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Science china(Life sciences) 2019年第4期62卷 535-543页

作者： Yamin Zhang Hongyan Ren Qiang Wang Wei Deng Weihua Yue Hao Yan Liwen Tan Qi Chen Guigang Yang Tianlan Lu Lifang Wang Fuquan Zhang Jianli Yang Keqing Li Luxian Lv Qingrong Tan Hongyan Zhang Xin Ma Fude Yang Lingjiang Li Chuanyue Wang Dai Zhang Liansheng Zhao Huiyao Wang Xiaojing Li Wanjun Guo Xun Hu Yang Tian Xiaohong Ma Tao Li chinese Antipsychotics Pharmacogenomics Consortium Mental Health Center and Psychiatric Laboratory the State Key Laboratory of BiotherapyWest China Hospital of Sichuan UniversityChengdu 610041China Tianjin Medical University General Hospital Tianjin Medical UniversityTianjin 300070China Hebei Mental Health Center Baoding 71000China Second Affiliated Hospital of Xinxiang Medical University Xinxiang 453002China Department of Psychiatry Xijing HospitalFourth Military Medical UniversityXi'an 710032China Huaxi Biobank West China Hospital of Sichuan UniversityChengdu 610041China West china Brain research Center West China Hospital of Sichuan UniversityChengdu 610041China Peking University Sixth Hospital(institute of Mental Health) Beijing 100191China National Clinical research Center for Mental Disorders & Key Laboratory of Mental Health Ministry of Health(Peking University)Beijing 100191China 5Second Xiangya Hospital Central South UniversityChangsha 410011China beijing Andi ng Hospital Beijing Institute for Brain DisordersCapital Medical UniversityBeijing 100088China beijing HuiLongGuan Hospital Beijing 100096China Wuxi Mental Health Center Nanjing Medical UniversityWuxi 214151China institute of Mental Health Tianjin Anding HospitalTianjin 300070China 不详

Antipsychotic-induced metabolic disturbance(AIMD) is a common adverse effect of antipsychotics with genetics partly underpinning variation in susceptibility among schizophrenia patients. Melanocortin4 receptor(MC4 R) gene, one of the candidate genes for AIMD, has been under-studied in the chinese patients. We conducted a pharmacogenetic study in a large cohort of chinese patients with schizophrenia. In this study, we investigated the genetic variation of MC4 R in chinese population by genotyping two SNPs(rs489693 and rs17782313) in 1,991 chinese patients and examined association of these variants with the metabolic effects that were often observed to be related to AIMD. Metabolic measures, including body mass index(BMI), waist circumference(WC), glucose, triglyceride, high-density lipoprotein(HDL), and low-density lipoprotein(LDL) levels were assessed at baseline and after 6-week antipsychotic treatment. We found that interaction of SNP×medication status(drug-na?ve/medicated) was significantly associated with BMI, WC, and HDL change %, respectively. Both SNPs were significantly associated with baseline BMI and WC in the medicated group. Moderate association of rs489693 with WC, Triglyceride, and HDL change % were observed in the whole sample. In the drug-na?ve group, we found recessive effects of rs489693 on BMI gain more than 7%, WC and Triglyceride change %, with AA incurring more metabolic adverse effects. In conclusion, the association between rs489693 and the metabolic measures is ubiquitous but moderate. Rs17782313 is less involved in AIMD. Two SNPs confer risk of AIMD to patients treated with different antipsychotics in a similar way.

关键词： MC4R antipsychotics metabolic disturbance

中度主动脉瓣狭窄人群的预后及左室收缩功能在其中的预测价值:一项来自china-VHD队列的研究

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Science Bulletin 2023年第22期68卷 2717-2720,M0004页

作者：刘先宝朱齐丰刘琼许海燕戴晗怡周瑶瑶陈俊 Stella Ng 鲁烨赵延延李喆吴永健王建安 china-VHD-研究团队 Department of Cardiology The Second Affiliated HospitalZhejiang University School of MedicineHangzhou 310009China Department of Cardiology Fuwai HospitalNational Center for Cardiovascular DiseaseChinese Academy of Medical Science and Peking Union Medical CollegeBeijing 100037China Department of Cardiology Affiliated Jinhua HospitalZhejiang University School of MedicineJinhua 321000China Medical research&Biometrics Center Fuwai HospitalNational Center for Cardiovascular DiseaseChinese Academy of Medical Science and Peking Union Medical CollegeBeijing 100037China

