11 research outputs found
Pedagogical Conditions for the Formation of Readiness of Students of Non-Linguistic Specialties for Foreign Language Communication in Future Professional Activity
ΠΠ° ΠΎΡΠ½ΠΎΠ²Π΅ Π°Π½Π°Π»ΠΈΠ·Π° ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠΉ ΡΠΎΡΠΌΡΠ»ΠΈΡΡΡΡΡΡ ΠΏΠ΅Π΄Π°Π³ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΡΡΠ»ΠΎΠ²ΠΈΡ, Π½Π΅ΠΎΠ±Ρ
ΠΎΠ΄ΠΈΠΌΡΠ΅ Π΄Π»Ρ ΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½ΠΈΡ Π³ΠΎΡΠΎΠ²Π½ΠΎΡΡΠΈ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ²-Π±ΡΠ΄ΡΡΠΈΡ
ΠΈΠ½ΠΆΠ΅Π½Π΅ΡΠΎΠ² ΠΊ ΠΈΠ½ΠΎΡΠ·ΡΡΠ½ΠΎΠΌΡ ΠΎΠ±ΡΠ΅Π½ΠΈΡ Π² ΠΏΡΠΎΡΠ΅ΡΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠΉ Π΄Π΅ΡΡΠ΅Π»ΡΠ½ΠΎΡΡΠΈ.Based on the research analysis, the pedagogical conditions necessary for the formation of the readiness of students-future engineers for foreign language communication in their professional activities are formulated
Deformation features of the central layers of Fe β3% Si(110)[hkl]alloy by rolling with a roll diameter of 90 mm
Π’Π΅ΠΊΡΡ ΡΡΠ°ΡΡΠΈ Π½Π΅ ΠΏΡΠ±Π»ΠΈΠΊΡΠ΅ΡΡΡ Π² ΠΎΡΠΊΡΡΡΠΎΠΌ Π΄ΠΎΡΡΡΠΏΠ΅ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΏΠΎΠ»ΠΈΡΠΈΠΊΠΎΠΉ ΠΆΡΡΠ½Π°Π»Π°.The article presents the results of studies of the effect of initial crystallographic orientation and deformation modes on rolling texture in the central layer of Fe β3% Si (110)[hkl] single crystals.
Several groups of samples of single crystals were rolled under laboratory conditions. The groups of samples were classified according to the final deformation rate, the ideal crystallographic orientation of the rolling plane and deflections of the direction of the ideal orientation plane from the rolling direction. The methodology of the experiment took into account the amount of reduction rate during one rolling.
Radiographic method was used to analyze the results of rolling.
The obtained data was superimposed on a stereographic projection, and straight pole figures were built. The results of decoding direct pole figures revealed differences in the formation of the texture from the previously obtained results. The research shows the manifestation of the one-component deformation texture in the central layer
DEFORMATION FEATURES OF THE CENTRAL LAYERS OF Fe - 3%Si(110)[hkl] ALLOY BY ROLLING WITH A ROLL DIAMETER OF 90 MM
The article presents the results of studies of the effect of initial crystallographic orientation and deformation modes on rolling texture in the central layer of Fe - 3%Si(110)[hkl] single crystals. Several groups of samples of single crystals were rolled under laboratory conditions. The groups of samples were classified according to the final deformation rate, the ideal crystallographic orientation of the rolling plane and deflections of the direction of the ideal orientation plane from the rolling direction. The methodology of the experiment took into account the amount of reduction rate during one rolling. Radiographic method was used to analyze the results of rolling. The obtained data was superimposed on a stereographic projection, and straight pole figures were built. The results of decoding direct pole figures revealed differences in the formation of the texture from the previously obtained results. The research shows the manifestation of the one-component deformation texture in the central layer
Isolation, identification, and chromatographic separation of N-methyl derivatives of glycoluril
Mono-, di-, and tetramethylglycolurils were synthesized, isolated, and purified. For the first time, the cis- and the trans-isomers of N,N-dimethylglycoluril were isolated as individual substances by semi-preparative HPLC method. The structures of the synthesized compounds were confirmed by 1H NMR, 13C NMR, and HRβHPLCβMS. The EI mass spectra of individual substances were obtained by the GCβMS. Retention and resolution of N-methyl glycolurils were investigated in the reversed-phase HPLC mode for different stationary phases: C18, SBβAq, and Luna 5u PFP(2). The retention of N-methyl glycolurils depended on the amount of CH3 groups and distance between the CH3 groups in the structure. The stationary phases provided different selectivity for glycoluril and its N-methyl derivatives due to different shape selectivity. Complete separation of the N-methyl derivatives of glycoluril was achieved in 4.5 min on the stationary phase with pentafluorophenyl propyl ligand in a gradient mode
