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When person`s diseases is result of bacterial infection, various characteristics of the organism are used for observation the course of the disease. Currently, one of these indicators is dynamics of cytokine concentrations are produced, mainly by cells of the immune system. There are many types of these low molecular weight proteins in human body and many species of animals. The study of cytokines is important for the interpretation of functional disorders of the body's immune system, assessment of the severity, monitoring the effectiveness of therapy, predicting of the course and outcome of treatment. Cytokine response of the body indicating characteristics of course of disease. For research regularities of such indication, experiments were conducted on laboratory mice. Experimental data are analyzed on the development of pneumonia and treatment with several drugs for bacterial infection of mice. As drugs used immunomodulatory drugs “Roncoleukin”, “Leikinferon” and “Tinrostim”. The data are presented by two types cytokines` concentration in lung tissue and animal blood. Multy-sided statistical ana non statistical analysis of the data allowed us to find common patterns of changes in the “cytokine profile” of the body and to link them with the properties of therapeutic preparations. The studies cytokine “Interleukin-10” (IL-10) and “Interferon Gamma” (IFN$\gamma$) in infected mice deviate from the normal level of infact animals indicating the development of the disease. Changes in cytokine concentrations in groups of treated mice are compared with those in a group of healthy (not infected) mice and a group of infected untreated mice. The comparison is made for groups of individuals, since the concentrations of cytokines are individual and differ significantly in different individuals. Under these conditions, only groups of individuals can indicate the regularities of the processes of the course of the disease. These groups of mice were being observed for two weeks. The dynamics of cytokine concentrations indicates characteristics of the disease course and efficiency of used therapeutic drugs. The effect of a medicinal product on organisms is monitored by the location of these groups of individuals in the space of cytokine concentrations. The Hausdorff distance between the sets of vectors of cytokine concentrations of individuals is used in this space. This is based on the Euclidean distance between the elements of these sets. It was found that the drug “Roncoleukin” and “Leukinferon” have a generally similar and different from the drug “Tinrostim” effect on the course of the disease.

Numerous contemporary studies confirm that neurons, astrocytes and blood vessels function as a unified dynamic system. Consequently, the concept of the integrated neurogliovascular unit (NGVU), encompassing these components, has emerged and gained significant traction in recent years. According to this framework, normal brain function relies on a broad complex of interactions between NGVU elements, while the disruption of these links may underlie various neuropathologies. Understanding the processes within a single NGVU, as well as the organization of connections between multiple units, is a prerequisite for successful diagnosis and therapy of neurological disorders.

In this work, we developed an NGVU model that, for the first time, integrates a detailed description of synaptically coupled excitatory and inhibitory neuronal networks (accounting for the E/I balance), extracellular environment dynamics (potassium, glutamate, GABA), and norepinephrine-modulated astrocytic activity, with subsequent regulation of local blood flow.

A key conceptual feature of the model is the integration of multiscale processes — ranging from ion dynamics at the level of individual Hodgkin – Huxley neurons to substance diffusion across a network of 100 NGVUs — into a single system of coupled nonlinear differential equations. This approach enabled the investigation of the ensemble’s collective dynamics and the identification of novel functional regimes.

Numerical experiments established that extracellular potassium dynamics and positive feedback play a decisive role in the formation of stable spatial excitation structures. It is shown that under local stimulation, activity remains confined due to potassium diffusion outflow; however, supercritical excitation initiates self-sustaining autowave regimes. The stabilization of these regimes leads to the formation of spatial patterns morphologically similar to Turing structures. These patterns, characterized by alternating zones of high and low activity, are independent of specific initial conditions but sensitive to parameter variations. This suggests that the system operates in a dynamic instability (chaos) regime, which is consistent with the concept of self-organized criticality of the brain under physiological conditions. The model successfully reproduces experimentally observed phenomena, including bursting and sensitivity to extracellular potassium. The results provide new perspectives for analyzing the pathophysiological mechanisms of brain function.

This paper presents the development and verification of a finite element algorithm for modeling nonlinear deformation in the functional spinal unit (FSU) of the human lumbar spine. The study is motivated by the increasing prevalence of musculoskeletal disorders and the need for improved predictive methods for spinal motion segment behavior under various loading conditions and pathologies.

Current modeling approaches often employ simplified geometry and linear deformation laws (Hooke’s law) for all spinal elements, including the intervertebral disc. However, these models inadequately represent human anatomy and the physical properties of the intervertebral disc, which comprises the annulus fibrosus and nucleus pulposus exhibiting pronounced nonlinear and viscoelastic characteristics.

To address these limitations, the authors propose a mathematical model wherein vertebrae are treated as linearly elastic, heterogeneous bodies, while the intervertebral disc is modeled according to nonlinear deformation theory. An anatomically accurate 3D reconstruction of the lumbar spine was generated from computed tomography (CT) images. Volumetric tetrahedral finite elements were employed for discretization. The intervertebral disc behavior is described using thin plate bending theory based on Kirchhoff-Love assumptions.

The algorithm’s key feature is a two-step solution procedure: an initial linear elastic solution is obtained, followed by the calculation of additional nodal forces for disc elements, enabling efficient nonlinear analysis without stiffness matrix modification.

Model verification was performed using two geometric representations of the lumbar spine: a simplified version comprising parallelepipeds and cylinders, and a patient-specific realistic model derived from CT data. Numerical simulation results demonstrate that incorporating nonlinear properties of the intervertebral disc substantially alters both deformation patterns and magnitudes compared to purely elastic models, producing nonlinear segments on stress-strain curves. The discrepancy between results obtained with the proposed algorithm and those from Comsol software did not exceed 10%, confirming the model’s validity. This algorithm shows promise for investigating lumbar spine biomechanics under normal conditions, degenerative changes, and post-surgical states.