检索结果-内蒙古大学图书馆

Research Square 2021年

作者： Zhang, Martin Jinye Hou, Kangcheng Pasaniuc, Bogdan Price, Alkes L. Dey, Kushal K. Jagadeesh, Karthik A. Weinand, Kathryn Sakaue, Saori Taychameekiatchai, Aris Rao, Poorvi Pisco, Angela Oliveira Zou, James Wang, Bruce Gandal, Michael Raychaudhuri, Soumya Harvard University United States Osaka University Japan Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Program in Medical and Population Genetics Broad Institute of MIT and Harvard CambridgeMA United States Bioinformatics Interdepartmental Program University of California Los Angeles Los AngelesCA United States Department of Pathology and Laboratory Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Center for Data Sciences Brigham and Women’s Hospital BostonMA United States Division of Genetics Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Division of Rheumatology Inflammation and Immunity Department of Medicine Brigham and Women’s Hospital Harvard Medical School BostonMA United States Department of Biomedical Informatics Harvard Medical School BostonMA United States Department of Medicine and Liver Center University of California San Francisco San FranciscoCA United States Developmental and Stem Cell Biology Graduate Program University of California San Francisco San FranciscoCA United States Chan Zuckerberg Biohub San FranciscoCA United States Department of Electrical Engineering Stanford University Palo AltoCA United States Department of Biomedical Data Science Stanford University Palo AltoCA United States Department of Psychiatry David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Program in Neurobehavioral Genetics Semel Institute David Geffen School of Medicine University of California Los Angeles Los AngelesCA United States Versus Arthritis Centre for Genetics and Genomic

Gene expression at the individual cell-level resolution, as quantified by single-cell RNA-sequencing (scRNA-seq), can provide unique insights into the pathology and cellular origin of diseases and complex traits. Here, we introduce single-cell Disease Relevance Score (scDRS), an approach that links scRNA-seq with polygenic risk of disease at individual cell resolution;scDRS identifies individual cells that show excess expression levels for genes in a disease-specific gene set constructed from GWAS data. We determined via simulations that scDRS is well-calibrated and powerful in identifying individual cells associated to disease. We applied scDRS to GWAS data from 74 diseases and complex traits (average N =341K) in conjunction with 16 scRNA-seq data sets spanning 1.3 million cells from 31 tissues and organs. At the cell type level, scDRS broadly recapitulated known links between classical cell types and disease, and also produced novel biologically plausible findings. At the individual cell level, scDRS identified subpopulations of disease-associated cells that are not captured by existing cell type labels, including subpopulations of CD4+ T cells associated with inflammatory bowel disease, partially characterized by their effector-like states;subpopulations of hippocampal CA1 pyramidal neurons associated with schizophrenia, partially characterized by their spatial location at the proximal part of the hippocampal CA1 region;and subpopulations of hepatocytes associated with triglyceride levels, partially characterized by their higher ploidy levels. At the gene level, we determined that genes whose expression across individual cells was correlated with the scDRS score (thus reflecting co-expression with GWAS disease genes) were strongly enriched for gold-standard drug target and Mendelian disease genes. © 2021, CC BY.

