in detail in a companion paper (Nelson, K.; Foley, J.; Yager, P. Anal. Chem. 2007, 79, 3542-3548.) uses surface plasmon resonance (SPR) imaging to rapidly measure the concentration of small molecules. To conduct this assay, antibody and analyte are introduced into the two inlets of a T-sensor (Weigl, B. H.; Yager, P. Science 1999, 283, 346-347. Kamholz, A. E.; Weigl, B. H.; Finlayson, B. A.; Yager, P. Anal. Chem. 1999, 71, 5340-5347). Several millimeters downstream, antibody molecules with open binding sites can bind to a surface functionalized with immobilized antigen. This space- and time-dependent binding can be sensitively observed using SPR imaging. In this paper, we describe a complex three-dimensional finite element model developed to better understand the dynamic processes occurring with this assay. The model shows strong qualitative agreement with experimental results for small-molecule detection. The model confirms the experimental finding that the position within the microchannel at which the antibody binds to the immobilized analyte may be used to quantify the concentration of analyte in the sample. In addition, the model was used to explore the sensitivity of assay performance to parameters such as antibody and analyte concentrations, thereby giving insight into ways to optimize analysis speed and accuracy. Given the experimental verification of the computational results, this model serves as an efficient method to explore the influence of the flow rate, microchannel dimensions, and antibody concentration on the sensitivity of the assay.
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Registered trials
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.