Higher media multi-tasking activity is associated with smaller gray-matter density in the anterior cingulate cortex

Media multitasking, or the concurrent consumption of multiple media forms, is increasingly prevalent in today’s society and has been associated with negative psychosocial and cognitive impacts. Individuals who engage in heavier media-multitasking are found to perform worse on cognitive control tasks and exhibit more socio-emotional difficulties. However, the neural processes associated with media multi-tasking remain unexplored. The present study investigated relationships between media multitasking activity and brain structure. Research has demonstrated that brain structure can be altered upon prolonged exposure to novel environments and experience. Thus, we expected differential engagements in media multitasking to correlate with brain structure variability. This was confirmed via Voxel-Based Morphometry (VBM) analyses: Individuals with higher Media Multitasking Index (MMI) scores had smaller gray matter density in the anterior cingulate cortex (ACC). Functional connectivity between this ACC region and the precuneus was negatively associated with MMI. Our findings suggest a possible structural correlate for the observed decreased cognitive control performance and socio-emotional regulation in heavy media-multitaskers. While the cross-sectional nature of our study does not allow us to specify the direction of causality, our results brought to light novel associations between individual media multitasking behaviors and ACC structure differences

Troppo - A Python framework for the reconstruction of context-specific metabolic models

The surge in high-throughput technology availability for molecular biology has enabled the development of powerful predictive tools for use in many applications, including (but not limited to) the diagnosis and treatment of human diseases such as cancer. Genome-scale metabolic models have shown some promise in clearing a path towards precise and personalized medicine, although some challenges still persist. The integration of omics data and subsequent creation of context-specific models for specific cells/tissues still poses a significant hurdle, and most current tools for this purpose have been implemented using proprietary software. Here, we present a new software tool developed in Python, troppo - Tissue-specific RecOnstruction and Phenotype Prediction using Omics data, implementing a large variety of context-specific reconstruction algorithms. Our framework and workflow are modular, which facilitates the development of newer algorithms or omics data sources.This study was supported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. The authors also thank the PhD scholarships funded by national funds through Fundacao para a Ciencia e Tecnologia, with references: SFRH/BD/133248/2017 (J.F.), SFRH/BD/118657/2016 (V.V.).info:eu-repo/semantics/publishedVersio

Investigation of attentional bias in obsessive compulsive disorder with and without depression in visual search

Copyright: © 2013 Morein-Zamir et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are creditedWhether Obsessive Compulsive Disorder (OCD) is associated with an increased attentional bias to emotive stimuli remains controversial. Additionally, it is unclear whether comorbid depression modulates abnormal emotional processing in OCD. This study examined attentional bias to OC-relevant scenes using a visual search task. Controls, non-depressed and depressed OCD patients searched for their personally selected positive images amongst their negative distractors, and vice versa. Whilst the OCD groups were slower than healthy individuals in rating the images, there were no group differences in the magnitude of negative bias to concern-related scenes. A second experiment employing a common set of images replicated the results on an additional sample of OCD patients. Although there was a larger bias to negative OC-related images without pre-exposure overall, no group differences in attentional bias were observed. However, OCD patients subsequently rated the images more slowly and more negatively, again suggesting post-attentional processing abnormalities. The results argue against a robust attentional bias in OCD patients, regardless of their depression status and speak to generalized difficulties disengaging from negative valence stimuli. Rather, post-attentional processing abnormalities may account for differences in emotional processing in OCD.Peer reviewedFinal Published versio

Influence of different treatment techniques on radiation dose to the LAD coronary artery

This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Low Dose Isoflurane Exerts Opposing Effects on Neuronal Network Excitability in Neocortex and Hippocampus

The anesthetic excitement phase occurring during induction of anesthesia with volatile anesthetics is a well-known phenomenon in clinical practice. However, the physiological mechanisms underlying anesthetic-induced excitation are still unclear. Here we provide evidence from in vitro experiments performed on rat brain slices that the general anesthetic isoflurane at a concentration of about 0.1 mM can enhance neuronal network excitability in the hippocampus, while simultaneously reducing it in the neocortex. In contrast, isoflurane tissue concentrations above 0.3 mM expectedly caused a pronounced reduction in both brain regions. Neuronal network excitability was assessed by combining simultaneous multisite stimulation via a multielectrode array with recording intrinsic optical signals as a measure of neuronal population activity

A lightweight sensing platform for monitoring sleep quality and posture: a simulated validation study

