22 research outputs found

    ΠœΠ°Ρ‚Π΅ΠΌΠ°Ρ‚ΠΈΡ‡Π΅ΡΠΊΠ°Ρ модСль ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·Π° скорости Ρ„ΠΈΠ±Ρ€ΠΎΠ·Π° ΠΏΠ΅Ρ‡Π΅Π½ΠΈ Ρƒ Π±ΠΎΠ»ΡŒΠ½Ρ‹Ρ… с хроничСским Π³Π΅ΠΏΠ°Ρ‚ΠΈΡ‚ΠΎΠΌ Π‘ Π½Π° основС ΠΊΠΎΠΌΠ±ΠΈΠ½Π°Ρ†ΠΈΠΉ Π³Π΅Π½ΠΎΠΌΠ½Ρ‹Ρ… ΠΌΠ°Ρ€ΠΊΠ΅Ρ€ΠΎΠ²

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    Aim of study. To evaluate clinical significance of different combinations of gene polymorphisms IL-1b, IL-6, IL-10, TNF, HFE, TGF-b, ATR1, NOS3894, CYBA, AGT, MTHFR, FII, FV, FVII, FXIII, ITGA2, ITGB3, FBG, PAI and their prognostic value for prediction of liver fibrosis progression rate in patients with chronic hepatitis C (CHC).Subjects and methods: 118 patients with CHC were divided into Β«fastΒ» and Β«slowΒ» (fibrosis rate progression β‰₯0,13 and 0,13 fibrosis units/yr; n =64 and n =54) fibrosis groups. Gene polymorphisms were determined. Statistical analysis was performed using Statistica 10.Results. A allele (p =0,012) and genotype AA (p =0,024) of AGT G-6T gene, as well as T allele (p =0,013) and MT+TT genotypes (p =0,005) of AGT 235 M/T gene were significantly more common in Β«fast fibrosersΒ» than in Β«slow fibrosersΒ». Patients with genotype TT of CYBA 242 C/T had a higher fibrosis progression rate than patients with CC+CT genotype (p =0,02). Our analysis showed a protective effect of TT genotype of ITGA2 807 C/T on fibrosis progression rate (p =0,03). There was a trend (p 0,15) to higher fibrosis progression rate in patients with mutant alleles and genotypes of TGFb +915 G/C, FXIII 103 G/T, PAI -675 5G/4G genes. Other gene polymorphisms were not associated with enhanced liver fibrosis. To build a mathematical model for prediction of liver fibrosis progression rate we performed coding with scores for genotypes and virus genotype. Total score correlated with the fibrosis progression rate (R =0,39, p =0,000).Conclusion: Determination of genetic profile of the patient and virus genotype allows to predict the course of CHC. ОбоснованиС. Π’ настоящСС врСмя большоС Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ удСляСтся поиску гСнСтичСских Ρ„Π°ΠΊΡ‚ΠΎΡ€ΠΎΠ², ΠΎΠ±ΡŠΡΡΠ½ΡΡŽΡ‰ΠΈΡ… Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅ хроничСского Π³Π΅ΠΏΠ°Ρ‚ΠΈΡ‚Π° Π‘ (Π₯Π“Π‘).ЦСль исслСдования: ΠΎΡ†Π΅Π½ΠΈΡ‚ΡŒ ΠΏΡ€ΠΎΠ³Π½ΠΎΡΡ‚ΠΈΡ‡Π΅ΡΠΊΡƒΡŽ Π·Π½Π°Ρ‡ΠΈΠΌΠΎΡΡ‚ΡŒ Π½ΠΎΡΠΈΡ‚Π΅Π»ΡŒΡΡ‚Π²Π° ΠΊΠΎΠΌΠ±ΠΈΠ½Π°Ρ†ΠΈΠΉ Π°Π»Π»Π΅Π»ΡŒΠ½Ρ‹Ρ… Π²Π°Ρ€ΠΈΠ°Π½Ρ‚ΠΎΠ² Π³Π΅Π½ΠΎΠ² IL 1b, IL 6, IL 10, TNF Ξ±, HFE, TGF b, ATR1, NOS3, CYBA, AGT, MTHFR, FII, FV, FVII, FXIII, ITGA2, ITGB3, FBG, PAI Π½Π° прогрСссированиС Ρ„ΠΈΠ±Ρ€ΠΎΠ·Π° ΠΏΠ΅Ρ‡Π΅Π½ΠΈ ΠΏΡ€ΠΈ Π₯Π“Π‘.ΠœΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Ρ‹ ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹: 118 ΠΏΠ°Ρ†ΠΈΠ΅Π½Ρ‚ΠΎΠ² с Π₯Π“Π‘ Ρ€Π°Π·Π΄Π΅Π»Π΅Π½Ρ‹ Π½Π° Π³Ρ€ΡƒΠΏΠΏΡ‹ с быстрым ΠΈ ΠΌΠ΅Π΄Π»Π΅Π½Π½Ρ‹ΠΌ (ΡΠΊΠΎΡ€ΠΎΡΡ‚ΡŒ Ρ„ΠΈΠ±Ρ€ΠΎΠ·Π° β‰₯0,13 ΠΈ 0,13 Π΅Π΄. Ρ„ΠΈΠ±Ρ€ΠΎΠ·Π°/Π³ΠΎΠ΄; n =64 ΠΈ n =54, соотвСтствСнно) Ρ„ΠΈΠ±Ρ€ΠΎΠ·ΠΎΠΌ. Π’Ρ‹ΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΠΏΠΎΠ»ΠΈΠΌΠΎΡ€Ρ„ΠΈΠ·ΠΌΠ°. Π‘Ρ‚Π°Ρ‚ΠΈΡΡ‚ΠΈΡ‡Π΅ΡΠΊΡƒΡŽ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΡƒ Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚ΠΎΠ² ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ с использованиСм ΠΏΠ°ΠΊΠ΅Ρ‚ΠΎΠ² ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌ Statistica 10.Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹. Π£ Π±ΠΎΠ»ΡŒΠ½Ρ‹Ρ… с быстрым Ρ„ΠΈΠ±Ρ€ΠΎΠ·ΠΎΠΌ Π² сравнСнии с Π³Ρ€ΡƒΠΏΠΏΠΎΠΉ с ΠΌΠ΅Π΄Π»Π΅Π½Π½Ρ‹ΠΌ Ρ‡Π°Ρ‰Π΅ Π²ΡΡ‚Ρ€Π΅Ρ‡Π°Π»ΠΈΡΡŒ аллСль А (Ρ€ =0,012) ΠΈ ΠΌΡƒΡ‚Π°Π½Ρ‚Π½Ρ‹ΠΉ Π³Π΅Π½ΠΎΡ‚ΠΈΠΏ АА (Ρ€ =0,024) Π³Π΅Π½Π° AGT G-6T, Ρ‚Π°ΠΊΠΆΠ΅ Π² Π΄Π°Π½Π½ΠΎΠΉ Π³Ρ€ΡƒΠΏΠΏΠ΅ Ρ‡Π°Ρ‰Π΅ выявляли аллСль Π’ (Ρ€ =0,013) ΠΈ Π³Π΅Π½ΠΎΡ‚ΠΈΠΏ МВ+Π’Π’ Π³Π΅Π½Π° AGT 235 M/T (Ρ€ =0,005). Π‘ΠΎΠ»ΡŒΠ½Ρ‹Π΅ с Π³Π΅Π½ΠΎΡ‚ΠΈΠΏΠΎΠΌ Π’Π’ Π³Π΅Π½Π° CYBA 242 C/T ΠΈΠΌΠ΅Π»ΠΈ Π±ΠΎΠ»Π΅Π΅ Π²Ρ‹ΡΠΎΠΊΡƒΡŽ ΡΠΊΠΎΡ€ΠΎΡΡ‚ΡŒ Ρ„ΠΈΠ±Ρ€ΠΎΠ·Π° ΠΏΠΎ ΡΡ€Π°Π²Π½Π΅Π½ΠΈΡŽ с Π±ΠΎΠ»ΡŒΠ½Ρ‹ΠΌΠΈ с Π³Π΅Π½ΠΎΡ‚ΠΈΠΏΠΎΠΌ Π‘Π‘+Π‘Π’ (Ρ€ =0,02). Π’ Ρ…ΠΎΠ΄Π΅ Π°Π½Π°Π»ΠΈΠ·Π° выявлСно ΠΏΡ€ΠΎΡ‚Π΅ΠΊΡ‚ΠΈΠ²Π½ΠΎΠ΅ влияниС Π³ΠΎΠΌΠΎΠ·ΠΈΠ³ΠΎΡ‚Ρ‹ Π’Π’ Π³Π΅Π½Π° ITGA2 807 C/T Π½Π° Ρ‚Π΅ΠΌΠΏΡ‹ Ρ„ΠΈΠ±Ρ€ΠΎΠ·Π° (Ρ€ =0,03). Наблюдались Ρ‚Π΅Π½Π΄Π΅Π½Ρ†ΠΈΠΈ ΠΊ Ρ€Π°Π·Π»ΠΈΡ‡ΠΈΡŽ ΠΏΠΎ встрСчаСмости Π°Π»Π»Π΅Π»Π΅ΠΉ ΠΈ Π³Π΅Π½ΠΎΡ‚ΠΈΠΏΠΎΠ² ΠΏΠΎΠ»ΠΈΠΌΠΎΡ€Ρ„Π½Ρ‹Ρ… ΠΌΠ°Ρ€ΠΊΠ΅Ρ€ΠΎΠ² TGFb +915 G/Π‘, FXIII 103 G/T, PAI -675 5G/4G ΠΌΠ΅ΠΆΠ΄Ρƒ двумя Π³Ρ€ΡƒΠΏΠΏΠ°ΠΌΠΈ. Для ΠΎΡΡ‚Π°Π»ΡŒΠ½Ρ‹Ρ… Π³Π΅Π½ΠΎΠ² достовСрных ΠΎΡ‚Π»ΠΈΡ‡ΠΈΠΉ Π½Π΅ ΠΎΠ±Π½Π°Ρ€ΡƒΠΆΠ΅Π½ΠΎ. Π’ дальнСйшСм построСна матСматичСская модСль, ΡƒΡ‡ΠΈΡ‚Ρ‹Π²Π°ΡŽΡ‰Π°Ρ ΠΏΡ€ΠΎΡ‚Π΅ΠΊΡ‚ΠΈΠ²Π½ΠΎΠ΅ ΠΈ ΠΏΡ€ΠΎΡ„ΠΈΠ±Ρ€ΠΎΠ³Π΅Π½Π½ΠΎΠ΅ влияниС Π³Π΅Π½ΠΎΠ², Π² Ρ‚Π°ΠΊΠΆΠ΅ влияниС Π³Π΅Π½ΠΎΡ‚ΠΈΠΏΠ° вируса. ВыявлСна коррСляция ΠΌΠ΅ΠΆΠ΄Ρƒ суммой Π±Π°Π»Π»ΠΎΠ² Π² этой ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΈ Ρ‚Π΅ΠΌΠΏΠΎΠΌ прогрСссирования Ρ„ΠΈΠ±Ρ€ΠΎΠ·Π° Π² ΠΏΠ΅Ρ‡Π΅Π½ΠΈ (R =0,39, p =0,000).Π—Π°ΠΊΠ»ΡŽΡ‡Π΅Π½ΠΈΠ΅: прСдлоТСнная матСматичСская модСль ΠΌΠΎΠΆΠ΅Ρ‚ ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·ΠΈΡ€ΠΎΠ²Π°Ρ‚ΡŒ Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅ Π±ΠΎΠ»Π΅Π·Π½ΠΈ

