ArticleDiagnostics (Basel, Switzerland)2020
Assessment of Renal Osteodystrophy via Computational Analysis of Label-free Raman Detection of Multiple Biomarkers.
Article in Diagnostics (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Physicochemical understanding of biomineralization by molecular vibrational spectroscopy: From mechanism to nature.Exploration (Beijing, China) · 2023Review
- Article
- Assessing Nordihydroguaiaretic Acid Therapeutic Effect for Glioblastoma Multiforme.Sensors (Basel, Switzerland) · 2022Article
- Direct monitoring of single-cell response to biomaterials by Raman spectroscopy.Journal of materials science. Materials in medicine · 2021Article
- Response of the ENPP1-Deficient Skeletal Phenotype to Oral Phosphate Supplementation and/or Enzyme Replacement Therapy: Comparative Studies in Humans and Mice.Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
Accurate clinical evaluation of renal osteodystrophy (ROD) is currently accomplished using invasive in vivo transiliac bone biopsy, followed by in vitro histomorphometry. In this study, we demonstrate that an alternative method for ROD assessment is through a fast, label-free Raman recording of multiple biomarkers combined with computational analysis for predicting the minimally required number of spectra for sample classification at defined accuracies. Four clinically relevant biomarkers: the mineral-to-matrix ratio, the carbonate-to-matrix ratio, phenylalanine, and calcium contents were experimentally determined and simultaneously considered as input to a linear discriminant analysis (LDA). Additionally, sample evaluation was performed with a linear support vector machine (LSVM) algorithm, with a 300 variable input. The computed probabilities based on a single spectrum were only marginally different (~80% from LDA and ~87% from LSVM), both providing an unacceptable classification power for a correct sample assignment. However, the Type I and Type II assignment errors confirm that a relatively small number of independent spectra (7 spectra for Type I and 5 spectra for Type II) is necessary for a
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