Background The prevalence of self-reported shoulder pain in the UK has been estimated at 16%. This has been linked with significant sleep disturbance. It is possible that this relationship is bidirectional, with both symptoms capable of causing the other. Within the field of sleep monitoring, there is a requirement for a mobile and unobtrusive device capable of monitoring sleep posture and quality. This study investigates the feasibility of a wearable sleep system (WSS) in accurately detecting sleeping posture and physical activity. Methods Sixteen healthy subjects were recruited and fitted with three wearable inertial sensors on the trunk and forearms. Ten participants were entered into a ‘Posture’ protocol; assuming a series of common sleeping postures in a simulated bedroom. Five participants completed an ‘Activity’ protocol, in which a triphasic simulated sleep was performed including awake, sleep and REM phases. A combined sleep posture and activity protocol was then conducted as a ‘Proof of Concept’ model. Data were used to train a posture detection algorithm, and added to activity to predict sleep phase. Classification accuracy of the WSS was measured during the simulations. Results The WSS was found to have an overall accuracy of 99.5% in detection of four major postures, and 92.5% in the detection of eight minor postures. Prediction of sleep phase using activity measurements was accurate in 97.3% of the simulations. The ability of the system to accurately detect both posture and activity enabled the design of a conceptual layout for a user-friendly tablet application. Conclusions The study presents a pervasive wearable sensor platform, which can accurately detect both sleeping posture and activity in non-specialised environments. The extent and accuracy of sleep metrics available advances the current state-of-the-art technology. This has potential diagnostic implications in musculoskeletal pathology and with the addition of alerts may provide therapeutic value in a range of areas including the prevention of pressure sores

The Increased Expression of Integrin α6 (ITGA6) Enhances Drug Resistance in EVI1high Leukemia

Ecotropic viral integration site-1 (EVI1) is one of the candidate oncogenes for human acute myeloid leukemia (AML) with chromosomal alterations at 3q26. High EVI1 expression (EVI1high) is a risk factor for AML with poor outcome. Using DNA microarray analysis, we previously identified that integrin α6 (ITGA6) was upregulated over 10-fold in EVI1high leukemia cells. In this study, we determined whether the increased expression of ITGA6 is associated with drug-resistance and increased cell adhesion, resulting in poor prognosis. To this end, we first confirmed the expression pattern of a series of integrin genes using semi-quantitative PCR and fluorescence-activated cell sorter (FACS) analysis and determined the cell adhesion ability in EVI1high leukemia cells. We found that the adhesion ability of EVI1high leukemia cells to laminin increased with the increased expression of ITGA6 and integrin β4 (ITGB4). The introduction of small-hairpin RNA against EVI1 (shEVI1) into EVI1high leukemia cells reduced the cell adhesion ability and downregulated the expression of ITGA6 and ITGB4. In addition, the overexpression of EVI1 in EVI1low leukemia cells enhanced their cell adhesion ability and increased the expression of ITGA6 and ITGB4. In a subsequent experiment, the introduction of shRNA against ITGA6 or ITGB4 into EVI1high AML cells downregulated their cell adhesion ability; however, the EVI1high AML cells transfected with shRNA against ITGA6 could not be maintained in culture. Moreover, treating EVI1high leukemia cells with neutralizing antibodies against ITGA6 or ITGB4 resulted in an enhanced responsiveness to anti-cancer drugs and a reduction of their cell adhesion ability. The expression of ITGA6 is significantly elevated in cells from relapsed and EVI1high AML cases; therefore, ITGA6 might represent an important therapeutic target for both refractory and EVI1high AML

An algorithm for network-based gene prioritization that encodes knowledge both in nodes and in links

Background: Candidate gene prioritization aims to identify promising new genes associated with a disease or a biological process from a larger set of candidate genes. In recent years, network-based methods - which utilize a knowledge network derived from biological knowledge - have been utilized for gene prioritization. Biological knowledge can be encoded either through the network's links or nodes. Current network-based methods can only encode knowledge through links. This paper describes a new network-based method that can encode knowledge in links as well as in nodes. Results: We developed a new network inference algorithm called the Knowledge Network Gene Prioritization (KNGP) algorithm which can incorporate both link and node knowledge. The performance of the KNGP algorithm was evaluated on both synthetic networks and on networks incorporating biological knowledge. The results showed that the combination of link knowledge and node knowledge provided a significant benefit across 19 experimental diseases over using link knowledge alone or node knowledge alone. Conclusions: The KNGP algorithm provides an advance over current network-based algorithms, because the algorithm can encode both link and node knowledge. We hope the algorithm will aid researchers with gene prioritization. © 2013 Kimmel, Visweswaran