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The active development of digital technologies, Internet of Things technologies, and virtual tests requires an increase in the volume of information collected and used, which is placed in data storage systems. The rapid growth of data volume leads to stricter requirements for storage. One of the main requirements for storage is to increase the reliability of storing large amounts of information. This implies the need to assess the reliability of storage equipment. For these purposes, it is necessary to evaluate such reliability indicators as the probability of failure-free operation, the probability of failures, the average residual resource, and the gamma percent resource. Traditionally, reliability indicators are evaluated with an exponential distribution of failure time. In a real situation, the samples of failure times of storage equipment are small, for which it is impossible to uniquely identify the initial distribution. In this article, a model is proposed for evaluating reliability indicators as a gamma percent residual resource in conditions of incomplete data presented by small samples of random variables of equipment uptime. The scientific novelty of the presented work consists in obtaining a general solution to the problem of determining the guaranteed gamma percent residual life of equipment in conditions of incomplete data presented by small samples of developments before equipment failure. The mathematical formalization of the problem of estimating the gamma percent residual life of storage equipment in conditions of incomplete data presented by small samples is performed in the form of a stochastic equation model, the solution of which is a guaranteed (lower, upper) estimate of the gamma percent residual life of equipment. A model for estimating the gamma percent residual life of storage equipment in conditions of incomplete data is presented. In the general case, the problem of finding guaranteed (lower and upper) estimates of the gamma percent residual life of equipment on a set of functions for the distribution of uptime of equipment with specified moments equal to sample moments determined from small samples is solved. At two points in the uptime of the equipment, analytical ratios were obtained to determine the gamma percent residual life. The performance of the model is demonstrated by the example of determining the lower guaranteed estimate of the gamma percent residual resource of the HP EVA P6500 disk array model. The results obtained can be used by specialists in evaluating and optimizing the gamma percent residual life of storage equipment.

Based on the material of the periodical press, texts of festive radio broadcasts and documents of the Soviet and party authorities of Leningrad, the features of using the international agenda as one of the tropes of festive propaganda discourse are recreated. On the example of jubilee celebrations in 1957 In the USSR, the author comes to the conclusion that the use of international themes in the information coverage of holidays contributed to strengthening the positive image of the USSR as an international leader in the eyes of Soviet citizens, and also allowed creating impressions of the celebrations as world-wide events.

Certain theoretical aspects of the stability of Russian banks under risk conditions have been studied. The relevance is due to the fact that in conditions of market uncertainty and risk, approaches to ensure the stability of banks using artificial intelligence are increasingly being used. The goal is to identify patterns between the characteristics of Assets and ROA (Return on Assets), an indicator of return on assets, and obtain a forecast value of Sberbank’s net profit. The result of the study was hierarchical clustering, as well as the generated Deep Learning model Random Forest, which calculated the predicted value of the Sberbank’s net profit. The novelty lies in the fact that the work puts forward and proves the hypothesis that using the Random Forest Deep learning model, a forecast of the net profit of commercial banks can be obtained, which predetermines the stability and dynamics of their development. The conclusions from the study boil down to the fact that a Deep Learning model Random Forest was developed to forecast the amount of net profit, which for Sberbank for 2023 amounted to 38,631 billion rubles, which coincided with its actual value. The area of application of the results obtained is commercial banks.

The study, dedicated to the medieval antiquities of the Otuz settlement, located in the southeastern Crimea, examines the mosque and gravestones of the XIV century. Gravestones dating back to the early Golden Horde period of the history of the settlement, belonging to natives of Asia Minor and Iraq, are an example of the initial links with major Muslim centers and the spread of Islam on the peninsula. It is established that by now most of the medieval sites of the settlement have been completely or partially lost. Written and epigraphic sources, stone sculptures, graphics and photographs of the XIX – early XX centuries were analyzed. The identified graphic works by K.F. von Kügelgen, A. de Paldo, F. DuBois de Montperreux give an idea of the settlement that preserved medieval planning and development in the XIX century. The topography of the location of the mosque and necropolis in the Beshik-Aziz area on the top of the Mal-Kaya hill has been determined. The religious complex was located in the southwestern part of the settlement. The architectural style of the mosque and minaret, which have not survived to this day, has been determined. The mosque building belongs to the type of structures that became widespread on the peninsula during the period of the Crimean Khanate. An assumption has been made that Otuz could have housed the summer residence of the rulers of Solkhat, the center of the ulus of the Golden Horde in Crimea.

This study is relevant because market uncertainty induces progressively more attempts at making accurate profits forecasts in the banking sector. The scientific novelty of this study lies in the profits forecasts for the Russian banking sector performed using a random forest machine learning (ML) model and a neural network regression model. Regarding technology, the two models are combined into a cognitive model, as they are executed in the same cloud service (Collab) and have a common dataset comprising a training set, scripts and result output. The aim of the study is to build two models: a random forest ML model and a neural network regression model. The dataset used in the random forest ML model and the regression model included data on the performance of the Russian banking sector and some macroeconomic data on the national economy and the stock market for the period 2017–2021. Specifically, the dataset for the models included the following: key rate (%), growth assets (%), overdue loans (%), gross domestic product (GDP, in billions of rubles), RTS index (points), USD rate (vs. RUB), investments in assets to GDP (%), exchange robots (%), capital outflow (in billions of rubles), bank assets (in trillions of rubles), stock accounts (pcs.), and bank profits (in billions of rubles). The practical relevance of this study is evidenced by the fact that the results of the digital profits forecasting for the Russian banking sector can be recommended for real-world use. In building the cognitive model, we used the Python language in the Collab cloud environment. The mean absolute error of the test set for the random forest ML model (DecisionTreeRegressor) was 414.67, which is 61% lower than for the linear regression model (LinearRegression), which had a mean absolute error of 667.65.

