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Prospective longitudinal follow‐up of left ventricular ejection fraction (LVEF) trajectories after acute cardiac decompensation of heart failure is lacking. We investigated changes in LVEF and covariates at 6‐months' follow‐up in patients with a predischarge LVEF ≤40%, and determined predictors and prognostic implications of LVEF changes through 18‐months' follow‐up.
Methods and Results
Interdisciplinary Network Heart Failure program participants (n=633) were categorized into subgroups based on LVEF at 6‐months' follow‐up: normalized LVEF (>50%; heart failure with normalized ejection fraction, n=147); midrange LVEF (41%–50%; heart failure with midrange ejection fraction, n=195), or persistently reduced LVEF (≤40%; heart failure with persistently reduced LVEF , n=291). All received guideline‐directed medical therapies. At 6‐months' follow‐up, compared with patients with heart failure with persistently reduced LVEF, heart failure with normalized LVEF or heart failure with midrange LVEF subgroups showed greater reductions in LV end‐diastolic/end‐systolic diameters (both P<0.001), and left atrial systolic diameter (P=0.002), more increased septal/posterior end‐diastolic wall‐thickness (both P<0.001), and significantly greater improvement in diastolic function, biomarkers, symptoms, and health status. Heart failure duration <1 year, female sex, higher predischarge blood pressure, and baseline LVEF were independent predictors of LVEF improvement. Mortality and event‐free survival rates were lower in patients with heart failure with normalized LVEF (P=0.002). Overall, LVEF increased further at 18‐months' follow‐up (P<0.001), while LV end‐diastolic diameter decreased (P=0.048). However, LVEF worsened (P=0.002) and LV end‐diastolic diameter increased (P=0.047) in patients with heart failure with normalized LVEF hospitalized between 6‐months' follow‐up and 18‐months' follow‐up.
Conclusions
Six‐month survivors of acute cardiac decompensation for systolic heart failure showed variable LVEF trajectories, with >50% showing improvements by ≥1 LVEF category. LVEF changes correlated with various parameters, suggesting multilevel reverse remodeling, were predictable from several baseline characteristics, and were associated with clinical outcomes at 18‐months' follow‐up. Repeat hospitalizations were associated with attenuation of reverse remodeling."
Tinnitus is an auditory phantom perception in the absence of an external sound stimulation. People with tinnitus often report severe constraints in their daily life. Interestingly, indications exist on gender differences between women and men both in the symptom profile as well as in the response to specific tinnitus treatments. In this paper, data of the TrackYourTinnitus platform (TYT) were analyzed to investigate whether the gender of users can be predicted. In general, the TYT mobile Health crowdsensing platform was developed to demystify the daily and momentary variations of tinnitus symptoms over time. The goal of the presented investigation is a better understanding of gender-related differences in the symptom profiles of users from TYT. Based on two questionnaires of TYT, four machine learning based classifiers were trained and analyzed. With respect to the provided daily answers, the gender of TYT users can be predicted with an accuracy of 81.7%. In this context, worries, difficulties in concentration, and irritability towards the family are the three most important characteristics for predicting the gender. Note that in contrast to existing studies on TYT, daily answers to the worst symptom question were firstly investigated in more detail. It was found that results of this question significantly contribute to the prediction of the gender of TYT users. Overall, our findings indicate gender-related differences in tinnitus and tinnitus-related symptoms. Based on evidence that gender impacts the development of tinnitus, the gathered insights can be considered relevant and justify further investigations in this direction.
The bounded input bounded output (BIBO) stability for a nonlinear Caputo fractional system with time‐varying bounded delay and nonlinear output is studied. Utilizing the Razumikhin method, Lyapunov functions and appropriate fractional derivatives of Lyapunov functions some new bounded input bounded output stability criteria are derived. Also, explicit and independent on the initial time bounds of the output are provided. Uniform BIBO stability and uniform BIBO stability with input threshold are studied. A numerical simulation is carried out to show the system's dynamic response, and demonstrate the effectiveness of our theoretical results.
