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相似文献

1
LitCovid in 2022: an information resource for the COVID-19 literature.2022 年的 LitCovid:COVID-19 文献信息资源。
Nucleic Acids Res. 2023 Jan 6;51(D1):D1512-D1518. doi: 10.1093/nar/gkac1005.
2
LitCovid: an open database of COVID-19 literature.LitCovid:一个 COVID-19 文献的开放数据库。
Nucleic Acids Res. 2021 Jan 8;49(D1):D1534-D1540. doi: 10.1093/nar/gkaa952.
3
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations.生物医学文献的多标签分类:BioCreative VII LitCovid 新冠文献主题标注挑战赛概述。
Database (Oxford). 2022 Aug 31;2022. doi: 10.1093/database/baac069.
4
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning.通过人工参与的机器学习全面识别长新冠相关文章。
Patterns (N Y). 2023 Jan 13;4(1):100659. doi: 10.1016/j.patter.2022.100659. Epub 2022 Dec 1.
5
LitCovid: A Database of Coronavirus Research.LitCovid:冠状病毒研究数据库。
Med Ref Serv Q. 2021 Jan-Mar;40(1):103-109. doi: 10.1080/02763869.2021.1873639.
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LitMC-BERT: Transformer-Based Multi-Label Classification of Biomedical Literature With An Application on COVID-19 Literature Curation.LitMC-BERT:基于 Transformer 的生物医学文献多标签分类及其在 COVID-19 文献整理中的应用。
IEEE/ACM Trans Comput Biol Bioinform. 2022 Sep-Oct;19(5):2584-2595. doi: 10.1109/TCBB.2022.3173562. Epub 2022 Oct 10.
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Features and Limitations of LitCovid Hub for Quick Access to Literature About COVID-19.LitCovid中心快速获取COVID-19相关文献的特点与局限性
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Covid-19 scientific publications from Turkey.土耳其的新冠病毒科研出版物。
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LitCovid ensemble learning for COVID-19 multi-label classification.LitCovid 用于 COVID-19 多标签分类的集成学习。
Database (Oxford). 2022 Nov 25;2022. doi: 10.1093/database/baac103.
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LitCovid, iSearch COVID-19 portfolio, and COVID-19 Global literature on coronavirus disease.LitCovid、iSearch COVID-19 组合以及关于冠状病毒病的 COVID-19 全球文献。
J Med Libr Assoc. 2022 Apr 1;110(2):279-280. doi: 10.5195/jmla.2022.1274.

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Academic case reports lack diversity: Assessing the presence and diversity of sociodemographic and behavioral factors related to Post COVID-19 Condition.学术病例报告缺乏多样性:评估与新冠后状况相关的社会人口学和行为因素的存在情况及多样性。
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Coronavirus research topics, tracking twenty years of research.冠状病毒研究主题,追踪二十年研究历程。
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Benchmarking large language models for biomedical natural language processing applications and recommendations.用于生物医学自然语言处理应用的大型语言模型基准测试及建议。
Nat Commun. 2025 Apr 6;16(1):3280. doi: 10.1038/s41467-025-56989-2.
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Computational tools and data integration to accelerate vaccine development: challenges, opportunities, and future directions.加速疫苗开发的计算工具与数据整合:挑战、机遇及未来方向
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How the National Library of Medicine should evolve in an era of artificial intelligence.国立医学图书馆在人工智能时代应如何发展。
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COVID-19-related research data availability and quality according to the FAIR principles: A meta-research study.基于 FAIR 原则的 COVID-19 相关研究数据的可用性和质量:一项元研究。
PLoS One. 2024 Nov 18;19(11):e0313991. doi: 10.1371/journal.pone.0313991. eCollection 2024.
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Is metadata of articles about COVID-19 enough for multilabel topic classification task?关于 COVID-19 的文章的元数据是否足以完成多标签主题分类任务?
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本文引用的文献

