Visually estimated ejection fraction by two dimensional and triplane echocardiography is closely correlated with quantitative ejection fraction by real-time three dimensional echocardiography

<p>Abstract</p> <p>Background</p> <p>Visual assessment of left ventricular ejection fraction (LVEF) is often used in clinical routine despite general recommendations to use quantitative biplane Simpsons (BPS) measurements. Even thou quantitative methods are well validated and from many reasons preferable, the feasibility of visual assessment (eyeballing) is superior. There is to date only sparse data comparing visual EF assessment in comparison to quantitative methods available. The aim of this study was to compare visual EF assessment by two-dimensional echocardiography (2DE) and triplane echocardiography (TPE) using quantitative real-time three-dimensional echocardiography (RT3DE) as the reference method.</p> <p>Methods</p> <p>Thirty patients were enrolled in the study. Eyeballing EF was assessed using apical 4-and 2 chamber views and TP mode by two experienced readers blinded to all clinical data. The measurements were compared to quantitative RT3DE.</p> <p>Results</p> <p>There were an excellent correlation between eyeballing EF by 2D and TP vs 3DE (r = 0.91 and 0.95 respectively) without any significant bias (-0.5 ± 3.7% and -0.2 ± 2.9% respectively). Intraobserver variability was 3.8% for eyeballing 2DE, 3.2% for eyeballing TP and 2.3% for quantitative 3D-EF. Interobserver variability was 7.5% for eyeballing 2D and 8.4% for eyeballing TP.</p> <p>Conclusion</p> <p>Visual estimation of LVEF both using 2D and TP by an experienced reader correlates well with quantitative EF determined by RT3DE. There is an apparent trend towards a smaller variability using TP in comparison to 2D, this was however not statistically significant.</p

Predicting olfactory receptor neuron responses from odorant structure

Background Olfactory receptors work at the interface between the chemical world of volatile molecules and the perception of scent in the brain. Their main purpose is to translate chemical space into information that can be processed by neural circuits. Assuming that these receptors have evolved to cope with this task, the analysis of their coding strategy promises to yield valuable insight in how to encode chemical information in an efficient way. Results We mimicked olfactory coding by modeling responses of primary olfactory neurons to small molecules using a large set of physicochemical molecular descriptors and artificial neural networks. We then tested these models by recording in vivo receptor neuron responses to a new set of odorants and successfully predicted the responses of five out of seven receptor neurons. Correlation coefficients ranged from 0.66 to 0.85, demonstrating the applicability of our approach for the analysis of olfactory receptor activation data. The molecular descriptors that are best-suited for response prediction vary for different receptor neurons, implying that each receptor neuron detects a different aspect of chemical space. Finally, we demonstrate that receptor responses themselves can be used as descriptors in a predictive model of neuron activation. Conclusions The chemical meaning of molecular descriptors helps understand structure-response relationships for olfactory receptors and their 'receptive fields'. Moreover, it is possible to predict receptor neuron activation from chemical structure using machine-learning techniques, although this is still complicated by a lack of training data

Fusion of metabolomics and proteomics data for biomarkers discovery: case study on the experimental autoimmune encephalomyelitis

Contains fulltext : 91843.pdf (publisher's version ) (Open Access)12 p

Parameter estimate of signal transduction pathways

BACKGROUND: The "inverse" problem is related to the determination of unknown causes on the bases of the observation of their effects. This is the opposite of the corresponding "direct" problem, which relates to the prediction of the effects generated by a complete description of some agencies. The solution of an inverse problem entails the construction of a mathematical model and takes the moves from a number of experimental data. In this respect, inverse problems are often ill-conditioned as the amount of experimental conditions available are often insufficient to unambiguously solve the mathematical model. Several approaches to solving inverse problems are possible, both computational and experimental, some of which are mentioned in this article. In this work, we will describe in details the attempt to solve an inverse problem which arose in the study of an intracellular signaling pathway. RESULTS: Using the Genetic Algorithm to find the sub-optimal solution to the optimization problem, we have estimated a set of unknown parameters describing a kinetic model of a signaling pathway in the neuronal cell. The model is composed of mass action ordinary differential equations, where the kinetic parameters describe protein-protein interactions, protein synthesis and degradation. The algorithm has been implemented on a parallel platform. Several potential solutions of the problem have been computed, each solution being a set of model parameters. A sub-set of parameters has been selected on the basis on their small coefficient of variation across the ensemble of solutions. CONCLUSION: Despite the lack of sufficiently reliable and homogeneous experimental data, the genetic algorithm approach has allowed to estimate the approximate value of a number of model parameters in a kinetic model of a signaling pathway: these parameters have been assessed to be relevant for the reproduction of the available experimental data

PLS and dimension reduction for classification

Linear discriminant analysis, Ridge regression, Shrinkage estimation, Misclassification rates, Principal components,