The olive tree is a venerable Mediterranean plant and often used in traditional medicine. The main aim of the present study was to evaluate the effect of Olea europaea L. cv. Arbosana leaf extract (OLE) and its encapsulation within a spanlastic dosage form on the improvement of its pro-oxidant and antiproliferative activity against HepG-2, MCF-7, and Caco-2 human cancer cell lines. The LC-HRESIMS-assisted metabolomic profile of OLE putatively annotated 20 major metabolites and showed considerable in vitro antiproliferative activity against HepG-2, MCF-7, and Caco-2 cell lines with IC\(_{50}\) values of 9.2 ± 0.8, 7.1 ± 0.9, and 6.5 ± 0.7 µg/mL, respectively. The encapsulation of OLE within a (spanlastic) nanocarrier system, using a spraying method and Span 40 and Tween 80 (4:1 molar ratio), was successfully carried out (size 41 ± 2.4 nm, zeta potential 13.6 ± 2.5, and EE 61.43 ± 2.03%). OLE showed enhanced thermal stability, and an improved in vitro antiproliferative effect against HepG-2, MCF-7, and Caco-2 (IC\(_{50}\) 3.6 ± 0.2, 2.3 ± 0.1, and 1.8 ± 0.1 µg/mL, respectively) in comparison to the unprocessed extract. Both preparations were found to exhibit pro-oxidant potential inside the cancer cells, through the potential inhibitory activity of OLE against glutathione reductase and superoxide dismutase (IC\(_{50}\) 1.18 ± 0.12 and 2.33 ± 0.19 µg/mL, respectively). These inhibitory activities were proposed via a comprehensive in silico study to be linked to the presence of certain compounds in OLE. Consequently, we assume that formulating such a herbal extract within a suitable nanocarrier would be a promising improvement of its therapeutic potential.
Metabolic glycoengineering enables a directed modification of cell surfaces by introducing target molecules to surface proteins displaying new features. Biochemical pathways involving glycans differ in dependence on the cell type; therefore, this technique should be tailored for the best results. We characterized metabolic glycoengineering in telomerase-immortalized human mesenchymal stromal cells (hMSC-TERT) as a model for primary hMSC, to investigate its applicability in TERT-modified cell lines. The metabolic incorporation of N-azidoacetylmannosamine (Ac\(_4\)ManNAz) and N-alkyneacetylmannosamine (Ac\(_4\)ManNAl) into the glycocalyx as a first step in the glycoengineering process revealed no adverse effects on cell viability or gene expression, and the in vitro multipotency (osteogenic and adipogenic differentiation potential) was maintained under these adapted culture conditions. In the second step, glycoengineered cells were modified with fluorescent dyes using Cu-mediated click chemistry. In these analyses, the two mannose derivatives showed superior incorporation efficiencies compared to glucose and galactose isomers. In time-dependent experiments, the incorporation of Ac\(_4\)ManNAz was detectable for up to six days while Ac\(_4\)ManNAl-derived metabolites were absent after two days. Taken together, these findings demonstrate the successful metabolic glycoengineering of immortalized hMSC resulting in transient cell surface modifications, and thus present a useful model to address different scientific questions regarding glycosylation processes in skeletal precursors.
Atherosclerosis is an inflammatory disease of large and medium-sized arteries, characterized by the growth of atherosclerotic lesions (plaques). These plaques often develop at inner curvatures of arteries, branchpoints, and bifurcations, where the endothelial wall shear stress is low and oscillatory. In conjunction with other processes such as lipid deposition, biomechanical factors lead to local vascular inflammation and plaque growth. There is also evidence that low and oscillatory shear stress contribute to arterial remodeling, entailing a loss in arterial elasticity and, therefore, an increased pulse-wave velocity. Although altered shear stress profiles, elasticity and inflammation are closely intertwined and critical for plaque growth, preclinical and clinical investigations for atherosclerosis mostly focus on the investigation of one of these parameters only due to the experimental limitations. However, cardiovascular magnetic resonance imaging (MRI) has been demonstrated to be a potent tool which can be used to provide insights into a large range of biological parameters in one experimental session. It enables the evaluation of the dynamic process of atherosclerotic lesion formation without the need for harmful radiation. Flow-sensitive MRI provides the assessment of hemodynamic parameters such as wall shear stress and pulse wave velocity which may replace invasive and radiation-based techniques for imaging of the vascular
function and the characterization of early plaque development. In combination with inflammation imaging, the analyses and correlations of these parameters could not only significantly advance basic preclinical investigations of atherosclerotic lesion formation and progression, but also the diagnostic clinical evaluation for early identification of high-risk plaques, which are prone to rupture. In this review, we summarize the key applications of magnetic resonance imaging for the evaluation of plaque characteristics through flow sensitive and morphological measurements. The simultaneous measurements of functional and structural parameters will further preclinical research on atherosclerosis and has the potential to fundamentally improve the detection of inflammation and vulnerable plaques in patients.
Growth, ageing and atherosclerotic plaque development alter the biomechanical forces acting on the vessel wall. However, monitoring the detailed local changes in wall shear stress (WSS) at distinct sites of the murine aortic arch over time has been challenging. Here, we studied the temporal and spatial changes in flow, WSS, oscillatory shear index (OSI) and elastic properties of healthy wildtype (WT, n = 5) and atherosclerotic apolipoprotein E-deficient (Apoe\(^{−/−}\), n = 6) mice during ageing and atherosclerosis using high-resolution 4D flow magnetic resonance imaging (MRI). Spatially resolved 2D projection maps of WSS and OSI of the complete aortic arch were generated, allowing the pixel-wise statistical analysis of inter- and intragroup hemodynamic changes over time and local correlations between WSS, pulse wave velocity (PWV), plaque and vessel wall characteristics. The study revealed converse differences of local hemodynamic profiles in healthy WT and atherosclerotic Apoe\(^{−/−}\) mice, and we identified the circumferential WSS as potential marker of plaque size and composition in advanced atherosclerosis and the radial strain as a potential marker for vascular elasticity. Two-dimensional (2D) projection maps of WSS and OSI, including statistical analysis provide a powerful tool to monitor local aortic hemodynamics during ageing and atherosclerosis. The correlation of spatially resolved hemodynamics and plaque characteristics could significantly improve our understanding of the impact of hemodynamics on atherosclerosis, which may be key to understand plaque progression towards vulnerability.