[Studying the structure of a gene pool population of the Russian White chicken breed by genome-wide SNP scan] ΠΠ·ΡΡΠ΅Π½ΠΈΠ΅ ΡΡΡΡΠΊΡΡΡΡ Π³Π΅Π½ΠΎΡΠΎΠ½Π΄Π½ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΡΡΡΡΠΊΠΎΠΉ Π±Π΅Π»ΠΎΠΉ ΠΏΠΎΡΠΎΠ΄Ρ ΠΊΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ Π³Π΅Π½ΠΎΠΌΠ½ΠΎΠ³ΠΎ SNP-ΡΠΊΠ°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ
A population of the Russian White chickens, bred at the gene pool farm of ARRIFAGB for 25 generations using individual selection, is characterized by resistance to a lowered temperature in the early postnatal period and white colour of the embryonic down. In 2002-2012, breeding was carried out by panmixia, and by now a new population of the Russian White chickens has been formed on the basis of the surviving stock. Comparison of the genetic variability of this population and the archival DNA of representatives of the 2001 population using microarray screening technology will help to assess the population structure and the preservation of the unique characteristics of its genome. The material for the study was DNA extracted from 162 chicken blood samples. Two groups of the Russian White breed were studied, the 2001 population and the current population. Genome-wide analysis using single nucleotide markers (SNP) included screening by means of the Illumina Chicken 60K SNP iSelect BeadChip microarray. Quality control of genotyping, determination of the population genetic structure by multidimensional scaling (MDS), calculation of linkage disequilibrium (LD) and allele frequency in the groups were carried out using PLINK 1.9 software program. The construction of a cluster delimitation model based on SNP genotypes was carried out using the ADMIXTURE program. According to the MDS analysis results, the current population can be divided into four MDS groups, which, when compared to the data of the pedigree, adequately reflect the origin of the studied individuals. The representatives of the ancestral population were genetically similar to the MDS3 group of the current population. Using the F-statistic of the two-way analysis of variance, a significant effect of the group, chromosome, chromosome in the group, and the distance between SNP markers on LD (r2) values was observed. In the 2001 group, the maximum r2 and the high incidence of LD equal to 1 were observed for all chromosomes, with a distance between SNP markers being 500-1000 Kb. There was also the greatest number of monomorphic alleles in this group. Based on the SNP analysis, we may conclude that the current Russian White chicken population is characterized by the disintegration of long LD regions of the ancestral population. Modelling clusters using the ADMIXTURE program revealed differences between the current population groups determined by MDS analysis. The groups composed of individuals included in MDS1 and MDS2 had a homogeneous structure and differed from each other at K = 4 and K = 5. The MDS4 group formed a genetically heterogeneous cluster different from the MDS1 and MDS2 groups at K of 2-5. The MDS3 group was phylogenetically close to the 2001 population (at K of 2-5). In general, the analysis of the current gene pool population of the Russian White chickens showed its heterogeneity while one of its groups (MDS3) was similar to the ancestral population of 2001, which in turn is characterized by a large number of monomorphic alleles and a high frequency of long LD regions. Thus, SNP scanning allowed evaluating the genetic similarity of individuals and the population structure of the Russian White chicken breed. Understanding the genetic structure is an important point in the panmictic breeding and tracking of historical changes in the molecular organization of the genome of a gene pool population with a limited number of animals.
