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Comparative Study on the Immunogenicity and Efficacy of Different Post-exposure Intramuscular Rabies Vaccination Regimens in China

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Biomedical and Environmental Sciences 2024年第2期37卷 178-186页

作者： SONG yun HE ying LU Xue xin ZHANG xiao Mei JIANG xiaO Lin SONG Qing huang Xue yong MA Hong xia YU peng Cheng zhu Wu Yang Henan Provincial Center for Disease Control and Prevention Infectious Disease Prevention and Control InstituteHenan Provincial Key Laboratory of Infectious Disease PathogensZhengzhou 450000HenanChina National Institute for Viral Disease Control and Prevention China CDCKey Laboratory of BiosafetyNational Health CommissionBeijing 102206China Shandong Provincial Center for Disease Control and Prevention Jinan 250014ShandongChina Qingdao Chengyang District Center for Disease Control and Prevention Qingdao 266108ShandongChina

Objective This study aimed to compare the current Essen rabies post-exposure immunization schedule(0-3-7-14-28)in China and the simple 4-dose schedule(0-3-7-14)newly recommended by the World Health Organization in terms of their safety,efficacy,and *** Mice were vaccinated according to different immunization schedules,and blood was collected for detection of rabies virus neutralizing antibodies(RVNAs)on days 14,21,28,35,and 120after the first ***,different groups of mice were injected with lethal doses of the CVS-11 virus on day 0,subjected to different rabies immunization schedules,and assessed for morbidity and death *** a clinical trial,185 rabies-exposed individuals were selected for post-exposure vaccination according to the Essen schedule,and blood was collected for RVNAs detection on days 28and 42 after the first *** A statistically significant difference in RVNAs between mice in the Essen and 0-3-7-14 schedule groups was observed on the 35th day(P0.05)in RVNAs levels at any time *** post-exposure immune protective test showed that the survival rate of mice in the control group was 20%,whereas that in the immunization groups was 40%.In the clinical trial,the RVNAs positive conversion rates on days 28(14 days after 4 doses)and 42(14 days after 5 doses)were both 100%,and no significant difference in RVNAs levels was observed(P>0.05).Conclusion The simple 4-dose schedule can produce sufficient RVNAs levels,with no significant effect of a delayed fourth vaccine dose(14–28 d)on the immunization potential.

关键词： Rabies Post-exposure immunization Essen regimen RVNAs

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液体圆柱射流破碎过程的实验

航空动力学报 2008年第2期23卷 208-214页

作者：万云霞黄勇朱英北京航空航天大学能源与动力工程学院北京100083

采用高速摄像仪对圆柱射流的表面剥离现象和射流破碎长度进行了实验,在低速射流下,分析了Rayleigh模式下主液滴和卫星液滴形成过程及运动规律.随着射流速度的增大,射流表面开始出现了表面剥离小液滴.并发现存在一个临界雷诺数,当超过临... 详细信息

采用高速摄像仪对圆柱射流的表面剥离现象和射流破碎长度进行了实验,在低速射流下,分析了Rayleigh模式下主液滴和卫星液滴形成过程及运动规律.随着射流速度的增大,射流表面开始出现了表面剥离小液滴.并发现存在一个临界雷诺数,当超过临界雷诺数时,射流表面出现表面剥离现象.通过对射流中表面剥离现象的实验和分析,给出了两种射流破碎长度随雷诺数变化规律和破碎长度拟合关系式.

关键词：航空、航天推进系统圆柱射流高速摄影表面剥离卫星液滴破碎长度

Significant loss of soil inorganic carbon at the continental scale

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National Science Review 2022年第2期9卷 13-20页

