Interface Focus最新文献

Cellular signal transduction takes place through a network of phosphorylation cycles. These pathways take the form of a multi-layered cascade of cycles. This work focuses on the sensitivity of single, double and n length cycles. Cycles that operate in the zero-order regime can become sensitive to changes in signal, resulting in zero-order ultrasensitivity (ZOU). Using frequency analysis, we confirm previous efforts that cascades can act as noise filters by computing the bandwidth. We show that n length cycles display what we term first-order ultrasensitivity which occurs even when the cycles are not operating in the zero-order regime. The magnitude of the sensitivity, however, has an upper bound equal to the number of cycles. It is known that ZOU can be significantly reduced in the presence of retroactivity. We show that the first-order ultrasensitivity is immune to retroactivity and that the ZOU and first-order ultrasensitivity can be blended to create systems with constant sensitivity over a wider range of signal. We show that the ZOU in a double cycle is only modestly higher compared with a single cycle. We therefore speculate that the double cycle has evolved to enable amplification even in the face of retroactivity.

How to optimize the allocation of enzymes in metabolic pathways has been a topic of study for many decades. Although the general problem is complex and nonlinear, we have previously shown that it can be solved by convex optimization. In this paper, we focus on unbranched metabolic pathways with simplified enzymatic rate laws and derive analytic solutions to the optimization problem. We revisit existing solutions based on the limit of mass-action rate laws and present new solutions for other rate laws. Furthermore, we revisit a known relationship between flux control coefficients and enzyme abundances in optimal metabolic states. We generalize this relationship to models with density constraints on enzymes and metabolites, and present a new local relationship between optimal reaction elasticities and enzyme amounts. Finally, we apply our theory to derive simple kinetics-based formulae for protein allocation during bacterial growth.

Assessment of left atrial (LA) fibrosis from late gadolinium enhancement (LGE) magnetic resonance imaging (MRI) adds to the management of patients with atrial fibrillation. However, accurate assessment of fibrosis in the LA wall remains challenging. Excluding anatomical structures in the LA proximity using clipping techniques can reduce misclassification of LA fibrosis. A novel FK-means approach for combined automatic clipping and automatic fibrosis segmentation was developed. This approach combines a feature-based Voronoi diagram with a hierarchical 3D K-means fractal-based method. The proposed automatic Voronoi clipping method was applied on LGE-MRI data and achieved a Dice score of 0.75, similar to the score obtained by a deep learning method (3D UNet) for clipping (0.74). The automatic fibrosis segmentation method, which uses the Voronoi clipping method, achieved a Dice score of 0.76. This outperformed a 3D UNet method for clipping and fibrosis classification, which had a Dice score of 0.69. Moreover, the proposed automatic fibrosis segmentation method achieved a Dice score of 0.90, using manual clipping of anatomical structures. The findings suggest that the automatic FK-means analysis approach enables reliable LA fibrosis segmentation and that clipping of anatomical structures in the atrial proximity can add to the assessment of atrial fibrosis.

A mutation to serine of a conserved threonine (T634S) in the hERG K⁺ channel S6 pore region has been identified as a variant of uncertain significance, showing a loss-of-function effect. However, its potential consequences for ventricular excitation and arrhythmogenesis have not been reported. This study evaluated possible functional effects of the T634S-hERG mutation on ventricular excitation and arrhythmogenesis by using multi-scale computer models of the human ventricle. A Markov chain model of the rapid delayed rectifier potassium current (I_Kr) was reconstructed for wild-type and T634S-hERG mutant conditions and incorporated into the ten Tusscher et al. models of human ventricles at cell and tissue (1D, 2D and 3D) levels. Possible functional impacts of the T634S-hERG mutation were evaluated by its effects on action potential durations (APDs) and their rate-dependence (APDr) at the cell level; and on the QT interval of pseudo-ECGs, tissue vulnerability to unidirectional conduction block (VW), spiral wave dynamics and repolarization dispersion at the tissue level. It was found that the T634S-hERG mutation prolonged cellular APDs, steepened APDr, prolonged the QT interval, increased VW, destablized re-entry and augmented repolarization dispersion across the ventricle. Collectively, these results imply potential pro-arrhythmic effects of the T634S-hERG mutation, consistent with LQT2.

Transverse (t)-tubule remodelling is a prominent feature of heart failure with reduced ejection fraction (HFrEF). In our previous research, we identified an increased amount of collagen within the t-tubules of HFrEF patients, suggesting fibrosis could contribute to the remodelling of t-tubules. In this research, we tested this hypothesis in a rodent model of myocardial infarction induced heart failure that was treated with the anti-fibrotic pirfenidone. Confocal microscopy demonstrated loss of t-tubules within the border zone region of the infarct. This was documented as a reduction in t-tubule frequency, area, length, and transverse elements. Eight weeks of pirfenidone treatment was able to significantly increase the area and length of the t-tubules within the border zone. Echocardiography showed no improvement with pirfenidone treatment. Surprisingly, pirfenidone significantly increased the thickness of the t-tubules in the remote left ventricle of heart failure animals. Dilation of t-tubules is a common feature in heart failure suggesting this may negatively impact function but there was no functional loss associated with pirfenidone treatment. However, due to the relatively short duration of treatment compared to that used clinically, the impact of long-term treatment on t-tubule structure should be investigated in future studies.

