TY - GEN
T1 - Discriminating drug-resistant bacteria using AI analysis on fine current changes from inner ION leakages
AU - Yoshikawa, Aomi
AU - Yasui, Takao
AU - Shimada, Taisuke
AU - Yamasaki, Seiji
AU - Nishino, Kunihiko
AU - Yanagida, Takeshi
AU - Nagashima, Kazuki
AU - Washio, Takashi
AU - Kawai, Tomoji
AU - Baba, Yoshinobu
N1 - Funding Information:
This work was funded by ImPACT Program of Council for Science, Technology and Innovation (Cabinet Office, Government of Japan).
Publisher Copyright:
© 2019 CBMS-0001.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85094965284&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85094965284&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85094965284
T3 - 23rd International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019
SP - 852
EP - 853
BT - 23rd International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019
PB - Chemical and Biological Microsystems Society
T2 - 23rd International Conference on Miniaturized Systems for Chemistry and Life Sciences, MicroTAS 2019
Y2 - 27 October 2019 through 31 October 2019
ER -