Association between right ventricular global longitudinal strain and mortality in intermediate-risk pulmonary embolism.

BACKGROUND: Right ventricular (RV) systolic dysfunction has been identified as a prognostic marker for adverse clinical events in patients presenting with acute pulmonary embolism (PE). However, challenges exist in identifying RV dysfunction using conventional echocardiography techniques. Strain echocardiography is an evolving imaging modality which measures myocardial deformation and can be used as an objective index of RV systolic function. This study evaluated RV Global Longitudinal Strain (RVGLS) in patients with intermediate risk PE as a parameter of RV dysfunction, and compared to traditional echocardiographic and CT parameters evaluating short-term mortality. METHODS: Retrospective single center cohort study of 251 patients with intermediate-risk PE between 2010 and 2018. The primary outcome was all-cause mortality at 30 days. Statistical analysis evaluated each parameter comparing survivors versus non-survivors at 30 days. Receiver operating characteristic (ROC) curves and Kaplan-Meier curves were used for comparison of the two cohorts. RESULTS: Altogether 251 patients were evaluated. Overall mortality rate was 12.4%. Utilizing an ROC curve, an absolute cutoff value of 17.7 for RVGLS demonstrated a sensitivity of 93% and specificity of 70% for observed 30-day mortality. Individuals with an RVGLS ≤17.7 had a 25 times higher mortality rate than those with RVGLS above 17.7 (HR 25.24, 95% CI = 6.0-106.4, p < .001). Area under the curve was (.855), RVGLS outperformed traditional echocardiographic parameters, CT findings, and cardiac biomarkers on univariable and multivariable analysis. CONCLUSIONS: Reduced RVGLS values on initial echocardiographic assessment of patients with intermediate-risk PE identified patients at higher risk for mortality at 30 days.

Interatrial shunt therapy in advanced heart failure: Outcomes from the open-label cohort of the RELIEVE-HF trial.

AIMS: Heart failure (HF) outcomes remain poor despite optimal guideline-directed medical therapy (GDMT). We assessed safety, effectiveness, and transthoracic echocardiographic (TTE) outcomes during the 12 months after Ventura shunt implantation in the RELIEVE-HF open-label roll-in cohort. METHODS AND RESULTS: Eligibility required symptomatic HF despite optimal GDMT with ≥1 HF hospitalization in the prior year or elevated natriuretic peptides. The safety endpoint was device-related major adverse cardiovascular or neurological events at 30 days, compared to a prespecified performance goal. Effectiveness evaluations included the Kansas City Cardiomyopathy Questionnaire (KCCQ) at baseline, 1, 3, 6, and 12 months and TTE at baseline and 12 months. Overall, 97 patients were enrolled and implanted at 64 sites. Average age was 70 ± 11 years, 97% were in New York Heart Association class III, and half had left ventricular ejection fraction (LVEF) ≤40%. The safety endpoint was achieved (event rate 0%, p < 0.001). KCCQ overall summary score was improved by 12-16 points at all follow-up timepoints (all p < 0.004), with similar outcomes in patients with reduced and preserved LVEF. At 12 months, left ventricular end-systolic and end-diastolic volumes were reduced (p = 0.020 and p = 0.038, respectively), LVEF improved (p = 0.009), right ventricular end-systolic and end-diastolic areas were reduced (p = 0.001 and p = 0.030, respectively), and right ventricular fractional area change (p < 0.001) and tricuspid annular plane systolic excursion (p < 0.001) improved. CONCLUSION: Interatrial shunting with the Ventura device was safe and resulted in favourable clinical effects in patients with HF, regardless of LVEF. Improvements of left and right ventricular structure and function were consistent with reverse myocardial remodelling. These results would support the potential of this shunt device as a treatment for HF.

PMechDB: A Public Database of Elementary Polar Reaction Steps.

Most online chemical reaction databases are not publicly accessible or are fully downloadable. These databases tend to contain reactions in noncanonicalized formats and often lack comprehensive information regarding reaction pathways, intermediates, and byproducts. Within the few publicly available databases, reactions are typically stored in the form of unbalanced, overall transformations with minimal interpretability of the underlying chemistry. These limitations present significant obstacles to data-driven applications including the development of machine learning models. As an effort to overcome these challenges, we introduce PMechDB, a publicly accessible platform designed to curate, aggregate, and share polar chemical reaction data in the form of elementary reaction steps. Our initial version of PMechDB consists of over 100,000 such steps. In the PMechDB, all reactions are stored as canonicalized and balanced elementary steps, featuring accurate atom mapping and arrow-pushing mechanisms. As an online interactive database, PMechDB provides multiple interfaces that enable users to search, download, and upload chemical reactions. We anticipate that the public availability of PMechDB and its standardized data representation will prove beneficial for chemoinformatics research and education and the development of data-driven, interpretable models for predicting reactions and pathways. PMechDB platform is accessible online at https://deeprxn.ics.uci.edu/pmechdb.

