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Your search keyword '"Xu, Mengxi"' showing total 158 results
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158 results on '"Xu, Mengxi"'

8. Identifying the cause of cardiac arrest through coronary computed tomography angiography with high heart rate under venoarterial extracorporeal membrane oxygenation support: a case description

Author

Wang, Xinhong, primary, Liu, Haipeng, additional, Xu, Mengxi, additional, Lin, Shuangxiang, additional, Wang, Kexin, additional, Ma, Linlin, additional, Hu, Daoxi, additional, Zhou, Wei, additional, Yu, Xiaoli, additional, Wang, Lijian, additional, and Xia, Ling, additional

Published
2024
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20. Genome-wide analysis of basic helix–loop–helix genes in Dendrobium catenatum and functional characterization of DcMYC2 in jasmonate-mediated immunity to Sclerotium delphinii

Author

Li, Cong, primary, Cai, Xiang, additional, Shen, Qiuyi, additional, Chen, Xueliang, additional, Xu, Mengxi, additional, Ye, Tianqi, additional, Si, Dun, additional, Wu, Lingshang, additional, Chen, Donghong, additional, Han, Zhigang, additional, and Si, Jinping, additional

Published
2022
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30. The relationship between hormone receptor status, tumor size and clinical stage and the efficacy of neoadjuvant chemotherapy for locally advanced breast cancer, predictors of survival, and the establishment of Cox-Based risk prediction model and nomogram

Author

Gan Fengjiao, Xu Mengxi, Li Xiaomei, Wang Lian, Zhang Mingyue, Xu Lin, Wu Qiaoyuan, Jiang Ni, Hu Kang, Li Yi, Sun Suhong, and Luo Qing

Abstract

Background: Neoadjuvant chemotherapy is currently mainly used for locally advanced breast cancer. However, there is currently no effective way to predict the prognosis of neoadjuvant chemotherapy patients. Methods: All relevant data before and after chemotherapy were collected and evaluate the efficacy of chemotherapy through RECIST1.1 and pathology. Survival curves were generated by the Kaplan-Meier method and compared using the log-rank test. A nomogram was developed using a binary logisti regression model with a cross validation. Results: A total of 158 patients were included in our study. The ORR was 64.6% (cCR, 3.8%; cPR, 60.8%). The pCR rates wer 12.0% for breast tumor and axillary nodes. The pCR rates were 17.1% for breast tumor and 30.4% for axillary nodes. Our study found that only differences in HER-2 status could predict different clinical efficacy, and clinical remission (CR+PR) was more likely to be achieved if HER-2 positive than negative (16.7% vs 7.5%, p=0.05). There was no significant difference in the clinical efficacy of neoadjuvant chemotherapy in age, clinical stage, and molecular classification of patients. In a single factor COX proportional hazard model study, it was found that ER positive relative to HR negative(HR 0.479,CI 95% 0.272-0.844, p=0.011), PR positive relative to PR negative (HR 0.423,CI 95% 0.235-0.763, p=0.004), HER-2 positive relative to HER-2 negative (HR 2.011, CI 95% 1.000-4.042, p=0.050), TNBC (HR 2.229, CI 95% 1.189-4.178, p=0.012), and HER-2 (HR 2.808, CI 95% 1.301-6.062, p=0.009) type are more effective than patients with Luminal type breast cancer 0btain a longer survival time, which is considered a protective factor. A nomogram was developed based on the clinical and statistically significant predictors. ROC curve for 3-year survial with area under the curve = 0.731 (95% confidence interval, 0.663-0.828); ROC curve for 5-year survial with area under the curve = 0.743 (95% confidence interval, 0.652-0.834); ROC curve for 10-year survial with area under the curve = 0.726 (95% confidence interval, 0.639-0.812). Conclusion: In conclusion, the hormone receptor status before chemotherapy can predict the prognosis of patients in advance, and we successfully constructed nomogram, which can help us predict the OS of patients with NAC.

Published
2020

40. Smart grid short-term load estimation model based on BP neural network

Author

Shi Chengchao, Jianqiang Shi, Xu Mengxi, and Han Lei

Subjects
Exploit, Artificial neural network, Continuous function, Computer science, Applied Mathematics, Data type, Theoretical Computer Science, Term (time), Computational Mathematics, Nonlinear system, Smart grid, Computational Theory and Mathematics, Control theory, Genetic algorithm
Abstract

As reasonable short-term load estimation system can provide reliable support for the operating, planning and designing of the smart grid, in this paper, we propose an effective smart grid short-term load estimation method. Different types of data are input to the BP neural network and then the output of BP neural network is represented as the load estimation results. Although BP neural network can approximate any nonlinear continuous function with the condition of a specific structure and suitable weights, it is very difficult to obtain the global minimum result. In order to obtain the global optimum solution in short-term load estimation, we exploit the genetic algorithm to optimise the weights and thresholds of the BP neural network, which is the main advantage of the proposed model. Finally, experimental results demonstrate that the proposed method can estimate short-term load of smart grid with higher accuracy and it can also clearly show the load requirement distribution in different time period.

Published
2020

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