检索结果-内蒙古大学图书馆

Npj health systems 2025年第1期2卷 4页

作者： Betina Idnay Zihan Xu William G Adams Mohammad Adibuzzaman Nicholas R Anderson Neil Bahroos Douglas S Bell Cody Bumgardner Thomas Campion Mario Castro James J Cimino I Glenn Cohen David Dorr Peter L Elkin Jungwei W Fan Todd Ferris David J Foran David Hanauer Mike Hogarth Kun Huang Jayashree Kalpathy-Cramer Manoj Kandpal Niranjan S Karnik Avnish Katoch Albert M Lai Christophe G Lambert Lang Li Christopher Lindsell Jinze Liu Zhiyong Lu Yuan Luo Peter McGarvey Eneida A Mendonca Parsa Mirhaji Shawn Murphy John D Osborne Ioannis C Paschalidis Paul A Harris Fred Prior Nicholas J Shaheen Nawar Shara Ida Sim Umberto Tachinardi Lemuel R Waitman Rosalind J Wright Adrian H Zai Kai Zheng Sandra Soo-Jin Lee Bradley A Malin Karthik Natarajan W Nicholson Price Ii Rui Zhang Yiye Zhang Hua Xu Jiang Bian Chunhua Weng Yifan Peng Department of Biomedical Informatics Columbia University Irving Medical Center New York NY USA. Department of Population Health Sciences Weill Cornell Medicine New York NY USA. Department of Pediatrics Boston Medical Center Boston MA USA Chobanian & Avedisian School of Medicine Boston University Boston MA USA. Oregon Clinical and Translational Research Institute Oregon Health and Science University Portland OR USA. Department of Public Health Sciences University of California Davis Davis CA USA. Keck School of Medicine University of Southern California Los Angeles CA USA. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. Department of Pathology and Laboratory Medicine University of Kentucky College of Medicine Lexington KY USA. Clinical and Translational Science Center Weill Cornell Medicine New York NY USA. Division of Pulmonary Critical Care and Sleep Medicine University of Kansas School of Medicine Kansas City KS USA. Department of Biomedical Informatics and Data Science Heersink School of Medicine University of Alabama Birmingham AL USA. Harvard Law School Petrie-Flom Center for Health Law Policy Biotechnology and Bioethics Harvard University Cambridge MA USA. Department of Biomedical Informatics University at Buffalo Buffalo NY USA. Center for Clinical and Translational Science Mayo Clinic Rochester MN USA. Technology and Digital Solutions Stanford Medicine Stanford University Stanford CA USA. Center for Biomedical Informatics Rutgers Cancer Institute New Brunswick NJ USA. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI USA. Altman Clinical and Translational Research Institute (ACTRI) University of California San Diego La Jolla CA USA. Department of Biostatistics and Health Data Science School of Medicine Indiana University Indianapolis IN USA. Department of Ophthalmology CCTSI University of Colorado Aurora CO USA. Center for Clinic

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies.

关键词： Health services Public health

来源：评论

学校读者我要写书评

暂无评论

Genes and comorbidities of thyroid cancer

引用

informatics in Medicine Unlocked 2021年 25卷

作者： Ljubic, Branimir Pavlovski, Martin Roychoudhury, Shoumik Van Neste, Christophe Salhi, Adil Essack, Magbubah Bajic, Vladimir B. Obradovic, Zoran Temple University Center for Data Analytics and Biomedical Informatics (DABI) Philadelphia 19122 PA United States Rutgers University the Office of Advanced Research Computing (OARC) Piscataway 08854 NJ United States Center for Medical Genetics (CMGG) Ghent University Ghent Belgium King Abdullah University of Science and Technology (KAUST) Computational Bioscience Research Center (CBRC) Computer Electrical and Mathematical Science and Engineering (CEMSE) Division Thuwal Saudi Arabia

