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TrackPad is an open-source, biologist-friendly, graphical user interface for tracking and annotating single-cell lineage fates from time-lapse movies. The software applies cross-correlative, template-matching (CCTM) to track each cell using transmitted or fluorescent images. Machine tracking is supervised by the investigator, who can manually annotate cell fates such as division, death, or changes in cell phenotype. Search parameters that minimise user interventions and tracking time are selected by simulating tracking using verified tracking data as a ground truth. Investigators who use this software can produce high-quality annotated cell trajectories and division pedigrees to understand how intrinsic and extrinsic factors influence cell states and fates.

Prostate imaging is indispensable for the diagnosis and management of prostatic carcinoma and other prostate lesions. Understanding imaging anatomy is the basis for accurate diagnosis and standardized reporting of the prostatic lesions. In this article, we will discuss about general anatomy of the prostate, zonal anatomy, imaging techniques for its assessment, modality specific appearance of different zones of the prostate in detail.

Loss of function of members of the muscleblind-like (MBNL) family of RNA binding proteins has been shown to play a key role in the spliceopathy of RNA toxicity in myotonic dystrophy type 1 (DM1), the most common muscular dystrophy affecting adults and children. MBNL1 and MBNL2 are the most abundantly expressed members in skeletal muscle. A key aspect of DM1 is poor muscle regeneration and repair, leading to dystrophy. We used a BaCl2-induced damage model of muscle injury to study regeneration and effects on skeletal muscle satellite cells (MuSCs) in Mbnl1∆E3/∆E3 and Mbnl2∆E2/∆E2 knockout mice. Similar experiments have previously shown deleterious effects on these parameters in mouse models of RNA toxicity. Muscle regeneration in Mbnl1 and Mbnl2 knockout mice progressed normally with no obvious deleterious effects on MuSC numbers or increased expression of markers of fibrosis. Skeletal muscles in Mbnl1∆E3/∆E3/ Mbnl2∆E2/+ mice showed increased histopathology but no deleterious reductions in MuSC numbers and only a slight increase in collagen deposition. These results suggest that factors beyond the loss of MBNL1/MBNL2 and the associated spliceopathy are likely to play a key role in the defects in skeletal muscle regeneration and deleterious effects on MuSCs that are seen in mouse models of RNA toxicity due to expanded CUG repeats.

Strongly coupled nonlinear phenomena such as those described by Earth system models (ESMs) are composed of multiple component models with independent mesh topologies and scalable numerical solvers. A common operation in ESMs is to remap or interpolate component solution fields defined on their computational mesh to another mesh with a different combinatorial structure and decomposition, e.g., from the atmosphere to the ocean, during the temporal integration of the coupled system. Several remapping schemes are currently in use or available for ESMs. However, a unified approach to compare the properties of these different schemes has not been attempted previously. We present a rigorous methodology for the evaluation and intercomparison of remapping methods through an independently implemented suite of metrics that measure the ability of a method to adhere to constraints such as grid independence, monotonicity, global conservation, and local extrema or feature preservation. A comprehensive set of numerical evaluations is conducted based on a progression of scalar fields from idealized and smooth to more general climate data with strong discontinuities and strict bounds. We examine four remapping algorithms with distinct design approaches, namely ESMF Regrid (Hill et al., 2004), TempestRemap (Ullrich and Taylor, 2015), generalized moving least squares (GMLS) (Trask and Kuberry, 2020) with post-processing filters, and WLS-ENOR (Li et al., 2020). By repeated iterative application of the high-order remapping methods to the test fields, we verify the accuracy of each scheme in terms of their observed convergence order for smooth data and determine the bounded error propagation using challenging, realistic field data on both uniform and regionally refined mesh cases. In addition to retaining high-order accuracy under idealized conditions, the methods also demonstrate robust remapping performance when dealing with non-smooth data. There is a failure to maintain monotonicity in the traditional L2-minimization approaches used in ESMF and TempestRemap, in contrast to stable recovery through nonlinear filters used in both meshless GMLS and hybrid mesh-based WLS-ENOR schemes. Local feature preservation analysis indicates that high-order methods perform better than low-order dissipative schemes for all test cases. The behavior of these remappers remains consistent when applied on regionally refined meshes, indicating mesh-invariant implementations. The MIRA intercomparison protocol proposed in this paper and the detailed comparison of the four algorithms demonstrate that the new schemes, namely GMLS and WLS-ENOR, are competitive compared to standard conservative minimization methods requiring computation of mesh intersections. The work presented in this paper provides a foundation that can be extended to include complex field definitions, realistic mesh topologies, and spectral element discretizations, thereby allowing for a more complete analysis of production-ready remapping packages.

