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中国民族民间医药 2009年第13期18卷 156-157页

作者：徐瑞妙陈石红潘锦梅甘保波罗绿文李芹芳广东省罗定市人民医院消化内分泌科广东罗定527200

目的:探讨禁食与进食对胃溃疡伴出血患者胃内PH值的影响。方法:将64例胃溃疡并出血(非大血)患者分为实验组(30例)和对照组(34例),实验组患者给予进食冷流质食物(常温),对照组患者给予常规禁食直至出血停止,分别观察两组在留置胃管后每... 详细信息

目的:探讨禁食与进食对胃溃疡伴出血患者胃内PH值的影响。方法:将64例胃溃疡并出血(非大血)患者分为实验组(30例)和对照组(34例),实验组患者给予进食冷流质食物(常温),对照组患者给予常规禁食直至出血停止,分别观察两组在留置胃管后每天胃液PH值的变化情况,采用抽取胃液(8Am、12N、5Pm、9Pm)作测定(试纸测定)并记录。

关键词：禁食与进食胃溃疡伴出血 PH值

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羟考酮在老年女性非体外循环冠脉旁路移植术后镇痛的临床效果观察

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北京医学 2023年第11期45卷 953-956页

作者：邹璐雯叶伟光马艳辉徐娜李洪利李超王天龙首都医科大学宣武医院麻醉科北京100053 首都医科大学宣武医院心脏外科北京100053

目的探讨羟考酮在老年女性非体外循环冠脉旁路移植(off-pump coronary artery bypass grafting,OPCABG)术后患者自控静脉镇痛(patient-controlled intravenous analgesia,PCIA)的效果及安全性。方法选取2021年10月至2022年12月首都医科大学宣武医院择期行OPCABG的老年女性患者64例,采用随机数字表法随机分为羟考酮组(O组)和舒芬太尼组(S组),每组32例。两组术后采用不同的PCIA方法,比较两组患者气管拔管时(T1),拔管后6 h(T2)、12 h(T3)、24 h(T4)和48 h(T5)的视觉模拟评分(visual analogue scale,VAS)和Ramsay镇静评分,并记录拔管后48 h内的PCIA有效按压次数、补救镇痛药使用率和镇痛相关并发症情况。结果64例患者平均年龄(68.0±3.5)岁,两组各时点的VAS、Ramsay镇静评分、自控按压次数、补救镇痛率、术后皮肤瘙痒及眩晕发生率的差异无统计学意义(P>0.05)。与O组相比,S组恶心呕吐发生率(34.4%比12.5%)及排气时间[(32.3±9.2)h比(25.5±6.1)h]均较高,差异有统计学意义(P<0.05)。结论与舒芬太尼相比,羟考酮用于老年女性OPCABG术后PCIA效果良好,且胃肠道不良反应较轻。

关键词：羟考酮非体外循环冠脉旁路移植术自控静脉镇痛老年女性

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循环机械牵张对骨髓间充质干细胞成纤维分化的促进作用

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郑州大学学报（医学版） 2019年第3期54卷 372-377页

作者：王肖帆王莹莹王君敏陈璐璐徐锋乔艳华张志磊任琛琛胡孟彩吴惠琰王鲁文崔世红赵冰郑州大学第三附属医院妇产科郑州450052 郑州大学基础医学院人体解剖学系郑州450001 许昌市中心医院妇产科许昌461000 郑州大学第三附属医院保健部郑州450052

目的:研究循环机械牵张(CMS)对骨髓间充质干细胞(BMSCs)体外定向分化为成纤维细胞的作用及可能机制。方法:用0%(对照)、1%、2%、4%、10%的CMS刺激BMSCs,采用Western blot法检测BMSCs中组蛋白去甲基化酶KDM4B及成纤维细胞标志蛋白赖氨酰... 详细信息