中度主动脉瓣狭窄(AS)患者的预后存在争议,尤其是左室收缩功能(LVEF)在该人群的应用价值仍不明确.该研究旨在探讨中度AS患者的预后及左室收缩功能对其的预测价值.研究通过全国多中心前瞻性队列研究china-VHD连续纳入了来自中国46家三甲... 详细信息

中度主动脉瓣狭窄(AS)患者的预后存在争议,尤其是左室收缩功能(LVEF)在该人群的应用价值仍不明确.该研究旨在探讨中度AS患者的预后及左室收缩功能对其的预测价值.研究通过全国多中心前瞻性队列研究china-VHD连续纳入了来自中国46家三甲医院的万余名严重瓣膜病患者,其中513例为中度AS,并依据LVEF界值分为三组,分别为LVEF≤40%(LVEF显著降低组),40%

关键词：主动脉瓣狭窄 Cox回归模型左室收缩功能预测价值三甲医院心源性死亡全因死亡瓣膜病

Entropy-based Mechanism for Bottom-up Visual Attention

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Entropy-based Mechanism for Bottom-up Visual Attention

EFFICIENTLY USING SPEAKER ADAPTATION DATA

The Third Asian Conference on Vision (ACV2004)

作者： Bangsheng Cheng. Xiaowei TangInterdiscipline Lab College of Science Zhejiang University Hangzhou 310027 P.R. china

＜正＞ We proposed a computational model of bottom-up selective visual attention based on the entropy measurement of the visual image. Instead of using Gabor filters and DoG （Difference of Gaussians） filters to compute the feature maps, we use a set of entropy filters to compute the entropy maps, and then integrate all the entropy maps to get a saliency map used to guide the selective attention. Contrast to the classical selective attention models, our

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EFFICIENTLY USING SPEAKER ADAPTATION DATA

Clinical practice guideline for transurethral plasmakinetic resection of prostate for benign prostatic hyperplasia(2021 Edition)

6～(th) International Conference on Spoken Language Processing

作者： Chengyi Zheng and Yonghong YanCenter for Spoken Language Understanding Oregon Graduate institute Intel Corporation USA

＜正＞Transformation based speaker adaptation techniques, such as Maximum Likelihood Linear Regression （MLLR） [1] require a large amount of adaptation data to robustly estimate the transform matrices. In this paper, we present a new adaptation scheme that adjusts the adaptation data according to the feedback from recognizer. By giving different weights to different parts of the adaptation data, the proposed scheme can make use of the adaptation data more efficiently. Experiments on the WSJ 20K task show that this method achieved an additional 10% relative word error rate reduction in supervised adaptation and 2% reduction in unsupervised adaptation compared with conventional MLLR approach.

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Military Medical research 2022年第5期9卷 515-533页

作者： Xian-Tao Zeng Ying-Hui Jin Tong-Zu Liu Fang-Ming Chen De-Gang Ding Meng Fu Xin-Quan Gu Bang-Min Han Xing Huang Zhi Hou Wan-Li Hu Xin-Li Kang Gong-Hui Li Jian-Xing Li Pei-Jun Li Chao-Zhao Liang Xiu-Heng Liu Zhi-Yu Liu Chun-Xiao Liu Jiu-Min Liu Guang-Heng Luo Yi Luo Wei-Jun Qin Jian-Hong Qiu Jian-Xin Qiu Xue-Jun Shang Ben-Kang Shi Fa Sun Guo-Xiang Tian Ye Tian Feng Wang Feng Wang Yin-Huai Wang Yu-Jie Wang Zhi-Ping Wang Zhong Wang Qiang Wei Min-Hui Xiao Wan-Hai Xu Fa-Xian Yi Chao-Yang Zhu Qian-Yuan Zhuang Li-Qun Zhou Xiao-Feng Zou Nian-Zeng Xing Da-Lin He Xing-Huan Wang 无 Department of Urology Institute of UrologyZhongnan Hospital of Wuhan UniversityWuhan430071China Center for Evidence-Based and Translational Medicine Zhongnan Hospital of Wuhan UniversityWuhan430071China Department of Urology Tianjin Third Central Hospital Affiliated To Nankai UniversityTianjin300170China Department of Urology Henan Provincial People’s HospitalZhengzhou University People’s HospitalZhengzhou450003China Department of Urology Beijing Tsinghua Changgung HospitalSchool of Clinical MedicineTsinghua UniversityBeijing102218China Department of Urology China-Japan Union Hospital of Jilin UniversityChangchun130033China Department of Urology Shanghai General HospitalShanghai Jiao Tong University School of MedicineShanghai200080China Department of Urology Qinghai University Affiliated HospitalXi’ning810012China Department of Urology People’s Hospital of Hainan ProvinceHainan Affiliated Hospital of Hainan Medical University HaikouHaikou570311China Department of Urology Sir Run Run Shaw HospitalZhejiang University School of MedicineHangzhou310016China Department of Urology General Hospital of Ningxia Medical UniversityYinchuan750003China Department of Urology The First Affiliated Hospital of Anhui Medical UniversityHefei230022China Department of Urology Renmin Hospital of Wuhan UniversityWuhan430060China Department of Urology The Second Hospital of Dalian Medical UniversityDalian116023LiaoningChina Department of Urology Zhujiang Hospital of Southern Medical UniversityGuangzhou510282China Department of Urology Guangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesGuangzhou510080China Department of Urology Surgery Guizhou Province People’s HospitalGuiyang550002China Department of Urology Xijing Hospital of Air Force Military Medical UniversityXi’an710032China Department of Urology The 980St Hospital of the PLA Joint Logistics Support Force(Bethune International Peace Hospital of PLA)Shijiazhuang050082China Department of Urology