作者： xiao-Dong Song Fei Yang Hua-yong Wu Jing Zhang De-Cheng Li Feng Liu Yu-Guo Zhao Jin-Ling Yang Bing Ju Chong-Fa Cai Biao huang Huai-Yu Long ying Lu Yue-Yu Sui Qiu-Bing wang ke-Ning Wu Feng-Rong Zhang Ming-Kui Zhang Zhou Shi wan-zhu Ma Gang xin Zhi-Ping Qi Qing-Rui Chang En Ci Da-Gang Yuan Yang-zhu Zhang Jun-Ping Bai Jia-ying Chen Jie Chen Yin-Jun Chen yun-Zhong Dong Chun-Lan Han Ling Li Li-Ming Liu Jian-Jun Pan Fu-peng Song Fu-Jun Sun Deng-Feng wang Tian-Wei wang xiang-Hua Wei Hong-Qi Wu xia Zhao Qing Zhou Gan-Lin Zhang State key Laboratory of Soil and Sustainable Agriculture Institute of Soil Science Chinese Academy of Sciences College of Advanced Agricultural Sciences University of Chinese Academy of Sciences College of Resources and Environment Huazhong Agricultural University Institute of Agricultural Resources and Regional Planning Chinese Academy of Agricultural Sciences College of Natural Resources and Environment South China Agricultural University Northeast Institute of Geography and Agroecology Chinese Academy of Sciences College of Land and Environment Shenyang Agricultural University School of Land Science and Technology China University of Geosciences College of Land Science and Technology China Agricultural University College of Environmental and Resource Sciences Zhejiang University Institute of Digital Agriculture Zhejiang Academy of Agricultural Sciences College of Agriculture Heilongjiang Bayi Agricultural University Tropical Crops Genetic Resources Institute Chinese Academy of Tropical Agricultural Sciences College of Natural Resources and Environment Northwest A & F University College of Resources and Environment Southwest University College of Resources Sichuan Agricultural University College of Resources and Environment Hunan Agricultural University Institute of Agricultural Product Quality Standard and Testing Research Tibet Academy of Agricultural and Animal Husbandry Sciences School of Agricultural Sciences Zhengzhou University Institute of Agriculture Environment and Resources Research Shanxi Academy of Agricultural Sciences College of Resources and Environment Henan Agricultural University College of Resources and Environment China Agricultural University College of Resources and Environmental Sciences Nanjing Agricultural University College of Resources and Environment Shandong Agricultural University Agricultural College Guangxi University College of Grassland and Environment Science Xinjiang Agricultural University College of Geographical Science Qinghai Normal University

Widespread soil acidification due to atmospheric acid deposition and agricultural fertilization may greatly accelerate soil carbonate dissolution and ***,to date,few studies have addressed these ***,we use meta-analysis and nationwide-survey datasets to investigate changes in soil inorganic carbon(SIC) stocks in *** observe an overall decrease in SIC stocks in topsoil(0-30 cm)(11.33 g C m-2yr-1) from the 1980 s to the 2010 *** SIC stocks have decreased by～8.99±2.24%(1.37±0.37 Pg C).The average SIC losses across China(0.046 Pg C yr-1) and in cropland(0.016 Pg C yr-1) account for～17.6%-24.0% of the terrestrial C sink and 57.1% of the soil organic carbon sink in cropland,*** deposition and climate change have profound influences on SIC *** estimate that～19.12%-19.47% of SIC stocks will be further lost by *** consumption of SIC may offset a large portion of global efforts aimed at ecosystem carbon sequestration,which emphasizes the importance of achieving a better understanding of the indirect coupling mechanisms of nitrogen and carbon cycling and of effective countermeasures to minimize SIC loss.

关键词： China soil inorganic carbon stocks global change carbonate soil acidification

Association of Overlapped and Un-overlapped Comorbidities with COVID-19 Severity and Treatment Outcomes: A Retrospective Cohort Study from Nine Provinces in China

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Biomedical and Environmental Sciences 2020年第12期33卷 893-905页