Understanding Treatment Preferences for Patients with Tricuspid Regurgitation.

Background. Tricuspid regurgitation (TR) is a high-prevalence disease associated with poor quality of life and mortality. This quantitative patient preference study aims to identify TR patients' perspectives on risk-benefit tradeoffs. Methods. A discrete-choice experiment was developed to explore TR treatment risk-benefit tradeoffs. Attributes (levels) tested were treatment (procedure, medical management), reintervention risk (0%, 1%, 5%, 10%), medications over 2 y (none, reduce, same, increase), shortness of breath (none/mild, moderate, severe), and swelling (never, 3× per week, daily). A mixed logit regression model estimated preferences and calculated predicted probabilities. Relative attribute importance was calculated. Subgroup analyses were performed. Results. An online survey was completed by 150 TR patients. Shortness of breath was the most important attribute and accounted for 65.8% of treatment decision making. The average patients' predicted probability of preferring a "procedure-like" profile over a "medical management-like" profile was 99.7%. This decreased to 78.9% for a level change from severe to moderate in shortness of breath in the "medical management-like" profile. Subgroup analysis confirmed that patients older than 64 y had a stronger preference to avoid severe shortness of breath compared with younger patients (P < 0.02), as did severe or worse TR patients relative to moderate. New York Heart Association class I/II patients more strongly preferred to avoid procedural reintervention risk relative to class III/IV patients (P < 0.03). Conclusion. TR patients are willing to accept higher procedural reintervention risk if shortness of breath is alleviated. This risk tolerance is higher for older and more symptomatic patients. These results emphasize the appropriateness of developing TR therapies and the importance of addressing symptom burden. Highlights: This study provides quantitative patient preference data from clinically confirmed tricuspid regurgitation (TR) patients to understand their treatment preferences.Using a targeted literature search and patient, physician, and Food and Drug Administration feedback, a cross-sectional survey with a discrete-choice experiment that focused on 5 of the most important attributes to TR patients was developed and administered online.TR patients are willing to accept higher procedural reintervention risk if shortness of breath is alleviated, and this risk tolerance is higher for older and more symptomatic patients.

Computed Tomography-Derived 3D Modeling to Guide Sizing and Planning of Transcatheter Mitral Valve Interventions.

A plethora of catheter-based strategies have been developed to treat mitral valve disease. Evolving 3-dimensional (3D) multidetector computed tomography (MDCT) technology can accurately reconstruct the mitral valve by means of 3-dimensional computational modeling (3DCM) to allow virtual implantation of catheter-based devices. 3D printing complements computational modeling and offers implanting physician teams the opportunity to evaluate devices in life-size replicas of patient-specific cardiac anatomy. MDCT-derived 3D computational and 3D-printed modeling provides unprecedented insights to facilitate hands-on procedural planning, device training, and retrospective procedural evaluation. This overview summarizes current concepts and provides insight into the application of MDCT-derived 3DCM and 3D printing for the planning of transcatheter mitral valve replacement and closure of paravalvular leaks. Additionally, future directions in the development of 3DCM will be discussed.

Efficient Processing of Spatio-Temporal Data Streams With Spiking Neural Networks.

Spiking neural networks (SNNs) are potentially highly efficient models for inference on fully parallel neuromorphic hardware, but existing training methods that convert conventional artificial neural networks (ANNs) into SNNs are unable to exploit these advantages. Although ANN-to-SNN conversion has achieved state-of-the-art accuracy for static image classification tasks, the following subtle but important difference in the way SNNs and ANNs integrate information over time makes the direct application of conversion techniques for sequence processing tasks challenging. Whereas all connections in SNNs have a certain propagation delay larger than zero, ANNs assign different roles to feed-forward connections, which immediately update all neurons within the same time step, and recurrent connections, which have to be rolled out in time and are typically assigned a delay of one time step. Here, we present a novel method to obtain highly accurate SNNs for sequence processing by modifying the ANN training before conversion, such that delays induced by ANN rollouts match the propagation delays in the targeted SNN implementation. Our method builds on the recently introduced framework of streaming rollouts, which aims for fully parallel model execution of ANNs and inherently allows for temporal integration by merging paths of different delays between input and output of the network. The resulting networks achieve state-of-the-art accuracy for multiple event-based benchmark datasets, including N-MNIST, CIFAR10-DVS, N-CARS, and DvsGesture, and through the use of spatio-temporal shortcut connections yield low-latency approximate network responses that improve over time as more of the input sequence is processed. In addition, our converted SNNs are consistently more energy-efficient than their corresponding ANNs.

