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The TB Portals: an Open-Access, Web-Based Platform for Global Drug-Resistant-Tuberculosis Data Sharing and Analysis.结核门户:一个开放获取、基于网络的全球耐多药结核病数据共享和分析平台。
J Clin Microbiol. 2017 Nov;55(11):3267-3282. doi: 10.1128/JCM.01013-17. Epub 2017 Sep 13.
2
TB DEPOT (Data Exploration Portal): A multi-domain tuberculosis data analysis resource.结核储存库(数据探索门户):一个多领域结核病数据分析资源。
PLoS One. 2019 May 23;14(5):e0217410. doi: 10.1371/journal.pone.0217410. eCollection 2019.
3
Comparative analysis of genomic variability for drug-resistant strains of Mycobacterium tuberculosis: The special case of Belarus.结核分枝杆菌耐药株的基因组变异性比较分析:白俄罗斯的特殊情况。
Infect Genet Evol. 2020 Mar;78:104137. doi: 10.1016/j.meegid.2019.104137. Epub 2019 Dec 12.
4
Strong Increase in Moxifloxacin Resistance Rate among Multidrug-Resistant Mycobacterium tuberculosis Isolates in China, 2007 to 2013.2007 年至 2013 年中国耐多药结核分枝杆菌分离株中莫西沙星耐药率的大幅增加。
Microbiol Spectr. 2021 Dec 22;9(3):e0040921. doi: 10.1128/Spectrum.00409-21. Epub 2021 Dec 1.
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Comparison of the socio-demographic and clinical features of pulmonary TB patients infected with sub-lineages within the W-Beijing and non-Beijing Mycobacterium tuberculosis.W-北京型和非北京型结核分枝杆菌亚谱系感染的肺结核患者的社会人口学和临床特征比较
Tuberculosis (Edinb). 2016 Mar;97:18-25. doi: 10.1016/j.tube.2015.11.007. Epub 2015 Dec 23.
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Whole-genome sequencing of clinical isolates from tuberculosis patients in India: real-world data indicates a high proportion of pre-XDR cases.对印度结核患者临床分离株进行全基因组测序:真实世界数据显示存在较高比例的预广泛耐药病例。
Microbiol Spectr. 2024 May 2;12(5):e0277023. doi: 10.1128/spectrum.02770-23. Epub 2024 Apr 10.
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Tuberculosis - A global emergency: Tools and methods to monitor, understand, and control the epidemic with specific example of the Beijing lineage.结核病——全球紧急情况:监测、理解和控制疫情的工具与方法,以北京家族分支为例
Tuberculosis (Edinb). 2015 Jun;95 Suppl 1:S177-89. doi: 10.1016/j.tube.2015.02.023. Epub 2015 Feb 13.
8
A retrospective genomic analysis of drug-resistant strains of M. tuberculosis in a high-burden setting, with an emphasis on comparative diagnostics and reactivation and reinfection status.在高负担环境下对耐药结核分枝杆菌菌株进行回顾性基因组分析,重点关注比较诊断以及再激活和再感染状态。
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Correlations between drug resistance of Beijing/W lineage clinical isolates of Mycobacterium tuberculosis and sublineages: a 2009-2013 prospective study in Xinjiang province, China.结核分枝杆菌北京/W 家族临床分离株耐药性与亚家族之间的相关性:2009 - 2013年中国新疆前瞻性研究
Med Sci Monit. 2015 May 7;21:1313-8. doi: 10.12659/MSM.892951.
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Drug-resistant Mycobacterium tuberculosis among Nepalese patients at a tuberculosis referral center.耐多药结核分枝杆菌在结核病转诊中心的尼泊尔患者中。
PLoS One. 2024 May 6;19(5):e0301210. doi: 10.1371/journal.pone.0301210. eCollection 2024.

