136 research outputs found
Impurity combination effect on oxygen absorption in Ξ±2-Ti3Al
The effect of substitutional impurities of the transition metals of VBβVIIB groups on the oxygen absorption in the doped 2-Ti3Al alloy was studied by the projector-augmented wave method within the density functional theory. It is established that all considered impurities prefer to substitute for a Ti atom rather than an Al atom. Changes in the absorption energy due to impurities being in the first neighbors of the oxygen atom were estimated. It was demonstrated that the doping resulted in a decrease in the oxygen absorption energy, which is mainly caused by the chemical contribution to it. The interaction energy between impurity atoms was calculated in the dependence on the interatomic distance. It was shown that the configuration with the impurity atoms being in the second neighbors of each other was stable in comparison with other possible configurations. The influence of two impurity atoms being in the first neighbors of oxygen is additively enhanced. It was revealed that the effect of two impurity atoms on the oxygen absorption energy can be estimated as the sum of the effects of separate impurities with an accuracy of more than ~90%
Profibrotic genetic polymorphisms as possible risk factors for the development of diastolic dysfunction in patients with epicardial adiposity
Aim. To determine the associations of variable sites of fibrogenesis genes with the risk of left ventricular (LV) diastolic dysfunction (DD) in patients with epicardial adiposity (EA).Material and methods. The study included 101 men with general obesity (Altai Territory) without cardiovascular diseases, diabetes and documented LVDD, of which, after determining the epicardial fat thickness (EFT), 2 groups were formed: group 1 β with EA (EA+), EFT β₯7 mm or more (n=70); group 2 β without EA (EA-), EFT <7 mm (n=31). The control group was formed from Kemerovo region residents of the corresponding sex and age and without a history of cardiovascular diseases and general obesity. Polymorphisms of the MMP9 rs17576, TGFB1 rs1800469, MMP3 rs6796620, MMP3 rs626750, MMP1 rs514921, LOC101927143 rs4290029, TIMP2 rs2277698 genes were determined in all patients using the polymerase chain reaction. After 4,7Β±0,3 years, all patients with general obesity underwent repeated echocardiography to assess LVDD.Results. We found that in the group with EA for rs626750 MMP3, the carriage of the homozygous T allele is 2 times more common (recessive inheritance, p=0,0022). After 4,7Β±0,3 years, LVDD was registered in 18 patients in the EA+ group and in 2 patients in the EA- group. When analyzing inheritance patterns, as well as comparing genotypes in groups of patients with EA with developed LVDD (n=20) and without LVDD (n=78), we found that patients with EA and LVDD are 3,4 times more likely to be a carrier of the homozygous T allele (recessive inheritance, p=0,02) for rs1800469 TGFB1.Conclusion. In patients with EA and LVDD, the carriage of the T rs1800469 TGFB1 allele is more common, which probably contributes to cardiac fibrosis and LVDD according to a recessive inheritance
Predicting the risk of left ventricular diastolic dysfunction in obesity
Obesity may develop heart failure with preserved ejection fraction, which is based on left ventricular diastolic dysfunction (LV DD). Currently, the search for effective predictors of LV DD is extremely relevant.Aim. To assess the prognostic value of key and additional metabolic risk factors (RFs), neurohumoral and profibrotic factors in the development of LV DD in obese patients.Material and methods. The study included 149 men with general obesity. The mean age was 49,7Β±7,9 years. The inclusion criteria was the presence of class I-III general obesity. The average body mass index was 32,9Β±3,6 kg/m2. The exclusion criteria were hypertension, coronary atherosclerosis, type 2 diabetes, as well as LV DD according to transthoracic echocardiography. Depending on the presence of epicardial adiposity, patients were divided into two groups: group 1 β epicardial adipose tissue (EAT) thickness β₯7 mm (n=70), group 2 β EAT <7 mm (n=31). In all patients, the following laboratory parameters were determined in blood serum using enzyme immunoassay: type I and III collagen, Procollagen I C-Terminal Propeptide (PICP), matrix metalloproteinase-3 (MMP-3), transforming growth factor Ξ²1, vascular endothelial growth factor, tumor necrosis factor-Ξ± (TNF-Ξ±), interleukin (IL)-6, IL-10, C-reactive protein (CRP), adiponectin, soluble leptin receptor, leptin, lipid parameters and free fatty acids (FFA). After 4,7Β±0,3 years, echocardiography was repeated in order to assess LV diastolic function.Results.Β Comparative analysis of metabolic risk factors revealed a significant increase in the level of total cholesterol (p=0,001), low-density lipoprotein cholesterol (LDL-C) (p<0,0001), triglycerides (TGs) (p<0,0001). These groups had no differences in such parameters as high-density lipoprotein cholesterol (p=0,09) and glucose (p=0,12). An increase in the level of such pro-inflammatory cytokines as TNF-Ξ± (p<0,0001), CRP (p<0,0001), IL-6 (p<0,0001) in group 1 was revealed, while differences in IL-10 (p=0,34) levels were not significant. In group 1, there was a significant increase in leptin levels (p<0,0001), a decrease in levels of adiponectin (p<0,0001) and leptin receptor (p=0,001). In group 1, an increase in the level of all studied profibrotic factors was revealed. After 4,7Β±0,3 years, repeated echocardiography revealed that selected groups were comparable in such parameters as A, E, E/A, E/eβ, eβ, and the peak tricuspid regurgitation velocity. There was a significant difference in left atrial volume index (p=0,0003). LV DD was detected in 20 patients. Binary logistic regression revealed the following most significant predictors of LV DD in obese patients: glucose, LDL-C, triglycerides, leptin receptor, leptin, MMP-3, FFA, PICP, and EAT thickness.Conclusion. Thus, the following most significant predictors of LV DD in obese patients were identified glucose, LDL-C, triglycerides, leptin receptor, leptin, MMP-3, FFA, PICP, and EAT thickness
The significance of melatonin as one of the signaling substrates in the expression of psychopathological disorders in the late period after traumatic brain injury
Aim. To discuss the functional properties of melatonin which plays a key role in the expression of psychopathological disorders following traumatic brain injury (TBI).
The influence of melatonin on neurochemical changes, which may lead to various mental disorders, has been analyzed. Specifically, its impact on asthenia, cognitive impairments, emotional instability, and insomnia, which are among the main symptoms accompanying TBI in the chronic phase, has been examined. The importance of studies on melatonin as a neurotransmitter and its role in the circadian rhythm regulation, which is often impaired as a consequence of TBI, has been emphasized. The mechanisms underlying these disorders may be related to dysfunction of the hypothalamus and interhemispheric interactions. Melatonin may influence the recovery processes after TBI, reducing inflammation and providing optimal conditions for tissue regeneration. It opens up new possibilities for the development of therapy aimed at reducing the impact of trauma consequences on patients. Earlier studies have also indicated the option of using melatonin as an antioxidant, which can protect brain tissue from further damage caused by recovery processes after injury.