目的:研究循环机械牵张(CMS)对骨髓间充质干细胞(BMSCs)体外定向分化为成纤维细胞的作用及可能机制。方法:用0%(对照)、1%、2%、4%、10%的CMS刺激BMSCs,采用Western blot法检测BMSCs中组蛋白去甲基化酶KDM4B及成纤维细胞标志蛋白赖氨酰氧化酶(LOX)、弹性蛋白(Elastin)的表达。另取BMSCs分为5组,空白对照组、KDM4B-shRNA组及阴性对照shRNA(NC-shRNA)组、KDM4B过表达组及过表达对照组,采用Western blot检测沉默和过表达KDM4B后细胞中LOX及Elastin蛋白的表达。分别用0%和10%CMS刺激BMSCs,用染色质免疫沉淀法结合qRT-PCR技术分析细胞中Elastin和LOX启动子区域组蛋白3第9位赖氨酸的三甲基化(H3K9me3)和KDM4B的表达。结果:CMS刺激后KDM4B、LOX、Elastin蛋白的表达均高于对照组,其中10%的CMS刺激下LOX和Elastin蛋白的表达最高,诱导分化效果最好(P <0. 05)。上调KDM4B的表达可以诱导BMSCs中Elastin和LOX蛋白的表达;反之,则抑制二者的表达(P <0. 05)。CMS通过调节Elastin和LOX启动子区域中KDM4B和H3K9me3的基因水平上调LOX和Elastin的表达(P <0. 05)。结论:CMS可能通过诱导KDM4B的分泌和抑制H3K9me3的修饰水平上调Elastin和LOX的表达,从而促进BMSCs向成纤维细胞分化。

关键词：循环机械牵张骨髓间充质干细胞 KDM4B 弹性蛋白赖氨酰氧化酶

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WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

arXiv

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arXiv 2022年

作者： Han, Chu Pan, Xipeng Yan, Lixu Lin, Huan Li, Bingbing Yao, Su Lv, Shanshan Shi, Zhenwei Mai, Jinhai Lin, Jiatai Zhao, Bingchao xu, Zeyan Wang, Zhizhen Wang, Yumeng Zhang, Yuan Wang, Huihui Zhu, Chao Lin, Chunhui Mao, Lijian Wu, Min Duan, luwen Zhu, Jingsong Hu, Dong Fang, Zijie Chen, Yang Zhang, Yongbing Li, Yi Zou, Yiwen Yu, Yiduo Li, Xiaomeng Li, Haiming Cui, Yanfen Han, Guoqiang xu, Yan xu, Jun Yang, Huihua Li, Chunming Liu, Zhenbing Lu, Cheng Chen, Xin Liang, Changhong Zhang, Qingling Liu, Zaiyi The Department of Radiology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Prov. Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The Department of Pathology Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The School of Computer Science and Information Security Guilin University of Electronic Technology Guangxi541004 China The Department of Pathology Guangdong Provicial People's Hospital Ganzhou Hospital Ganzhou China The College of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The School of Computer Science and Engineering South China University of Technology Guangzhou510640 China The Institute for AI in Medicine School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China The School of Biological Science and Medical Engineering State Key Laboratory of Software Development Environment Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education Research Institute of Beihang University in Shenzhen Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China The Department of Radiology Guangzhou First People's Hospital Hospital of South China University of Technology Guangzhou510180 China The Zhejiang Dahua Technology Co. Ltd Zhejiang310053 China The School of Biomedical Engineering University of Science and Technology of China Hefei230022 China The Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China Electronic and Computer Engineering & Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong The School of Automation Science and Engineering Xi'An Jiaotong University Xian710049 Chi

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outstanding weakly-supervised semantic segmentation (WSSS) techniques for histopathology images of LUAD. Participants have to design the algorithm to segment tumor epithelial, tumor-associated stroma and normal tissue with only patch-level labels. This challenge includes 10,091 patch-level annotations (the training set) and over 130 million labeled pixels (the validation and test sets), from 87 WSIs (67 from GDPH, 20 from TCGA). All the labels were generated by a pathologist-in-the-loop pipeline with the help of AI models and checked by the label review board. Among 532 registrations, 28 teams submitted the results in the test phase with over 1,000 submissions. Finally, the first place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919). According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS. Cutmix data augmentation has been widely adopted to generate more reliable samples. With the success of this challenge, we believe that WSSS approaches with patch-level annotations can be a complement to the traditional pixel annotations while reducing the annotation efforts. The entire dataset has been released to encourage more researches on computational pathology in LUAD and more novel WSSS techniques. © 2022, CC BY.