Modern highly sensitive and selective sensors are able to determine biologically active substances, which makes this direction one of the most popular areas of analytical chemistry. The study featured the electrochemical properties of new fiber materials based on single-wall carbon nanotubes with prospects of using them in the voltammetry of ascorbic acid. The authors developed a new technology to synthesize films from disordered single-wall carbon nanotubes by chemical vapor deposition. Fibers were produced from a solvent by wet-pulling of single-wall carbon nanotubes networks. Thin films of randomly oriented single-wall carbon nanotube bundles were deposited downstream of a floating aerosol CVD reactor, which included a high temperature furnace with a quartz tube. The synthesis of the single-wall carbon nanotube samples was performed at 825°C. Ethanol served as carbon source while ferrocene was used as catalyst precursor. The single-wall carbon nanotubes were collected on a nitrocellulose filter in the form of films with transmittances of 10% in the middle of the visible wavelength (550 nm). The method was optimized to involve air annealing at 300–320°C and a treatment with strong inorganic acids, i.e., HCl, HNO3 + H2 SO4 . The voltammetric curves recording included background electrolyte, scan rate, and preconditioning. These parameters were selected experimentally to obtain the maximal sensor response to ascorbic acid content. The anodic peak of ascorbic acid in the phosphate buffer electrolyte (pH 6.86) was observed at a potential of +0.2 V. The current and peak area of ascorbic acid oxidation depended neither on the time nor on the conditioning potential of the sensor. The linear dependences of these parameters on the concentration of ascorbic acid stayed within 50–500 μmol/L (8.8–90 mg/L) at а scan rate of 0.1 mV/s. The single-wall carbon nanotube microsensor had a length of 0.5 cm and an average width of 400 μm. Its sensitivity was two times as high as that of a disk glassy carbon electrode with a diameter of 5 mm. The experimental sensors proved effective in determining ascorbic acid in food products, pharmaceuticals, and biological fluids.

The article looks at linguistic units applied for creating an image of an event. The first section of the article dwells on the most important works of Russian scholars in the field under investigation. Here, the term “linguomedial reconstruction of an event” is introduced and defined as the creation of an image of an event in mass media, including the analysis of information, its interpretation, creating media texts, and their positioning and functioning in media space. The second section describes in detail the analysis of informational and analytical media texts from British and French quality press dedicated to the Coonavirus pandemic: spread of the new virus, measures taken by the authorities, and vaccination. The analysis was conducted on lexical (identification of key lexical components of the texts) and suprasegmental (identification of key structural elements of the text) levels. The third section contains the results of the study. It was found out that both informational and analytical media texts consist of roughly equal number (usually less than 6) of topic-based sections (TBS). Analytical texts explore each topic more fully than informational ones; besides, the former include such topic as ‘expert opinion’. Seemingly, the larger is the volume of articles describing certain topic, the higher position this topic takes in news agenda. Any TBS may be characterized with a particular set of words and expressions - its lexical base. The lexical base of the TBS differs between cultures; the study shows that British quality press media prefer words describing actions and deeds, while French ones pay more attention to emotional response to the event. Besides, it was noted that military lexical units were widely used in pandemic new coverage, thus conceptualizing anti-coronavirus measures as military actions.

This study addresses the problem of sustainable economic growth, a subject that is highly relevant in the current conditions of market uncertainty. Given the importance of having an accurate forecast of GDP in uncertain market conditions, this study proposes a digital cognitive model that includes an artificial intelligence (AI) system decision tree for forecasting GDP values. This study aims to test whether using a cognitive model with the application of the AI system decision tree can afford a more accurate forecast of GDP than known forecasting methods. To achieve this goal, this study: 1) investigated the theoretical fundamentals of sustainable economic growth; 2) identified the development trends of AI systems in economics and finance to create the model’s dataset; and 3) calculated the forecast value of GDP using the digital cognitive model that included the AI system decision tree. The methodology involves the formation and use of a cognitive model that uses a decision tree neural network based on the Python language in the Google Collab cloud environment. Further, monographic, analytical, and computational-constructive methods were used. The results showed that the developed digital cognitive model, which included an AI system decision tree, was capable of forming GDP forecast values under changing external parameters.

A significant part of the research on the problems of quantization of random variables is devoted to practical aspects of optimal quantization in the sense of filling in information. For these purposes, certain quantitative characteristics of quantized random variables are used, such as: mathematical expectation, variance and mean square deviation. At the same time, to determine the quantitative characteristics of quantized random variables, as a rule, well-known parametric distributions are used: uniform, exponential, normal and others. In real situations, it is not possible to identify the initial parametric distribution based on the available statistical information. In this paper, a nonparametric model is proposed for determining such numerical characteristics of a quantized random variable as the highest initial moments. The mathematical formalization of the problem of estimating the higher initial moments of a quantized random variable in the conditions of incomplete data represented by small samples of a quantized random variable is performed in the form of an optimization model of a certain integral of a piecewise continuous function satisfying certain conditions. The final estimates of the highest initial moments of the quantized random variable are found as extreme (lower and upper) estimates of a certain integral on a set of distribution functions with given moments equal to the sample moments of the quantized random variable. A model of the higher initial moments of a quantized random variable is presented in the form of a definite integral of a piecewise continuous function; in the general case, the problem of finding extreme (lower and upper) estimates of the higher initial moments of a quantized random variable on a set of distribution functions with given moments is solved. Examples of finding higher initial moments and optimal quantization of a random variable are given. The obtained results can be used by specialists in evaluating and optimizing the quantization of various information presented by random signals.

Theoretical foundations for the development of environmental protection measures in Russia and the world, the issues of investing in environmental protection are studied. The scientific novelty lies in the fact that based on the use of a variational series of the studied parameters, a machine learning model “Decision Tree” was developed, with the help of which a predictive value of the amount of investment in environmental protection measures was formed. The relevance lies in the fact that to determine the volume of investment in environmental measures in the country, an artificial intelligence system was used – machine learning “Decision tree” (ML “Decision tree”), which made it possible to successfully solve a complex problem due to the influence of many factors on the parameter under study. The use of the proposed approach is especially relevant in the light of the May Decrees of the President of the Russian Federation. In 2018, in the Decrees of the President, strategic principles for the development of the country were formulated, aimed at the formation of a “digital economy”, designed to ensure further movement in accordance with the “Strategy for Scientific and Technical Development of Russia” in the main direction, according to which, as a foundation for the implementation of innovative changes in the state will use digital technologies. With the help of the artificial intelligence system, a forecast was formed for such a parameter as the volume of investment in the country in environmental measures for the next year, namely 6.2221 million rubles.