关键词： RNA

来源：评论

学校读者我要写书评

暂无评论

Protect transgender scientists

引用

Science 2025年第6753期388卷 1283-1284页

作者： Nasa Sinnott-Armstrong Desiree Forsythe Jane M Benoit Callie R. Chappell Lisa S. Y. Coe Bruno Francesco Rodrigues de Oliveira Natalya Evans Anna C. Fagre Jonathan M. Gilligan Maria Hamilton Colin Moran Henneberry Suzanne L. Ishaq Juliet Johnston Erin Krichilsky Jamie Alcira Lopez Kay McMonigal Melanie Ortiz Alvarez de la Campa Rahmeen Rahman Nicole E. Schwartz Lauren Talluto Eric J. Taylor José M. Vargas-Muñiz J. L. Weissman Herbold Computational Biology Program Public Health Sciences Division Fred Hutchinson Cancer Center Seattle WA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Biology Santa Clara University Santa Clara CA USA. Department of Biological Science Florida State University Tallahassee FL USA. Department of Biology Stanford University Stanford CA USA. Department of Biology and Genetics Institute University of Florida Gainesville FL USA. School of Teaching and Learning College of Education University of Florida Gainesville FL USA. Department of Microbiology and Parasitology Biomedical Institute Fluminense Federal University Niterói RJ Brazil. Marine Science Institute University of California Santa Barbara Santa Barbara CA USA. Department of Microbiology Immunology and Pathology Colorado State University Fort Collins CO USA. Department of Earth and Environmental Sciences Vanderbilt University Nashville TN USA. Department Civil and Environmental Engineering Vanderbilt University Nashville TN USA. Department of Marine Sciences University of Georgia Athens GA USA. Department of Ecology and Evolution Stony Brook University Stony Brook NY USA. Department of Animal and Veterinary Sciences University of Maine Orono ME USA. Microbes and Social Equity working group Orono ME USA. School of Science and Math Hampshire College Amherst MA USA. American Museum of Natural History Richard Gilder Graduate School New York NY USA. Department of Ecology Evolution and Environmental Biology Columbia University New York NY USA. Department of Bioengineering Stanford University Stanford CA USA. Department of Applied Physics Stanford University Stanford CA USA. College of Fisheries and Ocean Sciences University of Alaska Fairbanks Fairbanks AK USA. Department of Molecular Microbiology and Immunology Brown University Providence RI USA. Department of Infectious Disease Imperial College London London UK. Depart

Transgender and gender nonconforming (TGnC) people are a primary target of the Trump administration. Multiple executive orders seek to erase TGnC protections; mandate denial of gender identity; and ban diversity, equity, and inclusion (DEI) (1). Changes to federal funding policies related to TGnC individuals and DEI threaten to curtail academic diversity and freedom (2). Institutions have been hesitant to resist, but for many TGnC scientists—especially TGnC individuals of color, immigrants, and those with disabilities—invisibility is not an option. Anticipatory obedience (3, 4), and even passive allyship, is insufficient; institutions must abandon neutrality and defend targeted communities to minimize further harm (5, 6). As queer and TGnC scientists, we call on researchers at all levels—from undergraduates to university presidents—to take direct, consistent, and rapid action.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Future trends in synthetic biology in Asia

Advanced genetics (Hoboken, N.J.)

引用

Advanced genetics (Hoboken, N.J.) 2021年第1期2卷 e10038页

作者： Ning Mao Nikhil Aggarwal Chueh Loo Poh Byung Kwan Cho Akihiko Kondo Chenli Liu Wen Shan Yew Matthew Wook Chang NUS Synthetic Biology for Clinical and Technological Innovation (SynCTI) National University of Singapore Singapore Singapore. Synthetic Biology Translational Research Program and Department of Biochemistry Yong Loo Ling School of Medicine National University of Singapore Singapore Singapore. Department of Biomedical Engineering National University of Singapore Singapore Singapore. Department of Biological Sciences and KI for the BioCentury Korea Advanced Institute of Science and Technology Daejeon South Korea. Graduate School of Science Technology and Innovation and Engineering Biology Research Center Kobe University Kobe Japan. CAS Key Laboratory for Quantitative Engineering Biology Shenzhen Institute of Synthetic Biology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China.

Synthetic biology research and technology translation has garnered increasing interest from the governments and private investors in Asia, where the technology has great potential in driving a sustainable bio-based economy. This Perspective reviews the latest developments in the key enabling technologies of synthetic biology and its application in bio-manufacturing, medicine, food and agriculture in Asia. Asia-centric strengths in synthetic biology to grow the bio-based economy, such as advances in genome editing and the presence of biofoundries combined with the availability of natural resources and vast markets, are also highlighted. The potential barriers to the sustainable development of the field, including inadequate infrastructure and policies, with suggestions to overcome these by building public-private partnerships, more effective multi-lateral collaborations and well-developed governance framework, are presented. Finally, the roles of technology, education and regulation in mitigating potential biosecurity risks are examined. Through these discussions, stakeholders from different groups, including academia, industry and government, are expectantly better positioned to contribute towards the establishment of innovation and bio-economy hubs in Asia.

