日期:
2020 年 — 2026 年
2020
2021
2022
2023
2024
2025
2026
影响因子:

Astragalus polysaccharides inhibit arsenic trioxide-induced BMSCs damage through inhibition of Jnk and p38 signaling pathways.

黄芪多糖通过抑制 Jnk 和 p38 信号通路来抑制三氧化二砷诱导的 BMSCs 损伤。

Wu Wei, Bamba Djibril, Zhang Zheng, Wu Feng, Li Yuan, Qi Wenyi, Liu Yingzhe, Zhang Tingting, Su Ying, Wang Xinyue, Wang Hongbo, Duan Shuqin, Ne Jingwen, Wang Wenbo, Liu Jingwei, Tang Jianyong, Li Fengda, Wu Qingchao, Li Yang, Yang Fan, Yang Lei

A nomogram based on the TyG index for the prediction of lower-limb venous thrombosis in patients with intracerebral hemorrhage

基于TyG指数的列线图用于预测脑出血患者下肢静脉血栓形成

Zhang, Hanyan; Huang, Lijie; Li, Fengda

Development and validation of a prognostic nomogram for predicting outcomes in brainstem hemorrhage patients

脑干出血患者预后预测列线图的建立与验证

Wei, Shuo; Gu, Longyuan; Fan, Yuechao; Ji, Peizhi; Yang, Liechi; Li, Fengda; Mei, Shuhong

The status and influencing factors of death anxiety among Chinese college students under the COVID-19 pandemic: a cross-sectional study

新冠肺炎疫情下中国大学生死亡焦虑现状及影响因素:一项横断面研究

Li, Guangjian; Wang, Zhou; Gao, Tingye; Gao, Xin; Sun, Junli; Li, Peng; Wu, Fengda; Wu, Shouzhi; Zhou, Jie; Kong, Yaping; Sun, Xugui

Prediction of 1p/19q state in glioma by integrated deep learning method based on MRI radiomics

基于MRI放射组学的集成深度学习方法预测胶质瘤中1p/19q状态

Li, Fengda; Li, Zeyi; Xu, Hong; Kong, Gang; Zhang, Ze; Cheng, Kaiyuan; Gu, Longyuan; Hua, Lei

Preoperative prediction of p53 overexpression in pituitary neuroendocrine tumors using MRI radiomics

利用MRI放射组学进行垂体神经内分泌肿瘤p53过表达的术前预测

Gu, Longyuan; Zhou, Fanghua; Wu, Bin; Yang, Jianpin; Li, Bin; Fan, Yuechao; Ji, Peizhi; Wu, Qian; Li, Fengda; Mei, Shuhong

Constructing a neural network model based on tumor-infiltrating lymphocytes (TILs) to predict the survival of hepatocellular carcinoma patients

构建基于肿瘤浸润淋巴细胞(TILs)的神经网络模型以预测肝细胞癌患者的生存期

Zhong, Wenqing; Zhao, Ziyin; Fang, Xin; Sun, Jingyi; Wei, Yanbing; Li, Fengda; Han, Bing; Jin, Cheng

Enhancing power equipment defect identification through multi-label classification methods

通过多标签分类方法增强电力设备缺陷识别

Zheng, Wenjie; Yang, Yi; Zhang, Fengda; Lv, Wenxiu; Li, Yong; Li, Sun

Machine learning predictors of risk of death within 7 days in patients with non-traumatic subarachnoid hemorrhage in the intensive care unit: A multicenter retrospective study

利用机器学习预测重症监护病房非创伤性蛛网膜下腔出血患者7天内死亡风险:一项多中心回顾性研究

Gu, Longyuan; Hu, Hongwei; Wu, Shinan; Li, Fengda; Li, Zeyi; Xiao, Yaodong; Li, Chuanqing; Zhang, Hui; Wang, Qiang; Li, Wenle; Fan, Yuechao

Predicting functional outcomes of patients with spontaneous intracerebral hemorrhage based on explainable machine learning models: a multicenter retrospective study

基于可解释机器学习模型预测自发性脑出血患者的功能预后:一项多中心回顾性研究

Pan, Bin; Li, Fengda; Liu, Chuanghong; Li, Zeyi; Sun, Chengfa; Xia, Kaijian; Xu, Hong; Kong, Gang; Gu, Longyuan; Cheng, Kaiyuan