Stable, covalent attachment of laminin to microposts improves the contractility of mouse neonatal cardiomyocytes.

The mechanical output of contracting cardiomyocytes, the muscle cells of the heart, relates to healthy and disease states of the heart. Culturing cardiomyocytes on arrays of elastomeric microposts can enable inexpensive and high-throughput studies of heart disease at the single-cell level. However, cardiomyocytes weakly adhere to these microposts, which limits the possibility of using biomechanical assays of single cardiomyocytes to study heart disease. We hypothesized that a stable covalent attachment of laminin to the surface of microposts improves cardiomyocyte contractility. We cultured cells on polydimethylsiloxane microposts with laminin covalently bonded with the organosilanes 3-glycidoxypropyltrimethoxysilane and 3-aminopropyltriethoxysilane with glutaraldehyde. We measured displacement of microposts induced by the contractility of mouse neonatal cardiomyocytes, which attach better than mature cardiomyocytes to substrates. We observed time-dependent changes in contractile parameters such as micropost deformation, contractility rates, contraction and relaxation speeds, and the times of contractions. These parameters were affected by the density of laminin on microposts and by the stability of laminin binding to micropost surfaces. Organosilane-mediated binding resulted in higher laminin surface density and laminin binding stability. 3-glycidoxypropyltrimethoxysilane provided the highest laminin density but did not provide stable protein binding with time. Higher surface protein binding stability and strength were observed with 3-aminopropyltriethoxysilane with glutaraldehyde. In cultured cardiomyocytes, contractility rate, contraction speeds, and contraction time increased with higher laminin stability. Given these variations in contractile function, we conclude that binding of laminin to microposts via 3-aminopropyltriethoxysilane with glutaraldehyde improves contractility observed by an increase in beating rate and contraction speed as it occurs during the postnatal maturation of cardiomyocytes. This approach is promising for future studies to mimic in vivo tissue environments

Experiences in deploying metadata analysis tools for institutional repositories

Current institutional repository software provides few tools to help metadata librarians understand and analyze their collections. In this article, we compare and contrast metadata analysis tools that were developed simultaneously, but independently, at two New Zealand institutions during a period of national investment in research repositories: the Metadata Analysis Tool (MAT) at The University of Waikato, and the Kiwi Research Information Service (KRIS) at the National Library of New Zealand. The tools have many similarities: they are convenient, online, on-demand services that harvest metadata using OAI-PMH; they were developed in response to feedback from repository administrators; and they both help pinpoint specific metadata errors as well as generating summary statistics. They also have significant differences: one is a dedicated tool wheres the other is part of a wider access tool; one gives a holistic view of the metadata whereas the other looks for specific problems; one seeks patterns in the data values whereas the other checks that those values conform to metadata standards. Both tools work in a complementary manner to existing Web-based administration tools. We have observed that discovery and correction of metadata errors can be quickly achieved by switching Web browser views from the analysis tool to the repository interface, and back. We summarize the findings from both tools' deployment into a checklist of requirements for metadata analysis tools

Value of Strain Imaging and Maximal Oxygen Consumption in Patients With Hypertrophic Cardiomyopathy

Longitudinal strain (LS) has been shown to be predictive of outcome in hypertrophic cardiomyopathy (HC). Percent predicted peak oxygen uptake (ppVO2), among other cardiopulmonary exercise testing (CPX) metrics, is a strong predictor of prognosis in HC. However, there has been limited investigation into the combination of LS and CPX metrics. This study sought to determine how LS and parameters of exercise performance contribute to prognosis in HC. One hundred and thirty-one consecutive patients with HC who underwent CPX and stress echocardiography were included. Global, septal, and lateral LS were assessed at rest and stress. Eighty matched individuals were used as controls. Patients were followed for the composite end point of death and worsening heart failure. All absolute LS components were lower in patients with HC than in controls (global 14.3\u2009\ub1\u20094.0% vs 18.8\u2009\ub1\u20092.2%, p 52\u2009ml/m2, and ppVO2 <80%. The combination of lateral LS, LAVI, and ppVO2 presents a simple model for outcome prediction

Angiotensin-Converting Enzyme Genotype Predicts Cardiac and Autonomic Responses to Prolonged Exercise

ObjectivesThe purpose of this study was to investigate the phenomenon of left ventricular (LV) dysfunction after ultraendurance exercise.BackgroundSubclinical LV dysfunction in response to endurance exercise up to 24 h duration has been described, but its mechanism remains elusive.MethodsWe tested 86 athletes before and after the Adrenalin Rush Adventure Race using echocardiography, impedance cardiography, and plasma immunoassay.ResultsAt baseline, athletes demonstrated physiology characteristic of extreme endurance training. After 90 to 120 h of almost-continuous exercise, LV systolic and diastolic function declined (fractional shortening before the race, 39.6 ± 0.65%; after, 32.2 ± 0.84%, p < 0.001; mitral inflow E-wave deceleration time before the race, 133 ± 5 ms; after, 160 ± 5 ms, n = 48, p < 0.001) without change in loading conditions as defined by LV end-diastolic dimension and total peripheral resistance estimated by thoracic impedance. There was a compensatory increase in heart rate (before, 55 ± 1.3 beats/min; after, 59 ± 1.5 beats/min, p = 0.05), which left cardiac output unchanged, as well as significant-but-subclinical increases in brain natriuretic peptide and troponin I. In addition, we found that athletes who were homozygous for the intron-16 insertion polymorphism of the angiotensin-converting enzyme (ACE) gene exhibited a significantly greater decrease in fractional shortening than athletes who were homozygous for the deletion allele. Heterozygotes showed an intermediate phenotype. In addition, the deletion group manifest an enhanced sympathovagal balance after the race, as evidenced by greater power in the low-frequency component of blood pressure variability.ConclusionsThe ACE genotype predicts the extent of reversible subclinical LV dysfunction after prolonged exercise and is associated with a differential postactivity augmentation of sympathetic nervous system function that may explain it