作者： MA Yan zhu Dong Shan CHEN Ren Bo SHI Nan Nan LIU Si Hong FAN Yi Pin WU Gui Hui YANG Pu Ye BAI Jiang Feng CHEN Hong CHEN Li ying FENG Qiao GUO Tuan Mao HOU yong HU Gui Fen HU xiao Mei HU yun Hong huang Jin huang Qiu Hua huang Shao Zhen JI Liang JIN Hai Hao LEI xiao LI Chun Yan LI Min Qing LI Qun Tang LI xian yong LIU Hong De LIU Jin Ping LIU Zhang MA Yu Ting MAO Ya MO Liu Fen NA Hui wanG Jing Wei SONG Fang Li SUN Sheng wanG Dong Ting wanG Ming Xuan wanG xiao Yan wanG Yin Zhen wanG Yu Dong WU Wei WU Lan Ping xiaO Yan Hua XIE Hai Jun XU Hong Ming XU Shou Fang XUE Rui xia YANG Chun YANG Kai Jun YUAN Sheng Li ZHANG Gong Qi ZHANG Jin Bo ZHANG Lin Song ZHAO Shu Sen ZHAO wan ying ZHENG Kai ZHOU ying Chun zhu Jun Teng zhu Tian Qing ZHANG Hua Min wanG Yan Ping wanG yong Yan 无 Institute of Basic Research in Clinical Medicine China Academy of Chinese Medical SciencesBeijing 100700China Centre for Health Management and Policy Research School of Public HealthCheeloo College of MedicineShandong UniversityJinan 250012ShandongChina Institute of Information on Traditional Chinese Medicine China Academy of Chinese Medical SciencesBeijing 100700China Department of Tuberculosis The Public Health Clinical Center of ChengduChengdu 610066SichuanChina Department of Infectious Disease Shaanxi Infectious Disease HospitalXi'an 610113ShaanxiChina Department of Infectious Disease Yulin Chinese Medicine HospitalYulin 719000ShaanxiChina President's Office The First Hospital of QiqiharQiqihar 161005HeilongjiangChina Department of Traditional Chinese Medicine Shangluo Central HospitalShangluo 726000ShaanxiChina Department of Traditional Chinese Medicine The People's Hospital of QitaiheQitaihe 154603HeilongjiangChina Department of Scientific Research Xianyang Central HospitalXianyang 712000ShaanxiChina Department of traditional Chinese medicine The First Affiliated Hospital of Anhui University of Traditional Chinese MedicineHefei 230012AnhuiChina Department of Infectious Disease Heping Hospital Affiliated to Changzhi Medical CollegeChangzhi 046000ShanxiChina Department of Traditional Chinese Medicine Langfang Hospital of Chinese MedicineLangfang 065000HeibeiChina Department of Traditional Chinese Medicine Xingtai Hospital of Chinese MedicineXingtai 054001HeibeiChina Department of Traditional Chinese Medicine The People's Hospital of Guangxi Zhuang Autonomous RegionNanning 168600GuangxiChina Department of Traditional Chinese Medicine The First People'sHospital of FangchenggangFangchenggang 538001GuangxiChina Department of Traditional Chinese Medicine Mianyang Hospital of Traditional Chinese MedicineMianyang 621000SichuanChina Department of Traditional Chinese Medicine Liuzhou People's HospitalLiuzhou 545006GansuChina Department of Traditional Chinese Medici

Objective Several COVID-19 patients have overlapping comorbidities. The independent role of each component contributing to the risk of COVID-19 is unknown, and how some non-cardiometabolic comorbidities affect the risk of COVID-19 remains *** A retrospective follow-up design was adopted. A total of 1,160 laboratory-confirmed patients were enrolled from nine provinces in China. Data on comorbidities were obtained from the patients’ medical records. Multivariable logistic regression models were used to estimate the odds ratio(OR) and 95% confidence interval(95% CI) of the associations between comorbidities(cardiometabolic or non-cardiometabolic diseases), clinical severity, and treatment outcomes of *** Overall, 158(13.6%) patients were diagnosed with severe illness and 32(2.7%) had unfavorable outcomes. Hypertension(2.87, 1.30–6.32), type 2 diabetes(T2 DM)(3.57, 2.32–5.49),cardiovascular disease(CVD)(3.78, 1.81–7.89), fatty liver disease(7.53, 1.96–28.96), hyperlipidemia(2.15, 1.26–3.67), other lung diseases(6.00, 3.01–11.96), and electrolyte imbalance(10.40, 3.00–26.10)were independently linked to increased odds of being severely ill. T2 DM(6.07, 2.89–12.75), CVD(8.47,6.03–11.89), and electrolyte imbalance(19.44, 11.47–32.96) were also strong predictors of unfavorable outcomes. Women with comorbidities were more likely to have severe disease on admission(5.46,3.25–9.19), while men with comorbidities were more likely to have unfavorable treatment outcomes(6.58, 1.46–29.64) within two *** Besides hypertension, diabetes, and CVD, fatty liver disease, hyperlipidemia, other lung diseases, and electrolyte imbalance were independent risk factors for COVID-19 severity and poor treatment outcome. Women with comorbidities were more likely to have severe disease, while men with comorbidities were more likely to have unfavorable treatment outcomes.

