Научная статья на тему 'Terahertz Spectroscopy of Mouse Blood Serum in the Dynamics of Experimental Glioblastoma'

Terahertz Spectroscopy of Mouse Blood Serum in the Dynamics of Experimental Glioblastoma Текст научной статьи по специальности «Медицинские технологии»

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Ключевые слова
terahertz time domain spectroscopy / mouse blood serum / U87 glioblastoma / Debye model

Аннотация научной статьи по медицинским технологиям, автор научной работы — Olga P. Cherkasova, Maria R. Konnikova, Maksim M. Nazarov, Denis A. Vrazhnov, Yury V. Kistenev

In this study terahertz (THz) absorption spectra of mouse blood serum in the dynamics of experimental U87 glioblastoma were investigated. Decrease of THz absorption with glioma growth was demonstrated. A two-component Debye model was used to analyze the experimental data. Analysis of the complex dielectric permittivity parameters of blood serum indicates an increase in the proportion of bound water in the samples in the dynamics of tumor growth. © 2023 Journal of Biomedical Photonics & Engineering.

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Текст научной работы на тему «Terahertz Spectroscopy of Mouse Blood Serum in the Dynamics of Experimental Glioblastoma»

Terahertz Spectroscopy of Mouse Blood Serum in the Dynamics of Experimental Glioblastoma

Olga P. Cherkasova1,2,3*, Maria R. Konnikova3,4,5, Maksim M. Nazarov6, Denis A. Vrazhnov5,7, Yury V. Kistenev5,7, and Alexander P. Shkurinov3,4,5

1 Institute of Automation and Electrometry, Siberian Branch of RAS, 1 Academician Koptyug Ave., Novosibirsk 630090, Russia

2 Institute of Laser Physics of SB RAS, 15B Academician Lavrentyev Ave., Novosibirsk 630090, Russia

3 Institute on Laser and Information Technologies, Branch of the Federal Scientific Research Centre "Crystallography and Photonics" of RAS, 1 Svyatoozerskaya str., Shatura 140700, Russia

4 Faculty of Physics, Lomonosov Moscow State University, 1 Leninskiye Gory, Moscow 119991, Russia

5 Laboratory of Laser Molecular Imaging and Machine Learning, Tomsk State University, 36 Lenin Ave., Tomsk 634050, Russia

6 National Research Centre "Kurchatov Institute", 1 Akademika Kurchatova pl., Moscow 123182, Russia

7 V.E. Zuev Institute of Atmospheric Optics SB RAS, 1 Academician Zuev Square, Tomsk 634055, Russia

*e-mail: o.p.cherkasova@gmail.com

Abstract. In this study terahertz (THz) absorption spectra of mouse blood serum in the dynamics of experimental U87 glioblastoma were investigated. Decrease of THz absorption with glioma growth was demonstrated. A two-component Debye model was used to analyze the experimental data. Analysis of the complex dielectric permittivity parameters of blood serum indicates an increase in the proportion of bound water in the samples in the dynamics of tumor growth. © 2023 Journal of Biomedical Photonics & Engineering.

Keywords: terahertz time domain spectroscopy; mouse blood serum; U87 glioblastoma; Debye model.

Paper #8990 received 3 Apr 2023; accepted for publication 21 Jun 2023; published online 15 Aug 2023. doi: 10.18287/JBPE23.09.030308.

1 Introduction

Glioblastoma, a type of glioma, tumors developing in the glial tissue of brain, represents almost half of all malignant tumors of the central nervous system [1] and is among the most rapidly progressing cancers with the worst prognosis of survival [2]. One reason for this is late diagnosis and treatment [3]. Therefore, it is relevant and important for the early detection of glioblastoma and the development of non-invasive and minimally invasive methods to monitor the effectiveness of treatment. The study of oncological molecular markers, their causes and pathways of formation, is a major direction in the diagnosis and therapy of malignant tumors [4]. Conventional methods such as chromatography and mass spectrometry [5-7], as well as nuclear magnetic resonance [8, 9], due to complex sample preparation, long testing time and inability to provide timely intraoperative analysis cannot provide fully reliable information. Early diagnosis of glioblastoma can be achieved by analyzing body fluids [10, 11], including the

use of more technological and simple spectroscopic methods [12, 13].

