Background Recent evidence showed that the characteristics and outcome of those with de novo heart failure (HF) and acutely decompensated chronic heart failure (ADCHF) were different. We aimed to perform a comprehensive search on the clinical characteristics and outcome of patients with de novo HF and ADCHF.Methods We performed a comprehensive search on de novo/new onset acute HF vs ADCHF from inception up until December 2019.Results There were 38320 patients from 15 studies. De novo HF were younger and, had less prevalent hypertension, diabetes mellitus, ischaemic heart disease, chronic obstructive pulmonary disease, atrial fibrillation, and history of stroke/transient ischaemic attack compared to ADCHF. Five studies showed a lower NT-proBNP in de novo HF patients, while one study showed no difference. Valvular heart disease as aetiology of heart failure was less frequent in de novo HF, and upon sensitivity analysis, hypertensive heart disease was more frequent in de novo HF. As for precipitating factors, ACS (OR 2.42; I289%) was more frequently seen in de novo HF, whereas infection was less frequently (OR 0.69; I232%) in ADCHF. De novo HF was associated with a significantly lower 3-month mortality (OR 0.63; I291%) and 1-year (OR 0.59; I259%) mortality. Meta-regression showed that 1-year mortality did not significantly vary with age (p = .106), baseline ejection fraction (p = .703), or HF reduced ejection fraction (p = .262).Conclusion Risk factors, aetiology, and precipitating factors of HF in de novo and ADCHF differ. De novo HF also had lower 1-year mortality and 3-month mortality compared to ADCHF.Background and Purpose- Classification of stroke as cardioembolic in etiology can be challenging, particularly since the predominant cause, atrial fibrillation (AF), may not be present at the time of stroke. Efficient tools that discriminate cardioembolic from noncardioembolic strokes may improve care as anticoagulation is frequently indicated after cardioembolism. find more We sought to assess and quantify the discriminative power of AF risk as a classifier for cardioembolism in a real-world population of patients with acute ischemic stroke. Methods- We performed a cross-sectional analysis of a multi-institutional sample of patients with acute ischemic stroke. We systematically adjudicated stroke subtype and examined associations between AF risk using CHA2DS2-VASc, Cohorts for Heart and Aging Research in Genomic Epidemiology-AF score, and the recently developed Electronic Health Record-Based AF score, and cardioembolic stroke using logistic regression. We compared the ability of AF risk to discriminate cardioembolism hresholds 0.90-0.99). Conclusions- AF risk scores associate with cardioembolic stroke and exhibit moderate discrimination. Utilization of AF risk scores at the time of stroke may be most useful for identifying individuals at low probability of cardioembolism. Future analyses are warranted to assess whether stroke subtype classification can be enhanced to improve outcomes in undifferentiated stroke.A means of quantifying continuous, free-living energy expenditure (EE) would advance the study of bioenergetics. The aim of this study was to apply a non-linear, machine learning algorithm (random forest) to predict minute level EE for a range of activities using acceleration, physiological signals (e.g., heart rate, body temperature, galvanic skin response), and participant characteristics (e.g., sex, age, height, weight, body composition) collected from wearable devices (Fitbit charge 2, Polar H7, SenseWear Armband Mini and Actigraph GT3-x) as potential inputs. By utilising a leave-one-out cross-validation approach in 59 subjects, we investigated the predictive accuracy in sedentary, ambulatory, household, and cycling activities compared to indirect calorimetry (Vyntus CPX). Over all activities, correlations of at least r = 0.85 were achieved by the models. Root mean squared error ranged from 1 to 1.37 METs and all overall models were statistically equivalent to the criterion measure. Significantly lower error was observed for Actigraph and Sensewear models, when compared to the manufacturer provided estimates of the Sensewear Armband (p  less then  0.05). A high degree of accuracy in EE estimation was achieved by applying non-linear models to wearable devices which may offer a means to capture the energy cost of free-living activities.We quantified the peak age of judokas during the World Championships (WC) and Olympic Games (OG) according to sex, weight category and competitive achievement and determined the relationship between competition year and athlete age. A retrospective study including 12,005 athletes who took part in the last 16 WC and 6 OG. Athletes were divided by sex, weight category and competitive achievement. Overall, females were younger than males, and older athletes competed at the OG compared to the WC. A weight category effect was also observed, with lighter athletes being younger than heavier athletes (p less then 0.05). A competitive achievement effect was found for females, with athletes being defeated in the eliminatory phases being younger than those advancing further in the competitions (p less then 0.05). Significant associations (p less then 0.05) were shown between competition year and age category for males at the WC and for females at both the WC and OG. In general, lighter athletes are younger than heavier ones (p less then 0.05). No difference in age was found  between males concerning their competitive achievement in WC and OG, whereas younger females are defeated in the eliminatory phases (p less then 0.05).Mowat-Wilson syndrome (MWS) is a syndromic form of Hirschsprung disease that is characterized by variable degrees of intellectual disability, characteristic facial dysmorphism, and a diverse set of other congenital malformations due to haploinsufficiency of ZEB2. A variety of brain malformations have been described in neuroimaging studies of MWS patients, and the role of ZEB2 in the brain has been studied in a multitude of genetically engineered mouse models that are now available. However, a paucity of autopsy information limits our ability to correlate data from neuroimaging studies and animal models with actual MWS patient tissues. Here, we report the autopsy neuropathology of a 19-year-old male patient with MWS. Autopsy neuropathology findings correlated well with the reported MWS neuroimaging data and are in keeping with data from genetically engineered MWS mouse models. This autopsy enhances our understanding of ZEB2 function in human brain development and demonstrates the reliability of MWS murine models.