    Implementing the education of future entrepreneurs in developing countries: Agile integration of traditions and innovations

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    Today, more and more attention all over the world is paid to entrepreneurship education, since such specialized education helps to fight against unemployment, and can stimulate innovation and economic growth. The purpose of this study is to analyze the socio-economic environment of developing countries to evaluate educational programs for future entrepreneurs. The business environment and innovation in the context of educational programs are investigated based on open statistics. The methodological and informational basis for the analysis was the Index of Economic Freedom (IEF), the rating of national higher education systems (U21) and the Global Entrepreneurship Monitoring (GEM). The analysis showed that the socio-cultural and economic environment is crucial for the successful implementation of entrepreneurial training programs, and countries pursuing a policy of economic freedom create favourable conditions for trade and commercial services, which determines the successful development of educational programs in the field of entrepreneurship. Entrepreneurship training provides the skills and knowledge necessary for developing business ideas, creating and developing enterprises. Thus, entrepreneurship entails innovation for the state, implementation, and independence-for the individual. Β© 2019, Allied Business Academies. All rights reserved

    Sputtering of Mo and Al in D2/N2 plasma cleaning discharge

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    Sputtering of Mo and Al (as Be proxy) in mixed D2/N2 DC glow discharge was studied in view of the first mirror performance. The composition of the working gas was varied from 100% D2 to 100% N2, while keeping a total pressure of 15Pa. The ion energies striking the sample surface were defined by its 100V biasing negative to a floating potential. It has been obtained that the sputtering yield of Mo and Al increases gradually with N2 concentration up to 4β€’16mol% and decreases with further N2 addition. In contrast, the sputtering yield of Be remains unchanged up to 10mol% of N2. Adding 16mol% leads to three-fold decrease in the sputtering rate. The sputtering behavior is discussed in context of surface data analysis and mass spectroscopy of the discharge gas exhaust. Variation in reflectivity of a single crystalline Mo due to plasma exposure under similar conditions is also presented and discussed
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