The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning methods.Our dataset contains data from patients who are prone to the epidem...The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning methods.Our dataset contains data from patients who are prone to the epidemic.It contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as Radiopaedia.For the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ROIs.Also,12 optimized statistical features are selected via the chi-square feature reduction technique.For the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features dataset.It is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed classifiers.As a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.展开更多
目的探讨GSK-J4通过抑制组蛋白去甲基化酶含Jumonji结构域蛋白3(Jumonji domain-containing protein 3,Jmjd3)并上调H3K27me3的表达影响HepG2肝癌细胞凋亡和侵袭的分子机制。方法体外培养人正常肝细胞L02及人肝细胞肝癌(hepatocellular ...目的探讨GSK-J4通过抑制组蛋白去甲基化酶含Jumonji结构域蛋白3(Jumonji domain-containing protein 3,Jmjd3)并上调H3K27me3的表达影响HepG2肝癌细胞凋亡和侵袭的分子机制。方法体外培养人正常肝细胞L02及人肝细胞肝癌(hepatocellular carcinoma,HCC)细胞株HepG2、SMMC7721,RT-PCR检测Jmjd3 m RNA表达,Western blot检测Jmjd3、H3K27me3蛋白表达;CCK-8法检测不同浓度GSK-J4(0、10、30、50μmol/mL)处理HepG2后增殖能力;流式细胞仪检测凋亡率;Transwell检测细胞侵袭力;Western blot检测Jmjd3、H3K27me3和凋亡相关蛋白Bcl2、Bax、Caspase3、上皮细胞间质转化(epithelial-mesenchymal transition,EMT)相关标记物E-钙黏蛋白(E-cadherin)、波形蛋白(Vimentin)以及p-STAT3、STAT3蛋白表达。结果与L02比,Jmjd3高表达,H3K27me3低表达于HepG2、SMMC7721肝癌细胞株(P<0.05);GSK-J4抑制HepG2增殖(P<0.05);GSK-J4处理组细胞凋亡率明显提高,细胞侵袭能力减弱(P<0.05);GSK-J4处理HepG2后Jmjd3水平降低,H3K27me3水平增高,Bcl2水平降低,Bax、Caspase3水平增高,E-cadherin表达增高,Vimentin水平降低(P<0.05);p-STAT3表达下调(P<0.01)。结论 GSK-J4通过抑制Jmjd3并上调H3K27me3表达而诱导HepG2发生凋亡并减弱侵袭,其可能与抑制EMT形成和STAT3的磷酸化有关。展开更多
基金support provided by the Center of Excellence in Theoretical and Computational Science(TaCS-CoE),KMUTT.Moreoverthis research project is supported by Thailand Science Research and Innovation(TSRI)Basic Research Fund:Fiscal year 2021,received by Dr.Poom Kumam,under project number 64A306000005,and sponsors URL:https://www.tsri.or.th/.
文摘The purpose of this research is the segmentation of lungs computed tomography(CT)scan for the diagnosis of COVID-19 by using machine learning methods.Our dataset contains data from patients who are prone to the epidemic.It contains three types of lungs CT images(Normal,Pneumonia,and COVID-19)collected from two different sources;the first one is the Radiology Department of Nishtar Hospital Multan and Civil Hospital Bahawalpur,Pakistan,and the second one is a publicly free available medical imaging database known as Radiopaedia.For the preprocessing,a novel fuzzy c-mean automated region-growing segmentation approach is deployed to take an automated region of interest(ROIs)and acquire 52 hybrid statistical features for each ROIs.Also,12 optimized statistical features are selected via the chi-square feature reduction technique.For the classification,five machine learning classifiers named as deep learning J4,multilayer perceptron,support vector machine,random forest,and naive Bayes are deployed to optimize the hybrid statistical features dataset.It is observed that the deep learning J4 has promising results(sensitivity and specificity:0.987;accuracy:98.67%)among all the deployed classifiers.As a complementary study,a statistical work is devoted to the use of a new statistical model to fit the main datasets of COVID-19 collected in Pakistan.