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MIDAS: a technology-enabled hub-and-spoke system for the collection and dissemination of high-quality medical datasets in India.MIDAS:一种在印度用于收集和传播高质量医学数据集的技术支持的中心辐射式系统。
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Molecular typing of Mycobacterium tuberculosis: a review of current methods, databases, softwares, and analytical tools.结核分枝杆菌的分子分型:当前方法、数据库、软件及分析工具综述
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Semantic Segmentation of TB in Chest X-rays: a New Dataset and Generalization Evaluation.胸部X光片中肺结核的语义分割:一个新数据集及泛化评估
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Clinical validation of an artificial intelligence algorithm for classifying tuberculosis and pulmonary findings in chest radiographs.用于胸部X光片中肺结核及肺部表现分类的人工智能算法的临床验证
Front Artif Intell. 2025 Feb 6;8:1512910. doi: 10.3389/frai.2025.1512910. eCollection 2025.
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Tuberculosis Chest X-Ray Image Retrieval System Using Deep Learning Based Biomarker Predictions.基于深度学习生物标志物预测的肺结核胸部X光图像检索系统
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J Imaging Inform Med. 2024 Oct;37(5):2173-2185. doi: 10.1007/s10278-024-01052-7. Epub 2024 Apr 8.
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DeepPulmoTB: A benchmark dataset for multi-task learning of tuberculosis lesions in lung computerized tomography (CT).DeepPulmoTB:用于肺部计算机断层扫描(CT)中结核病变多任务学习的基准数据集。
Heliyon. 2024 Feb 7;10(4):e25490. doi: 10.1016/j.heliyon.2024.e25490. eCollection 2024 Feb 29.
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Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis.多模态患者数据的综合分析确定了结核病治疗预后的个性化预测指标。
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The Tuberculosis Sentinel Research Network (TB-SRN) of the International epidemiology Databases to Evaluate AIDS (IeDEA): protocol for a prospective cohort study in Africa, Southeast Asia and Latin America.国际艾滋病流行病学数据库监测研究网络(TB-SRN):在非洲、东南亚和拉丁美洲开展的前瞻性队列研究方案。
BMJ Open. 2024 Jan 9;14(1):e079138. doi: 10.1136/bmjopen-2023-079138.
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Improving deep neural network generalization and robustness to background bias via layer-wise relevance propagation optimization.通过逐层相关性传播优化提高深度神经网络的泛化能力和对背景偏差的鲁棒性。
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本文引用的文献

1
Estimating the future burden of multidrug-resistant and extensively drug-resistant tuberculosis in India, the Philippines, Russia, and South Africa: a mathematical modelling study.估算印度、菲律宾、俄罗斯和南非耐多药及广泛耐药结核病的未来负担:一项数学建模研究
Lancet Infect Dis. 2017 Jul;17(7):707-715. doi: 10.1016/S1473-3099(17)30247-5. Epub 2017 May 9.
2
A tuberculosis biomarker database: the key to novel TB diagnostics.一个结核病生物标志物数据库:新型结核病诊断的关键。
Int J Infect Dis. 2017 Mar;56:253-257. doi: 10.1016/j.ijid.2017.01.025. Epub 2017 Jan 31.
3
Genomic analysis of globally diverse Mycobacterium tuberculosis strains provides insights into the emergence and spread of multidrug resistance.对全球不同结核分枝杆菌菌株的基因组分析为深入了解多重耐药性的出现和传播提供了线索。
Nat Genet. 2017 Mar;49(3):395-402. doi: 10.1038/ng.3767. Epub 2017 Jan 16.
4
Whole-Genome Sequencing of Mycobacterium tuberculosis Provides Insight into the Evolution and Genetic Composition of Drug-Resistant Tuberculosis in Belarus.结核分枝杆菌全基因组测序为深入了解白俄罗斯耐多药结核病的进化和基因组成提供了线索。
J Clin Microbiol. 2017 Feb;55(2):457-469. doi: 10.1128/JCM.02116-16. Epub 2016 Nov 30.
5
Genomic and functional analyses of Mycobacterium tuberculosis strains implicate ald in D-cycloserine resistance.结核分枝杆菌菌株的基因组和功能分析表明ald与D-环丝氨酸耐药性有关。
Nat Genet. 2016 May;48(5):544-51. doi: 10.1038/ng.3548. Epub 2016 Apr 11.
6
The FAIR Guiding Principles for scientific data management and stewardship.科学数据管理和保存的 FAIR 指导原则。
Sci Data. 2016 Mar 15;3:160018. doi: 10.1038/sdata.2016.18.
7
Evolution of Extensively Drug-Resistant Tuberculosis over Four Decades: Whole Genome Sequencing and Dating Analysis of Mycobacterium tuberculosis Isolates from KwaZulu-Natal.四十年间广泛耐药结核病的演变:来自夸祖鲁 - 纳塔尔省结核分枝杆菌分离株的全基因组测序与年代分析
PLoS Med. 2015 Sep 29;12(9):e1001880. doi: 10.1371/journal.pmed.1001880. eCollection 2015 Sep.
8
Xpert MTB/RIF assay for the diagnosis of pulmonary tuberculosis in children: a systematic review and meta-analysis.Xpert MTB/RIF assay 检测在儿童肺结核诊断中的应用:一项系统评价和荟萃分析。
Lancet Respir Med. 2015 Jun;3(6):451-61. doi: 10.1016/S2213-2600(15)00095-8. Epub 2015 Mar 24.
9
PET/CT imaging correlates with treatment outcome in patients with multidrug-resistant tuberculosis.正电子发射断层扫描/计算机断层扫描(PET/CT)成像与耐多药结核病患者的治疗结果相关。
Sci Transl Med. 2014 Dec 3;6(265):265ra166. doi: 10.1126/scitranslmed.3009501.
10
Pilon: an integrated tool for comprehensive microbial variant detection and genome assembly improvement.Pilon:一种用于全面微生物变异检测和基因组组装改进的集成工具。
PLoS One. 2014 Nov 19;9(11):e112963. doi: 10.1371/journal.pone.0112963. eCollection 2014.

