Anastasiya A Lykina1, Maksim M Nazarov2, Maria R Konnikova3,4, Ilia A Mustafin5, Vladimir L Vaks6, Vladimir A Anfertev6, Elena G Domracheva6, Mariya B Chernyaeva6, Yuri V Kistenev7, Denis A Vrazhnov8, Vladimir V Prischepa7, Yulia A Kononova9, Dmitry V Korolev9, Olga P Cherkasova3,10, Alexander P Shkurinov3,4, Alina Y Babenko9, Olga A Smolyanskaya1. 1. Institute of Photonics and Optical Information Technologies, ITMO University, Saint-Petersburg, Russia. 2. National Research Center "Kurchatov Institute," Moscow, Russia. 3. Institute on Laser and Information Technologies, Russian Academy of Sciences - Branch of Federal Sci, Russia. 4. Department of Physics, Lomonosov Moscow State University, Moscow, Russia. 5. Ioffe Institute, Saint-Petersburg, Russia. 6. Institute for Physics of Microstructures, Russian Academy of Sciences, Nizhny Novgorod, Russia. 7. Tomsk State University, Tomsk, Russia. 8. Institute of Strength Physics and Materials Science, Siberian Branch of the Russian Academy of Scien, Russia. 9. Almazov National Medical Research Centre, Saint-Petersburg, Russia. 10. Institute of Laser Physics, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia.
Abstract
SIGNIFICANCE: The creation of fundamentally new approaches to storing various biomaterial and estimation parameters, without irreversible loss of any biomaterial, is a pressing challenge in clinical practice. We present a technology for studying samples of diabetic and non-diabetic human blood plasma in the terahertz (THz) frequency range. AIM: The main idea of our study is to propose a method for diagnosis and storing the samples of diabetic and non-diabetic human blood plasma and to study these samples in the THz frequency range. APPROACH: Venous blood from patients with type 2 diabetes mellitus and conditionally healthy participants was collected. To limit the impact of water in the THz spectra, lyophilization of liquid samples and their pressing into a pellet were performed. These pellets were analyzed using THz time-domain spectroscopy. The differentiation between the THz spectral data was conducted using multivariate statistics to classify non-diabetic and diabetic groups' spectra. RESULTS: We present the density-normalized absorption and refractive index for diabetic and non-diabetic pellets in the range 0.2 to 1.4 THz. Over the entire THz frequency range, the normalized index of refraction of diabetes pellets exceeds this indicator of non-diabetic pellet on average by 9% to 12%. The non-diabetic and diabetic groups of the THz spectra are spatially separated in the principal component space. CONCLUSION: We illustrate the potential ability in clinical medicine to construct a predictive rule by supervised learning algorithms after collecting enough experimental data.
SIGNIFICANCE: The creation of fundamentally new approaches to storing various biomaterial and estimation parameters, without irreversible loss of any biomaterial, is a pressing challenge in clinical practice. We present a technology for studying samples of diabetic and non-diabetichuman blood plasma in the terahertz (THz) frequency range. AIM: The main idea of our study is to propose a method for diagnosis and storing the samples of diabetic and non-diabetichuman blood plasma and to study these samples in the THz frequency range. APPROACH: Venous blood from patients with type 2 diabetes mellitus and conditionally healthy participants was collected. To limit the impact of water in the THz spectra, lyophilization of liquid samples and their pressing into a pellet were performed. These pellets were analyzed using THz time-domain spectroscopy. The differentiation between the THz spectral data was conducted using multivariate statistics to classify non-diabetic and diabetic groups' spectra. RESULTS: We present the density-normalized absorption and refractive index for diabetic and non-diabetic pellets in the range 0.2 to 1.4 THz. Over the entire THz frequency range, the normalized index of refraction of diabetes pellets exceeds this indicator of non-diabetic pellet on average by 9% to 12%. The non-diabetic and diabetic groups of the THz spectra are spatially separated in the principal component space. CONCLUSION: We illustrate the potential ability in clinical medicine to construct a predictive rule by supervised learning algorithms after collecting enough experimental data.
Authors: Talia A Shmool; P J Woodhams; Markus Leutzsch; Amberley D Stephens; Mario U Gaimann; Michael D Mantle; Gabriele S Kaminski Schierle; Christopher F van der Walle; J Axel Zeitler Journal: Int J Pharm X Date: 2019-07-08