22 research outputs found
ΠΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·Π° ΡΠΊΠΎΡΠΎΡΡΠΈ ΡΠΈΠ±ΡΠΎΠ·Π° ΠΏΠ΅ΡΠ΅Π½ΠΈ Ρ Π±ΠΎΠ»ΡΠ½ΡΡ Ρ Ρ ΡΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΈΠΌ Π³Π΅ΠΏΠ°ΡΠΈΡΠΎΠΌ Π‘ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΠΊΠΎΠΌΠ±ΠΈΠ½Π°ΡΠΈΠΉ Π³Π΅Π½ΠΎΠΌΠ½ΡΡ ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ²
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.Β ΠΠ±ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠ΅. Π Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π±ΠΎΠ»ΡΡΠΎΠ΅ Π²Π½ΠΈΠΌΠ°Π½ΠΈΠ΅ ΡΠ΄Π΅Π»ΡΠ΅ΡΡΡ ΠΏΠΎΠΈΡΠΊΡ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ², ΠΎΠ±ΡΡΡΠ½ΡΡΡΠΈΡ
ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ Ρ
ΡΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π³Π΅ΠΏΠ°ΡΠΈΡΠ° Π‘ (Π₯ΠΠ‘).Π¦Π΅Π»Ρ ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: ΠΎΡΠ΅Π½ΠΈΡΡ ΠΏΡΠΎΠ³Π½ΠΎΡΡΠΈΡΠ΅ΡΠΊΡΡ Π·Π½Π°ΡΠΈΠΌΠΎΡΡΡ Π½ΠΎΡΠΈΡΠ΅Π»ΡΡΡΠ²Π° ΠΊΠΎΠΌΠ±ΠΈΠ½Π°ΡΠΈΠΉ Π°Π»Π»Π΅Π»ΡΠ½ΡΡ
Π²Π°ΡΠΈΠ°Π½ΡΠΎΠ² 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° ΠΏΡΠΎΠ³ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΡΠΈΠ±ΡΠΎΠ·Π° ΠΏΠ΅ΡΠ΅Π½ΠΈ ΠΏΡΠΈ Π₯ΠΠ‘.ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ: 118 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ Π₯ΠΠ‘ ΡΠ°Π·Π΄Π΅Π»Π΅Π½Ρ Π½Π° Π³ΡΡΠΏΠΏΡ Ρ Π±ΡΡΡΡΡΠΌ ΠΈ ΠΌΠ΅Π΄Π»Π΅Π½Π½ΡΠΌ (ΡΠΊΠΎΡΠΎΡΡΡ ΡΠΈΠ±ΡΠΎΠ·Π° β₯0,13 ΠΈ 0,13 Π΅Π΄. ΡΠΈΠ±ΡΠΎΠ·Π°/Π³ΠΎΠ΄; n =64 ΠΈ n =54, ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ) ΡΠΈΠ±ΡΠΎΠ·ΠΎΠΌ. ΠΡΠΏΠΎΠ»Π½Π΅Π½ΠΎ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΠΏΠΎΠ»ΠΈΠΌΠΎΡΡΠΈΠ·ΠΌΠ°. Π‘ΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΡΡ ΠΎΠ±ΡΠ°Π±ΠΎΡΠΊΡ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠΎΠ² ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΠ°ΠΊΠ΅ΡΠΎΠ² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌ Statistica 10.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. Π£ Π±ΠΎΠ»ΡΠ½ΡΡ
