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Background

Acute myocardial infarction (AMI) causes irreversible myocardial damage and release of inflammatory mediators, including cytokines, chemokines and miRNAs. We aimed to investigate changes in the levels of cytokines (IL-6, TNF-α and IL-10), miRNAs profiles (miR-146 and miR-155) and distribution of different monocyte subsets (CD14++CD16-, CD14++CD16+, CD14+CD16++) in the acute and post-healing phases of AMI.

Methods

In eighteen consecutive AMI patients (mean age 56.78?±?12.4 years, mean left ventricle ejection fraction – LVEF: 41.9?±?9.8%), treated invasively, monocyte subsets frequencies were evaluated (flow cytometry), cytokine concentrations were analyzed (ELISA) as well as plasma miRNAs were isolated twice – on admission and after 19.2?±?5.9 weeks of follow-up. Measurements were also performed among healthy volunteers.

Results

AMI patients presented significantly decreased frequencies of classical cells in comparison to healthy controls (median 71.22% [IQR: 64.4–79.04] vs. 84.35% [IQR: 81.2–86.7], p?=?0.001) and higher percent of both intermediate and non-classical cells, yet without statistical significance (median 6.54% [IQR: 5.14–16.64] vs. 5.87% [IQR: 4.48–8.6], p?=?0.37 and median 5.99% [IQR: 3.39–11.5] vs. 5.26% [IQR: 3.62–6.2], p?=?0.42, respectively). In AMI patients both, analyzed plasma miRNA concentrations were higher than in healthy subjects (miR-146: median 5.48 [IQR: 2.4–11.27] vs. 1.84 [IQR: 0.87–2.53], p?=?0.003; miR-155: median 25.35 [IQR: 8.17–43.15] vs. 8.4 [IQR: 0.08–16.9], p?=?0.027, respectively), and returned back to the values found in the control group in follow-up. miR-155/miR-146 ratio correlated with the frequencies of classical monocytes (r=0.6, p?=?0.01) and miR-155 correlated positively with the concentration of inflammatory cytokines ? IL-6 and TNF-α.

Conclusions

These results may suggest cooperation of both pro-inflammatory and anti-inflammatory signals in AMI in order to promote appropriate healing of the infarcted myocardium.  相似文献   
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ObjectivesThis study sought to identify the factors associated with incident atrial fibrillation (AF) in a well-characterized heart failure with preserved ejection fraction (HFpEF) population, with special focus on left atrial (LA) strain.BackgroundAF is associated with HFpEF, with adverse consequences. Effective risk evaluation might allow the initiation of protective strategies.MethodsClinical evaluation and echocardiography, including measurements of peak atrial longitudinal strain (PALS), peak atrial contraction strain (PACS), and LA volume index (LAVI), were obtained in 170 patients with symptomatic HFpEF (mean age, 65 ± 8 years), free of baseline AF. AF was identified by standard 12-lead electrocardiogram, review of relevant medical records (including Holter documentation), and surveillance with a portable single-lead electrocardiogram device over 2 weeks. Results were validated in the 103 patients with HFpEF from the Karolinska-Rennes (KaRen) study.ResultsOver a median follow-up of 49 months, incident AF was identified in 39 patients (23%). Patients who developed AF were older; had higher clinical risk scores, brain natriuretic peptide, creatinine, LAVI, and LV mass; lower LA strain and exercise capacity; and more impaired LV diastolic function. PACS, PALS, and LAVI were the most predictive parameters for AF (area under receiver-operating characteristic curve: 0.76 for PACS, 0.71 for PALS, and 0.72 for LAVI). Nested Cox regression models showed that the predictive value of PACS and PALS was independent from and incremental to clinical data, LAVI, and E/e’ ratio. Classification and regression trees analysis identified PACS ≤12.7%, PALS ≤29.4%, and LAVI >34.3 ml/m2 as discriminatory nodes for AF, with a 33-fold greater hazard of AF (p < 0.001) in patients categorized as high risk. The classification and regression trees algorithm discriminated high and low AF risk in the validation cohort.ConclusionsPACS and PALS provide incremental predictive information about incident AF in HFpEF. The inclusion of these LA strain components to the diagnostic algorithm may help guide screening and further monitoring for AF risk in this population.  相似文献   
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