148 research outputs found

    Analysis of changes in pharmacotherapy of stable angina over the five-year period at specialized out-patient level of medical care (pharmacoepidemiological study)

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    Investigate the dynamics of drug prescription rates in patients with stable angina over the five-year period on the example of routine clinical practice of outpatient cardiology institution of Moscow for the purpose of further eliminating the prescribing gap for guideline recommended pharmacological strategies. Our research work was performed as a retrospective pharmacoepidemiological study including two stages with five-year interval using cross-section metho

    Forecasting planned electricity consumption for the united power system using machine learning

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    The paper presents the results of studies of the predictive models development based on retrospective data on planned electricity consumption in the region with a significant share of enterprises in the mineral resource complex. Since the energy intensity of the industry remains quite high, the task of rationalizing the consumption of electricity is relevant. One of the ways to improve control accuracy when planning energy costs is to forecast electrical loads. Despite the large number of scientific papers on the topic of electricity consumption forecasting, this problem remains relevant due to the changing requirements of the wholesale electricity and power market to the accuracy of forecasts. Therefore, the purpose of this study is to support management decisions in the process of planning the volume of electricity consumption. To realize this, it is necessary to create a predictive model and determine the prospective power consumption of the power system. For this purpose, the collection and analysis of initial data, their preprocessing, selection of features, creation of models, and their optimization were carried out. The created models are based on historical data on planned power consumption, power system performance (frequency), as well as meteorological data. The research methods were: ensemble methods of machine learning (random forest, gradient boosting algorithms, such as XGBoost and CatBoost) and a long short-term memory recurrent neural network model (LSTM). The models obtained as a result of the conducted studies allow creating short-term forecasts of power consumption with a fairly high precision (for a period from one day to a week). The use of models based on gradient boosting algorithms and neural network models made it possible to obtain a forecast with an error of less than 1Β %, which makes it possible to recommend the models described in the paper for use in forecasting the planned electricity power consumption of united power systems

    Critical aspects of the management of stable coronary artery disease in primary care practice or how to increase the efficacy of evidence-based pharmacological therapy?

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    The publication describes a fragment of the pharmacoepidemiologic study conducted to review the quality of management of patients with stable coronary artery disease (SCAD) in primary care over a 12-year period. The aim of the study was to justify the application of standard operating procedures (SOPs). Such determinants of pharmacotherapy as non-pharmacological modification of cardiovascular risk factors (RFs) and medication adherence were analyze

    ИсслСдованиС ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌ ΠΏΡ€Π΅Π΄ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Ρ‚Π΅Π»ΡŒΡΠΊΠΎΠ³ΠΎ образования Π² России: оТидания стСйкхолдСров ΠΈ ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΠ° унивСрситСтов