关键词： biotechnology genetics or genomics genomics synthetic biology

来源：评论

学校读者我要写书评

暂无评论

The definition of sng: The expert consensus by the sng taxonomy Task force of the Taiwan sng Society

引用

Journal of the Formosan Medical Association 2025年

作者： Han, Der-Sheng Lin, Jiann-Her Hung, Chih-Hsien Huang, Shuping Chen, Jen-Yin Chiang, Yung-Hsiao Chiu, Hsiao-Yean Chou, Li-Wei Hsieh, Cheng-Ta Huang, Ming-Chyi Huang, Wei-Lieh Kang, Jiunn-Horng Lee, Hsun-Hua Lin, Chia-Shu Lin, Chien-Min Lin, Chih-Peng Lin, Ying-Tung Liu, Yen-Chin Ro, Long-Sun Shen, Chiung-Chyi Su, Lily I-Wen Su, Yu-Jih Sun, Jui-Ming Sung, Jia-Ying Tsai, Rong-Kung Wang, Man-Ling Wen, Yeong-Ray Weng, Hao-Jui Wu, Ming-Shun Yu, Yu-Wen Hsieh, Sung-Tsang Wang, Shuu-Jiun Sun, Wei-Zen Chen, Chih-Cheng Department of Physical Medicine and Rehabilitation National Taiwan University Hospital Beihu Branch Taipei Taiwan Department of Physical Medicine and Rehabilitation National Taiwan University College of Medicine Taipei Taiwan Division of Neurosurgery Department of Surgery Taipei Medical University Hospital Taipei Taiwan Department of Surgery School of Medicine College of Medicine Taipei Medical University Taipei Taiwan Taipei Neuroscience Institute Taipei Medical University Taipei Taiwan TMU Research Center of Neuroscience Taipei Medical University Taipei Taiwan Department of Neurology Kaohsiung Medical University Hospital Kaohsiung Taiwan School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Foreign Languages and Literature National Sun Yat-sen University Kaohsiung Taiwan Department of Anesthesiology Chi Mei Medical Center Tainan Taiwan School of Medicine College of Medicine National Sun Yat-sen University Kaohsiung Taiwan School of Nursing College of Nursing Taipei Medical University Taipei Taiwan Department of Nursing Taipei Medical University Hospital Taipei Taiwan Department of Physical Medicine and Rehabilitation China Medical University Hospital Taichung Taiwan Department of Physical Medicine and Rehabilitation Asia University Hospital Asia University Taichung Taiwan Department of Physical Therapy and Graduate Institute of Rehabilitation Science China Medical University Taichung Taiwan Division of Neurosurgery Department of Surgery Cathay General Hospital Taipei Taiwan School of Medicine National Tsing-Hua University Hsinchu Taiwan Department of Medicine School of Medicine Fu Jen Catholic University New Taipei City Taiwan Department of Psychiatry Taipei City Psychiatric Center Taipei City Hospital Taipei Taiwan Department of Psychiatry National Taiwan University Hospital Yunlin Branch Yunlin Taiwan Department of Physical Medicine and Rehabilitation Taipei Medical University Hospital Taip

“Sng” is a Taiwanese term describing a distinct state of soreness. Accumulating evidence indicates that sng is a specific somatosensory modality, independent of pain, with distinct receptors and transduction pathways. In recent years, variations in its presentation across different medical specialties have highlighted the need for a standardized definition of sng-related terminology. To address this, the Taiwan Sng Society established a multidisciplinary Sng Taxonomy Task Force, comprising 32 experts in both clinical and basic sciences related to sng. Here we summarize the discussions and expert consensus from the Task Force. A total of 16 sng-related terminology was defined. The first edition of sng terminology aims to establish a clear, standardized framework for the clinical practice and research of sng. © 2025 Formosan Medical Association