A generative growth model for thalamocortical axonal branching in primary visual cortex.

Axonal morphology displays large variability and complexity, yet the canonical regularities of the cortex suggest that such wiring is based on the repeated initiation of a small set of genetically encoded rules. Extracting underlying developmental principles can hence shed light on what genetically encoded instructions must be available during cortical development. Within a generative model, we investigate growth rules for axonal branching patterns in cat area 17, originating from the lateral geniculate nucleus of the thalamus. This target area of synaptic connections is characterized by extensive ramifications and a high bouton density, characteristics thought to preserve the spatial resolution of receptive fields and to enable connections for the ocular dominance columns. We compare individual and global statistics, such as a newly introduced length-weighted asymmetry index and the global segment-length distribution, of generated and biological branching patterns as the benchmark for growth rules. We show that the proposed model surpasses the statistical accuracy of the Galton-Watson model, which is the most commonly employed model for biological growth processes. In contrast to the Galton-Watson model, our model can recreate the log-normal segment-length distribution of the experimental dataset and is considerably more accurate in recreating individual axonal morphologies. To provide a biophysical interpretation for statistical quantifications of the axonal branching patterns, the generative model is ported into the physically accurate simulation framework of Cx3D. In this 3D simulation environment we demonstrate how the proposed growth process can be formulated as an interactive process between genetic growth rules and chemical cues in the local environment.

Conditioning by subthreshold synaptic input changes the intrinsic firing pattern of CA3 hippocampal neurons.

Unlike synaptic strength, intrinsic excitability is assumed to be a stable property of neurons. For example, learning of somatic conductances is generally not incorporated into computational models, and the discharge pattern of neurons in response to test stimuli is frequently used as a basis for phenotypic classification. However, it is increasingly evident that signal processing properties of neurons are more generally plastic on the timescale of minutes. Here we demonstrate that the intrinsic firing patterns of CA3 neurons of the rat hippocampus in vitro undergo rapid long-term plasticity in response to a few minutes of only subthreshold synaptic conditioning. This plasticity on the spike timing could also be induced by intrasomatic injection of subthreshold depolarizing pulses and was blocked by kinase inhibitors, indicating that discharge dynamics are modulated locally. Cluster analysis of firing patterns before and after conditioning revealed systematic transitions toward adapting and intrinsic burst behaviors, irrespective of the patterns initially exhibited by the cells. We used a conductance-based model to decide appropriate pharmacological blockade and found that the observed transitions are likely due to recruitment of low-voltage calcium and Kv7 potassium conductances. We conclude that CA3 neurons adapt their conductance profile to the subthreshold activity of their input, so that their intrinsic firing pattern is not a static signature, but rather a reflection of their history of subthreshold activity. In this way, recurrent output from CA3 neurons may collectively shape the temporal dynamics of their embedding circuits.NEW & NOTEWORTHY Although firing patterns are widely conserved across the animal phyla, it is still a mystery why nerve cells present such diversity of discharge dynamics upon somatic step currents. Adding a new timing dimension to the intrinsic plasticity literature, here we show that CA3 neurons rapidly adapt through the space of known firing patterns in response to the subthreshold signals that they receive from their embedding circuit, potentially adjusting their network processing to the temporal statistics of their circuit.

A Developmental Approach to Internal Medicine Residency Education: Lessons Learned from the Design and Implementation of a Novel Longitudinal Coaching Program.

