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

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    На основС Π°Π½Π°Π»ΠΈΠ·Π° исслСдований Ρ„ΠΎΡ€ΠΌΡƒΠ»ΠΈΡ€ΡƒΡŽΡ‚ΡΡ пСдагогичСскиС условия, Π½Π΅ΠΎΠ±Ρ…ΠΎΠ΄ΠΈΠΌΡ‹Π΅ для формирования готовности студСнтов-Π±ΡƒΠ΄ΡƒΡ‰ΠΈΡ… ΠΈΠ½ΠΆΠ΅Π½Π΅Ρ€ΠΎΠ² ΠΊ иноязычному ΠΎΠ±Ρ‰Π΅Π½ΠΈΡŽ Π² ΠΏΡ€ΠΎΡ„Π΅ΡΡΠΈΠΎΠ½Π°Π»ΡŒΠ½ΠΎΠΉ Π΄Π΅ΡΡ‚Π΅Π»ΡŒΠ½ΠΎΡΡ‚ΠΈ.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 –3% Si(110)[hkl]alloy by rolling with a roll diameter of 90 mm

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    ВСкст ΡΡ‚Π°Ρ‚ΡŒΠΈ Π½Π΅ публикуСтся Π² ΠΎΡ‚ΠΊΡ€Ρ‹Ρ‚ΠΎΠΌ доступС Π² соотвСтствии с ΠΏΠΎΠ»ΠΈΡ‚ΠΈΠΊΠΎΠΉ ΠΆΡƒΡ€Π½Π°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 –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

    DEFORMATION FEATURES OF THE CENTRAL LAYERS OF Fe - 3%Si(110)[hkl] ALLOY BY ROLLING WITH A ROLL DIAMETER OF 90 MM

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    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

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    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 HR–HPLC–MS. The EI mass spectra of individual substances were obtained by the GC–MS. Retention and resolution of N-methyl glycolurils were investigated in the reversed-phase HPLC mode for different stationary phases: C18, SB–Aq, 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] Π˜Π·ΡƒΡ‡Π΅Π½ΠΈΠ΅ структуры Π³Π΅Π½ΠΎΡ„ΠΎΠ½Π΄Π½ΠΎΠΉ популяции русской Π±Π΅Π»ΠΎΠΉ ΠΏΠΎΡ€ΠΎΠ΄Ρ‹ ΠΊΡƒΡ€ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ Π³Π΅Π½ΠΎΠΌΠ½ΠΎΠ³ΠΎ SNP-сканирования

