Optical and liquid biopsy of patients with chronic kidney diseases and heart failure
L. Bratchenko1, S. Al-Sammarrae1, Yu. Khristoforova1, D. Konovalova2, E. Typikova1, P. Lebedev2, M. Skuratova3, V. Zakharov1, I. Bratchenko1
1 Samara National Research University, Samara, Russia
2 Samara state medical university, Samara, Russia
3 Samara regional clinical hospital named after VD Seredavin, Samara, Russia
Abstract
In modern world practice, promising diagnostic methods are emerging, such as "optical biopsy" and "liquid biopsy", which are used for specific diseases biomarkers detection in biological tissues and fluids. Optical methods have the potential to overcome the limitations of traditional methods of clinical analysis. One of the most promising methods of optical analysis (and optical biopsy) is a Raman spectroscopy, which can contribute to understanding of molecular basis of diseases and creation of new bioanalytical tools for the diagnosis of diseases. In this study we demonstrate application of conventional Raman spectroscopy for the analysis of skin and application of SERS for serum analysis to determine the presence of kidney and heart diseases. Analyzed groups separation based on deep learning was implemented using a separate one-dimensional convolutional neural network (CNN).
Application of Raman spectroscopy to investigate the forearm skin has yielded the accuracy of 0.96, sensitivity of 0.94 and specificity of 0.99 in terms of identifying the target subjects with kidney failure. The autofluorescence analysis in the near infrared region identified the patients with kidney failure among healthy volunteers of the same age group with specificity, sensitivity, and accuracy of 0.91, 0.84, and 0.88, respectively. When classifying subjects by the presence of kidney failure using the PLS-DA method, the most informative Raman spectral bands are 1315 to 1330, 1450 to 1460, 1700 to 1800 cm−1. In general, the performed study demonstrates that for in vivo skin analysis, the conventional Raman spectroscopy can provide the basis for cost-effective and accurate detection of kidney failure and associated metabolic changes in the skin.
The results of the SERS data for CHF demonstrates that CNN significantly outperforms standard methods of analysis as projection on latent structures and allows for detection of CHF with 95-100% accuracy. By means of multivariate analysis, the informative spectral bands associated with the CHF during disease progression were identified. In addition, the analysis of the correlation between the serum spectral characteristics and urea, creatinine has made it possible to determine the spectral bands correlated with levels of creatinine and urea into the complex spectral characteristics of serum. In general, the reported approach may form the basis for monitoring the health status of CHF patients and find application in studying other pathological conditions of the human body.
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Speaker
Ivan Bratchenko
Samara university
Russia
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