ArticlePloS one2020
Spectral characteristics of urine from patients with end-stage kidney disease analyzed using Raman Chemometric Urinalysis (Rametrix).
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Utilizing Raman spectroscopy for urinalysis to diagnose acute kidney injury stages in cardiac surgery patients.Renal failure · 2024Article
- Cancer detection in dogs using rapid Raman molecular urinalysis.Frontiers in veterinary science · 2024Article
- Perspective: Raman spectroscopy for detection and management of diseases affecting the nervous system.Frontiers in veterinary science · 2024Article
- Analysis of urine Raman spectra differences from patients with diabetes mellitus and renal pathologies.PeerJ · 2023Article
- The Potential Applications of Raman Spectroscopy in Kidney Diseases.Journal of personalized medicine · 2022Review
- Raman Spectral Characterization of Urine for Rapid Diagnosis of Acute Kidney Injury.Journal of clinical medicine · 2022Article
- Integration of surface-enhanced Raman spectroscopy (SERS) and machine learning tools for coffee beverage classification.Digital Chemical Engineering · 2022Article
- Alterations in the molecular composition of COVID-19 patient urine, detected using Raman spectroscopic/computational analysis.PloS one · 2022Article
- Review
Corrections and comments
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Authors and funding
21 authors.
Funding
Abstract
Raman Chemometric Urinalysis (RametrixTM) was used to discern differences in Raman spectra from (i) 362 urine specimens from patients receiving peritoneal dialysis (PD) therapy for end-stage kidney disease (ESKD), (ii) 395 spent dialysate specimens from those PD therapies, and (iii) 235 urine specimens from healthy human volunteers. RametrixTM analysis includes spectral processing (e.g., truncation, baselining, and vector normalization); principal component analysis (PCA); statistical analyses (ANOVA and pairwise comparisons); discriminant analysis of principal components (DAPC); and testing DAPC models using a leave-one-out build/test validation procedure. Results showed distinct and statistically significant differences between the three types of specimens mentioned above. Further, when introducing "unknown" specimens, RametrixTM was able to identify the type of specimen (as PD patient urine or spent dialysate) with better than 98% accuracy, sensitivity, and specificity. RametrixTM was able to identify "unknown" urine specimens as from PD patients or healthy human volunteers with better than 96% accuracy (with better than 97% sensitivity and 94% specificity). This demonstrates that an entire Raman spectrum of a urine or spent dialysate specimen can be used to determine its identity or the presence of ESKD by the donor.
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