7 research outputs found
Π Π°Π·Π»ΠΈΡΠ°ΡΡΡΡ Π»ΠΈ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ ΡΡΠΎΠΌΠ±ΠΎΡΠΈΡΠΎΠ² ΠΈ ΠΏΠΎΠΊΠ°Π·Π°ΡΠ΅Π»ΠΈ Π²ΠΎΡΠΏΠ°Π»Π΅Π½ΠΈΡ Π² Π³ΡΡΠΏΠΏΠ°Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ Ρ ΡΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΡΡΡΡ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΡΡΠ½ΠΊΡΠΈΠΎΠ½Π°Π»ΡΠ½ΡΠΌΠΈ ΠΊΠ»Π°ΡΡΠ°ΠΌΠΈ NYHA?
It is stated in the literature that thrombosis in the chronic heart failure (CHF) patients may be caused by interaction of inflammation and platelets. The incidence of venous thromboembolism in heart failure patients is found to be the highest in the patients classified as NYHA IV. We aimed to test the hypothesis that prothrombotic state depends on inflammation. We have compared the C-reactive protein (CRP), fibrinogen concentration, platelet count (PLT), mean platelet volume (MPV) and platelet aggregation in CHF patientsβ groups according to New York Heart Association (NYHA). 203 patients with CHF with reduced ejection fraction (systolic heart failure classes IβIV according to NYHA) were included in the study. There were no statistically significant differences in fibrinogen concentration, CRP, PLT and platelet aggregation between the groups according to NYHA. The MPV was statistically significant higher in NYHA IV group than in NYHA III, NYHA II and NYHA I groups (10.86 Β± 1.14 and 9.78 Β± 1.21 and 9.65 Β± 1.22 and 9.21 Β± 0.59 respectively, p = 0.006). There was a weak correlation between CRP and PLT (r = 0.293, p = 0.010), and between MPV and fibrinogen concentration (r=0.205, p=0.012). There was a moderate correlation between MPV and NYHA (r = 0.361, p < 0.001) and between fibrinogen concentration and CRP (r = 0.381, p < 0.001). MPV rising in the patientsβ groups and correlation between MPV and NYHA class, and plasma fibrinogen concentration, correlation between PLT and CRP, correlation between CRP and NT-proBNP concentration confirm, that low inflammation can take place in the MPV rising.ΠΠΎ Π΄Π°Π½Π½ΡΠΌ ΡΡΠ΄Π° Π°Π²ΡΠΎΡΠΎΠ², ΡΡΠΎΠΌΠ±ΠΎΠ· Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ Ρ
ΡΠΎΠ½ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΡΡΡΡ (Π₯Π‘Π) ΠΌΠΎΠΆΠ΅Ρ Π±ΡΡΡ ΠΎΠ±ΡΡΠ»ΠΎΠ²Π»Π΅Π½ Π²Π·Π°ΠΈΠΌΠΎΠ΄Π΅ΠΉΡΡΠ²ΠΈΠ΅ΠΌ ΠΌΠ΅ΠΆΠ΄Ρ Π²ΠΎΡΠΏΠ°Π»Π΅Π½ΠΈΠ΅ΠΌ ΠΈ ΡΡΠΎΠΌΠ±ΠΎΡΠΈΡΠ°ΠΌΠΈ. Π£ΡΡΠ°Π½ΠΎΠ²Π»Π΅Π½ΠΎ, ΡΡΠΎ Ρ ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΡΡΡΡ IV ΠΊΠ»Π°ΡΡΠ° NYHA Π²Π΅Π½ΠΎΠ·Π½Π°Ρ ΡΡΠΎΠΌΠ±ΠΎΡΠΌΠ±ΠΎΠ»ΠΈΡ Π²ΡΡΡΠ΅ΡΠ°Π΅ΡΡΡ ΠΎΡΠΎΠ±Π΅Π½Π½ΠΎ ΡΠ°ΡΡΠΎ. Π§ΡΠΎΠ±Ρ ΠΏΡΠΎΠ²Π΅ΡΠΈΡΡ Π³ΠΈΠΏΠΎΡΠ΅Π·Ρ ΠΎ ΡΠΎΠΌ, ΡΡΠΎ ΠΏΡΠΎΡΡΠΎΠΌΠ±ΠΎΡΠΈΡΠ΅ΡΠΊΠΎΠ΅ ΡΠΎΡΡΠΎΡΠ½ΠΈΠ΅ Π·Π°Π²ΠΈΡΠΈΡ ΠΎΡ ΡΡΠΎΠ²Π½Ρ Π²ΠΎΡΠΏΠ°Π»Π΅Π½ΠΈΡ, ΠΌΡ ΡΡΠ°Π²Π½ΠΈΠ»ΠΈ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ Π‘-ΡΠ΅Π°ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ Π±Π΅Π»ΠΊΠ° (CRP) ΠΈ ΡΠΈΠ±ΡΠΈΠ½ΠΎΠ³Π΅Π½Π°, Π° ΡΠ°ΠΊ ΠΆΠ΅ ΠΊΠΎΠ»ΠΈΡΠ΅ΡΡΠ²ΠΎ ΡΡΠΎΠΌΠ±ΠΎΡΠΈΡΠΎΠ² (PLT), ΡΡΠ΅Π΄Π½ΠΈΠΉ ΠΎΠ±ΡΠ΅ΠΌ ΡΡΠΎΠΌΠ±ΠΎΡΠΈΡΠΎΠ² (MPV) ΠΈ Π°Π³ΡΠ΅Π³Π°ΡΠΈΡ ΡΡΠΎΠΌΠ±ΠΎΡΠΈΡΠΎΠ² Π² Π³ΡΡΠΏΠΏΠ°Ρ
ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² Ρ Π₯Π‘Π Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ ΠΊΠ»Π°ΡΡΠΈΡΠΈΠΊΠ°ΡΠΈΠ΅ΠΉ ΠΡΡ- ΠΠΎΡΠΊΡΠΊΠΎΠΉ ΠΊΠ°ΡΠ΄ΠΈΠΎΠ»ΠΎΠ³ΠΈΡΠ΅ΡΠΊΠΎΠΉ Π°ΡΡΠΎΡΠΈΠ°ΡΠΈΠΈ (NYHA). Π ΠΈΡΡΠ»Π΅Π΄ΠΎΠ²Π°Π½ΠΈΠ΅ Π±ΡΠ»ΠΈ Π²ΠΊΠ»ΡΡΠ΅Π½Ρ 203 ΠΏΠ°ΡΠΈΠ΅Π½ΡΠ° Ρ Π₯Π‘Π ΡΠΎ ΡΠ½ΠΈΠΆΠ΅Π½Π½ΠΎΠΉ ΡΡΠ°ΠΊΡΠΈΠ΅ΠΉ Π²ΡΠ±ΡΠΎΡΠ° (ΠΊΠ»Π°ΡΡΡ ΡΠΈΡΡΠΎΠ»ΠΈΡΠ΅ΡΠΊΠΎΠΉ ΡΠ΅ΡΠ΄Π΅ΡΠ½ΠΎΠΉ Π½Π΅Π΄ΠΎΡΡΠ°ΡΠΎΡΠ½ΠΎΡΡΠΈ I β IV ΠΏΠΎ NYHA). ΠΡ Π½Π΅ Π²ΡΡΠ²ΠΈΠ»ΠΈ ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΡΠ΅ ΡΠ°Π·Π»ΠΈΡΠΈΡ Π² ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠΈ ΡΠΈΠ±ΡΠΈΠ½ΠΎΠ³Π΅Π½Π°, CRP, PLT ΠΈ Π°Π³ΡΠ΅Π³Π°ΡΠΈΠΈ ΡΡΠΎΠΌΠ±ΠΎΡΠΈΡΠΎΠ² ΠΌΠ΅ΠΆΠ΄Ρ Π³ΡΡΠΏΠΏΠ°ΠΌΠΈ Π² ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²ΠΈΠΈ Ρ NYHA. MPV Π±ΡΠ» ΡΡΠ°ΡΠΈΡΡΠΈΡΠ΅ΡΠΊΠΈ Π·Π½Π°ΡΠΈΠΌΠΎ Π²ΡΡΠ΅ Π² Π³ΡΡΠΏΠΏΠ΅ NYHA IV, ΠΏΠΎ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ Ρ Π³ΡΡΠΏΠΏΠ°ΠΌΠΈ NYHA III, NYHA II ΠΈ NYHA I (10.86 Β± 1.14 ΠΈ 9.78 Β± 1.21 ΠΈ 9.65 Β± 1.22 ΠΈ 9.21 Β± 0.59 ΡΠΎΠΎΡΠ²Π΅ΡΡΡΠ²Π΅Π½Π½ΠΎ, p = 0.006). Π‘Π»Π°Π±Π°Ρ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΡ ΠΎΡΠΌΠ΅ΡΠ΅Π½Π° ΠΌΠ΅ΠΆΠ΄Ρ CRP ΠΈ PLT (r = 0.293, p = 0.010) ΠΈ ΠΌΠ΅ΠΆΠ΄Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠ΅ΠΉ MPV ΠΈ ΡΠΈΠ±ΡΠΈΠ½ΠΎΠ³Π΅Π½Π° (r = 0.205, p = 0.012). Π’Π°ΠΊΠΆΠ΅ Π½Π°Π±Π»ΡΠ΄Π°Π»Π°ΡΡ ΡΠΌΠ΅ΡΠ΅Π½Π½Π°Ρ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΡ ΠΌΠ΅ΠΆΠ΄Ρ MPV ΠΈ ΠΊΠ»Π°ΡΡΠ°ΠΌΠΈ NYHA (r = 0.361, Ρ < 0.001) ΠΈ ΠΌΠ΅ΠΆΠ΄Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠ΅ΠΉ ΡΠΈΠ±ΡΠΈΠ½ΠΎΠ³Π΅Π½Π° ΠΈ CRP (r = 0.381, Ρ < 0.001). ΠΠΎΠ²ΡΡΠ΅Π½ΠΈΠ΅ MPV Π² Π³ΡΡΠΏΠΏΠ°Ρ
ΠΏΠ°ΡΠΈΠ΅Π½ΡΠΎΠ² ΠΈ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΡ ΠΌΠ΅ΠΆΠ΄Ρ MPV ΠΈ ΠΊΠ»Π°ΡΡΠ°ΠΌΠΈ NYHA, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΡ ΡΠΈΠ±ΡΠΈΠ½ΠΎΠ³Π΅Π½Π° Π² ΠΏΠ»Π°Π·ΠΌΠ΅, ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΡ ΠΌΠ΅ΠΆΠ΄Ρ PLT ΠΈ CRP, Π° ΡΠ°ΠΊΠΆΠ΅ ΠΊΠΎΡΡΠ΅Π»ΡΡΠΈΡ ΠΌΠ΅ΠΆΠ΄Ρ ΠΊΠΎΠ½ΡΠ΅Π½ΡΡΠ°ΡΠΈΠ΅ΠΉ CRP ΠΈ NTβproBNP ΡΠ²ΠΈΠ΄Π΅ΡΠ΅Π»ΡΡΡΠ²ΡΡΡ Π² ΠΏΠΎΠ»ΡΠ·Ρ ΡΠΎΠ³ΠΎ, ΡΡΠΎ, ΡΡΠΎ ΠΏΡΠΈ ΠΏΠΎΠ²ΡΡΠ΅Π½ΠΈΠΈ MPV ΠΌΠΎΠΆΠ΅Ρ ΠΈΠΌΠ΅ΡΡ ΠΌΠ΅ΡΡΠΎ ΡΠ»Π°Π±ΠΎΠ΅ Π²ΠΎΡΠΏΠ°Π»Π΅Π½ΠΈΠ΅
