Study of dry pellets of blood plasma using THz spectroscopy

This work is devoted to the development of a suitable phantom of a biological object for measurements in the THz frequency range and for approbation with various diagnostic methods developed in different THz laboratories. The phantoms were represented as a pellet of human and a laboratory rat blood plasma in the diabetic and the control groups. These objects were analyzed in various laboratories, using THz pulsed spectroscopy and a high-resolution THz spectrometer based on a backward wave oscillator. The components of the dry blood plasma were identified by the detected spectral lines

Terahertz spectroscopy of diabetic and non-diabetic human blood plasma pellets

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 nondiabetic 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