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1,369 results on '"Pan, Ting"'

2. The receptor binding domain of SARS-CoV-2 Omicron subvariants targets Siglec-9 to decrease its immunogenicity by preventing macrophage phagocytosis

Author

He, Xin, Zhang, Xiantao, Wu, Bolin, Deng, Jieyi, Zhang, Yongli, Zhu, Airu, Yuan, Yaochang, Lin, Yingtong, Chen, Achun, Feng, Jinzhu, Wang, Xiumei, Wu, Shijian, Liu, Yingying, Liu, Jie, Wang, Yalin, Li, Rong, Liang, Chaofeng, Yuan, Quyu, Liang, Yu, Fang, Qiannan, Xi, Zhihui, Li, Wenjie, Liang, Liting, Zhang, Zhenglai, Tang, Hui, Peng, Yi, Ke, Changwen, Ma, Xiancai, Cai, Weibin, Pan, Ting, Liu, Bingfeng, Deng, Kai, Chen, Jun, Zhao, Jincun, Wei, Xuepeng, Chen, Ran, Zhang, Yiwen, and Zhang, Hui

Published
2024
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6. The value of the malignant subregion-based texture analysis in predicting the Ki-67 status in breast cancer

Author

Chao Hua, Chen Wenwen, Rui Huijuan, Pan Ting, and Zhang Jin

Subjects
breast cancer, texture analysis, malignant subregion, Ki-67 expression, machine learning, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

ObjectiveTo evaluate the value of the malignant subregion-based texture analysis in predicting Ki-67 status in breast cancer.Materials and methodsThe dynamic contrast-enhanced magnetic resonance imaging data of 119 histopathologically confirmed breast cancer patients (81 patients with high Ki-67 expression status) from January 2018 to February 2023 in our hospital were retrospectively collected. According to the enhancement curve of each voxel within the tumor, three subregions were divided: washout subregion, plateau subregion, and persistent subregion. The washout subregion and the plateau subregion were merged as the malignant subregion. The texture features of the malignant subregion were extracted using Pyradiomics software for texture analysis. The differences in texture features were compared between the low and high Ki-67 expression cohorts and then the receiver operating characteristic (ROC) curve analysis to evaluate the predictive performance of texture features on Ki-67 expression. Finally, a support vector machine (SVM) classifier was constructed based on differential features to predict the expression level of Ki-67, the performance of the classifier was evaluated using ROC analysis and confirmed using 10-fold cross-validation.ResultsThrough comparative analysis, 51 features exhibited significant differences between the low and high Ki-67 expression cohorts. Following feature reduction, 5 features were selected to build the SVM classifier, which achieved an area under the ROC curve (AUC) of 0.77 (0.68–0.87) for predicting the Ki-67 expression status. The accuracy, sensitivity, and specificity were 0.76, 0.80, and 0.68, respectively. The average AUC from the 10-fold cross-validation was 0.72 ± 0.14.ConclusionThe texture features of the malignant subregion in breast cancer were potential biomarkers for predicting Ki-67 expression level in breast cancer, which might be used to precisely diagnose and guide the treatment of breast cancer.

Published
2024
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47. Automatic detection advantage toward the intensity change of network signal cues among problematic internet users: an event-related potential study

Author

Nie, Yufeng, Pan, Ting, Zheng, Yang, Fan, Liyan, and He, Jinbo

Subjects
Evoked potentials (Electrophysiology) -- Analysis, Pathological Internet Use -- Diagnosis -- Development and progression, Psychology and mental health
Abstract

The network signal and its intensity determine the connectivity and fluency of the network. Previous studies revealed that problematic Internet users (PIUs) exhibit an automatic detection advantage toward network signal cues. The aim of present study was to test whether this advantage effect is affected by network signal intensity. This study recruited 30 PIUs and 30 healthy controls. The event-related potentials (ERPs) were recorded when participants performed a task-irrelevant deviant-standard reverse oddball task. Strong and weak network signal images were used as standard and deviant stimuli, respectively, to evoke visual mismatch negativity (vMMN). Results showed that the vMMNs elicited by the intensity change of network signal cues in PIUs were considerably enhanced compared with that in healthy controls. Meanwhile, the vMMN elicited by strong network signal cues in PIUs was larger than that by weak network signal cues and was correlated with the scores of Young's Internet Addiction Test and craving. However, the healthy controls did not show these effects. These results imply that the automatic detection advantage effect of PIUs for strong network signal cues was more pronounced compared with weak network signal cues, and the enhanced vMMNs can be considered as a candidate biomarker of PIU and the severity of its symptoms., Author(s): Yufeng Nie [sup.1] , Ting Pan [sup.1] , Yang Zheng [sup.1] , Liyan Fan [sup.1] , Jinbo He [sup.1] Author Affiliations: (1) grid.411407.7, 0000 0004 1760 2614, Key Laboratory [...]

Published
2022
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