Introduction: Thyroid cancer represents 3.1 % of diagnosed cancers in the United States. The objective of this research was to identify comorbidities and discover additional genes potentially related to thyroid cancer and improve current knowledge of genetics and comorbidities associated with this cancer. Methods: Healthcare Cost and Utilization Project (HCUP) California State Inpatient database (SID) was used to extract and rank the comorbidities of thyroid cancer. The text mining software - BeFree was utilized to identify and extract genes associated with thyroid cancer and the comorbidities from PubMed abstracts and the DisGeNET expert-curated repositories. Results: Female patients had 4,485, and male patients 2912 different comorbidities in early stages of thyroid cancer. Females had 3,587, and males 2817 different comorbidities in advanced stages. Through PubMed utilizing the BeFree method, 504 different genes associated with thyroid cancer were discovered, as well as five genes on DisGeNET. The most often genes on PubMed, associated with thyroid cancer were: BRAF, RET, SLC5A5, RAS, and PTEN. Genes found via DisGeNET were BRAF, RET, KRAS, NRAS, and PRKAR1A. Conclusion: Identified genes and comorbidities, as potential additional risk factors for thyroid cancer, not previously known, could improve the early diagnosis and the survival of patients with thyroid cancer. Genes discovered in this research in association with thyroid cancer could be used to direct decision making for optimal, more personalized treatment of thyroid cancer. © 2021

关键词： biomedical informatics Comorbidities DisGeNET Genes Text mining Thyroid cancer

来源：评论

学校读者我要写书评

暂无评论

Evidence-Based Health informatics as the Foundation for the COVID-19 Response: A Joint Call for Action

引用

Methods of Information in Medicine 2021年第6期59卷 183-192页

作者： Luis Fernandez-Luque Andre W. Kushniruk Andrew Georgiou Arindam Basu Carolyn Petersen Charlene Ronquillo Chris Paton Christian Nøhr Craig E. Kuziemsky Dari Alhuwail Diane Skiba Elaine Huesing Elia Gabarron Elizabeth M. Borycki Farah Magrabi Kerstin Denecke Linda W. P. Peute Max Topaz Najeeb Al-Shorbaji Paulette Lacroix Romaric Marcilly Ronald Cornet Shashi B. Gogia Shinji Kobayashi Sriram Iyengar Thomas M. Deserno Tobias Mettler Vivian Vimarlund Xinxin Zhu Adhera Health Inc. Palo Alto California United States School of Health Information Science University of Victoria Victoria Canada Australian Institute of Health Innovation Macquarie University Macquarie New South Wales Australia School of Health Sciences University of Canterbury Christchurch New Zealand Division of Biomedical Statistics and Informatics Mayo Clinic Rochester Minnesota United States Daphne Cockwell School of Nursing Ryerson University Ryerson Toronto Canada Department of Information Science University of Otago Dunedin New Zealand Nuffield Department of Medicine University of Oxford Oxford United Kingdom Centre for Health Informatics and Technology Maersk McKinney Moller Institute University of Southern Denmark Denmark Office of Research Services MacEwan University Edmonton AB Canada Department of Information Science Kuwait University Kuwait Health Informatics Unit Dasman Diabetes Institute Kuwait University of Colorado Denver Colorado United States IMIA CEO Norwegian Centre for E-health Research University Hospital of North Norway Tromsø Norway Institute for Medical Informatics Bern University of Applied Sciences Bern Switzerland Medical Informatics Amsterdam UMC University of Amsterdam Amsterdam The Netherlands Columbia University Medical Center Data Science Institute Columbia University Columbia United States Amman Jordan University of Victoria Victoria Canada Univ. Lille Inserm CHU Lille ULR 2694 - METRICS: Évaluation des technologies de santé et des pratiques médicales F-59000 Lille France Society for Administration of Telemedicine and Healthcare Informatics New Delhi India National Institute of Public Health Japan The University of Arizona Arizona United States Peter L. Reichertz Institute for Medical Informatics of TU Braunschweig and Hannover Medical School Braunschweig Germany Swiss Graduate School of Public Administration University of Lausanne Lausanne Switzerland Department of Computer and Information Science (IDA)