结核门户:一个开放获取、基于网络的全球耐多药结核病数据共享和分析平台。

The TB Portals: an Open-Access, Web-Based Platform for Global Drug-Resistant-Tuberculosis Data Sharing and Analysis.

机构信息

Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA

Office of Cyber Infrastructure and Computational Biology, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA.

出版信息

J Clin Microbiol. 2017 Nov;55(11):3267-3282. doi: 10.1128/JCM.01013-17. Epub 2017 Sep 13.

DOI:10.1128/JCM.01013-17
PMID:28904183
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5654911/
Abstract

The TB Portals program is an international consortium of physicians, radiologists, and microbiologists from countries with a heavy burden of drug-resistant tuberculosis working with data scientists and information technology professionals. Together, we have built the TB Portals, a repository of socioeconomic/geographic, clinical, laboratory, radiological, and genomic data from patient cases of drug-resistant tuberculosis backed by shareable, physical samples. Currently, there are 1,299 total cases from five country sites (Azerbaijan, Belarus, Moldova, Georgia, and Romania), 976 (75.1%) of which are multidrug or extensively drug resistant and 38.2%, 51.9%, and 36.3% of which contain X-ray, computed tomography (CT) scan, and genomic data, respectively. The top lineages represented among collected samples are Beijing, T1, and H3, and single nucleotide polymorphisms (SNPs) that confer resistance to isoniazid, rifampin, ofloxacin, and moxifloxacin occur the most frequently. These data and samples have promoted drug discovery efforts and research into genomics and quantitative image analysis to improve diagnostics while also serving as a valuable resource for researchers and clinical providers. The TB Portals database and associated projects are continually growing, and we invite new partners and collaborations to our initiative. The TB Portals data and their associated analytical and statistical tools are freely available at https://tbportals.niaid.nih.gov/.

摘要

TB 门户计划是一个由来自耐药结核病负担沉重国家的医生、放射科医生和微生物学家组成的国际联盟,他们与数据科学家和信息技术专业人员合作。我们共同建立了 TB 门户,这是一个包含耐药结核病患者的社会经济/地理位置、临床、实验室、放射学和基因组数据的存储库,这些数据由可共享的物理样本支持。目前,有来自五个国家(阿塞拜疆、白俄罗斯、摩尔多瓦、格鲁吉亚和罗马尼亚)的 1299 例总病例,其中 976 例(75.1%)为耐多药或广泛耐药,分别有 38.2%、51.9%和 36.3%的病例包含 X 射线、计算机断层扫描(CT)扫描和基因组数据。在所收集的样本中,代表的主要谱系是北京、T1 和 H3,并且最常出现与异烟肼、利福平、氧氟沙星和莫西沙星耐药相关的单核苷酸多态性(SNP)。这些数据和样本促进了药物发现工作以及基因组学和定量图像分析的研究,以改善诊断,同时也为研究人员和临床医生提供了有价值的资源。TB 门户数据库及其相关项目正在不断发展,我们邀请新的合作伙伴和合作关系加入我们的倡议。TB 门户数据及其相关的分析和统计工具可在 https://tbportals.niaid.nih.gov/ 免费获得。