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Li W, Lv XZ, Liu J, Zeng JH, Ye M, Li CL, Fan R, Lin H, Huang HL, Yao FJ. Assessment of Myocardial Dysfunction by Three-Dimensional Echocardiography Combined With Myocardial Contrast Echocardiography in Type 2 Diabetes Mellitus. Front Cardiovasc Med 2021; 8:677990. [PMID: 34164442 PMCID: PMC8215132 DOI: 10.3389/fcvm.2021.677990] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Accepted: 05/04/2021] [Indexed: 11/13/2022] Open
Abstract
Background: We aimed to explore the value of combining real-time three-dimensional echocardiography (RT-3DE) and myocardial contrast echocardiography (MCE) in the left ventricle (LV) evaluating myocardial dysfunction in type 2 diabetes mellitus (T2DM) patients. Patients and Methods: A total of 58 T2DM patients and 32 healthy individuals were selected for this study. T2DM patients were further divided into T2DM without microvascular complications (n = 29) and T2DM with microvascular complications (n = 29) subgroups. All participants underwent RT-3DE and MCE. The standard deviation (SD) and the maximum time difference (Dif) of the time to the minimum systolic volume (Tmsv) of the left ventricle were measured by RT-3DE. MCE was performed to obtain the perfusion measurement of each segment of the ventricular wall, including acoustic intensity (A), flow velocity (β), and A·β. Results: There were significant differences in all Tmsv indices except for Tmsv6-Dif among the three groups (all P < 0.05). After heart rate correction, all Tmsv indices of the T2DM with microvascular complications group were prolonged compared with the control group (all P < 0.05). The parameters of A, β, and A·β for overall segments showed a gradually decreasing trend in three groups, while the differences between the three groups were statistically significant (all P < 0.01). For segmental evaluation of MCE, the value of A, β, and A·β in all segments showed a decreasing trend and significantly differed among the three groups (all P < 0.05). Conclusions: The RT-3DE and MCE can detect subclinical myocardial dysfunction and impaired myocardial microvascular perfusion. Left ventricular dyssynchrony occurred in T2DM patients with or without microvascular complications and was related to left ventricular dysfunction. Myocardial perfusion was reduced in T2DM patients, presenting as diffuse damage, which was aggravated by microvascular complications in other organs.
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Affiliation(s)
- Wei Li
- Department of Medical Ultrasonics, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xiao-Zhou Lv
- Department of Traditional Chinese Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jia Liu
- Department of Medical Ultrasonics, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Jia-Hui Zeng
- Department of Medical Ultrasonics, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Min Ye
- Department of Medical Ultrasonics, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Cui-Ling Li
- Department of Medical Ultrasonics, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Rui Fan
- Department of Medical Ultrasonics, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Hong Lin
- Department of Medical Ultrasonics, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Hui-Ling Huang
- Department of Medical Ultrasonics, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China.,Department of Cardiology, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Feng-Juan Yao
- Department of Medical Ultrasonics, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
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Li W, Lv XZ, Zheng X, Ruan SM, Hu HT, Chen LD, Huang Y, Li X, Zhang CQ, Xie XY, Kuang M, Lu MD, Zhuang BW, Wang W. Machine Learning-Based Ultrasomics Improves the Diagnostic Performance in Differentiating Focal Nodular Hyperplasia and Atypical Hepatocellular Carcinoma. Front Oncol 2021; 11:544979. [PMID: 33842303 PMCID: PMC8033198 DOI: 10.3389/fonc.2021.544979] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 03/03/2021] [Indexed: 12/12/2022] Open
Abstract
