[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128509-en":3,"doc-seo-128509-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},128509,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Clostridioides difficile 高致病性与非高致病性核糖型的鉴别：MALDI-TOF 质谱与机器学习","Hypervirulent ribotypes (HVRTs) of Clostridioides difficile, including ribotype 027, are epidemiologically important. This study assessed whether MALDI-TOF can differentiate HVRT strains from non-HVRT strains commonly found in Europe. MALDI-TOF spectra from 157 clinical isolates served as training data, with an additional 83 isolates for validation. Direct spectral comparison was insufficient, while specific machine learning models achieved >95% accuracy and enabled sub-clustering of three HVRT subgroups. MALDI-TOF combined with ML offers rapid identification of major European HVRTs.","European Journal of Clinical Microbiology & Infectious Diseases (2023) 42:1373–1381 [https://doi.org/10.1007/s10096-023-04665-y](https://doi.org/10.1007/s10096-023-04665-y)  \nDiscrimination between hypervirulent and non‑hypervirulent ribotypes of Clostridioides difficile by MALDI‑TOF mass spectrometry and machine learning  \nAhmed Mohamed Mostafa Abdrabou1,2,3 · Issa Sy1 · Markus Bischoff1,3 · Manuel J. Arroyo4 · Sören L. Becker1 · Alexander Mellmann3,5 · Lutz von Müller3,6 · Barbara Gärtner1,3 · Fabian K. Berger1,3  \nReceived: 20 February 2023 / Accepted: 3 September 2023 / Published online: 18 September 2023 © The Author(s) 2023  \nAbstract  \nHypervirulent ribotypes (HVRTs) of Clostridioides difficile such as ribotype (RT) 027 are epidemiologically important. This study evaluated whether MALDI-TOF can distinguish between strains of HVRTs and non-HVRTs commonly found in Europe. Obtained spectra of clinical C. difficile isolates (training set, 157 isolates) covering epidemiologically relevant HVRTs and non-HVRTs found in Europe were used as an input for different machine learning (ML) models. Another 83 isolates were used as a validation set. Direct comparison of MALDI-TOF spectra obtained from HVRTs and non-HVRTs did not allow to discriminate between these two groups, while using these spectra with certain ML models could differentiate HVRTs from non-HVRTs with an accuracy >95% and allowed for a sub-clustering of three HVRT subgroups (RT027/ RT176, RT023, RT045/078/126/127) . MALDI-TOF combined with ML represents a reliable tool for rapid identification of major European HVRTs.  \nKeywords Clostridium difficile · Ribotypes · Anaerobic bacteria · MALDI-TOF mass spectrometry · Proteomic signature · Machine learning · Identification  \nAhmed Mohamed Mostafa Abdrabou and Issa Sy contributed equally to this article.  \n* Ahmed Mohamed Mostafa Abdrabou [ahmed.mostafa@uks.eu](ahmed.mostafa@uks.eu); [ahmedmostafa2020@mans.edu.eg](ahmedmostafa2020@mans.edu.eg)  \n1 Institute of Medical Microbiology and Hygiene, Saarland University, Kirrberger Straße 100, Building 43, D-66421 Homburg, Saar, Germany  \n2 Medical Microbiology and Immunology Department, Faculty of Medicine, Mansoura University, El Gomhouria Street, Mansoura 35516, Egypt  \n3 National Reference Center for Clostridioides (Clostridium) difficile, Homburg-Münster-Coesfeld, Germany  \n4 Clover Bioanalytical Software, Av. del Conocimiento, 41, 18016 Granada, Spain  \n5 Institute of Hygiene, University of Münster, Robert-Koch-Straße 41, 48149 Münster, Germany  \n6 Christophorus Kliniken Coesfeld, Coesfeld, Germany  \nIntroduction  \nClostridioides difficile is a significant cause of nosocomial diarrhea in industrialized nations [1] . Hypervirulent ribotypes (HVRTs) such as RT027 have influenced the global molecular epidemiology of C. difficile [2] leading to a higher disease burden [3] . RT027 has caused numerous outbreaks in Europe and the USA [4] . However, on a global scale, other HVRTs exist, e.g., RT023 being considered an emerging HVRT [5], and RT045 that might confer a zoonotic potential [6] . Besides the toxins A and B (genes: tcdA, tcdB) destroying the actin cytoskeleton, HVRT strains usually harbor a third toxin (binary toxin, gene: cdtAB) that increases bacterial adhesion through microtubular protrusions [7, 8] .  \nSeveral typing techniques have been developed to identify RTs of higher importance. These include in particular ribotyping [9] and whole genome sequencing (WGS) [10] . However, both methods are comparably time-and resourceconsuming and therefore usually not available in most laboratories. Matrix-assisted laser desorption ionization timeof-flight (MALDI-TOF) mass spectrometry (MS) is widely  \ndistributed and an easy-to-use tool for the identification of bacteria [11], which is also used for bacterial subtyping [12] .  \nMachine learning (ML) can further expand its capabilities, by training algorithms on a variety of databases garnered from analysis of bacterial proteins. ","cbCaik8TmaAB7FXe","https://ap.wps.com/l/cbCaik8TmaAB7FXe","pdf",1387688,1,9,"English","en",105,"# Abstract\n# Introduction\n# Material and methods\n## Strain collection and cultivation\n## Protein extraction, spectra acquisition, and species confirmation\n# Results\n# Discussion","[{\"question\":\"直接比较 MALDI-TOF 光谱能否区分高致病性与非高致病性的核糖型？\",\"answer\":\"不能。研究显示，HVRT 与非HVRT 的直接光谱比较不足以实现有效区分。\"},{\"question\":\"本研究使用了哪些数据集来训练和验证模型？\",\"answer\":\"训练集包含 157 株临床分离株，覆盖欧洲流行且具有流行病学意义的 HVRT 与非HVRT；验证集另外包含 83 株用于独立评估。\"},{\"question\":\"MALDI-TOF 联合机器学习的主要效果是什么？\",\"answer\":\"在特定机器学习模型下，HVRT 与非HVRT 可实现 \\u003e95% 的准确率，并可对三个 HVRT 亚群进行亚聚类，从而实现快速鉴别。\"}]","Clostridioides difficile 高致病性与非高致病性核糖型的鉴别：MALDI-TOF 质谱与机器学习 | PDF",1786001462,23,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"discrimination-between-hypervirulent-and-non-hypervirulent-ribotypes-of-clostridioides-difficile-maldi-tof-mass-spectrometry-and-machine-learning","",{"@graph":36,"@context":86},[37,54,69],{"@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/discrimination-between-hypervirulent-and-non-hypervirulent-ribotypes-of-clostridioides-difficile-maldi-tof-mass-spectrometry-and-machine-learning/128509/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"直接比较 MALDI-TOF 光谱能否区分高致病性与非高致病性的核糖型？","Question",{"text":76,"@type":77},"不能。研究显示，HVRT 与非HVRT 的直接光谱比较不足以实现有效区分。","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"本研究使用了哪些数据集来训练和验证模型？",{"text":81,"@type":77},"训练集包含 157 株临床分离株，覆盖欧洲流行且具有流行病学意义的 HVRT 与非HVRT；验证集另外包含 83 株用于独立评估。",{"name":83,"@type":74,"acceptedAnswer":84},"MALDI-TOF 联合机器学习的主要效果是什么？",{"text":85,"@type":77},"在特定机器学习模型下，HVRT 与非HVRT 可实现 >95% 的准确率，并可对三个 HVRT 亚群进行亚聚类，从而实现快速鉴别。","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},"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":107,"slug":138},19,"General","general"]