BACKGROUND: Resident physicians' achievement of professional competencies requires reflective practice skills and faculty coaching. Graduate medical education programs, however, struggle to operationalize these activities. OBJECTIVE: To (1) describe the process and strategies for implementing an Internal Medicine (IM) resident coaching program that evolved in response to challenges, (2) characterize residents' professional learning plans (PLPs) and their alignment with EPAs, and, (3) examine key lessons learned. DESIGN: The program began in 2013 and involved all postgraduate years (PGY) residents (n = 60, 100%), and 20 faculty coaches who were all IM trained and practicing in an IM-related specialty. One coach was linked with 3-4 residents for three years. Through 1:1 meetings, resident-coach pairs identified professional challenges ('disorienting dilemmas' or 'worst days'), reviewed successes ('best days'), and co-created professional learning plans. Typed summaries were requested following meetings. Coaches met monthly for professional development and to discuss program challenges/successes, which informed programmatic improvements; additionally, a survey was distributed after three program years. Data were analyzed using quantitative and qualitative methodologies. RESULTS: Disorienting dilemmas and professional learning plans mapped to all 16 EPAs and four additional themes: work-life balance, career planning, teaching skills, and research/scholarship. The most-frequently mapped topics included: PGY1 - leading and working within interprofessional care teams (EPA 10), research and scholarship, and work-life balance; PGY2 - improving quality of care (EPA 13), demonstrating personal habits of lifelong learning (EPA15), and research and scholarship; PGY3 - lifelong learning (EPA15); career planning was common across all years. CONCLUSIONS: Lessons learned included challenges in coordination of observations, identifying disorienting dilemmas, and creating a shared mental model between residents, faculty, and program leadership. The coaching program resulted in professional learning plans aligned with IM EPAs, in addition to other professional development topics. Operationalization of aspects of these results can inform the development of similar programs in residency education.

Radiation protection for the echocardiographers: "To each their own".

This article illustrates the effectiveness of targeted radioprotective strategies for the interventional echocardiographer. The reader should recognize the importance of engagement of all team members in the multifaceted process of radiation exposure mitigation. Future efforts/studies should focus on the impact of team oriented training, lab design, and development of novel supplies and equipment to mitigate radiation exposure of all personnel in the cardiac catheterization lab, particularly during more complex interventional procedures.

Morphological characterization and genotyping of the marbled crayfish and new evidence on its origin.

The obligately parthenogenetic marbled crayfish, Procambarus virginalis, is the first formally described asexual species of the Crustacea Decapoda. It is a triploid descendant of the sexually reproducing slough crayfish, Procambarus fallax. Here we describe the morphology of cultured and wild marbled crayfish of wide size ranges in detail and photodocument all taxonomically relevant characters. Some morphological traits and coloration showed considerable variation within populations despite the monoclonal nature of marbled crayfish. There were also significant differences between wild and laboratory populations with respect to body proportions, coloration and spination. Comparison with Procambarus fallax revealed no qualitative morphological characters that unambiguously identify the marbled crayfish. Analysis of the mitochondrial cytochrome c oxidase subunit I gene (COI) and nuclear microsatellites of marbled crayfish and Procambarus fallax from different sources indicated that the tri-allelic microsatellite PclG-02 is better suitable than COI to identify the marbled crayfish. A respective identification key is provided. The COI and microsatellites of Procambarus fallax from different areas of Florida and southern Georgia suggest that the parents of the first marbled crayfish may have come from northern Union County, northern Florida.

Deep Learning With Spiking Neurons: Opportunities and Challenges.

Spiking neural networks (SNNs) are inspired by information processing in biology, where sparse and asynchronous binary signals are communicated and processed in a massively parallel fashion. SNNs on neuromorphic hardware exhibit favorable properties such as low power consumption, fast inference, and event-driven information processing. This makes them interesting candidates for the efficient implementation of deep neural networks, the method of choice for many machine learning tasks. In this review, we address the opportunities that deep spiking networks offer and investigate in detail the challenges associated with training SNNs in a way that makes them competitive with conventional deep learning, but simultaneously allows for efficient mapping to hardware. A wide range of training methods for SNNs is presented, ranging from the conversion of conventional deep networks into SNNs, constrained training before conversion, spiking variants of backpropagation, and biologically motivated variants of STDP. The goal of our review is to define a categorization of SNN training methods, and summarize their advantages and drawbacks. We further discuss relationships between SNNs and binary networks, which are becoming popular for efficient digital hardware implementation. Neuromorphic hardware platforms have great potential to enable deep spiking networks in real-world applications. We compare the suitability of various neuromorphic systems that have been developed over the past years, and investigate potential use cases. Neuromorphic approaches and conventional machine learning should not be considered simply two solutions to the same classes of problems, instead it is possible to identify and exploit their task-specific advantages. Deep SNNs offer great opportunities to work with new types of event-based sensors, exploit temporal codes and local on-chip learning, and we have so far just scratched the surface of realizing these advantages in practical applications.