The article analyzes current trends in the use of artificial intelligence, cognitive technologies, Big Data and other innovations in order to predict the stability of the Russian banking system. The current trends in the development of banking innovations in the context of digitalization of the economy are summarized. Among the research methods used, the Random Forest artificial intelligence system, formed in the Google Collab cloud service in Python, should be noted. A neural network model for predicting the dynamics of bank profits based on the use of the Random Forest (RF – random forest) machine learning model is proposed. The results of the study indicate that, with the average value of the profit of banks for the period 2010-2021. 1126.0416 billion rubles, the mean absolute error (Mean Absolute Error) amounted to 129.96666 billion rubles, or 11.5%, which can be regarded as a good result in conditions of market uncertainty. The analysis showed that current trends are global in nature and lead to qualitative changes in the flow of business processes between consumers of banking services and credit institutions. Purpose: is to form a model that predicts the value of the target variable – the profit of the banking system using the Random Forest deep learning model, which is an ensemble of “decision trees”. The scientific novelty is to put forward and prove the hypothesis that with the help of the formed artificial intelligence system “Random Forest”, a profit forecast for commercial banks of the Russian Federation can be obtained. Method or methodology of the work. The work used such research methods as: monographic, analytical, artificial intelligence system – “Random Forest”, as well as comparative analysis. Calculations were performed in XL tables, Google Collab cloud service in Python. Results. The developed AI-system “Random Forest”, designed to predict the profits of banks, is presented. It is of practical importance to use the results of the Random Forest neural network to predict the development of credit institutions, which makes it possible to solve a major national economic problem - ensuring the stability of the banking system of the Russian Federation. A neural network model for predicting the dynamics of bank profits based on the use of the Random Forest (RF) machine learning model is proposed, which is a machine learning algorithm that consists in using an ensemble (set) of decision trees. The scope of the results: the financial sector, the banking sector, forecasting and ensuring the sustainable activity of credit institutions in the Russian Federation.

Objective: analysis of domestic scientific literature to identify ways of applying the Beers criteria, frequency of prescriptions and the structure of potentially inappropriate medications (PIMs) in elderly patients.Material and methods. The search for publications was carried out in Google Scholar abstract database, as wel as in eLibrary and CyberLeninka information-bearing networks for the period from 2013 to 2023. In total, 34 articles were found, among which 18 sources used Beers criteria as a tool for identifying PIMs.Results. In inpatient therapeutic departments, the maximum frequency of PIMs prescriptions according to the Beers criteria was 66.6% of cases, in surgical departments – 70%, in psychiatric departments – 90.4%, and in outpatient treatment settings – 28%. Non-steroidal anti-inflammatory drugs at different diseases were the most prescribed group of PIMs in both inpatients and outpatients. Surgical hospitals took the leading position in prescribing these drugs; specifically, more than 2/3 of elderly patients (75%) received them. According to two studies describing clinical cases of drug-induced falls in patients, the Beers criteria were effective in identifying drugs that can provoke this condition. One of papers presented application of Beers criteria to assess the economic affordability of replacing PIMs with safer new generation drugs.Conclusion. An analysis of domestic literature data has shown various ways of applying the Beers criteria, the frequency of prescriptions, and the most common PIMs. This information will help healthcare workers to be more wary of such drugs and think over applying the Beers criteria in routine practice of geriatric patients, which will further prevent or reduce the number of adverse drug reactions.

The COVID-19 mortality is associated with an increase in interleukin-6 (IL-6) levels. Levilimab is an anti–IL-6 receptor antibody with proven clinical efficacy in patients with severe COVID-19.The aim of the study was to assess the association of COVID-19 severity and levilimab effectiveness with IL-6 levels and to explore the potential for using levilimab in other conditions accompanied by cytokine release syndrome.Materials and methods: the subgroup analysis was based on the data of COVID patients with known baseline IL-6 levels from the CORONA clinical study. Subgroups were formed according to baseline IL-6 levels: ≤5 pg/mL (normal) and >5 pg/mL (elevated). The subgroup analysis included descriptive statistics of the patients and time courses of their clinical and laboratory findings (at screening, on the day of investigational product administration, and further until day 14). In order to compare the percentages of patients who had required rescue therapy, the authors used Fisher's exact test.Results: the subgroup analysis included 91 patients (47 from the levilimab group and 44 from the placebo group). At baseline, the authors observed elevated levels of IL-6 in 31/47 (66%) subjects in the levilimab group and 29/44 (48.4%) subjects in the placebo group. The subjects with elevated IL-6 demonstrated more pronounced clinical signs of pneumonia and abnormalities in inflammatory markers. Elevated baseline IL-6 levels were associated with the need for rescue therapy (OR=3.714; 95% CI: 1.317–9.747; p=0.0183); this association was stronger in the placebo group (OR=8.889; 95% CI: 2.098–33.31; p=0.0036). Also, the placebo group showed long-term abnormalities in the clinical and laboratory findings.Conclusions: IL-6 is one of the key elements in the pathogenesis of cytokine release syndrome related to COVID-19 and other conditions. Elevated IL-6 levels are associated with the severity of COVID-19. Inhibition of IL-6 receptors by levilimab leads to clinical improvement in patients with severe COVID-19, suggesting the effectiveness of levilimab in pathogenesis-oriented therapy for cytokine release syndrome of other aetiologies.

The development of the defense industry in Western Europe is of particular interest to political-academic research. This area has become especially relevant in the context of the escalation of the European security crisis in the spring of 2022. The study of the country’s defense industrial complex allows us to analyze the goals and objectives that are currently faced by the German political and military leadership. Procurement and R&D are a key focus of the study. The tasks set before the German DIC indicate the new plans of the country’s politico-military leadership for the development of the Bundeswehr. Their analysis may make it possible to predict the future development of the military-industrial complex. The purpose of the study is to analyze the current state of the German DIC. The hypothesis of the study is that the country’s military industry has many attributes indicating the country’s leadership in the organization of military-industrial cooperation between allies and partners, collective military and security policy in Europe. This article is a structured analysis of the development of the German DIC and is intended to fill the lack of information regarding future scenarios for the development of the German DIC.

The article discusses current trends in the use of artificial intelligence in the financial market. The relevance of the study is based on active use of exchange trading robots when conducting transactions on the exchange. However, despite some “splash” in 2021, the low investment activity of the population remains an acute problem. The purpose of the work is to solve a major national economic problem: increasing the investment activity of the population of the Russian Federation, strengthening the stability of the financial sector through the active use of private investors’ funds thanks to the introduction of a reliable robo-advisor. In the course of the study, the following tasks were solved: 1) theoretical basis for increasing the investment activity of the population was investigated; 2) an analysis of the current state of financial markets was carried out and trends in the use of artificial intelligence were identified; 3) a reliable highly efficient neural-network robo-advisor was developed. The scientific novelty lies in the fact that the proposed algorithm is based on the use of the machine learning algorithm of Random Forest, which allows you to get a reliable forecast for each next hour during the exchange trading of the SiH3 futures contract. The practical significance and value is that the developed recommendations can be implemented in practice, as they are confirmed by certificates of state registration for PC software. As a result of the study, conclusions were drawn: firstly, increasing the investment activity of the population contributing to the strengthening of the stability of the financial sector is important; secondly, the use of AI systems to support decision-making by private investors plays an important role in modern conditions; thirdly, the developed algorithm based on Random Forest machine learning is proposed, which allows you to get a reliable forecast of the price of the SiH3 futures contract for each next hour, providing a yield of 17,1 % during the exchange trading day. Among the directions for further scientific research, the use of Industry 5.0 technologies should be noted.

