L-2-Hydroxyglutarate (L-2HG) plays a significant role in many physiological processes and is considered as a biomarker for various types of oncology. So, its rapid and quantitative measurement in tissues and body fluids is of great clinical importance. The goal of this work is to develop highly efficient photonic sensor for L-2HG using the effect of nanoantenna-assisted plasmonic enhancement of terahertz absorption. We present the numerical results of the design optimization for L-2HG sensor based on Si/SiO2-wafer-backed arrays of golden nanoantennas (NA) of linear geometry for the resonant frequency of 1.337 THz. NA sensor was fabricated by nanolithography, characterized by scanning electron microscope and tested with terahertz time-domain spectroscopy (THz-TDS). Experimental validation of the NA sensor showed its specific sensitivity to L-2HG. A number of methods have been proposed to improve the accuracy of the NA sensor characterized using THz-TDS technique by an order of magnitude.

The issue of variability introduced into blood plasma and serum analysis by preanalytical procedures is the major obstacle to obtaining accurate and reproducible results. While the question of how to overcome this issue has been discussed in biochemical detection of analytes and omics technologies, its relevance to the field of optical spectroscopy remains mostly unexplored. In this work, we evaluated the freeze-thaw cycle (FTC)-induced alternations in blood serum optical properties by means of autofluorescence and Raman spectroscopy, including surface-enhanced Raman spectroscopy (SERS). In the case of regular Raman spectroscopy, FTC-specific spectral variability was estimated to be <1%, being significantly smaller than patient-specific variability, while the t-distributed stochastic neighbor embedding clustering of principal components yielded spectral grouping by patient ID independent of sample freezing. For SERS, FTC-specific and patient-specific spectral variabilities were 15% and >90%, respectively. Finally, parallel factor analysis of autofluorescence excitation-emission matrices revealed that patient-specific variability in the visible spectral range was 13%, whereas FTC-specific variability was 4%. We further evaluated disease-specific variability for two datasets, namely, for colorectal cancer diagnostics with autofluorescence and for chronic kidney disease diagnostics using SERS. Disease-associated variabilities were determined to be 8% and 49%, significantly exceeding the possible FTC-induced variability. Hence, the obtained results suggest that FTC blood serum samples can be used for disease diagnostics by Raman spectroscopy and SERS, as well as through autofluorescence spectroscopy, although the difference in FTC-induced and disease-induced variabilities was lowest in the latter case.

Raman spectroscopy (RS) is increasingly applied in medical fields to distinguish neoplastic from normal tissues, with recent advancements enabling its use in neurosurgery. This review explores RS as a diagnostic and surgical aid for brain gliomas, detailing its various modalities and applications. Through a comprehensive search in databases including PubMed, Google Scholar, and eLibrary, over 300 references were screened, resulting in 74 articles that met inclusion criteria. Key findings reveal RS's potential in neuro-oncology for examining native biopsy specimens, frozen and paraffin-embedded tissues, and body fluids, as well as performing intraoperative assessments. RS offers promise for identifying gliomas, differentiating them from healthy brain tissue, and establishing precise tumor boundaries during resection.

The possibility of the differentiation of glioblastoma from traumatic brain injury through blood serum analysis by terahertz time-domain spectroscopy and machine learning was studied using a small animal model. Samples of a culture medium and a U87 human glioblastoma cell suspension in the culture medium were injected into the subcortical brain structures of groups of mice referred to as the culture medium injection groups and glioblastoma groups, accordingly. Blood serum samples were collected in the first, second, and third weeks after the injection, and their terahertz transmission spectra were measured. The injection caused acute inflammation in the brain during the first week, so the culture medium injection group in the first week of the experiment corresponded to a traumatic brain injury state. In the third week of the experiment, acute inflammation practically disappeared in the culture medium injection groups. At the same time, the glioblastoma group subjected to a U87 human glioblastoma cell injection had the largest tumor size. The THz spectra were analyzed using two dimensionality reduction algorithms (principal component analysis and t-distributed Stochastic Neighbor Embedding) and three classification algorithms (Support Vector Machine, Random Forest, and Extreme Gradient Boosting Machine). Constructed prediction data models were verified using 10-fold cross-validation, the receiver operational characteristic curve, and a corresponding area under the curve analysis. The proposed machine learning pipeline allowed for distinguishing the traumatic brain injury group from the glioblastoma group with 95% sensitivity, 100% specificity, and 97% accuracy with the Extreme Gradient Boosting Machine. The most informative features for these groups’ differentiation were 0.37, 0.40, 0.55, 0.60, 0.70, and 0.90 THz. Thus, an analysis of mouse blood serum using terahertz time-domain spectroscopy and machine learning makes it possible to differentiate glioblastoma from traumatic brain injury.

