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

Identification of key modules and hub genes for sepsis-induced myopathy using weighted gene co-expression network analysis

利用加权基因共表达网络分析鉴定脓毒症诱导肌病的关键模块和枢纽基因

Lin, Siming; Cai, Kexin; Chen, Ai; Wu, Weibin; Lian, Guili; Feng, Shaodan; Lin, Zhihong; Xie, Liangdi

Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury

开发和验证基于机器学习的模型,用于识别与脓毒症相关急性肾损伤相关的基因

Lin, Chen; Zheng, Meng; Wu, Wensi; Wang, Zhishan; Lu, Guofeng; Feng, Shaodan; Zhang, Xinlan

Development and validation of an early acute kidney injury risk prediction model for patients with sepsis in emergency departments

针对急诊科脓毒症患者,开发并验证早期急性肾损伤风险预测模型

Lin, Chen; Lin, Siming; Zheng, Meng; Cai, Kexin; Wang, Jing; Luo, Yuqing; Lin, Zhihong; Feng, Shaodan

Identification of m5C-Related gene diagnostic biomarkers for sepsis: a machine learning study

利用机器学习方法鉴定脓毒症的m5C相关基因诊断生物标志物:一项研究

Lin, Siming; Cai, Kexin; Feng, Shaodan; Lin, Zhihong

Integrative omics analysis identifies biomarkers of septic cardiomyopathy.

整合组学分析鉴定脓毒症心肌病的生物标志物

Cai Kexin, Luo Yuqing, Chen Hongyin, Dong Yanfang, Su Yunyun, Lin Chen, Cai Chuanqi, Shi Yikbin, Lin Siming, Lian Guili, Lin Zhihong, Feng Shaodan

Elevated Activated Partial Thromboplastin Time as a Predictor of 28-Day Mortality in Sepsis-Associated Acute Kidney Injury: A Retrospective Cohort Analysis

活化部分凝血酶原时间升高作为脓毒症相关急性肾损伤28天死亡率的预测因子:一项回顾性队列分析

Lin, Chen; Wang, Jing; Cai, Kexin; Luo, Yuqing; Wu, Wensi; Lin, Siming; Lin, Zhihong; Feng, Shaodan

Development and validation of a simple-to-use nomogram to predict the deterioration and survival of patients with COVID-19

开发和验证一种简单易用的列线图,用于预测新冠肺炎患者的病情恶化和生存情况

Zeng, Zhiyong; Wu, Chaohui; Lin, Zhenlv; Ye, Yong; Feng, Shaodan; Fang, Yingying; Huang, Yanmei; Li, Minhua; Du, Debing; Chen, Gongping; Kang, Dezhi