Analyzing the serum of hemodialysis patients with end-stage chronic kidney disease by means of the combination of SERS and machine learning

Lyudmila A Bratchenko; Sahar Z Al-Sammarraie; Elena N Tupikova; Daria Y Konovalova; Peter A Lebedev; Valery P Zakharov; Ivan A Bratchenko

doi:10.1364/BOE.455549

Analyzing the serum of hemodialysis patients with end-stage chronic kidney disease by means of the combination of SERS and machine learning

Biomed Opt Express. 2022 Aug 24;13(9):4926-4938. doi: 10.1364/BOE.455549. eCollection 2022 Sep 1.

Authors

Lyudmila A Bratchenko^{1

2}, Sahar Z Al-Sammarraie¹, Elena N Tupikova³, Daria Y Konovalova⁴, Peter A Lebedev⁴, Valery P Zakharov¹, Ivan A Bratchenko^{1

5}

Affiliations

¹ Department of Laser and Biotechnical Systems, Samara University, 34 Moskovskoe Shosse, Samara, 443086, Russia.
² shamina94@inbox.ru.
³ Department of Chemistry, Samara University, 34 Moskovskoe Shosse, Samara, 443086, Russia.
⁴ Department of Internal Medicine, Samara State Medical University, 159 Tashkentskaya Street, Samara, 443095, Russia.
⁵ iabratchenko@gmail.com.

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^-1, 724 cm^-1, 1094 cm^-1 and 1393 cm^-1 bands are associated with the degree of kidney function inhibition; and the 1001 cm^-1 band is able to demonstrate the distinctive features of hemodialysis patients with end-stage CKD.