ΠΠΎΠΏΡΠ»ΡΡΠΈΡ ΡΡΡΡΠΊΠΈΡ
Π±Π΅Π»ΡΡ
ΠΊΡΡ ΡΠ΅Π»Π΅ΠΊΡΠΈΠΎΠ½ΠΈΡΠΎΠ²Π°Π»Π°ΡΡ Π² Π³Π΅Π½ΠΎΡΠΎΠ½Π΄Π½ΠΎΠΌ Ρ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅ ΠΡΠ΅ΡΠΎΡΡΠΈΠΉΡΠΊΠΎΠ³ΠΎ ΠΠΠ Π³Π΅Π½Π΅ΡΠΈΠΊΠΈ ΠΈ ΡΠ°Π·Π²Π΅Π΄Π΅Π½ΠΈΡ ΡΠ΅Π»ΡΡΠΊΠΎΡ
ΠΎΠ·ΡΠΉΡΡΠ²Π΅Π½Π½ΡΡ
ΠΆΠΈΠ²ΠΎΡΠ½ΡΡ
(ΠΠΠΠΠΠ Π) Π² ΡΠ΅ΡΠ΅Π½ΠΈΠ΅ 25 ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΠΉ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Π±ΠΎΡΠ°. ΠΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠΈ ΡΡΠΎΠΉ ΠΏΠΎΡΠΎΠ΄Ρ β ΡΡΡΠΎΠΉΡΠΈΠ²ΠΎΡΡΡ ΠΊ ΠΏΠΎΠ½ΠΈΠΆΠ΅Π½Π½ΠΎΠΉ ΡΠ΅ΠΌΠΏΠ΅ΡΠ°ΡΡΡΠ΅ Π²ΡΡΠ°ΡΠΈΠ²Π°Π½ΠΈΡ Π² ΡΠ°Π½Π½ΠΈΠΉ ΠΏΠΎΡΡΠ½Π°ΡΠ°Π»ΡΠ½ΡΠΉ ΠΏΠ΅ΡΠΈΠΎΠ΄ ΠΈ Π±Π΅Π»ΡΠΉ ΡΠ²Π΅Ρ ΡΠΌΠ±ΡΠΈΠΎΠ½Π°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΏΡΡ
Π°. Π 2002-2012 Π³ΠΎΠ΄Π°Ρ
Π΅Π΅ ΡΠ°Π·Π²Π΅Π΄Π΅Π½ΠΈΠ΅ ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ»ΠΎΡΡ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΏΠ°Π½ΠΌΠΈΠΊΡΠΈΠΈ, ΠΈ ΠΊ Π½Π°ΡΡΠΎΡΡΠ΅ΠΌΡ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ ΡΠΎΡ
ΡΠ°Π½ΠΈΠ²ΡΠ΅Π³ΠΎΡΡ ΠΏΠΎΠ³ΠΎΠ»ΠΎΠ²ΡΡ ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π° Π½ΠΎΠ²Π°Ρ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΡ ΡΡΡΡΠΊΠΈΡ
Π±Π΅Π»ΡΡ
ΠΊΡΡ. ΠΠ°ΡΠ΅ΠΉ ΡΠ΅Π»ΡΡ Π±ΡΠ»ΠΎ ΠΏΠΎΠΊΠ°Π·Π°ΡΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎΡΡΠΈ ΠΏΠΎΠ»Π½ΠΎΠ³Π΅Π½ΠΎΠΌΠ½ΠΎΠ³ΠΎ SNP-ΡΠΊΠ°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΡ (single nucleotide polymorphisms) Π΄Π»Ρ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΠ΅ΠΉ ΡΡΡΡΠΊΡΡΡΡ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΠΌΠ°Π»ΠΎΡΠΈΡΠ»Π΅Π½Π½ΡΡ
ΠΏΠΎΡΠΎΠ΄ ΠΊΡΡ ΠΎΡΠ΅ΡΠ΅ΡΡΠ²Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΡΠΎΠΈΡΡ
ΠΎΠΆΠ΄Π΅Π½ΠΈΡ ΠΈ Π΄ΠΈΠ½Π°ΠΌΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΡ ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΠΎΠΉ Π°ΡΡ
ΠΈΡΠ΅ΠΊΡΡΡΡ Π½Π° ΠΏΡΠΈΠΌΠ΅ΡΠ΅ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΡΡΡΡΠΊΠΎΠΉ Π±Π΅Π»ΠΎΠΉ ΠΏΠΎΡΠΎΠ΄Ρ Ρ ΠΏΡΠ΅Π΄ΠΊΠΎΠ²ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠ΅ΠΉ 2001 Π³ΠΎΠ΄Π°. ΠΡΠ»ΠΈ ΠΏΡΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡΠΎΠ²Π°Π½Ρ Π΄Π²Π΅ Π³ΡΡΠΏΠΏΡ ΠΊΡΡ: ΠΏΠΎΠΏΡΠ»ΡΡΠΈΡ 2001 Π³ΠΎΠ΄Π° (6 Π³ΠΎΠ»., Π½Π΅ΡΠΎΠ΄ΡΡΠ²Π΅Π½Π½ΡΠ΅ ΠΎΡΠΎΠ±ΠΈ ΠΈΠ· Π΄Π²ΡΡ
