Hypertrophic cardiomyopathy (HCM) is the most common monogenic heart disease with a frequency as high as 1 in 200. In many cases, HCM is caused by mutations in genes encoding the different components of the sarcomere apparatus. HCM is characterized by unexplained left ventricular hypertrophy (LVH), myofibrillar disarray, and myocardial fibrosis. The phenotypic expression is quite variable. While the majority of patients with HCM are asymptomatic, serious consequences are experienced in a subset of affected individuals who present initially with sudden cardiac death (SCD) or progress to refractory heart failure (HF). The HCMR study is a National Heart Lung and Blood Institute (NHLBI)-sponsored 2750 patient, 41 site, international registry and natural history study designed to address limitations in extant evidence to improve prognostication in HCM (NCT01915615). In addition to collection of standard demographic, clinical, and echocardiographic variables, patients will undergo state-of-the-art cardiac magnetic resonance (CMR) for assessment of left ventricular (LV) mass and volumes as well as replacement scarring and interstitial fibrosis. In addition, genetic and biomarker analysis will be performed. HCMR has the potential to change the paradigm of risk stratification in HCM, using novel markers to identify those at higher risk.
Clinically recognized atrial fibrillation (AF) is associated with higher risk of complications, including ischemic stroke, cognitive decline, heart failure, myocardial infarction, and death. It is increasingly recognized that AF frequently is undetected until complications such as stroke or heart failure occur. Hence, the public and clinicians have an intense interest in detecting AF earlier. However, the most appropriate strategies to detect undiagnosed AF (sometimes referred to as subclinical AF) and the prognostic and therapeutic implications of AF detected by screening are uncertain. Our report summarizes the National Heart, Lung, and Blood Institute’s virtual workshop focused on identifying key research priorities related to AF screening. Global experts reviewed major knowledge gaps and identified critical research priorities in the following areas: (1) role of opportunistic screening; (2) AF as a risk factor, risk marker, or both; (3) relationship between AF burden detected with long-term monitoring and outcomes/treatments; (4) designs of potential randomized trials of systematic AF screening with clinically relevant outcomes; and (5) role of AF screening after ischemic stroke. Our report aims to inform and catalyze AF screening research that will advance innovative, resource-efficient, and clinically relevant studies in diverse populations to improve the diagnosis, management, and prognosis of patients with undiagnosed AF.
Land cover and its associated biophysical parameters govern many land-atmosphere interactions. Several previous studies have demonstrated the utility of incorporating satellite-derived observations of land cover into climate models to improve prediction accuracy. In the developing world where agriculture is a primary livelihood, a better understanding of seasonal variability in precipitation and near-surface temperature is critical to constructing more effective coping strategies for climate changes and food security. However, relatively few studies have been able to assess the impacts of improved surface parameterisation on these variables and their seasonality. Using moderate resolution imaging spectroradiometer (MODIS)-derived products, we sought to address this shortcoming by adapting leaf area index (LAI) and vegetative fractional cover (FC) products, along with an improved representation of the land surface (i.e. land use land cover) into the Regional Atmospheric Modelling System in East Africa to evaluate the effect improved representations would have on simulated precipitation and land surface temperature (LST). In particular, we tested the hypothesis that improved phenological parameterisations could reduce error in precipitation and LST under dramatically different atmospheric conditions. The model was used to simulate dry/normal/wet rainfall years of 2000, 2001, and 2002 (respectively) in order to understand biases in this parameterisation under different boundary conditions. Our results show a dramatic improvement in LST simulation due to the use of the improved representations (spline functions) during most of the year, both spatially and temporally. Annual precipitation, which is dependent upon a much greater variety of surface and atmospheric characteristics, did not improve as much by adopting the spline representations of LAI and FC; the results were more equivocal. However, seasonal timing of precipitation improved in some areas, and this improvement has important consequences for integrated climate-agriculture assessments.
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