Benign prostatic hyperplasia (BPH) is highly prevalent among older men, impacting on their quality of life, sexual function, and genitourinary health, and has become an important global burden of disease. Transurethral plasmakinetic resection of prostate (TUPKP) is one of the foremost surgical procedures for the treatment of BPH. It has become well established in clinical practice with good efficacy and safety. In 2018, we issued the guideline “2018 Standard Edition”. However much new direct evidence has now emerged and this may change some of previous recommendations. The time is ripe to develop new evidence-based guidelines, so we formed a working group of clinical experts and methodologists. The steering group members posed 31 questions relevant to the management of TUPKP for BPH covering the following areas: questions relevant to the perioperative period (preoperative, intraoperative, and postoperative) of TUPKP in the treatment of BPH, postoperative complications and the level of surgeons’ surgical skill. We searched the literature for direct evidence on the management of TUPKP for BPH, and assessed its certainty generated recommendations using the grade criteria by the European Association of Urology. Recommendations were either strong or weak, or in the form of an ungraded consensus-based statement. Finally, we issued 36 statements. Among them, 23 carried strong recommendations, and 13 carried weak recommendations for the stated procedure. They covered questions relevant to the aforementioned three areas. The preoperative period for TUPKP in the treatment of BPH included indications and contraindications for TUPKP, precautions for preoperative preparation in patients with renal impairment and urinary tract infection due to urinary retention, and preoperative prophylactic use of antibiotics. Questions relevant to the intraoperative period incorporated surgical operation techniques and prevention and management of bladder explosion. The application to different

关键词： Transurethral plasmakinetic resection of prostate Benign prostatic hyperplasia Recommendation Treatment Guideline

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隶属云和隶属云发生器

计算机研究与发展 1995年第6期32卷 15-20页

作者：李德毅孟海军史雪梅总参谋部第六十一研究所南京通信工程学院计算机系

本文针对模糊集理论基石的隶属函数，提出了隶属云的新思想，给出了用数字特征描述隶属云的方法和正态隶属云的数学模型，探讨了隶属云发生器的实现技术及应用场合，从而为社会和自然科学中的诸多问题用定性和定量相结合的处理方法奠定... 详细信息

本文针对模糊集理论基石的隶属函数，提出了隶属云的新思想，给出了用数字特征描述隶属云的方法和正态隶属云的数学模型，探讨了隶属云发生器的实现技术及应用场合，从而为社会和自然科学中的诸多问题用定性和定量相结合的处理方法奠定了基础。

关键词：隶属云隶属云发生器模糊集模糊数学

Multidimensional dynamic prediction model for hospitalized patients with the omicron variant in china

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Infectious Disease Modelling 2023年第4期8卷 1097-1107页

作者： Yujie Chen Yao Wang Jieqing Chen Xudong Ma Longxiang Su Yuna Wei Linfeng Li Dandan Ma Feng Zhang Wen Zhu Xiaoyang Meng Guoqiang Sun Lian Ma Huizhen Jiang Chang Yin Taisheng Li Xiang Zhou china National Critical Care Quality Control Center Group Department of Critical Care Medicine Peking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100730China Yidu Cloud Technology Inc. BeijingChina Information Center Department State Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College HospitalPeking Union Medical College and Chinese Academy of Medical SciencesBeijing100730China Department of Medical Administration National Health Commission of the People's Republic of ChinaBeijing100044China Information Center State Key Laboratory of Complex Severe and Rare DiseasesPeking Union Medical College HospitalChinese Academy of Medical Sciences and Peking Union Medical CollegeBeijing100730China Department of Infectious Diseases Peking Union Medical College HospitalChinese Academy of Medical SciencesBeijingChina National institute of Hospital Administration BeijingChina

Purpose:To establish dynamic prediction models by machine learning using daily multidimensional data for coronavirus disease 2019(COVID-19)***:Hospitalized COVID-19 patients at Peking Union Medical College Hospital from Nov 2nd,2022,to Jan 13th,2023,were enrolled in this *** outcome was defined as deterioration or recovery of the patient's ***,comorbidities,laboratory test results,vital signs,and treatments were used to train the *** predict the following days,a separate XGBoost model was trained and *** Shapley additive explanations method was used to analyze feature ***:A total of 995 patients were enrolled,generating 7228 and 3170 observations for each prediction *** the deterioration prediction model,the minimum area under the receiver operating characteristic curve(AUROC)for the following 7 days was 0.786(95%CI 0.721-0.851),while the AUROC on the next day was 0.872(0.831-0.913).In the recovery prediction model,the minimum AUROC for the following 3 days was 0.675(0.583-0.767),while the AUROC on the next day was 0.823(0.770-0.876).The top 5 features for deterioration prediction on the 7th day were disease course,length of hospital stay,hypertension,and diastolic blood *** for recovery prediction on the 3rd day were age,D-dimer levels,disease course,creatinine levels and corticosteroid ***:The models could accurately predict the dynamics of Omicron patients’conditions using daily multidimensional variables,revealing important features including comorbidities(e.g.,hyperlipidemia),age,disease course,vital signs,D-dimer levels,corticosteroid therapy and oxygen therapy.

关键词： COVID-19 Omicron Prediction model Machine learning

Expert Consensus on Nutritional Support for Children with Congenital Heart Disease(2023 Edition)

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Congenital Heart Disease 2023年第6期18卷 571-593页

作者： Xuming Mo Wei Cai Jirong Qi Zhuoming Xu Ying Wang Weihui Yan Shoujun Li Nianguo Dong Xinxin Chen Jinfen Liu Qiang Shu Jimei Chen Haibo Zhang Hao Zhang Quansheng Xing Qi An Xiaofeng Li Xu Wang Yan He Junwu Su Taibing Fan Teng Ming Weibing Tang Li Hong Jinghao Zheng Ming Ye Guocheng Sun Yiqun Ding Liang Tao Yifeng Yang Zhongshi Wu Hua Cao Qiang Wang Keming Yang Libing Zhang Ping Wen Yanqin Cui Bo Zhai Yong Zou Qingya Tang Rui Chen Chun Wu Zhiyu Feng Caixia Liu Yaping Mi Rufang Zhang Ke Lin Xin Li Mingan Pi Xiangming Fan Shanshan Shi Peng Huang Zhengxia Pan Jiafeng Qi Renwei Chen Shuguang Tao Yaqin Shu Huifeng Zhang Lan Jiang Min Da Nishant Patel Liang Hu Cardiac Surgery Group of Pediatric Surgery Society of chinese Medical Association and Parenteral Enteral Nutrition Society of chinese Medical Association Department of Cardiothoracic Surgery Children’s Hospital of Nanjing Medical UniversityNanjing210008China Department of Pediatric Surgery and Nutrition Xinhua HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai200092China Department of Cardiothoracic Surgery Shanghai Children’s Medical CenterSchool of MedicineShanghai Jiao Tong UniversityShanghai200127China Division of Pediatric Gastroenterology and Nutrition Xinhua HospitalSchool of MedicineShanghai Jiao Tong UniversityShanghai200092China Pediatric Cardiac Surgery Center Fuwai HospitalNational Centre for Cardiovascular DiseasesState Key Laboratory of Cardiovascular DiseaseChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing100037China Department of Cardiovascular Surgery Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan430022China Cardiovascular Center Guangzhou Women and Children’s Medical CenterGuangzhou Medical UniversityGuangzhou510623China Department of Cardiac Surgery Children’s HospitalZhejiang University School of MedicineNational Clinical Research Center for Child HealthHangzhou310052China Department of Cardiovascular Surgery Guangdong Cardiovascular InstituteGuangdong Provincial People’s HospitalGuangdong Academy of Medical SciencesSouthern Medical UniversityGuangzhou519041China Heart Center and Shanghai Institue of Pediatric Congenital Heart Disease Shanghai Children’s Medical CenterNational Children’s Medical CenterSchool of MedicineShanghai Jiao Tong UniversityShanghai200127China Heart Center Qingdao Women and Children’s HospitalQingdao266034China Department of Cardiovascular Surgery West China HospitalSichuan UniversityChengdu610041China Department of Cardiac Surgery Beijing Children’s HospitalCapital Medical UniversityNational Center for Children’s HealthBeijing100045China Department of Pediatric Intensive Care Unit Fuwai HospitalChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing100037China Depar