关键词： Comorbidities COVID-19 Severity Gender Age Treatment outcome

Chemoprophylaxis, diagnosis, treatments, and discharge management of COVID-19: An evidence-based clinical practice guideline(updated version)

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Military Medical Research 2020年第3期7卷 249-282页

作者： ying-Hui Jin Qing-Yuan Zhan Zhi-yong peng Xue-Qun Ren Xun-Tao Yin Lin Cai Yu-Feng Yuan Ji-Rong Yue xiao-Chun Zhang Qi-Wen Yang Jianguang Ji Jian xia Yi-Rong Li Fu-xiang Zhou Ya-Dong Gao zhui Yu Feng Xu Ming-Li Tu Li-Ming Tan Min Yang Fang Chen xiao-Ju Zhang Mei Zeng Yu zhu xin-Can Liu Jian Yang Dong-Chi Zhao Yu-Feng Ding Ning Hou Fu-Bing wang Hao Chen yong-Gang Zhang Wei Li Wen Chen Yue-xian Shi Xiu-Zhi Yang Xue-Jun wang Yan-Jun Zhong Ming-Juan Zhao Bing-Hui Li Lin-Lu Ma Hao Zi Na wang yun-yun wang Shao-Fu Yu Lu-Yao Li Qiao huang Hong Weng xiang-ying Ren Li-Sha Luo Man-Ru Fan Di huang Hong-Yang Xue Lin-xin Yu Jin-Ping Gao Tong Deng xian-Tao Zeng Hong-Jun Li Zhen-Shun Cheng xiao-Mei Yao xing-Huan wang Center for Evidence-Based and Translational Medicine Zhongnan Hospital of Wuhan UniversityWuhan 430071China National Clinical Research Center for Respiratory Diseases China-Japan Friendship HospitalBeijing 100029China Department of Pulmonary and Critical Care Medicine China-Japan Friendship HospitalBeijing 10029China Leishenshan Hospital in Wuhan Wuhan 430200China Department of Critical Care Medicine Zhongnan Hospital of Wuhan UniversityWuhan 430071China Institutes of Evidence-based Medicine and Knowledge Translation Henan UniversityKaifeng 475000HenanChina Department of Medical Imaging Guizhou Provincial People’s HospitalGuiyang 550002China Departments of Orthopedics Zhongnan Hospital of Wuhan UniversityWuhan 430071China Department of Hepatobiliary Surgery Zhongnan Hospital of Wuhan UniversityWuhan 430071China National Clinical Research Center for Geriatrics West China Hospital of Sichuan UniversityChengdu 610041China Department of Geriatrics West China Hospital of Sichuan UniversityChengdu 610041China Department of Radiology Zhongnan Hospital of Wuhan UniversityWuhan 430071China Department of Clinical Laboratory Peking Union Medical College Hospital(PUMCH)Peking Union Medical CollegeChinese Academy of Medical Sciences(CAMS)Beijing 100730China Center for Primary Health Care Research Lund University and Region Skåne25002 MalmöSweden Emergency Center Zhongnan Hospital of Wuhan UniversityWuhan 403371China Department of Allergology Zhongnan Hospital of Wuhan UniversityWuhan 430071China Department of Radiation and Medical Oncology Zhongnan Hospital of Wuhan UniversityWuhan 43071China Department of Critical Care Medicine Renmin Hospital of Wuhan UniversityWuhan 430060China Department of Emergency Medicine and Chest Pain Center Qilu Hospital of Shandong UniversityJinan 250002China Department of Respiratory and Critical Care Medicine Suizhou Central HospitalHubei University of MedicineSuizhou 441300HubeiChina Department of Clinic Pharmacy Second People’s Hospital of Huaihua Ci

The novel severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)is the cause of a rapidly spreading illness,coronavirus disease 2019(COVID-19),affecting more than seventeen million people around the *** and treatment guidelines for clinicians caring for patients are *** the early stage,we have issued"A rapid advice guideline for the diagnosis and treatment of 2019 novel coronavirus(2019-nCoV)infected pneumonia(standard version)";now there are many direct evidences emerged and may change some of previous recommendations and it is ripe for develop an evidence-based *** formed a working group of clinical experts and *** steering group members proposed 29 questions that are relevant to the management of COVID-19 covering the following areas:chemoprophylaxis,diagnosis,treatments,and discharge *** searched the literature for direct evidence on the management of COVID-19,and assessed its certainty generated recommendations using the Grading of Recommendations,Assessment,Development and Evaluation(GRADE)*** were either strong or weak,or in the form of ungraded consensus-based ***,we issued 34 *** them,6 were strong recommendations for,14 were weak recommendations for,3 were weak recommendations against and 11 were ungraded consensus-based *** covered topics of chemoprophylaxis(including agents and Traditional Chinese Medicine(TCM)agents),diagnosis(including clinical manifestations,reverse transcription-polymerase chain reaction(RT-PCR),respiratory tract specimens,IgM and IgG antibody tests,chest computed tomography,chest X-ray,and CT features of asymptomatic infections),treatments(including lopinavirritonavir,umifenovir,favipiravir,interferon,remdesivir,combination of antiviral drugs,hydroxychloroquine/chloroquine,interleukin-6 inhibitors,interleukin-1 inhibitors,glucocorticoid,qingfei paidu decoction,lianhua qingwen granules/capsules,convalescent plasma,lung

