Center for Infectious Diseases and Infection Control, Jena University Hospital, 07740 Jena, Germany.
Center for Infectious Diseases and Infection Control, Jena University Hospital, 07740 Jena, Germany.
Int J Antimicrob Agents. 2014 Nov;44(5):424-30. doi: 10.1016/j.ijantimicag.2014.08.001. Epub 2014 Aug 25.
Monitoring the rapid global spread of antimicrobial resistance requires an over-regional and fast surveillance tool. Data from major surveillance studies based on aggregated results of selected sentinel laboratories or retrospective strain collections are not available for the whole scientific community and are limited by time and region. Thus, we tested an alternative approach to monitor resistance trends by automated semantic and scientometric analysis of all (>100000) related PubMed entries. A semantic search was done using 'Gene Ontology' and MeSH vocabulary and additional search terms for further data refinement. Data extraction was performed using the semantic search engine 'GoPubMed'. The timely relationship between introduction of novel β-lactam antibiotic classes into the market and emergence of respective resistance was investigated using nearly 22300 publications over the last 70 years. Further analysis was done with around 54000 publications related to 'infectious diseases' and an additional 50000 publications related to 'antimicrobial resistance' to estimate current trends in publication interest regarding resistance development since 1940. Scientometric results were compared with data from the major surveillance network EARS-Net. Furthermore, the relationship between micro-organism, year and antibiotic market introduction was investigated for eight key antibiotics using nearly 37500 publications. Owing to influencing factors such as availability of alternative antibiotics, scientometric analysis correlated only partly with resistance development. However, it provides a fast, reliable and global overview of the clinical and public health importance of a specific resistance including the period of the 1940s-1980s, when resistance surveillance studies were not yet established.
监测抗菌药物耐药性的快速全球传播需要一个超区域和快速的监测工具。基于选定的哨点实验室或回顾性菌株收集的汇总结果的主要监测研究的数据，无法为整个科学界所获得，并且受到时间和地区的限制。因此，我们测试了一种替代方法，通过对所有（> 100000）相关 PubMed 条目的自动语义和科学计量分析来监测耐药趋势。使用“基因本体论”和 MeSH 词汇以及其他搜索词进行语义搜索，并进行进一步的数据细化。使用语义搜索引擎“GoPubMed”进行数据提取。使用近 22300 篇出版物调查了新型β-内酰胺类抗生素引入市场与相应耐药性出现之间的时间关系，这些出版物涵盖了过去 70 年的数据。进一步分析了与“传染病”相关的约 54000 篇出版物和与“抗菌药物耐药性”相关的另外 50000 篇出版物，以估计自 1940 年以来与耐药性发展相关的出版物兴趣的当前趋势。科学计量学结果与 EARS-Net 主要监测网络的数据进行了比较。此外，使用近 37500 篇出版物调查了 8 种关键抗生素的微生物、年份和抗生素市场引入之间的关系。由于替代抗生素的可用性等影响因素，科学计量分析仅部分与耐药性发展相关。然而，它提供了一种快速、可靠和全球的概述，包括 1940 年代至 1980 年代的耐药性监测研究尚未建立的时期，说明了特定耐药性的临床和公共卫生重要性。