来源：评论

学校读者我要写书评

暂无评论

Bridging disconnected networks of first and second lines of biologic therapies in rheumatoid arthritis with registry data: Bayesian evidence synthesis with target trial emulation

arXiv

引用

arXiv 2022年

作者： Bujkiewicz, Sylwia Singh, Janharpreet Wheaton, Lorna Jenkins, David Martina, Reynaldo Hyrich, Kimme Abrams, Keith R. Biostatistics Research Group Department of Health Sciences University of Leicester University Road LeicesterLE1 7RH United Kingdom Centre for Health Informatics Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester United Kingdom NIHR Manchester Biomedical Research Centre Manchester University NHS Foundation Trust Manchester Academic Health Sciences Centre Manchester United Kingdom Versus Arthritis Centre for Epidemiology Centre for Musculoskeletal Research University of Manchester ManchesterM13 9PL United Kingdom Department of Statistics University of Warwick CoventryCV4 7AL United Kingdom Centre for Health Economics University of York YorkYO10 5DD United Kingdom

Objective: We aim to utilise real world data in evidence synthesis to optimise an evidence base for the effectiveness of biologic therapies in rheumatoid arthritis in order to allow for evidence on first-line therapies to inform second-line effectiveness estimates. Study design and setting: We use data from the British Society for Rheumatology Biologics Register for Rheumatoid Arthritis (BSRBR-RA) to supplement RCT evidence obtained from the literature, by emulating target trials of treatment sequences to estimate treatment effects in each line of therapy. Treatment effects estimates from the target trials inform a bivariate network meta-analysis (NMA) of first and second-line treatments. Results: Summary data were obtained from 21 trials of biologic therapies including 2 for second-line treatment and results from six emulated target trials of both treatment lines. Bivariate NMA resulted in a decrease in uncertainty around the effectiveness estimates of the second-line therapies, when compared to the results of univariate NMA, and allowed for predictions of treatment effects not evaluated in second-line RCTs. Conclusion: Bivariate NMA provides effectiveness estimates for all treatments in first- and second-line, including predicted effects in second-line where these estimates did not exist in the data. This novel methodology may have further applications, for example for bridging networks of trials in children and adults. © 2022, CC BY.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Factors Associated With Influenza Vaccination in a National Veteran Cohort

引用

AJPM Focus 2025年第1期4卷 100290-100290页

作者： Chen, Alissa S. Farmer, Melissa M. Han, Ling Runels, Tessa Bade, Brett Crothers, Kristina Bastian, Lori A. Bazan, Isabel S. Bean-Mayberry, Bevanne A. Brandt, Cynthia A. Akgün, Kathleen M. Section of General Medicine Department of Medicine Yale University School of Medicine New Haven Connecticut United States Center for Study of Healthcare Innovation Implementation and Policy (CSHIIP) VA Greater Los Angeles Healthcare System Los Angeles California United States Pain Research Informatics Multimorbidities and Education (PRIME) Center VA Connecticut Healthcare System West Haven CT United States Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Hempstead New York United States VA Puget Sound Seattle WA United States Division of Pulmonary Critical Care and Sleep Medicine University of Washington Seattle WA United States Pulmonary Critical Care & Sleep Medicine Yale University School of Medicine New Haven Connecticut United States Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles California United States Department of Emergency Medicine Yale University School of Medicine New Haven Connecticut United States Section of Biomedical Informatics and Data Science Yale University School of Medicine New Haven Connecticut United States

