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dc.contributor.authorHuttanus, Herbert M.en
dc.contributor.authorVu, Tommyen
dc.contributor.authorGuruli, Georgien
dc.contributor.authorTracey, Andrewen
dc.contributor.authorCarswell, Williamen
dc.contributor.authorSaid, Neveenen
dc.contributor.authorDu, Pangen
dc.contributor.authorParkinson, Bing G.en
dc.contributor.authorOrlando, Giuseppeen
dc.contributor.authorRobertson, John L.en
dc.contributor.authorSenger, Ryan S.en
dc.date.accessioned2020-10-05T13:49:42Zen
dc.date.available2020-10-05T13:49:42Zen
dc.date.issued2020-08-21en
dc.identifier.issn1932-6203en
dc.identifier.othere0237070en
dc.identifier.urihttp://hdl.handle.net/10919/100168en
dc.description.abstractBladder cancer (BCA) is relatively common and potentially recurrent/progressive disease. It is also costly to detect, treat, and control. Definitive diagnosis is made by examination of urine sediment, imaging, direct visualization (cystoscopy), and invasive biopsy of suspect bladder lesions. There are currently no widely-used BCA-specific biomarker urine screening tests for early BCA or for following patients during/after therapy. Urine metabolomic screening for biomarkers is costly and generally unavailable for clinical use. In response, we developed Raman spectroscopy-based chemometric urinalysis (Rametrix (TM)) as a direct liquid urine screening method for detecting complex molecular signatures in urine associated with BCA and other genitourinary tract pathologies. In particular, the Rametrix(TM)screen used principal components (PCs) of urine Raman spectra to build discriminant analysis models that indicate the presence/absence of disease. The number of PCs included was varied, and all models were cross-validated by leave-one-out analysis. In Study 1 reported here, we tested the Rametrix (TM) screen using urine specimens from 56 consented patients from a urology clinic. This proof-of-concept study contained 17 urine specimens with active BCA (BCA-positive), 32 urine specimens from patients with other genitourinary tract pathologies, seven specimens from healthy patients, and the urinalysis control Surine(TM). Using a model built with 22 PCs, BCA was detected with 80.4% accuracy, 82.4% sensitivity, 79.5% specificity, 63.6% positive predictive value (PPV), and 91.2% negative predictive value (NPV). Based on the number of PCs included, we found the Rametrix(TM)screen could be fine-tuned for either high sensitivity or specificity. In other studies reported here, Rametrix(TM)was also able to differentiate between urine specimens from patients with BCA and other genitourinary pathologies and those obtained from patients with end-stage kidney disease (ESKD). While larger studies are needed to improve Rametrix(TM)models and demonstrate clinical relevance, this study demonstrates the ability of the Rametrix(TM)screen to differentiate urine of BCA-positive patients. Molecular signature variances in the urine metabolome of BCA patients included changes in: phosphatidylinositol, nucleic acids, protein (particularly collagen), aromatic amino acids, and carotenoids.en
dc.description.sponsorshipCenter for Innovative Technology [CP15015-LS]; Virginia Tech Foundation; HATCHUnited States Department of Agriculture (USDA) [VA-160057]en
dc.format.mimetypeapplication/pdfen
dc.language.isoenen
dc.rightsCreative Commons Attribution 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleRaman chemometric urinalysis (Rametrix) as a screen for bladder canceren
dc.typeArticle - Refereeden
dc.contributor.departmentBiological Systems Engineeringen
dc.contributor.departmentChemical Engineeringen
dc.contributor.departmentStatisticsen
dc.contributor.departmentBiomedical Engineering and Mechanicsen
dc.description.notesThis research was funded in part the Center for Innovative Technology (award CP15015-LS), the Virginia Tech Foundation, and HATCH (project VA-160057). These funders provided support in salaries for authors (RSS) and materials. They had no role in study design, data collection and analysis, the decision to publish, and preparation of the manuscript. RSS and JLR are unsalaried co-founders of DialySensors, Inc., which was involved with study design, data collection and analysis, the decision to publish, and preparation of the manuscript.en
dc.title.serialPlos Oneen
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0237070en
dc.identifier.volume15en
dc.identifier.issue8en
dc.type.dcmitypeTexten
dc.identifier.pmid32822394en


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Creative Commons Attribution 4.0 International
License: Creative Commons Attribution 4.0 International