关键词： COVID-19 SARS-CoV-2 Recommendation Chemoprophylaxis Diagnosis Treatment Discharge management Traditional Chinese medicine guideline

基于个体化机器学习的原发性免疫性血小板减少症危重出血预测模型:一项全国前瞻性队列研究

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Science Bulletin 2023年第18期68卷 2106-2114,M0004页

作者： zhuo-Yu An Ye-Jun Wu Yu Hou Heng Mei Wei-xia Nong Wen-Qian Li Hu Zhou Ru Feng Jian-Ping Shen Jun peng Hai Zhou Yi Liu yong-Ping Song Lin-Hua Yang Mei-yun Fang Jian-yong Li yun-Feng Cheng peng Liu Ya-Jing Xu Zhao wang Yi Luo Zhen Cai Hui Liu Jing-Wen wang Juan Li Xi Zhang Zi-Min Sun xiao-Yu zhu xin wang Rong Fu Liang huang Shao-Yuan wang Tong-Hua Yang Li-Ping Su Liang-Ming Ma Xie-Qun Chen Dai-Hong Liu Hong-xia Yao Jia Feng Hong-Yu Zhang Ming Jiang Ze-Ping Zhou Wen-Sheng wang Xu-Liang Shen Yangjin Baima Yue-ying Li Qian-Fei wang Qiu-Sha huang Hai-xia Fu xiao-Lu zhu yun He Qian Jiang Hao Jiang Jin Lu xiang-Yu Zhao ying-Jun Chang Tao Wu Yao-zhu Pan Lin Qiu Da Gao A-Rong Jin Wei Li Su-Jun Gao Lei Zhang Ming Hou xiao-Jun huang xiao-Hui Zhang on behalf of the National Cooperative ITP Working Group Peking University People’s Hospital Peking University Institute of HematologyBeijing 100044China Collaborative Innovation Center of Hematology Peking UniversityBeijing 100044China Beijing key Laboratory of Hematopoietic Stem Cell Transplantation Beijing 100044China National Clinical Research Center for Hematologic Disease Beijing 100044China Department of Hematology Qilu HospitalCheeloo College of MedicineShandong UniversityJinan 250014China Institute of Hematology Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430074China Department of Hematology First Affiliated HospitalSchool of MedicineShihezi UniversityShihezi 832002China Department of Hematology Qinghai Provincial People’s HospitalXining 810007China Henan Cancer Hospital Affiliated Cancer Hospital of Zhengzhou UniversityHenan Institute of HematologyZhengzhou 450008China Department of Hematology Beijing HospitalBeijing 100044China Department of Hematology Zhejiang Provincial Hospital of Traditional Chinese MedicineHangzhou 310006China Department of Hematology Senior Department of Hematologythe Fifth Medical Center of PLA General HospitalBeijing 100044China Department of Hematology the Second Hospital of Shanxi Medical UniversityTaiyuan 030001China Department of Hematology Affiliated Zhongshan Hospital of Dalian UniversityDalian 116001China Department of Hematology The First Affiliated Hospital with Nanjing Medical University(Jiangsu Province Hospital)Nanjing 210029China Department of Hematology Zhongshan HospitalFudan UniversityShanghai 200032China Department of Hematology Xiangya HospitalCentral South UniversityChangsha 410008China Department of Hematology Beijing Friendship HospitalCapital Medical UniversityBeijing 100044China Institute of Hematology the First Affiliated HospitalZhejiang UniversityHangzhou 310058China Department of Hematology Beijing Tongren HospitalBeijing 100005China Department of Hematology The First Affiliated HospitalSun Yat-sen UniversityGuang

原发性免疫性血小板减少症(ITP)中少见但至关重要的危重出血事件,给患者的预后、生活质量和治疗决策带来严重影响。尽管有一些研究探讨了ITP中与危重出血相关的风险因素,但目前尚缺乏大样本数据、大规模多中心研究结果以及针对ITP患者... 详细信息