Π»ΠΈΠ½ΠΈΠΉ) ΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΡ (156 Π³ΠΎΠ».). SNP-Π°Π½Π°Π»ΠΈΠ· Π²ΠΊΠ»ΡΡΠ°Π» ΡΠΊΡΠΈΠ½ΠΈΠ½Π³ 162 ΠΎΠ±ΡΠ°Π·ΡΠΎΠ² ΠΠΠ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΌΠΈΠΊΡΠΎΡΠΈΠΏΠ° Illumina Chicken 60K SNP iSelect BeadChip (Β«IlluminaΒ», Π‘Π¨Π). ΠΠΎΠ½ΡΡΠΎΠ»Ρ ΠΊΠ°ΡΠ΅ΡΡΠ²Π° Π³Π΅Π½ΠΎΡΠΈΠΏΠΈΡΠΎΠ²Π°Π½ΠΈΡ, ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΡΡΠΊΡΡΡΡ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΌΠ½ΠΎΠ³ΠΎΠΌΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΠΊΠ°Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΡ (multidimensional scaling, MDS), ΡΠ°ΡΡΠ΅Ρ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ Π½Π΅ΡΠ°Π²Π½ΠΎΠ²Π΅ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ΅ΠΏΠ»Π΅Π½ΠΈΡ (linkage disequilibrium, LD) ΠΈ ΡΠ°ΡΡΠΎΡΡ Π²ΡΡΡΠ΅ΡΠ°Π΅ΠΌΠΎΡΡΠΈ Π°Π»Π»Π΅Π΅ΠΉ ΠΏΠΎ Π³ΡΡΠΏΠΏΠ°ΠΌ ΠΏΡΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Π² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ΅ PLINK 1.9. ΠΠΎΡΡΡΠΎΠ΅Π½ΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΡΠ°Π·Π³ΡΠ°Π½ΠΈΡΠ΅Π½ΠΈΡ ΠΊΠ»Π°ΡΡΠ΅ΡΠΎΠ² Π½Π° ΠΎΡΠ½ΠΎΠ²Π΅ SNP-Π³Π΅Π½ΠΎΡΠΈΠΏΠΎΠ² ΠΎΡΡΡΠ΅ΡΡΠ²Π»ΡΠ»ΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΡ ADMIXTURE. ΠΠΎ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΠ°ΠΌ MDS-Π°Π½Π°Π»ΠΈΠ·Π° ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΡ Π±ΡΠ»Π° ΡΡΠ»ΠΎΠ²Π½ΠΎ ΡΠ°Π·Π΄Π΅Π»Π΅Π½Π° Π½Π° ΡΠ΅ΡΡΡΠ΅ MDS-Π³ΡΡΠΏΠΏΡ, ΡΡΠΎ Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ Π΄Π°Π½Π½ΡΠΌΠΈ ΡΠΎΠ΄ΠΎΡΠ»ΠΎΠ²Π½ΠΎΠΉ Π°Π΄Π΅ΠΊΠ²Π°ΡΠ½ΠΎ ΠΎΡΡΠ°ΠΆΠ°Π΅Ρ ΠΏΡΠΎΠΈΡΡ
ΠΎΠΆΠ΄Π΅Π½ΠΈΠ΅ ΠΈΠ·ΡΡΠ΅Π½Π½ΡΡ
ΠΎΡΠΎΠ±Π΅ΠΉ. ΠΡΠ΅Π΄ΡΡΠ°Π²ΠΈΡΠ΅Π»ΠΈ ΠΏΡΠ΅Π΄ΠΊΠΎΠ²ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ Π±ΡΠ»ΠΈ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈ ΡΡ
ΠΎΠ΄Π½Ρ Ρ Π³ΡΡΠΏΠΏΠΎΠΉ MDS3. Π‘ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ F-ΡΡΠ°ΡΠΈΡΡΠΈΠΊΠΈ ΠΌΠ½ΠΎΠ³ΠΎΡΠ°ΠΊΡΠΎΡΠ½ΠΎΠ³ΠΎ Π΄ΠΈΡΠΏΠ΅ΡΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° Π²ΡΡΠ²Π»Π΅Π½ΠΎ Π΄ΠΎΡΡΠΎΠ²Π΅ΡΠ½ΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ Π³ΡΡΠΏΠΏΡ, Ρ
