AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability-Reference-Cited by-同舟云学术

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AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability

Published:2024-04 Issue:4 Volume:5 Page:101902
ISSN:2666-3864
Container-title:Cell Reports Physical Science
language:en
Short-container-title:Cell Reports Physical Science

Author:

Xu Xiaoyan,Feng Haowen,Zhao Ying,Shi Yunzhu,Feng Wei,Loh Xian Jun,Vancso G. Julius,Guo Shifeng

Funder

Department of Science and Technology of Guangdong Province

Shenzhen Science and Technology Innovation Commission

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference57 articles.

1. Optimisation of the protocol for the LIVE/DEAD® BacLight™ bacterial viability kit for rapid determination of bacterial load;Robertson;Front. Microbiol.,2019

2. Assessment of bacterial viability: a comprehensive review on recent advances and challenges;Kumar;Microbiology,2019

3. Effects of ‘healthy’ fecal microbiota transplantation against the deterioration of depression in fawn-hooded rats;Hu;mSystems,2022

4. The bacterial MtrAB Two-Component System regulates the cell wall homeostasis responding to environmental alkaline stress;Qin;Microbiol. Spectr.,2022

5. Antimicrobial susceptibility testing: a review of general principles and contemporary practices;Jorgensen;Clin. Infect. Dis.,2009

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Advances in microscopy-based techniques applied to the antimicrobial resistance of foodborne pathogens;Trends in Food Science & Technology;2024-10

2. Enhanced sensing performance of flexible non-enzymatic electrochemical glucose sensors using hollow Fe3O4 nanospheres of controllable morphologies;Ceramics International;2024-10

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