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    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. ΠŸΠΎΠΏΡƒΠ»ΡΡ†ΠΈΡ русских Π±Π΅Π»Ρ‹Ρ… ΠΊΡƒΡ€ ΡΠ΅Π»Π΅ΠΊΡ†ΠΈΠΎΠ½ΠΈΡ€ΠΎΠ²Π°Π»Π°ΡΡŒ Π² Π³Π΅Π½ΠΎΡ„ΠΎΠ½Π΄Π½ΠΎΠΌ хозяйствС ВсСроссийского НИИ Π³Π΅Π½Π΅Ρ‚ΠΈΠΊΠΈ ΠΈ развСдСния ΡΠ΅Π»ΡŒΡΠΊΠΎΡ…ΠΎΠ·ΡΠΉΡΡ‚Π²Π΅Π½Π½Ρ‹Ρ… ΠΆΠΈΠ²ΠΎΡ‚Π½Ρ‹Ρ… (Π’ΠΠ˜Π˜Π“Π Π–) Π² Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅ 25 ΠΏΠΎΠΊΠΎΠ»Π΅Π½ΠΈΠΉ с использованиСм ΠΈΠ½Π΄ΠΈΠ²ΠΈΠ΄ΡƒΠ°Π»ΡŒΠ½ΠΎΠ³ΠΎ ΠΏΠΎΠ΄Π±ΠΎΡ€Π°. ΠžΡΠΎΠ±Π΅Π½Π½ΠΎΡΡ‚ΠΈ этой ΠΏΠΎΡ€ΠΎΠ΄Ρ‹ β€” ΡƒΡΡ‚ΠΎΠΉΡ‡ΠΈΠ²ΠΎΡΡ‚ΡŒ ΠΊ ΠΏΠΎΠ½ΠΈΠΆΠ΅Π½Π½ΠΎΠΉ Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Π΅ выращивания Π² Ρ€Π°Π½Π½ΠΈΠΉ ΠΏΠΎΡΡ‚Π½Π°Ρ‚Π°Π»ΡŒΠ½Ρ‹ΠΉ ΠΏΠ΅Ρ€ΠΈΠΎΠ΄ ΠΈ Π±Π΅Π»Ρ‹ΠΉ Ρ†Π²Π΅Ρ‚ ΡΠΌΠ±Ρ€ΠΈΠΎΠ½Π°Π»ΡŒΠ½ΠΎΠ³ΠΎ ΠΏΡƒΡ…Π°. Π’ 2002-2012 Π³ΠΎΠ΄Π°Ρ… Π΅Π΅ Ρ€Π°Π·Π²Π΅Π΄Π΅Π½ΠΈΠ΅ ΠΎΡΡƒΡ‰Π΅ΡΡ‚Π²Π»ΡΠ»ΠΎΡΡŒ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ панмиксии, ΠΈ ΠΊ настоящСму Π²Ρ€Π΅ΠΌΠ΅Π½ΠΈ Π½Π° основС ΡΠΎΡ…Ρ€Π°Π½ΠΈΠ²ΡˆΠ΅Π³ΠΎΡΡ поголовья сформирована новая популяция русских Π±Π΅Π»Ρ‹Ρ… ΠΊΡƒΡ€. НашСй Ρ†Π΅Π»ΡŒΡŽ Π±Ρ‹Π»ΠΎ ΠΏΠΎΠΊΠ°Π·Π°Ρ‚ΡŒ возмоТности ΠΏΠΎΠ»Π½ΠΎΠ³Π΅Π½ΠΎΠΌΠ½ΠΎΠ³ΠΎ SNP-сканирования (single nucleotide polymorphisms) для изучСния гСнСтичСских особСнностСй структуры популяции малочислСнных ΠΏΠΎΡ€ΠΎΠ΄ ΠΊΡƒΡ€ отСчСствСнного происхоТдСния ΠΈ динамичСскиС измСнСния молСкулярной Π°Ρ€Ρ…ΠΈΡ‚Π΅ΠΊΡ‚ΡƒΡ€Ρ‹ Π½Π° ΠΏΡ€ΠΈΠΌΠ΅Ρ€Π΅ сравнСния соврСмСнной популяции русской Π±Π΅Π»ΠΎΠΉ ΠΏΠΎΡ€ΠΎΠ΄Ρ‹ с ΠΏΡ€Π΅Π΄ΠΊΠΎΠ²ΠΎΠΉ популяциСй 2001 Π³ΠΎΠ΄Π°. Π‘Ρ‹Π»ΠΈ ΠΏΡ€ΠΎΠ°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΠΎΠ²Π°Π½Ρ‹ Π΄Π²Π΅ Π³Ρ€ΡƒΠΏΠΏΡ‹ ΠΊΡƒΡ€: популяция 2001 Π³ΠΎΠ΄Π° (6 Π³ΠΎΠ»., нСродствСнныС особи ΠΈΠ· Π΄Π²ΡƒΡ… Π»ΠΈΠ½ΠΈΠΉ) ΠΈ соврСмСнная популяция (156 Π³ΠΎΠ».). SNP-Π°Π½Π°Π»ΠΈΠ· Π²ΠΊΠ»ΡŽΡ‡Π°Π» скрининг 162 ΠΎΠ±Ρ€Π°Π·Ρ†ΠΎΠ² Π”ΠΠš с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ ΠΌΠΈΠΊΡ€ΠΎΡ‡ΠΈΠΏΠ° Illumina Chicken 60K SNP iSelect BeadChip (Β«IlluminaΒ», БША). ΠšΠΎΠ½Ρ‚Ρ€ΠΎΠ»ΡŒ качСства гСнотипирования, ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ гСнСтичСской структуры популяции ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΎΠΌ ΠΌΠ½ΠΎΠ³ΠΎΠΌΠ΅Ρ€Π½ΠΎΠ³ΠΎ ΡˆΠΊΠ°Π»ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΡ (multidimensional scaling, MDS), расчСт ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»Π΅ΠΉ нСравновСсного сцСплСния (linkage disequilibrium, LD) ΠΈ частоты встрСчаСмости Π°Π»Π»Π΅Π΅ΠΉ ΠΏΠΎ Π³Ρ€ΡƒΠΏΠΏΠ°ΠΌ ΠΏΡ€ΠΎΠ²ΠΎΠ΄ΠΈΠ»ΠΈ Π² ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠ΅ PLINK 1.9. ΠŸΠΎΡΡ‚Ρ€ΠΎΠ΅Π½ΠΈΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ разграничСния кластСров Π½Π° основС SNP-Π³Π΅Π½ΠΎΡ‚ΠΈΠΏΠΎΠ² осущСствляли с использованиСм ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΡ‹ ADMIXTURE. По Ρ€Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Π°ΠΌ MDS-Π°Π½Π°Π»ΠΈΠ·Π° соврСмСнная популяция Π±Ρ‹Π»Π° условно Ρ€Π°Π·Π΄Π΅Π»Π΅Π½Π° Π½Π° Ρ‡Π΅Ρ‚Ρ‹Ρ€Π΅ MDS-Π³Ρ€ΡƒΠΏΠΏΡ‹, Ρ‡Ρ‚ΠΎ Π² сравнСнии с Π΄Π°Π½Π½Ρ‹ΠΌΠΈ родословной Π°Π΄Π΅ΠΊΠ²Π°Ρ‚Π½ΠΎ ΠΎΡ‚Ρ€Π°ΠΆΠ°Π΅Ρ‚ происхоТдСниС ΠΈΠ·ΡƒΡ‡Π΅Π½Π½Ρ‹Ρ… особСй. ΠŸΡ€Π΅Π΄ΡΡ‚Π°Π²ΠΈΡ‚Π΅Π»ΠΈ ΠΏΡ€Π΅Π΄ΠΊΠΎΠ²ΠΎΠΉ популяции Π±Ρ‹Π»ΠΈ гСнСтичСски сходны с Π³Ρ€ΡƒΠΏΠΏΠΎΠΉ MDS3. Π‘ ΠΏΡ€ΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ F-статистики ΠΌΠ½ΠΎΠ³ΠΎΡ„Π°ΠΊΡ‚ΠΎΡ€Π½ΠΎΠ³ΠΎ диспСрсионного Π°Π½Π°Π»ΠΈΠ·Π° выявлСно достовСрноС влияниС Π³Ρ€ΡƒΠΏΠΏΡ‹, хромосомы, хромосомы Π² Π³Ρ€ΡƒΠΏΠΏΠ΅ ΠΈ дистанции ΠΌΠ΅ΠΆΠ΄Ρƒ SNP-ΠΌΠ°Ρ€ΠΊΠ΅Ρ€Π°ΠΌΠΈ Π½Π° значСния LD (r2). Π’ Π³Ρ€ΡƒΠΏΠΏΠ΅ 2001 Π³ΠΎΠ΄Π° ΠΏΠΎ всСм хромосомам наблюдались ΠΌΠ°ΠΊΡΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹Π΅ ΠΏΠΎΠΊΠ°Π·Π°Ρ‚Π΅Π»ΠΈ r2 ΠΈ высокая частота встрСчаСмости LD, Ρ€Π°Π²Π½ΠΎΠ³ΠΎ 1, ΠΏΡ€ΠΈ расстоянии ΠΌΠ΅ΠΆΠ΄Ρƒ SNP-ΠΌΠ°Ρ€ΠΊΠ΅Ρ€Π°ΠΌΠΈ 500-1000 Кб. ΠšΠΎΠ»ΠΈΡ‡Π΅ΡΡ‚Π²ΠΎ ΠΌΠΎΠ½ΠΎΠΌΠΎΡ€Ρ„Π½Ρ‹Ρ… Π°Π»Π»Π΅Π»Π΅ΠΉ Π² этой Π³Ρ€ΡƒΠΏΠΏΠ΅ Ρ‚Π°ΠΊΠΆΠ΅ Π±Ρ‹Π»ΠΎ самым высоким. На основании SNP-Π°Π½Π°Π»ΠΈΠ·Π° сдСлан Π²Ρ‹Π²ΠΎΠ΄ ΠΎ Ρ‚ΠΎΠΌ, Ρ‡Ρ‚ΠΎ соврСмСнная популяция русских Π±Π΅Π»Ρ‹Ρ… ΠΊΡƒΡ€ характСризуСтся распадом