Sacubitril/Valsartan: A New Dawn has Begun! A Revisited Review.

Heart Failure (HF) is among the major causes of global morbidity as well as mortality. Increased prevalence, frequent and prolonged hospitalization, rehospitalization, long-term consumption of healthcare resources, absenteeism, and death upsurge the economic burden linked to HF. For decades, Angiotensin-Converting Enzyme Inhibitors (ACEIs), Angiotensin II Receptor Blockers (ARBs), Beta-Blockers (BBs), and mineralocorticoid receptor antagonists (MRA), have remained the mainstay of the standard of care for HF management. Despite their proven efficacy and cost-effectiveness, HF remains a global pandemic and is still increasing in prevalence. Sacubitril/ Valsartan (SAC/VAL) is an Angiotensin Receptor/Neprilysin Inhibitor (ARNI) that proved out to be a game-changer drug in HF treatment. Recent data indicated that SAC/VAL is more efficient and can improve the overall quality of life of HF patients with reduced ejection fraction (HFrEF) with fewer side effects. It is now incorporated in the guidelines as an alternative to ACEIs or ARBs to lower morbidity in addition to mortality in HFrEF patients. This review article will discuss the current guidelines-approved indications and highlight the potential emerging indications, in addition to the currently ongoing clinical trials that will expand the use of SAC/VAL.

Single-molecule nanopore sequencing reveals extreme target copy number heterogeneity in arylomycin-resistant mutants.

Tandem gene amplification is a frequent and dynamic source of antibiotic resistance in bacteria. Ongoing expansions and contractions of repeat arrays during population growth are expected to manifest as cell-to-cell differences in copy number (CN). As a result, a clonal bacterial culture could comprise subpopulations of cells with different levels of antibiotic sensitivity that result from variable gene dosage. Despite the high potential for misclassification of heterogenous cell populations as either antibiotic-susceptible or fully resistant in clinical settings, and the concomitant risk of inappropriate treatment, CN distribution among cells has defied analysis. Here, we use the MinION single-molecule nanopore sequencer to uncover CN heterogeneity in clonal populations of Escherichia coli and Acinetobacter baumannii grown from single cells isolated while selecting for resistance to an optimized arylomycin, a member of a recently discovered class of Gram-negative antibiotic. We found that gene amplification of the arylomycin target, bacterial type I signal peptidase LepB, is a mechanism of unstable arylomycin resistance and demonstrate in E. coli that amplification instability is independent of RecA. This instability drives the emergence of a nonuniform distribution of lepB CN among cells with a range of 1 to at least 50 copies of lepB identified in a single clonal population. In sum, this remarkable heterogeneity, and the evolutionary plasticity it fuels, illustrates how gene amplification can enable bacterial populations to respond rapidly to novel antibiotics. This study establishes a rationale for further nanopore-sequencing studies of heterogeneous cell populations to uncover CN variability at single-molecule resolution.

Impact of the updated diastolic function guidelines in the real world.

BACKGROUND: Echocardiographic assessment of diastolic function is complex but can aid in the diagnosis of heart failure, particularly in patients with preserved ejection fraction. In 2016, the American Society of Echocardiography (ASE) and European Association of Cardiovascular Imaging (EACVI) published an updated algorithm for the evaluation of diastolic function. The objective of our study was to assess its impact on diastolic function assessment in a real-world cohort of echo studies. METHODS: We retrospectively identified 71,727 consecutive transthoracic echo studies performed at a tertiary care center between February 2010 and March 2016 in which diastolic function was reported based on the 2009 ASE Guidelines. We then programmed a software algorithm to assess diastolic function in these echo studies according to the 2016 ASE/EACVI Guidelines. RESULTS: When diastolic function assessment based on the 2009 guidelines was compared to that using the 2016 guidelines, there were significant differences in proportion of studies classified as normal (23% vs. 32%) or indeterminate (43% vs. 36%) function, and mild (23% vs. 23%), moderate (10% vs. 8%), or severe (1% vs. 2%) diastolic dysfunction, with poor agreement between the two methods (Kappa 0.323, 95% CI 0.318-0.328). Furthermore, within the subgroup of studies with preserved ejection fraction and no evidence of myocardial disease, there was significant reclassification from mild diastolic dysfunction to normal diastolic function. CONCLUSION: The updated guidelines result in significant differences in diastolic function interpretation in the real world. Our findings have important implications for the identification of patients with or at risk for heart failure.

Automated estimation of echocardiogram image quality in hospitalized patients.

