Discriminating drug-resistant bacteria using AI analysis on fine current changes from inner ION leakages

Aomi Yoshikawa, Takao Yasui, Taisuke Shimada, Seiji Yamasaki, Kunihiko Nishino, Takeshi Yanagida, Kazuki Nagashima, Takashi Washio, Tomoji Kawai, Yoshinobu Baba

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As the WHO delivers a strong warning about drug-resistant bacteria, their types are increasing along with improper use of antibiotic drugs by medical professions, due to the fact that conventional methods cannot identify drug-resistant bacteria in a short time, and show a guideline for proper use of antibiotic drugs rapidly. Herein, we demonstrated a rational methodology for drug-resistant bacteria identification via machine learning analysis on fine current changes from bacteria inner ion leakages, which given by highly applied electric fields in microchannels We believe that our methodology opens up a new way for proper use of antibiotic drugs against drug-resistant bacteria.

Original languageEnglish
Title of host publication23rd International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019
PublisherChemical and Biological Microsystems Society
Pages852-853
Number of pages2
ISBN (Electronic)9781733419000
Publication statusPublished - 2019
Event23rd International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019 - Basel, Switzerland
Duration: Oct 27 2019Oct 31 2019

Publication series

Name23rd International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019

Conference

Conference23rd International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019
CountrySwitzerland
CityBasel
Period10/27/1910/31/19

All Science Journal Classification (ASJC) codes

  • Bioengineering
  • Chemical Engineering (miscellaneous)

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