Transitory activation of AMPK at reperfusion protects the ischaemic-reperfused rat myocardium against infarction

PURPOSE: AMPK plays a crucial role in the regulation of the energy metabolism of the heart. During ischaemia, AMPK activation is a known adaptative prosurvival mechanism that helps to maintain the energy levels of the myocardium. However, it still remains unclear if activation of AMPK during reperfusion is beneficial for the heart. Two known AMPK activators (metformin and AICAR) were used to verify the hypothesis that a transitory activation of AMPK at reperfusion may exert cardioprotection, as reflected in a reduction in myocardial infarct size. METHODS: Perfused rat hearts were subjected to 35 min ischaemia and 120 min reperfusion. Metformin (50 microM) or AICAR (0.5 mM) were added for 15 min at the onset of reperfusion alone or with Compound C (CC, 10 microM), an AMPK inhibitor. Infarct size and alpha-AMPK phosphorylation were measured. RESULTS: Metformin significantly reduced infarct size from 47.8 +/- 1.7% in control to 31.4 +/- 2.9%, an effect abolished by CC when the drugs were given concomitantly. Similarly, AICAR also induced a significant reduction in infarct size to 32.3 +/- 4.8%, an effect also abrogated by CC. However, metformin's protection was not abolished if CC was administered later in reperfusion. In addition, alpha-AMPK phosphorylation was significantly increased in the metformin treated group during the initial 30 min of reperfusion. CONCLUSIONS: Our data demonstrated that, in our ex vivo model of myocardial ischaemia-reperfusion injury, AMPK activation in early reperfusion is associated with a reduction in infarct size

Enhanced efficiency of genetic programming toward cardiomyocyte creation through topographical cues

AbstractGeneration of de novo cardiomyocytes through viral over-expression of key transcription factors represents a highly promising strategy for cardiac muscle tissue regeneration. Although the feasibility of cell reprogramming has been proven possible both in vitro and in vivo, the efficiency of the process remains extremely low. Here, we report a chemical-free technique in which topographical cues, more specifically parallel microgrooves, enhance the directed differentiation of cardiac progenitors into cardiomyocyte-like cells. Using a lentivirus-mediated direct reprogramming strategy for expression of Myocardin, Tbx5, and Mef2c, we showed that the microgrooved substrate provokes an increase in histone H3 acetylation (AcH3), known to be a permissive environment for reprogramming by “stemness” factors, as well as stimulation of myocardin sumoylation, a post-translational modification essential to the transcriptional function of this key co-activator. These biochemical effects mimicked those of a pharmacological histone deacetylase inhibitor, valproic acid (VPA), and like VPA markedly augmented the expression of cardiomyocyte-specific proteins by the genetically engineered cells. No instructive effect was seen in cells unresponsive to VPA. In addition, the anisotropy resulting from parallel microgrooves induced cellular alignment, mimicking the native ventricular myocardium and augmenting sarcomere organization

Prospective association between handgrip strength and cardiac structure and function in UK adults.

BACKGROUND: Handgrip strength, a measure of muscular fitness, is associated with cardiovascular (CV) events and CV mortality but its association with cardiac structure and function is unknown. The goal of this study was to determine if handgrip strength is associated with changes in cardiac structure and function in UK adults. METHODS AND RESULTS: Left ventricular (LV) ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV), stroke volume (SV), mass (M), and mass-to-volume ratio (MVR) were measured in a sample of 4,654 participants of the UK Biobank Study 6.3 ± 1 years after baseline using cardiovascular magnetic resonance (CMR). Handgrip strength was measured at baseline and at the imaging follow-up examination. We determined the association between handgrip strength at baseline as well as its change over time and each of the cardiac outcome parameters. After adjustment, higher level of handgrip strength at baseline was associated with higher LVEDV (difference per SD increase in handgrip strength: 1.3ml, 95% CI 0.1-2.4; p = 0.034), higher LVSV (1.0ml, 0.3-1.8; p = 0.006), lower LVM (-1.0g, -1.8 --0.3; p = 0.007), and lower LVMVR (-0.013g/ml, -0.018 --0.007; p<0.001). The association between handgrip strength and LVEDV and LVSV was strongest among younger individuals, while the association with LVM and LVMVR was strongest among older individuals. CONCLUSIONS: Better handgrip strength was associated with cardiac structure and function in a pattern indicative of less cardiac hypertrophy and remodeling. These characteristics are known to be associated with a lower risk of cardiovascular events

Quantitative CMR population imaging on 20,000 subjects of the UK Biobank imaging study: LV/RV quantification pipeline and its evaluation

