[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124032-en":3,"doc-seo-124032-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124032,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability","Detecting bacterial viability is essential across pharmaceutical, medical, and food sectors, but distinguishing intact live versus dead bacteria quickly and without damaging samples remains difficult. This study presents an accessible workflow combining atomic force microscopy (AFM) imaging, quantitative nano-mechanics, and machine-learning classification for gram-negative (E. coli) and gram-positive (S. aureus) bacteria. AFM force spectroscopy extracts deformation, spring constant, and Young’s modulus as key features, yielding >95% predictive accuracy and demonstrating universal, rapid, non-destructive viability detection.","ll  \nOPEN ACCESS  \nArticle  \nAFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability  \nXiaoyan Xu, Haowen Feng, Ying Zhao, ... , Xian Jun Loh, G. Julius Vancso, Shifeng Guo  \n[lohxj@imre.a-star.edu.sg](lohxj@imre.a-star.edu.sg) (X.J. L.)  \n[sf.guo@siat.ac.cn](sf.guo@siat.ac.cn) (S.G.)  \nHighlights  \nAFM-based nano-mechanics and machine learning to detect bacterial viability  \nDeformation, spring constant, and Young’s modulus utilized as detection features  \nRealized non-destructive, fast, and facile detection of bacterial death  \nA universal approach for both gram-positive and gram-negative bacteria  \nXu et al., Cell Reports Physical Science 5 , 101902  \nApril 17, 2024 ª 2024 The Author(s) . Published by Elsevier Inc.  \n[https://doi.org/10.1016/j.xcrp.2024.101902](https://doi.org/10.1016/j.xcrp.2024.101902)  \nll  \nOPEN ACCESS  \nArticle  \nAFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability  \nXiaoyan Xu, 1,2,4 Haowen Feng, 1,2 Ying Zhao,4 Yunzhu Shi,4 Wei Feng,2,3,4 Xian Jun Loh,6,*  \nG. Julius Vancso,7,8 and Shifeng Guo 1,2,3,4,5,9,*  \nSUMMARY  \nDetecting bacterial viability remains a critical necessity across the pharmaceutical, medical, and food sectors. Yet, a rapid, nondestructive approach for distinguishing between intact live and dead bacteria remains elusive. Here, this work introduces a robust and accessible methodology that integrates atomic force microscopy (AFM) imaging, quantitative nano-mechanics, and machine learning algorithms to assess the survival of gram-negative (Escherichia coli [E. coli]) and gram-positive (Staphylococcus aureus [S. aureus]) bacteria. The results reveal distinctive changes in ultraviolet-killed E. coli and S. aureus manifesting intact morphological structures but increased stiffness. Three specific features—bacterial deformation, spring constant, and Young’s modulus—extracted from AFM force spectroscopy are established as pivotal inputs fora machine-learning-based stacking classifier. Trained on extensive AFM datasets encompassing known bacterial viability, this methodology demonstrates exceptional predictive accuracy exceeding 95% for both E. coli and S. aureus. These results underscore its universal applicability, rapidity, and non-destructive nature, positioning it as a definitive method for universally detecting bacterial viability.  \nINTRODUCTION  \nDetecting bacterial viability stands as an imperative across various sectors including medicine, pharmacy, microbiology, and the food industry, highlighting its widespread significance.1 , 2 In fact, the microbiota has been shown to have an effect on the mental well-being of rats.3 However, despite its critical importance, reliable methods to effectively detect bacterial death through conventional means pose persistent challenges. Many studies have focused in the past on conditions that either promoted bacterial growth or hindered bacterial growth.4 Initially, culturebased techniques emerged as the primary approach for bacterial viability assessment, where live bacteria thrived on specific media while dead bacteria did not, marking a pivotal distinction.5 Yet, the prolonged culturing time associated with this method serves as a notable drawback.6 Consequently, culture-independent methods, encompassing techniques such as fluorescence staining, 1 viability polymerase chain reaction (PCR),6 transcription-based methods,7 and assessments based on cellular metabolism8 , 9 as well as sensors for NO emission, 10 have been developed to address this challenge. The introduction of these methods marks asignificant stride in bacterial viability assessment, yet each approach possesses inherent constraints that hinder their widespread application and effectiveness in  \n1Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, P. R. China  \n2University of Chines","cbCaidCtGvOHv2sk","https://ap.wps.com/l/cbCaidCtGvOHv2sk","pdf",5075883,1,18,"English","en",105,"# Highlights\n## AFM-based nano-mechanics与机器学习用于快速、无损检测细菌活性\n## 关键特征：形变、弹簧常数与杨氏模量\n## 对革兰氏阳性与阴性菌的通用性","[{\"question\":\"这项方法如何检测细菌活性？\",\"answer\":\"通过AFM成像与定量纳米力学获取力谱信息，再将形变、弹簧常数和杨氏模量等特征输入机器学习堆叠分类器完成活性判别。\"},{\"question\":\"用于分类的关键输入特征有哪些？\",\"answer\":\"从AFM力谱中提取的三项特征包括细菌形变、弹簧常数（spring constant）以及杨氏模量（Young’s modulus）。\"},{\"question\":\"该方法是否适用于不同类型的细菌？\",\"answer\":\"适用性覆盖革兰氏阴性与革兰氏阳性细菌，文中对紫外杀死的E. coli与S. aureus均给出了高准确率结果，并强调其通用性、快速性与无损性。\"}]","AFM-based nanomechanics and machine learning for rapid and non-destructive detection of bacterial viability | PDF",1785819966,45,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"afm-based-nanomechanics-and-machine-learning-for-rapid-and-non-destructive-detection-of-bacterial-viability","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/afm-based-nanomechanics-and-machine-learning-for-rapid-and-non-destructive-detection-of-bacterial-viability/124032/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"这项方法如何检测细菌活性？","Question",{"text":75,"@type":76},"通过AFM成像与定量纳米力学获取力谱信息，再将形变、弹簧常数和杨氏模量等特征输入机器学习堆叠分类器完成活性判别。","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"用于分类的关键输入特征有哪些？",{"text":80,"@type":76},"从AFM力谱中提取的三项特征包括细菌形变、弹簧常数（spring constant）以及杨氏模量（Young’s modulus）。",{"name":82,"@type":73,"acceptedAnswer":83},"该方法是否适用于不同类型的细菌？",{"text":84,"@type":76},"适用性覆盖革兰氏阴性与革兰氏阳性细菌，文中对紫外杀死的E. coli与S. aureus均给出了高准确率结果，并强调其通用性、快速性与无损性。","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]