关键词： Semantic Segmentation

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TELECHAT TECHNICAL REPORT

arXiv

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arXiv 2024年

作者： He, Zhongjiang Wang, Zihan Liu, Xinzhang Liu, Shixuan Yao, Yitong Huang, Yuyao Li, xuelong Li, Yongxiang Che, Zhonghao Zhang, Zhaoxi Wang, Yan Wang, Xin Pu, luwen xu, Huinan Fang, Ruiyu Zhao, Yu Zhang, Jie Huang, Xiaomeng Lu, Zhilong Peng, Jiaxin Zheng, Wenjun Wang, Shiquan Yang, Bingkai He, xuewei Jiang, Zhuoru Xie, Qiyi Zhang, Yanhan Li, Zhongqiu Shi, Lingling Fu, Weiwei Zhang, Yin Huang, Zilu Xiong, Sishi Zhang, Yuxiang Wang, Chao Song, Shuangyong

In this technical report, we present TeleChat, a collection of large language models (LLMs) with parameters of 3 billion, 7 billion and 12 billion. It includes pretrained language models as well as fine-tuned chat models that is aligned with human preferences. TeleChat is initially pretrained on an extensive corpus containing a diverse collection of texts from both English and Chinese languages, including trillions of tokens. Subsequently, the model undergoes fine-tuning to align with human preferences, following a detailed methodology that we describe. We evaluate the performance of TeleChat on various tasks, including language understanding, mathematics, reasoning, code generation, and knowledge-based question answering. Our findings indicate that TeleChat achieves comparable performance to other open-source models of similar size across a wide range of public benchmarks. To support future research and applications utilizing LLMs, we release the fine-tuned model checkpoints of TeleChat’s 7B and 12B variant, along with code and a portion of our pretraining data, to the public community. © 2024, CC BY.

关键词： Knowledge based systems

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Making China's water data accessible, usable and shareable

NATURE WATER

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NATURE WATER 2023年第4期1卷 328-335页

作者： Lin, Jingyu Bryan, Brett A. Zhou, xudong Lin, Peirong Do, Hong xuan Gao, Lei Gu, Xinchen Liu, Zhifeng Wan, luwen Tong, Shanlin Huang, Jiacong Wang, Qian Zhang, Yuan Gao, Hongkai Yin, Jiabo Chen, Zilong Duan, Weili Xie, Zheyu Cui, Tong Liu, Junzhi Li, Mingqian Li, Xiaodong xu, Zhenwu Guo, Fei Shu, Lele Li, Bin Zhang, Jingwen Zhang, Pingping Fan, Bo Wang, Yanwen Zhang, Yifan Huang, Jinliang Li, Xin Cai, Yanpeng Yang, Zhifeng Guangdong Univ Technol Sch Ecol Environm & Resources Guangdong Prov Key Lab Water Qual Improvement & Ec Guangzhou 510006 Peoples R China Southern Marine Sci & Engn Guangdong Lab Guangzhou Peoples R China Deakin Univ Sch Life & Environm Sci Burwood Vic Australia Univ Tokyo Inst Ind Sci Global Hydrol Predict Ctr Tokyo Japan Peking Univ Inst Remote Sensing & GIS Sch Earth & Space Sci Beijing Peoples R China Int Res Ctr Big Data Sustainable Dev Goals Beijing Peoples R China Nong Lam Univ Ho Chi Minh City Fac Environm & Nat Resources Ho Chi Minh City Vietnam Nong Lam Univ Ho Chi Minh City Ctr Technol Business Incubat Ho Chi Minh City Vietnam Commonwealth Sci & Ind Res Org CSIRO Urrbrae SA Australia Tianjin Univ Sch Civil Engn State Key Lab Hydraul Engn Simulat & Safety Tianjin Peoples R China Beijing Normal Univ Fac Geog Sci State Key Lab Earth Surface Proc & Resource Ecol E Beijing Peoples R China Beijing Normal Univ Fac Geog Sci Sch Nat Resources Beijing Peoples R China Michigan State Univ Dept Earth & Environm Sci E Lansing MI USA Wuhan Univ State Key Lab Water Resources & Hydropower Engn Sc Wuhan Peoples R China Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci Nanjing Peoples R China Hohai Univ Inst Water Sci & Technol Nanjing Peoples R China East China Normal Univ Key Lab Geog Informat Sci Minist Educ China Shanghai Peoples R China Xiamen Univ Coll Environm & Ecol Xiamen Peoples R China Chinese Acad Sci Xinjiang Inst Ecol & Geog Urumqi Peoples R China Tsinghua Univ Dept Hydraul Engn State Key Lab Hydrosci & Engn Beijing Peoples R China Lanzhou Univ Ctr Pan Pole Environm 3 Lanzhou Peoples R China Jilin Univ Key Lab Groundwater Resources & Environm Minist Educ Changchun Peoples R China Hohai Univ Coll Hydrol & Water Resources Nanjing Peoples R China Chinese Acad Sci Key Lab Water Cycle & Related Land Surface Proc Inst Geog Sci & Nat Resources Res Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R C

