OBJECTIVE To assess the utility of machine learning algorithms for automatically estimating prognosis in patients with repaired tetralogy of Fallot (ToF) using cardiac magnetic resonance (CMR). METHODS We included 372 patients with ToF who had undergone CMR imaging as part of a nationwide prospective study. Cine loops were retrieved and subjected to automatic deep learning (DL)-based image analysis, trained on independent, local CMR data, to derive measures of cardiac dimensions and function. This information was combined with established clinical parameters and ECG markers of prognosis. RESULTS Over a median follow-up period of 10 years, 23 patients experienced an endpoint of death/aborted cardiac arrest or documented ventricular tachycardia (defined as >3 documented consecutive ventricular beats). On univariate Cox analysis, various DL parameters, including right atrial median area (HR 1.11/cm², p=0.003) and right ventricular long-axis strain (HR 0.80/%, p=0.009) emerged as significant predictors of outcome. DL parameters were related to adverse outcome independently of left and right ventricular ejection fraction and peak oxygen uptake (p less then 0.05 for all). A composite score of enlarged right atrial area and depressed right ventricular longitudinal function identified a ToF subgroup at significantly increased risk of adverse outcome (HR 2.1/unit, p=0.007). CONCLUSIONS We present data on the utility of machine learning algorithms trained on external imaging datasets to automatically estimate prognosis in patients with ToF. Due to the automated analysis process these two-dimensional-based algorithms may serve as surrogates for labour-intensive manually attained imaging parameters in patients with ToF. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.This review article is focused on the role of echocardiography, cardiac CT and cardiac magnetic resonance (CMR) imaging in diagnosing and managing patients with post-cardiac injury syndrome (PCIS). Clinically, the spectrum of pericardial diseases under PCIS varies not only in form and severity of presentation but also in the timing varying from weeks to months, thus making it difficult to diagnose. Pericarditis developing after recent or remote myocardial infarction, cardiac surgery or ablation if left untreated or under-treated could worsen into complicated pericarditis which can lead to decreased quality of life and increased morbidity. Colchicine in combination with other anti-inflammatory agents (non-steroidal anti-inflammatory drugs) is proven to prevent and treat acute pericarditis as well as its relapses under various scenarios. Imaging modalities such as echocardiography, CT and CMR play a pivotal role in diagnosing PCIS especially in difficult cases or when clinical suspicion is low. Echocardiography is the tool of choice for emergent bedside evaluation for cardiac tamponade and to electively study the haemodynamics impact of constrictive pericarditis. CT can provide information on pericardial thickening, calcification, effusions and lead perforations. CMR can provide pericardial tissue characterisation, haemodynamics changes and guide long-term treatment course with anti-inflammatory agents. It is important to be familiar with the indications as well as findings from these multimodality imaging tools for clinical decision-making. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.This paper aims to raise awareness of cyberbullying and online safety among health practitioners and provide some useful advice and key messages to help facilitate conversations with children and young people about internet use. The paper also discusses the role of 'SOCKS' (Stamp Out Cyberbullying & Keep Safe), a novel teaching workshop aimed at primary school children, which aims to generate awareness and understanding before they become regularly exposed to the dangers of the online world. © Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.OBJECTIVE To determine whether bicycle commuting is associated with risk of injury. DESIGN Prospective population based study. SETTING UK Biobank. PARTICIPANTS 230 390 commuters (52.1% women; mean age 52.4 years) recruited from 22 sites across the UK compared by mode of transport used (walking, cycling, mixed mode versus non-active (car or public transport)) to commute to and from work on a typical day. MAIN OUTCOME MEASURE First incident admission to hospital for injury. RESULTS 5704 (2.5%) participants reported cycling as their main form of commuter transport. check details Median follow-up was 8.9 years (interquartile range 8.2-9.5 years), and overall 10 241 (4.4%) participants experienced an injury. Injuries occurred in 397 (7.0%) of the commuters who cycled and 7698 (4.3%) of the commuters who used a non-active mode of transport. After adjustment for major confounding sociodemographic, health, and lifestyle factors, cycling to work was associated with a higher risk of injury compared with commuting by a non-active modewer deaths. CONCLUSION Compared with non-active commuting to work, commuting by cycling was associated with a higher risk of hospital admission for a first injury and higher risk of transport related incidents specifically. These risks should be viewed in context of the health benefits of active commuting and underscore the need for a safer infrastructure for cycling in the UK. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http//group.bmj.com/group/rights-licensing/permissions.Group 1 metabotropic glutamate receptors (Gp1 mGluRs), including mGluR1 and mGluR5, are critical regulators for neuronal and synaptic plasticity. Dysregulated Gp1 mGluR signaling is observed with various neurologic disorders, including Alzheimer's disease, Parkinson's disease, epilepsy, and autism spectrum disorders (ASDs). It is well established that acute activation of Gp1 mGluRs leads to elevation of neuronal intrinsic excitability and long-term synaptic depression. However, it remains unknown how chronic activation of Gp1 mGluRs can affect neural activity and what molecular mechanisms might be involved. In the current study, we employed a multielectrode array (MEA) recording system to evaluate neural network activity of primary mouse cortical neuron cultures. We demonstrated that chronic activation of Gp1 mGluRs leads to elevation of spontaneous spike frequency while burst activity and cross-electrode synchronization are maintained at the baseline. We further showed that these neural network properties are achieved through proteasomal degradation of Akt that is dependent on the tumor suppressor p53.