ArticleBiomedical optics express2022
Analyzing the serum of hemodialysis patients with end-stage chronic kidney disease by means of the combination of SERS and machine learning.
Article in Biomedical optics express, 2022. 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.
- Integrated SERS and Machine Learning Workflow for Nanoplastic Detection on a Plasmonic Membrane.Analytical chemistry · 2026Article
- Comment on "Early cancer detection by serum biomolecular fingerprinting spectroscopy with machine learning".Light, science & applications · 2025Article
- Raman liquid biopsy: a new approach to the multiple sclerosis diagnostics.Frontiers in neurology · 2025Article
- Comment on Borșa et al. Developing New Diagnostic Tools Based on SERS Analysis of Filtered Salivary Samples for Oral Cancer Detection.International journal of molecular sciences · 2024Article
- Comment on "utilizing Raman spectroscopy for urinalysis to diagnose acute kidney injury stages in cardiac surgery patients".Renal failure · 2024Article
- Differences in whole blood before and after hemodialysis session of subjects with chronic kidney disease measured by Raman spectroscopy.Lasers in medical science · 2024Article
- Raman-Based Techniques in Medical Applications for Diagnostic Tasks: A Review.International journal of molecular sciences · 2023Review
- Serum laser Raman spectroscopy as a potential diagnostic tool to discriminate the benignancy or malignancy of pulmonary nodules.iScience · 2023Article
- Recent Trends in SERS-Based Plasmonic Sensors for Disease Diagnostics, Biomolecules Detection, and Machine Learning Techniques.Biosensors · 2023Review
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The aim of this paper is a multivariate analysis of SERS characteristics of serum in hemodialysis patients, which includes constructing classification models (PLS-DA, CNN) by the presence/absence of end-stage chronic kidney disease (CKD) with dialysis and determining the most informative spectral bands for identifying dialysis patients by variable importance distribution. We found the spectral bands that are informative for detecting the hemodialysis patients: the 641 cm
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Registered trials
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