Coping and emotional state in the acute phase of myocardial infarction

Σκοπός: Η ψυχολογική προσαρμογή στην οξεία φάση ενός Εμφράγματος του Μυοκαρδίου (ΕΜ) αποτελεί αντισταθμιστικό μηχανισμό των ασθενών στην προσαρμογή στη νέα πραγματικότητα μιας ανάλογης απειλητικής συνθήκης. Υπάρχουν ελάχιστα δεδομένα που καταδεικνύουν τη σχέση ανάμεσα στις στρατηγικές διαχείρισης από ενδονοσοκομειακούς ασθενείς και στη συναισθηματική τους κατάσταση. Η μελέτη ερευνά τη συγκεκριμένη σχέση σε ασθενείς που έχουν υποστεί ένα ΕΜ.Μέθοδος: Εκατό άνδρες ασθενείς που έχουν υποστεί ΕΜ, ηλικίας 60.5 (Διάστημα Εμπιστοσύνης (Δ.Ε): 57.5-62.7) ετών, έδωσαν συνέντευξη μετά την οξεία φάση ΕΜ κατά την τελευταία μέρα παραμονής τους στη Στεφανιαία Μονάδα. Ιατρικές πληροφορίες δόθηκαν από τους ιατρικούς φακέλους των ασθενών. Τα ερωτηματολόγια Προσανατολισμός Διαχείρισης Προβλημάτων (COPE) και Προφίλ Ψυχολογικής Διάθεσης (POMS) χρησιμοποιήθηκαν στην αξιολόγηση του τρόπου διαχείρισης και της συναισθηματικής κατάστασης αντίστοιχα.Αποτελέσματα: Η ενεργή διαχείριση σχετίστηκε θετικά με το άγχος και το θυμό. Η συναισθηματική υποστήριξη βρέθηκε να σχετίζεται αρνητικά με τις συγκεκριμένες μεταβλητές. Η πνευματική αναζήτηση και η συμπεριφορική αποδέσμευση σχετίστηκαν θετικά με την κατάθλιψη. Σημαντική διαφορά βρέθηκε στις στρατηγικές διαχείρισης και τη συναισθηματική διάθεση συγκρινόμενες με την υποκειμενική αντίληψη της σοβαρότητας.Συμπέρασμα:Oι ασθενείς μετά απο ένα ΕΜ, αναπτύσσουν στρατηγικές διαχείρισης που σχετίζονται με την συναισθηματική διάθεση. Η σημασία αυτών των συσχετίσεων μένει να εξακριβωθεί και σε μελλοντικές μελέτες.Objective: Emotional and coping adaptation after the acute phase of a myocardial infarction (MI) is a compensatory mechanism developed for patients’ adjustment to the new reality following this life threatening situation. There are however scarce data on the relationship between hospitalized MI patients’ coping behavior and their emotional state. The study investigates the associations between coping strategies and the affective status in patients surviving a MI.Methods: One-hundred male patients surviving a MI, aged 60.5 (Confidence Intervals (CI): 57.5-62.7) years, were interviewed after the acute phase of MI, at the last day of their coronary unit stay. Medical data were obtained by their medical records. Coping Orientation to Problems Experienced (COPE) and Profile of Mood States (POMS) questionnaires were used to examine coping styles and patients’ mood state respectively.Results: Active coping was positively related to anxiety and anger. Emotional support was negatively associated with these variables. Religious coping and behavioral disengagement were positively related to depression. Significant difference was found in coping styles and mood states scores between the different perceived severity beliefs.Conclusion: Patients surviving after a MI, develop coping strategies related to their emotional state. The significance of these relations remains to be clarified in future studies

Personalized e-Learning Implementation - The GIS Case

Personalized e-learning implementation is recognized as among one of the most interesting research areas in the distance learning Web-based education. In particular, the GIS e-learning initiatives that incorporate —by default— a number of sequencing spatial techniques (i.e. spatial objects selection and sequencing), will well benefit from a welldefined personalized e-learning implementation with embedded spatial functionality. This is the case addressed in this paper. The GIS e-learning implementation introduced in the current paper is based on a set of teaching (lecturing) rules according to the cognitive style of learning preferences of both the learners and the lecturers as well. It is important to note that, in spite of the fact that most of these teaching rules are generic (i.e. domain, view and user independent), there are no so far well-defined and commonly accepted rules on how the learning spatial GIS objects and techniques should be selected and how they should be sequenced to make “instructional sense" in a Web-based GIS course