原发性免疫性血小板减少症(ITP)中少见但至关重要的危重出血事件,给患者的预后、生活质量和治疗决策带来严重影响。尽管有一些研究探讨了ITP中与危重出血相关的风险因素,但目前尚缺乏大样本数据、大规模多中心研究结果以及针对ITP患者致命出血事件的预测模型。本研究首次采用国际血栓与止血学会新提出的ITP致命出血标准,利用大样本数据开发了首个基于机器学习的在线应用,用于预测ITP患者的致命出血.研究中,我们使用中国各地大型多中心数据进行开发,并对全国39家医疗中心进行为期一年的外部测试,得到了较好的训练、验证和测试数据集预测能力该基于新算法的便捷网络工具能够快速识别ITP患者的出血风险,辅助临床决策,有望未来降低不良事件的发生。

关键词： Critical bleeding Severe bleeding Immune thrombocytopenia Machine learning Prediction model

The first data release(DR1) of the LAMOST regular survey

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Research in Astronomy and Astrophysics 2015年第8期15卷 1095-1124页

作者： A-Li Luo yong-Heng Zhao Gang Zhao Li-Cai Deng xiao-Wei Liu Yi-peng Jing Gang wang Hao-Tong Zhang Jian-Rong Shi xiang-Qun Cui Yao-Quan Chu Guo-Ping Li Zhong-Rui Bai Yue Wu Yan Cai Shu-yun Cao Zi-huang Cao Jeffrey L.Carlin Hai-Yuan Chen Jian-Jun Chen Kun-xin Chen Li Chen Xue-Lei Chen xiao-Yan Chen ying Chen Norbert Christlieb Jia-Ru Chu Chen-Zhou Cui Yi-Qiao Dong Bing Du Dong-Wei Fan Lei Feng Jian-Ning Fu peng Gao Xue-Fei Gong Bo-Zhong Gu Yan-xin Guo Zhan-Wen Han Bo-Liang He Jin-Liang Hou yong-Hui Hou Wen Hou Hong-zhuan Hu Ning-Sheng Hu Zhong-Wen Hu Zhi-ying Huo Lei Jia Fang-Hua Jiang xiang Jiang Zhi-Bo Jiang Ge Jin xiao Kong Xu Kong Ya-Juan Lei Ai-Hua Li Chang-Hua Li Guang-Wei Li Hai-Ning Li Jian Li Qi Li Shuang Li Sha-Sha Li xin-Nan Li Yan Li Yin-Bi Li Ye-Ping Li Yuan Liang Chien-Cheng Lin Chao Liu Gen-Rong Liu Guan-Qun Liu Zhi-Gang Liu Wen-Zhi Lu Yu Luo Yin-Dun Mao Heidi Newberg Ji-Jun Ni Zhao-xiang Qi yong-Jun Qi Shi-Yin Shen Huo-Ming Shi Jing Song Yi-Han Song Ding-Qiang Su Hong-Jun Su Zheng-Hong Tang Qing-Sheng Tao Yuan Tian Dan wang Da-Qi wang Feng-Fei wang Guo-Min wang Hai wang Hong-Chi wang Jian wang Jia-Ning wang Jian-Ling wang Jian-Ping wang Jun-xian wang Lei wang Meng-xin wang Shou-Guan wang Shu-Qing wang xia wang Ya-Nan wang You wang Yue-Fei wang You-Fen wang peng Wei Ming-Zhi Wei Hong Wu ke-Fei Wu Xue-Bing Wu Yu-Zhong Wu xiao-Zheng xing Ling-Zhe Xu xin-Qi Xu Yan Xu Tai-Sheng Yan De-Hua Yang Hai-Feng Yang Hui-Qin Yang Ming Yang Zheng-Qiu Yao yong Yu Hui Yuan Hai-Bo Yuan Hai-Long Yuan Wei-Min Yuan Chao Zhai En-peng Zhang Hua-Wei Zhang Jian-Nan Zhang Li-Pin Zhang Wei Zhang yong Zhang Yan-xia Zhang Zheng-Chao Zhang Ming Zhao Fang Zhou Xu Zhou Jie zhu yong-Tian zhu Si-Cheng Zou Fang Zuo National Astronomical Observatories Chinese Academy of Sciences Department of Astronomy Peking University Shanghai Astronomical Observatory Chinese Academy of Sciences Nanjing Institute of Astronomical Optics & Technology National Astronomical ObservatoriesChinese Academy of Sciences University of Science and Technology of China Department of Physics Applied Physics and AstronomyRensselaer Polytechnic InstituteTroyNY 12180USA Department of Physics and Astronomy Earlham CollegeRichmondIN 47374USA Universitt Heidelberg Zentrum für AstronomieLandessternwarteKnigstuhl 1269117 HeidelbergGermany Department of Astronomy Beijing Normal University yunnan Observatories Chinese Academy of Sciences Purple Mountain Observatory Chinese Academy of Sciences

