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Photobiomodulation Under Electroencephalographic Controls of Sleep for Stimulation of Lymphatic Removal of Toxins from Mouse Brain

The meningeal lymphatic vessels (MLVs) play an important role in the removal of toxins from the brain. The development of innovative technologies for the ...

Saratov State University

A coarse-graining procedure, which involves averaging time series in non-overlapping windows followed by processing of the obtained multiple data sets, is the initial step in the multiscale entropy computation method. In this paper, we discuss how this procedure can be applied with other methods of time series analysis. Based on extended detrended fluctuation analysis (EDFA), we compare signal processing results for data sets with and without coarse-graining. Using the simulated data provided by the interacting nephrons model, we show how this procedure increases, up to 48%, the distinctions between local scaling exponents quantifying synchronous and asynchronous chaotic oscillations. Based on the experimental data of electrocorticograms (ECoG) of mice, an improvement in differences in local scaling exponents up to 41% and Student's -values up to 34% was revealed.

Extended Detrended Fluctuation Analysis of Coarse-Grained Time Series.

Long-range correlations are often used as diagnostic markers in physiological research. Due to the limitations of conventional techniques, their characterizations are typically carried out with alternative approaches, such as the detrended fluctuation analysis (DFA). In our previous works, we found EEG-related markers of the blood-brain barrier (BBB), which limits the penetration of major drugs into the brain. However, anesthetics can penetrate the BBB, affecting its function in a dose-related manner. Here, we study two types of anesthesia widely used in experiments on animals, including zoletil/xylazine and isoflurane in optimal doses not associated with changes in the BBB. Based on DFA, we reveal informative characteristics of the electrical activity of the brain during such doses that are important for controlling the depth of anesthesia in long-term experiments using magnetic resonance imaging, multiphoton microscopy, etc., which are crucial for the interpretation of experimental results. These findings provide an important informative platform for the enhancement and refinement of surgery, since the EEG-based DFA analysis of BBB can easily be used during surgery as a tool for characterizing normal BBB functions under anesthesia.

Characterization of Anesthesia in Rats from EEG in Terms of Long-Range Correlations.

Anesthesia enables the painless performance of complex surgical procedures. However, the effects of anesthesia on the brain may not be limited only by its duration. Also, anesthetic agents may cause long-lasting changes in the brain. There is growing evidence that anesthesia can disrupt the integrity of the blood-brain barrier (BBB), leading to neuroinflammation and neurotoxicity. However, there are no widely used methods for real-time BBB monitoring during surgery. The development of technologies for an express diagnosis of the opening of the BBB (OBBB) is a challenge for reducing post-surgical/anesthesia consequences. In this study on male rats, we demonstrate a successful application of machine learning technology, such as artificial neural networks (ANNs), to recognize the OBBB induced by isoflurane, which is widely used in surgery. The ANNs were trained on our previously presented data obtained on the sound-induced OBBB with an 85% testing accuracy. Using an optical and nonlinear analysis of the OBBB, we found that 1% isoflurane does not induce any changes in the BBB, while 4% isoflurane caused significant BBB leakage in all tested rats. Both 1% and 4% isoflurane stimulate the brain's drainage system (BDS) in a dose-related manner. We show that ANNs can recognize the OBBB induced by 4% isoflurane in 57% of rats and BDS activation induced by 1% isoflurane in 81% of rats. These results open new perspectives for the development of clinically significant bedside technologies for EEG-monitoring of OBBB and BDS.

Machine Learning Technology for EEG-Forecast of the Blood-Brain Barrier Leakage and the Activation of the Brain's Drainage System during Isoflurane Anesthesia.

There is an association between sleep quality and glioma-specific outcomes, including survival. The critical role of sleep in survival among subjects with glioma may be due to sleep-induced activation of brain drainage (BD), that is dramatically suppressed in subjects with glioma. Emerging evidence demonstrates that photobiomodulation (PBM) is an effective technology for both the stimulation of BD and as an add-on therapy for glioma. Emerging evidence suggests that PBM during sleep stimulates BD more strongly than when awake. In this study on male  rats, we clearly demonstrate that the PBM course during sleep vs. when awake more effectively suppresses glioma growth and increases survival compared with the control. The study of the mechanisms of this phenomenon revealed stronger effects of the PBM course in sleeping vs. awake rats on the stimulation of BD and an immune response against glioma, including an increase in the number of CD8+ in glioma cells, activation of apoptosis, and blockage of the proliferation of glioma cells. Our new technology for sleep-phototherapy opens a new strategy to improve the quality of medical care for patients with brain cancer, using promising smart-sleep and non-invasive approaches of glioma treatment.

Article	Year
Extended Detrended Fluctuation Analysis of Coarse-Grained Time Series. Diagnostics (Basel, Switzerland)\| PubMed ID: 36611385	2022
Characterization of Anesthesia in Rats from EEG in Terms of Long-Range Correlations. Diagnostics (Basel, Switzerland)\| PubMed ID: 36766531	2023
Machine Learning Technology for EEG-Forecast of the Blood-Brain Barrier Leakage and the Activation of the Brain's Drainage System during Isoflurane Anesthesia. Biomolecules\| PubMed ID: 38002287	2023
Different Effects of Phototherapy for Rat Glioma during Sleep and Wakefulness. Biomedicines\| PubMed ID: 38397864	2024

Matvey Tuzhilkin

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