Expert Council: Drapkina O. M., Vavilova T. V., Karpov Yu. A., Kobalava Zh. D., Lomakin N. V., Martynov A. I., Roitman E. V., Sychev D. A.Scientific communities: the Russian Society for the Prevention of Non-Communicable Diseases (ROPNIZ), the Russian Scientific Medical Society of Therapists (RNMOT), the Russian Antithrombotic Forum (RAF), the National Association for Thrombosis and Hemostasis (NATH).

The plans of the Bakhchisarai Khan Palace of the 18th–20th centuries (G. Trombaro, W. Hastie, N. P. Krasnov, A. L. Rotach) feature an ‘Old Palace’ of the Crimean khans, probably built in the 16th century under Khan Sahib I Gerai (reigned in 1532–1551), and later sources only refer to a surviving gallery. Throughout its existence, the palace was guarded by the khans and was invariably restored after the destruction, being one of the first structures in the territory of the palace complex, serving as a symbol of Khan's power associated with the birth of the new capital – Bakhchisaray. In the 1820s the ‘Old Palace’ was dismantled as a completely dilapidated structure, but the restorers preserved its gallery, which was later named the Selyamet Gerai Gazebo. The gazebo with a fountain in the center of the room, which stood on a high basement, with a marble colonnade and arched openings of a lancet shape, was decorated with stucco decor and painting in naturalistic and baroque style, which was included in the art and craft of Turkey and the Crimean Khanate in the second half of the 16th–18th centuries. The gazebo was destroyed in the middle of the 20th century.

Theoretical foundations of GDP dynamics under conditions of market uncertainty and the formation of a digital economy are studied. Forecasting GDP is important because it allows to ensure balance, sustainability in the development of various sectors of the national economy, and its economic security, which determines the practical significance of the study. Purpose. To put forward and prove the hypothesis that with the help of the neural network model “perceptron” and the semantic model of knowledge representation, it is possible to obtain a forecast of Russia’s GDP for the next year. The scientific novelty lies in the fact that forecasts of the RF GDP were generated using a semantic knowledge representation model and the Perceptron AI system. Method or methodology of the work. The work used such methods as monographic, analytical, artificial intelligence system - perceptron, Mind histogram, as well as comparative analysis. Calculations were performed in tables XL perceptron was formed on the Deductor platform. We used a semantic knowledge representation model developed in the DOT language using the Graphviz 2.38 program. Results. Theoretical foundations of the use of neural networks in economic forecasts have been studied. A semantic model of knowledge representation has been formed regarding forecasting the GDP of the Russian Federation by a neural network. The developed AI-system “perceptron” is presented, designed to forecast Russia’s GDP based on input parameters representing a set of data reflecting the development of the real sector of the economy and the financial sector of Russia for 2011–2021. The scope of the results: economics, the financial sector, forecasting and planning of economic and financial activities.

The methods of risk assessment and decision-making in the management of agrotechnology were studied in order to develop a methodical approach to assessing the risks of possible yield losses in case of deviations from the project parameters in the implementation of agrotechnology. The study uses methods of analyzing information from the subject area of risk management in the management of agricultural technology. A registry of possible deviations in the design values of process parameters in the implementation of agricultural technologies has been compiled. A new approach has been developed to assess the risks of possible yield losses in the implementation of agrotechnology with deviations in process parameters from project values. Using the proposed approach will provide an automated ranking of options for decisions on the degree of risk of possible crop failure in case of deviations from the designed values, which will facilitate the transition to intelligent management of crop production.

The article presents an overview of publications describing the mechanisms of the development of the pathological process in patients infected with SARS-CoV-2 coronavirus. The authors analysed publications showing that damage to the endothelium and endothelial glycocalyx is the main factor influencing the development of coronavirus COVID-19 infection. It is the endothelium inflammation caused by a virus that leads to the dysfunction of the vascular system and the development of coagulopathy. Using scanning electron microscopy and flow cytofluorimetry, we showed that patients with COVID-19 of moderate severity (CT2) had significant desquamation of endothelial cells (concentration of circulating endothelial cells (CEC) in blood is 300-400 cells/ml whereas the normal range is no more than 10 cells/ ml). Such desquamation should cause exposure of the pro-inflammatory and thrombogenic subendothelial matrix, which, as a results, leads to the development of thrombotic disorders of the circulatory system. Therefore, it is only natural to try to counteract disease progression by protecting the endothelial glycocalyx from damage. Sulodexide, a mixture of fast-moving heparin fraction (80%) and the glycocalyx element dermatan sulfate (20%) obtained from the mucous membrane of the small intestine of pigs, is very promising in this regard. This drug can significantly reduce the inflammatory process, protect the glycocalyx and endothelium from damage, resulting in lowering the degree of thrombus formation in patients with coronavirus COVID-19 infection, which relieves the course of the disease and improves its outcome. The experimental data presented in the review, although obtained in not large enough population of patients, allow us to consider sulodexide a promising drug that protects the endothelium and suppresses thrombosis in COVID-19.

Introduction. Plant raw materials can be a source of biologically active substances and increase the nutritional value of food products. The present research objective was to determine the content of biologically active substances in powdered viburnum and barberry. Study objects and methods. The study featured viburnum (Viburnum opulus L.) and barberry (Berberis vulgaris L.), dried by convection and crushed into particles of 50 microns. Results and discussion. The total content of phenolic compounds in powdered viburnum was 3114.07 mg/100 g, in powdered barberry – 2272.7 mg/100 g. The content of flavonoids in powdered viburnum was 324.52 mg/100 g, in powdered barberry – 390.00 mg/100 g. The flavonoid profile of the powders included rutin, hyperoside, quercitrin, isoquercintrin, and astralagin. The total content of catechins was 446 mg/100 g for viburnum and 506 mg/100 g for barberry. The catechins included mainly epigallocatechin and catechin. In powdered viburnum, the catechin composition was as follows: epicatechin – 196, catechin – 118, and epigallocatechin – 89 mg/100 g; in powdered barberry: epigallocatechin – 173, catechin – 111, and epicatechin – 74 mg/100 g. The antiradical activity in relation to trolox equivalent was 7560 mg/100 g in powdered viburnum and 9460 mg/100 g in powdered barberry. Conclusion. The obtained viburnum and barberry powders can fortify food with biologically active substances and expand the range of functional products.

