Furthermore, toxicity was demonstrated to be significantly reduced after biotransformation. Together, this study introduced a strategy to use V. diabolicus strain L2-2 to realize simultaneous removal and detoxification of multiple SAs in freshwater and seawater, and revealed SAs removal pathways and relevant molecular mechanism.Background Cardiac sympathetic blockade is a therapeutic approach for arrhythmias and heart failure and may be a beneficial effect of high thoracic epidural anesthesia. These treatments require detailed knowledge of the spatial location and distribution of cardiac autonomic nerves, however, there are controversies on this subject in humans. Objective To provide a systematic overview of current knowledge on human anatomy of the cardiac autonomic nervous system. Results In contrast to the often claimed assumption that human preganglionic sympathetic cardiac neurons originate mainly from thoracic spinal segments T1-T4 or T5, there is ample evidence indicating involvement of cervical spinal segment C8 and thoracic spinal segments below T5. Whether cervical ganglia besides the stellate ganglion play a role in transmission of cardiac sympathetic signals is unclear. Similarly, there is debate on the origin of cardiac nerves from different thoracic ganglia. Most human studies report thoracic cardiac nerves emerging from the first to fourth thoracic paravertebral ganglia; others report contributions from the fifth, sixth and even the seventh thoracic ganglia. There is no agreement on the precise composition of nerve plexuses at the cardiac level. After years of debate, it is generally accepted that the vagal nerve contributes to ventricular innervation. Vagal distribution appears higher in atria, whereas adrenergic fibers exceed the number of vagal fibers in the ventricles. Conclusion Anatomy of the human cardiac autonomic nervous system is highly variable and likely extends beyond generally assumed boundaries. This information is relevant for thoracic epidural anesthesia and procedures targeting neuronal modulation of cardiac sympathetic innervation.For a poor quality optical coherence tomography (OCT) image, quality enhancement is limited to speckle residue and edge blur as well as texture loss, especially at the background region near edges. To solve this problem, in this paper we propose a de-speckling method based on the convolutional neural network (CNN). In the proposed method, we use a deep nonlinear CNN mapping model in the serial architecture, here named as OCTNet. Our OCTNet in the proposed method can fully utilize the deep information on speckles and edges as well as fine textures of an original OCT image. And also we construct an available pertinent dataset by combining three existing methods to train the model. With the proposed method, we can accurately get the speckle noise from an original OCT image. We test our method on four experimental human retinal OCT images and also compare it with three state-of-the-art methods, including the adaptive complex diffusion (ACD) method and the curvelet shrinkage (Curvelet) method as well as the shearlet-based total variation (STV) method. The performance of these methods is quantitatively evaluated in terms of image distinguishability, contrast, smoothness and edge sharpness, and also qualitatively analyzed at aspects of speckle reduction, texture protection and edge preservation. The experimental results show that our OCTNet can reduce the speckle noise and protect the structural information as well as preserve the edge features effectively and simultaneously, even where the background region near edges. And also our OCTNet has full advantages on excellent generalization, adaptiveness, robust and batch performance. read more These advantages make our method be suitable to process a great mass of different images rapidly without any parameter fine-turning under a time-constrained real-time situation.Deep learning-based systems can achieve a diagnostic performance comparable to physicians in a variety of medical use cases including the diagnosis of diabetic retinopathy. To be useful in clinical practice, it is necessary to have well calibrated measures of the uncertainty with which these systems report their decisions. However, deep neural networks (DNNs) are being often overconfident in their predictions, and are not amenable to a straightforward probabilistic treatment. Here, we describe an intuitive framework based on test-time data augmentation for quantifying the diagnostic uncertainty of a state-of-the-art DNN for diagnosing diabetic retinopathy. We show that the derived measure of uncertainty is well-calibrated and that experienced physicians likewise find cases with uncertain diagnosis difficult to evaluate. This paves the way for an integrated treatment of uncertainty in DNN-based diagnostic systems.Bivalirudin is a reversible direct thrombin inhibitor that inhibits both bound and free thrombin and binds to the active (catalytic) and fibrinogen-binding sites of thrombin, with high affinity and specificity. Off-label use of bivalirudin in the paediatric population has increased, as an alternative to heparin, particularly in the setting of anticoagulation for patients undergoing coronary bypass surgery (CPB), extracorporeal life support (ECLS) and those on ventricular assist devices (VAD). This study aimed to determine the age-specific in vitro effect of bivalirudin in children compared to adults. Age-specific pools (neonates, ≤2 years, >2 to 5 years, 6 to 10 years, 11 to 17 years and Adults) were prepared using platelet poor plasma samples from 20 individuals per age group. Pooled plasma was spiked with increasing concentrations of Bivalirudin (from 0 g/mL to 10μg/mL), and thrombin inhibition was measured using standard coagulation assays. There was a significantly increased response to bivalirudin across all paediatric age groups as compared to adults. The age-specific difference in response to bivalirudin was specifically evident in neonates, where the potential to generate thrombin was decreased 2-fold compared to adults (p less then 0.001). Our findings support the concept of age-specific pharmaco-dynamic responses to Bivalirudin and support the need for further ex vivo studies in hospitalised children to determine accurate clinical dosing recommendations.