Purpose: The incidence of acute myocardial infarctions (AMI) shows circadian variation typically peaking during morning hours with a decline at night. However, this variation does not occur in patients with diabetes mellitus (DM). The night's decline of AMI may be partially explained by melatonin-related platelet inhibition. Whether this effect is absent in diabetic patients is unknown. The aim was to study the effect of melatonin on in-vitro platelet aggregation in healthy individuals and patients with type 2 DM. Methods: Platelet aggregation was measured in blood samples from healthy individuals (n = 15) and type 2 DM patients (n = 15) using multiple electrode aggregometry. Adenosine diphosphate (ADP), arachidonic acid (ASPI) and thrombin (TRAP) were used as agonists. Aggregability for each subject was tested after adding melatonin in two concentrations. Results: In healthy individuals, melatonin inhibited platelet aggregation in both higher (10-5 M) and lower concentrations (10-9 M) induced by ADP, ASPI, and TRAP (p < 0.001, p = 0.002, p = 0.029, respectively). In DM patients, melatonin did not affect platelet aggregation in both concentrations induced by ADP, ASPI, and TRAP. Melatonin decreased platelet aggregation induced by ADP, ASPI, and TRAP significantly more in healthy individuals compared to patients with DM. (p = 0.005, p = 0.045 and p = 0.048, respectively). Conclusion: Platelet aggregation was inhibited by melatonin in healthy individuals. In-vitro antiplatelet effect of melatonin in type 2 DM patients is significantly attenuated.

Introduction Recent advances in machine learning provide new possibilities to process and analyse observational patient data to predict patient outcomes. In this paper, we introduce a data processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The ability to identify high-risk patients could possibly allow taking pre-emptive measures and thus prevent the development of CS. Methods We mainly focus on techniques for the imputation of missing data by generating a pipeline for imputation and comparing the performance of various multivariate imputation algorithms, including k-nearest neighbours, two singular value decomposition (SVD)—based methods, and Multiple Imputation by Chained Equations. After imputation, we select the final subjects and variables from the imputed dataset and showcase the performance of the gradient-boosted framework that uses a tree-based classifier for cardiogenic shock prediction. Results We achieved good classification performance thanks to data cleaning and imputation (cross-validated mean area under the curve 0.805) without hyperparameter optimization. Conclusion We believe our pre-processing pipeline would prove helpful also for other classification and regression experiments.

Objectives: Purpose of this study was to evaluate properties of apelin, a peptide detectable in peripheral blood, for atrial fibrillation (AF) detection in a diverse population of patients covering a broad spectrum from healthy to polymorbid patients. Background: AF is the most common cardiac arrhythmia with constantly increasing incidence and prevalence. Currently available diagnostic tools do not provide sufficient detection rate. Large proportion of patients with AF remains undiagnosed and the possibility of screening at-risk groups would be significantly beneficial. Methods: We designed this study as a multi-centre retrospective study. Study population included 183 patients. 64 in non-AF and 119 in AF group. Results: Apelin plasma concentration was significantly lower in AF group compared to non-AF group (p < 0.001). Receiver operating characteristic analysis of apelin as a predictor of AF scored area under the curve of 0.79, sensitivity = 0.941 and specificity = 0.578. Multivariate analysis using logistic regression adjusted for age, BMI, apelin, dilated LV, dilated LA, arterial hypertension, and gender showed only apelin and age to be statistically significant contributors for AF. Conclusion: Apelin might be a promising biomarker for detecting AF in our study population. These results suggest promising potential of apelin as a screening biomarker for AF (Tab. 2, Fig. 1, Ref. 46). Text in PDF www.elis.sk Keywords: biomarker, apelin, arrhythmia, atrial fibrillation.

Left ventricular hypertrophy (LVH) refers to a complex rebuilding of the left ventricle that can gradually lead to serious complications—heart failure and life-threatening ventricular arrhythmias. LVH is defined as an increase in the size of the left ventricle (i.e., anatomically), therefore the basic diagnosis detecting the increase in the LV size is the domain of imaging methods such as echocardiography and cardiac magnetic resonance. However, to evaluate the functional status indicating the gradual deterioration of the left ventricular myocardium, additional methods are available approaching the complex process of hypertrophic remodeling. The novel molecular and genetic biomarkers provide insights on the underlying processes, representing a potential basis for targeted therapy. This review summarizes the spectrum of the main biomarkers employed in the LVH valuation.

Purpose Oxidative stress is an important contributor to the etiology of atrial fibrillation (AF). Our aim was to study oxidative stress biomarkers in patients undergoing pulmonary vein isolation (PVI) for paroxysmal AF with radiofrequency catheter ablation and to assess its prognostic value in predicting long-term PVI outcome. Methods In this prospective cohort study, we included 62 patients (mean age 55±8 years, 12 females and 50 males) with paroxysmal AF and implanted ECG loop recorders who underwent PVI. Plasmatic concentrations of advanced glycation end-products (AGEs), fructosamine, advanced oxidation protein products, and thiobarbituric-acid reacting substances were measured before PVI. AF burden (percentage of time spent in AF) was continually assessed during the follow-up period (1063±271 days). Results Nineteen patients (31%) were defined as optimal responders (oR) with AF burden < 0.5% after PVI. Remaining 43 patients (69%) were defined as sub-optimal responders. Concentration of AGEs was significantly lower in oR by 3.7 g/g (CI: −6.5 to −1.7; P=0.0003). After adjustment for age, sex, BMI, left atrial size, arterial hypertension, and AF burden before PVI, only low concentration of AGEs remained significantly associated with oR (odds ratio: 1.3; P=0.04). AGEs concentration achieved area under the curve of 0.78 for predicting optimal long-term PVI response. Conclusions AGEs concentration before PVI was associated with long-term PVI outcome in patients with paroxysmal AF. Further research will show if this biomarker could contribute to optimal patient selection for catheter ablation.