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    Introduction. Entrepreneurial education, as an area of educational practice in higher education, is a relatively new area of activity for Russian universities. In this area, due to the special dynamics of development and transformation, especially in a pandemic, there is the most significant gap between the competencies formed by universities and in demand on the labour market. The rationale for the research stemmed from two major trends in the economy and society: industry demand for workforce with greater enterprise skills, at the same time a new generation, generation Z, seeks more flexible and more fulfilling career path. Therefore, to address these trends, universities have to diversify the skill set included in the academic curriculum. Aim. This study is aimed at studying the problems of interaction between universities and their stakeholders in curricula improvement. Methodology and research methods. Taken into consideration the regulatory nature of the curricula design in Russian Higher Education Institutions (HEIs) a two-step strategy has been adopted for this research. The first step was a concern with meta-analysis of the competencies outlined in Federal State Educational Standard (FSES) in Management through the lens of entrepreneurial competencies. The second step was to investigate inclusion of soft skills in entrepreneurship curricula in across Russian HEIs. To address the objective of research, descriptive statistics and non-parametric Mann-Whitney U-test were applied. Results. The research findings suggest in the environment where the degree programmes have to comply with set Governmental standards, curricula in entrepreneurship struggle to develop essential soft entrepreneurial skills. Most of the analysed curricula are heavily loaded with hard and cognitive skills. Even though the government proclaims a need for innovative development of the nation, creative and innovative thinking is not mentioned either in the FSES nor analysed curricula. The research findings also led to a surprising conclusion that very few core β€˜business’ modules include the development of social or action-oriented skills in their learning outcomes. Scientific novelty. The scientific novelty of this study lies in the fact that for the first time the problems of ensuring the development of soft skills in entrepreneurial education in Russia have been studied. Practical significance. The results of the study will find their application in the design of entrepreneurial curricula to achieve the necessary balance of competencies in them.