The Large sky Area Multi-Object Fiber Spectroscopic Telescope（LAMOST） general survey is a spectroscopic survey that will eventually cover approximately half of the celestial sphere and collect 10 million spectra of stars, galaxies and QSOs. Objects in both the pilot survey and the first year regular survey are included in the LAMOST DR1. The pilot survey started in October 2011 and ended in June 2012, and the data have been released to the public as the LAMOST Pilot Data Release in August 2012. The regular survey started in September 2012, and completed its first year of operation in June 2013. The LAMOST DR1 includes a total of 1202 plates containing 2 955 336 spectra, of which 1 790 879 spectra have observed signalto-noise ratio（SNR） ≥ 10. All data with SNR ≥ 2 are formally released as LAMOST DR1 under the LAMOST data policy. This data release contains a total of 2 204 696 spectra, of which 1 944 329 are stellar spectra, 12 082 are galaxy spectra and 5017 are quasars. The DR1 not only includes spectra, but also three stellar catalogs with measured parameters： late A,FGK-type stars with high quality spectra（1 061 918 entries）, A-type stars（100 073 entries）, and M-type stars（121 522 entries）. This paper introduces the survey design, the observational and instrumental limitations, data reduction and analysis, and some caveats. A description of the FITS structure of spectral files and parameter catalogs is also provided.

关键词： techniques: spectroscopic survey—data release—catalog

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Biomarkers of aging

Science China(Life Sciences) 2023年第5期66卷 893-1066页

作者： Aging Biomarker Consortium Hainan Bao Jiani Cao Mengting Chen Min Chen Wei Chen xiao Chen Yanhao Chen Yu Chen Yutian Chen Zhiyang Chen Jagadish K Chhetri yingjie Ding Junlin Feng Jun Guo Mengmeng Guo Chuting He Yujuan Jia Haiping Jiang ying Jing Dingfeng Li Jiaming Li Jingyi Li Qinhao Liang Rui Liang Feng Liu xiaoqian Liu Zuojun Liu Oscar Junhong Luo Jianwei Lv Jingyi Ma kehang Mao Jiawei Nie xinhua Qiao xinpei Sun xiaoqiang Tang Jianfang wang Qiaoran wang Siyuan wang Xuan wang Yaning wang Yuhan wang Rimo Wu Kai xia Fu-Hui xiao Lingyan Xu yingying Xu Haoteng Yan Liang Yang Ruici Yang Yuanxin Yang Yilin ying Le Zhang Weiwei Zhang Wenwan Zhang xing Zhang zhuo Zhang Min Zhou Rui Zhou Qingchen zhu Zhengmao zhu Feng Cao Zhongwei Cao Piu Chan Chang Chen Guobing Chen Hou-Zao Chen Jun Chen Weimin Ci Bi-Sen Ding Qiurong Ding Feng Gao Jing-Dong JHan Kai huang Zhenyu Ju Qing-peng Kong Ji Li Jian Li xin Li Baohua Liu Feng Liu Lin Liu Qiang Liu Qiang Liu xingguo Liu yong Liu xianghang Luo Shuai Ma xinran Ma Zhiyong Mao Jing Nie Yaojin peng Jing Qu Jie Ren Ruibao Ren Moshi Song Zhou Songyang Yi Eve Sun Yu Sun Mei Tian Shusen wang Si wang xia wang xiaoning wang Yan-Jiang wang yunfang wang Catherine CL Wong Andy peng xiang Yichuan xiao Zhengwei Xie Daichao Xu Jing Ye Rui Yue Cuntai Zhang Hongbo Zhang Liang Zhang Weiqi Zhang yong Zhang yun-Wu Zhang zhuohua Zhang Tongbiao Zhao Yuzheng Zhao Dahai zhu Weiguo Zou Gang Pei Guang-Hui Liu Aging Biomarker Consortium(ABC) Beijing 100101China CAS key Laboratory of Genomic and Precision Medicine Beijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing 100101China State key Laboratory of Stem Cell and Reproductive Biology Institute of ZoologyChinese Academy of SciencesBeijing 100101China Department of Dermatology Xiangya HospitalCentral South UniversityChangsha 410008China Hunan key Laboratory of Aging Biology Xiangya HospitalCentral South UniversityChangsha 410008China National Clinical Research Center for Geriatric Disorders Xiangya HospitalCentral South UniversityChangsha 410008China Clinic Center of Human Gene Research Union HospitalTongji Medical CollegeHuazhong University of Science and TechnologyWuhan 430022China Hubei Clinical Research Center of Metabolic and Cardiovascular Disease Huazhong University of Science and TechnologyWuhan 430022China Hubei key Laboratory of Metabolic Abnormalities and Vascular Aging Huazhong University of Science and TechnologyWuhan 430022China Stem Cell Translational Research Center Tongji HospitalTongji University School of MedicineShanghai 200065China Department of Nuclear Medicine Daping HospitalThird Military Medical UniversityChongqing 400042China CAS key Laboratory of Nutrition Metabolism and Food SafetyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Shanghai key Laboratory of Maternal Fetal Medicine Clinical and Translational Research Center of Shanghai First Maternity and Infant HospitalFrontier Science Center for Stem Cell ResearchShanghai Key Laboratory of Signaling and Disease ResearchSchool of Life Sciences and TechnologyTongji UniversityShanghai 200092China The Department of Endovascular Surgery The First Affiliated Hospital of Zhengzhou UniversityZhengzhou 450052China key Laboratory of Regenerative Medicine of Ministry of Education Institute of Ageing and Regenerative MedicineJ