Studies on pharmacological modulation of the melatonin system could be useful for developing new methods for treatment and prevention of mental disorders associated with TBI. Examining the action of melatonin receptor antagonists and agonists can help to choose optimal treatment strategies aimed at correcting disorders arising as a result of trauma. It holds promise for faster and more complete recovery of mental health in TBI patients.
Conclusions. Considering the significance of melatonin in regulation of physiological processes, further studies may enrich understanding of its role, ensuring the development of more effective treatment methods. Given the safe profile of melatonin and its availability as a supplement, supportive studies in this direction have the potential to positively impact clinical practice
Π‘ΡΠ±ΡΠ΅ΠΊΡΠΈΠ²Π½ΠΎΠ΅ Π±Π»Π°Π³ΠΎΠΏΠΎΠ»ΡΡΠΈΠ΅ ΡΡΡΠ΄Π΅Π½ΡΠΎΠ² ΠΏΡΠΈΡ ΠΎΠ»ΠΎΠ³ΠΎ-ΠΏΠ΅Π΄Π°Π³ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΈΡ Π½Π°ΠΏΡΠ°Π²Π»Π΅Π½ΠΈΠΉ Π² ΠΏΠ΅ΡΠΈΠΎΠ΄ ΡΠΏΠΈΠ΄Π΅ΠΌΠΈΠΈ COVID-19
Introduction. With the development of humanistic attitudes in society, the importance of issues related to the subjective well-being of the individual increases. It is important not only how successful a person is at work or in educational activities, but also how well he/she feels. The COVID-19 pandemic has significantly changed the learning environment for university students. The study of the factors of subjective well-being of students expands the instrumental capabilities of psychological support during the pandemic. In the case of a repetition of a similar situation, this knowledge will be useful for helping students, and potentially a wider circle of people, to maintain subjective well-being. Aim. The present research aimed to investigate the subjective well-being of students of psychological and pedagogical directions of universities during the pandemic with an emphasis on its emotional component. Methodology and research methods. The research methodology is based on the subjective approach, which considers a student as an active subject, capable of successfully adapting to the changed conditions of an educational activity. In the course of the research, the authors identified the interrelationships of subjective well-being, its semantic markers and self-organisation to expand the possibilities of diagnosing subjective well-being and maintaining it during the periods of extreme social situations, as well as to use semantic markers for self-analysis. To assess subjective well-being, three methods were applied. Self-assessment of satisfaction with one's condition on a 10-point scale was carried out according to the following parameters: sleep, food, communication with family, communication with friends, studies, hobbies, and mood. The authors employed the scale of subjective well-being (by Π. Perrudet-Badoux, G. Mendelsohn, J. Chiche, adapted by M. V. Sokolova) and psychosemantic characteristics of the subjective attitude to the situation of distance learning at the university due to the COVID-19 pandemic. To assess the ability of students to organise themselves in the changed learning conditions, the questionnaire of self-organisation of activities (N. T. Feather ΠΈ M. J. Bond, adapted by E. Yu. Mandrikova) was used. The study involved 406 students between the ages of 18 and 45 years (383 women and 23 men) studying in the areas of psychology and pedagogy at the University of Tyumen and State Kurgan State University. For statistical analysis of the research data, the Mann-Whitney U Test and correlation analysis were used. Results. It was found that the ability to self-organise leads to higher subjective well-being, and this, in turn, stimulates self-organisation. Semantic markers of subjective well-being associated with educational activities during the pandemic, such as comfortable and uncomfortable, interesting and uninteresting, tired and vigorous, were highlighted. The authors revealed objective parameters associated with self-organisation and subjective well-being, namely sleep disturbances. This can lead to the fact that there is not enough daytime and the student works at night, thereby resulting in the disturbance of night sleep, and consequently - poor self-organisation. Scientific