Population imaging studies generate data for developing and implementing personalised health strategies to prevent, or more effectively treat disease. Large prospective epidemiological studies acquire imaging for pre-symptomatic populations. These studies enable the early discovery of alterations due to impending disease, and enable early identification of individuals at risk. Such studies pose new challenges requiring automatic image analysis. To date, few large-scale population-level cardiac imaging studies have been conducted. One such study stands out for its sheer size, careful implementation, and availability of top quality expert annotation; the UK Biobank (UKB). The resulting massive imaging datasets (targeting ca. 100,000 subjects) has put published approaches for cardiac image quantification to the test. In this paper, we present and evaluate a cardiac magnetic resonance (CMR) image analysis pipeline that properly scales up and can provide a fully automatic analysis of the UKB CMR study. Without manual user interactions, our pipeline performs end-to-end image analytics from multi-view cine CMR images all the way to anatomical and functional bi-ventricular quantification. All this, while maintaining relevant quality controls of the CMR input images, and resulting image segmentations. To the best of our knowledge, this is the first published attempt to fully automate the extraction of global and regional reference ranges of all key functional cardiovascular indexes, from both left and right cardiac ventricles, for a population of 20,000 subjects imaged at 50 time frames per subject, for a total of one million CMR volumes. In addition, our pipeline provides 3D anatomical bi-ventricular models of the heart. These models enable the extraction of detailed information of the morphodynamics of the two ventricles for subsequent association to genetic, omics, lifestyle habits, exposure information, and other information provided in population imaging studies. We validated our proposed CMR analytics pipeline against manual expert readings on a reference cohort of 4620 subjects with contour delineations and corresponding clinical indexes. Our results show broad significant agreement between the manually obtained reference indexes, and those automatically computed via our framework. 80.67% of subjects were processed with mean contour distance of less than 1 pixel, and 17.50% with mean contour distance between 1 and 2 pixels. Finally, we compare our pipeline with a recently published approach reporting on UKB data, and based on deep learning. Our comparison shows similar performance in terms of segmentation accuracy with respect to human experts

Disruption of TGF-β Signaling Improves Ocular Surface Epithelial Disease in Experimental Autoimmune Keratoconjunctivitis Sicca

TGF-β is a pleiotropic cytokine that can have pro- or anti-inflammatory effects depending on the context. Elevated levels of bioactive TGF-β1 in tears and elevated TGF-β1mRNA transcripts in conjunctiva and minor salivary glands of human Sjögren's Syndrome patients has also been reported. The purpose of this study was to evaluate the response to desiccating stress (DS), an experimental model of dry eye, in dominant-negative TGF-β type II receptor (CD4-DNTGFβRII) mice. These mice have a truncated TGF-β receptor in CD4(+) T cells, rendering them unresponsive to TGF-β.DS was induced by subcutaneous injection of scopolamine and exposure to a drafty low humidity environment in CD4-DNTGFβRII and wild-type (WT) mice, aged 14 weeks, for 5 days. Nonstressed (NS) mice served as controls. Parameters of ocular surface disease included corneal smoothness, corneal barrier function and conjunctival goblet cell density. NS CD4-DNTGFβRII at 14 weeks of age mice exhibited a spontaneous dry eye phenotype; however, DS improved their corneal barrier function and corneal surface irregularity, increased their number of PAS+ GC, and lowered CD4(+) T cell infiltration in conjunctiva. In contrast to WT, CD4-DNTGFβRII mice did not generate a Th-17 and Th-1 response, and they failed to upregulate MMP-9, IL-23, IL-17A, RORγT, IFN-γ and T-bet mRNA transcripts in conjunctiva. RAG1KO recipients of adoptively transferred CD4+T cells isolated from DS5 CD4-DNTGFβRII showed milder dry eye phenotype and less conjunctival inflammation than recipients of WT control.Our results showed that disruption of TGF-β signaling in CD4(+) T cells causes paradoxical improvement of dry eye disease in mice subjected to desiccating stress

Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images

Background: Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g., same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. / Methods: We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). / Results: The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium, and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. / Conclusions: The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task

Improving the Generalizability of Convolutional Neural Network-Based Segmentation on CMR Images

Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g. same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium and 0.82 for the right ventricle on the ACDC dataset; and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task

Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging

“The final authenticated version is available online at https://doi.org/10.1007/978-3-030-32245-8_83.”© 2019, Springer Nature Switzerland AG. Recent progress in fully-automated image segmentation has enabled efficient extraction of clinical parameters in large-scale clinical imaging studies, reducing laborious manual processing. However, the current state-of-the-art automatic image segmentation may still fail, especially when it comes to atypical cases. Visual inspection of segmentation quality is often required, thus diminishing the improvements in efficiency. This drives an increasing need to enhance the overall data processing pipeline with robust automatic quality scoring, especially for clinical applications. We present a novel quality control-driven (QCD) framework to provide reliable segmentation using a set of different neural networks. In contrast to the prior segmentation and quality scoring methods, the proposed framework automatically selects the optimal segmentation on-the-fly from the multiple candidate segmentations available, directly utilizing the inherent Dice similarity coefficient (DSC) predictions. We trained and evaluated the framework on a large-scale cardiovascular magnetic resonance aortic cine image sequences from the UK Biobank Study. The framework achieved segmentation accuracy of mean DSC at 0.966, mean prediction error of DSC within 0.015, and mean error in estimating lumen area ≤17.6 mm2 for both ascending aorta and proximal descending aorta. This novel QCD framework successfully integrates the automatic image segmentation along with detection of critical errors on a per-case basis, paving the way towards reliable fully-automatic extraction of clinical parameters for large-scale imaging studies