Treatment of an obstructive, recurrent, syncytial myoepithelioma of the trachea with tracheal resection and reconstruction.

Myoepithelioma is a rare occurrence in the trachea and respiratory tract with only 11 cases reported in the literature. We present a case report of a 10-year-old female who was found to have an anterior tracheal mass causing near total obstruction of the airway on bronchoscopy. Characteristics of the mass were consistent with syncytial myoepithelioma. The patient experienced multiple recurrences requiring tracheal resection with end-to-end reanastomosis. To date there have not been any reported cases of myoepithelioma of the trachea in a child and no reports of syncytial myoepithelioma in the trachea or respiratory tract.

Identifying eustachian tube dysfunction prior to hyperbaric oxygen therapy: Who is at risk for intolerance?

PURPOSE: Determine whether specific risk factors, symptoms and clinical examination findings are associated with hyperbaric oxygen therapy (HBOT) intolerance and subsequent tympanotomy tube placement. MATERIALS AND METHODS: A retrospective case series with chart review was conducted from 2007 to 2016 of patients undergoing HBOT clearance at a tertiary care university hospital in an urban city. Eighty-one (n=81) patient charts were reviewed for risk factors, symptoms and clinical examination findings related to HBOT eustachian tube dysfunction and middle ear barotrauma. Relative risk was calculated for each variable to determine risk for HBOT intolerance and need for tympanotomy tube placement. Risk factor, symptom, physical examination and HBOT complication-susceptibility scores were calculated for each patient. RESULTS: Mean risk factor, clinical and HBOT complication-susceptibility scores were significantly higher in patients who did not tolerate HBOT compared to patients who tolerated HBOT. Patients reporting a history of otitis media, tinnitus, and prior ear surgery were at a higher risk for HBOT intolerance. Patients reporting a history of pressure intolerance and prior ear surgery were more likely to undergo tympanotomy tube placement. Patients noted to have otologic findings prior to HBOT were at a higher risk for both HBOT intolerance and tympanotomy tube placement. CONCLUSIONS: A thorough otolaryngological evaluation can potentially predict and identify patients at risk for HBOT intolerance and tympanotomy tube placement.

Segmentation and classification of colon glands with deep convolutional neural networks and total variation regularization.

Segmentation of histopathology sections is a necessary preprocessing step for digital pathology. Due to the large variability of biological tissue, machine learning techniques have shown superior performance over conventional image processing methods. Here we present our deep neural network-based approach for segmentation and classification of glands in tissue of benign and malignant colorectal cancer, which was developed to participate in the GlaS@MICCAI2015 colon gland segmentation challenge. We use two distinct deep convolutional neural networks (CNN) for pixel-wise classification of Hematoxylin-Eosin stained images. While the first classifier separates glands from background, the second classifier identifies gland-separating structures. In a subsequent step, a figure-ground segmentation based on weighted total variation produces the final segmentation result by regularizing the CNN predictions. We present both quantitative and qualitative segmentation results on the recently released and publicly available Warwick-QU colon adenocarcinoma dataset associated with the GlaS@MICCAI2015 challenge and compare our approach to the simultaneously developed other approaches that participated in the same challenge. On two test sets, we demonstrate our segmentation performance and show that we achieve a tissue classification accuracy of 98% and 95%, making use of the inherent capability of our system to distinguish between benign and malignant tissue. Our results show that deep learning approaches can yield highly accurate and reproducible results for biomedical image analysis, with the potential to significantly improve the quality and speed of medical diagnoses.

Gland segmentation in colon histology images: The glas challenge contest.

Colorectal adenocarcinoma originating in intestinal glandular structures is the most common form of colon cancer. In clinical practice, the morphology of intestinal glands, including architectural appearance and glandular formation, is used by pathologists to inform prognosis and plan the treatment of individual patients. However, achieving good inter-observer as well as intra-observer reproducibility of cancer grading is still a major challenge in modern pathology. An automated approach which quantifies the morphology of glands is a solution to the problem. This paper provides an overview to the Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at MICCAI'2015. Details of the challenge, including organization, dataset and evaluation criteria, are presented, along with the method descriptions and evaluation results from the top performing methods.

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs) can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications.

Training Deep Spiking Neural Networks Using Backpropagation.

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.