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Article type: Research Article
Authors: Bindal, Priyadarshnia | Bindal, Umeshd | Lin, Chai Wena | Kasim, Noor Hayaty Abua | Ramasamy, Thamil Selvee A.Pb | Dabbagh, Alic | Salwana, Elye | Shamshirband, Shahaboddinf; g; *
Affiliations: [a] Department of Restorative Dentistry, Faculty of Dentistry, University of Malaya, Kuala Lumpur, Malaysia | [b] Department of Molecular Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia | [c] Wellness Research Cluster, Institute of Research Management and Monitoring (IPPP), University of Malaya, Kuala Lumpur, Malaysia | [d] School of Medicine, Taylor’s University, Selangor, Malaysia | [e] Institute of Virtual Informatics, Universiti Kebangsan, Malaysia, Malaysia | [f] Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam | [g] Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City, Vietnam
Correspondence: [*] Corresponding author: Shahaboddin Shamshirband, Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam. E-mail: [email protected].
Abstract: Dental stem cells isolated for human dental pulp are an excellent source for regenerative medicine and dentistry. Simulation of clinical scenario is one of the crucial challenges for evaluation of the efficacy of DPSCs in various regenerative therapies. In this study we evaluated the viability of DPSCs after treatment with artificial bacterial lipopolysaccharides (LPS) as the main component responsible for inducing inflammatory response in majority of the inflammatory conditions in clinical scenario. Although a number of studies have previously treated stem cells with LPS from bacteria, however the accuracy level of the outcome was not established. Here we have analyzed the outcome using adaptive neuro-fuzzy inferences system (ANFIS) to predict the viability of human DPSCs after treatment with bacterial LPS.
Keywords: Dental pulp stem cells, lipopolysaccharides, neuro-fuzzy, ANFIS, prediction
DOI: 10.3233/THC-170922
Journal: Technology and Health Care, vol. 25, no. 6, pp. 1041-1051, 2017
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