CC BY
9B-I-5
BIOMEDICAL PHOTONICS
Raman-based liquid biopsy of chronic heart failure patients
L. Bartchenko1, S. Al-Sammarrae1, P. Lebedev2, M. Skuratova3, 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 Main author email address: iabratchenko@gmail.com
Today, one of the most accessible methods of primary assessment of the human body state is a blood test [1]. However, in identifying a specific pathological process in the body, the prognostic significance of a particular biochemical blood index or a set of indices may be insufficient. Improved prognostic significance of a blood test for identifying pathological processes is possible by examining a complex of changes in the blood component composition. Raman spectroscopy is a promising method to reach this goal [2].
In this study, the in vitro analysis of human serum was performed for 205 subjects, including 69 healthy subjects and 61 patients with chronic heart failure (CHF). For surface-enhanced Raman spectroscopy (SERS) analysis, each serum sample was dropped in a volume of 1.5 ^l on aluminum foil with a layer of silver structures and dried. The analysis of the serum spectral characteristics was carried out using an experimental stand consisting of a spectro-metric system (EnSpectr R785, Spektr-M, Chernogolovka, Russia) and a microscope (ADF U300, ADF, China). Analyzed groups separation based on deep learning was implemented using a separate one-dimensional convolutional neural network (CNN). The choice of the CNN architecture for recognition of the current SERS dataset consisted of several consecutive stages. At the first stage, the verified CNN configurations and advanced deep learning practices based on CNN were examined. Analysis of the work by other research teams has shown that the following CNN configurations are characterized by their possible abilities to recognize Raman spectra: sequential CNNs, CNNs containing the Inception module, CNNs with residual connections, ensemble CNNs, CNNs based on a combination of convolutional layers with recurrent layers [3].
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.
[1] J. Watson, I. de Salis, J. Banks, and C. Salisbury, "What do tests do for doctors? A qualitative study of blood testing in UK primary care," Family Practice 34(6), 735-739 (2017).
[2] C. G. Atkins, K. Buckley, M. W. Blades, and R. F.B. Turner, "Raman spectroscopy of blood and blood components," Appl. Spectrosc. 71(5), 767-793 (2017).
[3] P. Wang, L. Guo, Y. Tian, J. Chen, S. Huang, C. Wang, P. Bai, D. Chen, W. Zhu, H. Yang, W. Yao, and J. Gao, "Discrimination of blood species using Raman spectroscopy combined with a recurrent neural network," OSA Continuum 4, 672-687 (2021).
ALT'22