novelty._The parameters of subjective well-being and self-organisation of students in a new, extreme social situation, during the COVID-19 pandemic are considered. Practical significance._The data obtained can be used to develop a strategy for teaching students in a pandemic situation and forced self-isolation, as well as to increase subjective well-being in a new social situation. The research results can be applied in psychodiagnostics for a more complete interpretation of the parameters of subjective well-being, as well as for the use of the identified relationships in the programmes of psychological support for students of psychological and pedagogical specialities. Semantic markers of subjective well-being that have received empirical justification can be employed to create a diagnostic scale. Β© 2021 Russian State Vocational Pedagogical University. All rights reserved.Acknowledgements. The research was funded by the Russian Foundation for Basic Research (RFBR) and Tyumen Region within the research project β 20-413-720004
Cytokines and HIF-1Ξ± as dysregulation factors of migration and differentiation of monocyte progenitor cells of endotheliocytes in the pathogenesis of ischemic cardiomyopathy
Background. Angiogenic endothelial dysfunction and progenitor endothelial cells (EPCs) in ischemic cardiomyopathy (ICMP) have not been studied enough.The aim. To establish the nature of changes in the cytokine profile and HIF-1Ξ± in blood and bone marrow associated with impaired differentiation of monocytic progenitor cells of endotheliocytes (CD14+VEGFR2+) in the bone marrow and their migration into the blood in patients with coronary heart disease (CHD), suffering and not suffering from ICMP.Materials and methods. A single-stage, single-centre, observational case-control study was conducted involving 74 patients with CHD, suffering and not suffering from ICMP (30 and 44 people, respectively), and 25 healthy donors. In patients with CHD, bone marrow was obtained during coronary bypass surgery, peripheral blood β before surgery. Healthy donors were taken peripheral blood. The number of CD14+VEGFR2+ in bone marrow and blood was determined by flow cytometry; the concentration of IL-6, TNF-Ξ±, M-CSF, GM-CSF, MCP-1 and HIF-1Ξ± β by the method of enzyme immunoassay.Results. A high content of CD14+VEGFR2+ cells in the blood of patients with CHD without cardiomyopathy was established relative to patients with ICMP against the background of a comparable number of these cells in myeloid tissue. Regardless of the presence of ICMP in the blood, patients with CHD showed an excess of TNF-Ξ±, a normal concentration of IL-6, GM-CSF, HIF-1Ξ± and a deficiency of M-CSF, and in the bone marrow supernatant, the concentration of IL-6 and TNF-Ξ± exceeded that in the blood plasma (the level of GM-CSF β only in patients without cardiomyopathy). With ICMP, the normal concentration of MCP-1 was determined in the blood plasma, and with CHD without cardiomyopathy, its elevated content was determined.Conclusion. The formation of ICMP is accompanied by insufficient activation of EPCs migration with the CD14+VEGFR2+ phenotype in blood without disruption of their differentiation in the bone marrow, which associated with the absence of an increase in the concentration of MCP-1 in blood plasma and not associated with the plasma content of M-CSF, GM-CSF, HIF-1Ξ±, IL-6 and TNF-Ξ±
RISK ASSESSMENT MODEL FOR CORONARY ATHEROSCLEROSIS IN PATIENTS WITH VISCERAL OBESITY
Aim. To invent a model for coronary atherosclerosis risk prediction in patients with visceral obesity and to conduct comparison research for this model with the other known Framingham and PROCAM.Material and methods. Totally 67 men included, of the age 40-65 (50,95Β±6,54 y.o.) without angina pectoris and clinical signs of another localization atherosclerosis. Patients had general obesity of I-III grade with BMI 35,16Β±3,32 kg/m , and visceral obesity by the thickness of epicaridal fat >7 mm. After coronary arteriography or multidetector computed tomography of coronary arteries we selected 2 comparison groups: group I (n=25) β patients with coronary atherosclerosis, group II (n=42) β without. For the invention of the prognostic score we used regression model with regression