Structural identifiability analyses of candidate models for in vitro Pitavastatin hepatic uptake

In this paper a review of the application of four different techniques (a version of the similarity transformation approach for autonomous uncontrolled systems, a non-differential input/output observable normal form approach, the characteristic set differential algebra and a recent algebraic input/output relationship approach) to determine the structural identifiability of certain in vitro nonlinear pharmacokinetic models is provided. The Organic Anion Transporting Polypeptide (OATP) substrate, Pitavastatin, is used as a probe on freshly isolated animal and human hepatocytes. Candidate pharmacokinetic non-linear compartmental models have been derived to characterise the uptake process of Pitavastatin. As a prerequisite to parameter estimation, structural identifiability analyses are performed to establish that all unknown parameters can be identified from the experimental observations available

Imaging Informatics and the Human Brain Project: the Role of Structure, Review

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Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

.Blood flow patterns estimation in the left ventricle with low-rate 2D and 3D dynamic contrast-enhanced ultrasound

a b s t r a c t Background and Objective : Left ventricle (LV) dysfunction always occurs at early heart-failure stages, pro- ducing variations in the LV flow patterns. Cardiac diagnostics may therefore benefit from flow-pattern analysis. Several visualization tools have been proposed that require ultrafast ultrasound acquisitions. However, ultrafast ultrasound is not standard in clinical scanners. Meanwhile techniques that can handle low frame rates are still lacking. As a result, the clinical translation of these techniques remains limited, especially for 3D acquisitions where the volume rates are intrinsically low. Methods : To overcome these limitations, we propose a novel technique for the estimation of LV blood velocity and relative-pressure fields from dynamic contrast-enhanced ultrasound (DCE-US) at low frame rates. Different from other methods, our method is based on the time-delays between time-intensity curves measured at neighbor pixels in the DCE-US loops. Using Navier-Stokes equation, we regularize the obtained velocity fields and derive relative-pressure estimates. Blood flow patterns were characterized with regard to their vorticity, relative-pressure changes (dp/dt) in the LV outflow tract, and viscous energy loss, as these reflect the ejection efficiency. Results : We evaluated the proposed method on 18 patients (9 responders and 9 non-responders) who un- derwent cardiac resynchronization therapy (CRT). After CRT, the responder group evidenced a significant (p < 0.05) increase in vorticity and peak dp/dt, and a non-significant decrease in viscous energy loss. No significant difference was found in the non-responder group. Relative feature variation before and after CRT evidenced a significant difference (p < 0.05) between responders and non-responders for vorticity and peak dp/dt. Finally, the method feasibility is also shown with 3D DCE-US. Conclusions : Using the proposed method, adequate visualization and quantification of blood flow patterns are successfully enabled based on low-rate DCE-US of the LV, facilitating the clinical adoption of the method using standard ultrasound scanners. The clinical value of the method in the context of CRT is also shown