Terahertz (THz) pulse spectroscopy, which has a number of features determining its application in creating new methods of medical diagnostics, has received intensive development [14-16]. The ability to directly measure the refractive index, absorption coefficient and dielectric function spectrum of the studied sample is a distinctive feature of this method [17, 18]. This makes it possible to obtain a detailed spectral characterization of the analyzed sample in a single measurement, giving the prospect of developing noninvasive rapid diagnostics on this basis [19]. Previously, THz spectroscopy methods have been used to study the features of THz images of mouse and rat brain tissue, freshly dissected [20, 21] and encased in paraffin blocks, in the dynamics of glioma development [22-28]. It was shown that the main contrast source in THz frequency range is water content, which increased with increasing tumor size and degree of malignancy [22, 27]. Other sources of contrast were increased cell density [22] and lipid content [26],

appearance of necrosis zones [28]. In these works, blood was not considered as an object of investigation. Meanwhile, blood composition significantly changes during the development of pathologies in organs and tissues of the body [29, 30], which can be used to create minimally invasive diagnostic methods [31, 32].

In this work we investigated THz absorption spectra of mouse blood serum in the dynamics of experimental U87 glioblastoma development. Currently, glioblastoma models with continuous cell lines such as U87 derived from primary human tumor cells transplanted intracranially into the mouse brain are most common [33, 34]. Using glioblastoma models in animals makes it possible to follow tumor development and blood composition changes in dynamics.

2 Materials and Methods

2.1 Terahertz Time Domain Spectroscopy (THz-TDS)

Mouse serum samples were measured on a pulsed THz spectrometer, which is described in detail in previous articles [35-37]. A femtosecond laser (Spectra Physics Tsunami) with wavelength X = 800 nm, pulse duration t = 80 fs, and modulation frequency f = 80 MHz was used as pumping. The THz generator was a multidipole photoconductive antenna (iPCA-21-05-1000-800-h, Batop, Germany), the detector was a 300 ^m ZnTe crystal. Photoconductive antenna emission power was in the range of 70^75 mW, bias supply voltage was 15 V. Spectral range for reliable measurements was from 0.6 to 1.8 THz, with small attenuation of the passed radiation. The range was determined by the signal-to-noise ratio, which reached 102 or 20 dB across the field in the absence of sample. The experimental samples were placed in a demountable liquid cuvette (Bruker, USA) with two identical polystyrene windows. The refractive index of windows was 1.5 at 0.6-1.8 THz. The thickness of the liquid layer given by PTFE spacer was 180 ± 5 ^m, which provided a transmittance value in the range 0.05-0.50, depending on the sample type. The lowest transmittance coefficient corresponded to the transmittance of distilled water at high frequencies. Measurements were performed at temperature of 21 ± 1 °C and 35% relative humidity. The cuvette was installed in THz beam tangle with a diameter of 300 ± 15 ^m. The time shape of pulse that passed through the cuvette was recorded. To exclude the statistical error associated with changes in the laser power during the experiment, the transmission measurements of empty cuvette, cuvette with water and sample were carried out alternately. A time sampling of 25 ps from 1024 points with a signal accumulation time in each point of 300 ms was used. To increase the reliability of measurements, three independent measurements were averaged for each serum sample. The measurement error was calculated as the standard deviation from the group mean. The time shapes of group-averaged THz pulses while passing through air

(without a cuvette), an empty cuvette, and a cuvette filled with water and serum are shown in Fig. 1.

Fig. 1 Time shapes of group-averaged THz pulses: without cuvette (gray line), passed through empty cuvette (dark gray line), cuvette filled with water (blue line), serum of control mice (green line), after 7 (yellow line) and 21 days (red line) after injection of U87 glioblastoma cells.

Fourier transform of measured temporal shape of THz pulse gives complex spectrum E(ro) of radiation, i.e. amplitude | E(ro) | and phase arg(E(ro)) of spectrum, where E is electric component of electromagnetic wave field, ro = 2nf , f is frequency. The transmittance spectrum of sample was calculated as follows:

T(w) =

g|>)

(1)

where E0 (ro) is the reference spectrum of the THz pulse passing through an empty cuvette, E(ro) is the transmission of the cuvette with distilled water or blood serum.