Introduction: Only 53% of American adults receive influenza vaccination, and disparities in vaccination exist among particular racial and ethnic groups. This study determines how race, ethnicity, sex, and rurality are associated with influenza vaccination adherence in a national Veteran Health Affairs Administration cohort. Methods: The authors examined differences in documented influenza vaccinations for the 2019–2020 influenza season among Veteran Health Affairs Administration patients in a retrospective cohort study using Veteran Health Affairs Administration administrative electronic health record data. The author used logistic regression to model receipt of influenza vaccination in association with race, ethnicity, sex, and rurality while controlling for clinical diagnoses, demographics, and ambulatory care utilization. The authors also stratified the models by sex and rurality. Results: Among 5,943,918 veterans, 48.6% received influenza vaccination. Unadjusted comparisons showed that those who were vaccinated were more likely to be White, to be of male sex, and to be older. Similar proportions of unvaccinated and unvaccinated veterans were from rural settings. In adjusted models, Black race was most strongly associated with decreased vaccination (AOR=0.69;95% CI=0.69, 0.70), and American Indian/Alaskan Native race also had reduced odds of vaccination (AOR=0.94;95% CI=0.92, 0.95) compared with White race. Female veterans had increased odds of vaccination (AOR=1.20;95% CI=1.19, 1.20) compared with men. Rurality (AOR=0.97;95% CI=0.96, 0.97) was associated with a small decreased odds of vaccination compared with urban. In stratified models, Black veterans were less likely to receive influenza vaccination regardless of sex and rurality than White veterans. American Indian/Alaska Native female veterans had equal odds of vaccination as White female veterans, whereas American Indian/Alaska Native male veterans had reduced odds of vaccination compared with White male v

关键词： Influenza vaccination vaccination coverage vaccine hesitancy veterans

来源：评论

学校读者我要写书评

暂无评论

Echocardiographic Features of Right Ventricular Reverse Remodeling in Adults with Repaired Tetralogy of Fallot after Pulmonary Valve Replacement and Impact of Age at Time of Intervention

引用

Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography 2025年

作者： Rebecca L Moore Jan Leonard Alexis Z Tomlinson Stephanie Fuller Yan Wang Laura Mercer-Rosa Yuli Y Kim Division of Cardiology Department of Pediatrics Children's Hospital of Philadelphia Philadelphia PA USA Division of Cardiology Department of Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Electronic address: Rebecca.moore2@pennmedicine.upenn.edu. Data Science and Biostatistics Unit Department of Biomedical and Health Informatics Children's Hospital of Philadelphia Philadelphia PA USA. Division of Cardiothoracic Surgery Department of Surgery Children's Hospital of Philadelphia Philadelphia PA USA. Division of Cardiology Department of Pediatrics Children's Hospital of Philadelphia Philadelphia PA USA. Division of Cardiology Department of Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA.

来源：评论

学校读者我要写书评

暂无评论

Causal Inference with Double/Debiased Machine Learning for Evaluating the Health Effects of Multiple Mismeasured Pollutants

arXiv

引用

arXiv 2024年

作者： Xu, Gang Zhou, Xin Wang, Molin Zhang, Boya Jiang, Wenhao Laden, Francine Suh, Helen H. Szpiro, Adam A. Spiegelman, Donna Wang, Zuoheng Department of Biostatistics Yale School of Public Health New HavenCT United States Center for Methods in Implementation and Prevention Science Yale School of Public Health New HavenCT United States Department of Epidemiology Harvard T.H. Chan School of Public Health BostonMA United States Department of Biostatistics Harvard T.H. Chan School of Public Health BostonMA United States Channing Division of Network Medicine Harvard Medical School Brigham and Women’s Hospital BostonMA United States Department of Environmental Health Harvard T.H. Chan School of Public Health BostonMA United States Department of Biostatistics and Epidemiology School of Public Health and Health Sciences University of Massachusetts AmherstMA United States Department of Civil and Environmental Engineering Tufts University MedfordMA United States Department of Biostatistics University of Washington SeattleWA United States Department of Biomedical Informatics & Data Science Yale School of Medicine New HavenCT United States