Severe mitral valve regurgitation (MR) is a heart valve disease that progresses to end-stage congestive heart failure and death if left untreated. Surgical repair or replacement of the mitral valve (MV) remains the gold standard for treatment of severe MR, with repair techniques aiming to restore the native geometry of the MV. However, patients with extensive co-morbidities may be ineligible for surgical intervention. With the emergence of transcatheter MV repair (TMVR) treatment paradigms for MR will evolve. The longer-term outcomes of TMVR and its effectiveness compared to surgical repair remain unknown given the differing patient eligibility for either treatment at this time. Advances in computational modeling will elucidate answers to these questions, employing techniques such as finite element method and fluid structure interactions. Use of clinical imaging will permit patient-specific MV models to be created with high accuracy and replicate MV pathophysiology. It is anticipated that TMVR technology will gradually expand to treat lower-risk patient groups, thus pre-procedural computational modeling will play a crucial role guiding clinicians towards the optimal intervention. Additionally, concerted efforts to create MV models will establish atlases of pathologies and biomechanics profiles which could delineate which patient populations would best benefit from specific surgical vs. TMVR options. In this review, we describe recent literature on MV computational modeling, its relevance to MV repair techniques, and future directions for translational application of computational modeling for treatment of MR.

Background With improving survival of patients with single ventricle physiology who underwent Fontan palliation, there is also an increase in the prevalence of overweight and obesity in these patients. This tertiary care single‐center study aims to determine the association of body mass index (BMI) with the clinical characteristics and outcomes in adults with Fontan. Methods and Results Adult patients (aged ≥18 years) with Fontan who were managed at a single tertiary care center between January 1, 2000, and July 1, 2019, and had BMI data available were identified via retrospective review of medical records. Univariate and multivariable (after adjusting for age, sex, functional class, and type of Fontan) linear and logistic regression, as appropriate, were utilized to evaluate associations between BMI and diagnostic testing and clinical outcomes. A total of 163 adult patients with Fontan were included (mean age, 29.9±9.08 years), with a mean BMI of 24.2±5.21 kg/m2 (37.4% of patients had BMI ≥25 kg/m2). Echocardiography data were available for 95.7% of patients, exercise testing for 39.3% of patients, and catheterization for 53.7% of patients. Each SD increase in BMI was significantly associated with decreased peak oxygen consumption (P=0.010) on univariate analysis and with increased Fontan pressure (P=0.035) and pulmonary capillary wedge pressure (P=0.037) on multivariable analysis. In addition, BMI ≥25 kg/m2 was independently associated with heart failure hospitalization (adjusted odds ratio [AOR], 10.2; 95% CI, 2.79–37.1 [P

Introduction: Severe mitral regurgitation (MR) is a mitral valve disease that can lead to lifethreatening complications. MitraClip (MC) therapy is a percutaneous solution for patients who cannot tolerate surgical solutions. In MC therapy, a clip is implanted in the heart to reduce MR. To achieve optimal MC therapy, the cardiologist needs to foresee the outcomes of different scenarios for MC implantation, including the location of the MC. Although finite element (FE) modeling can simulate the outcomes of different MC scenarios, it is not suitable for clinical usage because it requires several hours to complete.Methods: In this paper, we used machine learning (ML) to predict the outcomes of MC therapy in less than 1 s. Two ML algorithms were used: XGBoost, which is a decision tree model, and a feed-forward deep learning (DL) model. The MC location, the geometrical attributes of the models and baseline stress and MR were the features of the ML models, and the predictions were performed for MR and maximum von Mises stress in the leaflets. The parameters of the ML models were determined to achieve the minimum errors obtained by applying the ML models on the validation set.Results: The results for the test set (not used during training) showed relative agreement between ML predictions and ground truth FE predictions. The accuracy of the XGBoost models were better than DL models. Mean absolute percentage error (MAPE) for the XGBoost predictions were 0.115 and 0.231, and the MAPE for DL predictions were 0.154 and 0.310, for MR and stress, respectively.Discussion: The ML models reduced the FE runtime from 6 hours (on average) to less than 1 s. The accuracy of ML models can be increased by increasing the dataset size. The results of this study have important implications for improving the outcomes of MC therapy by providing information about the outcomes of MC implantation in real-time.

Patients with Fontan circulation are now surviving longer into adulthood and experience a multitude of extracardiac, non-hepatic sequelae that impact their quality of life and prognosis. These sequelae include pulmonary complications (pulmonary vascular disease, sleep-disordered breathing, restrictive lung disease, plastic bronchitis), gastrointestinal manifestations (protein losing enteropathy), chronic venous insufficiency, kidney abnormalities, reproductive issues (pregnancy, contraception and sexual dysfunction), neurocognitive changes, endocrinopathies (glycemic abnormalities, thyroid disorders) and hematological abnormalities (thromboembolic complications and secondary erythrocytosis). This review article aims to present a contemporary overview of the extracardiac, non-hepatic sequelae observed in adults with Fontan circulation.