The absorption coefficient and refractive index spectra were calculated using the methods described in detail in previous papers [14, 15, 31, 32, 35, 37, 38]; the sample absorption coefficient was calculated as follows:

a(w)=-MLM + Miz^2, (2)

where d is sample thickness, Ra = —— is reflection

F a na+1

coefficient for real part of refractive index averaged over all frequencies, na = 1 + At^ is averaged refractive index, A t is pulse delay in passing through the sample. The refractive index was calculated as follows:

n{œ) = - arg(r (œ)) • c +1. œ • d

(3)

In this case, the complex permittivity has the form:

(4)

e(ra) = («(ra) - ia(ra) — ) + 1l .

ra

2.2 Samples

The model of orthotopic xenotransplantation of U87 human glioblastoma cells into immunodeficient SCID mice was used [39]. A 5 ^l cell suspension (500,000 U87 MG cells per animal) was injected in the subcortical brain structure through a hole in the animal's cranium. A 5 ^l culture medium was introduced to animals from the control group according to a similar scheme. Tumor size was measured in vivo on a horizontal tomograph with a magnetic field strength of 11.7 Tesla (Biospec 117/16, Bruker, USA) [40]. After that, animals of experimental and corresponding control groups were removed from the experiment by decapitation on days 7 and 21 after injection. Blood was collected in separate tubes, centrifuged and serum was frozen at -80 °C until the day of analysis. There were 10 mice in each group. The study was carried out in accordance with the EU Directive 2010/63/EU and the ARRIVE 2.0 guidelines, and approved by the Inter-Institutional Commission on Biological Ethics at the Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences (Permission #78, 16 April 2021).

3 Results and Discussion

Tumor size increases significantly in the dynamics of U87 glioblastoma development. Thus, the tumor size is 2.6 ± 0.4 mm3 on day 7, and already 89.6 ± 11.5 mm3 on day 21 of the experiment. The THz response of blood serum also changes significantly, which is expressed in decrease of absorption coefficient at the last stage of the experiment (Fig. 2, a).

The high water content in blood serum determines the nature of its dielectric response in THz frequency range [15, 41]. Water can be in free or bound to the surrounding molecules. The reason for changes in the THz response of blood serum is the transition of water from free to bound state and back [15, 41]. The THz spectrum of water is characterized by the absence of well-defined spectral features in the frequency range of 0.1-3.0 THz. A similar type of spectra was obtained previously by us and some authors when studying of human blood [42], serum of rats with experimental liver cancer [31], diabetes [43, 44], as well as blood plasma from patients with thyroid disease [32]. The absorption spectra of control mice serum and on the 7th day of experiment are indistinguishable within the limits of error and have a smaller amplitude compared to the absorption spectrum of water. It is known that metabolic changes associated specifically with brain injury and the development of an inflammation reaction are observed in the first week after the introduction of culture medium or tumor cells into the brain [45]. Then blood composition changes associated with tumor development become

dominant [46]. The absorption coefficient of blood serum of mice on the 21st day of the experiment is significantly lower than the absorption coefficient of the other spectra in the entire range under study. At the same time, with increasing frequency, there is a greater degree of differentiation of absorption spectra for all samples. No significant differences in the refractive index spectra were observed (see Fig. 2, b).

Fig. 2 Absorption coefficient (a) and refractive index (b) of water (blue line), blood serum of control mice (green line), and at 7 (yellow line) and 21 days (red line) after injection of U87 glioblastoma cells.

Based on comparison of experimentally obtained dielectric permittivity spectra with the model dielectric function of water e(œ), which can be described by the two-component Debye model and the Lorentz term [15, 31, 37, 47-50], we analyzed the observed differences in THz spectra:

e(w) = £m +

A .

+

Ae1

+

ae2

1+i2uf\1 1+Î2 n/x2

-+

(5)

M01-(2n/)2 + i2n/yOi'

where ii and T2 are relaxation time of first ("slow") and second ("fast") Debye terms, Agi is contributions to dielectric permittivity of first and second Debye terms, Ai is amplitude, œoi is resonance frequency, yoi is haf-

width from the Lorentz term and ew is dielectric permittivity of water at high frequencies.

