PROCEEDINGS OF THE INTERNATIONAL CONFERENCE AND SCHOOL ON PHYSICS IN MEDICINE AND BIOSYSTEM (ICSPMB): Physics Contribution in Medicine and Biomedical Applications最新文献

筛选

英文中文

Preliminary study in genetic polymorphism of hOGG1 and risk factor for thyroid cancer in Indonesia 印度尼西亚人hOGG1基因多态性与甲状腺癌危险因素的初步研究

PROCEEDINGS OF THE INTERNATIONAL CONFERENCE AND SCHOOL ON PHYSICS IN MEDICINE AND BIOSYSTEM (ICSPMB): Physics Contribution in Medicine and Biomedical Applications Pub Date : 2021-03-29 DOI: 10.1063/5.0047941

H. N. E. Surniyantoro, N. Hidayati

{"title":"Preliminary study in genetic polymorphism of hOGG1 and risk factor for thyroid cancer in Indonesia","authors":"H. N. E. Surniyantoro, N. Hidayati","doi":"10.1063/5.0047941","DOIUrl":"https://doi.org/10.1063/5.0047941","url":null,"abstract":"The human 8-oxoguanine DNA N-glycosylase-1 (hOGG1) is the most important DNA repair enzyme in base excision repair (BER) pathways and has been reported to have a relationship with the risk of developing various cancers. The study was aimed to assess the genetic polymorphism of hOGG1 as a risk factor for thyroid cancer. A total of 19 participants were enrolled in this study, consisted of ten thyroid cancer patients as a case group and nine non-cancer patients as a control group. Examination of hOGG1 genotype was carried out by using Polymerase Chain Reaction-Restriction Fragment Length Polymorphism (PCR-RFLP) method and statistical analysis using a Chi-square test. In this study, we found the frequency of hOGG1 genetic polymorphism was not significantly different between cancer patients and control groups and we predict that the polymorphism was not a risk factor for cancer (P>0.05). Results showed the frequency of G allele (mutant) was 0.5 in the case group and 0.33 in the control group. We found that frequency of hOGG1 genetic polymorphism was not significantly different between cancer patients and control groups and this work predicts that the polymorphism was not a risk factor for thyroid cancer. In further studies, it is necessary to assess genetic polymorphisms in populations with controlled non-genetic factors, such as diet, lifestyle, and environmental factor. The relatively small sample size must be considered as a limitation of the study, and thus further research is needed in different populations with larger sample sizes.","PeriodicalId":379310,"journal":{"name":"PROCEEDINGS OF THE INTERNATIONAL CONFERENCE AND SCHOOL ON PHYSICS IN MEDICINE AND BIOSYSTEM (ICSPMB): Physics Contribution in Medicine and Biomedical Applications","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124506523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comparison of two convolutional neural network models for automated classification of brain cancer types 两种卷积神经网络模型在脑癌类型自动分类中的比较

M. Fuad, C. Anam, K. Adi, G. Dougherty

{"title":"Comparison of two convolutional neural network models for automated classification of brain cancer types","authors":"M. Fuad, C. Anam, K. Adi, G. Dougherty","doi":"10.1063/5.0047750","DOIUrl":"https://doi.org/10.1063/5.0047750","url":null,"abstract":"Convolutional neutral network (CNN) is widely used in the classification of types of brain cancer and many architectures of the CNN have been developed. Comparasions of various architectures on a specific clinical task is essential. This study aims to compare a deep transfer learning model with AlexNet and GoogleNet architectures for brain tumor classification on the T1-w magnetic resonance imaging (MR)I images. The comparison of the AlexNet and the GoogleNet architectures was implemented on the T1-w MRI images with three tumor types: glioma, meningioma and pituitary. The total images were 3,064 consisted of 1,426 gliomas, 708 meningiomas, and 930 pituitaries. 80% of datasets were for training and 20% of datasets were for testing. It is found that the accuracies for the AlexNet is 94.6% and for the GoogleNet is 92%. The sensitivity, specificity, precision and recall for the AlexNet are 94%, 95.2%, 94.6% and 46.9%, respectively. While sensitivity, specificity, precision and recall for the GoogleNet are 96.3%, 96.8%, 87.3% and 45.9%, respectively.","PeriodicalId":379310,"journal":{"name":"PROCEEDINGS OF THE INTERNATIONAL CONFERENCE AND SCHOOL ON PHYSICS IN MEDICINE AND BIOSYSTEM (ICSPMB): Physics Contribution in Medicine and Biomedical Applications","volume":"109 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134255925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 7