[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125561-en":3,"doc-seo-125561-105":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},125561,1099513958762,"Logic","https://ap-avatar.wpscdn.com/avatar/1000023916a998db790?x-image-process=image/resize,m_fixed,w_180,h_180&k=1784791008015729253",8,"Research & Report","Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning - read online free","Biopsy-based diagnosis supports long-term kidney allograft survival by enabling prompt treatment of graft complications, yet traditional histology suffers from subjective interpretation, limited reproducibility, and imprecise quantification of disease burden. This study develops a molecular diagnostic framework for T-cell-mediated rejection using quantitative label-free proteomics from FFPE kidney transplant biopsies, combining differentially expressed protein features with machine learning classifiers. A random-forest model shows strong performance on validation and independent blind testing, and generalizes to external transcriptome datasets with measurable sensitivity and specificity.","TYPE Original Research PUBLISHED 06 February 2023 DOI 10.3389/fimmu.2023.1090373  \nOPEN ACCESS  \nEDITED BY  \nXuanchuan Wang, Fudan University, China  \nREVIEWED BY  \nGiuseppe Gianni Figueiredo Leite, Federal University of São Paulo, Brazil Hu Linkun,  \nSoochow University, China  \n*CORRESPONDENCE Kunhong Xiao  \n [kunhongkevin.xiao@ahn.org](kunhongkevin.xiao@ahn.org)  \n†These authors have contributed equally to this work  \nSPECIALTY SECTION  \nThis article was submitted to Alloimmunity and Transplantation, a section of the journal  \nFrontiers in Immunology  \nRECEIVED 05 November 2022  \nACCEPTED 23 January 2023  \nPUBLISHED 06 February 2023  \nCITATION  \nFang F, Liu P, Song L, Wagner P, Bartlett D, Ma L, Li X, Rahimian MA, Tseng G, Randhawa P and Xiao K (2023) Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning.  \nFront. Immunol. 14:1090373 .  \ndoi: 10.3389/fimmu.2023.1090373  \nCOPYRIGHT  \n© 2023 Fang, Liu, Song, Wagner, Bartlett, Ma, Li, Rahimian, Tseng, Randhawa and Xiao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nDiagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning  \nFei Fang 1†, Peng Liu 2†, Lei Song 1†, Patrick Wagner 3,  \nDavid Bartlett 3, Liane Ma 3, Xue Li 4, M. Amin Rahimian 5, George Tseng2, Parmjeet Randhawa 6 and Kunhong Xiao 1,3,7,8*  \n1 Department of Pharmacology and Chemical Biology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, United States, 2 Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, United States, 3Allegheny Health Network Cancer Institute, Pittsburgh, PA, United States,  \n4 Department of Chemistry, Michigan State University, East Lansing, MI, United States, 5 Department of Industrial Engineering, University of Pittsburgh, Pittsburgh, PA, United States, 6 Department of Pathology, The Thomas E Starzl Transplantation Institute, University of Pittsburgh, Pittsburgh, PA, United States,  \n7Center for Proteomics & Artificial Intelligence, Allegheny Health Network Cancer Institute, Pittsburgh, PA, United States, 8Center for Clinical Mass Spectrometry, Allegheny Health Network Cancer Institute, Pittsburgh, PA, United States  \nBackground: Biopsy-based diagnosis is essential for maintaining kidney allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficie ncy, accuracy, and reproducibility of traditional histologic methods.  \nMethods: Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from kidney transplant patients, including five samples each with diagnosis of T-cell-mediated rejection (TCMR), polyomavirus BK nephropathy (BKPyVN), and stable (STA) kidney function control tissue. Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR.  \nResults: The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this stud","cbCaiemQqATLOQlb","https://ap.wps.com/l/cbCaiemQqATLOQlb","pdf",6926798,1,12,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusions\n# Keywords\n# Introduction","[{\"question\":\"Why is biopsy-based diagnosis important in kidney transplantation, and what are its limitations?\",\"answer\":\"Timely biopsy-based diagnosis is essential to preserve allograft longevity by enabling prompt treatment. Histologic assessment is considered the gold standard but is limited by subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden.\"},{\"question\":\"How were proteomic biomarkers and machine learning used to diagnose TCMR?\",\"answer\":\"Quantitative label-free mass spectrometry was performed on FFPE biopsy samples from TCMR, BKPyVN, and stable control tissues. Differentially expressed proteins served as features to build and test machine learning models for TCMR prediction.\"},{\"question\":\"What performance did the best model achieve in validation and blind testing?\",\"answer\":\"Leave-one-out cross-validation indicated the random forest model had the best predictive power. In a follow-up blind test with an independent sample set, the model achieved 80% accuracy for TCMR and 100% for stable tissue.\"}]","Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning - read online free | PDF",1785899863,30,{"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},"diagnosis-of-t-cell-mediated-kidney-rejection-by-biopsy-based-proteomic-biomarkers-and-machine-learning-read-online-free","",{"@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/diagnosis-of-t-cell-mediated-kidney-rejection-by-biopsy-based-proteomic-biomarkers-and-machine-learning-read-online-free/125561/",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","2026-08-05",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},"Why is biopsy-based diagnosis important in kidney transplantation, and what are its limitations?","Question",{"text":75,"@type":76},"Timely biopsy-based diagnosis is essential to preserve allograft longevity by enabling prompt treatment. Histologic assessment is considered the gold standard but is limited by subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were proteomic biomarkers and machine learning used to diagnose TCMR?",{"text":80,"@type":76},"Quantitative label-free mass spectrometry was performed on FFPE biopsy samples from TCMR, BKPyVN, and stable control tissues. Differentially expressed proteins served as features to build and test machine learning models for TCMR prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance did the best model achieve in validation and blind testing?",{"text":84,"@type":76},"Leave-one-out cross-validation indicated the random forest model had the best predictive power. In a follow-up blind test with an independent sample set, the model achieved 80% accuracy for TCMR and 100% for stable tissue.","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},[93,97,101,105,110,115,120,122,127,130,134],{"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":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]