The following model parameters for water were obtained: Aei = 74 + 9, n=9.47 + 0.5 ps,

Ag2 = 1.67 + 0.03, T2=0.24 + 0.01 ps, ^=31 THz2, raoi = 5.3 THz, y0 = 7 THz, ew = 2.3 + 0.2. For frequency range of 0.05-2.5 THz at a temperature of 20-22 °C the given set of values is optimal. Comparison of the experimental imaginary Im(e) and real Re(e) parts of dielectric permittivity with model spectra for water are shown by blue line and dotted line in Fig. 3, respectively.

(a)

- 2

ill1 — Water experiment ■ i ■ - - Water model

- Control experiment - - - Control model -

7th day experiment - - 7th day model

— 21 th day experiment - - - 21 th day model

:

-

^^'•il'-A----*

■ .■.I. i.i.

0.6 0.8 1.0 1.2 1.4 1.6 1.8 Frequency, THz

(b)

Fig. 3 Imaginary Im(e) (a) and real Re(e) (b) parts of dielectric permittivity for experimental data presented in Fig. 1 and their model curves: for water (blue line), blood serum of control mice (green line), and after 7 (yellow line) and 21 days (red line) after injection of U87 glioblastoma cells.

Earlier we demonstrated for the THz range that any aqueous solution of biomolecules, including blood serum, can be described with sufficient accuracy mainly by changes in the amplitude Aex and the relaxation time Ti of the first Debye term [31, 37, 43, 47-49]. This approach is valid also for a model spectrum of serum dielectric permittivity in the dynamics of glioma development. By changing Aex and T1, all other terms of the model (5) have fixed values corresponding to water parameters in the chosen frequency range. Results of comparison of experimental and model dielectric

permittivity spectra are shown in Fig. 3. The parameters of Debye model Aex and T1 for all investigated samples are given in Table 1.

Table 1 Parameters Aex and T1 of studied samples. Sample/Parameter Aex ti (ps)

Distilled water 74.9 ± 4.1 9.28 ± 0.51

Blood serum of control mice 74.3 ± 4.9 9.56 ± 0.31

Blood serum of mice after 7 days 68.2 ± 5.1 10.42 ± 0.32

Blood serum of mice after 21 days

57.9 ± 5.4 11.80 ± 0.29

It should be noted that for frequencies above 0.1 THz we cannot separate the contribution of the decrease Ag] or increase T1 to the model spectrum. Only the change in the ratio Ae1/T1 can be clearly discerned [47]. We have previously shown a 1.2-fold decrease in the ratio compared to water for blood plasma of rat with experimental diabetes, which is associated with a high level of glucose in the blood plasma [43]. It was also shown that as both glucose concentration and protein concentration increase in solutions, the proportion of water bound to biomolecules increases, which has a lower value of Ag and a high value of relaxation time T1 as compared to water molecules not bound by hydrogen bonds [47, 49].

Table 1 shows that in the dynamics of U87 glioblastoma there is a decrease in Ag1 and an increase in T1, which leads to a decrease in the ratio Ae1/T1. As noted above, on day 21 of the experiment, there is a more than 34-fold increase in tumor size compared to 7 days after the introduction of glioblastoma U87 cells. This leads to a significant change in blood serum composition [46]. The decrease of Ag1 contribution to dielectric permittivity of samples in the dynamics of tumor growth is related to the decrease of free water fraction with simultaneous increase of bound water fraction. The increase of relaxation time T1 in the dynamics of tumor growth indicates a "slowing down" of the process of water molecules reorientation in the hydrogen bonding network. Thus, a change in blood serum composition in the dynamics of glioma development leads to a change in complex dielectric permittivity parameters.

4 Conclusion

The THz absorption spectra of mouse blood serum in frequency range 0.6-1.8 THz in the dynamics of experimental U87 glioblastoma were studied. It is shown that the absorption coefficients of healthy mouse serum and on the 7th day of experiment do not differ. The absorption coefficient of mouse serum on 21st day of the experiment is significantly lower compared to water and serum of other experimental groups. This difference in THz response is explained by changes in blood composition at different stages of U87 glioblastoma

development. Analysis of complex dielectric permittivity parameters of blood serum in the dynamics of glioblastoma development in mice indicates an increase in proportion of bound water with tumor growth.

