an enhanced platform to analyse low-affinity amyloid β protein by integration of electrical detection and preconcentrator
Clicks: 203
ID: 135181
2017
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
Popular Article
30.0
/100
203 views
34 readers
AI Quality Assessment
Not analyzed
Readership in this journal
PopularRanked #67 of 107 articles by views in historia de la educación
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Abstract Sensitivity and limit of detection (LOD) enhancement are essential criteria for the development of ultrasensitive molecular sensors. Although various sensor types have been investigated to enhance sensitivity and LOD, analyte detection and its quantification are still challenging, particularly for protein-protein interactions with low association constants. To solve this problem, here, we used ion concentration polarization (ICP)-based preconcentration to increase the local concentration of analytes in a microfluidic platform for LOD improvement. This was the first demonstration of a microfluidic device with an integrated ICP preconcentrator and interdigitated microelectrode (IME) sensor to detect small changes in surface binding between antigens and antibodies. We detected the amyloid beta (Aβ) protein, an Alzheimer’s disease marker, with low binding affinity to its antibodies by adopting ICP preconcentration phenomena. We demonstrated that a combination of ICP preconcentrator and IME sensor increased the LOD by 13.8-fold to femtomolar level (8.15 fM), which corresponds to a significant advance for clinical applications.
| Reference Key |
yoo2017scientifican
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | ;Yong Kyoung Yoo;Dae Sung Yoon;Gangeun Kim;Jinsik Kim;Sung Il Han;Junwoo Lee;Myung-Sic Chae;Sang-Myung Lee;Kyu Hyoung Lee;Kyo Seon Hwang;Jeong Hoon Lee |
| Journal | historia de la educación |
| Year | 2017 |
| DOI |
10.1038/s41598-017-14338-4
|
| URL | |
| Keywords |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.