Qualitative and quantitative detection of liver injury with terahertz time-domain spectroscopy

Ensemble learning prediction framework for EGFR amplification status of glioma based on terahertz spectral features

Epidermal growth factor receptor (EGFR) plays a pivotal role in the initiation and progression of gliomas. In particular, in glioblastoma, EGFR amplification emerges as a catalyst for invasion, proliferation, and resistance to radiotherapy and chemotherapy. Current approaches are not capable of providing rapid diagnostic results of molecular pathology. In this study, we propose a terahertz spectroscopic approach for predicting the EGFR amplification status of gliomas for the first time. A machine learning model was constructed using the terahertz response of the measured glioma tissues, including the absorption coefficient, refractive index, and dielectric loss tangent. The novelty of our model is the integration of three classical base classifiers, i.e., support vector machine, random forest, and extreme gradient boosting. The ensemble learning method combines the advantages of various base classifiers, this model has more generalization ability. The effectiveness of the proposed method was validated by applying an individual test set. The optimal performance of the integrated algorithm was verified with an area under the curve (AUC) maximum of 85.8 %. This signifies a significant stride toward more effective and rapid diagnostic tools for guiding postoperative therapy in gliomas.

Study on the detection method of biological characteristics of hepatoma cells based on terahertz time-domain spectroscopy

Liver cancer usually has a high degree of malignancy and its early symptoms are hidden, therefore, it is of significant research value to develop early-stage detection methods of liver cancer for pathological screening. In this paper, a biometric detection method for living human hepatocytes based on terahertz time-domain spectroscopy was proposed. The difference in terahertz response between normal and cancer cells was analyzed, including five characteristic parameters in the response, namely refractive index, absorption coefficient, dielectric constant, dielectric loss and dielectric loss tangent. Based on class separability and variable correlation, absorption coefficient and dielectric loss were selected to better characterize cellular properties. Maximum information coefficient and principal component analysis were employed for feature extraction, and a cell classification model of support vector machine was constructed. The results showed that the algorithm based on parameter feature fusion can achieve an accuracy of 91.6% for human hepatoma cell lines and one normal cell line. This work provides a promising solution for the qualitative evaluation of living cells in liquid environment.

Prediction of IDH mutation status of glioma based on terahertz spectral data

Gliomas are the most common type of primary tumor in the central nervous system in adults. Isocitrate dehydrogenase (IDH) mutation status is an important molecular biomarker for adult diffuse gliomas. In this study, we were aiming to predict IDH mutation status based on terahertz time-domain spectroscopy technology. Ninety-two frozen sections of glioma tissue from nine patients were included, and terahertz spectroscopy data were obtained. Through Least Absolute Shrinkage and Selection Operator (LASSO), Principal component analysis (PCA), and Random forest (RF) algorithms, a predictive model for predicting IDH mutation status in gliomas was established based on the terahertz spectroscopy dataset with an AUC of 0.844. These results indicate that gliomas with different IDH mutation status have different terahertz spectral features, and the use of terahertz spectroscopy can establish a predictive model of IDH mutation status, providing a new way for glioma research.

PEG-fibrin conjugates: the PEG impact on the polymerization dynamics

Fibrin and its modifications, particularly those with functionalized polyethylene glycol (PEG), remain highly attractive as a biomaterial in drug delivery and regenerative medicine. Despite the extensive knowledge of fibrinogenesis, there is little information on the processes occurring after its modification. Previously, we found structural differences between native fibrin and its conjugates with PEG that allows us to hypothesize that a combination of methods such as terahertz (THz) pulsed spectroscopy and rheology may contribute to the characterization of gelation and reveal the effect of PEG on the polymerization dynamics. Compared to native fibrin, PEGylated fibrins had a homogenously soft surface; PEGylation also led to a significant decrease in the gelation time: from 42.75 min for native fibrin to 31.26 min and 35.09 min for 5 : 1 and 10 : 1 PEGylated fibrin, respectively. It is worth noting that THz pulsed spectroscopy makes it possible to reliably investigate only the polymerization process itself, while it does not allow us to observe statistically significant differences between the distinct PEGylated fibrin gels. The polymerization time constant of native fibrin measured by THz pulsed spectroscopy was 14.4 ± 2.8 min. However, it could not be calculated for PEGylated fibrin because the structural changes were too rapid. These results, together with those previously reported, led us to speculate that PEG-fibrin conjugates formed homogenously distributed highly water-shelled aggregates without bundling compared to native fibrin, ensuring rapid gelation and stabilization of the system without increasing its complexity.