Ρ Π±ΡΡΡΡΡΠΌ ΡΠΈΠ±ΡΠΎΠ·ΠΎΠΌ Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ Π³ΡΡΠΏΠΏΠΎΠΉ Ρ ΠΌΠ΅Π΄Π»Π΅Π½Π½ΡΠΌ ΡΠ°ΡΠ΅ Π²ΡΡΡΠ΅ΡΠ°Π»ΠΈΡΡ Π°Π»Π»Π΅Π»Ρ Π (Ρ =0,012) ΠΈ ΠΌΡΡΠ°Π½ΡΠ½ΡΠΉ Π³Π΅Π½ΠΎΡΠΈΠΏ ΠΠ (Ρ =0,024) Π³Π΅Π½Π° AGT G-6T, ΡΠ°ΠΊΠΆΠ΅ Π² Π΄Π°Π½Π½ΠΎΠΉ Π³ΡΡΠΏΠΏΠ΅ ΡΠ°ΡΠ΅ Π²ΡΡΠ²Π»ΡΠ»ΠΈ Π°Π»Π»Π΅Π»Ρ Π’ (Ρ =0,013) ΠΈ Π³Π΅Π½ΠΎΡΠΈΠΏ ΠΠ’+Π’Π’ Π³Π΅Π½Π° AGT 235 M/T (Ρ =0,005). ΠΠΎΠ»ΡΠ½ΡΠ΅ Ρ Π³Π΅Π½ΠΎΡΠΈΠΏΠΎΠΌ Π’Π’ Π³Π΅Π½Π° CYBA 242 C/T ΠΈΠΌΠ΅Π»ΠΈ Π±ΠΎΠ»Π΅Π΅ Π²ΡΡΠΎΠΊΡΡ ΡΠΊΠΎΡΠΎΡΡΡ ΡΠΈΠ±ΡΠΎΠ·Π° ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ Π±ΠΎΠ»ΡΠ½ΡΠΌΠΈ Ρ Π³Π΅Π½ΠΎΡΠΈΠΏΠΎΠΌ Π‘Π‘+Π‘Π’ (Ρ =0,02). Π Ρ
ΠΎΠ΄Π΅ Π°Π½Π°Π»ΠΈΠ·Π° Π²ΡΡΠ²Π»Π΅Π½ΠΎ ΠΏΡΠΎΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π³ΠΎΠΌΠΎΠ·ΠΈΠ³ΠΎΡΡ Π’Π’ Π³Π΅Π½Π° ITGA2 807 C/T Π½Π° ΡΠ΅ΠΌΠΏΡ ΡΠΈΠ±ΡΠΎΠ·Π° (Ρ =0,03). ΠΠ°Π±Π»ΡΠ΄Π°Π»ΠΈΡΡ ΡΠ΅Π½Π΄Π΅Π½ΡΠΈΠΈ ΠΊ ΡΠ°Π·Π»ΠΈΡΠΈΡ ΠΏΠΎ Π²ΡΡΡΠ΅ΡΠ°Π΅ΠΌΠΎΡΡΠΈ Π°Π»Π»Π΅Π»Π΅ΠΉ ΠΈ Π³Π΅Π½ΠΎΡΠΈΠΏΠΎΠ² ΠΏΠΎΠ»ΠΈΠΌΠΎΡΡΠ½ΡΡ
ΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² TGFb +915 G/Π‘, FXIII 103 G/T, PAI -675 5G/4G ΠΌΠ΅ΠΆΠ΄Ρ Π΄Π²ΡΠΌΡ Π³ΡΡΠΏΠΏΠ°ΠΌΠΈ. ΠΠ»Ρ ΠΎΡΡΠ°Π»ΡΠ½ΡΡ
Π³Π΅Π½ΠΎΠ² Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΡΡ
ΠΎΡΠ»ΠΈΡΠΈΠΉ Π½Π΅ ΠΎΠ±Π½Π°ΡΡΠΆΠ΅Π½ΠΎ. Π Π΄Π°Π»ΡΠ½Π΅ΠΉΡΠ΅ΠΌ ΠΏΠΎΡΡΡΠΎΠ΅Π½Π° ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ, ΡΡΠΈΡΡΠ²Π°ΡΡΠ°Ρ ΠΏΡΠΎΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ΅ ΠΈ ΠΏΡΠΎΡΠΈΠ±ΡΠΎΠ³Π΅Π½Π½ΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π³Π΅Π½ΠΎΠ², Π² ΡΠ°ΠΊΠΆΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π³Π΅Π½ΠΎΡΠΈΠΏΠ° Π²ΠΈΡΡΡΠ°. ΠΡΡΠ²Π»Π΅Π½Π° ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΡ ΠΌΠ΅ΠΆΠ΄Ρ ΡΡΠΌΠΌΠΎΠΉ Π±Π°Π»Π»ΠΎΠ² Π² ΡΡΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΈ ΡΠ΅ΠΌΠΏΠΎΠΌ ΠΏΡΠΎΠ³ΡΠ΅ΡΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ ΡΠΈΠ±ΡΠΎΠ·Π° Π² ΠΏΠ΅ΡΠ΅Π½ΠΈ (R =0,39, p =0,000).ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅: ΠΏΡΠ΅Π΄Π»ΠΎΠΆΠ΅Π½Π½Π°Ρ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠ°Ρ ΠΌΠΎΠ΄Π΅Π»Ρ ΠΌΠΎΠΆΠ΅Ρ ΠΏΡΠΎΠ³Π½ΠΎΠ·ΠΈΡΠΎΠ²Π°ΡΡ ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ Π±ΠΎΠ»Π΅Π·Π½ΠΈ
Implementing the education of future entrepreneurs in developing countries: Agile integration of traditions and innovations
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
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 4β16mol% 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