Diagnosis of Glioma Molecular Markers by Terahertz Technologies

Fano resonances induced by strong conductive coupling in cross-shaped metasurfaces for tunable EIT-like phenomena

Highly efficient Metamaterials are necessary for applications in sensing, communication, etc. Fano resonance and electromagnetically induced transparency-like phenomena are essential for obtaining high Q-factor and sensitive Metamaterials. Employing both numerical simulations and experimental analysis, we investigate the emergence of Fano resonance in cross-resonator Metamaterials facilitated by the conductive coupling between dark and bright resonators. We analyze the gradual shift of the fano resonance by tuning the dark resonator and finally form an electromagnetically induced transparency-like transmission peak. The strong coupling of the resonator is observed in the form of an anti-crossing and discussed through analytical models. We demonstrate that the coupling strength of the dark and bright resonance in our metamaterial is proportional to the asymmetry parameter, albeit at the cost of the Fano resonance's Q-factor. The findings and methods introduced in this study can be used to develop highly efficient THz Metamaterials for various applications operable in room conditions.

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.

Full-Stokes polarization laser-induced breakdown spectroscopy detection of infiltrative glioma boundary tissue

The glioma boundary is difficult to identify during surgery due to the infiltrative characteristics of tumor cells. In order to ensure a full resection rate and increase the postoperative survival of patients, it is often necessary to make an expansion range resection, which may have harmful effects on the quality of the patient's survival. A full-Stokes laser-induced breakdown spectroscopy (FSLIBS) theory with a corresponding system is proposed to combine the elemental composition information and polarization information for glioma boundary detection. To verify the elemental content of brain tissues and provide an analytical basis, inductively coupled plasma mass spectrometry (ICP-MS) and LIBS are also applied to analyze the healthy, boundary, and glioma tissues. Totally, 42 fresh tissue samples are analyzed, and the Ca, Na, K elemental lines and CN, C₂ molecular fragmental bands are proved to take an important role in the different tissue identification. The FSLIBS provides complete polarization information and elemental information than conventional LIBS elemental analysis. The Stokes parameter spectra can significantly reduce the under-fitting phenomenon of artificial intelligence identification models. Meanwhile, the FSLIBS spectral features within glioma samples are relatively more stable than boundary and healthy tissues. Other tissues may be affected obviously by individual differences in lesion positions and patients. In the future, the FSLIBS may be used for the precise identification of glioma boundaries based on polarization and elemental characterizing ability.

Quantitative analysis of aqueous biomolecular mixtures by THz spectroscopy based on high-power LiNbO3 radiation source

The rapid and accurate identification of the types and contents of early pathological markers by THz technology are of particular importance for the prevention and treatment of major diseases. Nevertheless, these markers usually contain interference from water and other non-target molecules, resulting in low signal-to-noise ratio (SNR) and making identification and quantitative analysis difficult. Here, based on THz spectroscopy from a high-power THz source radiated by LiNbO₃, we perform quantitative and real-time THz detection of mixtures (α-lactose monohydrate and 4-aminobenzoic acid) in liquids. The results demonstrate that the absorption spectra of the aqueous biomolecular mixtures exhibit an accumulation of THz features of each pure product, i.e., the amplitude of the absorption peaks is proportional to the mixing ratio, while the corresponding absorption baseline increases with decreasing concentration. Furthermore, the content of the target substance can be calculated from the linear relationship between the absorption spectra of pure and mixed samples. This technology will support the future application of THz-TDS in early disease diagnosis under complex states and environments.

Discovering Glioma Tissue through Its Biomarkers' Detection in Blood by Raman Spectroscopy and Machine Learning

The most commonly occurring malignant brain tumors are gliomas, and among them is glioblastoma multiforme. The main idea of the paper is to estimate dependency between glioma tissue and blood serum biomarkers using Raman spectroscopy. We used the most common model of human glioma when continuous cell lines, such as U87, derived from primary human tumor cells, are transplanted intracranially into the mouse brain. We studied the separability of the experimental and control groups by machine learning methods and discovered the most informative Raman spectral bands. During the glioblastoma development, an increase in the contribution of lactate, tryptophan, fatty acids, and lipids in dried blood serum Raman spectra were observed. This overlaps with analogous results of glioma tissues from direct Raman spectroscopy studies. A non-linear relationship between specific Raman spectral lines and tumor size was discovered. Therefore, the analysis of blood serum can track the change in the state of brain tissues during the glioma development.