关键词： Definition sng Sngception Taxonomy Terminology

来源：评论

学校读者我要写书评

暂无评论

Considering discrepancy when calibrating a mechanistic electrophysiology model

arXiv

引用

arXiv 2020年

作者： Lei, Chon Lok Ghosh, Sanmitra Whittaker, Dominic G. Aboelkassem, Yasser Beattie, Kylie A. Cantwell, Chris D. Delhaas, Tammo Houston, Charles Novaes, Gustavo Montes Panfilov, Alexander V. Pathmanathan, Pras Riabiz, Marina Weber dos Santos, Rodrigo Worden, Keith Mirams, Gary R. Wilkinson, Richard D. Computational Biology & Health Informatics Dept. of Computer Science University of Oxford United Kingdom MRC Biostatistics Unit University of Cambridge United Kingdom Centre for Mathematical Medicine & Biology School of Mathematical Sciences University of Nottingham United Kingdom Department of Bioengineering University of California San Diego United States Systems Modeling and Translational Biology GlaxoSmithKline R&D Stevenage United Kingdom ElectroCardioMaths Programme Centre for Cardiac Engineering Imperial College London United Kingdom CARIM School for Cardiovascular Diseases Maastricht University Netherlands Graduate Program in Computational Modeling Universidade Federal de Juiz de Fora Brazil Department of Physics and Astronomy Ghent University Belgium Laboratory of Computational Biology and Medicine Ural Federal University Ekaterinburg Russia U.S. Food and Drug Administration Center for Devices and Radiological Health Office of Science and Engineering Laboratories United States Department of Biomedical Engineering King’s College London Alan Turing Institute United Kingdom Dynamics Research Group Department of Mechanical Engineering University of Sheffield United Kingdom School of Mathematics and Statistics University of Sheffield United Kingdom

Uncertainty quantification (UQ) is a vital step in using mathematical models and simulations to take decisions. The field of cardiac simulation has begun to explore and adopt UQ methods to characterise uncertainty in model inputs and how that propagates through to outputs or predictions. In this perspective piece we draw attention to an important and under-addressed source of uncertainty in our predictions — that of uncertainty in the model structure or the equations themselves. The difference between imperfect models and reality is termed model discrepancy, and we are often uncertain as to the size and consequences of this discrepancy. Here we provide two examples of the consequences of discrepancy when calibrating models at the ion channel and action potential scales. Furthermore, we attempt to account for this discrepancy when calibrating and validating an ion channel model using different methods, based on modelling the discrepancy using Gaussian processes (GPs) and autoregressive-moving-average (ARMA) models, then highlight the advantages and shortcomings of each approach. Finally, suggestions and lines of enquiry for future work are provided. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

来源：评论

学校读者我要写书评

暂无评论

Non-Invasive and Non-Destructive Determination of Corneal and Scleral Biomechanics Using Vibrational Optical Coherence Tomography: Preliminary Observations

引用

Materials Sciences and Applications 2018年第7期9卷 657-669页

作者： Frederick H. Silver Ruchit G. Shah Dominick Benedetto Department of Pathology and Laboratory Medicine Robert Wood Johnson Medical School Rutgers The State University of New Jersey Piscataway NJ USA Graduate Program in Biomedical Engineering Rutgers The State University of New Jersey Piscataway NJ USA Morristown Ophthalmology Associates Morristown NJ USA

Experimental measurements made in this study on human and porcine eyes suggest that the resonant frequency for both cornea and sclera varies from 130 to 150 Hz and increases slightly with increasing intraocular pressure. The values of the moduli calculated using the experimental values of the thickness are close to 2 MPa. Similar values of the modulus for cornea and sclera suggest that there is very little stress concentration at the cornea-scleral junction and that any stress concentration that occurs probably resides at the scleral attachment laterally and posteriorly. These moduli are close to those measured in vivo on human skin suggesting that the mechanism of tensile deformation of skin, cornea and sclera are similar. Our results suggest that the modulus of cornea and sclera can be measured non-invasively and non-destructively using vibrational OCT. Results of these studies will assist clinicians to better understand the influence of biomechanics on the outcome of corneal refractive surgery as well as the pathogenesis of eye disorders such as glaucoma, myopia and keratoconus.

关键词： Collagen Cornea Sclera Skin Biomechanics Vibrational OCT Optical Coherence Tomography (OCT) Modulus Resonant Frequency Mechanical Properties Myopia Keratoconus Glaucoma

来源：评论

学校读者我要写书评

暂无评论

Subject-specific left ventricle biomechanical model using composite material mechanics approach

Subject-specific left ventricle biomechanical model using co...