We developed a machine learning model for efficient analysis of echocardiographic image quality in hospitalized patients. This study applied a machine learning model for automated transthoracic echo (TTE) image quality scoring in three inpatient groups. Our objectives were: (1) Assess the feasibility of a machine learning model for echo image quality analysis, (2) Establish the comprehensiveness of real-world TTE reporting by clinical group, and (3) Determine the relationship between machine learning image quality and comprehensiveness of TTE reporting. A machine learning model was developed and applied to TTEs from three matched cohorts for image quality of nine standard views. Case TTEs were comprehensive studies in mechanically ventilated patients between 01/01/2010 and 12/31/2015. For each case TTE, there were two matched spontaneously breathing controls (Control 1: Inpatients scanned in the lab and Control 2: Portable studies). We report the overall mean maximum and view specific quality scores for each TTE. The comprehensiveness of an echo report was calculated as the documented proportion of 12 standard parameters. An inverse probability weighted regression model was fit to determine the relationship between machine learning quality score and the completeness of a TTE report. 175 mechanically ventilated TTEs were included with 350 non-intubated samples (175 Control 1: Lab and 175 Control 2: Portable). In total, the machine learning model analyzed 14,086 echo video clips for quality. The overall accuracy of the model with regard to the expert ground truth for the view classification was 87.0%. The overall mean maximum quality score was lower for mechanically ventilated TTEs (0.55 [95% CI 0.54, 0.56]) versus 0.61 (95% CI 0.59, 0.62) for Control 1: Lab and 0.64 (95% CI 0.63, 0.66) for Control 2: Portable; p = 0.002. Furthermore, mechanically ventilated TTE reports were the least comprehensive, with fewer reported parameters. The regression model demonstrated the correlation of echo image quality and completeness of TTE reporting regardless of the clinical group. Mechanically ventilated TTEs were of inferior quality and clinical utility compared to spontaneously breathing controls and machine learning derived image quality correlates with completeness of TTE reporting regardless of the clinical group.

Cardiac point-of-care to cart-based ultrasound translation using constrained CycleGAN.

PURPOSE:  The emerging market of cardiac handheld ultrasound (US) is on the rise. Despite the advantages in ease of access and the lower cost, a gap in image quality can still be observed between the echocardiography (echo) data captured by point-of-care ultrasound (POCUS) compared to conventional cart-based US, which limits the further adaptation of POCUS. In this work, we aim to present a machine learning solution based on recent advances in adversarial training to investigate the feasibility of translating POCUS echo images to the quality level of high-end cart-based US systems. METHODS:  We propose a constrained cycle-consistent generative adversarial architecture for unpaired translation of cardiac POCUS to cart-based US data. We impose a structured shape-wise regularization via a critic segmentation network to preserve the underlying shape of the heart during quality translation. The proposed deep transfer model is constrained to the anatomy of the left ventricle (LV) in apical two-chamber (AP2) echo views. RESULTS:  A total of 1089 echo studies from 841 patients are used in this study. The AP2 frames are captured by POCUS (Philips Lumify and Clarius) and cart-based (Philips iE33 and Vivid E9) US machines. The dataset of quality translation comprises a total of 441 echo studies from 395 patients. Data from both POCUS and cart-based systems of the same patient were available in 122 cases. The deep-quality transfer model is integrated into a pipeline for an automated cardiac evaluation task, namely segmentation of LV in AP2 view. By transferring the low-quality POCUS data to the cart-based US, a significant average improvement of 30% and 34 mm is obtained in the LV segmentation Dice score and Hausdorff distance metrics, respectively. CONCLUSION:  This paper presents the feasibility of a machine learning solution to transform the image quality of POCUS data to that of high-quality high-end cart-based systems. The experiments show that by leveraging the quality translation through the proposed constrained adversarial training, the accuracy of automatic segmentation with POCUS data could be improved.

On Modelling Label Uncertainty in Deep Neural Networks: Automatic Estimation of Intra- Observer Variability in 2D Echocardiography Quality Assessment.