Acknowledgements

This work was performed partly within the State Assignment of IA&E SB RAS, ILP SB RAS and FSRC "Crystallography and Photonics" RAS. This work has been supported by the Interdisciplinary Scientific and Educational School of Moscow University "Photonic and

Quantum Technologies. Digital Medicine" for part of the results analysis. The research was carried out partly with the support of the Tomsk State University Development Program (Priority-2030). The work of D. V. and Yu. K. was supported by the Ministry of Science and Higher Education of the Russian Federation (budget funds for the V. E. Zuev Institute of Atmospheric Optics of the Siberian Branch of the Russian Academy of Sciences).

Disclosures

The authors declare no conflict of interest.

References

1. Q. T. Ostrom, G. Cioffi, K. Waite, C. Kruchko, and J. C. Barnholtz-Sloan, "CBTRUS Statistical Report: Primary Brain and Other Central Nervous System Tumors Diagnosed in the United States in 2014-2018," Neuro-Oncology, 23(Supplement S3), iii1-iii105 (2021).

2. T. Tykocki, M. Eltayeb, "Ten-year survival in glioblastoma. A systematic review," Journal of Clinical Neuroscience 54, 7-13 (2018).

3. M. Hishii, T. Matsumoto, and H. Arai, "Diagnosis and Treatment of Early-Stage Glioblastoma," Asian Journal of Neurosurgery 14(2), 589-592 (2019).

4. L. Wang, X. Liu, and Q. Yang, "Application of Metabolomics in Cancer Research: As a Powerful Tool to Screen Biomarker for Diagnosis, Monitoring and Prognosis of Cancer," Biomarkers Journal 4(3), 12 (2018).

5. V. Poinsignon, L. Mercier, K. Nakabayashi, M. D. David, A. Lalli, V. Penard- Lacronique, C. Quivoron, V. Saada, S. D. Botton, S. Broutin, and A. Paci, "Quantitation of isocitrate dehydrogenase (IDH)-induced D and L enantiomers of 2-hydroxyglutaric acid in biological fluids by a fully validated liquid tandem mass spectrometry method, suitable for clinical applications," Journal of Chromatography B 1022, 290-297 (2016).

6. M. Touat, A. Duran-Peña, A. Alentorn, L. Lacroix, C. Massard, and A. Idbaih, "Emerging circulating biomarkers in glioblastoma: promises and challenges," Expert Reviews 15(10), 1311-1323 (2015).

7. E. Miyauchi, T. Furuta, S. Ohtsuki, M. Tachikawa, Y. Uchida, H. Sabit, W. Obuchi, T. Baba, M. Watanabe, T. Terasaki, and M. Nakada, "Identification of blood biomarkers in glioblastoma by SWATH mass spectrometry and quantitative targeted absolute proteomics," PLoS ONE 13(3), e0193799 (2018).

iНе можете найти то, что вам нужно? Попробуйте сервис подбора литературы.

8. E. Baranovicová, T. Galanda, M. Galanda, J. Hatok, B. Kolarovszki, R. Richterová, and P. Racay, "Metabolomic profiling of blood plasma in patients with primary brain tumours: Basal plasma metabolites correlated with tumour grade and plasma biomarker analysis predicts feasibility of the successful statistical discrimination from healthy subjects - a preliminary study," IUBMB Life 71(12), 1994-2002 (2019).

9. J. E. Lee, S. S. Jeun, S. H. Kim, C. Y. Yoo, H.-M. Baek, and S. H. Yang, "Metabolic profiling of human gliomas assessed with NMR," Journal of Clinical Neuroscience 68, 275-280 (2019).

10. J. M. Figueroa, "Detection of glioblastoma in biofluids," Journal of Neurosurgery 129(2), 334-340 (2018).

11. O. Cherkasova, Y. Peng, M. Konnikova, Y. Kistenev, C. Shi, D. Vrazhnov, O. Shevelev, E. Zavjalov, S. Kuznetsov, and A. Shkurinov, "Diagnosis of Glioma Molecular Markers by Terahertz Technologies," Photonics 8(1), 22 (2021).

12. H. J. Butler, P. M. Brennan, J. M. Cameron, D. Finlayson, M. G. Hegarty, M. D. Jenkinson, D. S. Palmer, B. R. Smith, and M. J. Baker, "Development of high-throughput ATR-FTIR technology for rapid triage of brain cancer," Nature Communications 10, 4501 (2019).