引用

SPIE Medical Imaging 2017

作者： Haddad, Seyyed M. H. Karami, E. Samani, Abbas Graduate Program in Biomedical Engineering Western University LondonON Canada Dept. of Electrical and Computer Engineering Western University LondonON Canada Dept. of Medical Biophysics Western University LondonON Canada

来源：评论

学校读者我要写书评

暂无评论

Identification of gene expression signatures for psoriasis classification using machine learning techniques

引用

Medicine in Omics 2021年 1卷

作者： Nguyen Quoc Khanh Le Duyen Thi Do Trinh-Trung-Duong Nguyen Ngan Thi Kim Nguyen Truong Nguyen Khanh Hung Nguyen Thi Thu Trang Professional Master Program in Artificial Intelligence in Medicine College of Medicine Taipei Medical University Taipei City 106 Taiwan Research Center for Artificial Intelligence in Medicine Taipei Medical University Taipei City 106 Taiwan Graduate Institute of Biomedical Informatics Taipei Medical University Taipei City 106 Taiwan Department of Computer Science and Engineering Yuan Ze University Chung-Li 32003 Taiwan School of Nutrition and Health Sciences Taipei Medical University Taipei City 110 Taiwan International Master/Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan Orthopedic and Trauma Department Cho Ray Hospital Ho Chi Minh City Viet Nam International PhD Program for Cell Therapy and Regeneration Medicine College of Medicine Taipei Medical University Taipei City 110 Taiwan

Psoriasis classification requires the accurate identification of the lesional types for the early and effective diagnosis and it is worth interesting that the normal and psoriasis cell tissues exhibit different gene expression. Therefore, gene expression data is an effective source for psoriasis classification and there is a challenge regarding the selection of suitable gene signatures for its purpose. In this present study, the gene expression-based microarray data were used and 35 expression features linked with psoriasis were utilized to feed into our machine learning model. Overall, the performance of our model based on 35 mentioned-above features surpassed that of other state-of-the-art classifiers with an average accuracy of 98.3%, recall of 98.6%, and precision of 98% in 5-fold cross-validation tests. We also validate our model on two different sets of psoriasis and the performance results are significant. These results have suggested that our 35 expression signatures have been identified as key features for classifying samples between lesion and non-lesion. More specifically, the expression levels of few genes i.e., FABP5 , TGM1 , or BCAR3 are discovered as newly potential biomarkers for psoriasis classification and treatment with high confidence. This study, therefore, could shed light on developing the prediction models for psoriasis classification and treatment using gene expression profiles.

关键词： Psoriasis classification Random forest Gene expression Feature selection Machine learning FABP5 Dermatology

来源：评论

学校读者我要写书评

暂无评论

Corrigendum to “Application of an optically induced dielectrophoresis (ODEP)-based microfluidic system for the detection and isolation of bacteria with heterogeneity of antibiotic susceptibility” [Sens. Actuators B: Chem. 307 (15 March) (2020) 127540]

引用

Sensors and Actuators B: Chemical 2020年 323卷

作者： Hsin-Yao Wang Chih-Yu Chen Po-Yu Chu Yu-Xian Zhu Chia-Hsun Hsieh Jang-Jih Lu Min-Hsien Wu Department of Laboratory Medicine Chang Gung Memorial Hospital at Linkou Taoyuan City Taiwan ROC Ph.D. Program in Biomedical Engineering Chang Gung University Taoyuan City Taiwan Graduate Institute of Biomedical Engineering Chang Gung University Taoyuan City Taiwan ROC Division of Haematology/Oncology Department of Internal Medicine Chang Gung Memorial Hospital at Linkou Taoyuan City Taiwan ROC School of Medicine Chang Gung University Taoyuan City Taiwan ROC Department of Medical Biotechnology and Laboratory Science Chang Gung University Taoyuan City Taiwan ROC

来源：评论

学校读者我要写书评

暂无评论

Aptamer/doxorubicin-conjugated nanoparticles target membranous CEMIP2 in colorectal cancer