Uncertainty of labels in clinical data resulting from intra-observer variability can have direct impact on the reliability of assessments made by deep neural networks. In this paper, we propose a method for modelling such uncertainty in the context of 2D echocardiography (echo), which is a routine procedure for detecting cardiovascular disease at point-of-care. Echo imaging quality and acquisition time is highly dependent on the operator's experience level. Recent developments have shown the possibility of automating echo image quality quantification by mapping an expert's assessment of quality to the echo image via deep learning techniques. Nevertheless, the observer variability in the expert's assessment can impact the quality quantification accuracy. Here, we aim to model the intra-observer variability in echo quality assessment as an aleatoric uncertainty modelling regression problem with the introduction of a novel method that handles the regression problem with categorical labels. A key feature of our design is that only a single forward pass is sufficient to estimate the level of uncertainty for the network output. Compared to the 0.11 ± 0.09 absolute error (in a scale from 0 to 1) archived by the conventional regression method, the proposed method brings the error down to 0.09 ± 0.08, where the improvement is statistically significant and equivalents to 5.7% test accuracy improvement. The simplicity of the proposed approach means that it could be generalized to other applications of deep learning in medical imaging, where there is often uncertainty in clinical labels.

Automatic biplane left ventricular ejection fraction estimation with mobile point-of-care ultrasound using multi-task learning and adversarial training.

PURPOSE: Left ventricular ejection fraction (LVEF) is one of the key metrics to assess the heart functionality, and cardiac ultrasound (echo) is a standard imaging modality for EF measurement. There is an emerging interest to exploit the point-of-care ultrasound (POCUS) usability due to low cost and ease of access. In this work, we aim to present a computationally efficient mobile application for accurate LVEF estimation. METHODS: Our proposed mobile application for LVEF estimation runs in real time on Android mobile devices that have either a wired or wireless connection to a cardiac POCUS device. We propose a pipeline for biplane ejection fraction estimation using apical two-chamber (AP2) and apical four-chamber (AP4) echo views. A computationally efficient multi-task deep fully convolutional network is proposed for simultaneous LV segmentation and landmark detection in these views, which is integrated into the LVEF estimation pipeline. An adversarial critic model is used in the training phase to impose a shape prior on the LV segmentation output. RESULTS: The system is evaluated on a dataset of 427 patients. Each patient has a pair of captured AP2 and AP4 echo studies, resulting in a total of more than 40,000 echo frames. The mobile system reaches a noticeably high average Dice score of 92% for LV segmentation, an average Euclidean distance error of 2.85 pixels for the detection of anatomical landmarks used in LVEF calculation, and a median absolute error of 6.2% for LVEF estimation compared to the expert cardiologist's annotations and measurements. CONCLUSION: The proposed system runs in real time on mobile devices. The experiments show the effectiveness of the proposed system for automatic LVEF estimation by demonstrating an adequate correlation with the cardiologist's examination.

Cardiac Phase Detection in Echocardiograms With Densely Gated Recurrent Neural Networks and Global Extrema Loss.

Accurate detection of end-systolic (ES) and end-diastolic (ED) frames in an echocardiographic cine series can be difficult but necessary pre-processing step for the development of automatic systems to measure cardiac parameters. The detection task is challenging due to variations in cardiac anatomy and heart rate often associated with pathological conditions. We formulate this problem as a regression problem and propose several deep learning-based architectures that minimize a novel global extrema structured loss function to localize the ED and ES frames. The proposed architectures integrate convolution neural networks (CNNs)-based image feature extraction model and recurrent neural networks (RNNs) to model temporal dependencies between each frame in a sequence. We explore two CNN architectures: DenseNet and ResNet, and four RNN architectures: long short-term memory, bi-directional LSTM, gated recurrent unit (GRU), and Bi-GRU, and compare the performance of these models. The optimal deep learning model consists of a DenseNet and GRU trained with the proposed loss function. On average, we achieved 0.20 and 1.43 frame mismatch for the ED and ES frames, respectively, which are within reported inter-observer variability for the manual detection of these frames.

Optimized arylomycins are a new class of Gram-negative antibiotics.

Multidrug-resistant bacteria are spreading at alarming rates, and despite extensive efforts no new class of antibiotic with activity against Gram-negative bacteria has been approved in over fifty years. Natural products and their derivatives have a key role in combating Gram-negative pathogens. Here we report chemical optimization of the arylomycins-a class of natural products with weak activity and limited spectrum-to obtain G0775, a molecule with potent, broad-spectrum activity against Gram-negative bacteria. G0775 inhibits the essential bacterial type I signal peptidase, a new antibiotic target, through an unprecedented molecular mechanism. It circumvents existing antibiotic resistance mechanisms and retains activity against contemporary multidrug-resistant Gram-negative clinical isolates in vitro and in several in vivo infection models. These findings demonstrate that optimized arylomycin analogues such as G0775 could translate into new therapies to address the growing threat of multidrug-resistant Gram-negative infections.

B-type Natriuretic Peptide as an Index of Symptoms and Severity of Chronic Rheumatic Mitral Regurgitation.

