Improvement of the Bonding Properties of Mineral Trioxide Aggregate by Elastin-Like Polypeptide Supplementation.
Clicks: 416
ID: 46639
2019
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
Emerging Content
69.1
/100
416 views
282 readers
Trending
AI Quality Assessment
Not analyzed
Readership in this journal
EmergingRanked #1 of 53 articles by views in scanning
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
Elastin-like polypeptide (ELP) supplementation was previously reported to enhance the physical properties of mineral trioxide aggregate (MTA). The aim of this study was to investigate the effect of ELP supplementation on the bonding properties of MTA to dentin.Two types of ELPs were synthesized and mixed with MTA in a 0.3 liquid/powder ratio. The push-out bond strength test and interfacial observation with scanning electron microscopy were performed for ELP-supplemented MTA. The porosity of MTA fillings in the cavity was observed with microcomputed tomography. The stickiness, flow rate, and contact angle were additionally measured for potential increased bonding properties.ELP supplementation improved the bond strength of MTA to dentin. MTA supplemented by a specific ELP exhibited a less porous structure, higher stickiness, and higher flow rate. ELPs also decreased the contact angle to dentin.This research data verifies that ELP improves the bonding properties of MTA to a tooth structure. The sticky and highly flowable characteristics of ELP-supplemented MTA may provide intimate contact with dentin and supply a less porous cement structure, which might improve the bonding properties of MTA.
| Reference Key |
kim2019improvementscanning
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Kim, Hyun-Jung;Lee, Donghyun;Cho, Seungryong;Jang, Ji-Hyun;Kim, Sahng Gyoon;Kim, Sun-Young; |
| Journal | scanning |
| Year | 2019 |
| DOI |
10.1155/2019/3484396
|
| URL | |
| Keywords |
counting
image classification
blood
object detection
learning (artificial intelligence)
medical image processing
automatic identification
blood cells detection
blood smear images
cellular biophysics
complete blood cell count
machine learning approach
neural nets
patient diagnosis
red blood cells
white blood cells
|
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.