RESUMO
The quantification of left ventricular ejection fraction (LVEF) has important clinical utility in the assessment of cardiac function and is vital for the diagnosis of cardiovascular diseases. A transthoracic echocardiogram serves as the most commonly used tool for LVEF assessment for several reasons, including, its noninvasive nature, great safety profile, real-time image processing ability, portability, and cost-effectiveness. However, transthoracic echocardiogram is highly dependent on the clinical skill of the sonographer and interpreting physician. Moreover, even amongst well-trained clinicians, significant interobserver variability exists in the quantification of LVEF. In search of possible solutions, the usage of artificial intelligence (AI) has been increasingly tested in the clinical setting. While AI-derived ejection fraction is in the preliminary stages of development, it has shown promise in its ability to rapidly quantify LVEF, decrease variability, increase accuracy, and utilize higher-order processing capabilities. This review will delineate the latest advancements of AI in evaluating LVEF through echocardiography and explore the challenges and future trajectory of this emerging domain.
RESUMO
The study of lipoprotein(a) [Lp(a)] has long been a source of interest as a possible independent risk factor for atherosclerotic cardiovascular disease (ASCVD). The results of large sample observational studies, genome-wide association studies, and Mendelian randomization studies have been strong indicators supporting the link between ASCVD and Lp(a) despite early studies, with less sensitive assays, failing to show a connection. The recommendations for the indications and frequency of testing Lp(a) levels vary between US, Canadian, and European organizations due to the uncertain role of Lp(a) in ASCVD. The innovation of recent therapies, such as antisense oligonucleotides and small interfering RNA, designed to specifically target and reduce Lp(a) levels by targeting mRNA translation have once more thrust LP(a) into the spotlight of inquiry. These emerging modalities serve the dual purpose of definitively elucidating the connection between elevated Lp(a) levels and atherosclerotic cardiovascular risk, as well as the possibility of providing clinicians with the tools necessary to manage elevated Lp(a) levels in vulnerable populations. This review seeks to examine the mechanisms of atherogenicity of Lp(a) and explore the most current pharmacologic therapies currently in development.
RESUMO
Two-thirds of individuals experience adversity during childhood such as neglect, abuse or highly-stressful events. Early-life adversity (ELA) increases the life-long risk of developing mood and substance use disorders. Reward-related deficits has emerged as a key endophenotype of such psychiatric disorders. Animal models are invaluable for studying how ELA leads to reward deficits. However, the existing literature is heterogenous with difficult to reconcile findings. To create an overview, we conducted a systematic review containing multiple meta-analyses regarding the effects of ELA on reward processes overall and on specific aspects of reward processing in animal models. A comprehensive search identified 120 studies. Most studies omitted key details resulting in unclear risk of bias. Overall meta-analysis showed that ELA significantly reduced reward behaviors (SMD: -0.42 [-0.60; -0.24]). The magnitude of ELA effects significantly increased with longer exposure. When reward domains were analyzed separately, ELA only significantly dampened reward responsiveness (SMD: -0.525[-0.786; -0.264]) and social reward processing (SMD: -0.374 [-0.663; -0.084]), suggesting that ELA might lead to deficits in specific reward domains.