In the course of the study, a nonlinear dependence of the GDP dynamics on the increase in freight turnover was revealed. A regression model has been developed that determines the value of GDP depending on the dynamics of freight turnover. It should be stated that in Russia there is a rather low level of application of digital innovations, in comparison with foreign companies in developed countries, and continues to remain at a low level. Purpose. A hypothesis was put forward and proved that using a mathematical model, it is possible to obtain a forecast of Russia’s GDP for the next year based on the use of both a linear regression model reflecting the dependence of the GDP value on the dynamics of freight turnover, and using the AI-model “perceptron”, which includes the aggregate data reflecting the development of the economy. Method or methodology of the work. The work used research methods such as: monographic, analytical, linear regression and nonlinear mathematical model, as well as analysis, study and generalization. Results. A developed AI model (artificial intelligence model) “Perceptron” is presented, designed to predict Russia’s GDP based on input parameters representing a set of data reflecting the development of the real sector of the Russian economy in 2011–2019, including the dynamics of freight turnover. The experience of using artificial intelligence systems for forecasting time series, including the RF GDP, is considered. Field of application of the results: economics, financial sphere, forecasting and planning of economic and financial activities, economic security

The paper presents the results of a prognostic model of the occurrence of deviant behavior based on neurophysiological and neuropsychological indicators of behavioral regulation functions in adolescents. We used data on the state of the regulatory systems of the brain obtained on the basis of EEG analysis, data on the state of brain executive functions (EF), obtained on the basis of a neuropsychological examination, and the results of computer tests aimed at evaluating various components of attention. The study involved 166 adolescents, 69 of them with signs of deviant behavior. Using logistic regression analysis, it was shown that EEG-signs of suboptimal RS performance and evaluation of individual EF components (i.e. the ability to form and maintain an acquired action plan, signs of inertia and perseveration) can be significant predictors of deviant behavior. Computer test results showed poor predictive ability. A general model is also proposed that includes predicted probabilities of adolescents belonging to a group with signs of deviant behavior based on the results of using all three methods. A prognostic accuracy of the model is quite high.

The treatment of scars and impact by them their deformations, as well as the formation of pathological scars, remain one of the most difficult and insufficiently explored problems of plastic and reconstructive surgery. The most severe type of pathological scars is keloid a tumor-like growth of immature connective tissue due to uncontrolled proliferative fibroblast activity. The simple removal of keloids even within healthy tissue was often accompanied by their recurrence. Here presented clinical observation of the patient with benign fibroma of 1 finger of the right foot, who had after excision of formation, keloid scar has developed. Repeated operations with keloid excision and the use of free skin plastic have proved ineffective. Only long-term therapy with diprospan injections for 1.5 years allowed to achieve cure of the patient.

Progressive ischaemic stroke is a clinical form of unfavourable progression of an acute cerebrovascular accident, found in around 20% of cases during the acute period. The strategy and approach to managing patients with an apparent deterioration during the acute period of stroke have not been fully established. Various types of antithrombotic therapy are discussed in the literature, with the use of antiplatelet drugs considered the most promising. The authors analyse all types of antithrombotic therapy for progressive ischaemic stroke and examine in detail the only known case of late thrombolysis, which was performed in 1968.

Aim. To compare the kinetics of dissolution (in vitro) and some pharmacodynamic and pharmacokinetic parameters of branded generic preparations of acetylsalicylic acid (ASA) in buffered form in cardioprotective doses [ASA 75 mg+Mg(OH)2 15.2 mg], which differ in the composition of excipients, with a reference drug.Material and methods. Patients with cardiovascular diseases who had indications for ASA monotherapy (n=75) were included in a one-center open postregistration comparative non-randomized study. Patients were divided into 3 groups for treatment with one of the three studied drugs: Cardiomagnyl® (comparison drug; group 1; n=25), Trombital® (group 2; n=25) and Fazostabil® (group 3; n=25). A study of the kinetics of dissolution of the studied drugs in vitro under conditions of pH 1.2 has been performed. Also, platelet aggregation in response to arachidonic acid, the concentration of salicylic acid and the level of serum thromboxane B2 were studied in the compared groups.Results. The average release profile of ASA by the 30th minute from Cardiomagnyl® was higher than for Trombital® and Fazostabil® (95.7%, 84.8%, 76.5% respectively). The similarity factor (f2) of ASA release for Trombital® was 39.3, and for Fazostabil® - 34.2. An index of f2

The article is presented the description of rare clinical observation of the patient with pharyngeal lipofibroma complicated by compression syndrome, accompanied by impaired swallowing and breathing. The patient was successfully operated on with cervical access.

Introduction. The article focuses mainly on the activities of I.V. Stalin Crimean State Medical Institute during the Great Patriotic War, its main areas of work. Comprehensive scientific developments on this issue are not available in Russian historiography. Methods and materials. The research is carried out based on a variety of archival sources from the funds of the State Archive of the Russian Federation (SARF, Moscow), which are introduced into scientific discourse for the first time. Various groups of documents are widely involved: correspondence of S.R. Tatevosov, director of the institute, with the authorities on the functioning of the university; directives, decrees of state institutions reflected in the activities of the medical institute; university reporting and planning documentation. Analysis. Employees and students of Crimean Medical Institute were forced to leave the peninsula after the outbreak of the Great Patriotic War, from the moment of the threat of occupation of the Crimea. The university team had to be evacuated along the route: Armavir – Tbilisi – Krasnovodsk – Dzhambul – again Armavir – Ordzhonikidze – Baku – Krasnovodsk – Kzyl-Orda. Their wanderings lasted almost a year, however, the university did not stop its activities for a single day as the front was in dire need of medical specialists. Two turnouts graduated in Kzyl-Orda in incredibly difficult conditions. In total, from June 1941 to July 1944, the institute trained 850 doctors, wherefore the university staff was awarded with personal thanks of I.V. Stalin. Results. The heroic history of I.V. Stalin Crimean State Medical Institute during the Great Patriotic War has been restored. The activity of lecturers of the institute during the period of forced evacuation to the territory of the Kazakh Soviet Socialist Republic (the city of Kzyl-Orda) has been analyzed. The stage of resuming the work of the university after its re-evacuation to Simferopol has been recreated in detail, the measures taken to prepare for the beginning of the first academic year in the Crimea in the autumn of 1944 have been restored. In order to prepare the study, the authors have carried out a detailed historiographic analysis, and have selected the source base carefully.

