As the fundamental unit of life, cells coordinate biological activities through the interaction between microscopic molecular mechanisms and macroscopic tissue organization. Traditional research studies, experiments, and biochemical analyses, give rise to important insights, although they are restricted in spatiotemporal resolution and processing power, thereby precluding the understanding of dynamic cross-scale biological events . Breakthroughs in artificial intelligence (AI) have given birth to the AI virtual cell (AIVC) as a new way to do research. By integrating multi-omics data and mixing methods from multidisciplinary models, AIVC establishes a digital twin system to simulate cell functions and behaviors. AIVC still faces a number of pressing challenges that need to be addressed in its current development stage. In this review, we are proposing a unified definition and technical framework for AIVC and analyze in detail the cross-scale coupling mechanisms of the "gene-protein-pathway-cell" hierarchy. Furthermore, we decompose the technical construction framework of AIVC from cross-scale representation engineering, functional submodule design, and multi-component dynamic regulation mechanisms. Additionally, we summarize the existing models and datasets in the field to provide reference resources for researchers. Finally, we deeply discuss the challenges faced by AIVC, such as data heterogeneity and model interpretability, and aim to accelerate the research progress in the AIVC field while driving the life sciences to shift from observational analysis to a paradigm that integrates predictability and innovation. Despite being in the early stage, AIVC is a trending topic that has garnered widespread interest. This review aims to integrate existing models, datasets, and technical ideas to provide a unified framework for field development.

QRS complex detection is a crucial preprocessing step in ECG-based arrhythmia recognition. Traditional methods often fail to maintain stability when confronted with noisy interference, baseline drift, and high inter-patient variability in ECG signals. Although deep learning approaches achieve superior detection performance, their high computational complexity hinders deployment on mobile and edge devices. Therefore, this study proposes a lightweight encoder-decoder network named MSCRAG for robust QRS complex detection. The architecture integrates joint time and frequency analysis with multi-scale convolutional to enable adaptive frequency decomposition, and incorporates a channel attention to enhance QRS time and frequency feature representation. In the encoder and decoder, an improved GhostV2Block (GhostV2BlockMS) reduces model parameters and computational cost while preserving feature extraction efficiency. Furthermore, the Multi-Scale Dynamic Feature Convolution Plus (MSDFC+) module which introduces in the bottleneck enhances the global receptive field and provides dynamic feature modulation, allowing the network to adaptively focus on critical temporal regions and capture long-range dependencies in ECG signals. Evaluations on standard ECG datasets demonstrate that our method achieves 99.1 % accuracy, 97.2 % sensitivity, 99.6 % positive predictive value, and 99.3% F1-score, outperforming existing state-of-the-art approaches. Its high accuracy, lightweight design, and low computational cost render it suitable for both static analysis and real-time cardiac monitoring in dynamic and wearable applications, offering significant potential for clinical and portable healthcare scenarios.

Neoadjuvant chemoradiotherapy (nCRT) is the standard treatment for locally advanced rectal cancer (LARC). However, substantial inter-patient variability in response to nCRT poses a significant challenge for accurately predicting treatment outcomes based on preoperative data, thereby complicating clinical decision-making. With the rapid advancement of artificial intelligence technologies, there has been growing interest in leveraging AI for predictive modeling in cancer therapy. In this study, we propose a novel multi-modal prediction framework, FMPNet, which integrates preoperative magnetic resonance imaging (MRI) and whole-slide image (WSI) features to predict nCRT efficacy. Specifically, for WSI processing, we develop an efficient tumor cell segmentation strategy and incorporate a deep subspace clustering mechanism into the feature extraction pipeline to enhance the model's representational capacity. Comprehensive experiments demonstrate that FMPNet consistently outperforms ten other feature fusion-based prediction models on both internal test sets and external validation cohorts across multiple metrics, including accuracy, precision, recall, F1-score and ROC-AUC curves. These results not only confirm the superior performance of our model but also underscore its potential to support more accurate and personalized clinical decision-making for patients with LARC.

Automated quality assessment of cardiac ultrasound images is crucial for ensuring diagnostic accuracy and clinical decision-making reliability. Hospitals face significant challenges in efficiently screening ultrasound image quality, where manual expert review is time-consuming and subjective. However, dedicated methods for ultrasound image quality assessment remain scarce, with most adapted from natural image quality metrics that fail to capture clinically relevant diagnostic factors. In this paper, we propose a novel dual-domain framework that models both spatial anatomical features and frequency-domain spectral characteristics using specialized neural modules. Our approach incorporates cardiac-specific attention mechanisms and wavelet-based artifact detection to enable comprehensive, clinically aligned evaluation. Extensive experiments on a largescale clinical dataset demonstrate the superiority of our method, achieving 88.1 % overall accuracy, a macro-averaged F1-score of 88.1 %, and 100 % precision and recall in detecting diagnostically unacceptable images. The proposed framework is designed to meet clinical reliability standards, paving the way for safe integration into diagnostic workflows.