13. A. G. Theakstone, P. M. Brennan, M. D. Jenkinson, S. J. Mills, K. Syed, C. Rinaldi, Y. Xu, R. Goodacre, H. J. Butler, D. S. Palmer, B. R. Smith, and M. J. Baker, "Rapid Spectroscopic Liquid Biopsy for the Universal Detection of Brain Tumours," Cancers 13(15), 3851 (2021).

14. G. R. Musina, P. V. Nikitin, N. V. Chernomyrdin, I. N. Dolganova, A. A. Gavdush, G. A. Komandin, D. S. Ponomarev, A. A. Potapov, I. V. Reshetov, V. V. Tuchin, and K. I. Zaytsev, "Prospects of terahertz technology in diagnosis of human brain tumors - a review," Journal of Biomedical Photonics & Engineering 6(2), 020201 (2020).

15. O. A. Smolyanskaya, N. V. Chernomyrdin, A. A. Konovko, K. I. Zaytsev, I. A. Ozheredov, O. P. Cherkasova, M. M. Nazarov, J.-P. Guillet, S. A. Kozlov, Yu. V. Kistenev, J.-L. Coutaz, P. Mounaix, V. L. Vaks, J.-H. Son, H. Cheon, V. P. Wallace, Yu. Feldman, I. Popov, and V. V. Tuchin, "Terahertz biophotonics as a tool for studies of dielectric and spectral properties of biological tissues and liquids," Progress in Quantum Electronics 62, 1-77 (2018).

16. K. I. Zaytsev, I. N. Dolganova, N. V. Chernomyrdin, G. M. Katyba, A. A. Gavdush, O. P. Cherkasova, G. A. Komandin, M. A. Shchedrina, A. N. Khodan, D. S. Ponomarev, I. V. Reshetov, V. E. Karasik, M. Skorobogatiy, V.N. Kurlov, and V. V. Tuchin, "The progress and perspectives of terahertz technology for diagnosis of neoplasms: A review," Journal of Optics 22(1), 013001 (2020).

17. K. P. Cheung, D. H. Auston, "A novel technique for measuring far-infrared absorption and dispersion," Infrared Physics 26(1), 23-27 (1986).

18. D. Grischkowsky, S. Keiding, M. van Exter, and Ch. Fattinger, "Far-infrared time-domain spectroscopy with terahertz beams of dielectrics and semiconductors," Journal of the Optical Society of America B 7(10), 2006-2015 (1990).

19. O. Cherkasova, M. Nazarov, and A. Shkurinov, "Noninvasive blood glucose monitoring in the terahertz frequency range," Optical and Quantum Electronics 48(3), 217 (2016).

20. A. Gavdush, N. Chernomyrdin, K. Malakhov, S.-I. Beshplav, I. Dolganova, A. Kosyrkova, P. Nikitin, G. Musina, G. Katyba, I. Reshetov, O. Cherkasova, G. Komandin, V. Karasik, A. Potapov, V. Tuchin, and K. Zaytsev, "Terahertz spectroscopy of gelatin-embedded human brain gliomas of different grades: a road toward intraoperative THz diagnosis," Journal of Biomedical Optics 24(2), 027001 (2019).

21. A. A. Gavdush, N. V. Chernomyrdin, G. A. Komandin, I. N. Dolganova, P. V. Nikitin, G. R. Musina, G. M. Katyba, A. S. Kucheryavenko, I. V. Reshetov, A. A. Potapov, V. V. Tuchin, and K. I. Zaytsev, "Terahertz dielectric spectroscopy of human brain gliomas and intact tissues ex vivo: double-Debye and double-overdamped-oscillator models of dielectric response," Biomedical Optics Express 12(1), 69-83 (2020).

22. S. J. Oh, S.-H. Kim, Y. B. Ji, K. Jeong, Y. Park, J. Yang, D. W. Park, S. K. Noh, S.-G. Kang, Y.-M. Huh, J.-H. Son, and J.-S. Suh, "Study of freshly excised brain tissues using terahertz imaging," Biomedical Optics Express 5(8), 2837-2842 (2014).

23. K. Meng, T.-N. Chen, T. Chen, L.-G. Zhu, Q. Liu, Z. Li, F. Li, S.-C. Zhong, Z.-R. Li, H. Feng, and J.-H. Zhao, "Terahertz pulsed spectroscopy of paraffin-embedded brain glioma," Journal of Biomedical Optics 19(7), 077001 (2014).