Π’Π²Π΅Π΄Π΅Π½ΠΈΠ΅. ΠŸΡ€Π΅Π΄ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Ρ‚Π΅Π»ΡŒΡΠΊΠΎΠ΅ ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠ°ΠΊ ΠΎΠ±Π»Π°ΡΡ‚ΡŒ ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½ΠΎΠΉ ΠΏΡ€Π°ΠΊΡ‚ΠΈΠΊΠΈ Π² Π²Ρ‹ΡΡˆΠ΅ΠΉ школС являСтся ΠΎΡ‚Π½ΠΎΡΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎ Π½ΠΎΠ²ΠΎΠΉ сфСрой Π΄Π΅ΡΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ для российских Π²ΡƒΠ·ΠΎΠ², Π² ΠΊΠΎΡ‚ΠΎΡ€ΠΎΠΉ Π² силу особой Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ развития ΠΈ трансформации, особСнно Π² условиях ΠΏΠ°Π½Π΄Π΅ΠΌΠΈΠΈ, Π½Π°Π±Π»ΡŽΠ΄Π°Π΅Ρ‚ΡΡ Π½Π°ΠΈΠ±ΠΎΠ»Π΅Π΅ Π·Π½Π°Ρ‡ΠΈΡ‚Π΅Π»ΡŒΠ½Ρ‹ΠΉ Ρ€Π°Π·Ρ€Ρ‹Π² ΠΌΠ΅ΠΆΠ΄Ρƒ компСтСнциями, сформированными Π²ΡƒΠ·Π°ΠΌΠΈ ΠΈ вострСбованными Π½Π° Ρ€Ρ‹Π½ΠΊΠ΅ Ρ‚Ρ€ΡƒΠ΄Π°. НастоящСС исслСдованиС базируСтся Π½Π° Π΄Π²ΡƒΡ… основных тСндСнциях Π² экономикС ΠΈ общСствС: отраслСвой спрос Π½Π° Ρ€Π°Π±ΠΎΡ‡ΡƒΡŽ силу с Π±ΠΎΠ»Π΅Π΅ высокими Π½Π°Π²Ρ‹ΠΊΠ°ΠΌΠΈ ΠΏΡ€Π΅Π΄ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Ρ‚Π΅Π»ΡŒΡΡ‚Π²Π° ΠΈ Π² Ρ‚ΠΎ ΠΆΠ΅ врСмя поиск ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΠ΅ΠΌ Z Π±ΠΎΠ»Π΅Π΅ Π³ΠΈΠ±ΠΊΠΈΡ… ΠΈ насыщСнных ΠΊΠ°Ρ€ΡŒΠ΅Ρ€Π½Ρ‹Ρ… пСрспСктив. ΠŸΠΎΡΡ‚ΠΎΠΌΡƒ Π² ΠΎΡ‚Π²Π΅Ρ‚ Π½Π° эти Ρ‚Π΅Π½Π΄Π΅Π½Ρ†ΠΈΠΈ унивСрситСты Π΄ΠΎΠ»ΠΆΠ½Ρ‹ Ρ€Π°Π·Π½ΠΎΠΎΠ±Ρ€Π°Π·ΠΈΡ‚ΡŒ Π½Π°Π±ΠΎΡ€ ΠΊΠΎΠΌΠΏΠ΅Ρ‚Π΅Π½Ρ†ΠΈΠΉ, Ρ„ΠΎΡ€ΠΌΠΈΡ€ΡƒΠ΅ΠΌΡ‹Ρ… ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹ΠΌΠΈ ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠ°ΠΌΠΈ. ЦСль. Π”Π°Π½Π½ΠΎΠ΅ исслСдованиС Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½ΠΎ Π½Π° ΠΈΠ·ΡƒΡ‡Π΅Π½ΠΈΠ΅ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌ взаимодСйствия унивСрситСтов ΠΈ ΠΈΡ… стСйкхолдСров Π² ΡΠΎΠ²Π΅Ρ€ΡˆΠ΅Π½ΡΡ‚Π²ΠΎΠ²Π°Π½ΠΈΠΈ ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌ. ΠœΠ΅Ρ‚ΠΎΠ΄ΠΎΠ»ΠΎΠ³ΠΈΡ, ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΠΈ. Π‘ ΡƒΡ‡Π΅Ρ‚ΠΎΠΌ Π½ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½ΠΎΠ³ΠΎ Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€Π° Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌ Π² российских Π²ΡƒΠ·Π°Ρ… для Π΄Π°Π½Π½ΠΎΠ³ΠΎ исслСдования Π±Ρ‹Π»Π° принята двухэтапная стратСгия. На ΠΏΠ΅Ρ€Π²ΠΎΠΌ этапС Π±Ρ‹Π» ΠΏΡ€ΠΎΠ²Π΅Π΄Π΅Π½ ΠΌΠ΅Ρ‚Π°Π°Π½Π°Π»ΠΈΠ· ΠΊΠΎΠΌΠΏΠ΅Ρ‚Π΅Π½Ρ†ΠΈΠΉ Π€Π“ΠžΠ‘ ΠΏΠΎ Π½Π°ΠΏΡ€Π°Π²Π»Π΅Π½ΠΈΡŽ ΠΏΠΎΠ΄Π³ΠΎΡ‚ΠΎΠ²ΠΊΠΈ Β«ΠœΠ΅Π½Π΅Π΄ΠΆΠΌΠ΅Π½Ρ‚Β» Ρ‡Π΅Ρ€Π΅Π· ΠΏΡ€ΠΈΠ·ΠΌΡƒ ΠΏΡ€Π΅Π΄ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Ρ‚Π΅Π»ΡŒΡΠΊΠΈΡ… ΠΊΠΎΠΌΠΏΠ΅Ρ‚Π΅Π½Ρ†ΠΈΠΉ. Π’Ρ‚ΠΎΡ€Ρ‹ΠΌ шагом Π±Ρ‹Π»ΠΎ исслСдованиС Π²ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΡ мягких Π½Π°Π²Ρ‹ΠΊΠΎΠ² Π² ΡƒΡ‡Π΅Π±Π½Ρ‹Π΅ ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΡ‹ ΠΏΠΎ ΠΏΡ€Π΅Π΄ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Ρ‚Π΅Π»ΡŒΡΡ‚Π²Ρƒ Π² российских Π²ΡƒΠ·Π°Ρ…, Ρ€Π΅Π°Π»ΠΈΠ·ΡƒΡŽΡ‰ΠΈΡ… ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΡ‹ ΠΏΡ€Π΅Π΄ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Ρ‚Π΅Π»ΡŒΡΠΊΠΎΠ³ΠΎ образования. Для Ρ€Π΅ΡˆΠ΅Π½ΠΈΡ Π·Π°Π΄Π°Ρ‡ΠΈ исслСдования ΠΏΡ€ΠΈΠΌΠ΅Π½ΡΠ»ΠΈΡΡŒ ΠΎΠΏΠΈΡΠ°Ρ‚Π΅Π»ΡŒΠ½Π°Ρ статистика, Π° Ρ‚Π°ΠΊΠΆΠ΅ нСпарамСтричСский U-ΠΊΡ€ΠΈΡ‚Π΅Ρ€ΠΈΠΉ Манна – Π£ΠΈΡ‚Π½ΠΈ. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ исслСдования ΠΏΠΎΠΊΠ°Π·Ρ‹Π²Π°ΡŽΡ‚, Ρ‡Ρ‚ΠΎ Π² ΠΏΡ€Π΅Π΄ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Ρ‚Π΅Π»ΡŒΡΠΊΠΈΡ… ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠ°Ρ…, ΡΠΎΠΎΡ‚Π²Π΅Ρ‚ΡΡ‚Π²ΡƒΡŽΡ‰ΠΈΡ… Π€Π“ΠžΠ‘, удСляСтся Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ Ρ€Π°Π·Π²ΠΈΡ‚ΠΈΡŽ Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΡ‹Ρ… мягких ΠΏΡ€Π΅Π΄ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Ρ‚Π΅Π»ΡŒΡΠΊΠΈΡ… Π½Π°Π²Ρ‹ΠΊΠΎΠ². Однако Π±ΠΎΠ»ΡŒΡˆΠΈΠ½ΡΡ‚Π²ΠΎ ΠΏΡ€ΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Π½Ρ‹Ρ… ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌ ΠΏΠ΅Ρ€Π΅Π³Ρ€ΡƒΠΆΠ΅Π½Ρ‹ дисциплинами, Ρ„ΠΎΡ€ΠΌΠΈΡ€ΡƒΡŽΡ‰ΠΈΠΌΠΈ Ρ€ΡƒΡ‚ΠΈΠ½Π½Ρ‹Π΅ ΠΈ ΠΊΠΎΠ³Π½ΠΈΡ‚ΠΈΠ²Π½Ρ‹Π΅ Π½Π°Π²Ρ‹ΠΊΠΈ. НСсмотря Π½Π° Ρ‚ΠΎ Ρ‡Ρ‚ΠΎ государство ΠΏΡ€ΠΎΠ²ΠΎΠ·Π³Π»Π°ΡˆΠ°Π΅Ρ‚ Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎΡΡ‚ΡŒ ΠΈΠ½Π½ΠΎΠ²Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ развития Π½Π°Ρ†ΠΈΠΈ, творчСскоС ΠΈ ΠΈΠ½Π½ΠΎΠ²Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠ΅ ΠΌΡ‹ΡˆΠ»Π΅Π½ΠΈΠ΅ Π½Π΅ упоминаСтся Π½ΠΈ Π² Ρ„Π΅Π΄Π΅Ρ€Π°Π»ΡŒΠ½Ρ‹Ρ… государствСнных ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… стандартах (Π€Π“ΠžΠ‘), Π½ΠΈ Π² Π°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΡƒΠ΅ΠΌΡ‹Ρ… ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠ°Ρ…. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ исслСдования Ρ‚Π°ΠΊΠΆΠ΅ ΠΏΡ€ΠΈΠ²Π΅Π»ΠΈ ΠΊ Π½Π΅ΠΎΠΆΠΈΠ΄Π°Π½Π½ΠΎΠΌΡƒ Π²Ρ‹Π²ΠΎΠ΄Ρƒ ΠΎ Ρ‚ΠΎΠΌ, Ρ‡Ρ‚ΠΎ ΠΎΡ‡Π΅Π½ΡŒ Π½Π΅ΠΌΠ½ΠΎΠ³ΠΈΠ΅ Β«ΠΏΡ€Π΅Π΄ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Ρ‚Π΅Π»ΡŒΡΠΊΠΈΠ΅Β» ΠΌΠΎΠ΄ΡƒΠ»ΠΈ ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Π°Ρ‚Π΅Π»ΡŒΠ½Ρ‹Ρ… ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌ Π²ΠΊΠ»ΡŽΡ‡Π°ΡŽΡ‚ Π² свои Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ обучСния Ρ€Π°Π·Π²ΠΈΡ‚ΠΈΠ΅ ΡΠΎΡ†ΠΈΠ°Π»ΡŒΠ½Ρ‹Ρ… ΠΈΠ»ΠΈ практичСских Π½Π°Π²Ρ‹ΠΊΠΎΠ². Научная Π½ΠΎΠ²ΠΈΠ·Π½Π°. Научная Π½ΠΎΠ²ΠΈΠ·Π½Π° настоящСго исслСдования состоит Π² Ρ‚ΠΎΠΌ, Ρ‡Ρ‚ΠΎ Π²ΠΏΠ΅Ρ€Π²Ρ‹Π΅ ΠΈΠ·ΡƒΡ‡Π΅Π½Ρ‹ ΠΏΡ€ΠΎΠ±Π»Π΅ΠΌΡ‹ обСспСчСния развития мягких Π½Π°Π²Ρ‹ΠΊΠΎΠ² Π² ΠΎΠ±ΡƒΡ‡Π΅Π½ΠΈΠΈ ΠΏΡ€Π΅Π΄ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Ρ‚Π΅Π»ΡŒΡΡ‚Π²Ρƒ Π² России. ΠŸΡ€Π°ΠΊΡ‚ΠΈΡ‡Π΅ΡΠΊΠ°Ρ Π·Π½Π°Ρ‡ΠΈΠΌΠΎΡΡ‚ΡŒ. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹ исслСдования Π½Π°ΠΉΠ΄ΡƒΡ‚ своС ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΏΡ€ΠΈ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚ΠΊΠ΅ ΠΏΡ€Π΅Π΄ΠΏΡ€ΠΈΠ½ΠΈΠΌΠ°Ρ‚Π΅Π»ΡŒΡΠΊΠΈΡ… ΡƒΡ‡Π΅Π±Π½Ρ‹Ρ… ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌ для достиТСния Π² Π½ΠΈΡ… Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΠΎΠ³ΠΎ баланса ΠΊΠΎΠΌΠΏΠ΅Ρ‚Π΅Π½Ρ†ΠΈΠΉ.The research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University Programme of Development within the Priority-2030 Programme) is gratefully acknowledged.ИсслСдованиС Π²Ρ‹ΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΏΡ€ΠΈ финансовой ΠΏΠΎΠ΄Π΄Π΅Ρ€ΠΆΠΊΠ΅ ΠœΠΈΠ½ΠΈΡΡ‚Π΅Ρ€ΡΡ‚Π²Π° Π½Π°ΡƒΠΊΠΈ ΠΈ Π²Ρ‹ΡΡˆΠ΅Π³ΠΎ образования Российской Π€Π΅Π΄Π΅Ρ€Π°Ρ†ΠΈΠΈ Π² Ρ€Π°ΠΌΠΊΠ°Ρ… ΠŸΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΡ‹ развития Π£Ρ€Π°Π»ΡŒΡΠΊΠΎΠ³ΠΎ Ρ„Π΅Π΄Π΅Ρ€Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ унивСрситСта ΠΈΠΌΠ΅Π½ΠΈ ΠΏΠ΅Ρ€Π²ΠΎΠ³ΠΎ ΠŸΡ€Π΅Π·ΠΈΠ΄Π΅Π½Ρ‚Π° России Π‘. Н. Π•Π»ΡŒΡ†ΠΈΠ½Π° Π² соотвСтствии с ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠΎΠΉ стратСгичСского акадСмичСского лидСрства Β«ΠŸΡ€ΠΈΠΎΡ€ΠΈΡ‚Π΅Ρ‚-2030Β»