Development and validation of a rapid visual technique for left ventricular hypertrophy detection from the electrocardiogram

IntroductionLeft ventricular hypertrophy (LVH) detection techniques on by electrocardiogram (ECG) are cumbersome to remember with modest performance. This study validated a rapid technique for LVH detection and measured its performance against other techniques.MethodsThis was a retrospective cohort study of patients at Stanford Health Care who received ECGs and resting transthoracic echocardiograms (TTE) from 2006 through 2018. The novel technique, Witteles-Somani (WS), assesses for S- and R-wave overlap on adjacent precordial leads. The WS, Sokolow-Lyon, Cornell, and Peguero-Lo Presti techniques were algorithmically implemented on ECGs. Classification metrics, receiver-operator curves, and Pearson correlations measured performance. Age- and sex-adjusted Cox proportional hazard models evaluated associations between incident cardiovascular outcomes and each technique.ResultsA total of 53,333 ECG-TTE pairs from 18,873 patients were identified. Of all ECG-TTE pairs, 21,638 (40.6%) had TTE-diagnosed LVH. The WS technique had a sensitivity of 0.46, specificity of 0.66, and AUROC of 0.56, compared to Sokolow-Lyon (AUROC 0.55), Cornell (AUROC 0.63), and Peguero-Lo Presti (AUROC 0.63). Patients meeting LVH by WS technique had a higher risk of cardiovascular mortality [HR 1.18, 95% CI (1.12, 1.24), P &lt; 0.001] and a higher risk of developing any cardiovascular disease [HR 1.29, 95% CI (1.22, 1.36), P &lt; 0.001], myocardial infarction [HR 1.60, 95% CI (1.44, 1.78), P &lt; 0.005], and heart failure [HR 1.24, 95% CI (1.17, 1.32), P &lt; 0.001].ConclusionsThe WS criteria is a rapid visual technique for LVH detection with performance like other LVH detection techniques and is associated with incident cardiovascular outcomes

A Generalizable Deep Learning System for Cardiac MRI

Cardiac MRI allows for a comprehensive assessment of myocardial structure, function, and tissue characteristics. Here we describe a foundational vision system for cardiac MRI, capable of representing the breadth of human cardiovascular disease and health. Our deep learning model is trained via self-supervised contrastive learning, by which visual concepts in cine-sequence cardiac MRI scans are learned from the raw text of the accompanying radiology reports. We train and evaluate our model on data from four large academic clinical institutions in the United States. We additionally showcase the performance of our models on the UK BioBank, and two additional publicly available external datasets. We explore emergent zero-shot capabilities of our system, and demonstrate remarkable performance across a range of tasks; including the problem of left ventricular ejection fraction regression, and the diagnosis of 35 different conditions such as cardiac amyloidosis and hypertrophic cardiomyopathy. We show that our deep learning system is capable of not only understanding the staggering complexity of human cardiovascular disease, but can be directed towards clinical problems of interest yielding impressive, clinical grade diagnostic accuracy with a fraction of the training data typically required for such tasks.Comment: 21 page main manuscript, 4 figures. Supplementary Appendix and code will be made available on publicatio

Identification of rare-disease genes using blood transcriptome sequencing and large control cohorts.

It is estimated that 350 million individuals worldwide suffer from rare diseases, which are predominantly caused by mutation in a single gene1. The current molecular diagnostic rate is estimated at 50%, with whole-exome sequencing (WES) among the most successful approaches2-5. For patients in whom WES is uninformative, RNA sequencing (RNA-seq) has shown diagnostic utility in specific tissues and diseases6-8. This includes muscle biopsies from patients with undiagnosed rare muscle disorders6,9, and cultured fibroblasts from patients with mitochondrial disorders7. However, for many individuals, biopsies are not performed for clinical care, and tissues are difficult to access. We sought to assess the utility of RNA-seq from blood as a diagnostic tool for rare diseases of different pathophysiologies. We generated whole-blood RNA-seq from 94 individuals with undiagnosed rare diseases spanning 16 diverse disease categories. We developed a robust approach to compare data from these individuals with large sets of RNA-seq data for controls (n = 1,594 unrelated controls and n = 49 family members) and demonstrated the impacts of expression, splicing, gene and variant filtering strategies on disease gene identification. Across our cohort, we observed that RNA-seq yields a 7.5% diagnostic rate, and an additional 16.7% with improved candidate gene resolution

Pathologic gene network rewiring implicates PPP1R3A as a central regulator in pressure overload heart failure

Heart failure is a leading cause of mortality, yet our understanding of the genetic interactions underlying this disease remains incomplete. Here, we harvest 1352 healthy and failing human hearts directly from transplant center operating rooms, and obtain genome-wide genotyping and gene expression measurements for a subset of 313. We build failing and non-failing cardiac regulatory gene networks, revealing important regulators and cardiac expression quantitative trait loci (eQTLs). PPP1R3A emerges as a regulator whose network connectivity changes significantly between health and disease. RNA sequencing after PPP1R3A knockdown validates network-based predictions, and highlights metabolic pathway regulation associated with increased cardiomyocyte size and perturbed respiratory metabolism. Mice lacking PPP1R3A are protected against pressure-overload heart failure. We present a global gene interaction map of the human heart failure transition, identify previously unreported cardiac eQTLs, and demonstrate the discovery potential of disease-specific networks through the description of PPP1R3A as a central regulator in heart failure