Introduction: Thalassemia disorder is a genetic disease that causes the blood to have less hemoglobin than normal, the main requirement to control thalassemia’s propagation is to educate the entire society. Methodolo...Introduction: Thalassemia disorder is a genetic disease that causes the blood to have less hemoglobin than normal, the main requirement to control thalassemia’s propagation is to educate the entire society. Methodology: A descriptive survey was taken to evaluate the awareness of thalassemia among Saudi Arabia’s society, with a sample size of 384. Results: The results were written in frequencies, and it shows that most of the participants were unaware and lacking information on thalassemia syndrome. Discussion: The results of this study provide valuable insights into the awareness of thalassemia in Saudi Arabia and highlight the need to raise awareness of this disease. Conclusion: This study is not comprehensive because the survey was not disrupted evenly, but it can give us an overview of the awareness of thalassemia in Saudi Arabia, and it shows that most of the participants were unaware and lacked information on thalassemia.展开更多
This study aimed to explore the value of deep learning(DL)-assisted quantitative susceptibility mapping(QSM)in glioma grading and molecular subtyping.Forty-two patients with gliomas,who underwent preoperative T2 fluid...This study aimed to explore the value of deep learning(DL)-assisted quantitative susceptibility mapping(QSM)in glioma grading and molecular subtyping.Forty-two patients with gliomas,who underwent preoperative T2 fluid-attenuated inversion recovery(T2 FLAIR),contrast-enhanced T1-weighted imaging(T1WI+C),and QSM scanning at 3.0T magnetic resonance imaging(MRI)were included in this study.Histopathology and immunohistochemistry staining were used to determine glioma grades,and isocitrate dehydrogenase(IDH)1 and alpha thalassemia/mental retardation syndrome X-linked gene(ATRX)subtypes.Tumor segmentation was performed manually using Insight Toolkit-SNAP program(www.itksnap.org).An inception convolutional neural network(CNN)with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI slices.Fivefold cross-validation was utilized as the training strategy(seven samples for each fold),and the ratio of sample size of the training,validation,and test dataset was 4:1:1.The performance was evalu-ated by the accuracy and area under the curve(AUC).With the inception CNN,single modal of QSM showed better perfor-mance in differentiating glioblastomas(GBM)and other grade gliomas(OGG,grade II–III),and predicting IDH1 mutation and ATRX loss(accuracy:0.80,0.77,0.60)than either T2 FLAIR(0.69,0.57,0.54)or T1WI+C(0.74,0.57,0.46).When combining three modalities,compared with any single modality,the best AUC/accuracy/F1-scores were reached in grading gliomas(OGG and GBM:0.91/0.89/0.87,low-grade and high-grade gliomas:0.83/0.86/0.81),predicting IDH1 mutation(0.88/0.89/0.85),and predicting ATRX loss(0.78/0.71/0.67).As a supplement to conventional MRI,DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades,IDH1 mutation,and ATRX loss.展开更多
文摘Introduction: Thalassemia disorder is a genetic disease that causes the blood to have less hemoglobin than normal, the main requirement to control thalassemia’s propagation is to educate the entire society. Methodology: A descriptive survey was taken to evaluate the awareness of thalassemia among Saudi Arabia’s society, with a sample size of 384. Results: The results were written in frequencies, and it shows that most of the participants were unaware and lacking information on thalassemia syndrome. Discussion: The results of this study provide valuable insights into the awareness of thalassemia in Saudi Arabia and highlight the need to raise awareness of this disease. Conclusion: This study is not comprehensive because the survey was not disrupted evenly, but it can give us an overview of the awareness of thalassemia in Saudi Arabia, and it shows that most of the participants were unaware and lacked information on thalassemia.
基金supported in part by Science and Technology Commission of Shanghai Municipality(grant number 18411967300,20ZR1407800)Shanghai Municipal Science and Technology Major Project(2018SHZDZX01)the National Natural Science Foundation of China(81873893).
文摘This study aimed to explore the value of deep learning(DL)-assisted quantitative susceptibility mapping(QSM)in glioma grading and molecular subtyping.Forty-two patients with gliomas,who underwent preoperative T2 fluid-attenuated inversion recovery(T2 FLAIR),contrast-enhanced T1-weighted imaging(T1WI+C),and QSM scanning at 3.0T magnetic resonance imaging(MRI)were included in this study.Histopathology and immunohistochemistry staining were used to determine glioma grades,and isocitrate dehydrogenase(IDH)1 and alpha thalassemia/mental retardation syndrome X-linked gene(ATRX)subtypes.Tumor segmentation was performed manually using Insight Toolkit-SNAP program(www.itksnap.org).An inception convolutional neural network(CNN)with a subsequent linear layer was employed as the training encoder to capture multi-scale features from MRI slices.Fivefold cross-validation was utilized as the training strategy(seven samples for each fold),and the ratio of sample size of the training,validation,and test dataset was 4:1:1.The performance was evalu-ated by the accuracy and area under the curve(AUC).With the inception CNN,single modal of QSM showed better perfor-mance in differentiating glioblastomas(GBM)and other grade gliomas(OGG,grade II–III),and predicting IDH1 mutation and ATRX loss(accuracy:0.80,0.77,0.60)than either T2 FLAIR(0.69,0.57,0.54)or T1WI+C(0.74,0.57,0.46).When combining three modalities,compared with any single modality,the best AUC/accuracy/F1-scores were reached in grading gliomas(OGG and GBM:0.91/0.89/0.87,low-grade and high-grade gliomas:0.83/0.86/0.81),predicting IDH1 mutation(0.88/0.89/0.85),and predicting ATRX loss(0.78/0.71/0.67).As a supplement to conventional MRI,DL-assisted QSM is a promising molecular imaging method to evaluate glioma grades,IDH1 mutation,and ATRX loss.