The Effect of Oxidant Hypochlorous Acid on Platelet Aggregation and Dityrosine Concentration in Chronic Heart Failure Patients and Healthy Controls
Background and objective: One of the reasons for thrombosis in chronic heart failure (CHF) might be reactive forms of oxygen activating platelets. The aim of this study was to evaluate the effect of oxidant hypochlorous acid (HOCl) on platelet aggregation and dityrosine concentration in CHF patients and healthy controls. Materials and Methods: CHF patients (n = 67) and healthy (n = 31) were investigated. Heart echoscopy, 6-min walking test, complete blood count, platelet aggregation, and dityrosine concentration were performed. Platelet aggregation and dityrosine concentration were measured in plasma samples after incubation with different HOCl concentrations (0.15, 0.0778, and 0.0389 mmol/L). Results: Platelet aggregation without oxidant was lower (p = 0.049) in CHF patients than in controls. The spontaneous platelet aggregation with oxidant added was higher in CHF patients (p = 0.004). Dityrosine concentration was also higher (p = 0.032) in CHF patients. Platelet aggregation was the highest in samples with the highest oxidant concentration in both healthy controls (p = 0.0006) and in CHF patients (p = 0.036). Platelet aggregation was higher in NYHA III group in comparison to NYHA II group (p = 0.0014). Concentration of dityrosine was significantly higher in CHF samples (p = 0.032). The highest concentration of dityrosine was obtained in NYHA IV group samples (p < 0.05). Intensity of platelet aggregation, analyzed with ADP, was correlated with LV EF (r = 0.42, p = 0.007). Dityrosine concentration was correlated with NYHA functional class (r = 0.27, p < 0.05). Conclusions: The increase in platelet aggregation in CHF and healthy controls shows the oxidant effect on platelets. The increase in dityrosine concentration in higher NYHA functional classes shows a higher oxidative stress in patients with worse condition
The Effect of PAI-1 4G/5G Polymorphism and Clinical Factors on Coronary Artery Occlusion in Myocardial Infarction