Π΄Π»ΠΈΠ½Π½Ρ‹Ρ… LD-Ρ€Π°ΠΉΠΎΠ½ΠΎΠ² ΠΏΡ€Π΅Π΄ΠΊΠΎΠ²ΠΎΠΉ популяции. ΠœΠΎΠ΄Π΅Π»ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ кластСров Π² ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠ΅ ADMIXTURE выявило различия ΠΌΠ΅ΠΆΠ΄Ρƒ Π³Ρ€ΡƒΠΏΠΏΠ°ΠΌΠΈ соврСмСнной популяции, ΠΎΠΏΡ€Π΅Π΄Π΅Π»Π΅Π½Π½Ρ‹ΠΌΠΈ с ΠΏΠΎΠΌΠΎΡ‰ΡŒΡŽ MDS-Π°Π½Π°Π»ΠΈΠ·Π°. Π“Ρ€ΡƒΠΏΠΏΡ‹, сформированныС ΠΈΠ· особСй, входящих Π² MDS1 ΠΈ MDS2, ΠΈΠΌΠ΅Π»ΠΈ ΠΎΠ΄Π½ΠΎΡ€ΠΎΠ΄Π½ΡƒΡŽ структуру ΠΈ Ρ€Π°Π·Π»ΠΈΡ‡Π°Π»ΠΈΡΡŒ ΠΌΠ΅ΠΆΠ΄Ρƒ собой ΠΏΡ€ΠΈ K = 4 ΠΈ K = 5. Π“Ρ€ΡƒΠΏΠΏΠ° MDS4 ΠΎΠ±Ρ€Π°Π·ΠΎΠ²Ρ‹Π²Π°Π»Π° гСнСтичСски Π½Π΅ΠΎΠ΄Π½ΠΎΡ€ΠΎΠ΄Π½Ρ‹ΠΉ кластСр, ΠΎΡ‚Π»ΠΈΡ‡Π°ΡŽΡ‰ΠΈΠΉΡΡ ΠΎΡ‚ Π³Ρ€ΡƒΠΏΠΏ MDS1 ΠΈ MDS2 ΠΏΡ€ΠΈ значСниях K ΠΎΡ‚ 2 Π΄ΠΎ 5. Π“Ρ€ΡƒΠΏΠΏΠ° MDS3 Π±Ρ‹Π»Π° филогСнСтичСски Π±Π»ΠΈΠ·ΠΊΠ° ΠΊ популяции 2001 Π³ΠΎΠ΄Π° (ΠΏΡ€ΠΈ K ΠΎΡ‚ 2 Π΄ΠΎ 5). Π’Π°ΠΊΠΈΠΌ ΠΎΠ±Ρ€Π°Π·ΠΎΠΌ, Π°Π½Π°Π»ΠΈΠ· соврСмСнной Π³Π΅Π½ΠΎΡ„ΠΎΠ½Π΄Π½ΠΎΠΉ популяции русских Π±Π΅Π»Ρ‹Ρ… ΠΊΡƒΡ€ ΠΏΠΎΠΊΠ°Π·Π°Π» Π΅Π΅ Π½Π΅ΠΎΠ΄Π½ΠΎΡ€ΠΎΠ΄Π½ΠΎΡΡ‚ΡŒ ΠΈ сходство Π³Ρ€ΡƒΠΏΠΏΡ‹ MDS3 с ΠΏΡ€Π΅Π΄ΠΊΠΎΠ²ΠΎΠΉ популяциСй 2001 Π³ΠΎΠ΄Π°, которая, Π² свою ΠΎΡ‡Π΅Ρ€Π΅Π΄ΡŒ, Ρ…Π°Ρ€Π°ΠΊΡ‚Π΅Ρ€ΠΈΠ·ΠΎΠ²Π°Π»Π°ΡΡŒ большим числом ΠΌΠΎΠ½ΠΎΠΌΠΎΡ€Ρ„Π½Ρ‹Ρ… Π°Π»Π»Π΅Π»Π΅ΠΉ ΠΈ высокой частотой встрСчаСмости Π΄Π»ΠΈΠ½Π½Ρ‹Ρ… LD-Ρ€Π°ΠΉΠΎΠ½ΠΎΠ². SNP-сканированиС ΠΏΠΎΠ·Π²ΠΎΠ»ΠΈΠ»ΠΎ ΠΎΡ†Π΅Π½ΠΈΡ‚ΡŒ гСнСтичСскоС сходство особСй ΠΈ ΠΏΠΎΠΏΡƒΠ»ΡΡ†ΠΈΠΎΠ½Π½ΡƒΡŽ структуру русской Π±Π΅Π»ΠΎΠΉ ΠΏΠΎΡ€ΠΎΠ΄Ρ‹ ΠΊΡƒΡ€. ПониманиС гСнСтичСской структуры Π²Π°ΠΆΠ½ΠΎ ΠΏΡ€ΠΈ панмиктичСском Ρ€Π°Π·Π²Π΅Π΄Π΅Π½ΠΈΠΈ ΠΈ отслСТивании историчСских ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ Π² молСкулярной ΠΎΡ€Π³Π°Π½ΠΈΠ·Π°Ρ†ΠΈΠΈ Π³Π΅Π½ΠΎΠΌΠ° Π³Π΅Π½ΠΎΡ„ΠΎΠ½Π΄Π½ΠΎΠΉ популяции с ΠΎΠ³Ρ€Π°Π½ΠΈΡ‡Π΅Π½Π½Ρ‹ΠΌ поголовьСм

    Diversities in the Gut Microbial Patterns in Patients with Atherosclerotic Cardiovascular Diseases and Certain Heart Failure Phenotypes

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    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
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