Arterial thrombosis is a result of complex interaction between blood cells, soluble coagulation factors in plasma and vessel wall. Antiplatelet drugs do not always provide the necessary antithrombotic effect of sufficient strength, because their influence does not extend to all three factors involved in this process. Low doses of direct oral inhibitors of thrombin are able to potentiate antithrombotic effect of antiplatelet therapy. The combination of rivaroxaban in a dose of 2.5 mg and standard double antiplatelet therapy turned out to be the most promising for clinical use, since studies with dabigatran and apixaban at the II and III stages of the trials were found to be unsuccessful due to the unacceptably high frequency of bleeding. Studies of the combination of rivaroxaban at a dose of 2.5 mg and standard antiplatelet therapy conducted in previous years among patients with acute myocardial infarction showed a decrease in the frequency of complications of atherothrombosis associated with their ischemic nature, while at the same time there was a slight increase in hemorrhagic complications. In the COMPASS study the combination of rivaroxaban (2.5 mg) plus aspirin reduced the risk of the primary endpoint (myocardial infarction, ischemic stroke, cardiovascular death) more significantly than aspirin alone in patients with stable ischemic heart disease and ischemic brain disease. The pathophysiological rationales for the use of low doses of rivaroxaban when added to dual antiplatelet therapy are considered, and the significance of recent studies in patients with acute coronary syndrome, stable ischemic heart disease and in the prevention of ischemic stroke is discussed.

The article presents a rare variant of recurrent retrosternal goiter located in the posterior mediastinum. The localization and size of the goiter required the supplementation of traditional cervical access with a partial longitudinal sternotomy. It was shown relationship between the development of recurrent retrosternal goiter and nonradically performed primary operation.

The essential role of the Crimean Scientific Center of Shigabutdin Mardzhani Institute of History of Academy of Sciences of the Republic Tatarstan in study, preservation and promotion of the cultural heritage of the Republic Crimea has been specified. The third volume of the «Set of monuments of history, architecture and culture of the Crimean Tatars», dedicated to the antiquities of Muslim origin of the city of Simferopol, has been presented, its content has been analyzed in detail. A number of subjective remarks and comments have been indicated.

Based on the generalization of literary and cartographic materials, including the space survey data, the features of the spatial position, segmentation and tectonic position of the global trans-Euroasian dislocation belt and planetary fracturing, the Karpinsky lineament, were clarified. The described geomorphic structure is an integral part of the naturally spatially oriented ancient reghmatogenic fault framework of Eurasia, formed in the early stages of the rigid crust formation under the influence of cosmic rotational factors. The lineament at its various sites consists of various morphotectonic geostructural elements (linear grabens, shaft-like uplifts, folded depressions and orogens, suture zones, etc.). It dissects and divides the blocks of the earth's crust, which are very different in their nature and rank (ancient and young platforms, shields, zones of the Mezo-Cenozoic epiplatform orogeny, folded conflict zones), being a single transcontinental cross-cutting structure. The conjugation of these heterogeneous elements within one belt of concentration of deep faults is controlled by a long-lived quasi-stationary ordered planetary regmatic fault network, its north-west diagonal system. The Karpinsky lineament is divided in the longitudinal direction into two parts of the largest rank – the north-western paleorifthogenic (Pripyat graben – Tuarkyr folded zone) and the south-eastern collision shear (Central Mangyshlak – South Tien Shan), which differ in the structural pattern of faults and the prevail-ing dynamic mode of formation. The extent of the total longitudinal shear dislocations for the north-west paleorifthogenic part of the line-ament is within the first tens of kilometers, in most cases several kilometers; for the southeastern collision shear part, this order is larger, but does not exceed 2% of the total belt length, which makes it possible to characterize these dislocations as relatively low-shear.

The paper analyzes the properties of the constitutive relations of the theory of plasticity for a continuum, which plastic properties depend on the type of stress state. The plasticity condition presented in the corresponding generalized form is used, where the parameter of the type of stress state is introduced, which is the ratio of the hydrostatic stress component to the equivalent von Mises stress, named in the literature the stress triaxiality. For a particular type of plasticity condition, an analytical solution of the problem for a solid with a circular hole under plane strain is obtained. The stress distributions corresponding to the obtained solution are compared with the ones for a solid whose plastic properties are invariant to the stress state using the Huber – Mises plasticity condition. The influence of the degree of sensitivity of materials properties to the type of stress state on the stress distributions in plastic areas are studied.

Review presents data on medicines from the new group of cardiovascular drugs, direct oral anticoagulants (DOACs) inhibitors, as antidotes for DOAC when stopping life-threatening bleeding. DOAC therapy is accorded by hemorrhages with lower frequency than therapy by indirect anticoagulants, but really exist. New antidotes for DOACs are idarucizumab, andexanet, ciraparantag. The need in antidotes for DOAC may suddenly appear in spontaneous bleeding, during surgical operation, invasive procedure, due to trauma, in patients with stroke, kidney or liver failure. Data is given on the frequency of the main types of bleeding while taking new oral anticoagulants. Information concerning use of antidotes for DOACs in bleedings as well as use of non-specific therapy are reviewed.

Thrombocytopenia in blood count may be a reflex of haemostatic problems of different origin – from autoimmune disease to iatrogenic nature. Sometimes, thrombocytopenia may be revealed in patient with coronary heart disease before PCI procedure as well as some hours or days later. Dual antithrombotic therapy and thrombocytopenia have the same main side effect – bleeding. Etiology of disease and details of medical decision before percutаneous coronary intervention (PCI) in CHD patients are discussed.