ΡΠΎΠΌΠΎΡΠΎΠΌΡ, Ρ
ΡΠΎΠΌΠΎΡΠΎΠΌΡ Π² Π³ΡΡΠΏΠΏΠ΅ ΠΈ Π΄ΠΈΡΡΠ°Π½ΡΠΈΠΈ ΠΌΠ΅ΠΆΠ΄Ρ SNP-ΠΌΠ°ΡΠΊΠ΅ΡΠ°ΠΌΠΈ Π½Π° Π·Π½Π°ΡΠ΅Π½ΠΈΡ LD (r2). Π Π³ΡΡΠΏΠΏΠ΅ 2001 Π³ΠΎΠ΄Π° ΠΏΠΎ Π²ΡΠ΅ΠΌ Ρ
ΡΠΎΠΌΠΎΡΠΎΠΌΠ°ΠΌ Π½Π°Π±Π»ΡΠ΄Π°Π»ΠΈΡΡ ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡΠ½ΡΠ΅ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ r2 ΠΈ Π²ΡΡΠΎΠΊΠ°Ρ ΡΠ°ΡΡΠΎΡΠ° Π²ΡΡΡΠ΅ΡΠ°Π΅ΠΌΠΎΡΡΠΈ LD, ΡΠ°Π²Π½ΠΎΠ³ΠΎ 1, ΠΏΡΠΈ ΡΠ°ΡΡΡΠΎΡΠ½ΠΈΠΈ ΠΌΠ΅ΠΆΠ΄Ρ SNP-ΠΌΠ°ΡΠΊΠ΅ΡΠ°ΠΌΠΈ 500-1000 ΠΠ±. ΠΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΠΌΠΎΠ½ΠΎΠΌΠΎΡΡΠ½ΡΡ
Π°Π»Π»Π΅Π»Π΅ΠΉ Π² ΡΡΠΎΠΉ Π³ΡΡΠΏΠΏΠ΅ ΡΠ°ΠΊΠΆΠ΅ Π±ΡΠ»ΠΎ ΡΠ°ΠΌΡΠΌ Π²ΡΡΠΎΠΊΠΈΠΌ. ΠΠ° ΠΎΡΠ½ΠΎΠ²Π°Π½ΠΈΠΈ SNP-Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ΄Π΅Π»Π°Π½ Π²ΡΠ²ΠΎΠ΄ ΠΎ ΡΠΎΠΌ, ΡΡΠΎ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½Π°Ρ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΡ ΡΡΡΡΠΊΠΈΡ
Π±Π΅Π»ΡΡ
ΠΊΡΡ Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΡΠ΅ΡΡΡ ΡΠ°ΡΠΏΠ°Π΄ΠΎΠΌ Π΄Π»ΠΈΠ½Π½ΡΡ
LD-ΡΠ°ΠΉΠΎΠ½ΠΎΠ² ΠΏΡΠ΅Π΄ΠΊΠΎΠ²ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ. ΠΠΎΠ΄Π΅Π»ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΊΠ»Π°ΡΡΠ΅ΡΠΎΠ² Π² ΠΏΡΠΎΠ³ΡΠ°ΠΌΠΌΠ΅ ADMIXTURE Π²ΡΡΠ²ΠΈΠ»ΠΎ ΡΠ°Π·Π»ΠΈΡΠΈΡ ΠΌΠ΅ΠΆΠ΄Ρ Π³ΡΡΠΏΠΏΠ°ΠΌΠΈ ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ, ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΡΠΌΠΈ Ρ ΠΏΠΎΠΌΠΎΡΡΡ MDS-Π°Π½Π°Π»ΠΈΠ·Π°. ΠΡΡΠΏΠΏΡ, ΡΡΠΎΡΠΌΠΈΡΠΎΠ²Π°Π½Π½ΡΠ΅ ΠΈΠ· ΠΎΡΠΎΠ±Π΅ΠΉ, Π²Ρ
ΠΎΠ΄ΡΡΠΈΡ
Π² MDS1 ΠΈ MDS2, ΠΈΠΌΠ΅Π»ΠΈ ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΡΡ ΡΡΡΡΠΊΡΡΡΡ ΠΈ ΡΠ°Π·Π»ΠΈΡΠ°Π»ΠΈΡΡ ΠΌΠ΅ΠΆΠ΄Ρ ΡΠΎΠ±ΠΎΠΉ ΠΏΡΠΈ K = 4 ΠΈ K = 5. ΠΡΡΠΏΠΏΠ° MDS4 ΠΎΠ±ΡΠ°Π·ΠΎΠ²ΡΠ²Π°Π»Π° Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈ Π½Π΅ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΡΠΉ ΠΊΠ»Π°ΡΡΠ΅Ρ, ΠΎΡΠ»ΠΈΡΠ°ΡΡΠΈΠΉΡΡ ΠΎΡ Π³ΡΡΠΏΠΏ MDS1 ΠΈ MDS2 ΠΏΡΠΈ Π·Π½Π°ΡΠ΅Π½ΠΈΡΡ
K ΠΎΡ 2 Π΄ΠΎ 5. ΠΡΡΠΏΠΏΠ° MDS3 Π±ΡΠ»Π° ΡΠΈΠ»ΠΎΠ³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΈ Π±Π»ΠΈΠ·ΠΊΠ° ΠΊ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ 2001 Π³ΠΎΠ΄Π° (ΠΏΡΠΈ K ΠΎΡ 2 Π΄ΠΎ 5). Π’Π°ΠΊΠΈΠΌ ΠΎΠ±ΡΠ°Π·ΠΎΠΌ, Π°Π½Π°Π»ΠΈΠ· ΡΠΎΠ²ΡΠ΅ΠΌΠ΅Π½Π½ΠΎΠΉ Π³Π΅Π½ΠΎΡΠΎΠ½Π΄Π½ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ ΡΡΡΡΠΊΠΈΡ