    Comparative analysis of oxidative metabolism indicators at acute alcohol and acute surrogate alcohol intoxication

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    High level of population alcoholization is the cause of many cases of acute alcohol and alcoholic surrogate intoxication. The number of alcohol intoxication cases in Kazakhstan in 2014 amounted to 13891 (80.3 per 100 000 people), the number of fatal intoxication cases amounted to 882 (5.1 per 100 000 people). The problem of alcoholization in Russia remains urgent as well: according to the statistics of 2014,152 551 cases of acute intoxication of chemical etiology were registered, 33.9 % of cases occurred due to alcohol intoxication. Alcoholic beverages in the course of their biotransformation to acetic acid can form oxygen free radicals in particular superoxide anion as a byproduct of acetic aldehyde oxidation reaction. Studies on oxidative metabolism of ethanol intoxication are currently being conducted. At the same time, the state of oxidative metabolism during alcoholic surrogate intoxication was not practically investigated. Evaluation of oxidative metabolism depending on the severity of alcohol or its surrogate intoxication is of special interest. The aim was to compare oxidative metabolism indicators among patients with acute alcohol and alcoholic surrogate intoxication of different severity. The object of the study was blood of 62 people with diagnosed moderate or severe degrees of acute alcohol and alcoholic surrogate intoxication. Indicators of oxidative metabolism in erythrocytes and blood plasma were estimated. Significant differences were found in product concentration of protein oxidation containing bityrosine crosslinks in blood plasma under increase of alcohol intoxication degree

