Xiaofeng Zhou
China-Japan Friendship Hospital
9 Papers
1 Citations
Xiaofeng Zhou is an academic researcher from China-Japan Friendship Hospital. The author has contributed to research in topics: Medicine & Cancer. The author has an hindex of 4, co-authored 5 publications. Previous affiliations of Xiaofeng Zhou include Peking University.
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Papers
Metformin use and prostate cancer risk
Zhaohan Feng,Xiaofeng Zhou,Nai-bo Liu,Jianfeng Wang,Xing Chen,Xin Xu +5 more
TL;DR: A large meta-analysis of cohort studies did not find an association between metformin use and prostate cancer risk and indicated that no single study dominated the pooled RR.
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Prognostic role of platelet to lymphocyte ratio in prostate cancer: A meta-analysis
TL;DR: A meta-analysis showed that a high PLR was correlated with poor DFS and OS in patients with prostate cancer, and remained a significant prognostic factor for OS irrespective of ethnicity, tumor stage, or cut-off value.
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A novel messenger RNA and long noncoding RNA signature associated with the progression of nonmuscle invasive bladder cancer
Yuhui He,Yuhui He,Yi‐sen Deng,Yi‐sen Deng,Pan‐xin Peng,Pan‐xin Peng,Ning Wang,Jianfeng Wang,Zhenshan Ding,Xing Chen,Xiaofeng Zhou,Xiaofeng Zhou +11 more
TL;DR: To explore the molecular mechanism of nonmuscle invasive bladder cancer (NMIBC), matched normal, and cancer tissues of 10 NMIBC were examined for RNA sequencing.
15
lncRNA-UCA1 in the diagnosis of bladder cancer: A meta-analysis.
TL;DR: Wang et al. as mentioned in this paper systematically evaluated the diagnostic value of long-chain non-coding RNA urothelial carcinoembryonic antigen 1 (lncRNA-UCA1) for bladder cancer, and provided a scientific basis for the diagnosis of bladder cancer.
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Contrast between traditional and machine learning algorithms based on a urine culture predictive model: a multicenter retrospective study in patients with urinary calculi
Yuhui He,Pan-xin Peng,Wenwei Ying,Qinwei Wang,Yan Wang,Xiankui Liu,Wen-jian Song,Yue Gao,Peizhe Li,Jie Wang,Weijie Zhu,Wen Gao,Xiaofeng Zhou,Xuesong Li,Liqun Zhou +14 more
TL;DR: The results indicate that machine learning algorithms may be useful tools for urine culture outcome prediction in patients with urinary calculi because they exhibit superior performance compared with the logistic regression model.
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