One way to quantify exposure to air pollution and its constituents in epidemiologic studies is to use an individual’s nearest monitor. This strategy results in potential inaccuracy in the actual personal exposure, introducing bias in estimating the health effects of air pollution and its constituents, especially when evaluating the causal effects of correlated multi-pollutant constituents measured with correlated error. This paper addresses estimation and inference for the causal effect of one constituent in the presence of other PM2.5 constituents, accounting for measurement error and correlations. We used a linear regression calibration model, fitted with generalized estimating equations in an external validation study, and extended a double/debiased machine learning (DML) approach to correct for measurement error and estimate the effect of interest in the main study. We demonstrated that the DML estimator with regression calibration is consistent and derived its asymptotic variance. Simulations showed that the proposed estimator reduced bias and attained nominal coverage probability across most simulation settings. We applied this method to assess the causal effects of PM2.5 constituents on cognitive function in the Nurses’ Health Study and identified two PM2.5 constituents, Br and Mn, that showed a negative causal effect on cognitive function after measurement error correction. Copyright © 2024, The Authors. All rights reserved.

关键词： Regression analysis

来源：评论

学校读者我要写书评

暂无评论

A systematic review of causal methods enabling predictions under hypothetical interventions

arXiv

引用

arXiv 2020年

作者： Lin, Lijing Sperrin, Matthew Jenkins, David A. Martin, Glen P. Peek, Niels Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom NIHR Greater Manchester Patient Safety Translational Research Centre University of Manchester United Kingdom NIHR Manchester Biomedical Research Centre University of Manchester Manchester Academic Health Science Centre United Kingdom

Background The methods with which prediction models are usually developed mean that neither the parameters nor the predictions should be interpreted causally. For many applications this is perfectly acceptable. However, when prediction models are used to support decision making, there is often a need for predicting outcomes under hypothetical interventions. Aims We aimed to identify and compare published methods for developing and validating prediction models that enable risk estimation of outcomes under hypothetical interventions, utilizing causal inference. We aimed to identify the main methodological approaches, their underlying assumptions, targeted estimands, and possible sources of bias. Finally, we aimed to highlight unresolved methodological challenges. Methods We systematically reviewed literature published by December 2019, considering papers in the health domain that used causal considerations to enable prediction models to be used to evaluate predictions under hypothetical interventions. We included both methodology development studies and applied studies. Results We identified 4919 papers through database searches and a further 115 papers through manual searches. Of these, 87 papers were retained for full text screening, of which 12 were selected for inclusion. We found papers from both the statistical and the machine learning literature. Most of the identified methods for causal inference from observational data were based on marginal structural models and g-estimation. Conclusions There exist two broad methodological approaches for allowing prediction under hypothetical intervention into clinical prediction models: 1) enriching prediction models derived from observational studies with estimated causal effects from clinical trials and meta-analyses;and 2) estimating prediction models and causal effects directly from observational data. These methods require extending to dynamic treatment regimes, and consideration of multiple interventions. Techniques

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

Epicure:ensemble pretrained models for extracting cancer mutations from literature

arXiv

引用

arXiv 2021年

作者： Cao, Jiarun van Veen, Elke M. Peek, Niels Renehan, Andrew G. Ananiadou, Sophia National Centre for Text Mining Dept. of Computer Science The University of Manchester Manchester United Kingdom Manchester Centre for Genomic Medicine St Mary's Hospital Manchester University Hospitals NHS Foundation Trust Division of Evolution and Genomic Sciences School of Biological Sciences Faculty of Biology Medicine and Health The University of Manchester Manchester United Kingdom Division of Informatics Imaging and Data Science NIHR Manchester Biomedical Research Centre Faculty of Biology Medicine and Health The University of Manchester Manchester United Kingdom Division of Cancer Sciences School of Medical Sciences Faculty of Biology Medicine and Health The University of Manchester United Kingdom Surgery Christie NHS Foundation Trust Manchester United Kingdom