24. S. Yamaguchi, Y. Fukushi, O. Kubota, T. Itsuji, T. Ouchi, and S. Yamamoto, "Brain tumor imaging of rat fresh tissue using terahertz spectroscopy," Scientific Reports 6, 30124 (2016).

25. L. Wu, D. Xu, Y. Wang, B. Liao, Z. Jiang, L. Zhao, Z. Sun, N. Wu, T. Chen, H. Feng, and J. Yao, "Study of in vivo brain glioma in a mouse model using continuous-wave terahertz reflection imaging," Biomedical Optics Express 10(8), 3953-3962 (2019).

26. Y. B. Ji, S. J. Oh, S.-G. Kang, J. Heo, S.-H. Kim, Y. Choi, S. Song, H. Y. Son, S. H. Kim, J. H. Lee, S. J. Haam, Y. M. Huh, J. H. Chang, C. Joo, and J.-S. Suh, "Terahertz reflectometry imaging for low and high grade gliomas," Scientific Reports 6, 36040 (2016).

27. L. Wu, Y. Wang, B. Liao, L. Zhao, K. Chen, M. Ge, H. Li, T. Chen, H. Feng, D. Xu, and J. Yao, "Temperature dependent terahertz spectroscopy and imaging of orthotopic brain gliomas in mouse models," Biomedical Optics Express 13(1), 93-104 (2022).

28. A. S. Kucheryavenko, N. V. Chernomyrdin, A. A. Gavdush, A. I. Alekseeva, P. V. Nikitin, I. N. Dolganova, P. A. Karalkin, A. S. Khalansky, I. E. Spektor, M. Skorobogatiy, V. V. Tuchin, and K. I. Zaytsev, "Terahertz dielectric spectroscopy and solid immersion microscopy of ex vivo glioma model 101.8: brain tissue heterogeneity," Biomedical Optics Express 12(8), 5272-5289 (2021).

29. A. G. Kamkin, A. A. Kamensky (Eds.), Fundamental and Clinical Physiology, Training manual, The Academy Moscow (2004). ISBN: 5-7695-1675-5.

30. O. P. Cherkasova, M. M. Nazarov, and A. P. Shkurinov, "Study of blood and its components by terahertz pulsed spectroscopy," EPJ Web of Conferences 195, 10003 (2018).

31. M. M. Nazarov, O. P. Cherkasova, E. N. Lazareva, A. B. Bucharskaya, N. A. Navolokin, V. V. Tuchin, and A. P. Shkurinov, "A complex study of the peculiarities of blood serum absorption of rats with experimental liver cancer," Optics and Spectroscopy 126(6), 721-729 (2019).

32. M. R. Konnikova, O. P. Cherkasova, M. M. Nazarov, D. A. Vrazhnov, Yu. V. Kistenev, S. E. Titov, E. V. Kopeikina, S. P. Shevchenko, and A. P. Shkurinov, "Malignant and benign thyroid nodule differentiation through the analysis of blood plasma with terahertz spectroscopy," Biomedical Optics Express 12(2), 1020-1035 (2021).

33. J. A. Koutcher, X. Hu, S. Xu, T. P. Gade, N. Leeds, X. J. Zhou, D. Zagzag, and E. S. Holland, "MRI of Mouse Models for Gliomas Shows Similarities to Humans and Can Be Used to Identify Mice for Preclinical Trials," Neoplasia 4(6), 480-485 (2002).

34. E. L. Zavjalov, I. A. Razumov, L. A. Gerlinskaya, and A. V. Romashchenko, "In vivo MRI Visualization of U87 Glioblastoma Development Dynamics in the Model of Orthotopic Xenotransplantation to the SCID Mouse," Russian Journal of Genetics: Applied Research 6, 448-453 (2016).

35. M. M. Nazarov, A. P. Shkurinov, E. A. Kuleshov, and V. V. Tuchin, "Terahertz time-domain spectroscopy of biological tissues," Quantum Electronics 38(7), 647 (2008).

36. O. P. Cherkasova, M. M. Nazarov, A. P. Shkurinov, and V. I. Fedorov, "Terahertz spectroscopy of biological molecules," Radiophysics and Quantum Electronics 52(7), 518-523 (2009).

