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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. [ABSTRACT FROM AUTHOR]

Health systems challenges that emerged during the COVID-19 pandemic, such as a lack of resources and medical staff, are forcing solutions which optimize healthcare performance. One of the solutions is the development of clinical decision support systems (CDSS) based on artificial intelligence (AI). We classified AI-based clinical decision-supporting systems used during the pandemic and evaluated the mathematical algorithms present in these systems. Materials and methods: we searched for articles relevant to the aim of the study in the Scopus publication database. Results: depending on the purpose of the development a clinical decision support system based on artificial intelligence during pandemic, we identified three groups of tasks: organizational, scientific and diagnostic. Tasks such as predicting of pandemic parameters, searching of analogies in pandemic progression, prioritization of patients, use of telemedicine are solved for the purposes of healthcare organization. Artificial intelligence in drugs and vaccine development, alongside personalized treatment programs, apply to new scientific knowledge acquisition. Diagnostic tasks include the development of mathematical models for assessing COVID-19 outcomes, prediction of disease severity, analysis of factors influencing COVID-19 complications. Conclusion: artificial intelligence methods can be effectively implemented for decision support systems in solving tasks that face healthcare during pandemic. [ABSTRACT FROM AUTHOR]

Adipose tissue (AT) optical properties for physiological temperatures and in vivo conditions are still insufficiently studied. The AT is composed mainly of packed cells close to spherical shape. It is a possible reason that AT demonstrates a very complicated spatial structure of reflected or transmitted light. It was shown with a cellular tissue phantom, is split into a fan of narrow tracks, originating from the insertion point and representing filament-like light distribution. The development of suitable approaches for describing light propagation in a AT is urgently needed. A mathematical model of the propagation of light through the layers of fat cells is proposed. It has been shown that the sharp local focusing of optical radiation (light localized near the shadow surface of the cells) and its cleavage by coupling whispering gallery modes depends on the optical thickness of the cell layer. The optical coherence tomography numerical simulation and experimental studies results demonstrate the importance of sharp local focusing in AT for understanding its optical properties for physiological conditions and at AT heating. [ABSTRACT FROM AUTHOR]

Cancer is a life-threatening disease that has claimed the lives of many people worldwide. With the current diagnostic methods, it is hard to determine cancer at an early stage, due to its versatile nature and lack of genomic biomarkers. The rapid development of biophotonics has emerged as a potential tool in cancer detection and diagnosis. Using the fluorescence, scattering, and absorption characteristics of cells and tissues, it is possible to detect cancer at an early stage. The diagnostic techniques addressed in this review are highly sensitive to the chemical and morphological changes in the cell and tissue during disease progression. These changes alter the fluorescence signal of the cell/tissue and are detected using spectroscopy and microscopy techniques including confocal and two-photon fluorescence (TPF). Further, second harmonic generation (SHG) microscopy reveals the morphological changes that occurred in non-centrosymmetric structures in the tissue, such as collagen. Again, Raman spectroscopy is a non-destructive method that provides a fingerprinting technique to differentiate benign and malignant tissue based on Raman signal. Photoacoustic microscopy and spectroscopy of tissue allow molecule-specific detection with high spatial resolution and penetration depth. In addition, terahertz spectroscopic studies reveal the variation of tissue water content during disease progression. In this review, we address the applications of spectroscopic and microscopic techniques for cancer detection based on the optical properties of the tissue. The discussed state-of-the-art techniques successfully determines malignancy to its rapid diagnosis. [ABSTRACT FROM AUTHOR]

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 non-diabetic 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. [ABSTRACT FROM AUTHOR]