Π±Π΅Π»ΡΡ
ΠΊΡΡ ΠΏΠΎΠΊΠ°Π·Π°Π» Π΅Π΅ Π½Π΅ΠΎΠ΄Π½ΠΎΡΠΎΠ΄Π½ΠΎΡΡΡ ΠΈ ΡΡ
ΠΎΠ΄ΡΡΠ²ΠΎ Π³ΡΡΠΏΠΏΡ MDS3 Ρ ΠΏΡΠ΅Π΄ΠΊΠΎΠ²ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠ΅ΠΉ 2001 Π³ΠΎΠ΄Π°, ΠΊΠΎΡΠΎΡΠ°Ρ, Π² ΡΠ²ΠΎΡ ΠΎΡΠ΅ΡΠ΅Π΄Ρ, Ρ
Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΠ·ΠΎΠ²Π°Π»Π°ΡΡ Π±ΠΎΠ»ΡΡΠΈΠΌ ΡΠΈΡΠ»ΠΎΠΌ ΠΌΠΎΠ½ΠΎΠΌΠΎΡΡΠ½ΡΡ
Π°Π»Π»Π΅Π»Π΅ΠΉ ΠΈ Π²ΡΡΠΎΠΊΠΎΠΉ ΡΠ°ΡΡΠΎΡΠΎΠΉ Π²ΡΡΡΠ΅ΡΠ°Π΅ΠΌΠΎΡΡΠΈ Π΄Π»ΠΈΠ½Π½ΡΡ
LD-ΡΠ°ΠΉΠΎΠ½ΠΎΠ². SNP-ΡΠΊΠ°Π½ΠΈΡΠΎΠ²Π°Π½ΠΈΠ΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ ΠΎΡΠ΅Π½ΠΈΡΡ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΡΡ
ΠΎΠ΄ΡΡΠ²ΠΎ ΠΎΡΠΎΠ±Π΅ΠΉ ΠΈ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΎΠ½Π½ΡΡ ΡΡΡΡΠΊΡΡΡΡ ΡΡΡΡΠΊΠΎΠΉ Π±Π΅Π»ΠΎΠΉ ΠΏΠΎΡΠΎΠ΄Ρ ΠΊΡΡ. ΠΠΎΠ½ΠΈΠΌΠ°Π½ΠΈΠ΅ Π³Π΅Π½Π΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΡΡΠΊΡΡΡΡ Π²Π°ΠΆΠ½ΠΎ ΠΏΡΠΈ ΠΏΠ°Π½ΠΌΠΈΠΊΡΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΠ°Π·Π²Π΅Π΄Π΅Π½ΠΈΠΈ ΠΈ ΠΎΡΡΠ»Π΅ΠΆΠΈΠ²Π°Π½ΠΈΠΈ ΠΈΡΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π² ΠΌΠΎΠ»Π΅ΠΊΡΠ»ΡΡΠ½ΠΎΠΉ ΠΎΡΠ³Π°Π½ΠΈΠ·Π°ΡΠΈΠΈ Π³Π΅Π½ΠΎΠΌΠ° Π³Π΅Π½ΠΎΡΠΎΠ½Π΄Π½ΠΎΠΉ ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΈ Ρ ΠΎΠ³ΡΠ°Π½ΠΈΡΠ΅Π½Π½ΡΠΌ ΠΏΠΎΠ³ΠΎΠ»ΠΎΠ²ΡΠ΅ΠΌ
Diversities in the Gut Microbial Patterns in Patients with Atherosclerotic Cardiovascular Diseases and Certain Heart Failure Phenotypes
To continue progress in the treatment of cardiovascular disease, there is a need to improve the overall understanding of the processes that contribute to the pathogenesis of cardiovascular disease (CVD). Exploring the role of gut microbiota in various heart diseases is a topic of great interest since it is not so easy to find such reliable connections despite the fact that microbiota undoubtedly affect all body systems. The present study was conducted to investigate the composition of gut microbiota in patients with atherosclerotic cardiovascular disease (ASCVD) and heart failure