Translating research to support practitioners in addressing disparities in child and adolescent mental health and services in the United States.

Despite increased recognition of disparities in youth mental health, racial/ethnic disparities in mental health burden and in mental health service use persist. This phenomenon suggests that research documenting disparities alone has not led to extensive action in practice settings in order to significantly reduce disparities. In this commentary, we present a framework to actively target this research-to-practice gap by describing the development of a resource titled, "Addressing the Mental Health Needs of Racial and Ethnic Minority Youth-A Guide for Practitioners." We begin by presenting social justice as the impetus for eliminating disparities and then reviewing current knowledge and efforts aimed at reducing disparities. Subsequently, we describe knowledge transfer frameworks and goals guiding our work. Finally, we detail the steps taken in our approach to translation and implications for subsequent dissemination of this guide. Translation focused on evidence-based information on (a) mechanisms that contribute to disparities, and (b) strategies for providers to address disparities in their work. We reflect on the framework guiding our translation to offer future directions for others interested in bridging research and action. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

Bayesian Classification Models for Premature Ventricular Contraction Detection on ECG Traces.

According to the American Heart Association, in its latest commission about Ventricular Arrhythmias and Sudden Death 2006, the epidemiology of the ventricular arrhythmias ranges from a series of risk descriptors and clinical markers that go from ventricular premature complexes and nonsustained ventricular tachycardia to sudden cardiac death due to ventricular tachycardia in patients with or without clinical history. The premature ventricular complexes (PVCs) are known to be associated with malignant ventricular arrhythmias and sudden cardiac death (SCD) cases. Detecting this kind of arrhythmia has been crucial in clinical applications. The electrocardiogram (ECG) is a clinical test used to measure the heart electrical activity for inferences and diagnosis. Analyzing large ECG traces from several thousands of beats has brought the necessity to develop mathematical models that can automatically make assumptions about the heart condition. In this work, 80 different features from 108,653 ECG classified beats of the gold-standard MIT-BIH database were extracted in order to classify the Normal, PVC, and other kind of ECG beats. Three well-known Bayesian classification algorithms were trained and tested using these extracted features. Experimental results show that the F1 scores for each class were above 0.95, giving almost the perfect value for the PVC class. This gave us a promising path in the development of automated mechanisms for the detection of PVC complexes.

Meningoencefalitis aguda debida a primoinfección por VIH / Acute meningoencephalitis in a patient with primary HIV infection

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