Water data are essential for monitoring, managing, modelling and projecting water resources. Yet despite such data-including water quantity, quality, demand and ecology-being extensively collected in China, it remains difficult to access, use and share them. These challenges have led to poor data quality, duplication of effort and wasting of resources, limiting their utility for supporting decision-making in water resources policy and management. In this Perspective we discuss the current state of China's water data collection, governance and sharing, the barriers to open-access water data and its impacts, and outline a path to establishing a national water data infrastructure to reform water resource management in China and support global water-data sharing initiatives.

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Two-dimensional materials for future information technology: status and prospects

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Science China(Information Sciences) 2024年第6期67卷 1-147页

作者： Hao QIU Zhihao YU Tiange ZHAO Qi ZHANG Mingsheng xu Peifeng LI Taotao LI Wenzhong BAO Yang CHAI Shula CHEN Yiqi CHEN Hui-Ming CHENG Daoxin DAI Zengfeng DI Zhuo DONG Xidong DUAN Yuhan FENG Yu FU Jingshu GUO Pengwen GUO Yue HAO Jun HE Xiao HE Jingyi HU Weida HU Zehua HU Xinyue HUANG Ziyang HUANG Ali IMRAN Ziqiang KONG Jia LI Qian LI Weisheng LI Lei LIAO Bilu LIU Can LIU Chunsen LIU Guanyu LIU Kaihui LIU Liwei LIU Sheng LIU Yuan LIU Donglin LU Likuan MA Feng MIAO Zhenhua NI Jing NING Anlian PAN Tian-Ling REN Haowen SHU Litao SUN Yue SUN Quanyang TAO Zi-Ao TIAN Dong WANG Hao WANG Haomin WANG Jialong WANG Junyong WANG Wenhui WANG Xingjun WANG Yeliang WANG Yuwei WANG Zhenyu WANG Yao WE... Department of Applied Physics The Hong Kong Polytechnic University Key Laboratory for Micro-Nano Physics and Technology of Hunan Province College of Materials Science and EngineeringHunan University College of Integrated Circuits Zhejiang University Shenzhen Geim Graphene Center Shenzhen Key Laboratory of Layered Materials for Value-Added ApplicationsTsinghua-Berkeley Shenzhen Institute & Institute of Materials ResearchTsinghua Shenzhen International Graduate SchoolTsinghua University State Key Laboratory of Extreme Photonics and Instrumentation College of Optical Science and EngineeringInternational Research Center for Advanced PhotonicsZijingang CampusZhejiang University National Key Laboratory of Materials for Integrated Circuits Shanghai Institute of Microsystem and Information TechnologyChinese Academy of Sciences CAS Key Laboratory of Nanophotonic Materials and Devices & Key Laboratory of Nanodevices and Applications i-LabSuzhou Institute of Nano-Tech and Nano-Bionics (SINANO)Chinese Academy of Sciences College of Chemistry and Chemical Engineering Hunan University Key Laboratory of Quantum State Construction and Manipulation (Ministry of Education) Department of PhysicsRenmin University of China School of Integrated Circuits Beijing National Research Center for Information Science and Technology (BNRist)Tsinghua University School of Electronic Science and Engineering Nanjing University The State Key Discipline Laboratory of Wide Band Gap Semiconductor Technology Shaanxi Joint Key Laboratory of GrapheneSchool of MicroelectronicsXidian University Key Laboratory of Artificial Micro- and Nano-Structures of Ministry of Education School of Physics and TechnologyWuhan University School of Integrated Circuits Huazhong University of Science and Technology Center for Nanochemistry (CNC) Beijing Science and Engineering Center for NanocarbonsBeijing National Laboratory for Molecular SciencesCollege of Chemistry and Molecular EngineeringPeking University State Key Laboratory for Artificial Microstruct

Over the past 70 years, the semiconductor industry has undergone transformative changes,largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional(2D) materials like transition metal dichalcogenides(TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

关键词： two-dimensional (2D) materials information technology production technology standardization of characterization microelectronic devices optoelectronic devices multi-functional integration

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