Modelling complex systems, like the human heart, has made great progress over the last decades. Patient-specific models, called 'digital twins', can aid in diagnosing arrhythmias and personalizing treatments. However, building highly accurate predictive heart models requires a delicate balance between mathematical complexity, parameterization from measurements and validation of predictions. Cardiac electrophysiology (EP) models range from complex biophysical models to simplified phenomenological models. Complex models are accurate but computationally intensive and challenging to parameterize, while simplified models are computationally efficient but less realistic. In this paper, we propose a hybrid approach by leveraging deep learning to complete a simplified cardiac model from data. Our novel framework has two components, decomposing the dynamics into a physics based and a data-driven term. This construction allows our framework to learn from data of different complexity, while simultaneously estimating model parameters. First, using in silico data, we demonstrate that this framework can reproduce the complex dynamics of cardiac transmembrane potential even in the presence of noise in the data. Second, using ex vivo optical data of action potentials (APs), we demonstrate that our framework can identify key physical parameters for anatomical zones with different electrical properties, as well as to reproduce the AP wave characteristics obtained from various pacing locations. Our physics-based data-driven approach may improve cardiac EP modelling by providing a robust biophysical tool for predictions.

Metabolic syndrome (MetS) has been linked to a higher prevalence of cardiac arrhythmias, the most frequent being atrial fibrillation, but the mechanisms are not well understood. One possible underlying mechanism may be an abnormal modulation of autonomic nervous system activity, which can be quantified by analysing heart rate variability (HRV). Our aim was to investigate the modifications of long-term HRV in an experimental model of diet-induced MetS to identify the early changes in HRV and the link between autonomic dysregulation and MetS components. NZW rabbits were randomly assigned to control (n = 10) or MetS (n = 10) groups, fed 28 weeks with high-fat, high-sucrose diet. 24-hour recordings were used to analyse HRV at week 28 using time-domain, frequency-domain and nonlinear analyses. Time-domain analysis showed a decrease in RR interval and triangular index (Ti). In the frequency domain, we found a decrease in the low frequency band. Nonlinear analyses showed a decrease in DFA-α1 and DFA-α2 (detrended fluctuations analysis) and maximum multiscale entropy. The strongest association between HRV parameters and markers of MetS was found between Ti and mean arterial pressure, and Ti and left atrial diameter, which could point towards the initial changes induced by the autonomic imbalance in MetS.

Persistent atrial fibrillation (AF) is not effectively treated due to a lack of adequate tools for identifying patient-specific AF substrates. Recent studies revealed that in 30-50% of patients, persistent AF is maintained by localized drivers not only in the left atrium (LA) but also in the right atrium (RA). The chamber-specific atrial wall thickness (AWT) features underlying AF remain elusive, though the important role of AWT in AF is widely acknowledged. We aimed to provide direct evidence of the existence of distinguished RA and LA AWT features underlying AF drivers by analysing functionally and structurally mapped human hearts ex vivo. Coronary-perfused intact human atria (n = 7, 47 ± 14 y.o.; two female) were mapped using panoramic near-infrared optical mapping during pacing-induced AF. Then the hearts were imaged at approximately 170 µm³ resolution by 9.4 T gadolinium-enhanced MRI. The heart was segmented, and 3D AWT throughout atrial chambers was estimated and analysed. Optical mapping identified six localized RA re-entrant drivers in four hearts and four LA drivers in three hearts. All RA AF drivers were anchored to the pectinate muscle junctions with the crista terminalis or atrial walls. The four LA AF drivers were in the posterior LA. RA (n = 4) with AF drivers were thicker with greater AWT variation than RA (n = 3) without drivers (5.4 ± 2.6 mm versus 5.0 ± 2.4 mm, T-test p < 0.05; F-test p < 0.05). Furthermore, AWT in RA driver regions was thicker and varied more than in RA non-driver regions (5.1 ± 2.5 mm versus 4.4 ± 2.2 mm, T-test p < 0.05; F-test p < 0.05). On the other hand, LA (n = 3) with drivers was thinner than the LA (n = 4) without drivers. In particular, LA driver regions were thinner than the rest of LA regions (3.4 ± 1.0 mm versus 4.2 ± 1.0 mm, T-test p < 0.05). This study demonstrates chamber-specific AWT features of AF drivers. In RA, driver regions are thicker and have more variable AWT than non-driver regions. By contrast, LA drivers are thinner than non-drivers. Robust evaluation of patient-specific AWT features should be considered for chamber-specific targeted ablation.