and optimal scaling.Results. Potential predictors of coronary atherosclerosis riskas a result of two groups comparison were: arterial hypertension, carbohydrate metabolism disorders, triglycerides, leptin, adiponectin and C-rective protein. As the result of regression analysis each predictor got its own significance mark. The rate of correctclassifications reached 79,1% that shows good prognostic value of this regression model. While using Framingham and PROCAM model the prognostic value of subclinical coronary atherosclerosis was 24,6% and 21,6% lower, resp., than the new risk assessment. Conclusion. The model invented of the risk assessment in visceral obesity patients makes it possible to take into account the main pathogenetic mechanisms that connect obesity and coronary atherosclerosis
Expression of CD80 and HLA-DR molecules on blood monocytes in patients with pulmonary tuberculosis
We examined expression pattern of CD80 and HLA-DR pro-inflammatory molecules on the monocytes in patients with pulmonary tuberculosis (TB), depending on the clinical form of the disease and susceptibility of the pathogen to anti-tuberculosis drugs. The study involved forty-five patients with newly diagnosed pulmonary TB (25 men and 20 women aged 18 to 55 years, average age β 44.0Β±12.4 years). The control group included 15 healthy donors with similar socio-demographic characteristics as in TB patients. Venous blood was used as biomaterial for assays. Studies of the monocyte immunophenotype were carried out by flow cytometry of whole blood cells using Cytoflex flow cytometer (Beckman Coulter, USA) with specific monoclonal antibodies (eBioscience, USA). We determined the content of cells expressing surface markers of monocytes, i.e., CD14, CD45, CD80, and HLA-DR. The results of this study were evaluated using SPSS Statistics 17.0 standard software package and Microsoft Excel. In the course of the study, we have suggested a working hypothesis that the monocytes in TB patients, still being in circulation, can express activation markers during their migration to inflammation focus, especially CD80 and HLA-DR molecules. Analysis of the total CD14+ monocyte number showed its decrease in all forms and variants of clinical course of pulmonary tuberculosis compared with the control group. Assessment of pro-inflammatory markers expressed on CD14 positive monocytes, i.e., HLA-DR activation marker and CD80 co-stimulatory molecule, showed that the number of monocytes with HLA-DR expression in all TB patients was higher than in healthy donors. HLA- DR expression on CD14+ monocytes in the group of patients with infiltrative TB proved to be 15% higher than in patients with disseminated TB. The expression of CD80 on CD14+ monocytes in TB patients showed no differences between the groups and varied within the normal range. Hence, an imbalance within monocyte population in patients with pulmonary tuberculosis, regardless of its clinical form and drug sensitivity of the pathogen is developed, due to decrease in total number of CD14+ cells, along with increased relative number of monocytes expressing HLA-DR activation marker (pro-inflammatory phenotype). Meanwhile, expression of the CD80 co-stimulatory molecule on monocytes was within normal values
ΠΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΠ°ΡΠΈΡ ΠΈ ΡΡΠ±ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΎΠ½Π½ΡΠΉ ΡΠΎΡΡΠ°Π² VEGFR2+ ΠΌΠΎΠ½ΠΎΡΠΈΡΠΎΠ² ΠΊΡΠΎΠ²ΠΈ ΠΈ ΠΊΠΎΡΡΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° ΠΏΡΠΈ ΠΈΡΠ΅ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠ°ΡΠ΄ΠΈΠΎΠΌΠΈΠΎΠΏΠ°ΡΠΈΠΈ
Aim. To identify disturbances of differentiation and subpopulation composition of VEGFR2+ cells in the blood and bone marrow associated with the features of the cytokine profile in the blood and bone marrow in patients with coronary artery disease (CAD) with and without ischemic cardiomyopathy (ICM).Materials and methods. The study included 74 patients with Π‘AD with and without ICM (30 and 44 people, respectively) and 18 healthy donors. In all patients with Π‘AD, peripheral blood sampling was performed immediately before coronary artery bypass grafting, and bone marrow samples were taken during the surgery via a sternal incision. In the healthy donors, only