Aging biomarkers are a combination of biological parameters to(i)assess age-related changes,(ii)track the physiological aging process,and(iii)predict the transition into a pathological *** a broad spectrum of aging biomarkers has been developed,their potential uses and limitations remain poorly *** immediate goal of biomarkers is to help us answer the following three fundamental questions in aging research:How old are we?Why do we get old?And how can we age slower?This review aims to address this ***,we summarize our current knowledge of biomarkers developed for cellular,organ,and organismal levels of aging,comprising six pillars:physiological characteristics,medical imaging,histological features,cellular alterations,molecular changes,and secretory *** fulfill all these requisites,we propose that aging biomarkers should qualify for being specific,systemic,and clinically relevant.

关键词： aging senescence biomarker clock

两种高通量测序平台应用于不同SARS-CoV-2变异株的对比研究

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中国人兽共患病学报 2022年第9期38卷 771-777页

作者：李东晓李懿朱琳宋云马红霞王海峰叶莹黄学勇郭万申河南省疾病预防控制中心河南省传染病病原生物重点实验室郑州450016

目的利用Illumina MiSeq和Oxford Nanopore高通量测序平台对河南省新型冠状病毒肺炎(COVID-19)确诊病例的上呼吸道样本进行全基因组测序,为新型冠状病毒(SARS-CoV-2)全基因组监测工作提供参考。方法收集河南省2021年6月至2022年1月共10... 详细信息

目的利用Illumina MiSeq和Oxford Nanopore高通量测序平台对河南省新型冠状病毒肺炎(COVID-19)确诊病例的上呼吸道样本进行全基因组测序,为新型冠状病毒(SARS-CoV-2)全基因组监测工作提供参考。方法收集河南省2021年6月至2022年1月共10份COVID-19确诊病例的上呼吸道样本,分别采用二代和三代测序技术进行测序,获得SARS-CoV-2全基因组序列,运用生物信息学软件CLC Genomics Workbench(CLC)进行序列比对分析。结果与武汉参考株(Wuhan-Hu-1)相比,10份样本中3份属于Omicron(BA.1)变异株,核苷酸变异位点55个和61个;1份属于Alpha(B.1.1.7)变异株,核苷酸变异位点41个;6份属于Delta(B.1.617.2)变异株,核苷酸变异位点35个、42个和47个。二代测序识别碱基变异位点的准确率更高,6份样本的二代和三代测序变异位点100%同源,7份样本S基因编码区共享一致数目的变异位点。Ct值0.05)。结论两种高通量测序平台均能满足SARS-CoV-2变异株的检测需求,Illumina MiSeq平台对SARS-CoV-2变异位点识别更精准;Oxford Nanopore平台可用于SARS-CoV-2的快速鉴定分型。

关键词：高通量测序 Illumina Nanopore 覆盖度变异位点