Optimizing timing of defibrillation by evaluating the likelihood of a successful outcome could significantly enhance resuscitation. Previous studies employed conventional machine learning approaches and hand-crafted features to address this issue, but none have achieved superior performance to be widely accepted. This study proposes a novel approach in which predictive features are automatically learned. A raw 4s VF episode immediately prior to first defibrillation shock was feed to a 3-stage CNN feature extractor. Each stage was composed of 4 components convolution, rectified linear unit activation, dropout and max-pooling. At the end of feature extractor, the feature map was flattened and connected to a fully connected multi-layer perceptron for classification. For model evaluation, a 10 fold cross-validation was employed. To balance classes, SMOTE oversampling method has been applied to minority class. The obtained results show that the proposed model is highly accurate in predicting defibrillation oach benefits from being fully automatic by fusing feature extraction, selection and classification into a single learning model. It provides a superior strategy that can be used as a tool to guide treatment of OHCA patients in bringing optimal decision of precedence treatment. Furthermore, for encouraging replicability, the dataset has been made publicly available to the research community.Cardiac magnetic resonance quantitative T1-mapping is increasingly used for advanced myocardial tissue characterisation. However, cardiac or respiratory motion can significantly affect the diagnostic utility of T1-maps, and thus motion artefact detection is critical for quality control and clinically-robust T1 measurements. Manual quality control of T1-maps may provide reassurance, but is laborious and prone to error. We present a deep learning approach with attention supervision for automated motion artefact detection in quality control of cardiac T1-mapping. Firstly, we customised a multi-stream Convolutional Neural Network (CNN) image classifier to streamline the process of automatic motion artefact detection. Secondly, we imposed attention supervision to guide the CNN to focus on targeted myocardial segments. Thirdly, when there was disagreement between the human operator and machine, a second human validator reviewed and rescored the cases for adjudication and to identify the source of disagreement. The multi-stream neural networks demonstrated 89.8% agreement, 87.4% ROC-AUC on motion artefact detection with the human operator in the 2568 T1 maps. Trained with additional supervision on attention, agreements and AUC significantly improved to 91.5% and 89.1%, respectively (p less then 0.001). Rescoring of disagreed cases by the second human validator revealed that human operator error was the primary cause of disagreement. Deep learning with attention supervision provides a quick and high-quality assurance of clinical images, and outperforms human operators. This systematic review sought to determine the effectiveness of mental practice (MP) on the activity limitations of the upper limb in individuals after stroke, as well as when, in whom, and how MP should be delivered. Ten electronic databases were searched from November 2009 to May 2020. Search terms included Arm, Practice, Stroke rehabilitation, Imagination, Paresis, Recovery of function, and Stroke. Studies from a Cochrane review of MP (up to November 2009) were automatically included. The review was registered with the PROSPERO database of systematic reviews (reference no. CRD42019126044). Randomized controlled trials of adults after stroke using MP for the upper limb were included if they compared MP to usual care, conventional therapy, or no treatment and reported activity limitations of the upper limb as outcomes. Independent screening was conducted by 2 reviewers. One reviewer extracted data using a tool based on the Template for Intervention Description and Replication. Data extraction was indndividuals with the most severe upper limb dysfunction. There was no clear pattern of the ideal dosage of MP.Atopy is defined by the propensity to develop an exaggerated type-2 inflammatory response to environmental molecules. Clinically, atopy is diagnosed when atopic disease occurs atopic dermatitis, food allergy, atopic asthma and allergic rhinitis and conjunctivitis. https://www.selleckchem.com/products/apd334.html Whereas the classical "atopic march" is increasingly challenged through epidemiological studies, type-2 cellular inflammation is a characteristic shared by the atopic diseases. This inflammation can be innate (non-specific eosinophils, mast cells, dendritic cells, innate lymphoid cells [ILC]), or adaptive (antigen-specific, involving T cells). Interleukins (IL-)4, 5 and 13 are major actors of type-2 inflammation and are mainly produced by ILC and T cells. The efficacy of treatments targeting these type-2 cytokines highlight the importance of type-2 inflammation in atopic diseases. However, several patients do not respond to type-2 targeting treatments, highlighting the presence of other actors in pathophysiology of atopic diseases alteration of epithelial barrier, IgE-mediated allergic responses, type-17 inflammation. Thus, the term "endotype" can illustrate this diversity in pathophysiology. Finally, a global approach of atopic diseases, as type-2 inflammatory diseases, is fundamental, but not sufficient. An approach by endotype is advisable, in a personalized medicine perspective. © 2020 Elsevier Masson SAS. All rights reserved.Atopic dermatitis has a very significant impact on children and adolescents. Having visible lesions, but above all almost permanent pruritus or sometimes skin pain inevitably has consequences on all aspects of daily life, including sleep, education and relationships with others, family and emotional life. It also has an impact on the whole family. Stigmatization may occur. Treatment and especially local care can be very demanding. Adherence to treatment is therefore often difficult. Quality of life can be severely impaired and atopic dermatitis can be a heavy burden. The psychological consequences can be major. Family problems related to the disease often arise. The best way out of it is probably to have very effective and well-tolerated treatments. © 2020 Elsevier Masson SAS. All rights reserved.