Studying the genotoxic effects of high intensity terahertz radiation on fibroblasts and CNS tumor cells

The data is obtained on the effect of high-intensity pulses of terahertz (THz) radiation with a broad spectrum (0.2-3 THz) on cell cultures. We have evaluated the threshold exposure parameters of THz radiation causing genotoxic effects in fibroblasts. Phosphorylation of histone H2AX at Ser 139 (γH2AX) was chosen as a marker for genotoxicity and a quantitative estimation of γH2AX foci number in fibroblasts was performed after cell irradiation with THz pulses for 30 min. No genotoxic effects of THz radiation were observed in fibroblasts unless peak intensity and electric field strength exceeded 21 GW cm^-2 and 2.8 MV cm^-1 , respectively. In tumor cell lines (neuroblastoma (SK-N-BE (2)) and glioblastoma (U87)), exposure to THz pulses with peak intensity of 21 GW cm^-2 for 30 min caused no morphological changes as well as no statistically significant increase in histone phosphorylation foci number.

Machine Learning Enhanced Optical Spectroscopy for Disease Detection

Optical spectroscopy plays an important role in disease detection. Improving the sensitivity and specificity of spectral detection has great importance in the development of accurate diagnosis. The development of artificial intelligence technology provides a great opportunity to improve the detection accuracy through machine learning methods. In this Perspective, we focus on the combination of machine learning methods with the optical spectroscopy methods widely used for disease detection, including absorbance, fluorescence, scattering, FTIR, terahertz, etc. By comparing the spectral analysis with different machine learning methods, we illustrate that the support vector machine and convolutional neural network are most effective, which have potential to further improve the classification accuracy to distinguish disease subtypes if these machine learning methods are used. This Perspective broadens the scope of optical spectroscopy enhanced by machine learning and will be useful for the development of disease detection.

Improving the accuracy of tumor surgery by THz imaging and making the results of pathological anatomy faster by THz spectroscopy

Background

The terahertz radiation is a specific part of the electromagnetic radiation spectrum and has multiple significant applications in multiple scientific researches such as the applications in the medicine. An important application of the terahertz is its use in tumor imaging which is very important in the tumor surgery; however, lots of physicians and workers in the medical field have little information or having no information at all, dealing with this significant part of the electromagnetic spectrum.

Results

In this work, we interviewed a number of local surgeons in Syrian Arab Republic, who reported that they visually delineate the contour of tumors to be removed, and in order to reduce the number of future possible interventions, a large margin of healthy tissue is often excised. Furthermore, a number of pathologists who reported that preparing samples of excised tissues for examination takes a long period of time which may extend to several days, and that the results of histopathology indicate in some cases the integrity of removed tissues.

Conclusion

We have found that a significant number of participants in the survey demonstrated that the importance of dealing with terahertz imaging and terahertz spectroscopy, encouraging to implement the technique in the Syrian Arab Republic.

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.

Ex vivo sensing and imaging of corneal scar tissues using terahertz time domain spectroscopy

Pulsed terahertz (THz) broadband spectroscopy and imaging were applied to investigate ex vivo rabbit corneal scar tissues. Scars with different depths and densities were created by laser ablation on four corneal samples while two corneal samples untouched were used as control. The THz time-domain spectroscopy and reconstructed images illustrated the scar tissue density and gave out depth variation distribution profiles. Calculated absorption coefficient spectra displayed extra absorption peaks compared with control corneas and refractive index spectra showed the optical properties changed at the corneal scar locations. THz spectroscopy and imaging demonstrated adequate contrast in reviewing the scar densities and locations and showed potential applications in depicting spatial distribution and composition changes of corneal scars.