Ileal Transcriptome Profiles of Japanese Quail Divergent in Phosphorus Utilization.

Phosphorus (P) is an essential component for all living beings. Low P diets prompt phenotypic and molecular adaptations to maintain P homeostasis and increase P utilization (PU). Knowledge of the molecular mechanisms of PU is needed to enable targeted approaches to improve PU efficiency and thus lower P excretion in animal husbandry. In a previous population study, Japanese quail were subjected to a low P diet lacking mineral P and exogenous phytase. Individual PU was determined based on total P intake and excretion. A subset of 20 extreme siblings discordant for PU was selected to retrieve gene expression patterns of ileum (n = 10 per PU group). Sequencing reads have been successfully mapped to the current Coturnix japonica reference genome with an average mapping rate of 86%. In total, 640 genes were found to be differentially abundant between the low and high PU groups (false discovery rate ≤ 0.05). Transcriptional patterns suggest a link between improved PU and mitochondrial energy metabolism, accelerated cell proliferation of enterocytes, and gut integrity. In assessing indicators of the efficient use of macro- and micronutrients, further research on turnover and proliferation rates of intestinal cells could provide an approach to improve P efficiency in poultry species.

CFD- and Bernoulli-based pressure drop estimates: A comparison using patient anatomies from heart and aortic valve segmentation of CT images.

PURPOSE: An aortic valve stenosis is an abnormal narrowing of the aortic valve (AV). It impedes blood flow and is often quantified by the geometric orifice area of the AV (AVA) and the pressure drop (PD). Using the Bernoulli equation, a relation between the PD and the effective orifice area (EOA) represented by the area of the vena contracta (VC) downstream of the AV can be derived. We investigate the relation between the AVA and the EOA using patient anatomies derived from cardiac computed tomography (CT) angiography images and computational fluid dynamic (CFD) simulations. METHODS: We developed a shape-constrained deformable model for segmenting the AV, the ascending aorta (AA), and the left ventricle (LV) in cardiac CT images. In particular, we designed a structured AV mesh model, trained the model on CT scans, and integrated it with an available model for heart segmentation. The planimetric AVA was determined from the cross-sectional slice with minimum AV opening area. In addition, the AVA was determined as the nonobstructed area along the AV axis by projecting the AV leaflet rims on a plane perpendicular to the AV axis. The flow rate was derived from the LV volume change. Steady-state CFD simulations were performed on the patient anatomies resulting from segmentation. RESULTS: Heart and valve segmentation was used to retrospectively analyze 22 cardiac CT angiography image sequences of patients with noncalcified and (partially) severely calcified tricuspid AVs. Resulting AVAs were in the range of 1-4.5 cm2 and ejection fractions (EFs) between 20 and 75%. AVA values computed by projection were smaller than those computed by planimetry, and both were strongly correlated (R2 = 0.995). EOA values computed via the Bernoulli equation from CFD-based PD results were strongly correlated with both AVA values (R2 = 0.97). EOA values were ∼10% smaller than planimetric AVA values. For EOA values < 2.0 cm2 , the EOA was up to ∼15% larger than the projected AVA. CONCLUSIONS: The presented segmentation algorithm allowed to construct detailed AV models for 22 patient cases. Because of the crown-like 3D structure of the AV, the planimetric AVA is larger than the projected AVA formed by the free edges of the AV leaflets. The AVA formed by the free edges of the AV leaflets was smaller than the EOA for EOA values <2.0cm2. This contradiction with respect to previous studies that reported the EOA to be always smaller or equal to the geometric AVA is explained by the more detailed AV models used within this study.

Initiation of atrial fibrillation by interaction of pacemakers with geometrical constraints.