peripheral blood sampling was performed. In the bone marrow and blood samples, the number of VEGFR2+ cells (CD14+VEGFR2+ cells) and their immunophenotypes CD14++CD16-VEGFR2+, CD14++CD16+VEGFR2+, CD14+CD16++VEGFR2+, and CD14+CD16-VEGFR2+ was determined by flow cytometry. Using enzyme-linked immunosorbent assay, the levels of VΠGF-Π, TNFΞ±, M-CSF, and IL-13, as well as the content of MCP-1 (only in the blood) and the M-CSF / IL-13 ratio (only in the bone marrow) were determined.Results. The content of CD14+VEGFR2+ cells in the blood of CAD patients with and without ICM was higher than normal values due to the greater number of CD14++CD16-VEGFR2+, CD14++CD16+VEGFR2+, and CD14+CD16++VEGFR2+. In the bone marrow of the patients with ICM, the content of CD14++CD16-VEGFR2+, CD14+CD16++VEGFR2+, and CD14+CD16-VEGFR2+ was lower than in patients with CAD without ICM, and the number of CD14++CD16+VEGFR2+ cells corresponded to that in the controls. Regardless of the presence of ICM in CAD, a high concentration of TNFΞ± and normal levels of VEGF-A and IL-13 were observed in the blood. In CAD without ICM, an excess of MCP-1 and deficiency of M-CSF were revealed in the blood. In the bone marrow, the levels of VEGF-A, TNFΞ±, M-CSF, and IL-13 were comparable between the groups of patients against the background of a decrease in the M-CSF / IL-13 ratio in the patients with ICM.Conclusion. Unlike CAD without cardiomyopathy, in ICM, no excess of VEGFR2+ cells and MCP-1 in the blood is observed, which hinders active migration of CD14+CD16++VEGFR2+ cells from the myeloid tissue, and a decrease in the M-CSF / IL-13 ratio in the bone marrow disrupts differentiation of other forms of VEGFR2+ cells, preventing vascular repair.Π¦Π΅Π»Ρ: ΡΡΡΠ°Π½ΠΎΠ²ΠΈΡΡ Π½Π°ΡΡΡΠ΅Π½ΠΈΡ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΡΠΎΠ²ΠΊΠΈ ΠΈ ΡΡΠ±ΠΏΠΎΠΏΡΠ»ΡΡΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΡΠΎΡΡΠ°Π²Π° VEGFR2+ ΠΌΠΎΠ½ΠΎΡΠΈΡΠΎΠ² Π² ΠΊΡΠΎΠ²ΠΈ ΠΈ ΠΊΠΎΡΡΠ½ΠΎΠΌ ΠΌΠΎΠ·Π³Π΅ Π²ΠΎ Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·ΠΈ Ρ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎΡΡΡΠΌΠΈ ΡΠΈΡΠΎΠΊΠΈΠ½ΠΎΠ²ΠΎΠ³ΠΎ ΠΏΡΠΎΡΠΈΠ»Ρ ΠΊΡΠΎΠ²ΠΈ ΠΈ ΠΊΠΎΡΡΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° Ρ Π±ΠΎΠ»ΡΠ½ΡΡ
ΠΈΡΠ΅ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ Π±ΠΎΠ»Π΅Π·Π½ΡΡ ΡΠ΅ΡΠ΄ΡΠ° (ΠΠΠ‘), ΡΡΡΠ°Π΄Π°ΡΡΠΈΡ
ΠΈ Π½Π΅ ΡΡΡΠ°Π΄Π°ΡΡΠΈΡ
ΠΈΡΠ΅ΠΌΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΠ°ΡΠ΄ΠΈΠΎΠΌΠΈΠΎΠΏΠ°ΡΠΈΠ΅ΠΉ (ΠΠΠΠ).ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. Π ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π²ΠΎΡΠ»ΠΈ 74 Π±ΠΎΠ»ΡΠ½ΡΡ
ΠΠΠ‘, ΡΡΡΠ°Π΄Π°ΡΡΠΈΡ
ΠΈ Π½Π΅ ΡΡΡΠ°Π΄Π°ΡΡΠΈΡ
ΠΠΠΠ (30 ΠΈ 44 ΡΠ΅Π»ΠΎΠ²Π΅ΠΊΠ° ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ), ΠΈ 18 Π·Π΄ΠΎΡΠΎΠ²ΡΡ
Π΄ΠΎΠ½ΠΎΡΠΎΠ². Π£ Π²ΡΠ΅Ρ
Π±ΠΎΠ»ΡΠ½ΡΡ
ΠΠΠ‘ Π·Π°Π±ΠΎΡ ΠΏΠ΅ΡΠΈΡΠ΅ΡΠΈΡΠ΅ΡΠΊΠΎΠΉ ΠΊΡΠΎΠ²ΠΈ ΠΏΡΠΎΠΈΠ·Π²ΠΎΠ΄ΠΈΠ»ΡΡ Π½Π΅ΠΏΠΎΡΡΠ΅Π΄ΡΡΠ²Π΅Π½Π½ΠΎ ΠΏΠ΅ΡΠ΅Π΄ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠ΅ΠΉ ΠΊΠΎΡΠΎΠ½Π°ΡΠ½ΠΎΠ³ΠΎ ΡΡΠ½ΡΠΈΡΠΎΠ²Π°Π½ΠΈΡ, Π° ΠΊΠΎΡΡΠ½ΠΎΠ³ΠΎ ΠΌΠΎΠ·Π³Π° β ΠΈΠ· ΡΠ°Π·ΡΠ΅Π·Π° Π³ΡΡΠ΄ΠΈΠ½Ρ Π²ΠΎ Π²ΡΠ΅ΠΌΡ ΠΎΠΏΠ΅ΡΠ°ΡΠΈΠΈ. Π£ Π·Π΄ΠΎΡΠΎΠ²ΡΡ
Π΄ΠΎΠ½ΠΎΡΠΎΠ² Π·Π°Π±ΠΈΡΠ°Π»ΠΈ ΡΠΎΠ»ΡΠΊΠΎ ΠΏΠ΅ΡΠΈΡΠ΅ΡΠΈΡΠ΅ΡΠΊΡΡ ΠΊΡΠΎΠ²Ρ.Β Π ΠΊΠΎΡΡΠ½ΠΎΠΌ ΠΌΠΎΠ·Π³Π΅ ΠΈ ΠΊΡΠΎΠ²ΠΈ ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΏΡΠΎΡΠΎΡΠ½ΠΎΠΉ ΡΠΈΡΠΎΡΠ»ΡΠΎΡΠΈΠΌΠ΅ΡΡΠΈΠΈ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ»ΠΈ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΡ VEGFR2+ ΠΌΠΎΠ½ΠΎΡΠΈΡΠΎΠ² (CD14+VΠGFR2+ ΠΊΠ»Π΅ΡΠΎΠΊ) ΠΈ ΠΈΡ
ΠΈΠΌΠΌΡΠ½ΠΎΡΠ΅Π½ΠΎΡΠΈΠΏΠΎΠ² CD14++CD16-VEGFR2+, CD14++CD16+VEGFR2+, CD14+CD16++VEGFR2+, CD14+CD16-VEGFR2+, ΠΌΠ΅ΡΠΎΠ΄ΠΎΠΌ ΠΈΠΌΠΌΡΠ½ΠΎΡΠ΅ΡΠΌΠ΅Π½ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π° ΡΠ΅Π³ΠΈΡΡΡΠΈΡΠΎΠ²Π°Π»ΠΈ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ VΠGF-Π, TNFΞ±, M-CSF, IL-13, Π° ΡΠ°ΠΊΠΆΠ΅ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠ΅ MCP-1 (ΡΠΎΠ»ΡΠΊΠΎ Π² ΠΊΡΠΎΠ²ΠΈ) ΠΈ ΡΠΎΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠ΅ M-CSF/IL-13 (ΡΠΎΠ»ΡΠΊΠΎ Π² ΠΊΠΎΡΡΠ½ΠΎΠΌ ΠΌΠΎΠ·Π³Π΅).Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. Π‘ΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠ΅ CD14+VEGFR2+ ΠΊΠ»Π΅ΡΠΎΠΊ Π² ΠΊΡΠΎΠ²ΠΈ Ρ Π±ΠΎΠ»ΡΠ½ΡΡ
ΠΠΠ‘ Π±Π΅Π· ΠΊΠ°ΡΠ΄ΠΈΠΎΠΌΠΈΠΎΠΏΠ°ΡΠΈΠΈ ΠΈ Ρ ΠΠΠΠ Π±ΡΠ»ΠΎ Π²ΡΡΠ΅ Π½ΠΎΡΠΌΡ ΠΈΠ·-Π·Π° Π±ΠΎΠ»ΡΡΠ΅ΠΉ ΡΠΈΡΠ»Π΅Π½Π½ΠΎΡΡΠΈ CD14++CD16-VEGFR2+, CD14++CD16+VEGFR2+ ΠΈ CD14+CD16++VEGFR2+ ΡΠΎΡΠΌ. Π ΠΊΠΎΡΡΠ½ΠΎΠΌ ΠΌΠΎΠ·Π³Π΅ Ρ Π±ΠΎΠ»ΡΠ½ΡΡ
