Nanomedicine Research Journal

Nanomedicine Research Journal

A New Approach to Ceramics Based on the Tissue Reaction: A Versatile Ceramic for Pharmaceutical, Dental and Ancient Artifacts Applications Using Machine Learning (ML) Modeling

Document Type : Original Research Article

Authors
Xingang Tan 1, 2
Samira Basir Shabestari 3
Bahareh Noshadi 4
Atefeh Ghorbani 5
Foad Iranmanesh 6
Azadeh Asefnejad 7
1 School of History and Society, Chongqing Normal University, China, 401331
2 School of Cultural Heritage, Chongqing College of Arts and Sciences, China, 402160
3 Department of ENT and Head and Neck, Firoozgar Hospital, School of Medicine, ENT and Head and Neck Research Center, Iran University of Medical Sciences, Tehran, Iran
4 Faculty of Pharmacy, Department of Pharmaceutical Chemistry, Near East University, Nicosia/TRNC, Mersin 10, Turkey
5 Biotechnology Department, Falavarjan Branch, Islamic Azad University, Isfahan, Iran
6 Endodontic Department, Dental school, Rafsanjan University of Medical Sciences, Rafsanjan, Iran
7 Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
10.22034/nmrj.2024.03.006
Abstract
The field of bioceramics has emerged as a critical component in various medical and dental applications, with calcium phosphate (CaP) materials like tricalcium phosphate (TCP) gaining significant attention. CaP bioceramics are valued for their exceptional biocompatibility, osteoconductivity, and ability to promote new bone formation, making them invaluable in the optimization of dental implant integration and performance. This study explores a novel approach to developing versatile CaP-based ceramics that can find applications in the pharmaceutical, dental, and even ancient artifacts preservation domains, leveraging the power of machine learning (ML) modeling techniques. Tricalcium phosphate, a widely studied CaP ceramic, was the focus of this investigation, as it can be fabricated with varying degrees of crystallinity and porosity to tailor its biodegradation and bone regeneration properties. Through the use of a feedforward artificial neural network (FFANN), the researchers were able to predict the changes in dental ceramics, biocompatibility, and tissue reactions across a wide range of non-toxicity and bone growth parameters. The FFANN modeling approach provided valuable insights into the relationships between these key attributes, allowing for the optimization of CaP-based ceramics for specific clinical and preservation applications. The versatility of TCP extends beyond dental implants, with applications in periodontal regeneration, tooth root repair, and even direct pulp capping procedures. By manipulating the material's composition and microstructure, researchers and clinicians can tailor the performance of CaP bioceramics to meet the diverse needs of the healthcare and cultural heritage sectors. As the field of bioceramics continues to evolve, the integration of advanced ML modeling techniques, such as the FFANN approach employed in this study, promises to unlock new possibilities for the development of innovative, tissue-friendly ceramics that can revolutionize dentistry, pharmaceutical formulations, and the preservation of precious ancient artifacts.
Keywords
Ceramics
Calcium Phosphate (CaP)
Dentistry
Machine Learning (ML) Modeling
Ancient Artifacts

Subjects

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Nanomedicine Research Journal
Volume 9, Issue 3
Autumn 2024
Pages 283-297