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Hybrid machine learning approaches in viability assessment of dental pulp stem cells treated with platelet-rich concentrates on different periods

Citation

Bindal, P and Bindal, U and Kazemipoor, M and Kazemipoor, M and Jha, SK, Hybrid machine learning approaches in viability assessment of dental pulp stem cells treated with platelet-rich concentrates on different periods, Applied Medical Informatics, 41, (3) pp. 93-101. ISSN 1224-5593 (2019) [Refereed Article]


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Copyright 2019 The Authors. Licensed under Creative Commons Attribution 4.0 International (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/

Official URL: https://ami.info.umfcluj.ro/index.php/AMI/article/...

Abstract

The unique characteristics of dental pulp stem cells (DPSCs), like multi-lineage differentiation, have attracted considerable interest among clinicians and researchers for the treatment of various diseases. Platelet-derived concentrates (PRSs) are utilized for wound healing, due to the plethora of growth factors that are released from platelets. In this study, DPSCs were cultured with one of the three culture supplements, including fetal bovine serum (FBS), human platelet-rich plasma (PRP), and human platelet lysate (HPL). The viability effects of these platelet-derived culture supplements on DPSCs were evaluated using hybrid approaches of fuzzy-genetic methods. The results showed that DPSCs cultured in HPL have higher viability than FBS and PRP. It is suggested that fuzzy-genetic algorithm (GA) is an accurate approach to estimate the effect of platelet concentrates on the proliferation of stem cells derived from the human tooth.

Item Details

Item Type:Refereed Article
Keywords:stem cells, AI, human platelet-rich concentrates, fuzzy-genetic algorithm
Research Division:Biological Sciences
Research Group:Biochemistry and Cell Biology
Research Field:Cellular Interactions (incl. Adhesion, Matrix, Cell Wall)
Objective Division:Expanding Knowledge
Objective Group:Expanding Knowledge
Objective Field:Expanding Knowledge in the Medical and Health Sciences
UTAS Author:Bindal, P (Dr Priyadarshni Bindal)
ID Code:136091
Year Published:2019
Deposited By:Centre for Rural Health
Deposited On:2019-11-29
Last Modified:2019-12-16
Downloads:3 View Download Statistics

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