BACKGROUND: The most common causes of severe mitral regurgitation (MR) in developing countries are rheumatic heart disease. The plasma level of B-type natriuretic peptide (BNP) is known to increase with left ventricular (LV) dysfunction. AIM OF THE WORK: To study BNP level as an index of symptoms and severity of chronic rheumatic MR. PATIENTS AND METHODS: One hundred and forty patients with rheumatic MR and LV ejection fractions (EFs) of >55% underwent assessment of symptoms, transthoracic echocardiography, and measurement of BNP. RESULTS: The level of BNP rose with increasing left atrium (LA) dimensions and volumes, LV dimensions and volumes, echocardiographic parameters of MR severity (width of the vena contracta, regurgitation jet area, effective regurgitation orifice area, and regurgitant volume), and E waves. RESULTS: BNP was significantly higher in patients with severe MR compared with moderate and mild MR (P < 0.001), and using cutoff point of 61 pg/mL mm had 97% sensitivity and 89% specificity for predicting patients with severe MR (0.99, 95% confidence interval [CI] 0.9-1). BNP was significantly higher in patients with New York Heart Association (NYHA III) compared with NYHA II, I and asymptomatic patients (P < 0.001) and using cutoff point of 53 pg/mL had 97% sensitivity and 87% specificity for predicting symptomatic patients with symptomatic MR (0.81, 95% CI 0.70-0.92). CONCLUSIONS: BNP level increase with increasing severity of rheumatic MR and are higher in symptomatic compared to asymptomatic patients, even in the presence of normal EF%.

MICU2, a paralog of MICU1, resides within the mitochondrial uniporter complex to regulate calcium handling.

Mitochondrial calcium uptake is present in nearly all vertebrate tissues and is believed to be critical in shaping calcium signaling, regulating ATP synthesis and controlling cell death. Calcium uptake occurs through a channel called the uniporter that resides in the inner mitochondrial membrane. Recently, we used comparative genomics to identify MICU1 and MCU as the key regulatory and putative pore-forming subunits of this channel, respectively. Using bioinformatics, we now report that the human genome encodes two additional paralogs of MICU1, which we call MICU2 and MICU3, each of which likely arose by gene duplication and exhibits distinct patterns of organ expression. We demonstrate that MICU1 and MICU2 are expressed in HeLa and HEK293T cells, and provide multiple lines of biochemical evidence that MCU, MICU1 and MICU2 reside within a complex and cross-stabilize each other's protein expression in a cell-type dependent manner. Using in vivo RNAi technology to silence MICU1, MICU2 or both proteins in mouse liver, we observe an additive impairment in calcium handling without adversely impacting mitochondrial respiration or membrane potential. The results identify MICU2 as a new component of the uniporter complex that may contribute to the tissue-specific regulation of this channel.

Large mutational target size for rapid emergence of bacterial persistence.

Phenotypic heterogeneity displayed by a clonal bacterial population permits a small fraction of cells to survive prolonged exposure to antibiotics. Although first described over 60 y ago, the molecular mechanisms underlying this behavior, termed persistence, remain largely unknown. To systematically explore the genetic basis of persistence, we selected a library of transposon-mutagenized Escherichia coli cells for survival to multiple rounds of lethal ampicillin exposure. Application of microarray-based genetic footprinting revealed a large number of loci that drastically elevate persistence frequency through null mutations and domain disruptions. In one case, the C-terminal disruption of methionyl-tRNA synthetase (MetG) results in a 10,000-fold higher persistence frequency than wild type. We discovered a mechanism by which null mutations in transketolase A (tktA) and glycerol-3-phosphate (G3P) dehydrogenase (glpD) increase persistence through metabolic flux alterations that increase intracellular levels of the growth-inhibitory metabolite methylglyoxal. Systematic double-mutant analyses revealed the genetic network context in which such persistent mutants function. Our findings reveal a large mutational target size for increasing persistence frequency, which has fundamental implications for the emergence of antibiotic tolerance in the clinical setting.

Integrative genomics identifies MCU as an essential component of the mitochondrial calcium uniporter.

