A Mixture of Ceramic Biomaterials (Hydroxyapatite and β-Tricalcium Phosphate) and Chitosan as a Scaffold For Critical Sized Defect Bone

Background: Bone is a living tissue that undergoes a continuous regeneration-remodeling process and the second largest organ implanted after the blood transfusion process. Bones can heal completely, but Critical Size Defects (CSD) require graft materials to support the healing process. There are several graft materials, namely: autologous, allogenous, xenograft, and alloplastic material with their respective advantages and disadvantages through the properties: osteogenesis, osteoconduction, osteoinduction, and others. One of the alloplastic materials is Hydroxypatite/HA and β-Tricalcium Phosphate/β-TCP widely used in the grafting process. HA has the disadvantage of having a low degree of solubility, while β-TCP has a high solubility level when exposed to body fluids. Purpose: To explain the mixture of ceramic biomaterials (Hydroxyapatite and β-Tricalcium Phosphate) and Chitosan as a Scaffold for Critical Sized Defect Bone. Reviews: The CSDs are condition where the bone can not heal by itself. It needs bone graft to bridge the heal of CSDs. One of the transplant materials is ceramic biomaterials contains of HA and β-TCP. Each of material has its strengths and weaknesses so that mixture of these ingredients will increase the positive effects and reduce the negative effects of each ingredient. CSD healing requires the suitable scaffold and biopolymer that can help in healing process. Conclusion: CSD healing requires a scaffold that mimics cancellous bone in the healing process of bone defects played by the mixture of BCP as a bioceramic material and chitosan as a natural biopolymer with low toxicity and high biocompatibility

Utilization of artificial intelligence-assisted histopathological detection in surveillance of oral squamous cell carcinoma staging: A narrative review

Background: Oral squamous cell carcinoma (OSCC) is defined as an oral malignancy with worldwide prevalence of 90%. In 2018, the number of cases observed is 354.864 with 177.384 deaths globally. Early diagnosis for determining OSCC stage due to histopathological examination is required to sustain prognosis and minimize mortality. Determining the stage is mostly done manually and highly dependent on skill and experiences of the pathologist thus having a high tendency of misdiagnosis. Artificial intelligence (AI) is a technology that modifies machines with human-like intelligence thus making them able to solve the tasks. Utilization of AI in analyzing histopathological samples is known to give such a precision analysis then diagnosing the OSCC stage accurately Purpose: This study describes utilization of AI-assisted histopathological detection in determining OSCC staging. Review: Developmental process of OSCC begins with gene damage causing disruption of cell regulation, manifesting in impaired differentiation and proliferation of keratinocytes in the epithelium which is characterized by keratin pearl formation. AI-assisted histopathological detection is able to identify the percentage of keratinization and keratin pearls in histopathological images by convolutional neural network (CNN). CNN is a deep learning architecture specifically designed to recognize two-dimensional visual patterns with minimal preprocessing. CNN works by analyzing input in the form of visual images from histopathological images and producing output as keratinization percentage in related samples then being used to determine the staging of OSCC. Conclusion: AI-assisted histopathological detection may potential to be used in determining OSCC staging