Machine Learning Identification of Organic Compounds Using Visible Light

Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and vibrational information for remote chemical identification. This has been exploited using the fingerprint region of infrared absorption spectra, which involves a dense set of absorption peaks that are unique to individual molecules, thus facilitating chemical identification. However, optical identification using visible light has not been realized. Using decades of experimental refractive index data in the scientific literature of pure organic compounds and polymers over a broad range of frequencies from the ultraviolet to the far-infrared, we develop a machine learning classifier that can accurately identify organic species based on a single-wavelength dispersive measurement in the visible spectral region, away from absorption resonances. The optical classifier proposed here could be applied to autonomous material identification protocols and applications.

Measurement of tissue optical properties in a wide spectral range: a review [Invited]

A distinctive feature of this review is a critical analysis of methods and results of measurements of the optical properties of tissues in a wide spectral range from deep UV to terahertz waves. Much attention is paid to measurements of the refractive index of biological tissues and liquids, the knowledge of which is necessary for the effective application of many methods of optical imaging and diagnostics. The optical parameters of healthy and pathological tissues are presented, and the reasons for their differences are discussed, which is important for the discrimination of pathologies and the demarcation of their boundaries. When considering the interaction of terahertz radiation with tissues, the concept of an effective medium is discussed, and relaxation models of the effective optical properties of tissues are presented. Attention is drawn to the manifestation of the scattering properties of tissues in the THz range and the problems of measuring the optical properties of tissues in this range are discussed. In conclusion, a method for the dynamic analysis of the optical properties of tissues under optical clearing using an application of immersion agents is presented. The main mechanisms and technologies of optical clearing, as well as examples of the successful application for differentiation of healthy and pathological tissues, are analyzed.

Moxa Wool in Different Purities and Different Growing Years Measured by Terahertz Spectroscopy

Moxa wool is a traditional Chinese herbal medicine, which can warm channels to dispel coldness. At present, there is no unified index to evaluate the purity and growing years of moxa wool in the market. Terpineol is one of the effective substances in the volatile oil of moxa wool. Here, we characterize the purity and growing years of moxa wool by studying terpineol. Gas chromatography-mass spectrometry (GC-MS) and high-performance liquid chromatography (HPLC) are the methods for monitoring terpineol at present, all of which have defects of complicated procedures. We established linear fitting to distinguish the different purities of moxa wool through the intensities (areas) of terpineol, the characteristic peaks, and the consequence presented; the coefficient of determination (R2) was higher than 0.90. Furthermore, based on the characteristic peak position of standard terpineol, the correlation model with the purity and growing year of moxa wool was set up, thereby differentiating the quality of moxa wool. We have built the partial least squares (PLS) model of the growing years of moxa wool with high accuracy, and the determination coefficient is greater than 0.98. In addition, we compare the quantitative accuracy of Raman spectroscopy with terahertz technology. Finally, a new method of terahertz spectroscopy to evaluate quality of moxa wool was found. It provides a new idea for the identification of inferior moxa wool in the market and a new method for identifying the quality of moxa wool in traditional Chinese medicine.