Mitochondria from diverse organisms are capable of transporting large amounts of Ca(2+) via a ruthenium-red-sensitive, membrane-potential-dependent mechanism called the uniporter. Although the uniporter's biophysical properties have been studied extensively, its molecular composition remains elusive. We recently used comparative proteomics to identify MICU1 (also known as CBARA1), an EF-hand-containing protein that serves as a putative regulator of the uniporter. Here, we use whole-genome phylogenetic profiling, genome-wide RNA co-expression analysis and organelle-wide protein coexpression analysis to predict proteins functionally related to MICU1. All three methods converge on a novel predicted transmembrane protein, CCDC109A, that we now call 'mitochondrial calcium uniporter' (MCU). MCU forms oligomers in the mitochondrial inner membrane, physically interacts with MICU1, and resides within a large molecular weight complex. Silencing MCU in cultured cells or in vivo in mouse liver severely abrogates mitochondrial Ca(2+) uptake, whereas mitochondrial respiration and membrane potential remain fully intact. MCU has two predicted transmembrane helices, which are separated by a highly conserved linker facing the intermembrane space. Acidic residues in this linker are required for its full activity. However, an S259A point mutation retains function but confers resistance to Ru360, the most potent inhibitor of the uniporter. Our genomic, physiological, biochemical and pharmacological data firmly establish MCU as an essential component of the mitochondrial Ca(2+) uniporter.

MICU1 encodes a mitochondrial EF hand protein required for Ca(2+) uptake.

Mitochondrial calcium uptake has a central role in cell physiology by stimulating ATP production, shaping cytosolic calcium transients and regulating cell death. The biophysical properties of mitochondrial calcium uptake have been studied in detail, but the underlying proteins remain elusive. Here we use an integrative strategy to predict human genes involved in mitochondrial calcium entry based on clues from comparative physiology, evolutionary genomics and organelle proteomics. RNA interference against 13 top candidates highlighted one gene, CBARA1, that we call hereafter mitochondrial calcium uptake 1 (MICU1). Silencing MICU1 does not disrupt mitochondrial respiration or membrane potential but abolishes mitochondrial calcium entry in intact and permeabilized cells, and attenuates the metabolic coupling between cytosolic calcium transients and activation of matrix dehydrogenases. MICU1 is associated with the mitochondrial inner membrane and has two canonical EF hands that are essential for its activity, indicating a role in calcium sensing. MICU1 represents the founding member of a set of proteins required for high-capacity mitochondrial calcium uptake. Its discovery may lead to the complete molecular characterization of mitochondrial calcium uptake pathways, and offers genetic strategies for understanding their contribution to normal physiology and disease.

Genetic architecture of intrinsic antibiotic susceptibility.

BACKGROUND: Antibiotic exposure rapidly selects for more resistant bacterial strains, and both a drug's chemical structure and a bacterium's cellular network affect the types of mutations acquired. METHODOLOGY/PRINCIPAL FINDINGS: To better characterize the genetic determinants of antibiotic susceptibility, we exposed a transposon-mutagenized library of Escherichia coli to each of 17 antibiotics that encompass a wide range of drug classes and mechanisms of action. Propagating the library for multiple generations with drug concentrations that moderately inhibited the growth of the isogenic parental strain caused the abundance of strains with even minor fitness advantages or disadvantages to change measurably and reproducibly. Using a microarray-based genetic footprinting strategy, we then determined the quantitative contribution of each gene to E. coli's intrinsic antibiotic susceptibility. We found both loci whose removal increased general antibiotic tolerance as well as pathways whose down-regulation increased tolerance to specific drugs and drug classes. The beneficial mutations identified span multiple pathways, and we identified pairs of mutations that individually provide only minor decreases in antibiotic susceptibility but that combine to provide higher tolerance. CONCLUSIONS/SIGNIFICANCE: Our results illustrate that a wide-range of mutations can modulate the activity of many cellular resistance processes and demonstrate that E. coli has a large mutational target size for increasing antibiotic tolerance. Furthermore, the work suggests that clinical levels of antibiotic resistance might develop through the sequential accumulation of chromosomal mutations of small individual effect.

A comprehensive genetic characterization of bacterial motility.

We have developed a powerful experimental framework that combines competitive selection and microarray-based genetic footprinting to comprehensively reveal the genetic basis of bacterial behaviors. Application of this method to Escherichia coli motility identifies 95% of the known flagellar and chemotaxis genes, and reveals three dozen novel loci that, to varying degrees and through diverse mechanisms, affect motility. To probe the network context in which these genes function, we developed a method that uncovers genome-wide epistatic interactions through comprehensive analyses of double-mutant phenotypes. This allows us to place the novel genes within the context of signaling and regulatory networks, including the Rcs phosphorelay pathway and the cyclic di-GMP second-messenger system. This unifying framework enables sensitive and comprehensive genetic characterization of complex behaviors across the microbial biosphere.