ΠΠΠΠ ΡΠΎΠ΄Π΅ΡΠΆΠ°Π½ΠΈΠ΅ CD14++CD16-VEGFR2+, CD14+CD16++VEGFR2+ ΠΈ CD14+CD16-VEGFR2+ ΡΠΎΡΠΌ Π±ΡΠ»ΠΎ Π½ΠΈΠΆΠ΅, ΡΠ΅ΠΌ Ρ Π±ΠΎΠ»ΡΠ½ΡΡ
ΠΠΠ‘ Π±Π΅Π· ΠΊΠ°ΡΠ΄ΠΈΠΎΠΌΠΈΠΎΠΏΠ°ΡΠΈΠΈ, Π° ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ CD14++CD16+VEGFR2+ ΠΊΠ»Π΅ΡΠΎΠΊ ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΎΠ²Π°Π»ΠΎ ΠΈΡ
ΡΠΈΡΠ»Ρ Π² Π³ΡΡΠΏΠΏΠ΅ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ. ΠΠ½Π΅ Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ Π½Π°Π»ΠΈΡΠΈΡ ΠΠΠΠ ΠΏΡΠΈ ΠΠΠ‘ Π² ΠΊΡΠΎΠ²ΠΈ ΠΎΡΠΌΠ΅ΡΠ°Π»Π°ΡΡ Π²ΡΡΠΎΠΊΠ°Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ TNFΞ±, Π½ΠΎΡΠΌΠ°Π»ΡΠ½ΡΠΉ ΡΡΠΎΠ²Π΅Π½Ρ VEGF-Π ΠΈ IL-13; ΠΏΡΠΈ ΠΠΠ‘ Π±Π΅Π· ΠΊΠ°ΡΠ΄ΠΈΠΎΠΌΠΈΠΎΠΏΠ°ΡΠΈΠΈ β ΠΈΠ·Π±ΡΡΠΎΠΊ ΠΠ‘Π -1 ΠΈ Π΄Π΅ΡΠΈΡΠΈΡ M-CSF Π² ΠΊΡΠΎΠ²ΠΈ. Π ΠΊΠΎΡΡΠ½ΠΎΠΌ ΠΌΠΎΠ·Π³Π΅ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ VΠGF-Π, TNFΞ±, M-CSF, IL-13 Π±ΡΠ»Π° ΡΠΎΠΏΠΎΡΡΠ°Π²ΠΈΠΌΠΎΠΉ ΠΌΠ΅ΠΆΠ΄Ρ Π³ΡΡΠΏΠΏΠ°ΠΌΠΈ Π±ΠΎΠ»ΡΠ½ΡΡ
Π½Π° ΡΠΎΠ½Π΅ ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΡ M-CSF/IL-13 Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΠΠΠ.ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. Π ΠΎΡΠ»ΠΈΡΠΈΠ΅ ΠΎΡ ΠΠΠ‘ Π±Π΅Π· ΠΊΠ°ΡΠ΄ΠΈΠΎΠΌΠΈΠΎΠΏΠ°ΡΠΈΠΈ ΠΏΡΠΈ ΠΠΠΠ Π½Π΅ ΡΠΎΡΠΌΠΈΡΡΠ΅ΡΡΡ ΠΈΠ·Π±ΡΡΠΎΠΊ VEGFR2+ ΠΌΠΎΠ½ΠΎΡΠΈΡΠΎΠ² ΠΈ ΠΠ‘Π -1 Π² ΠΊΡΠΎΠ²ΠΈ, ΡΡΠΎ Π·Π°ΡΡΡΠ΄Π½ΡΠ΅Ρ Π°ΠΊΡΠΈΠ²Π½ΡΡ ΠΌΠΈΠ³ΡΠ°ΡΠΈΡ CD14+CD16++VEGFR2+ ΠΊΠ»Π΅ΡΠΎΠΊ ΠΈΠ· ΠΌΠΈΠ΅Π»ΠΎΠΈΠ΄Π½ΠΎΠΉ ΡΠΊΠ°Π½ΠΈ, Π° ΡΠ½ΠΈΠΆΠ΅Π½ΠΈΠ΅ M-CSF/IL-13 Π² ΠΊΠΎΡΡΠ½ΠΎΠΌ ΠΌΠΎΠ·Π³Π΅ Π½Π°ΡΡΡΠ°Π΅Ρ Π΄ΠΈΡΡΠ΅ΡΠ΅Π½ΡΠΈΡΠΎΠ²ΠΊΡ ΠΎΡΡΠ°Π»ΡΠ½ΡΡ
ΡΠΎΡΠΌ VEGFR2+ ΠΌΠΎΠ½ΠΎΡΠΈΡΠΎΠ², ΠΏΡΠ΅ΠΏΡΡΡΡΠ²ΡΡ ΡΠ΅ΠΏΠ°ΡΠ°ΡΠΈΠΈ ΡΠΎΡΡΠ΄ΠΎΠ²
Π‘ΡΠ²ΠΎΡΠΎΡΠΎΡΠ½ΡΠ΅ Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΡ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΡ ΠΌΠ΅Ρ Π°Π½ΠΈΠΊΠΈ Π»Π΅Π²ΠΎΠ³ΠΎ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠ° Π² ΡΠ°Π½Π½Π΅ΠΉ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ΅ Π΄ΠΈΠ°ΡΡΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΡΡΡΠ½ΠΊΡΠΈΠΈ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΡΠΏΠΈΠΊΠ°ΡΠ΄ΠΈΠ°Π»ΡΠ½ΡΠΌ ΠΎΠΆΠΈΡΠ΅Π½ΠΈΠ΅ΠΌ
Highlights. Patients with epicardial obesity develop myocardial fibrosis (the underlying mechanism of left ventricular diastolic dysfunction) the preclinical diagnosis of which is difficult to perform. In this regard, the search for non-invasive methods for diagnosing diastolic dysfunction at an early stage, including the methods of determining the serum level of biomarkers of heart failure and studying the parameters of left ventricular mechanics using speckle-tracking echocardiography, seems quite relevant.Background. Currently, the search for serum biomarkers and non-invasive methods for diagnosing diastolic dysfunction (DD) of the left ventricle (LV) at the preclinical stage in obese patients is relevant.Aim. To study the levels of heart failure biomarkers and their association with profibrotic factors and LV mechanics in patients depending on the presence of epicardial obesity (EO).Methods. Out of 143 men with general obesity, depending on the severity of EO, determined by the thickness of epicardial adipose tissue (tEΠT), 2 groups of patients were identified: the EO (+) group with tEΠT 7 mm or more (n = 70), and the EO (β) group with tEΠT less than 7 mm (n = 40). The exclusion criteria from the study were: arterial hypertension, type 2 diabetes mellitus, coronary artery disease, and the presence of LVDD detected by echocardiography (echo). Levels of profibrotic factors (type I and type III collagen, procollagen type I C-terminal propeptide (PICP), matrix metalloproteinase-3 (MMP-3), transforming growth factor-Ξ² (TGF-Ξ²), vascular endothelial growth factor A (VEGF-A), sST2, and NT-proBNP were determined in all patients using enzyme immunoassay. With the help of speckle-tracking echocardiography, the mechanics of LV were analyzed.Results. The EO (+) group presented with increased sST2 level (22.11Β±7.36 ng/mL) compared to the EO (β) group (sST2 level 9.79Β±3.14 ng/mL (p<0.0001). In the EO (+) group, a significant influence of tEAT on sST2 level was identified (F = 8.57; p = 0.005). In the EO (+) group, an increase in the level of MMP-3, type I collagen, type III collagen, PICP, transforming growth factor-Ξ², and VEGF-A was revealed. Moreover, in the EO (+) group, a statistically significant relationship between sST2 and type III collagen was revealed (p = 0.01). When comparing the parameters of speckle-tracking echo, the EO group (+) presented with increased LV untwisting rate of β128.31 (β142.0; β118.0) deg/s-1 (p = 0.002), and increased time to LV peak untwisting rate of β 476.44 (510.0; 411.0) msec compared with the EO group (β) (p = 0.03). Moreover, a significant association between LV untwisting rate and sST2 level was revealed in the EO (+) group (r = 0.35; p = 0.02).