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Myrna Nurlatifah Zakaria,Arief Cahyanto,Ahmed El-Ghannam 한국생체재료학회 2018 생체재료학회지 Vol.22 No.4
Background: Carbonate apatite (CO3Ap) and silica-calcium phosphate composite (SCPC) are bone substitutes with good prospect for dental application. SCPC creates a hydroxyapatite surface layer and stimulate bone cell function while, CO3Ap induce apatite crystal formation with good adaptation providing good seal between cement and the bone. Together, these materials will add favorable properties as a pulp capping material to stimulate mineral barrier and maintain pulp vitality. The aim of this study is to investigate modification of CO3Ap cement combined with SCPC, later term as CO3Ap-SCPC cement (CAS) in means of its chemical (Calcium release) and physical properties (setting time, DTS and pH value). Methods: The study consist of three groups; group 1 (100% calcium hydroxide, group 2 CO3Ap (60% DCPA: 40% vaterite, and group 3 CAS (60% DCPA: 20% vaterite: 20% SCPC. Distilled water was employed as a solution for group 1, and 0.2 mol/L Na3PO4 used for group 2 and group 3. Samples were evaluated with respect to important properties for pulp capping application such as pH, setting time, mechanical strength and calcium release evaluation. Results: The fastest setting time was in CO3Ap cement group without SCPC, while the addition of 20% SCPC slightly increase the pH value but did not improved the cement mechanical strength, however, the mechanical strength of both CO3Ap groups were significantly higher than calcium hydroxide. All three groups released calcium ions and had alkaline pH. Highest pH level, as well as calcium released level, was in the control group. Conclusion: The CAS cement had good mechanical and acceptable chemical properties for pulp capping application compared to calcium hydroxide as a gold standard. However, improvements and in vivo studies are to be carried out with the further development of this material.
Ika Octariyani Safitri,Dian Anggraini Suroto,Sardjono Sardjono,Muhammad Nur Cahyanto,Jaka Widada 한국미생물학회 2022 미생물학회지 Vol.58 No.3
A whole genome sequence was performed on Trichoderma asperellum MLT1J1 isolated from coconut husk in Maluku, Indonesia. This strain is a white fungus that has glucoseresistant properties in the production of alpha-amylase and glucoamylase. In this study, the genome sequence of Trichoderma asperellum MLT1J1 was sequenced using Illumina NovaSeq PE150. The genome assembly has a length of 48.66 Mb with a GC content of 52.32%, 471 scaffolds and 14,103 protein-coding genes. Based on additional analyses, the number of genes encoding carbohydrate-active enzymes and secondary metabolites gene clusters were revealed. The result of this study will provide useful genomic information that can be compared with other Trichoderma species.
Musri, Nabilla,Christie, Brenda,Ichwan, Solachuddin Jauhari Arief,Cahyanto, Arief Korean Academy of Oral and Maxillofacial Radiology 2021 Imaging Science in Dentistry Vol.51 No.3
Purpose: The aim of this study was to analyse and review deep learning convolutional neural networks for detecting and diagnosing early-stage dental caries on periapical radiographs. Materials and Methods: In order to conduct this review, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines were followed. Studies published from 2015 to 2021 under the keywords(deep convolutional neural network) AND (caries), (deep learning caries) AND (convolutional neural network) AND (caries) were systematically reviewed. Results: When dental caries is improperly diagnosed, the lesion may eventually invade the enamel, dentin, and pulp tissue, leading to loss of tooth function. Rapid and precise detection and diagnosis are vital for implementing appropriate prevention and treatment of dental caries. Radiography and intraoral images are considered to play a vital role in detecting dental caries; nevertheless, studies have shown that 20% of suspicious areas are mistakenly diagnosed as dental caries using this technique; hence, diagnosis via radiography alone without an objective assessment is inaccurate. Identifying caries with a deep convolutional neural network-based detector enables the operator to distinguish changes in the location and morphological features of dental caries lesions. Deep learning algorithms have broader and more profound layers and are continually being developed, remarkably enhancing their precision in detecting and segmenting objects. Conclusion: Clinical applications of deep learning convolutional neural networks in the dental field have shown significant accuracy in detecting and diagnosing dental caries, and these models hold promise in supporting dental practitioners to improve patient outcomes.