AIM The aim of this study was to examine whether the termination of long-term clopidogrel therapy results in a proinflammatory state and whether lipid parameters influence the inflammatory response after stopping the drug. METHODS A prospective, multicenter study was conducted among 200 patients with implanted coronary stents who received dual antiplatelet therapy for one year, without ischemic or bleeding events. According to the guidelines, clopidogrel was discontinued after one year. In all patients, the high-sensitivity C-reactive protein (hsCRP), soluble CD40 ligand (sCD40L) and lipid [total cholesterol, triglycerides, high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C)] levels were measured twice: on the day of cessation of clopidogrel and 45 days after the termination of clopidogrel treatment. RESULTS In men (n=151), the sCD40L serum levels were significantly higher 45 days after the discontinuation of clopidogrel (p=0.007), while the hsCRP levels were not significantly different (p=0.407). Furthermore, when analyzed across the HDL-C quartiles, the hsCRP and sCD40L values were found to be associated with the levels of HDL-C after the discontinuation of clopidogrel in men. In addition, the men in the first HDL-C quartile exhibited the most pronounced increase in the sCD40L levels (p=0.001) and had significantly higher hsCRP levels (p=0.001) compared to the subjects in the other quartiles. Other lipid parameters did not show any associations with the sCD40L or hsCRP levels. CONCLUSIONS The discontinuation of clopidogrel is associated with higher increments in the sCD40L level, and a pronounced proinflammatory response is associated with a lower HDL-C concentration.

Background There is consensus that Heart Rate Variability is associated with the risk of vascular events. However, Heart Rate Variability predictive value for vascular events is not completely clear. The aim of this study is to develop novel predictive models based on data-mining algorithms to provide an automatic risk stratification tool for hypertensive patients. Methods A database of 139 Holter recordings with clinical data of hypertensive patients followed up for at least 12 months were collected ad hoc. Subjects who experienced a vascular event (i.e., myocardial infarction, stroke, syncopal event) were considered as high-risk subjects. Several data-mining algorithms (such as support vector machine, tree-based classifier, artificial neural network) were used to develop automatic classifiers and their accuracy was tested by assessing the receiver-operator characteristics curve. Moreover, we tested the echographic parameters, which have been showed as powerful predictors of future vascular events. Results The best predictive model was based on random forest and enabled to identify high-risk hypertensive patients with sensitivity and specificity rates of 71.4% and 87.8%, respectively. The Heart Rate Variability based classifier showed higher predictive values than the conventional echographic parameters, which are considered as significant cardiovascular risk factors. Conclusions Combination of Heart Rate Variability measures, analyzed with data-mining algorithm, could be a reliable tool for identifying hypertensive patients at high risk to develop future vascular events.

We consider high-order above-threshold ionization (HATI) of polyatomic molecules ionized by a strong linearly polarized laser field. Improved molecular strong-field approximation by which the HATI process on polyatomic molecular species can be described is developed. Using this theory we calculate photoelectron angular-energy spectra for different triatomic molecules. Special attention is devoted to the minima that are observed in the calculated high-energy electron spectra of the ozone and carbon dioxide molecules. A key difference between these minima and minima that are observed in the corresponding spectra of diatomic molecules are presented.