Monoclonal antibody (MAb) to rat liver cyto-chrome P-450j isozyme, an activating enzyme specific to nitrosamine metabolism, was used coupled with immunoblotting, densitometer scanning of SDS-PAGE gels and immunohistoc...Monoclonal antibody (MAb) to rat liver cyto-chrome P-450j isozyme, an activating enzyme specific to nitrosamine metabolism, was used coupled with immunoblotting, densitometer scanning of SDS-PAGE gels and immunohistochemical technique. The trace P-450HSj isozyme (Mr. 51.5 Kd) was found in human gastric mucosa. It was similar to P-450j in molecular weight, catalytic and immunochemical properties. The concentrations of P-450HSj in mucosa of lesser curvature were higher than those in greater curvature. This might be one of the important reasons that lesser curvature is the commonest area for gastric carcinoma. But there was possibly less P-450HSj in gastric mucosa with cancer. Im-munohistochemically, P-450HSj was discovered in the cytoplasm of some glandular epithelial cells, especially in the glands with hyperplastic and intestinal metaplastic changes adjacent to carcinoma. It was also found in some normal glands and in tumor cells of high-differentiated adenocarcinoma, but not in those of low-differentiated ones. Following subjects are discussed: (1) the method of detecting trace P-450HSj, (2) the rule of distribution of P-450HSj, and (3) the relationship between the isozyme and the occurrence of gastric cancer caused by nitrosa-mines.展开更多
Background Accurately identifying and localizing the five subtypes of intracranial hemorrhage(ICH)are crucial steps for subsequent clinical treatment;however,the lack of a large computed tomography(CT)dataset with ann...Background Accurately identifying and localizing the five subtypes of intracranial hemorrhage(ICH)are crucial steps for subsequent clinical treatment;however,the lack of a large computed tomography(CT)dataset with annotations of the categorization and localization of ICH considerably limits the development of deep learningbased identification and localization methods.We aimed to construct this large dataset and develop a deep learning-based model to identify and localize the five ICH subtypes,including intraventricular hemorrhage(IVH),intraparenchymal hemorrhage(IPH),subdural hemorrhage(SDH),subarachnoid hemorrhage(SAH),and epidural hemorrhage(EDH),in non-contrast head CT scans.Methods Based on the public Radiological Society of North America(RSNA)2019 dataset,we constructed a large CT dataset named RSNA 2019+that was annotated for bleeding localization of the five ICH subtypes by three radiologists.An improved YOLOv8 architecture with the bidirectional feature pyramid network was proposed and trained using the RSNA 2019+training dataset and evaluated on the RSNA 2019+test dataset.The public CQ500,and two private datasets collected from the Xinqiao and Sunshine Union Hospitals,respectively,were also annotated to perform multicenter validation.Furthermore,the performance of the deep learning model was compared with that of four radiologists.Multiple performance metrics,including the average precision(AP),precision,recall and F1-score,were used for performance evaluation.The McNemar and chi-squared tests were performed,and the 95%Wilson confidence intervals were given for the precision and recall.Results There were 175,125;4,707;8,259;and 3,104 bounding boxes after annotation on the RSNA 2019+;CQ500+;and the PD 1 and PD 2 datasets,respectively.With an intersection-over-union threshold of 0.5,the APs of IVH,IPH,SAH,SDH and EDH are 0.852,0.820,0.574,0.639,and 0.558,respectively,yielding a mean average precision(mAP)of 0.688 for our proposed deep learning model on the RSNA 2019+test dataset.For the multicenter validation involving the three external datasets,the mAPs for CQ500,PD1,and PD2 were 0.594,0.734,and 0.66,respectively,which is comparable to those of radiologist with eight years of experience in head CT interpretation.Conclusion The deep learning model developed from the constructed RSNA 2019+dataset exhibited good potential in identifying and localizing the five ICH subtypes in CT slices and has the potential to assist in the clinical diagnosis.展开更多
文摘Monoclonal antibody (MAb) to rat liver cyto-chrome P-450j isozyme, an activating enzyme specific to nitrosamine metabolism, was used coupled with immunoblotting, densitometer scanning of SDS-PAGE gels and immunohistochemical technique. The trace P-450HSj isozyme (Mr. 51.5 Kd) was found in human gastric mucosa. It was similar to P-450j in molecular weight, catalytic and immunochemical properties. The concentrations of P-450HSj in mucosa of lesser curvature were higher than those in greater curvature. This might be one of the important reasons that lesser curvature is the commonest area for gastric carcinoma. But there was possibly less P-450HSj in gastric mucosa with cancer. Im-munohistochemically, P-450HSj was discovered in the cytoplasm of some glandular epithelial cells, especially in the glands with hyperplastic and intestinal metaplastic changes adjacent to carcinoma. It was also found in some normal glands and in tumor cells of high-differentiated adenocarcinoma, but not in those of low-differentiated ones. Following subjects are discussed: (1) the method of detecting trace P-450HSj, (2) the rule of distribution of P-450HSj, and (3) the relationship between the isozyme and the occurrence of gastric cancer caused by nitrosa-mines.
基金supported by Science and Technology Research Project of Chongqing Education Commission(Grant No.KJQN202212804).
文摘Background Accurately identifying and localizing the five subtypes of intracranial hemorrhage(ICH)are crucial steps for subsequent clinical treatment;however,the lack of a large computed tomography(CT)dataset with annotations of the categorization and localization of ICH considerably limits the development of deep learningbased identification and localization methods.We aimed to construct this large dataset and develop a deep learning-based model to identify and localize the five ICH subtypes,including intraventricular hemorrhage(IVH),intraparenchymal hemorrhage(IPH),subdural hemorrhage(SDH),subarachnoid hemorrhage(SAH),and epidural hemorrhage(EDH),in non-contrast head CT scans.Methods Based on the public Radiological Society of North America(RSNA)2019 dataset,we constructed a large CT dataset named RSNA 2019+that was annotated for bleeding localization of the five ICH subtypes by three radiologists.An improved YOLOv8 architecture with the bidirectional feature pyramid network was proposed and trained using the RSNA 2019+training dataset and evaluated on the RSNA 2019+test dataset.The public CQ500,and two private datasets collected from the Xinqiao and Sunshine Union Hospitals,respectively,were also annotated to perform multicenter validation.Furthermore,the performance of the deep learning model was compared with that of four radiologists.Multiple performance metrics,including the average precision(AP),precision,recall and F1-score,were used for performance evaluation.The McNemar and chi-squared tests were performed,and the 95%Wilson confidence intervals were given for the precision and recall.Results There were 175,125;4,707;8,259;and 3,104 bounding boxes after annotation on the RSNA 2019+;CQ500+;and the PD 1 and PD 2 datasets,respectively.With an intersection-over-union threshold of 0.5,the APs of IVH,IPH,SAH,SDH and EDH are 0.852,0.820,0.574,0.639,and 0.558,respectively,yielding a mean average precision(mAP)of 0.688 for our proposed deep learning model on the RSNA 2019+test dataset.For the multicenter validation involving the three external datasets,the mAPs for CQ500,PD1,and PD2 were 0.594,0.734,and 0.66,respectively,which is comparable to those of radiologist with eight years of experience in head CT interpretation.Conclusion The deep learning model developed from the constructed RSNA 2019+dataset exhibited good potential in identifying and localizing the five ICH subtypes in CT slices and has the potential to assist in the clinical diagnosis.