Applications of THz Spectral Imaging in the Detection of Agricultural Products

Agricultural products need to be inspected for quality and safety, and the issue of safety of agricultural products caused by quality is frequently investigated. Safety testing should be carried out before agricultural products are consumed. The existing technologies for inspecting agricultural products are time-consuming and require complex operation, and there is motivation to develop a rapid, safe, and non-destructive inspection technology. In recent years, with the continuous progress of THz technology, THz spectral imaging, with the advantages of its unique characteristics, such as low energies, superior spatial resolution, and high sensitivity to water, has been recognized as an efficient and feasible identification tool, which has been widely used for the qualitative and quantitative analyses of agricultural production. In this paper, the current main performance achievements of the use of THz images are presented. In addition, recent advances in the application of THz spectral imaging technology for inspection of agricultural products are reviewed, including internal component detection, seed classification, pesticide residues detection, and foreign body and packaging inspection. Furthermore, machine learning methods applied in THz spectral imaging are discussed. Finally, the existing problems of THz spectral imaging technology are analyzed, and future research directions for THz spectral imaging technology are proposed. Recent rapid development of THz spectral imaging has demonstrated the advantages of THz radiation and its potential application in agricultural products. The rapid development of THz spectroscopic imaging combined with deep learning can be expected to have great potential for widespread application in the fields of agriculture and food engineering.

Cellular effects of terahertz waves

SIGNIFICANCE

An increasing interest in the area of biological effects at exposure of tissues and cells to the terahertz (THz) radiation is driven by a rapid progress in THz biophotonics, observed during the past decades. Despite the attractiveness of THz technology for medical diagnosis and therapy, there is still quite limited knowledge about safe limits of THz exposure. Different modes of THz exposure of tissues and cells, including continuous-wave versus pulsed radiation, various powers, and number and duration of exposure cycles, ought to be systematically studied.

AIM

We provide an overview of recent research results in the area of biological effects at exposure of tissues and cells to THz waves.

APPROACH

We start with a brief overview of general features of the THz-wave-tissue interactions, as well as modern THz emitters, with an emphasis on those that are reliable for studying the biological effects of THz waves. Then, we consider three levels of biological system organization, at which the exposure effects are considered: (i) solutions of biological molecules; (ii) cultures of cells, individual cells, and cell structures; and (iii) entire organs or organisms; special attention is devoted to the cellular level. We distinguish thermal and nonthermal mechanisms of THz-wave-cell interactions and discuss a problem of adequate estimation of the THz biological effects' specificity. The problem of experimental data reproducibility, caused by rareness of the THz experimental setups and an absence of unitary protocols, is also considered.

RESULTS

The summarized data demonstrate the current stage of the research activity and knowledge about the THz exposure on living objects.

CONCLUSIONS

This review helps the biomedical optics community to summarize up-to-date knowledge in the area of cell exposure to THz radiation, and paves the ways for the development of THz safety standards and THz therapeutic applications.

Moisture adsorption by decellularized bovine pericardium collagen matrices studied by terahertz pulsed spectroscopy and solid immersion microscopy

In this paper, terahertz (THz) pulsed spectroscopy and solid immersion microscopy were applied to study interactions between water vapor and tissue scaffolds-the decellularized bovine pericardium (DBP) collagen matrices, in intact form, cross-linked with the glutaraldehyde or treated by plasma. The water-absorbing properties of biomaterials are prognostic for future cell-mediated reactions of the recipient tissue with the scaffold. Complex dielectric permittivity of DBPs was measured in the 0.4-2.0 THz frequency range, while the samples were first dehydrated and then exposed to water vapor atmosphere with 80.0 ± 5.0% relative humidity. These THz dielectric measurements of DBPs and the results of their weighting allowed to estimate the adsorption time constants, an increase of tissue mass, as well as dispersion of these parameters. During the adsorption process, changes in the DBPs' dielectric permittivity feature an exponential character, with the typical time constant of =8-10 min, the transient process saturation at =30 min, and the tissue mass improvement by =1-3%. No statistically-relevant differences between the measured properties of the intact and treated DBPs were observed. Then, contact angles of wettability were measured for the considered DBPs using a recumbent drop method, while the observed results showed that treatments of DBP somewhat affects their surface energies, polarity, and hydrophilicity. Thus, our studies revealed that glutaraldehyde and plasma treatment overall impact the DBP-water interactions, but the resultant effects appear to be quite complex and comparable to the natural variability of the tissue properties. Such a variability was attributed to the natural heterogeneity of tissues, which was confirmed by the THz microscopy data. Our findings are important for further optimization of the scaffolds' preparation and treatment technologies. They pave the way for THz technology use as a non-invasive diagnosis tool in tissue engineering and regenerative medicine.