Orthotopic transplantation of glioblastoma cells in the brain of laboratory mice is a common animal model for studying brain tumors. It was shown that 1H magnetic resonance spectroscopy (MRS) enables monitoring of the tumor’s occurrence and its development during therapy based on the ratio of several metabolites. However, in studying new approaches to the therapy of glioblastoma in the model of orthotopic xenotransplantation of glioma cells into the brain of mice, it is necessary to understand which metabolites are produced by a growing tumor and which are the result of tumor cells injection along the modeling of the pathology. Currently, there are no data on the dynamic metabolic processes in the brain that occur after the introduction of glioblastoma cells into the brain of mice. In addition, there is a lack of data on the delayed effects of invasive brain damage. Therefore, this study investigates the long-term dyna mics of the neurometabolic profile, assessed using 1H MRS, after intracranial injection of a culture medium used in orthotopic modeling of glioma in mice. Levels of N-acetylaspartate, N-acetylaspartylglutamic acid, myoinositol, taurine, glutathione, the sum of glycerophosphocholine and phosphocholine, glutamic acid (Glu), glutamine (Gln), and gamma aminobutyric acid (GABA) indicate patterns of neurometabolites in the early stage after intracranial injection similar to brain trauma ones. Most of the metabolites, with the exception of Gln, Glu and GABA, returned to their original values on day 28 after injection. A progressive increase in the Glu/Gln and Glu/GABA ratio up to 28 days after surgery potentially indicates an impaired turnover of these metabolites or increased neurotransmission. Thus, the data indicate that the recovery processes are largely completed on day 28 after the traumatic event in the brain tissue, leaving open the question of the neurotransmitter system impairment. Consequently, when using animal models of human glioma, researchers should clearly distinguish between which changes in neurometabolites are a response to the injection of cancer cells into the brain, and which processes may indicate the early development of a brain tumor. It is important to keep this in mind when modeling human glioblastoma in mice and monitoring new treatments. In addition, these results may be important in the development of approaches for non-invasive diagnostics of traumatic brain injury as well as recovery and rehabilitation processes of patients after certain brain surgeries.

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.

Gliomas, one of the most severe malignant tumors of the central nervous system, have a high mortality rate and an increased risk of recurrence. Therefore, early glioma diagnosis and the control of treatment have great significance. The blood plasma samples of glioma patients, patients with skull craniectomy defects, and healthy donors were studied using terahertz time-domain spectroscopy (THz-TDS). An analysis of experimental THz data was performed by machine learning (ML). The ML pipeline included (i) THz spectra smoothing using the Savitzky–Golay filter, (ii) dimension reduction with principal component analysis and t-distribution stochastic neighborhood embedding methods; (iii) data separability analyzed using Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The ML models’ performance was evaluated by a k-fold cross validation technique using ROC-AUC, sensitivity, and specificity metrics. It was shown that tree-based ensemble methods work more accurately than SVM. RF and XGBoost provided a better differentiation of the group of patients with glioma from healthy donors and patients with skull craniectomy defects. THz-TDS combined with ML was shown to make it possible to separate the blood plasma of patients before and after tumor removal surgery (AUC = 0.92). Thus, the applicability of THz-TDS and ML for the diagnosis of glioma and treatment monitoring has been shown.

