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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.

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.

Computed tomography (CT) has undergone a phenomenal evolution since its introduction in 1971 and has revolutionized diagnostic radiology. It is now the cornerstone of diagnostic imaging and has become an inevitable part of the management of patients. Among all the advancements and breakthroughs witnessed over the years, the most recent and most advanced is the dual-energy CT (DECT), also known as spectral CT, introduced in 2006. In DECT, two datasets are obtained by scanning with two different energy spectra (low and high energy). The difference in attenuation can differentiate materials with different elemental compositions but similar attenuation in single-energy CT. Therefore, it has widespread clinical applications based on its potential for material decomposition and virtual monoenergetic imaging. In this review, the principle and hardware of DECT will be presented with an overview of its clinical applications.

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.

We introduce cytoNet, a cloud-based tool to characterize cell populations from microscopy images. cytoNet quantifies spatial topology and functional relationships in cell communities using principles of network science. Capturing multicellular dynamics through graph features, cytoNet also evaluates the effect of cell-cell interactions on individual cell phenotypes. We demonstrate cytoNet's capabilities in four case studies: 1) characterizing the temporal dynamics of neural progenitor cell communities during neural differentiation, 2) identifying communities of pain-sensing neurons in vivo, 3) capturing the effect of cell community on endothelial cell morphology, and 4) investigating the effect of laminin α4 on perivascular niches in adipose tissue. The analytical framework introduced here can be used to study the dynamics of complex cell communities in a quantitative manner, leading to a deeper understanding of environmental effects on cellular behavior. The versatile, cloud-based format of cytoNet makes the image analysis framework accessible to researchers across domains.

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.

Severe mitral regurgitation (MR) is a cardiac disease that can lead to fatal consequences. MitraClip (MC) intervention is a percutaneous procedure whereby the mitral valve (MV) leaflets are connected along the edge using MCs. The outcomes of the MC intervention are not known in advance, i.e., the outcomes are quite variable. Artificial intelligence (AI) can be used to guide the cardiologist in selecting optimal MC scenarios. In this study, we describe an atlas of shapes as well as different scenarios for MC implantation for such an AI analysis. We generated the MV geometrical data from three different sources. First, the patients' 3-dimensional echo images were used. The pixel data from six key points were obtained from three views of the echo images. Using PyGem, an open-source morphing library in Python, these coordinates were used to create the geometry by morphing a template geometry. Second, the dimensions of the MV, from the literature were used to create data. Third, we used machine learning methods, principal component analysis, and generative adversarial networks to generate more shapes. We used the finite element (FE) software ABAQUS to simulate smoothed particle hydrodynamics in different scenarios for MC intervention. The MR and stresses in the leaflets were post-processed. Our physics-based FE models simulated the outcomes of MC intervention for different scenarios. The MR and stresses in the leaflets were computed by the FE models for a single clip at different locations as well as two and three clips. Results from FE simulations showed that the location and number of MCs affect subsequent residual MR, and that leaflet stresses do not follow a simple pattern. Furthermore, FE models need several hours to provide the results, and they are not applicable for clinical usage where the predicted outcomes of MC therapy are needed in real-time. In this study, we generated the required dataset for the AI models which can provide the results in a matter of seconds.

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.

Objective: To evaluate the predictive value of Computed Tomography Angiography (CTA) measurements of the RVOT for transcatheter valve sizing. Background: Transcatheter pulmonary valve replacement (TPVR) provides an alternative to surgery in patients with right ventricular outflow tract (RVOT) dysfunction. We studied 18 patients who underwent catheterization for potential TPVR to determine whether CT imaging can be used to accurately predict implant size. Methods: Cases were grouped by RVOT characteristics: native or transannular patch (n = 8), conduit (n = 5) or bioprosthetic valve (n = 5). TPVR was undertaken in 14/18 cases, after balloon-sizing was used to confirm suitability and select implant size. Retrospective CT measurements of the RVOT (circumference-derived (Dcirc) and area-derived (Darea) diameters) were obtained at the level of the annulus, bioprosthesis or conduit. Using manufacturer sizing guidance, a valve size was generated and a predicted valve category assigned: (1) 29 mm. Predicted and implanted valves were compared for inter-rater agreement using Cohen’s kappa coefficient. Results: The median age of patients was 37 years old (IQR: 30–49); 55% were male. Diagnoses included: Tetralogy of Fallot (12/18), d-Transposition repair (3/18), congenital pulmonary stenosis (2/18) and carcinoid heart disease (1/18). Measurements of Darea (κ = 0.697, p

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.

Myotonic dystrophy type 1 (DM1), the most common muscular dystrophy affecting adults and children, is a multi-systemic disorder affecting skeletal, cardiac, and smooth muscles as well as neurologic, endocrine and other systems. This review is on the cardiac pathology associated with DM1. The heart is one of the primary organs affected in DM1. Cardiac conduction defects are seen in up to 75% of adult DM1 cases and sudden death due to cardiac arrhythmias is one of the most common causes of death in DM1. Unfortunately, the pathogenesis of cardiac manifestations in DM1 is ill defined. In this review, we provide an overview of the history of cardiac studies in DM1, clinical manifestations, and pathology of the heart in DM1. This is followed by a discussion of emerging data about the utility of cardiac magnetic resonance imaging (CMR) as a biomarker for cardiac disease in DM1, and ends with a discussion on models of cardiac RNA toxicity in DM1 and recent clinical guidelines for cardiologic management of individuals with DM1.

Left ventricle—right atrial communication could be congenital (Gerbode defect) or acquired as a complication of surgery or infective endocarditis and leads to volume overloading of pulmonary circulation. Two types, direct and indirect types are known depending on the involvement of septal tricuspid leaflet. Transcatheter closure of this defect is feasible and appears an attractive alternative to surgical management. Various devices like Amplatzer duct occluder I, II, Muscular ventricular septal defect device etc. have been used to close this defect. We report two patients, a preteen boy with direct left ventricle-right atrial communication as post operative complication and an adult female with indirect communication who underwent transcatheter closure with Cera duct occluder (Lifetech Scientific (Shenzhen), China).