引用

International journal of biological macromolecules 2023年 245卷 125510页

作者： Maryam Kianpour Ching-Wen Huang Pichpisith Pierre Vejvisithsakul Jaw-Yuan Wang Chien-Feng Li Meng-Shin Shiao Cheng-Tang Pan Yow-Ling Shiue Institute of Biomedical Sciences College of Medicine National Sun Yat-sen University Kaohsiung 80424 Taiwan. Division of Colorectal Surgery Department of Surgery Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung 80756 Taiwan Department of Surgery Faculty of Medicine College of Medicine Kaohsiung Medical University Kaohsiung 80756 Taiwan. Institute of Biomedical Sciences College of Medicine National Sun Yat-sen University Kaohsiung 80424 Taiwan Program in Translational Medicine Faculty of Medicine Ramathibodi Hospital Mahidol University Bangkok 10400 Thailand. Department of Surgery Faculty of Medicine College of Medicine Kaohsiung Medical University Kaohsiung 80756 Taiwan Pingtung Hospital Ministry of Health and Welfare Pingtung 900214 Taiwan Graduate Institute of Clinical Medicine College of Medicine Kaohsiung Medical University Kaohsiung 80756 Taiwan Graduate Institute of Medicine College of Medicine Kaohsiung Medical University Kaohsiung 80756 Taiwan Center for Cancer Research Kaohsiung Medical University Kaohsiung 80756 Taiwan. Department of Medical Research Chi Mei Medical Center Tainan 71004 Taiwan Department of Pathology Chi Mei Medical Center Tainan 71004 Taiwan National Institute of Cancer Research National Health Research Institutes Tainan 70456 Taiwan. Research Center Faculty of Medicine Ramathibodi Hospital Mahidol University Bangkok 10400 Thailand. Institute of Precision Medicine College of Medicine National Sun Yat-sen University Kaohsiung 80424 Taiwan Department of Mechanical and Electro-Mechanical Engineering National Sun Yat-sen University Kaohsiung 80424 Taiwan. Electronic address: pan@mem.nsysu.edu.tw. Department of Mechanical and Electro-Mechanical Engineering National Sun Yat-sen University Kaohsiung 80424 Taiwan. Electronic address: shirley@imst.nsysu.edu.tw.

The objectives were to identify the functional domains of a potential oncoprotein, cell migration inducing hyaluronidase 2 (CEMIP2), evaluate its expression levels and roles in colorectal cancer (CRC), and develop an aptamer-based nanoparticle for targeted therapy. Data mining on TCGA identified that CEMIP2 might play oncogenic roles in CRC. In a local cohort, CEMIP2 mRNA levels significantly stepwise increase in CRC patients with higher stages, and high CEMIP2 confers worse disease-free survival. In addition, CEMIP2 mRNA levels significantly correlated to hyaluronan levels in sera from CRC patients. Deletion mapping identified that CEMIP2 containing G8 and PANDER-like domains preserved hyaluronidase activity and oncogenic roles, including cell proliferation, anchorage-independent cell growth, cell migration and invasion, and human umbilical vein endothelial cell (HUVEC) tube formation in CRC-derived cells. A customized monoclonal mouse anti-human CEMIP2 antibody probing the PANDER-like domain (anti-289307) counteracted CEMIP2-mediated carcinogenesis in vitro. Cell-SELEX pinpointed an aptamer, aptCEMIP2(101), specifically interacted with the full-length CEMIP2, potentially involving its 3D structure. Treatments with aptCEMIP2(101) significantly reduced CEMIP2-mediated tumorigenesis in vitro. Mesoporous silica nanoparticles (MSN) carrying atpCEMIP2(101) and Dox were fabricated. Dox@MSN, MSN-aptCEMIP2(101), and Dox@MSN-aptCEMIP2(101) significantly suppressed tumorigenesis in vitro compared to the Mock, while Dox@MSN-aptCEMIP2(101) showed substantially higher effects compared to Dox@MSN and MSN-aptCEMIP2(101) in CRC-derived cells. Our study identified a novel oncogene and developed an effective aptamer-based targeted therapeutic strategy.

关键词： Aptamer CEMIP2 Cell migration inducing hyaluronidase 2 Colorectal cancer Mesoporous silica nanoparticle

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：