The second edition of the expert consensus on pediatric nutrition was formed based on a global update of pedia-tric nutrition guidelines or consensus worldwide,the management of congenital heart disease,and the results of multi-center clinical nutrition research for congenital heart disease following theﬁrst chinese consensus edition of *** consensus was also shaped by the results of three discussion sessions and two questionnaires con-ducted by the 13-member collaboration *** process was informed by both clinical guidelines and expert *** quality of literature,both in English and chinese,and the level of recommendations were evaluated using the Grading of Recommendations Assessment,Development,and Evaluations(GRADE)system.

关键词： Congenital heart disease nutrition enteral parenteral expert consensus

Development of a prediction model to identify undiagnosed chronic obstructive pulmonary disease patients in primary care settings in china

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chinese Medical Journal 2023年第6期136卷 676-682页

作者： Buyu Zhang Dong Sun Hongtao Niu Fen Dong Jun Lyu Yu Guo Huaidong Du Yalin Chen Junshi Chen Weihua Cao Ting Yang Canqing Yu Zhengming Chen Liming Li on behalf of the china Kadoorie Biobank Collaborative Group Department of Epidemiology and Biostatistics School of Public HealthPeking UniversityBeijing 100191China Department of Pulmonary and Critical Care Medicine Center of Respiratory MedicineChina–Japan Friendship HospitalBeijing 100029China National Center for Respiratory Medicine and National Clinical research Center for Respiratory Diseases Beijing 100029China institute of Respiratory Medicine Chinese Academy of Medical SciencesBeijing 100007China Peking University Center for Public Health and Epidemic Preparedness and Response Beijing 100191China Key Laboratory of Molecular Cardiovascular sciences(Peking University) Ministry of EducationBeijing 100191China National Center for Cardiovascular Diseases Fuwai HospitalChinese Academy of Medical SciencesBeijing 100037China Medical research Council Population Health research Unit at the University of Oxford Oxford OX37LFUK Clinical Trial Service Unit and Epidemiological Studies Unit Nuffield Department of Population HealthUniversity of OxfordOxford OX37LFUK Maiji Center for Disease Control and Prevention TianshuiGansu 741020China china National Center for Food Safety Risk Assessment Beijing 100022China 不详

Background:At present,a large number of chronic obstructive pulmonary disease(COPD)patients are undiagnosed in ***,this study aimed to develop a simple prediction model as a screening tool to identify patients at risk for ***:The study was based on the data of 22,943 subjects aged 30 to 79 years and enrolled in the second resurvey of china Kadoorie Biobank during 2012 and 2013 in *** stepwisely selected the predictors using logistic regression *** we tested the model validity through P-P graph,area under the receiver operating characteristic curve(AUROC),ten-fold cross validation and an external validation in a sample of 3492 individuals from the Enjoying Breathing Program in ***:The final prediction model involved 14 independent variables,including age,sex,location(urban/rural),region,educational background,smoking status,smoking amount(pack-years),years of exposure to air pollution by cooking fuel,family history of COPD,history of tuberculosis,body mass index,shortness of breath,sputum and *** model showed an area under curve(AUC)of 0.72(95%confidence interval[CI]:0.72-0.73)for detecting undiagnosed COPD patients,with the cutoff of predicted probability of COPD=0.22,presenting a sensitivity of 70.13%and a specificity of 62.25%.The AUROC value for screening undiagnosed patients with clinically significant COPD was 0.68(95%CI:0.66-0.69).Moreover,the ten-fold cross validation reported an AUC of 0.72(95%CI:0.71-0.73),and the external validation presented an AUC of 0.69(95%CI:0.68-0.71).Conclusion:This prediction model can serve as a first-stage screening tool for undiagnosed COPD patients in primary care settings.

关键词： Chronic obstructive pulmonary disease Screening Prediction model china Kadoorie Biobank