A Comparative Analysis of Coronary and Aortic Flow Waveforms

Dang, Charles Q.; Nenadic, Zoran; Kassab, Ghassan S.

doi:10.1007/s10439-008-9488-x

Cited by 6 publications

(5 citation statements)

References 23 publications

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“…Swine models are commonly used to study the mechanisms of heart failure and to develop new diagnostics and therapies. We have previously demonstrated that a biotelemetry system can reliably monitor blood flow and pressure in freely roaming normal animals [3], [8]. In the swine model, the present study demonstrated that continuous recording of pressure, flow, and electrical activity of the heart using an implantable telemetric system effectively revealed hemodynamic changes during the progression of heart failure.…”

Section: Discussionsupporting

confidence: 50%

Longitudinal Hemodynamic Measurements in Swine Heart Failure Using a Fully Implantable Telemetry System

et al. 2014

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Chronic monitoring of heart rate, blood pressure, and flow in conscious free-roaming large animals can offer considerable opportunity to understand the progression of cardiovascular diseases and can test new diagnostics and therapeutics. The objective of this study was to demonstrate the feasibility of chronic, simultaneous measurement of several hemodynamic parameters (left ventricular pressure, systemic pressure, blood flow velocity, and heart rate) using a totally implantable multichannel telemetry system in swine heart failure models. Two solid-state blood pressure sensors were inserted in the left ventricle and the descending aorta for pressure measurements. Two Doppler probes were placed around the left anterior descending (LAD) and the brachiocephalic arteries for blood flow velocity measurements. Electrocardiographic (ECG) electrodes were attached to the surface of the left ventricle to monitor heart rate. The telemeter body was implanted in the right side of the abdomen under the skin for approximately 4 to 6 weeks. The animals were subjected to various heart failure models, including volume overload (A-V fistula, n = 3), pressure overload (aortic banding, n = 2) and dilated cardiomyopathy (pacing-induced tachycardia, n = 3). Longitudinal changes in hemodynamics were monitored during the progression of the disease. In the pacing-induced tachycardia animals, the systemic blood pressure progressively decreased within the first 2 weeks and returned to baseline levels thereafter. In the aortic banding animals, the pressure progressively increased during the development of the disease. The pressure in the A-V fistula animals only showed a small increase during the first week and remained stable thereafter. The results demonstrated the ability of this telemetry system of long-term, simultaneous monitoring of blood flow, pressure and heart rate in heart failure models, which may offer significant utility for understanding cardiovascular disease progression and treatment.

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Section: Discussionsupporting

confidence: 50%

Longitudinal Hemodynamic Measurements in Swine Heart Failure Using a Fully Implantable Telemetry System

et al. 2014

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“…It was found that the popular methods of Fourier analysis, and wavelets introduce confusing artifacts because they require the hypothesis of stationary random oscillations, and usually miss out on the nonlinear sub harmonic oscillations (22). We found Huang’s IMF method invented for study of ocean waves fits the need.…”

Section: Mathematical Expression Of Cause Functionsmentioning

confidence: 99%

“…The pressure signal was treated with the IMF method, and resolved into the mean trends and oscillations about each mean trend. A later study was carried out for blood flow which was analyzed by the IMF-Hilbert Spectrum method and compared with popular methods such as wavelet analysis and principal component analysis (22). A set of “mean trends” was obtained, each of which represents a “mean” value in a definitive sense, and together they represent the mean trend systematically with different degrees of oscilatory content.…”

Section: Mathematical Expression Of Cause Functionsmentioning

confidence: 99%

A Systems Approach to Tissue Remodeling

Kassab

2009

Journal of Biomechanical Engineering

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Remodeling of tissue in response to physical stress is a very complex process. The changes of stimulus (cause) and response (effect) must be measured and the results must be organized into mathematical forms that are suitable for predictions and applications. An experiment where the stimulus (pressure, flow, shear stress, etc.) can be changed approximately as a step function (a step plus a perturbation) and the response (structure, material properties, function, etc.) can be measured over time can be simulated by indicial response functions (IRF). The IRF is a mathematical expression of the ratio of the change of a particular feature of the system in response to a unit step change of stimulus. The IRF approach provides a quantitative description of the remodeling process and simplifies the interpretation of data and greatly increases the potential of using the experimental data for prediction of the outcome of future experiments. The objective of this review is to provide an overview of the IRF approach including some exampleary systems. The goal is to illustrate how the indicial expressions make it possible to integrate biological complexity by convolution. The time courses of stimuli represent half of the convolution while the time course of changes in respone respresents the second half of the convolution. The IRF approach provides an understanding of physiological problems with mathematical accuracy and may be conducive to new findings.

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“…Computational approaches to detect miRNAs have been developed to overcome the limitation, and most methods for pre-miRNA detection are based on machine learning [5][6][7]. With the exception of few approaches based on one class classification [8][9][10], most methods rely on two class classification. While all parts contributing to model establishment are important [11], the selection of negative data is crucial since no gold standard is available.…”

Section: Introductionmentioning

confidence: 99%

Categorization of species based on their microRNAs employing sequence motifs, information-theoretic sequence feature extraction, and k-mers

Yousef

Nigatu

Levy

et al. 2017

EURASIP J. Adv. Signal Process.

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Background: Diseases like cancer can manifest themselves through changes in protein abundance, and microRNAs (miRNAs) play a key role in the modulation of protein quantity. MicroRNAs are used throughout all kingdoms and have been shown to be exploited by viruses to modulate their host environment. Since the experimental detection of miRNAs is difficult, computational methods have been developed. Many such tools employ machine learning for pre-miRNA detection, and many features for miRNA parameterization have been proposed. To train machine learning models, negative data is of importance yet hard to come by; therefore, we recently started to employ pre-miRNAs from one species as positive data versus another species' pre-miRNAs as negative examples based on sequence motifs and k-mers. Here, we introduce the additional usage of information-theoretic (IT) features. Results: Pre-miRNAs from one species were used as positive and another species' pre-miRNAs as negative training data for machine learning. The categorization capability of IT and k-mer features was investigated. Both feature sets and their combinations yielded a very high accuracy, which is as good as the previously suggested sequence motif and k-mer based method. However, for obtaining a high performance, a sufficiently large phylogenetic distance between the species and sufficiently high number of pre-miRNAs in the training set is required. To examine the contribution of the IT and k-mer features, an information gain-based feature ranking was performed. Although the top 3 are IT features, 80% of the top 100 features are k-mers. The comparison of all three individual approaches (motifs, IT, and k-mers) shows that the distinction of species based on their pre-miRNAs k-mers are sufficient. Conclusions: IT sequence feature extraction enables the distinction among species and is less computationally expensive than motif calculations. However, since IT features need larger amounts of data to have enough statistics for producing highly accurate results, future categorization into species can be effectively done using k-mers only. The biological reasoning for this is the existence of a codon bias between species which can, at least, be observed in exonic miRNAs. Future work in this direction will be the ab initio detection of pre-miRNA. In addition, prediction of pre-miRNA from RNA-seq can be done.

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A Comparative Analysis of Coronary and Aortic Flow Waveforms

Cited by 6 publications

References 23 publications

Longitudinal Hemodynamic Measurements in Swine Heart Failure Using a Fully Implantable Telemetry System

Longitudinal Hemodynamic Measurements in Swine Heart Failure Using a Fully Implantable Telemetry System

A Systems Approach to Tissue Remodeling

Categorization of species based on their microRNAs employing sequence motifs, information-theoretic sequence feature extraction, and k-mers

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