To interpret the genetic profile present in a patient sample, it is necessary to know which mutations have important roles in the development of the corresponding cancer type. Named entity recognition (NER) is a core step in the text mining pipeline which facilitates mining valuable cancer information from the scientific literature. However, due to the scarcity of related datasets, previous NER attempts in this domain either suffer from low performance when deep learning based models are deployed, or they apply feature-based machine learning models or rule-based models to tackle this problem, which requires intensive efforts from domain experts, and limit the model generalization capability. In this paper, we propose EPICURE, an ensemble pretrained model equipped with a conditional random field pattern (CRF) layer and a span prediction pattern (Span) layer to extract cancer mutations from text. We also adopt a data augmentation strategy to expand our training set from multiple datasets. Experimental results on three benchmark datasets show competitive results compared to the baseline models, validating our model's effectiveness and advances in generalization capability. © 2021, CC BY.

关键词： Diseases

来源：评论

学校读者我要写书评

暂无评论

Lessons Learned from the Immrep23 Tcr-Epitope Prediction Challenge

SSRN

引用

SSRN 2024年

作者： Nielsen, Morten Eugster, Anne Jensen, Mathias Fynbo Goel, Manisha Tiffeau-Mayer, Andreas Pelissier, Aurelien Valkiers, Sebastiaan Martínez, María Rodríguez Meynard-Piganeeau, Barthélémy Greiff, Victor Mora, Thierry Walczak, Aleksandra M. Croce, Giancarlo Moreno, Dana L. Gfeller, David Meysman, Pieter Barton, Justin Department of Health Technology Technical University of Denmark LyngbyDK-2800 Denmark Center for Regenerative Therapies Dresden Faculty of Medicine TU Dresden Dresden Germany Division of Infection and Immunity University College London United Kingdom Institute of Computational Life Sciences ZHAW Winterthur8400 Switzerland Biomedical Informatics and Data Science Yale School of Medicine New Haven06510 United States Computational and Quantitative Biology LCQB UMR 7238 Institut de Biologie Paris Seine CNRS Sorbonne Universite Paris75005 France Department of Immunology and Transfusion Medicine University of Oslo Oslo University Hospital Oslo Norway Laboratoire de Physique de l’Ecole Normale Supérieure ENS Université PSL CNRS Sorbonne Université Université de Paris Cité ParisF-75005 France Department of Oncology UNIL CHUV Ludwig Institute for Cancer Research University of Lausanne Lausanne Switzerland University of Antwerp Antwerp Belgium Institute of Structural and Molecular Biology University of London United Kingdom

Here, we present the findings from IMMREP23, the second benchmark competition focused on predicting the specificity of TCR-pMHC *** interaction of T cell receptors (TCR) towards their pMHC target is the cornerstone of the cellular immune system. Over the last decade, substantial progress has been made within the field of TCR specificity prediction, providing proof of concept for predicting TCR-pMHC interactions in a narrow antigen space where substantial training data is available. However, a significant challenge persists in extending the predictive capability to novel "unseen" pMHC targets. Furthermore, typical of advancements in prediction methodologies, the performance of proposed methods is often challenged when evaluated outside the initial publication and data *** address these issues, IMMREP23 challenge invited participants to predict, for a given test set of TCR-pMHC pairs, the likelihood that a pair would bind. A total of 53 teams participated, providing a total of 398 *** benchmark confirms that current methods achieve reasonable performance in the "seen" pHLA setting. However, most participating methods had close to random performance on the subset of "unseen" peptides, underlining that this prediction challenge remains essentially ***, another key lesson from the benchmark is the critical issue of data leakage. Specifically, the procedure employed in IMMREP23 led to biases in the negative test data set. These biases were identified by several participating teams, and complicated the interpretation of the benchmark results. Based on these results, we put forward suggestions on how future competitions could avoid such data leakages and biases. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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：