1
Comprehensively identifying Long Covid articles with human-in-the-loop machine learning.通过人工参与的机器学习全面识别长新冠相关文章。
Patterns (N Y). 2023 Jan 13;4(1):100659. doi: 10.1016/j.patter.2022.100659. Epub 2022 Dec 1.
2
Mendelian Susceptibility to Mycobacterial Disease: Retrospective Clinical and Genetic Study in Mexico.孟德尔易感性与分枝杆菌病:墨西哥的回顾性临床与遗传研究。
J Clin Immunol. 2023 Jan;43(1):123-135. doi: 10.1007/s10875-022-01357-8. Epub 2022 Aug 31.
3
Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations.生物医学文献的多标签分类:BioCreative VII LitCovid 新冠文献主题标注挑战赛概述。
Database (Oxford). 2022 Aug 31;2022. doi: 10.1093/database/baac069.
4
Gastrointestinal manifestations of long COVID: A systematic review and meta-analysis.新冠长期症状的胃肠道表现:一项系统评价与荟萃分析
Therap Adv Gastroenterol. 2022 Aug 19;15:17562848221118403. doi: 10.1177/17562848221118403. eCollection 2022.
5
Discovery and Mechanistic Study of PafA Inhibitors.发现并研究 PafA 抑制剂。
J Med Chem. 2022 Aug 25;65(16):11058-11065. doi: 10.1021/acs.jmedchem.2c00289. Epub 2022 Aug 4.
6
Opportunities to Improve Long COVID Care: Implications from Semi-structured Interviews with Black Patients.改善长新冠护理的机会:对黑人患者进行半结构化访谈的启示。
Patient. 2022 Nov;15(6):715-728. doi: 10.1007/s40271-022-00594-8. Epub 2022 Jul 30.
7
Epipharyngeal Abrasive Therapy (EAT) Has Potential as a Novel Method for Long COVID Treatment.咽上磨蚀疗法(EAT)有望成为治疗长新冠的新方法。
Viruses. 2022 Apr 27;14(5):907. doi: 10.3390/v14050907.
8
LitMC-BERT: Transformer-Based Multi-Label Classification of Biomedical Literature With An Application on COVID-19 Literature Curation.LitMC-BERT:基于 Transformer 的生物医学文献多标签分类及其在 COVID-19 文献整理中的应用。
IEEE/ACM Trans Comput Biol Bioinform. 2022 Sep-Oct;19(5):2584-2595. doi: 10.1109/TCBB.2022.3173562. Epub 2022 Oct 10.
9
Short-Term and Long-Term COVID-19 Pandemic Forecasting Revisited with the Emergence of OMICRON Variant in Jordan.随着约旦出现奥密克戎变种对短期和长期新冠疫情预测的重新审视
Vaccines (Basel). 2022 Apr 7;10(4):569. doi: 10.3390/vaccines10040569.
10
Histopathology of persistent long COVID toe: A case report.持续性长新冠脚趾的组织病理学:一例报告。
J Cutan Pathol. 2022 Sep;49(9):791-794. doi: 10.1111/cup.14240. Epub 2022 Apr 18.

2022 年的 LitCovid:COVID-19 文献信息资源。

LitCovid in 2022: an information resource for the COVID-19 literature.

机构信息

National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, MD, USA.

Towson University, Towson, MD, USA.

出版信息

Nucleic Acids Res. 2023 Jan 6;51(D1):D1512-D1518. doi: 10.1093/nar/gkac1005.

DOI:10.1093/nar/gkac1005
PMID:36350613
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9825538/
Abstract

LitCovid (https://www.ncbi.nlm.nih.gov/research/coronavirus/)-first launched in February 2020-is a first-of-its-kind literature hub for tracking up-to-date published research on COVID-19. The number of articles in LitCovid has increased from 55 000 to ∼300 000 over the past 2.5 years, with a consistent growth rate of ∼10 000 articles per month. In addition to the rapid literature growth, the COVID-19 pandemic has evolved dramatically. For instance, the Omicron variant has now accounted for over 98% of new infections in the United States. In response to the continuing evolution of the COVID-19 pandemic, this article describes significant updates to LitCovid over the last 2 years. First, we introduced the long Covid collection consisting of the articles on COVID-19 survivors experiencing ongoing multisystemic symptoms, including respiratory issues, cardiovascular disease, cognitive impairment, and profound fatigue. Second, we provided new annotations on the latest COVID-19 strains and vaccines mentioned in the literature. Third, we improved several existing features with more accurate machine learning algorithms for annotating topics and classifying articles relevant to COVID-19. LitCovid has been widely used with millions of accesses by users worldwide on various information needs and continues to play a critical role in collecting, curating and standardizing the latest knowledge on the COVID-19 literature.

摘要

LitCovid(https://www.ncbi.nlm.nih.gov/research/coronavirus/)于 2020 年 2 月首次推出,是一个专门用于追踪 COVID-19 最新已发表研究的文献枢纽。在过去的 2.5 年中,LitCovid 中的文章数量从 55000 篇增加到了约 300000 篇,每月的增长率稳定在约 10000 篇。除了文献数量的快速增长,COVID-19 大流行也发生了巨大变化。例如,奥密克戎变异株现在已经占美国新感染病例的 98%以上。为了应对 COVID-19 大流行的持续演变,本文描述了过去 2 年来 LitCovid 的重大更新。首先,我们引入了长新冠集合,其中包含了关于 COVID-19 幸存者经历持续多系统症状(包括呼吸问题、心血管疾病、认知障碍和严重疲劳)的文章。其次,我们对文献中提到的最新 COVID-19 株和疫苗提供了新的注释。第三,我们改进了几个现有的功能,使用更准确的机器学习算法来注释主题和对与 COVID-19 相关的文章进行分类。LitCovid 已被全球数百万用户广泛使用,用于满足各种信息需求,并且继续在收集、整理和规范 COVID-19 文献的最新知识方面发挥关键作用。