37. M. M. Nazarov, O. P. Cherkasova, and A. P. Shkurinov, "A Comprehensive Study of Albumin Solutions in the Extended Terahertz Frequency Range," Journal of Infrared, Millimeter, and Terahertz Waves 39, 840-853 (2018).

38. M. R. Konnikova, O. P. Cherkasova, T. A. Geints, E. S. Dizer, A. A. Man'kova, I. S. Vasilievskii, A. A. Butylin, Yu. V. Kistenev, V. V. Tuchin, and A. P. Shkurinov, "Study of adsorption of the SARS-CoV-2 virus spike protein by vibrational spectroscopy using terahertz metamaterials," Quantum Electronics 52(1), 2 (2022).

39. D. Vrazhnov, A. Knyazkova, M. Konnikova, O. Shevelev, I. Razumov, E. Zavjalov, Y. Kistenev, A. Shkurinov, and O. Cherkasova, "Analysis of Mouse Blood Serum in the Dynamics of U87 Glioblastoma by Terahertz Spectroscopy and Machine Learning," Applied Sciences 12(20), 10533 (2022).

40. O. B. Shevelev, A. A. Seryapina, E. L. Zavjalov, L. A. Gerlinskaya, T. N. Goryachkovskaya, N. M. Slynko, L. V. Kuibida, S. E. Peltek, A. L. Markel, and M. P. Moshkin, "Hypotensive and neurometabolic effects of intragastric Reishi (Ganoderma lucidum) administration in hypertensive ISIAH rat strain," Phytomedicine 41, 1-6 (2018).

41. M. Nazarov, A. Shkurinov, V. V. Tuchin, and X.-C. Zhang, Handbook of Photonics for Biomedical Science. Series in Medical Physics and Biomedical Engineering, V. V. Tuchin (Ed.), 1st ed., CRC Press, Boca Raton (2010). ISBN: 9780429131271.

42. T.-F. Tseng, B. You, H.-C. Gao, T.-D. Wang, and C.-K. Sun, "Pilot clinical study to investigate the human whole blood spectrum characteristics in the sub-THz region," Optics Express 23(7), 9440-9451 (2015).

43. O. P. Cherkasova, M. M. Nazarov, A. A. Angeluts, and A. P. Shkurinov, "Analysis of blood plasma at terahertz frequencies," Optics and Spectroscopy 120, 50-57 (2016).

44. O. P. Cherkasova, M. M. Nazarov, I. N. Smirnova, A. A. Angeluts, and A. P. Shkurinov, "Application of timedomain THz spectroscopy for studying blood plasma of rats with experimental diabetes," Physics of Wave Phenomena 22, 185-188 (2014).

45. Y. Wang, G. Wang, D. Xu, B. Jiang, M. Ge, L. Wu, C. Yang, N. Mu, S. Wang, C. Chang, T. Chen, H. Feng, and J. Yao, "Terahertz spectroscopic diagnosis of early blast-induced traumatic brain injury in rats," Biomedical Optics Express 11(8), 4085-4098 (2020).

46. D. Vrazhnov, A. Mankova, E. Stupak, Y. Kistenev, A. Shkurinov, and O. Cherkasova, "Discovering Glioma Tissue through Its Biomarkers' Detection in Blood by Raman Spectroscopy and Machine Learning," Pharmaceutics 15(1), 203 (2023).

47. M. M. Nazarov, O. P. Cherkasova, and A. P. Shkurinov, "Study of the dielectric function of aqueous solutions of glucose and albumin by THz time-domain spectroscopy," Quantum Electronics 46 (6), 488 (2016).

48. O. Cherkasova, M. Nazarov, and A. Shkurinov, "Properties of aqueous solutions in THz frequency range," Journal of Physics: Conference Series 793(1), 012005 (2017).

49. O. P. Cherkasova, M. M. Nazarov, M. R. Konnikova, and A. P. Shkurinov, "THz Spectroscopy of Bound Water in Glucose: Direct Measurements from Crystalline to Dissolved State," Journal of Infrared, Millimeter, and Terahertz Waves 41, 1057-1068 (2020).

50. H. Yada, M. Nagai, and K. Tanaka, "Origin of the fast relaxation component of water and heavy water revealed by terahertz time-domain attenuated total reflection spectroscopy," Chemical Physics Letters 464 (4-6), 166-170 (2009).

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