Atrial fibrillation (AF) is the most common arrhythmia of the heart in industrialized countries. Its generation and the transitory behavior of paroxysmal AF are still not well understood. In this work we examine the interaction of two activation sources via an isthmus as possible cause for the initiation of fibrillation episodes. For this study, the electrophysiological model of Bueno-Orovio, Cherry and Fenton is adapted to atrial electrophysiology, both for physiological and electrophysiologically remodeled conditions due to AF. We show that the interaction of the pacemakers, combined with the geometrical constraints of the isthmus, can produce fibrillatory-type irregularities, which we quantify by the loss of spatial phase coherence in the transmembrane voltage. Transitions to irregular behavior occur when the frequencies of the pacemakers exceed certain thresholds, suggesting that AF episodes are initiated by frequency changes of the activating sources (sinus node, ectopic focus).

Computational modeling of the human atrial anatomy and electrophysiology.

This review article gives a comprehensive survey of the progress made in computational modeling of the human atria during the last 10 years. Modeling the anatomy has emerged from simple "peanut"-like structures to very detailed models including atrial wall and fiber direction. Electrophysiological models started with just two cellular models in 1998. Today, five models exist considering e.g. details of intracellular compartments and atrial heterogeneity. On the pathological side, modeling atrial remodeling and fibrotic tissue are the other important aspects. The bridge to data that are measured in the catheter laboratory and on the body surface (ECG) is under construction. Every measurement can be used either for model personalization or for validation. Potential clinical applications are briefly outlined and future research perspectives are suggested.

Comparing measured and simulated wave directions in the left atrium - a workflow for model personalization and validation.

Atrial arrhythmias are frequently treated using catheter ablation during electrophysiological (EP) studies. However, success rates are only moderate and could be improved with the help of personalized simulation models of the atria. In this work, we present a workflow to generate and validate personalized EP simulation models based on routine clinical computed tomography (CT) scans and intracardiac electrograms. From four patient data sets, we created anatomical models from angiographic CT data with an automatic segmentation algorithm. From clinical intracardiac catheter recordings, individual conduction velocities were calculated. In these subject-specific EP models, we simulated different pacing maneuvers and measurements with circular mapping catheters that were applied in the respective patients. This way, normal sinus rhythm and pacing from a coronary sinus catheter were simulated. Wave directions and conduction velocities were quantitatively analyzed in both clinical measurements and simulated data and were compared. On average, the overall difference of wave directions was 15° (8%), and the difference of conduction velocities was 16 cm/s (17%). The method is based on routine clinical measurements and is thus easy to integrate into clinical practice. In the long run, such personalized simulations could therefore assist treatment planning and increase success rates for atrial arrhythmias.

Conduction velocity restitution of the human atrium--an efficient measurement protocol for clinical electrophysiological studies.

Conduction velocity (CV) and CV restitution are important substrate parameters for understanding atrial arrhythmias. The aim of this work is to (i) present a simple but feasible method to measure CV restitution in-vivo using standard circular catheters, and (ii) validate its feasibility with data measured during incremental pacing. From five patients undergoing catheter ablation, we analyzed eight datasets from sinus rhythm and incremental pacing sequences. Every wavefront was measured with a circular catheter and the electrograms were analyzed with a cosine-fit method that calculated the local CV. For each pacing cycle length, the mean local CV was determined. Furthermore, changes in global CV were estimated from the time delay between pacing stimulus and wavefront arrival. Comparing local and global CV between pacing at 500 and 300 ms, we found significant changes in seven of eight pacing sequences. On average, local CV decreased by 20 ± 15% and global CV by 17 ± 13%. The method allows for in-vivo measurements of absolute CV and CV restitution during standard clinical procedures. Such data may provide valuable insights into mechanisms of atrial arrhythmias. This is important both for improving cardiac models and also for clinical applications, such as characterizing arrhythmogenic substrates during sinus rhythm.

Predicting tissue conductivity influences on body surface potentials-an efficient approach based on principal component analysis.

