Evidence map›Paper›PMID 30906180›Full record

ArticleInstrumentation science & technology2017

Evaluation of Kinetics Using Label-Free Optical Biosensors.

Yung-Shin Sun, James P Landry, X D Zhu

Abstract read
In one paragraph

Article in Instrumentation science & technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

3 authors.

Yung-Shin SunDepartment of Physics, Fu-Jen Catholic University, New Taipei City, Taiwan.
James P LandryDepartment of Physics, University of California at Davis, Davis, CA, USA.
X D ZhuDepartment of Physics, University of California at Davis, Davis, CA, USA.

Funding

High-Speed Label-Free Optical Detection System for Small-Molecule MicroarraysR01HG003827 · NHGRI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI ZHU, XIANGDONG · 2006 to 2009
$2.2M
NHGRI NIH HHS R01 HG003827
6 · The paper itself

Abstract

Optical biosensors provide a platform for qualitatively and quantitatively analyzing various biomolecular interactions. In addition to advantages such as label-free and high-throughput detection, these devices are also capable of measuring real-time binding curves in response to changes in optical properties of biomolecules. These kinetic data may be fitted to models to extract binding affinities such as association rates, dissociation rates, and equilibrium dissociation constants. In these biosensors, one of the binding pair is usually immobilized on a solid substrate for capturing the other. Due to the nature of these surface-based methods, mass transport effects and immobilization heterogenetity may cause problems when fitting the kinetic curves with the simple one-to-one Langmuir model. Here real-time binding curves of various antibody-antigen reactions were obtained by using an ellipsometry-based biosensor, and the results were fitted to the simple one-to-one model as well as a more sophisticated approach. The results show that the one-to-two model fitted much better to the curves than the one-to-one model. The two-site model may be explained by assuming two immobilization configurations on the surface. In summary, in fitting real-time curves obtained from optical biosensors, more sophisticated models are usually required to take surface-related issues, such as immobilization heterogenetity and mass transport effects within targets, into account.

Indexed as

biomolecular interactionlabel-free optical biosensorLangmuir equationoblique-incidence reflectivity difference (OI-RD) microscopyreaction rates

Identifiers

PMID30906180
PMCPMC6430235

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Registered trials

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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.