This study aimed to create an imaging-derived patient-specific computational model of low-flow, low-gradient (LFLG) aortic stenosis (AS) to obtain biomechanics data about the left ventricle. LFLG AS is now a commonly recognized sub-type of aortic stenosis. There remains much controversy over its management, and investigation into ventricular biomechanics may elucidate pathophysiology and better identify patients for valve replacement. ECG-gated cardiac computed tomography images from a patient with LFLG AS were obtained to provide patient-specific geometry for the computational model. Surfaces of the left atrium, left ventricle (LV), and outflow track were segmented. A previously validated multi-scale, multi-physics computational human heart model was adapted to the patient-specific geometry, yielding a model consisting of 91,000 solid elements. This model was coupled to a virtual circulatory system and calibrated to clinically measured parameters from echocardiography and cardiac catheterization data. The simulation replicated key physiologic parameters within 10% of their clinically measured values. Global LV systolic myocardial stress was 7.1 ± 1.8 kPa. Mean stress of the basal, middle, and apical segments were 7.7 ± 1.8 kPa, 9.1 ± 3.8 kPa, and 6.4 ± 0.4 kPa, respectively. This is the first patient-specific computational model of LFLG AS based on clinical imaging. Low myocardial stress correlated with low ejection fraction and eccentric LV remodeling. Further studies are needed to understand how alterations in LV biomechanics correlates with clinical outcomes of AS.

We present this case of a 22-year-old woman with congenital heart disease as the youngest reported patient, to the best of our knowledge, to successfully undergo treatment of medically refractory torrential tricuspid regurgitation with the MitraClip system as an adjunct to guideline directed therapy. (Level of Difficulty: Advanced.)

Abstract The ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has profoundly impacted all fields of medicine. Infection with SARS-CoV-2 and the resulting coronavirus of 2019 (COVID-19) syndrome has multiorgan effects. The pandemic has united researchers from bench to bedside in attempts to understand the pathophysiology of the disease and define optimal treatment strategies. Cardiovascular disease is highly prevalent and a leading cause of death across gender, race, and ethnic groups. As the pandemic spreads, there is increasing concern about the cardiovascular effects of the viral infection and the interaction of infection with existing cardiovascular disease. Additionally, there are concerns about the cardiac effects of the numerous treatment agents under study. It will be essential for cardiologists to understand the interplay between underlying cardiac comorbidities, acute cardiovascular effects of COVID-19 disease, and adverse effects of new treatments. Here we describe emerging evidence of the epidemiology of SARS-CoV-2 infection and underlying cardiovascular disease, the evidence for direct myocardial injury in SARS-CoV-2 infection, the specific presentations of cardiovascular involvement by SARS-CoV-2, and the cardiac effects of emerging treatments.

One of the fundamental factors contributing to the spatiotemporal inaccuracy in climate modeling is the mapping of solution field data between different discretizations and numerical grids used in the coupled component models. The typical climate computational workflow involves evaluation and serialization of the remapping weights during the preprocessing step, which is then consumed by the coupled driver infrastructure during simulation to compute field projections. Tools like Earth System Modeling Framework (ESMF) (Hill et al., 2004) and TempestRemap (Ullrich et al., 2013) offer capability to generate conservative remapping weights, while the Model Coupling Toolkit (MCT) (Larson et al., 2001) that is utilized in many production climate models exposes functionality to make use of the operators to solve the coupled problem. However, such multistep processes present several hurdles in terms of the scientific workflow and impede research productivity. In order to overcome these limitations, we present a fully integrated infrastructure based on the Mesh Oriented datABase (MOAB) (Tautges et al., 2004; Mahadevan et al., 2015) library, which allows for a complete description of the numerical grids and solution data used in each submodel. Through a scalable advancing-front intersection algorithm, the supermesh of the source and target grids are computed, which is then used to assemble the high-order, conservative, and monotonicity-preserving remapping weights between discretization specifications. The Fortran-compatible interfaces in MOAB are utilized to directly link the submodels in the Energy Exascale Earth System Model (E3SM) to enable online remapping strategies in order to simplify the coupled workflow process. We demonstrate the superior computational efficiency of the remapping algorithms in comparison with other state-of-the-science tools and present strong scaling results on large-scale machines for computing remapping weights between the spectral element atmosphere and finite volume discretizations on the polygonal ocean grids.