Background The typical enhancement patterns of hepatocellular carcinoma (HCC) on contrast-enhanced ultrasound (CEUS) are hyper-enhanced in the arterial phase and washed out during the portal venous and late phases. However, atypical variations make a differential diagnosis both challenging and crucial. We aimed to investigate whether machine learning-based ultrasonic signatures derived from CEUS images could improve the diagnostic performance in differentiating focal nodular hyperplasia (FNH) and atypical hepatocellular carcinoma (aHCC). Patients and Methods A total of 226 focal liver lesions, including 107 aHCC and 119 FNH lesions, examined by CEUS were reviewed retrospectively. For machine learning-based ultrasomics, 3,132 features were extracted from the images of the baseline, arterial, and portal phases. An ultrasomics signature was generated by a machine learning model. The predictive model was constructed using the support vector machine method trained with the following groups: ultrasomics features, radiologist’s score, and combination of ultrasomics features and radiologist’s score. The diagnostic performance was explored using the area under the receiver operating characteristic curve (AUC). Results A total of 14 ultrasomics features were chosen to build an ultrasomics model, and they presented good performance in differentiating FNH and aHCC with an AUC of 0.86 (95% confidence interval [CI]: 0.80, 0.89), a sensitivity of 76.6% (95% CI: 67.5%, 84.3%), and a specificity of 80.5% (95% CI: 70.6%, 85.9%). The model trained with a combination of ultrasomics features and the radiologist’s score achieved a significantly higher AUC (0.93, 95% CI: 0.89, 0.96) than that trained with the radiologist’s score (AUC: 0.84, 95% CI: 0.79, 0.89, P < 0.001). For the sub-group of HCC with normal AFP value, the model trained with a combination of ultrasomics features, and the radiologist’s score remain achieved the highest AUC of 0.92 (95% CI: 0.87, 0.96) compared to that with the ultrasomics features (AUC: 0.86, 95% CI: 0.74, 0.89, P < 0.001) and radiologist’s score (AUC: 0.86, 95% CI: 0.79, 0.91, P < 0.001). Conclusions Machine learning-based ultrasomics performs as well as the staff radiologist in predicting the differential diagnosis of FNH and aHCC. Incorporating an ultrasomics signature into the radiologist’s score improves the diagnostic performance in differentiating FNH and aHCC.
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Affiliation(s)
- Wei Li
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xiao-Zhou Lv
- Department of Traditional Chinese Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, China
| | - Xin Zheng
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Si-Min Ruan
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Hang-Tong Hu
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Li-Da Chen
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Yang Huang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Xin Li
- Research Center, GE Healthcare, Shanghai, China
| | - Chu-Qing Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Xiao-Yan Xie
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming Kuang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.,Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Ming-De Lu
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.,Department of Hepatobiliary Surgery, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Bo-Wen Zhuang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
| | - Wei Wang
- Department of Medical Ultrasonics, Institute of Diagnostic and Interventional Ultrasound, Ultrasomics Artificial Intelligence X-Lab, The First Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China
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Abstract
The antagonistic effects of supplementation of Zn and Se to the soil on vegetables were studied in this work. In the pot experiment, Se (Se4+) and Zn (Zn2+) were applied, respectively, to the soil, in which the Chinese cabbage (Brassica rapa) and the lettuce (Lactuca sativa L.) were planted. As a result, Se and Zn were enriched evidently in the two vegetables. The contents of Pb and Cd in the two vegetables were decreased markedly while contents of some healthy mineral elements, like Mn and Mg, were increased to some extent when Se and Zn were applied. The antagonism of Se and Zn against Pb and Cd in plants was suggested. The farmland experiment on the lettuce was conducted to explore further the effect of supplementation of Zn and Se under the actual field conditions. Result came out to be that the enrichment of Zn and Se restrained the accumulation of Pb and Cd in the lettuce remarkably, as well as enhanced the absorption of some other nutritional elements, like Fe, Mn, Cu, Ca and Mg. Therefore, application of Se and Zn was proved to be an effective and feasible method to improve trace elements nutrition in the vegetables.
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Affiliation(s)
- P P He
- Center of Green Science and Technology, School of Chemistry, University of Science and Technology of China, Hefei, 230026, People's Republic of China.
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