Subtypes of Aggressive Behavior in Children with Autism in the Context of Emotion Recognition, Hostile Attribution Bias, and Dysfunctional Emotion Regulation

The causes of aggressive behavior in children with autism are poorly understood, which limits treatment options. Therefore, this study used behavioral testing and parent reports of 60 children with autism to investigate the interplay of emotion misinterpretation and hostile attribution bias in the prediction of different aggressive behaviors. Further, the additional impact of dysfunctional emotion regulation was examined. Path analyses indicated that hostile attribution bias increased verbal and covert aggression but not physical aggression and bullying. Dysfunctional emotion regulation had an additional impact on bullying, verbal aggression, and covert aggression. Emotion recognition was positively associated with hostile attribution bias. These findings provide a first insight into a complex interplay of socio-emotional variables; longitudinal studies are needed to examine causal relationships.stiftung irene (germany)berlin school of mind and brainmedical-scientific funds of the mayor of vienna (austria)Humboldt-Universität zu Berlin (1034)Peer Reviewe

Performance of a Qualitative Point-of-Care Strip Test to Detect DOAC Exposure at the Emergency Department:A Cohort-Type Cross-Sectional Diagnostic Accuracy Study

An accurate point-of-care test for detecting effective anticoagulation by direct oral anticoagulants (DOACs) in emergencies is an unmet need. We investigated the accuracy of a urinary qualitative strip test (DOAC Dipstick) to detect relevant DOAC exposure in patients who presented to an emergency department. In this prospective single-center cohort-type cross-sectional study, adults on DOAC treatment were enrolled. We assessed clinical sensitivity and specificity of DOAC Dipstick factor Xa and thrombin inhibitor pads to detect DOAC plasma levels ≥30 ng/mL using urine samples as the testing matrix. Liquid chromatography coupled with tandem-mass spectrometry was used as the reference standard method for plasma and urine measurement of DOAC concentrations. Of 293 patients enrolled, 265 patients were included in the analysis, of whom 92 were treated with rivaroxaban, 65 with apixaban, 77 with edoxaban, and 31 with dabigatran. The clinical sensitivity and specificity of the dipstick on urine samples to detect ≥30 ng/mL dabigatran plasma levels were 100% (95% confidence interval [CI]: 87–100%) and 98% (95% CI: 95–99%), respectively. The sensitivity and specificity of the dipstick to detect ≥30 ng/mL factor Xa inhibitor plasma levels were 97% (95% CI: 94–99%) and 69% (95% CI: 56–79%), respectively. The DOAC Dipstick sensitively identified effective thrombin and factor Xa inhibition in a real-world cohort of patients presenting at an emergency department. Therefore, the dipstick might provide a valuable test to detect relevant DOAC exposure in emergencies, although further studies will be needed to confirm these findings

Viewing Visual Analytics as Model Building

To complement the currently existing definitions and conceptual frameworks of visual analytics, which focus mainly on activities performed by analysts and types of techniques they use, we attempt to define the expected results of these activities. We argue that the main goal of doing visual analytics is to build a mental and/or formal model of a certain piece of reality reflected in data. The purpose of the model may be to understand, to forecast or to control this piece of reality. Based on this model-building perspective, we propose a detailed conceptual framework in which the visual analytics process is considered as a goal-oriented workflow producing a model as a result. We demonstrate how this framework can be used for performing an analytical survey of the visual analytics research field and identifying the directions and areas where further research is needed