Functional connectivity (FC) networks are typically inferred from resting-state fMRI data using the Pearson correlation between BOLD time series from pairs of brain regions. However, alternative methods of estimating functional connectivity have not been systematically tested for their sensitivity or robustness to head motion artifact. Here, we evaluate the sensitivity of eight different functional connectivity measures to motion artifact using resting-state data from the Human Connectome Project. We report that FC estimated using full correlation has a relatively high residual distance-dependent relationship with motion compared to partial correlation, coherence, and information theory-based measures, even after implementing rigorous methods for motion artifact mitigation. This disadvantage of full correlation, however, may be offset by higher test-retest reliability, fingerprinting accuracy, and system identifiability. FC estimated by partial correlation offers the best of both worlds, with low sensitivity to motion artifact and intermediate system identifiability, with the caveat of low test-retest reliability and fingerprinting accuracy. We highlight spatial differences in the sub-networks affected by motion with different FC metrics. Further, we report that intra-network edges in the default mode and retrosplenial temporal sub-networks are highly correlated with motion in all FC methods. Our findings indicate that the method of estimating functional connectivity is an important consideration in resting-state fMRI studies and must be chosen carefully based on the parameters of the study.

Structured Abstract: Background: While percutaneous closure is preferred in adults with a patent ductus arteriosus (PDA), the extent of cardiac remodeling following percutaneous PDA closure in adults is not well understood. We performed a meta-analysis to characterize cardiac remodeling following percutaneous PDA closure in adults. Methods: MEDLINE and EMBASE were systematically searched for original studies that reported echocardiographic variables at baseline, immediately post-procedure (24 h), and at follow-up (>1 month) in adults undergoing percutaneous PDA closure. Additionally, we included data from patients >18 years of age that underwent percutaneous PDA closure between January 2015–December 2019 at our centre. Weighted averages were calculated for parameters with sufficient data. Pooled differences were presented as weighted mean differences (WMD). Results: After screening 278 abstracts, 5 studies were identified. When combined with our cohort, our meta-analysis encompassed 244 patients. Immediately post-procedure, LVEF was the only parameter to change significantly (64 vs 52%, WMD -8.64 95%, CI -14.04 to −3.25). All parameters significantly decreased from baseline to follow-up: LVEF: 64 vs 60% (WMD -3.95, CI -5.80 to −2.09), LVEDD: 62 vs 53 mm (WMD -8.79, CI -12.78 to −4.81) LVEDVI 172 vs 123 mL/m2 (WMD -26.60, CI -45.4 to −7.85), LVESD 42 vs 36 mm (WMD -4.64, CI -9.10 to −0.17) and LA dimension: 42 vs 37 mm (WMD -5.87, CI -9.85 to −1.90). Conclusions: As compared to the baseline values, left ventricular and left atrial sizes decreased. The LVEF decreased immediately following PDA closure and, while it recovers, it remained lower on follow-up testing.

Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods and Results Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the appropriate anatomic auscultation location detected moderate‐to‐severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate‐to‐severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%. Conclusions The deep learning algorithm’s ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front‐line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease. Registration URL: https://clinicaltrials.gov; Unique Identifier: NCT03458806.

Background As patients with congenital heart disease (CHD) are living longer, understanding the comorbidities they develop as they age is increasingly important. However, there are no published population‐based estimates of the comorbidity burden among the US adult patients with CHD. Methods and Results Using the IBM MarketScan commercial claims database from 2010 to 2016, we identified adults aged ≥18 years with CHD and 2 full years of continuous enrollment. These were frequency matched with adults without CHD within categories jointly defined by age, sex, and dates of enrollment in the database. A total of 40 127 patients with CHD met the inclusion criteria (mean [SD] age, 36.8 [14.6] years; and 48.2% were women). Adults with CHD were nearly twice as likely to have any comorbidity than those without CHD (P

Background: India currently is posed by the double threat of thinness and overweight/obesity among children. Different growth charts have taken different population and give different cut-off points to assess these conditions. Objective: The objective of this study is to assess the anthropometry of school children, 5-18 years of age and thereby estimate the prevalence of childhood thinness, overweight and obesity. To analyze how the study population compares with that of Agarwal′s growth chart. Materials and Methods: The anthropometric measurements of all the students who were studying from 1 st to 12 th standards were taken from 27 randomly selected Government and private schools. Prevalence of thinness, overweight and obesity were assessed using two standards - Indian standard given by Agarwal and International Standards given by International Obesity Task Force (IOTF). Results: The prevalence of thinness, overweight and obesity among 18,001 students enrolled as per Indian standard were 12.2%, 9.5% and 3% and as per International standard were 15.3%, 8.1% and 2.6% respectively. The mean and the 95 th percentile values of body mass index for both boys and girls at all ages in this study are falling short of Agarwal′s and IOTF values. Using international cut-offs as well as Indian cut-offs given by Agarwal, underestimate the prevalence of obesity among boys and girls of all age groups. Conclusion: This study shows that under and over-nutrition among school children is in almost equal proportions. There is an underestimation of obesity among children whenever an Indian or an International growth chart is used. Thus, this study brings out the need for a really representative growth chart.