Global S&T Development Trend Analysis Platform of Resources and Environment
A video-based deep learning algorithm-EchoNet-Dynamic-accurately identifies subtle changes in ejection fraction and classifies heart failure with reduced ejection fraction using information from multiple cardiac cycles.
Accurate assessment of cardiac function is crucial for the diagnosis of cardiovascular disease(1), screening for cardiotoxicity(2) and decisions regarding the clinical management of patients with a critical illness(3). However, human assessment of cardiac function focuses on a limited sampling of cardiac cycles and has considerable inter-observer variability despite years of training(4,5). Here, to overcome this challenge, we present a video-based deep learning algorithm-EchoNet-Dynamic-that surpasses the performance of human experts in the critical tasks of segmenting the left ventricle, estimating ejection fraction and assessing cardiomyopathy. Trained on echocardiogram videos, our model accurately segments the left ventricle with a Dice similarity coefficient of 0.92, predicts ejection fraction with a mean absolute error of 4.1% and reliably classifies heart failure with reduced ejection fraction (area under the curve of 0.97). In an external dataset from another healthcare system, EchoNet-Dynamic predicts the ejection fraction with a mean absolute error of 6.0% and classifies heart failure with reduced ejection fraction with an area under the curve of 0.96. Prospective evaluation with repeated human measurements confirms that the model has variance that is comparable to or less than that of human experts. By leveraging information across multiple cardiac cycles, our model can rapidly identify subtle changes in ejection fraction, is more reproducible than human evaluation and lays the foundation for precise diagnosis of cardiovascular disease in real time. As a resource to promote further innovation, we also make publicly available a large dataset of 10,030 annotated echocardiogram videos.
Most magmatism occurring on Earth is conventionally attributed to passive mantle upwelling at mid-ocean ridges, to slab devolatilization at subduction zones, or to mantle plumes. However, the widespread Cenozoic intraplate volcanism in northeast China(1-3) and the young petit-spot volcanoes(4-7) offshore of the Japan Trench cannot readily be associated with any of these mechanisms. In addition, the mantle beneath these types of volcanism is characterized by zones of anomalously low seismic velocity above and below the transition zone(8-12) (a mantle level located at depths between 410 and 660 kilometres). A comprehensive interpretation of these phenomena is lacking. Here we show that most (or possibly all) of the intraplate and petit-spot volcanism and low-velocity zones around the Japanese subduction zone can be explained by the Cenozoic interaction of the subducting Pacific slab with a hydrous mantle transition zone. Numerical modelling indicates that 0.2 to 0.3 weight per cent of water dissolved in mantle minerals that are driven out from the transition zone in response to subduction and retreat of a tectonic plate is sufficient to reproduce the observations. This suggests that a critical amount of water may have accumulated in the transition zone around this subduction zone, as well as in others of the Tethyan tectonic belt(13) that are characterized by intraplate or petit-spot volcanism and low-velocity zones in the underlying mantle.
The widespread intraplate volcanism in northeast China and the unusual '
Neural control of the function of visceral organs is essential for homeostasis and health. Intestinal peristalsis is critical for digestive physiology and host defence, and is often dysregulated in gastrointestinal disorders(1). Luminal factors, such as diet and microbiota, regulate neurogenic programs of gut motility(2-5), but the underlying molecular mechanisms remain unclear. Here we show that the transcription factor aryl hydrocarbon receptor (AHR) functions as a biosensor in intestinal neural circuits, linking their functional output to the microbial environment of the gut lumen. Using nuclear RNA sequencing of mouse enteric neurons that represent distinct intestinal segments and microbiota states, we demonstrate that the intrinsic neural networks of the colon exhibit unique transcriptional profiles that are controlled by the combined effects of host genetic programs and microbial colonization. Microbiota-induced expression of AHR in neurons of the distal gastrointestinal tract enables these neurons to respond to the luminal environment and to induce expression of neuron-specific effector mechanisms. Neuron-specific deletion of Ahr, or constitutive overexpression of its negative feedback regulator CYP1A1, results in reduced peristaltic activity of the colon, similar to that observed in microbiota-depleted mice. Finally, expression of Ahr in the enteric neurons of mice treated with antibiotics partially restores intestinal motility. Together, our experiments identify AHR signalling in enteric neurons as a regulatory node that integrates the luminal environment with the physiological output of intestinal neural circuits to maintain gut homeostasis and health.
In a mouse model, aryl hydrocarbon receptor signalling in enteric neurons is revealed as a mechanism that helps to maintain gut homeostasis by integrating the luminal environment with the physiology of intestinal neural circuits.