The author reviews the history of research on the medieval settlement Eski-Yurt, located in the territory of the modern city of Bakhchisarai, identifying the main stages of its research and summarizing the progression of localization and identification of the monument in Russian historiography. The first attempts to preserve and protect architectural and archaeological objects were connected to the activities of the staff of the Central State Restoration Workshops, associated with the Bakhchisarai State Palace-Museum of Turkic-Tatar Culture, in the mid-1920s. The author describes the development of archaeological research of the site (expeditions led by A.S. Bashkirov and U.A. Bo­daninsky (1924), V.L. Myts (1991), S.V. Karlov (2005)). Objective: To accumulate, systematize and summarize the experience of researching the settlement of Eski-Yurt – possibly the largest Golden Horde center on the Crimean Peninsula after the decline of Solkhat’s role as an economic, military, cultural and, above all, administrative center, and the transfer of the capital to western Crimea. Research materials: Travel records of numerous voyagers who visited the Crimean Peninsula, both for personal purposes and in the call of duty (Martin Bronevsky, F. Dubois de Montpéreux, Jacob Lyzlov, P.S. Pallas, S.I. Tarbeev, Evliya Çelebi, and others); scientific developments brought about by Russian researchers in the field of monu­mentology of the Crimean peninsula of the Golden Horde period, particularly related to the study of the Eski-Yurt settlement (A.S. Bashkirov, U.A. Bodaninsky, P.I. Gollandsky, B.N. Zasypkin, A.A. Ivanov, S.V. Karlov, V.P. Kirilko and others). Archival materials from the Archives of the Monument Protection Department of the Bakhchisarai Historical and Cultural Archaeological Museum-Reserve (Bakhchisarai), of the Scientific Archives of the Institute of Archaeology of the Crimea, of the Russian Academy of Sciences (Simferopol), and of the Central State Archives of the city of Moscow. Results and novelty of the research: Three stages of the study of the site can be distinguished: 1. Late 18th – early 20th centuries: sketches in travel notes of the researcher-encyclopedists who frequently visited the Crimea after its accession to Russia (P.S. Pallas, F. Dubois de Montpéreux and others). The first scientific experience of the development of the site (A.F. Negri, V.D. Smirnov and others); 2. The 1920s: the beginnings of archaeo­logical research on the territory of the settlement (expedition of A.S. Bashkirov, U.A. Bodaninsky, 1924). Attempts to draw the attention of the authorities and the public to the problem of preserving cultural heritage sites at Eski-Yurt (activities of A.S. Bashkirov, U.A. Bodaninsky, P.I. Gollandsky, B.N. Zasypkin); 3. The last quarter of the 20th century – contemporary stage: the revival of scientific interest in the settlement (works of A.E. Gaivoronsky, A.A. Ivanov, V.P. Kirilko, O.M. Stoykova, M.M. Choref, etc.), and the continuation of archaeological research of the monument (expedition of S.V. Karlov, V.L. Myts). The placing of architectural and archaeological monuments of the settlement on the state record. The available material was systematized and summarized by the author who presented the architectural analysis of the monuments of the settlement. He analyzed the current state of objects of cultural heritage and pointed to the further need for the earliest possible archaeological study of the territory of the medieval settlement of Eski-Yurt.

Extracting various valuable medical information from head MRI and CT series is one of the most important and challenging tasks in the area of medical image analysis. Due to the lack of automation for many of these tasks, they require meticulous preprocessing from the medical experts. Nevertheless, some of these problems may have semi-automatic solutions, but they are still dependent on the person's competence. The main goal of our research project is to create an instrument that maximizes series processing automation degree. Our project consists of two parts: a set of algorithms for medical image processing and tools for its results interpretation. In this paper we present an overview of the best existing approaches in this field, as well the description of our own algorithms developed for similar tissue segmentation problems such as eye bony orbit and brain tumor segmentation based on convolutional neural networks. The investigation of performance of different neural network models for both tasks as well as neural ensembles applied to brain tumor segmentation is presented. We also introduce our software named "MISO Tool" which is created specifically for this type of problems. It allows tissues segmentation using pre-trained neural networks, DICOM pixel data manipulation and 3D reconstruction of segmented areas.

Background. The paper describes the results of the experimental study of working memory in early schoolchildren and adolescents. The capacity of working memory develops through preadolescence to adolescence, while the developmental changes of representations of serial order are not clear enough so far. Objective. The accuracy of delayed reproduction of movements (the open polygonal chains, trajectories) reveals. The sample included 32 early schoolchildren (mean age 10.4±0.75 years old) and 25 adolescents (mean age 14.6±0.3 years old). The subjects reproduced trajectories of different length (from 4 to 6 elements) using the graphical tablet. Another factor in the experiment carried out was a mode of stimulus presentation: the trajectories in were presented either as a static line-drawing (static mode) or a small moving pointer (dynamic mode). Design. The errors of reproduction in two age groups and in different conditions were analyzed. We distinguished three types of errors: (1) severe distortions which are caused by inability to keep the sequence in working memory; (2) insertions or omissions of elements of the trajectory which are related to the problems of encoding the visual spatial information in the motor representation; (3) distortions of proportions of the trajectories, which are related with visual-motor coordination during motor reproduction of the sequences. Research results showed that in adolescence the accuracy of delayed reproduction is better than in early ages. The most obvious difference in accuracy between the groups of early schoolchildren and adolescents were observed in dynamic mode. The analysis of different types of errors revealed that adolescents less often make insertions and omissions of elements. It may be caused by development of executive functions in adolescents. Conclusion. The study showed that the capacity of working memory increases in adolescents. In addition, the precision of the representation of serial information are higher in adolescents in the more complicated dynamic mode.

Use of antiplatelet therapy in the world indicates real difference in individual drug effect between patients, including the effect on prognosis. Problems of personification of individual approach, concerning antiplatelet therapy, are discussed.

The article reviews foreign and Russian literature on the organization of headache (HA) treatment. The economic burden of HA has been convincingly proven. The medical aid appealability of patients with HA has been established to be low (20–60%). This is frequently due to a lack of confidence in the success of a doctor's advice. The paper considers various models for the management of patients with HA in foreign countries. There is a low reliability of the diagnosis established during questionnaire surveys. Whether the patients can be managed using telecommunications, such as telephone and online counseling, is described. In Russia, there are substantially fewer treatment options for patients with HA; at the same time the available models are inclined to foreign ones. The paper describes world and Russian practices in educating the physicians and underlines the need for patients with HA to have educational classes. The sites where both the doctor and the patient can receive relevant information about HA are considered.

