首页> 外文会议>International Conference on Internet of Things: Systems, Management and Security >Detection of Carbapenems Resistant K-mer Sequences in Bacteria of Critical Priority by the World Health Organization (Pseudomonas aeruginosa and Acinetobacter baumannii)
【24h】

Detection of Carbapenems Resistant K-mer Sequences in Bacteria of Critical Priority by the World Health Organization (Pseudomonas aeruginosa and Acinetobacter baumannii)

机译:世界卫生组织批判优先术细菌耐药碱序列(假单胞菌铜绿假单胞菌和肺杆菌)的抗菌K-MER序列检测

获取原文

摘要

Antimicrobial resistance (AMR) (or drug resistance) is a natural phenomenon where microorganisms change their molecular, physical, or chemical structures to resist the drugs created by infections. The World Health Organization (WHO) had released for the first time a list of Multidrug-Resistant Bacteria (MRB) that pose the greatest threat to human health, and for which new antibiotics are desperately needed. Acinetobacter baumannii and Pseudomonas aeruginosa resistant to carbapenems are part of the Gramnegative group non-fermenting bacilli with critical priority according to the WHO. For this, the research final purpose was to create and train a bioinformatic study capable of finding critical k-mers that could differentiate those strains of P. aeruginosa and A. baumannii resistant to carbapenems. At the end, two sets of k-mers for both pathogens were obtained. Three Machine Learning algorithms were performed to prove the use of these k-mers in antimicrobial prediction Random Forest, Adaboost, and Xgboost. For Pseudomonas aeruginosa, an accuracy of 0.8 was obtained using Random Forest, an accuracy of 0.92 using Adaboost, and an accuracy of 0.84 when using Xgboost. In the case of Acinetobacter baumannii, an accuracy of 0.98 was obtained when using Random Forest and an accuracy of 0.99 when using Adaboost or Xgboost. To investigate the sequences of the k-mers obtained, the National Center for Biotechnology Information (NCBI) Basic Local Alignment Search Tool BLAST was used. Twenty sequences built with the k-mers were investigated for each bacteria. For A. baumannii, 18 out of 20 sequences represented a key sequence in antibiotic resistance. In the case of P. aeruginosa, 16 out of 20 sequences represented a key sequence. Further investigation over these sequences can be applied in creating new directed antibiotics or detecting easily resistant strains of Pseudomona aeruginosa or Acinetobacter baumannii resistant to carbapenems.
机译:抗微生物抗性(AMR)(或耐药性)是微生物改变其分子,物理或化学结构的自然现象,以抵抗受感染产生的药物。世界卫生组织(世卫组织)首次发布了对人类健康最大威胁的多药抗性细菌(MRB)列表,以及迫切需要新的抗生素。抗肉豆蔻酸的肺杆菌和铜绿假单胞菌是克巴彭氏菌的一部分,根据世卫组织,克明群非发酵杆菌的一部分。为此,研究最终目的是创造和培训能够找到能够区分那些可以区分那些铜绿一素的菌株和抗肉豆蔻菌菌株的批判性K-MERS的生物信息研究。最后,获得两组用于两种病原体的K-MERS。进行三种机器学习算法以证明在抗微生物预测随机林,Adaboost和XGBoost中使用这些K-MERS。对于假单胞菌铜绿假单胞菌,使用随机森林获得0.8的精度,使用Adaboost 0.92的精度,使用XGBoost时的精度为0.84。在肺杆菌的情况下,使用随机森林时获得0.98的精度,并且使用Adaboost或Xgboost时为0.99的精度。为了研究所获得的K-MERS的序列,使用国家生物技术信息中心(NCBI)基本的局部对准搜索工具爆炸。为每种细菌研究了用K-MERS构建的20个序列。对于A.Baumannii,20个序列中的18个表示抗生素抗性的关键序列。在P.铜绿假单胞菌的情况下,20个序列中的16个表示键序列。通过这些序列的进一步调查可以应用于创造新的定向抗生素或检测易抗性铜绿假单胞菌或抗肉豆蔻菌的植物抗菌菌菌株。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号