Machine Learning Techniques for THz Imaging and Time-Domain Spectroscopy

Terahertz imaging and time-domain spectroscopy have been widely used to characterize the properties of test samples in various biomedical and engineering fields. Many of these tasks require the analysis of acquired terahertz signals to extract embedded information, which can be achieved using machine learning. Recently, machine learning techniques have developed rapidly, and many new learning models and learning algorithms have been investigated. Therefore, combined with state-of-the-art machine learning techniques, terahertz applications can be performed with high performance that cannot be achieved using modeling techniques that precede the machine learning era. In this review, we introduce the concept of machine learning and basic machine learning techniques and examine the methods for performance evaluation. We then summarize representative examples of terahertz imaging and time-domain spectroscopy that are conducted using machine learning.

In situ terahertz monitoring of an ice ball formation during tissue cryosurgery: a feasibility test

SIGNIFICANCE

Uncontrolled cryoablation of tissues is a strong reason limiting the wide application of cryosurgery and cryotherapy due to the certain risks of unpredicted damaging of healthy tissues. The existing guiding techniques are unable to be applied in situ or provide insufficient spatial resolution. Terahertz (THz) pulsed spectroscopy (TPS) based on sensitivity of THz time-domain signal to changes of tissue properties caused by freezing could form the basis of an instrument for observation of the ice ball formation.

AIM

The ability of TPS for in situ monitoring of tissue freezing depth is studied experimentally.

APPROACH

A THz pulsed spectrometer operated in reflection mode and equipped with a cooled sample holder and ex vivo samples of bovine visceral adipose tissue is applied. Signal spectrograms are used to analyze the changes of THz time-domain signals caused by the interface between frozen and unfrozen tissue parts.

RESULTS

Experimental observation of TPS signals reflected from freezing tissue demonstrates the feasibility of TPS to detect ice ball formation up to 657-μm depth.

CONCLUSIONS

TPS could become the promising instrument for in situ control of cryoablation, enabling observation of the freezing front propagation, which could find applications in various fields of oncology, regenerative medicine, and THz biophotonics.

Optimal hyperosmotic agents for tissue immersion optical clearing in terahertz biophotonics

In this work, a thorough analysis of hyperosmotic agents for the immersion optical clearing (IOC) in terahertz (THz) range was performed. It was aimed at the selection of agents for the efficient enhancement of penetration depth of THz waves into biological tissues. Pulsed spectroscopy in the frequency range of 0.1 to 2.5 THz was applied for investigation of the optical properties of common IOC agents. Using the collimated transmission spectroscopy in visible range, binary diffusion coefficients of tissue water and agent in ex vivo rat brain tissue were measured. IOC agents were objectively compared using two-dimensional nomogram, accounting for their THz-wave absorption coefficients and binary diffusion coefficients. The results of this study demonstrate an interplay between the penetration depth enhancement and the diffusion rate and allow for pointing out glycerol as an optimal agent among the considered ones for particular applications in THz biophotonics.

Quantitative analysis of homocysteine in liquid by terahertz spectroscopy

Homocysteine (C₄H₉NO₂S) is a variant of the amino acid cysteine, a harmful substance to the human body, which is closely related to cardiovascular disease, senile dementia, fractures, et al. At present, conventional methods for detecting homocysteine in biological samples include high performance liquid chromatography (HPLC), fluorescence polarization immunoassay (FPIA), and enzymatic cycling methods. These methods have the disadvantages of being time-consuming, sample-losing, chemical reagent-using and operation-cumbersome. Here, we present a method for the quantitative detection of homocysteine in liquid based on terahertz spectroscopy. Considering the strong absorption of water for terahertz beam, we also put forward a pretreatment method for drying samples at low temperature. These methods make the detection limit for homocysteine reach 10 µmol/L (human normal concentration). Based on the linear relationship between the homocysteine concentration and the THz spectral intensity, we can successfully achieve quantitative, accurate and real-time detection of homocysteine. As compared to Raman spectroscopy, the correlation coefficient of THz spectrum ( R 16.24 THz 2 = 0.99809) is much larger than that of the Raman spectrum ( R 2558.26 c m - 1 2  = 0.80022, R 2937.32 c m - 1 2 = 0.8028). These results are greatly useful for the accurate evaluation of pathological stage.