A novel Desmoplakin mutation associated with left dominant arrhythmogenic cardiomyopathy and cutaneous phenotype

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Ibutilide for the Cardioversion of Paroxysmal Atrial Fibrillation during Radiofrequency Ablation of Supraventricular Tachycardias

Direct current electrical cardioversion (DC-ECV) is the preferred treatment for the termination of paroxysmal atrial fibrillation (AF) that occurs during radiofrequency ablation (RFA) of supraventricular tachycardias (SVT). Intravenous Ibutilide may be an alternative option in this setting. Thirty-four out of 386 patients who underwent SVT-RFA presented paroxysmal AF during the procedure and were randomized into receiving ibutilide or DC-ECV. Ibutilide infusion successfully cardioverted 16 out of 17 patients (94%) within 17.37 ± 7.87  min. DC-ECV was successful in all patients (100%) within 17.29 ± 3.04  min. Efficacy and total time to cardioversion did not differ between the study groups. No adverse events were observed. RFA was successfully performed in 16 patients (94%) in the ibutilide arm and in all patients (100%) in the DC-ECV arm, p = NS. In conclusion, ibutilide is a safe and effective alternative treatment for restoring sinus rhythm in cases of paroxysmal AF complicating SVT-RFA

Cardiac magnetic resonance for ventricular arrhythmias: a systematic review and meta-analysis

Background: Cardiac magnetic resonance (CMR) allows comprehensive myocardial tissue characterisation, revealing areas of myocardial inflammation or fibrosis that may predispose to ventricular arrhythmias (VAs). With this study, we aimed to estimate the prevalence of structural heart disease (SHD) and decipher the prognostic implications of CMR in selected patients presenting with significant VAs. Methods: Electronic databases were searched for studies enrolling adult patients that underwent CMR for diagnostic or prognostic purposes in the setting of significant VAs. A random effects model meta-analysis of proportions was performed to estimate the prevalence of SHD. HRs were pooled together in order to evaluate the prognostic value of CMR. Results: The prevalence of SHD was reported in 18 studies. In all-comers with significant VAs, the pooled rate of SHD post-CMR evaluation was 39% (24% in the subgroup of premature ventricular contractions and/or non-sustained ventricular tachycardia vs 63% in the subgroup of more complex VAs). A change in diagnosis after use of CMR ranged from 21% to 66% with a pooled average of 35% (29%–41%). A non-ischaemic cardiomyopathy was the most frequently identified SHD (56%), followed by ischaemic heart disease (21%) and hypertrophic cardiomyopathy (5%). After pooling together data from six studies, we found that the presence of late gadolinium enhancement was associated with increased risk of major adverse outcomes in patients with significant VAs (pooled HR: 1.79; 95% CI 1.33 to 2.42). Conclusion: CMR is a valuable tool in the diagnostic and prognostic evaluation of patients with VAs. CMR should be considered early after initial evaluation in the diagnostic algorithm for VAs of unclear aetiology as this strategy may also define prognosis and improve risk stratification

A Resource Reservation Protocol with Linear Traffic Prediction for OBS Networks

This paper addresses the issue of providing resource reservation mechanism for OBS networks. We propose a linear prediction mechanism based on least mean square (LMS) method to reduce the burst delay at edge nodes. A reservation method is proposed to increase the reservation probability and to improve the delay reduction performance

Exploring Clustering Techniques for Analyzing User Engagement Patterns in Twitter Data

Social media platforms have revolutionized information exchange and socialization in today’s world. Twitter, as one of the prominent platforms, enables users to connect with others and express their opinions. This study focuses on analyzing user engagement levels on Twitter using graph mining and clustering techniques. We measure user engagement based on various tweet attributes, including retweets, replies, and more. Specifically, we explore the strength of user connections in Twitter networks by examining the diversity of edges. Our approach incorporates graph mining models that assign different weights to evaluate the significance of each connection. Additionally, clustering techniques are employed to group users based on their engagement patterns and behaviors. Statistical analysis was conducted to assess the similarity between user profiles, as well as attributes, such as friendship, followings, and interactions within the Twitter social network. The findings highlight the discovery of closely linked user groups and the identification of distinct clusters based on engagement levels. This research emphasizes the importance of understanding both individual and group behaviors in comprehending user engagement dynamics on Twitter