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Potential bioactive coating system for high-performance absorbable magnesium bone implants

Sarian MN, Iqbal N, Sotoudehbagha P, Razavi M, Ahmed QU, Sukotjo C, et al.

Bioact Mater, 2022 Jun;12:42-63.
PMID: 35087962 DOI: 10.1016/j.bioactmat.2021.10.034

Magnesium alloys are considered the most suitable absorbable metals for bone fracture fixation implants. The main challenge in absorbable magnesium alloys is their high corrosion/degradation rate that needs to be controlled. Various coatings have been applied to magnesium alloys to slow down their corrosion rates to match their corrosion rate to the regeneration rate of the bone fracture. In this review, a bioactive coating is proposed to slow down the corrosion rate of magnesium alloys and accelerate the bone fracture healing process. The main aim of the bioactive coatings is to enhance the direct attachment of living tissues and thereby facilitate osteoconduction. Hydroxyapatite, collagen type I, recombinant human bone morphogenetic proteins 2, simvastatin, zoledronate, and strontium are six bioactive agents that show high potential for developing a bioactive coating system for high-performance absorbable magnesium bone implants. In addition to coating, the substrate itself can be made bioactive by alloying magnesium with calcium, zinc, copper, and manganese that were found to promote bone regeneration.
Fulltext Bioactive Carbonate Apatite Cement with Enhanced Compressive Strength via Incorporation of Silica Calcium Phosphate Composites and Calcium Hydroxide

Cahyanto A, Liemidia M, Karlina E, Zakaria MN, Shariff KA, Sukotjo C, et al.

Materials (Basel), 2023 Mar 03;16(5).
PMID: 36903186 DOI: 10.3390/ma16052071

Carbonate apatite (CO3Ap) is a bioceramic material with excellent properties for bone and dentin regeneration. To enhance its mechanical strength and bioactivity, silica calcium phosphate composites (Si-CaP) and calcium hydroxide (Ca(OH)2) were added to CO3Ap cement. The aim of this study was to investigate the effect of Si-CaP and Ca(OH)2 on the mechanical properties in terms of the compressive strength and biological characteristics of CO3Ap cement, specifically the formation of an apatite layer and the exchange of Ca, P, and Si elements. Five groups were prepared by mixing CO3Ap powder consisting of dicalcium phosphate anhydrous and vaterite powder added by varying ratios of Si-CaP and Ca(OH)2 and 0.2 mol/L Na2HPO4 as a liquid. All groups underwent compressive strength testing, and the group with the highest strength was evaluated for bioactivity by soaking it in simulated body fluid (SBF) for one, seven, 14, and 21 days. The group that added 3% Si-CaP and 7% Ca(OH)2 had the highest compressive strength among the groups. SEM analysis revealed the formation of needle-like apatite crystals from the first day of SBF soaking, and EDS analysis indicated an increase in Ca, P, and Si elements. XRD and FTIR analyses confirmed the presence of apatite. This combination of additives improved the compressive strength and showed the good bioactivity performance of CO3Ap cement, making it a potential biomaterial for bone and dental engineering applications.
Fulltext Knowledge, Perceived Risk, and Preventive Behaviors amidst Covid-19 Pandemic among Dental Students in Malaysia

Ismail A, Ismail NH, Abu Kassim NYM, Lestari W, Ismail AF, Sukotjo C

Dent J (Basel), 2021 Dec 14;9(12).
PMID: 34940048 DOI: 10.3390/dj9120151

PURPOSE: Coronavirus disease 19 (COVID-19) has affected dental education in Malaysia. However, studies on dental students' knowledge, perception, and behaviors with regards to COVID-19 are very limited. Thus, this study aims to determine the knowledge status, perceived risk, and preventive behaviors of dental students in Malaysia regarding COVID-19.
METHODS: A cross-sectional study was conducted among students from 13 dental schools across Malaysia using online questionnaires.
RESULTS: From 355 respondents, 93.5% obtained a high score of knowledge of COVID-19. Female respondents scored higher than males in perceived risks and preventive behaviors. Chinese respondents scored highest in knowledge, while Malay respondents had the highest perceived risk score. The mean preventive behavior score did not vary across ethnicity. On-campus students scored higher in knowledge and perceived risk whereas off-campus students practiced more preventive behaviors. Clinical students' knowledge score was higher than preclinical students. Final year students scored higher in knowledge and perceived risk compared to their juniors.
CONCLUSION: The majority of dental students have good knowledge and a high perceived risk of COVID-19, and they practiced most of the preventive behaviors. However, the latest information on this disease should be incorporated into dental schools' curriculums and updated periodically.
Artificial intelligence to predict pre-clinical dental student academic performance based on pre-university results: A preliminary study

Lestari W, Abdullah AS, Amin AMA, Nurfaridah, Sukotjo C, Ismail A, et al.

J Dent Educ, 2024 Jul 30.
PMID: 39080875 DOI: 10.1002/jdd.13673

PURPOSE/OBJECTIVES: Admission into dental school involves selecting applicants for successful completion of the course. This study aimed to predict the academic performance of Kulliyyah of Dentistry, International Islamic University Malaysia pre-clinical dental students based on admission results using artificial intelligence machine learning (ML) models, and Pearson correlation coefficient (PCC).
METHODS: ML algorithms logistic regression (LR), decision tree (DT), random forest (RF), and support vector machine (SVM) models were applied. Academic performance prediction in pre-clinical years was made using three input parameters: age during admission, pre-university Cumulative Grade Point Average (CGPA), and total matriculation semester. PCC was deployed to identify the correlation between pre-university CGPA and dental school grades. The proposed models' classification accuracy ranged from 29% to 57%, ranked from highest to lowest as follows: RF, SVM, DT, and LR. Pre-university CGPA was shown to be predictive of dental students' academic performance; however, alone they did not yield optimal outcomes. RF was the most precise algorithm for predicting grades A, B, and C, followed by LR, DT, and SVM. In forecasting failure, LR predicted three grades with the highest recall, SVM predicted two grades, and DT predicted one. RF performance was insignificant.
CONCLUSION: The findings demonstrated the application of ML algorithms and PCC to predict dental students' academic performance. However, it was limited by several factors. Each algorithm has unique performance qualities, and trade-offs between different performance metrics may be necessary. No definitive model stood out as the best algorithm for predicting student academic success in this study.