In this paper, we present an efficient method to estimate changes in forward-calculated body surface potential maps (BSPMs) caused by variations in tissue conductivities. For blood, skeletal muscle, lungs, and fat, the influence of conductivity variations was analyzed using the principal component analysis (PCA). For each single tissue, we obtained the first PCA eigenvector from seven sample simulations with conductivities between ±75% of the default value. We showed that this eigenvector was sufficient to estimate the signal over the whole conductivity range of ±75%. By aligning the origins of the different PCA coordinate systems and superimposing the single tissue effects, it was possible to estimate the BSPM for combined conductivity variations in all four tissues. Furthermore, the method can be used to easily calculate confidence intervals for the signal, i.e., the minimal and maximal possible amplitudes for given conductivity uncertainties. In addition to that, it was possible to determine the most probable conductivity values for a given BSPM signal. This was achieved by probing hundreds of different conductivity combinations with a numerical optimization scheme. In conclusion, our method allows to efficiently predict forward-calculated BSPMs over a wide range of conductivity values from few sample simulations.

A framework for personalization of computational models of the human atria.

A framework for step-by-step personalization of a computational model of human atria is presented. Beginning with anatomical modeling based on CT or MRI data, next fiber structure is superimposed using a rule-based method. If available, late-enhancement-MRI images can be considered in order to mark fibrotic tissue. A first estimate of individual electrophysiology is gained from BSPM data solving the inverse problem of ECG. A final adjustment of electrophysiology is realized using intracardiac measurements. The framework is applied using several patient data. First clinical application will be computer assisted planning of RF-ablation for treatment of atrial flutter and atrial fibrillation.

Ranking the influence of tissue conductivities on forward-calculated ECGs.

This paper examined the effects that different tissue conductivities had on forward-calculated ECGs. To this end, we ranked the influence of tissues by performing repetitive forward calculations while varying the respective tissue conductivity. The torso model included all major anatomical structures like blood, lungs, fat, anisotropic skeletal muscle, intestine, liver, kidneys, bone, cartilage, and spleen. Cardiac electrical sources were derived from realistic atrial and ventricular simulations. The conductivity rankings were based on one of two methods: First, we considered fixed percental conductivity changes to probe the sensitivity of the ECG regarding conductivity alterations. Second, we set conductivities to the reported minimum and maximum values to evaluate the effects of the existing conductivity uncertainties. The amplitudes of both atrial and ventricular ECGs were most sensitive for blood, skeletal muscle conductivity and anisotropy as well as for heart, fat, and lungs. If signal morphology was considered, fat was more important whereas skeletal muscle was less important. When comparing atria and ventricles, the lungs had a larger effect on the atria yet the heart conductivity had a stronger impact on the ventricles. The effects of conductivity uncertainties were significant. Future studies dealing with electrocardiographic simulations should consider these effects.

Wave-direction and conduction-velocity analysis from intracardiac electrograms--a single-shot technique.

Atrial arrhythmias, such as atrial flutter or fibrillation, are frequent indications for catheter ablation. Recorded intracardiac electrograms (EGMs) are, however, mostly evaluated subjectively by the physicians. In this paper, we present a method to quantitatively extract the wave direction and the local conduction velocity from one single beat in a circular mapping catheter signal. We simulated typical clinical EGMs to validate the method. We then showed that even with noise, the average directional error was below 10(°) and the average velocity error was below 5.4 cm/s. In a realistic atrial simulation, the method could clearly distinguish between stimuli from different pulmonary veins. We further analyzed eight clinical data segments from three patients in normal sinus rhythm and with stimulation. We obtained stable wave directions for each segment and conduction velocities between 70 and 115 cm/s. We conclude that the method allows for easy quantitative analysis of single macroscopic wavefronts in intracardiac EGMs, such as during atrial flutter or in typical clinical stimulation procedures after termination of atrial fibrillation. With corresponding simulated data, it can provide an interface to personalize electrophysiological (EP) models. Furthermore, it could be integrated into EP navigation systems to provide quantitative data of high diagnostic value to the physician.