    НСйровизуализационныС особСнности строСния Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° Ρƒ Π΄Π΅Ρ‚Π΅ΠΉ с дСтским Ρ†Π΅Ρ€Π΅Π±Ρ€Π°Π»ΡŒΠ½Ρ‹ΠΌ ΠΏΠ°Ρ€Π°Π»ΠΈΡ‡ΠΎΠΌ, ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Π΅ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ ΠΌΠ°Π³Π½ΠΈΡ‚Π½ΠΎ-рСзонансной Ρ‚Ρ€Π°ΠΊΡ‚ΠΎΠ³Ρ€Π°Ρ„ΠΈΠΈ

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    Aim. To perform quantitative evaluation of the degree of white matter tract abnormalities in children with spastic cerebral palsy by magnetic resonance tractography to determine severity of the disease, as well as to carry out a dynamic assessment of treatment effectiveness.Materials and methods. The study included 46 children (32 males, 14 females; average age 5.4 Β± 1.1 years). The participants were divided into two groups. The experimental group consisted of 23 children with spastic cerebral palsy. The control group included 23 children without any neurological disorder. Examination of the brain was performed on the Siemens Essenza 1,5 Π’ system (Siemens, Germany) and included magnetic resonance tractography to reconstruct the major white matter tracts. The number of fibers, average fractional anisotropy value, apparent diffusion coefficient, and coefficient of myelination of major white matter tracts in the brain were calculated and analyzed.Results. We found a significant difference in the above-stated parameters between the groups. The experimental group showed a decrease in the absolute number of fibers at the central and posterior segments of the corpus callosum, corticospinal tracts, and left inferior longitudinal fasciculus. Besides, we detected a decrease in fractional anisotropy at 2–5 segments of the corpus callosum and right lateral corticospinal tract, an increase in the apparent diffusion coefficient at 2, 4, and 5 segments of the corpus callosum and left lateral corticospinal tract, and a decrease in the myelination coefficient in all the examined tracts, except for superior longitudinal fasciculus. We revealed a positive correlation between the intensity of the motor disturbance and the coefficient of myelination at the anterior corpus callosum and inferior longitudinal fasciculus.Conclusion. Magnetic resonance tractography is an informative technique for unbiased evaluation of white matter tract anatomy, as well the level and degree of motor tract damage. The most useful characteristics of white matter tract anatomy are the absolute number of fibers in the tract, fractional anisotropy, and coefficient of myelination. Some of them correlated with the intensity of motor disturbance, so they can be regarded as potential predictors of rehabilitation potential. ЦСль. ΠšΠΎΠ»ΠΈΡ‡Π΅ΡΡ‚Π²Π΅Π½Π½Π°Ρ ΠΎΡ†Π΅Π½ΠΊΠ° стСпСни Π½Π°Ρ€ΡƒΡˆΠ΅Π½ΠΈΠΉ развития проводящих ΠΏΡƒΡ‚Π΅ΠΉ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° Ρƒ Π΄Π΅Ρ‚Π΅ΠΉ со спастичСскими Ρ„ΠΎΡ€ΠΌΠ°ΠΌΠΈ дСтского Ρ†Π΅Ρ€Π΅Π±Ρ€Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ ΠΏΠ°Ρ€Π°Π»ΠΈΡ‡Π° (Π”Π¦ΠŸ) ΠΌΠ΅Ρ‚ΠΎΠ΄Π°ΠΌΠΈ ΠΌΠ°Π³Π½ΠΈΡ‚Π½ΠΎ-рСзонансной (МР) Ρ‚Ρ€Π°ΠΊΡ‚ΠΎΠ³Ρ€Π°Ρ„ΠΈΠΈ для опрСдСлСния тяТСсти заболСвания, Π° Ρ‚Π°ΠΊΠΆΠ΅ ΠΎΡ†Π΅Π½ΠΊΠ° Π΄ΠΈΠ½Π°ΠΌΠΈΠΊΠΈ эффСктивности лСчСния.ΠœΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Ρ‹ ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹. ΠžΠ±ΡΠ»Π΅Π΄ΠΎΠ²Π°Π½Ρ‹ 46 Π΄Π΅Ρ‚Π΅ΠΉ 4–7 Π»Π΅Ρ‚ (срСдний возраст (5,4 Β± 1,1) Π»Π΅Ρ‚), ΠΈΠ· Π½ΠΈΡ… 14 Π΄Π΅Π²ΠΎΡ‡Π΅ΠΊ (33%) ΠΈ 32 ΠΌΠ°Π»ΡŒΡ‡ΠΈΠΊΠ° (66%). ΠŸΠ°Ρ†ΠΈΠ΅Π½Ρ‚Ρ‹ Ρ€Π°Π·Π΄Π΅Π»Π΅Π½Ρ‹ Π½Π° Π΄Π²Π΅ Π³Ρ€ΡƒΠΏΠΏΡ‹. Π˜ΡΡΠ»Π΅Π΄ΡƒΠ΅ΠΌΡƒΡŽ Π³Ρ€ΡƒΠΏΠΏΡƒ составили 23 ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π° со спастичСскими Ρ„ΠΎΡ€ΠΌΠ°ΠΌΠΈ Π”Π¦ΠŸ. Π’ ΠΊΠΎΠ½Ρ‚Ρ€ΠΎΠ»ΡŒΠ½ΡƒΡŽ Π³Ρ€ΡƒΠΏΠΏΡƒ вошли 23 Ρ€Π΅Π±Π΅Π½ΠΊΠ° Π±Π΅Π· нСврологичСского Π΄Π΅Ρ„ΠΈΡ†ΠΈΡ‚Π°. ИсслСдованиС Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΎΡΡŒ Π½Π° МР-Ρ‚ΠΎΠΌΠΎΠ³Ρ€Π°Ρ„Π΅ Siemens Essenza 1,5 Π’ (Siemens, ГСрмания) ΠΈ Π²ΠΊΠ»ΡŽΡ‡Π°Π»ΠΎ ΠΌΠ΅Ρ‚ΠΎΠ΄ МР-Ρ‚Ρ€Π°ΠΊΡ‚ΠΎΠ³Ρ€Π°Ρ„ΠΈΠΈ. Π‘Ρ‹Π»ΠΈ рассчитаны ΠΈ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚Π°Π½Ρ‹: количСство Π²ΠΎΠ»ΠΎΠΊΠΎΠ½, срСдний ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒ Ρ„Ρ€Π°ΠΊΡ†ΠΈΠΎΠ½Π½ΠΎΠΉ Π°Π½ΠΈΠ·ΠΎΡ‚Ρ€ΠΎΠΏΠΈΠΈ, коэффициСнт Π΄ΠΈΡ„Ρ„ΡƒΠ·ΠΈΠΈ, коэффициСнт ΠΌΠΈΠ΅Π»ΠΈΠ½ΠΈΠ·Π°Ρ†ΠΈΠΈ основных проводящих ΠΏΡƒΡ‚Π΅ΠΉ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π°.Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹. ВыявлСна достовСрная Ρ€Π°Π·Π½ΠΈΡ†Π° ΡƒΠΊΠ°Π·Π°Π½Π½Ρ‹Ρ… Π²Ρ‹ΡˆΠ΅ ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ ΠΌΠ΅ΠΆΠ΄Ρƒ ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚Π°ΠΌΠΈ исслСдуСмой ΠΈ ΠΊΠΎΠ½Ρ‚Ρ€ΠΎΠ»ΡŒΠ½ΠΎΠΉ Π³Ρ€ΡƒΠΏΠΏ. Π£ Π΄Π΅Ρ‚Π΅ΠΉ с Π”Π¦ΠŸ ΠΎΡ‚ΠΌΠ΅Ρ‡Π°Π»ΠΎΡΡŒ сниТСниС Π°Π±ΡΠΎΠ»ΡŽΡ‚Π½ΠΎΠ³ΠΎ количСства Π²ΠΎΠ»ΠΎΠΊΠΎΠ½ Π² области Ρ†Π΅Π½Ρ‚Ρ€Π°Π»ΡŒΠ½Ρ‹Ρ… ΠΈ Π·Π°Π΄Π½Π΅Π³ΠΎ сСгмСнтов мозолистого Ρ‚Π΅Π»Π°, ΠΊΠΎΡ€Ρ‚ΠΈΠΊΠΎΡΠΏΠΈΠ½Π°Π»ΡŒΠ½Ρ‹Ρ… Ρ‚Ρ€Π°ΠΊΡ‚ΠΎΠ² ΠΈ Π»Π΅Π²ΠΎΠ³ΠΎ Π½ΠΈΠΆΠ½Π΅Π³ΠΎ ΠΏΡ€ΠΎΠ΄ΠΎΠ»ΡŒΠ½ΠΎΠ³ΠΎ ΠΏΡƒΡ‡ΠΊΠ°. Π’Π°ΠΊΠΆΠ΅ ΠΎΠΏΡ€Π΅Π΄Π΅Π»ΡΠ»ΠΎΡΡŒ сниТСниС показатСля Ρ„Ρ€Π°ΠΊΡ†ΠΈΠΎΠ½Π½ΠΎΠΉ Π°Π½ΠΈΠ·ΠΎΡ‚Ρ€ΠΎΠΏΠΈΠΈ Π²ΠΎΠ»ΠΎΠΊΠΎΠ½ Π² области 2–5-Π³ΠΎ сСгмСнтов мозолистого Ρ‚Π΅Π»Π°, ΠΏΡ€Π°Π²ΠΎΠ³ΠΎ ΠΊΠΎΡ€Ρ‚ΠΈΠΊΠΎΡΠΏΠΈΠ½Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ Ρ‚Ρ€Π°ΠΊΡ‚Π°; ΠΏΠΎΠ²Ρ‹ΡˆΠ΅Π½ΠΈΠ΅ коэффициСнта Π΄ΠΈΡ„Ρ„ΡƒΠ·ΠΈΠΈ Π² области 2, 4, 5-Π³ΠΎ сСгмСнтов ΠΈ Π»Π΅Π²ΠΎΠ³ΠΎ ΠΊΠΎΡ€Ρ‚ΠΈΠΊΠΎΡΠΏΠΈΠ½Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ Ρ‚Ρ€Π°ΠΊΡ‚Π°; сниТСниС коэффициСнта ΠΌΠΈΠ΅Π»ΠΈΠ½ΠΈΠ·Π°Ρ†ΠΈΠΈ Π²ΠΎ всСх исслСдуСмых Ρ‚Ρ€Π°ΠΊΡ‚Π°Ρ…, Π·Π° ΠΈΡΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΠ΅ΠΌ Π²Π΅Ρ€Ρ…Π½ΠΈΡ… ΠΏΡ€ΠΎΠ΄ΠΎΠ»ΡŒΠ½Ρ‹Ρ… ΠΏΡƒΡ‡ΠΊΠΎΠ². ВыявлСна ΠΏΠΎΠ»ΠΎΠΆΠΈΡ‚Π΅Π»ΡŒΠ½Π°Ρ коррСляция ΠΌΠ΅ΠΆΠ΄Ρƒ Β Π²Ρ‹Ρ€Π°ΠΆΠ΅Π½Π½ΠΎΡΡ‚ΡŒΡŽ ΠΌΠΎΡ‚ΠΎΡ€Π½ΠΎΠ³ΠΎ Π΄Π΅Ρ„ΠΈΡ†ΠΈΡ‚Π° ΠΈ коэффициСнтом ΠΌΠΈΠ΅Π»ΠΈΠ½ΠΈΠ·Π°Ρ†ΠΈΠΈ Π² области ΠΏΠ΅Ρ€Π΅Π΄Π½Π΅Π³ΠΎ сСгмСнта мозолистого Ρ‚Π΅Π»Π° ΠΈ Π½ΠΈΠΆΠ½ΠΈΡ… ΠΏΡ€ΠΎΠ΄ΠΎΠ»ΡŒΠ½Ρ‹Ρ… ΠΏΡƒΡ‡ΠΊΠΎΠ².Π—Π°ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΠ΅. МР-трактография являСтся ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½Ρ‹ΠΌ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ ΠΎΠ±ΡŠΠ΅ΠΊΡ‚ΠΈΠ²Π½ΠΎΠΉ ΠΎΡ†Π΅Π½ΠΊΠΈ ΠΎΡ€Π³Π°Π½ΠΈΠ·Π°Ρ†ΠΈΠΈ проводящих ΠΏΡƒΡ‚Π΅ΠΉ Π³ΠΎΠ»ΠΎΠ²Π½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π°, уровня ΠΈ стСпСни пораТСния ΠΌΠΎΡ‚ΠΎΡ€Π½Ρ‹Ρ… Ρ‚Ρ€Π°ΠΊΡ‚ΠΎΠ². НаиболСС ΠΈΠ½Ρ„ΠΎΡ€ΠΌΠ°Ρ‚ΠΈΠ²Π½Ρ‹ΠΌΠΈ характСристиками ΠΎΡ€Π³Π°Π½ΠΈΠ·Π°Ρ†ΠΈΠΈ проводящих ΠΏΡƒΡ‚Π΅ΠΉ ΡΠ²Π»ΡΡŽΡ‚ΡΡ Π°Π±ΡΠΎΠ»ΡŽΡ‚Π½ΠΎΠ΅ количСство Π²ΠΎΠ»ΠΎΠΊΠΎΠ½ Π² Ρ‚Ρ€Π°ΠΊΡ‚Π΅, ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒ Ρ„Ρ€Π°ΠΊΡ†ΠΈΠΎΠ½Π½ΠΎΠΉ Π°Π½ΠΈΠ·ΠΎΡ‚Ρ€ΠΎΠΏΠΈΠΈ, Π° Ρ‚Π°ΠΊΠΆΠ΅ расчСтный ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΡŒ – коэффициСнт ΠΌΠΈΠ΅Π»ΠΈΠ½ΠΈΠ·Π°Ρ†ΠΈΠΈ. НСкоторыС ΠΈΠ· выявлСнных ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ ΠΊΠΎΡ€Ρ€Π΅Π»ΠΈΡ€ΠΎΠ²Π°Π»ΠΈ с Π²Ρ‹Ρ€Π°ΠΆΠ΅Π½Π½ΠΎΡΡ‚ΡŒΡŽ ΠΌΠΎΡ‚ΠΎΡ€Π½ΠΎΠ³ΠΎ Π΄Π΅Ρ„ΠΈΡ†ΠΈΡ‚Π°, Ρ‡Ρ‚ΠΎ позволяСт Ρ€Π°ΡΡΠΌΠ°Ρ‚Ρ€ΠΈΠ²Π°Ρ‚ΡŒ ΠΈΡ… ΠΊΠ°ΠΊ ΠΏΠΎΡ‚Π΅Π½Ρ†ΠΈΠ°Π»ΡŒΠ½Ρ‹Π΅ ΠΏΡ€Π΅Π΄ΠΈΠΊΡ‚ΠΎΡ€Ρ‹ Ρ€Π΅Π°Π±ΠΈΠ»ΠΈΡ‚Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·Π°

