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Machine perfusion versus cold storage of livers: a meta-analysis
null
《医学前沿(英文)》 2016年 第10卷 第4期 页码 451-464 doi: 10.1007/s11684-016-0474-7
Different organ preservation methods are key factors influencing the results of liver transplantation. In this study, the outcomes of experimental models receiving donation after cardiac death (DCD) livers preserved through machine perfusion (MP) or static cold storage (CS) were compared by conducting a meta-analysis. Standardized mean difference (SMD) and 95% confidence interval (CI) were calculated to compare pooled data from two animal species. Twenty-four studies involving MP preservation were included in the meta-analysis. Compared with CS preservation, MP can reduce the levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), lactate dehydrogenase (LDH), and hyaluronic acid (HA) and the changes in liver weight. By contrast, MP can enhance bile production and portal vein flow (PVF). Alkaline phosphatase (ALP) levels and histological changes significantly differed between the two preservation methods. In conclusion, MP of DCD livers is superior to CS in experimental animals.
“机械灌注+”应用于扩大标准供肝移植——肝尽其用 Review
王周城, Jack Martin, 余炯杰, 汪恺, Kourosh Saeb-Parsy, 徐骁
《工程(英文)》 2024年 第32卷 第1期 页码 30-41 doi: 10.1016/j.eng.2023.11.003
移植是终末期肝病最有效的治疗方法,但由于供体器官的短缺而受到限制。扩大标准供体(ECD)供肝在临床实践中越来越多地用于缓解这一挑战。然而,这些移植物耐受缺血的能力下降,危及冷保存期间器官的活力。机器灌注(MP)旨在改善器官保存并减少移植后并发症。然而,越来越明显的是,单独使用MP无法达到ECD供肝的最佳保存。因此,人们逐渐开始重视改良的 MP 策略,包括使用不同的灌注剂、改良的灌注方式并结合不同的治疗干预策略。本文中,我们提出了“机械灌注+”的新理念,代表这些旨在提升器官功能并可能实现ECD供肝体外再生的机械灌注新策略。为此,我们总结了现有和改良的MP策略,并阐述其在临床场景中修复不同ECD供肝的优势。
Weiwei CHEN, Jianpin QI, Wenzhen ZHU, Wenhua HUANG, Jinmei SONG
《医学前沿(英文)》 2009年 第3卷 第2期 页码 230-235 doi: 10.1007/s11684-009-0023-8
关键词: computed tomography angiography computed tomography perfusion
Hui YANG, Hairong FANG, Yuefa FANG, Xiangyun LI
《机械工程前沿(英文)》 2021年 第16卷 第1期 页码 46-60 doi: 10.1007/s11465-020-0606-2
关键词: 5-DOF hybrid manipulator reconfigurable base large workspace dimensional synthesis optimal design
WANG Hong, HUANG Lan, JIN Jun, SONG Yaoming, GENG Zhaohua, YU Xuejun, QIN Jun, ZHAO Gang, GAO Yunhua, LIU Zheng
《医学前沿(英文)》 2007年 第1卷 第1期 页码 62-67 doi: 10.1007/s11684-007-0013-7
Challenges of human–machine collaboration in risky decision-making
《工程管理前沿(英文)》 2022年 第9卷 第1期 页码 89-103 doi: 10.1007/s42524-021-0182-0
关键词: human–machine collaboration risky decision-making human–machine team and interaction task allocation human–machine relationship
Spatial prediction of soil contamination based on machine learning: a review
《环境科学与工程前沿(英文)》 2023年 第17卷 第8期 doi: 10.1007/s11783-023-1693-1
● A review of machine learning (ML) for spatial prediction of soil contamination.
关键词: Soil contamination Machine learning Prediction Spatial distribution
Predicting the elemental compositions of solid waste using ATR-FTIR and machine learning
《环境科学与工程前沿(英文)》 2023年 第17卷 第10期 doi: 10.1007/s11783-023-1721-1
● A method based on ATR-FTIR and ML was developed to predict CHNS contents in waste.
关键词: Elemental composition Infrared spectroscopy Machine learning Moisture interference Solid waste Spectral noise
State-of-the-art applications of machine learning in the life cycle of solid waste management
《环境科学与工程前沿(英文)》 2023年 第17卷 第4期 doi: 10.1007/s11783-023-1644-x
● State-of-the-art applications of machine learning (ML) in solid waste (SW) is presented.
关键词: Machine learning (ML) Solid waste (SW) Bibliometrics SW management Energy utilization Life cycle
Luosi WEI, Zongxia JIAO
《机械工程前沿(英文)》 2009年 第4卷 第2期 页码 184-191 doi: 10.1007/s11465-009-0034-9
关键词: machine vision visual location solder paste printing VisionPro
Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method
《环境科学与工程前沿(英文)》 2023年 第17卷 第11期 doi: 10.1007/s11783-023-1738-5
● A novel integrated machine learning method to analyze O3 changes is proposed.
Evaluation and prediction of slope stability using machine learning approaches
《结构与土木工程前沿(英文)》 2021年 第15卷 第4期 页码 821-833 doi: 10.1007/s11709-021-0742-8
关键词: slope stability factor of safety regression machine learning repeated cross-validation
Liquefaction prediction using support vector machine model based on cone penetration data
Pijush SAMUI
《结构与土木工程前沿(英文)》 2013年 第7卷 第1期 页码 72-82 doi: 10.1007/s11709-013-0185-y
关键词: earthquake cone penetration test liquefaction support vector machine (SVM) prediction
《化学科学与工程前沿(英文)》 2022年 第16卷 第2期 页码 183-197 doi: 10.1007/s11705-021-2073-7
关键词: machine learning flowsheet simulations constraints exploration
Big data and machine learning: A roadmap towards smart plants
《工程管理前沿(英文)》 页码 623-639 doi: 10.1007/s42524-022-0218-0
关键词: big data machine learning artificial intelligence smart sensor cyber–physical system Industry 4.0 intelligent system digitalization
标题 作者 时间 类型 操作
Simultaneous acquisition of CT angiography and whole brain CT perfusion images by using multiphase dynamic
Weiwei CHEN, Jianpin QI, Wenzhen ZHU, Wenhua HUANG, Jinmei SONG
期刊论文
Dimensional synthesis of a novel 5-DOF reconfigurable hybrid perfusion manipulator for large-scale sphericalhoneycomb perfusion
Hui YANG, Hairong FANG, Yuefa FANG, Xiangyun LI
期刊论文
Evaluation of the effect of myocardial perfusion after percutaneous coronary intervention in coronary
WANG Hong, HUANG Lan, JIN Jun, SONG Yaoming, GENG Zhaohua, YU Xuejun, QIN Jun, ZHAO Gang, GAO Yunhua, LIU Zheng
期刊论文
Research and application of visual location technology for solder paste printing based on machine vision
Luosi WEI, Zongxia JIAO
期刊论文
Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method
期刊论文
Liquefaction prediction using support vector machine model based on cone penetration data
Pijush SAMUI
期刊论文
Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet
期刊论文