Background
International consensus criteria (ICC) have redefined borderline resectability for pancreatic ductal adenocarcinoma (PDAC) according to three dimensions: anatomical (BR-A), biological (BR-B), and conditional (BR-C). The present definition acknowledges that resectability is not just about the anatomic relationship between the tumour and vessels but that biological and conditional dimensions also are important.
Methods
Patients’ tumours were retrospectively defined borderline resectable according to ICC. The study cohort was grouped into either BR-A or BR-B and compared with patients considered primarily resectable (R). Differences in postoperative complications, pathological reports, overall (OS), and disease-free survival were assessed.
Results
A total of 345 patients underwent resection for PDAC. By applying ICC in routine preoperative assessment, 30 patients were classified as stage BR-A and 62 patients as stage BR-B. In total, 253 patients were considered R. The cohort did not contain BR-C patients. No differences in postoperative complications were detected. Median OS was significantly shorter in BR-A (15 months) and BR-B (12 months) compared with R (20 months) patients (BR-A vs. R: p = 0.09 and BR-B vs. R: p < 0.001). CA19-9, as the determining factor of BR-B patients, turned out to be an independent prognostic risk factor for OS.
Conclusions
Preoperative staging defining surgical resectability in PDAC according to ICC is crucial for patient survival. Patients with PDAC BR-B should be considered for multimodal neoadjuvant therapy even if considered anatomically resectable.
Sensitivity analysis for interpretation of machine learning based segmentation models in cardiac MRI
(2021)
Background
Image segmentation is a common task in medical imaging e.g., for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Here we present a generic method for segmentation model interpretation. Sensitivity analysis is an approach where model input is modified in a controlled manner and the effect of these modifications on the model output is evaluated. This method yields insights into the sensitivity of the model to these alterations and therefore to the importance of certain features on segmentation performance.
Results
We present an open-source Python library (misas), that facilitates the use of sensitivity analysis with arbitrary data and models. We show that this method is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We demonstrate this in two case studies on cardiac magnetic resonance imaging. The first case study explores the suitability of a published network for use on a public dataset the network has not been trained on. The second case study demonstrates how sensitivity analysis can be used to evaluate the robustness of a newly trained model.
Conclusions
Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox, enabling and improving interpretability of segmentation models. Enhancing our understanding of neural networks through sensitivity analysis also assists in decision making. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.
Purpose
Artificial neural networks show promising performance in automatic segmentation of cardiac MRI. However, training requires large amounts of annotated data and generalization to different vendors, field strengths, sequence parameters, and pathologies is limited. Transfer learning addresses this challenge, but specific recommendations regarding type and amount of data required is lacking. In this study, we assess data requirements for transfer learning to experimental cardiac MRI at 7T where the segmentation task can be challenging. In addition, we provide guidelines, tools, and annotated data to enable transfer learning approaches by other researchers and clinicians.
Methods
A publicly available segmentation model was used to annotate a publicly available data set. This labeled data set was subsequently used to train a neural network for segmentation of left ventricle and myocardium in cardiac cine MRI. The network is used as starting point for transfer learning to 7T cine data of healthy volunteers (n = 22; 7873 images) by updating the pre-trained weights. Structured and random data subsets of different sizes were used to systematically assess data requirements for successful transfer learning.
Results
Inconsistencies in the publically available data set were corrected, labels created, and a neural network trained. On 7T cardiac cine images the model pre-trained on public imaging data, acquired at 1.5T and 3T, achieved DICE\(_{LV}\) = 0.835 and DICE\(_{MY}\) = 0.670. Transfer learning using 7T cine data and ImageNet weight initialization improved model performance to DICE\(_{LV}\) = 0.900 and DICE\(_{MY}\) = 0.791. Using only end-systolic and end-diastolic images reduced training data by 90%, with no negative impact on segmentation performance (DICE\(_{LV}\) = 0.908, DICE\(_{MY}\) = 0.805).
Conclusions
This work demonstrates and quantifies the benefits of transfer learning for cardiac cine image segmentation. We provide practical guidelines for researchers planning transfer learning projects in cardiac MRI and make data, models, and code publicly available.