Objective: to assess the possibility of using the markers of acute kidney injury to predict the preoperative risk for the subsequent decline in glomerular filtration rate in different types of surgical treatment of renal cell carcinoma (RCC). Material and methods. 60 patients with histologically confirmed RCC T1-3M0N0 operated in the clinic of urology of Saratov State Medical University n.a. V. I. Razumovsky in the volume of nephrectomy or partial nephrectomy. Before surgery all patients underwent standard examination intended for patients with kidney tumors: ultrasound, MRI, excretory urography and dynamic renoscintigraphy, the perioperative values of serum creatinine and glomerular filtration rate were determined. Using the method of immuno-enzymatic analysis the concentrations of excreted with the urine NGAL and IL-18 in serum samples at the preoperative stage, after 5 days and after 1 month of postoperative follow-up were investigated. Differences in clinical data and clinical variables were compared by using Spearman rank correlations and t-test. Results. The determined parameters of acute kidney injury markers IL-18 and NGAL in the early postoperative period were increased in patients after open nephrectomy. Moreover, the correlation analysis according to the Spearman method revealed a strong significant correlation between the preoperative levels of IL-18 and GFR after surgery (r=1; p

The results of experimental study of memorizing and delayed reproduction (copying) of unfamiliar contour shapes in children and adult subjects are presented in the article. We analyzed the age-related characteristics of retention of the shapes (sequences) in working memory. 21 children (average age 7.2 y.o) participated in the experiment. They were shown trajectories (consisted of 6 vertical and horizontal lines) and were asked to remember and to reproduce them after acoustical go signal (short click). Go signal was delayed relatively to the end of trajectories exposure by T = 0, 500, 1000, 2000 or 4000 ms. We analyzed the number of errors, reaction time (RT), mean movement time (MT) along a single segment of trajectory, and the mean dwell time (DT) in the vertices of the trajectory. We compared the results with the analogous data collected previously in the sample of adult subjects. The analysis shows that children made more errors. Beside among children the accuracy of the reproduction decreases whit increasing of the delay of go signal. Also it is shown that RT depends on the delay T, and the shape of the dependence is similar among both children and adults. The results allow to assume the transformation of primary sensory-specific representation in an abstract representation of the sequence both in children and in adults. This work was supported by grant RSF № 14-18-037037.

Dual antiplatelet therapy, including clopidogrel and aspirin, in a significant share of patients after acute coronary syndrome (ACS) is characterized by high level of platelet reactivity, which is associated with an increased incidence of cardiovascular events. Perhaps it will make reasonable the prescription of new antiplatelet drugs, particularly the combination of ticagrelor with aspirin.Aim. To assess the cost-effectiveness of VerifyNow P2Y12 platelet reactivity testing in patients after ACS.Material and methods. The analysis was performed for patients aged 55 years after ACS by modeling based on the results of the PLATO trial considering Russian epidemiological data. The time horizon of simulation was 5 years. It was assumed that the patients were receiving either generic clopidogrel or ticagrelor for 1 year, or before maintenance treatment VerifyNow P2Y12 assay had been performed, and the patients with platelet reactivity index >230 24-48 hours after ACS were receiving ticagrelor and the remaining patients - generic clopidogrel. It was expected that after 1 year the patients would discontinue treatment with clopidogrel or ticagrelor, and hereafter additional therapeutic effect of their use would be absent. The costs of antiplatelet agents in the reference case corresponded to the weighted average price of public procurement in 2013 in Russia. The costs of treatment of complications corresponded to the compulsory health insurance rates for St. Petersburg in 2014. The cost and life expectancy were discounted at 3.5% per year.Results. The platelet reactivity test and the prescription by its results of the combination of clopidogrel plus aspirin or ticagrelor plus aspirin can prevent 5 myocardial infarction and 6 deaths per 1000 patients additionally as compared with the prescription of clopidogrel plus aspirin combination to all patients. The costs for one additional year of life as compared with the combination of clopidogrel plus aspirin were 376.5 and 373.2 thousand rubles, respectively, the costs for one additional quality adjusted life year (QALY) were 433.8 and 509.8 thousand rubles, respectively. The total predictive value of costs per 1 patient with ACS using the test was 32% lower than in the routine use of ticagrelor in all patients.Conclusion. Assessment of platelets reactivity using VerifyNow P2Y12 assay in patients with ACS and modification of antithrombotic therapy in accordance with the received data can be considered as a cost-effective intervention which can reduce treatment costs as compared with the routine use of new antithrombotic drugs.

The paper presents a review of the main results of current works on the functionalization methods for carbon nanotubes and graphene, modification of their properties as a result of doping and possible use of materials based on them. The methods of covalent attachment of functional groups to graphene and carbon nanotubes, heteroatom substitution and noncovalent inclusion of nanoparticles and molecules are considered. The perspectives of the use of chemically modified carbon nanotubes and graphene in production of a new generation of electronic devices with high mechanical strength, flexibility and elasticity are shown.

Aim. To analyze the influence of pharmacogenetic factors on the risk of clopidogrel resistance and cardiovascular events during 18-months follow-up. Subjects and methods. Two hundred and fifty patients taking clopidogrel were examined. Platelet function was determined by optical aggregometry. Thromboxane A synthase 1 (TBS1) gene polymorphism was investigated in all the patients. The impact of TBS1 gene polymorphism on the risk of clopidogrel resistance and cardiovascular events was analyzed during 18 months of follow-up. Results. The carriage of TBS1 gene polymorphism AA was shown to affect the risk of clopidogrel resistance. Cardiovascular complications significantly less frequently occurred in TBS1 gene polymorphism AA carriers during 18 months. Conclusion. The carriage of a slow AA allele of the TBS1 gene is suggested to be a clinically significant protective factor in the secondary prevention of coronary heart disease

Objective: to draw the attention of experts on rare cause of secondary parkinsonism — a brain tumor. Material and methods. The clinical observation of the patient 52 years with Parkinson's syndrome. Brain MRI. Results. In parallel the patient had classical progressing clinic bilateral parkinsonism in combination with progressive cognitive decline. MRI research revealed the left frontal lobe tumor. Conclusions. Secondary parkinsonism as a result of intracerebral neoplasms are caused mostly by compression of basal ganglia or mesencephalon increasing tumors. Hemiparkinsonism in these cases usually precedes the other focal neurological symptoms.

Aim. To evaluate the clinical efficacy, safety, and effects of the nonsteroidal anti-inflammatory drug lornoxicam on inflammatory markers in non-ST-segment elevation acute coronary syndrome (NSTEACS). Subjects and methods. Eighty-five patients with NSTESCS were enrolled in a prospective randomized study. They were divided into 2 groups: 1) patients received lornoxicam (8 or 12 mg/daily) for 15 days in addition to standard treatment and 2) controls. Cardiovascular outcomes and the levels of C-reactive protein (CRP), IL-6, and IL-10 were determined. Results. The lornoxicam group exhibited a significant reduction in CRP and a decrease in IL-6. There was also a significant increase in IL-10 levels in this group. A 6-month follow-up indicated a significant reduction in the number of cardiovascular events (nonfatal myocardial infarction, unstable angina, coronary death). Conclusion. Lornoxicam reduces the risk of poor cardiovascular outcomes in patients with NSTEACS and the levels of inflammatory markers.