syndromes with reduced ejection fraction (HFrEF) and HF with preserved EF (HFpEF), and to compare these results with the microbiota of individuals without those diseases (control group). Fecal microbiota were evaluated by three methods: living organisms were determined using bacterial cultures, total DNA taxonomic composition was estimated by next generation sequencing (NGS) of 16S rRNA gene (V3–V4) and quantitative assessment of several taxa was performed using qPCR (quantitative polymerase chain reaction). Regarding the bacterial culture method, all disease groups demonstrated a decrease in abundance of Enterococcus faecium and Enterococcus faecalis in comparison to the control group. The HFrEF group was characterized by an increased abundance of Streptococcus sanguinus and Streptococcus parasanguinis. NGS analysis was conducted at the family level. No significant differences between patient’s groups were observed in alpha-diversity indices (Shannon, Faith, Pielou, Chao1, Simpson, and Strong) with the exception of the Faith index for the HFrEF and control groups. Erysipelotrichaceae were significantly increased in all three groups; Streptococcaceae and Lactobacillaceae were significantly increased in ASCVD and HFrEF groups. These observations were indirectly confirmed with the culture method: two species of Streptococcus were significantly increased in the HFrEF group and Lactobacillus plantarum was significantly increased in the ASCVD group. The latter observation was also confirmed with qPCR of Lactobacillus sp. Acidaminococcaceae and Odoribacteraceae were significantly decreased in the ASCVD and HFrEF groups. Participants from the HFpEF group showed the least difference compared to the control group in all three study methods. The patterns found expand the knowledge base on possible correlations of gut microbiota with cardiovascular diseases. The similarities and differences in conclusions obtained by the three methods of this study demonstrate the need for a comprehensive approach to the analysis of microbiota