    Landscape science: a Russian geographical tradition

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    The Russian geographical tradition of landscape science (landshaftovedenie) is analyzed with particular reference to its initiator, Lev Semenovich Berg (1876-1950). The differences between prevailing Russian and Western concepts of landscape in geography are discussed, and their common origins in German geographical thought in the late nineteenth and early twentieth centuries are delineated. It is argued that the principal differences are accounted for by a number of factors, of which Russia's own distinctive tradition in environmental science deriving from the work of V. V. Dokuchaev (1846-1903), the activities of certain key individuals (such as Berg and C. O. Sauer), and the very different social and political circumstances in different parts of the world appear to be the most significant. At the same time it is noted that neither in Russia nor in the West have geographers succeeded in specifying an agreed and unproblematic understanding of landscape, or more broadly in promoting a common geographical conception of human-environment relationships. In light of such uncertainties, the latter part of the article argues for closer international links between the variant landscape traditions in geography as an important contribution to the quest for sustainability

    Complex Estimation of Strength Properties of Functional Materials on the Basis of the Analysis of Grain-Phase Structure Parameters

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    The technique allows analysis using grain-phase structure of the functional material to evaluate its performance, particularly strength properties. The technique is based on the use of linguistic variable in the process of comprehensive evaluation. An example of estimating the strength properties of steel reinforcement, subject to special heat treatment to obtain the desired grain-phase structure
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