Objective. Data on the impact of PAI-1-675 4G/5G genotype for fibrinolysis during myocardial infarction are inconsistent. The aim of our study was to evaluate the association of clinical and genetic (PAI-1-675 4G/5G polymorphism) factors with coronary artery occlusion in patients with myocardial infarction. Materials and Methods. PAI-1-675 4G/5G detection was achieved by using Sanger sequencing in a sample of patients hospitalized for stent implantation due to myocardial infarction. We categorized the patients into two groups: patients with coronary artery occlusion and patients without coronary artery occlusion according to angiographic evaluation. Results. We identified n=122 (32.4%) 4G/4G, n=186 (49.5%) 4G/5G, and n=68 (18.1%) 5G/5G PAI-1 genotype carriers. Univariate and multivariate analysis showed that only the 4G/5G genotype was associated with coronary artery occlusion (OR: 1.656 and 95% CI: 1.009β2.718, p=0.046). Conclusions. Our results showed that carriers of PAI-1 4G/5G genotype with myocardial infarction have increased odds of coronary artery occlusion more than 1.6 times in comparison to the carriers of homozygous genotypes
Genetical SignatureβAn Example of a Personalized Skin Aging Investigation with Possible Implementation in Clinical Practice
We conducted a research study to create the groundwork for personalized solutions within a skin aging segment. This test utilizes genetic and general laboratory data to predict individual susceptibility to weak skin characteristics, leveraging the research on genetic polymorphisms related to skin functional properties. A cross-sectional study was conducted in a collaboration between the Private Clinic Medicina Practica Laboratory (Vilnius, Lithuania) and the Public Institution Lithuanian University of Health Sciences (Kaunas, Lithuania). A total of 370 participants agreed to participate in the project. The median age of the respondents was 40, with a range of 19 to 74 years. After the literature search, we selected 15 polymorphisms of the genes related to skin aging, which were subsequently categorized in terms of different skin functions: SOD2 (rs4880), GPX1 (rs1050450), NQO1 (rs1800566), CAT (rs1001179), TYR (rs1126809), SLC45A2 (rs26722), SLC45A2 (rs16891982), MMP1 (rs1799750), ELN (rs7787362), COL1A1 (rs1800012), AHR (rs2066853), IL6 (rs1800795), IL1Beta (rs1143634), TNF-Ξ± (rs1800629), and AQP3 (rs17553719). RT genotyping, blood count, and immunochemistry results were analyzed using statistical methods. The obtained results show significant associations between genotyping models and routine blood screens. These findings demonstrate the personalized medicine approach for the aging segment and further add to the growing literature. Further investigation is warranted to fully understand the complex interplay between genetic factors, environmental influences, and skin aging