>Λ0.0001). In the EO (+) group, a significant influence of tEAT on sST2 level was identified (F = 8.57; p = 0.005). In the EO (+) group, an increase in the level of MMP-3, type I collagen, type III collagen, PICP, transforming growth factor-Ξ², and VEGF-A was revealed. Moreover, in the EO (+) group, a statistically significant relationship between sST2 and type III collagen was revealed (p = 0.01). When comparing the parameters of speckle-tracking echo, the EO group (+) presented with increased LV untwisting rate of β128.31 (β142.0; β118.0) deg/s-1 (p = 0.002), and increased time to LV peak untwisting rate of β 476.44 (510.0; 411.0) msec compared with the EO group (β) (p = 0.03). Moreover, a significant association between LV untwisting rate and sST2 level was revealed in the EO (+) group (r = 0.35; p = 0.02).Conclusion. The data obtained indicate that patients with EO have LVDD, which could not be detected using echo criteria for LVDD, and the determination of serum levels of the heart failure biomarker - sST2 can be used for the diagnosis of LVDD at the early stage.ΠΡΠ½ΠΎΠ²Π½ΡΠ΅ ΠΏΠΎΠ»ΠΎΠΆΠ΅Π½ΠΈΡ. Π£ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΡΠΏΠΈΠΊΠ°ΡΠ΄ΠΈΠ°Π»ΡΠ½ΡΠΌ ΠΎΠΆΠΈΡΠ΅Π½ΠΈΠ΅ΠΌ ΡΠ°Π·Π²ΠΈΠ²Π°Π΅ΡΡΡ ΡΠΈΠ±ΡΠΎΠ· ΠΌΠΈΠΎΠΊΠ°ΡΠ΄Π°, Π»Π΅ΠΆΠ°ΡΠΈΠΉ Π² ΠΎΡΠ½ΠΎΠ²Π΅ Π½Π°ΡΡΡΠ΅Π½ΠΈΡ Π΄ΠΈΠ°ΡΡΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ Π»Π΅Π²ΠΎΠ³ΠΎ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠ°, Π΄ΠΎΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠ°Ρ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠ° ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ Π·Π°ΡΡΡΠ΄Π½ΠΈΡΠ΅Π»ΡΠ½Π°. Π ΡΠ²ΡΠ·ΠΈ Ρ ΡΡΠΈΠΌ ΠΊΡΠ°ΠΉΠ½Π΅ Π°ΠΊΡΡΠ°Π»Π΅Π½ ΠΏΠΎΠΈΡΠΊ Π½Π΅ΠΈΠ½Π²Π°Π·ΠΈΠ²Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Π΄ΠΈΠ°ΡΡΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΡΡΡΠ½ΠΊΡΠΈΠΈ Π½Π° ΡΠ°Π½Π½Π΅ΠΉ ΡΡΠ°Π΄ΠΈΠΈ, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΡΡΠ²ΠΎΡΠΎΡΠΎΡΠ½ΠΎΠ³ΠΎ ΡΡΠΎΠ²Π½Ρ Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΈ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠΎΠ² ΠΌΠ΅Ρ
Π°Π½ΠΈΠΊΠΈ Π»Π΅Π²ΠΎΠ³ΠΎ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠ° Ρ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ΠΌ speckle-tracking ΡΡ
ΠΎΠΊΠ°ΡΠ΄ΠΈΠΎΠ³ΡΠ°ΡΠΈΠΈ.ΠΠΊΡΡΠ°Π»ΡΠ½ΠΎΡΡΡ. Π Π½Π°ΡΡΠΎΡΡΠ΅Π΅ Π²ΡΠ΅ΠΌΡ Π°ΠΊΡΡΠ°Π»Π΅Π½ ΠΏΠΎΠΈΡΠΊ Π½Π΅ΠΈΠ½Π²Π°Π·ΠΈΠ²Π½ΡΡ
ΠΌΠ΅ΡΠΎΠ΄ΠΎΠ² Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ Π΄ΠΈΠ°ΡΡΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΎΠΉ Π΄ΠΈΡΡΡΠ½ΠΊΡΠΈΠΈ (ΠΠ) Π»Π΅Π²ΠΎΠ³ΠΎ ΠΆΠ΅Π»ΡΠ΄ΠΎΡΠΊΠ° (ΠΠ) Π½Π° Π΄ΠΎΠΊΠ»ΠΈΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΌ ΡΡΠ°ΠΏΠ΅, Π² ΡΠΎΠΌ ΡΠΈΡΠ»Π΅ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΎΠΆΠΈΡΠ΅Π½ΠΈΠ΅ΠΌ.Π¦Π΅Π»Ρ. ΠΠ·ΡΡΠΈΡΡ ΡΡΠΎΠ²Π½ΠΈ Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠΎΠ² ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΡΡΠΈ ΠΈ ΠΈΡ
Π°ΡΡΠΎΡΠΈΠ°ΡΠΈΡ Ρ ΠΏΡΠΎΡΠΈΠ±ΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΠΌΠΈ ΡΠ°ΠΊΡΠΎΡΠ°ΠΌΠΈ ΠΈ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ°ΠΌΠΈ ΠΌΠ΅Ρ
Π°Π½ΠΈΠΊΠΈ ΠΠ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ Π½Π°Π»ΠΈΡΠΈΡ ΡΠΏΠΈΠΊΠ°ΡΠ΄ΠΈΠ°Π»ΡΠ½ΠΎΠ³ΠΎ ΠΎΠΆΠΈΡΠ΅Π½ΠΈΡ (ΠΠ).ΠΠ°ΡΠ΅ΡΠΈΠ°Π»Ρ ΠΈ ΠΌΠ΅ΡΠΎΠ΄Ρ. ΠΠ· 143 ΠΌΡΠΆΡΠΈΠ½ Ρ ΠΎΠ±ΡΠΈΠΌ ΠΎΠΆΠΈΡΠ΅Π½ΠΈΠ΅ΠΌ Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ ΡΡΠ΅ΠΏΠ΅Π½ΠΈ Π²ΡΡΠ°ΠΆΠ΅Π½Π½ΠΎΡΡΠΈ ΠΠ, ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π½ΠΎΠ³ΠΎ ΠΏΠΎ ΡΠΎΠ»ΡΠΈΠ½Π΅ ΡΠΏΠΈΠΊΠ°ΡΠ΄ΠΈΠ°Π»ΡΠ½ΠΎΠΉ ΠΆΠΈΡΠΎΠ²ΠΎΠΉ ΡΠΊΠ°Π½ΠΈ (ΡΠΠΠ’), Π²ΡΠ΄Π΅Π»Π΅Π½Ρ Π΄Π²Π΅ Π³ΡΡΠΏΠΏΡ: ΠΠ(+) β ΡΠΠΠ’ 7 ΠΈ Π±ΠΎΠ»Π΅Π΅ ΠΌΠΌ (n = 70), ΠΠ(β) β ΡΠΠΠ’ ΠΌΠ΅Π½Π΅Π΅ 7 ΠΌΠΌ (n = 40). ΠΡΠΈΡΠ΅ΡΠΈΠΈ ΠΈΡΠΊΠ»ΡΡΠ΅Π½ΠΈΡ ΠΈΠ· ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΡ: Π°ΡΡΠ΅ΡΠΈΠ°Π»ΡΠ½Π°Ρ Π³ΠΈΠΏΠ΅ΡΡΠ΅Π½Π·ΠΈΡ, ΡΠ°Ρ