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.

In this research, an experimental U87 glioblastoma small animal model was studied. The association between glioblastoma stages and the spectral patterns of mouse blood serum measured in the terahertz range was analyzed by terahertz time-domain spectroscopy (THz-TDS) and machine learning. The THz spectra preprocessing included (i) smoothing using the Savitsky–Golay filter, (ii) outlier removing using isolation forest (IF), and (iii) Z-score normalization. The sequential informative feature-selection approach was developed using a combination of principal component analysis (PCA) and a support vector machine (SVM) model. The predictive data model was created using SVM with a linear kernel. This model was tested using k-fold cross-validation. Achieved prediction accuracy, sensitivity, specificity were over 90%. Also, a relation was established between tumor size and the THz spectral profile of blood serum samples. Thereby, the possibility of detecting glioma stages using blood serum spectral patterns in the terahertz range was demonstrated.

This chapter demonstrates the possibility of application of THz time-domain spectroscopy for the assessment of diabetic complications. The optical properties of human blood with the different glucose level were studied. The increasing refractive index of blood at the glucose-level growth was shown for a series of experiments. The dispersion of complex refractive index of human nails was obtained. Based on these data, the noninvasive glucose-measuring technique was proposed, which utilizes the reflection of the THz pulse from the nail plate/nail bed interface. The possibility of development of metafilm-based method for detecting glucose level in blood using THz time-domain spectroscopy was demonstrated. In addition, the influence of concentration of bilirubin, creatine, uric acid, and triglycerides on the refractive index of blood was shown.

Infectious diseases are among the most severe threats to modern society. Current methods of virus infection detection based on genome tests need reagents and specialized laboratories. The desired characteristics of new virus detection methods are noninvasiveness, simplicity of implementation, real-time, low cost and label-free detection. There are two groups of methods for molecular biomarkers' detection and analysis: (i) a sample physical separation into individual molecular components and their identification, and (ii) sample content analysis by laser spectroscopy. Variations in the spectral data are typically minor. It requires the use of sophisticated analytical methods like machine learning. This review examines the current technological level of laser spectroscopy and machine learning methods in applications for virus infection detection.

Gliomas are invasive brain tumors with high rates of recurrence and mortality. It has been shown that specific markers for glioma’s differential diagnostics are enantiomers of 2-hydroxyglutarate (L-2HG and D-2HG) in brain tissues and blood. These isomers have unique absorption peaks originating from vibrational and rotational transitions in their molecules. In particular, the peaks centered at 1.337 THz and 1.695 THz correspond to L-2HG and D-2HG isomers, respectively. The goal of this work is to develop highly efficient frequency-selective sensors for L-2HG and D-2HG isomers using the effect of nanoantenna-assisted plasmonic enhancement of THz absorption. Such an approach provides a noticeable increase in detection sensitivity versus direct non-resonant methods. In this paper, we present the numerical results of the design optimization for L-2HG and D-2HG sensors based on Si/SiO 2 -wafer-backed arrays of golden nanoantennas of linear geometry. The optimal structural parameters of the arrays found through integral averaging of the square of the surface electric field over an array unit cell are recommended for further nanolithographic fabrication of this kind of THz sensor.

Adhesion of the spike protein of the SARS-CoV-2 virus is studied by vibrational spectroscopy using terahertz metamaterials. The features of metastructure absorption upon the deposition of histidine, albumin, and the receptor-binding domain of the spike protein films are investigated. An original technique for quantitative assessment of the efficiency of virus adhesion on the metamaterial surfaces are proposed and experimentally tested.

Noninvasive glioblastoma diagnosis can be achieved by analyzing blood by Terahertz, Infrared and Raman spectroscopy. The model of xenotransplantation of the U87 human glioblastoma cells into immunodeficient mice was used. The most informative frequencies, separating glioblastoma's and healthy groups were identified by machine learning methods.