[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120932-en":3,"doc-seo-120932-105":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},120932,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in clinical isolates of Salmonella Typhimurium - Reviewer comments","Peer review feedback evaluates a manuscript that predicts ciprofloxacin susceptibility in Salmonella Typhimurium using high-content imaging combined with machine learning. The reviewer highlights strengths in demonstrating morphology-based prediction without drug exposure, using a random forest classifier to identify key morphology features, and testing multiple clinical isolates across regions. Major critiques focus on the unclear generalizability to other antibiotics/species, the absence of a well-defined end-to-end workflow, and figure presentation issues including formatting, missing scale bars, and consistency.","Peer Review File  \nCombining machine learning with high-content imaging to infer ciprofloxacin susceptibility in clinical isolates of Salmonella Typhimurium  \nOpen Access This file is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. In the cases where the authors are anonymous, such as is the case for the reports of anonymous peer reviewers, author attribution should be to 'Anonymous Referee' followed by a clear attribution to the source work. The images or other third party material in this file are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material isnot included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/) .  \nREVIEWER COMMENTS  \nReviewer \\#1 (Remarks to the Author):  \nThe manuscript entitled “Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in clinical isolates of Salmonella Typhimurium” by Tran and Sridhar et al. describes a novel approach utilizing high content imaging paired with machine learning to predict ciprofloxacin susceptibility in Salmonella Typhimurium isolates. The authors begin by demonstrating that there are quantifiable differences in four different Salmonella Typhimurium strains exposed to ciprofloxacin. Using a random forest classifier, the authors determine which morphology properties are most important for distinguishing ciprofloxacin concentrations, treatment times, and strains. Next, the authors demonstrate that there are morphological differences between resistant and susceptible strains without exposing these bacteria to ciprofloxacin. These morphological properties are defined, refined, and utilized by several different machine learning classifiers to discern ciprofloxacin susceptibility of 13 additional clinical isolates.  \nOverall, the manuscript was rigorous and tells an interesting story.  \nThe major strength of this manuscript is the demonstration that high content imaging and machine learning can be used to predict ciprofloxacin susceptibility without the need for drug exposure. This capability is novel, would save time and resources compared to traditional AST, and has significant potential utility for the field of antimicrobial resistance. An additional strength is the demonstration of this technology with multiple clinical isolates from different regions.  \nThe major disadvantages of this manuscript include the uncertainty of how well this method would work for different antibiotics, different/mixed bacterial species, and diverse resistance mechanisms. However, the majority of these limitations were discussed, and some points are perhaps outside the scope of the current manuscript to address experimentally. The other major weaknesses are the lack of a welldefined workflow and the presentation of figures (see below) .  \nGeneral concerns:  \nMany of the figures need to be re-formatted for consistency and so that they are large enough to read. All font sizes for axis, keys, etc., should be the same size across figures.  \nScale bars are missing from HCI images.  \n“gyrA” is italicized in the text, but not in the figures.  \nPlease ensure figures are colorblind friendly.  \nWhat would the ultimate readout for this assay be (as in sensitive vs resistant, or more of a gradient that would require interpretation)? What would the potential workflow look like? The authors demonstrated proof-of-principal, but do not explicitly de","cbCaijYNr4XlsR9V","https://ap.wps.com/l/cbCaijYNr4XlsR9V","pdf",3479999,1,17,"English","en",105,"# Reviewer Comments\n## Overall Assessment\n## General Concerns\n## Major Disadvantages\n## Specific Questions and Clarifications\n## Minor Concerns and Clarity Improvements","[{\"question\":\"What is the core idea assessed in the reviewer comments?\",\"answer\":\"The manuscript pairs high-content imaging with machine learning to infer ciprofloxacin susceptibility in Salmonella Typhimurium clinical isolates based on morphological features rather than exposing bacteria to the drug.\"},{\"question\":\"What strengths does the reviewer identify?\",\"answer\":\"The reviewer commends the ability to predict susceptibility without drug exposure, the time/resource-saving potential versus traditional AST, and the demonstration using multiple clinical isolates from different regions.\"},{\"question\":\"What key issues does the reviewer raise?\",\"answer\":\"The reviewer notes uncertainty about performance across different antibiotics, mixed bacterial species, and resistance mechanisms, and highlights the lack of a clearly defined workflow plus multiple figure-quality and consistency problems.\"}]","Combining machine learning with high-content imaging to infer ciprofloxacin susceptibility in clinical isolates of Salmonella Typhimurium - 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