Π°ΡΠ½ΡΠΉ Π΄ΠΈΠ°Π±Π΅Ρ 2-Π³ΠΎ ΡΠΈΠΏΠ°, ΠΈΡΠ΅ΠΌΠΈΡΠ΅ΡΠΊΠ°Ρ Π±ΠΎΠ»Π΅Π·Π½Ρ ΡΠ΅ΡΠ΄ΡΠ°, Π° ΡΠ°ΠΊΠΆΠ΅ Π½Π°Π»ΠΈΡΠΈΠ΅ ΠΠ ΠΠ, Π²ΡΡΠ²Π»Π΅Π½Π½ΠΎΠΉ ΠΏΠΎ Π΄Π°Π½Π½ΡΠΌ ΡΡ
ΠΎΠΊΠ°ΡΠ΄ΠΈΠΎΠ³ΡΠ°ΡΠΈΠΈ (ΠΡ
ΠΎΠΠ). ΠΡΠ΅ΠΌ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ°ΠΌ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΠ»ΠΈ ΡΡΠΎΠ²Π΅Π½Ρ ΠΏΡΠΎΡΠΈΠ±ΡΠΎΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΡΠ°ΠΊΡΠΎΡΠΎΠ² (ΠΊΠΎΠ»Π»Π°Π³Π΅Π½ I ΠΈ III ΡΠΈΠΏΠΎΠ², ΠΏΡΠΎΠΊΠΎΠ»Π»Π°Π³Π΅Π½ I C-ΠΊΠΎΠ½ΡΠ΅Π²ΠΎΠ³ΠΎ ΠΏΡΠΎΠΏΠ΅ΠΏΡΠΈΠ΄Π° (PICP), ΠΌΠ°ΡΡΠΈΠΊΡΠ½Π°Ρ ΠΌΠ΅ΡΠ°Π»Π»ΠΎΠΏΡΠΎΡΠ΅ΠΈΠ½Π°Π·Π°-3 (MMΠ-3), ΡΡΠ°Π½ΡΡΠΎΡΠΌΠΈΡΡΡΡΠΈΠΉ ΡΠ°ΠΊΡΠΎΡ ΡΠΎΡΡΠ°-Ξ² (TGF-Ξ²), ΡΠΎΡΡΠ΄ΠΈΡΡΡΠΉ ΡΠ½Π΄ΠΎΡΠ΅Π»ΠΈΠ°Π»ΡΠ½ΡΠΉ ΡΠ°ΠΊΡΠΎΡ ΡΠΎΡΡ (VEGFA)), sST2 ΠΈ NT-proBNP ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π»ΠΈ Ρ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ ΠΈΠΌΠΌΡΠ½ΠΎΡΠ΅ΡΠΌΠ΅Π½ΡΠ½ΠΎΠ³ΠΎ Π°Π½Π°Π»ΠΈΠ·Π°. Π‘ ΠΏΠΎΠΌΠΎΡΡΡ speckle-tracking ΠΡ
ΠΎΠΠ ΠΈΠ·ΡΡΠ΅Π½Π° ΠΌΠ΅Ρ
Π°Π½ΠΈΠΊΠ° ΠΠ.Π Π΅Π·ΡΠ»ΡΡΠ°ΡΡ. Π Π³ΡΡΠΏΠΏΠ΅ ΠΠ(+) Π²ΡΡΠ²Π»Π΅Π½ΠΎ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΡΡΠΎΠ²Π½Ρ sST2 Π΄ΠΎ 22,11Β±7,36 Π½Π³/ΠΌΠ» Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ Π³ΡΡΠΏΠΏΠΎΠΉ ΠΠ(β), Π³Π΄Π΅ ΡΡΠΎΠ²Π΅Π½Ρ sST2 ΡΠΎΡΡΠ°Π²ΠΈΠ» 9,79Β±3,14 Π½Π³/ΠΌΠ» (Ρ<0,0001). Π Π³ΡΡΠΏΠΏΠ΅ ΠΠ(+) ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΎ Π·Π½Π°ΡΠΈΠΌΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΡΠΠΠ’ Π½Π° ΡΡΠΎΠ²Π΅Π½Ρ sST2 (F = 8,57; p = 0,005). Π’Π°ΠΊΠΆΠ΅ Π² Π³ΡΡΠΏΠΏΠ΅ ΠΠ(+) Π·Π°ΡΠ΅Π³ΠΈΡΡΡΠΈΡΠΎΠ²Π°Π½ΠΎ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ ΡΡΠΎΠ²Π½Ρ ΠΠΠ-3, ΠΊΠΎΠ»Π»Π°Π³Π΅Π½Π° I ΠΈ III ΡΠΈΠΏΠΎΠ², PICP, TGF-Ξ², VEGFA; ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π° ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΠ°Ρ Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·Ρ sST2 ΠΈ ΠΊΠΎΠ»Π»Π°Π³Π΅Π½Π° III ΡΠΈΠΏΠ° (p = 0,01). ΠΡΠΈ ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ speckle-tracking ΠΡ
ΠΎΠΠ Π² Π³ΡΡΠΏΠΏΠ΅ ΠΠ(+) Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ Π³ΡΡΠΏΠΏΠΎΠΉ ΠΠ(β) ΠΎΡΠΌΠ΅ΡΠ΅Π½ΠΎ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΡΠΊΠΎΡΠΎΡΡΠΈ ΡΠ°ΡΠΊΡΡΡΠΈΠ²Π°Π½ΠΈΡ ΠΠ Π΄ΠΎ β128,31 (β142,0; β118,0) Π³ΡΠ°Π΄ΡΡΠ°/Ρβ1 (p = 0,002) ΠΈ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ Π΄ΠΎ ΠΏΠΈΠΊΠ° ΡΠ°ΡΠΊΡΡΡΠΈΠ²Π°Π½ΠΈΡ ΠΠ Π΄ΠΎ 476,44 (510,0; 411,0) ΠΌΡΠ΅ΠΊ (p = 0,03). Π Π΄Π°Π½Π½ΠΎΠΉ Π³ΡΡΠΏΠΏΠ΅ Π²ΡΡΠ²Π»Π΅Π½Π° Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·Ρ ΡΠΊΠΎΡΠΎΡΡΠΈ ΡΠ°ΡΠΊΡΡΡΠΈΠ²Π°Π½ΠΈΡ ΠΠ ΠΈ ΡΡΠΎΠ²Π½Ρ sST2 (r = 0,35; p = 0,02). >Λ0,0001). Π Π³ΡΡΠΏΠΏΠ΅ ΠΠ(+) ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΎ Π·Π½Π°ΡΠΈΠΌΠΎΠ΅ Π²Π»ΠΈΡΠ½ΠΈΠ΅ ΡΠΠΠ’ Π½Π° ΡΡΠΎΠ²Π΅Π½Ρ sST2 (F = 8,57; p = 0,005). Π’Π°ΠΊΠΆΠ΅ Π² Π³ΡΡΠΏΠΏΠ΅ ΠΠ(+) Π·Π°ΡΠ΅Π³ΠΈΡΡΡΠΈΡΠΎΠ²Π°Π½ΠΎ ΡΠ²Π΅Π»ΠΈΡΠ΅Π½ΠΈΠ΅ ΡΡΠΎΠ²Π½Ρ ΠΠΠ-3, ΠΊΠΎΠ»Π»Π°Π³Π΅Π½Π° I ΠΈ III ΡΠΈΠΏΠΎΠ², PICP, TGF-Ξ², VEGFA; ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½Π° ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΠ°Ρ Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·Ρ sST2 ΠΈ ΠΊΠΎΠ»Π»Π°Π³Π΅Π½Π° III ΡΠΈΠΏΠ° (p = 0,01). ΠΡΠΈ ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»Π΅ΠΉ speckle-tracking ΠΡ
ΠΎΠΠ Π² Π³ΡΡΠΏΠΏΠ΅ ΠΠ(+) Π² ΡΡΠ°Π²Π½Π΅Π½ΠΈΠΈ Ρ Π³ΡΡΠΏΠΏΠΎΠΉ ΠΠ(β) ΠΎΡΠΌΠ΅ΡΠ΅Π½ΠΎ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ ΡΠΊΠΎΡΠΎΡΡΠΈ ΡΠ°ΡΠΊΡΡΡΠΈΠ²Π°Π½ΠΈΡ ΠΠ Π΄ΠΎ β128,31 (β142,0; β118,0) Π³ΡΠ°Π΄ΡΡΠ°/Ρβ1 (p = 0,002) ΠΈ Π²ΡΠ΅ΠΌΠ΅Π½ΠΈ Π΄ΠΎ ΠΏΠΈΠΊΠ° ΡΠ°ΡΠΊΡΡΡΠΈΠ²Π°Π½ΠΈΡ ΠΠ Π΄ΠΎ 476,44 (510,0; 411,0) ΠΌΡΠ΅ΠΊ (p = 0,03). Π Π΄Π°Π½Π½ΠΎΠΉ Π³ΡΡΠΏΠΏΠ΅ Π²ΡΡΠ²Π»Π΅Π½Π° Π²Π·Π°ΠΈΠΌΠΎΡΠ²ΡΠ·Ρ ΡΠΊΠΎΡΠΎΡΡΠΈ ΡΠ°ΡΠΊΡΡΡΠΈΠ²Π°Π½ΠΈΡ ΠΠ ΠΈ ΡΡΠΎΠ²Π½Ρ sST2 (r = 0,35; p = 0,02).ΠΠ°ΠΊΠ»ΡΡΠ΅Π½ΠΈΠ΅. ΠΠΎΠ»ΡΡΠ΅Π½Π½ΡΠ΅ Π΄Π°Π½Π½ΡΠ΅ ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡ ΠΏΡΠ΅Π΄ΠΏΠΎΠ»ΠΎΠΆΠΈΡΡ, ΡΡΠΎ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΠΠ ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΠ ΠΠ, Π½Π΅ Π²ΡΡΠ²Π»Π΅Π½Π½Π°Ρ Ρ ΠΏΠΎΠΌΠΎΡΡΡ ΠΡ
ΠΎΠΠ-ΠΊΡΠΈΡΠ΅ΡΠΈΠ΅Π² Π½Π°ΡΡΡΠ΅Π½ΠΈΡ Π΄ΠΈΠ°ΡΡΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΡΠ½ΠΊΡΠΈΠΈ ΠΠ, Π° ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΠ΅ ΡΡΠΎΠ²Π½Ρ ΡΡΠ²ΠΎΡΠΎΡΠΎΡΠ½ΠΎΠ³ΠΎ Π±ΠΈΠΎΠΌΠ°ΡΠΊΠ΅ΡΠ° ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΡΡΠΈ sST2 Π²ΠΎΠ·ΠΌΠΎΠΆΠ½ΠΎ Π΄Π»Ρ Π΄ΠΈΠ°Π³Π½ΠΎΡΡΠΈΠΊΠΈ ΠΠ Π½Π° ΡΠ°Π½Π½Π΅ΠΉ ΡΡΠ°Π΄ΠΈΠΈ
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