Atrial fibrillation (AF) significantly affects morbidity and mortality rates. Class III antiarrhythmic drugs (AADs) play a crucial role in managing AF but often exhibit gender-specific complications. Our study aims to identify gender-specific Class III AADs by integrating in vitro measurements, in silico models, and machine learning (ML). By simulating drug effects on a diverse cardiomyocyte model population (5,663 males and 6,184 females), we classified drugs based on changes in action potentials and calcium transients. Using sex-dependent Support Vector Machine (SVM) algorithms, we achieved high prediction accuracy (>89%) and F1 score (>87%). Key features included changes in resting membrane potential and action potential amplitude, duration and area. Gender differences in drug responses were attributed to lower IK1, INa, and Ito in females.

Patients with abbreviated cardiac repolarization are at increased risk of cardiac arrhythmias including ventricular and atrial fibrillation (AF). In this computational simulation study, we investigated pro-arrhythmic effects of loss-of-function missense mutations in CACNA1C (A39V and G490R Cav1.2) identified in patients with a phenotype combining Brugada syndrome with shorter-than-normal QT intervals. Biophysically-detailed computational models of human atrial cells were modified to incorporate the functional impact of the CACNA1C encoded A39V and G490R mutations on the reduction of the maximal conductance (gCaL) of L-type calcium channels (LTCC). Varying levels of gCaL reduction were considered. Effects of deficient LTCC on atrial excitation and propagation were investigated by using cellular and multi-dimensional tissue models that included a one-dimensional atrial strand, a two-dimensional idealized atrial sheet and three-dimensional human atria with realistic anatomical structure and detailed electrophysiology. Our results showed that reduced LTCC activity from the CACNA1C A39V and G490R mutations accelerated atrial repolarization, leading to shortened action potential duration and effective refractory period, as well as the loss of their rate-dependence. At the tissue level, decreased gCaL shortened the wavelength of atrial excitation waves, slowed down atrial conduction velocity (CV) at low pacing rates but increased it at high pacing rates. It also showed bi-phasic arrhythmogenic effects in One-dimensional (1D), Two-dimensional (2D) and Three-dimensional (3D) tissue simulations. A large reduction in ICaL increased tissue susceptibility to initiation and maintenance of atrial re-entrant excitation waves, while a moderate reduction showed anti-arrhythmic effects due to an increased meandering area of re-entrant excitation waves that led to early self-termination of the reentry. In conclusion, this study provides new mechanistic insights into understanding of biphasic effects of loss-of-function LTCC mutations on atrial pro-arrhythmias.

The right atrium (RA) is critical for cardiac hemodynamics but is often overlooked in clinical diagnostics. This study presents a benchmark framework for RA cavity segmentation from late gadolinium-enhanced magnetic resonance imaging (LGE-MRIs), leveraging a two-stage strategy and a novel 3D deep learning network, RASnet. The architecture addresses challenges in class imbalance and anatomical variability by incorporating multi-path input, multi-scale feature fusion modules, Vision Transformers, context interaction mechanisms, and deep supervision. Evaluated on datasets comprising 354 LGE-MRIs, RASnet achieves SOTA performance with a Dice score of 92.19% on a primary dataset and demonstrates robust generalizability on an independent dataset. The proposed framework establishes a benchmark for RA cavity segmentation, enabling accurate and efficient analysis for cardiac imaging applications. Open-source code (https://github.com/zjinw/RAS) and data (https://zenodo.org/records/15524472) are provided to facilitate further research and clinical adoption.

The forward problem of electrocardiography is essential for understanding the mechanisms underlying cardiac electrical activity, validating and optimizing the inverse problem, and advancing personalized medicine through patient-specific modeling. Comprehensive computation of the forward problem requires multi-scale modeling and the execution of complex computational processes. The challenges associated with constructing, solving, and validating multi-scale models arise from the need to accurately represent biophysical processes by establishing precise interactions across various spatial and temporal scales. In this study, we aimed to integrate single-cell modeling, which captures the electrophysiological activity of cardiac cells, with finite element methods (FEM), which simulates the propagation of electrical signals from cardiac tissue to the body surface, to achieve a comprehensive simulation of the electrocardiogram (ECG) forward problem. We developed a multi-scale tissue structure model, incorporating it with single-cell models and FEM to compute the distribution of body surface potentials. Additionally, we simulated a 12-lead ECG by appropriately positioning electrodes. The results demonstrate that our proposed approach effectively simulates the QRS and T-wave components of the ECG and accurately captures variations in sub-cellular parameters under both normal and abnormal conditions as reflected in the ECG.

This paper presents a comprehensive evaluation of the performance of different numerical methods in cardiac electrophysiological simulations, including the Explicit Euler (EE) Method, Implicit Euler (IE) Method, Trapezoidal Rule (TR), and the Fourth-Order Runge-Kutta (RK4) Method within the framework of one-dimensional (1D), two-dimensional (2D), and three-dimensional (3D) models of the human ventricles. Simulation accuracy was investigated for varying the time step (h) for different tissue scales and complexities. It was shown that, as the dimension of the model increased, the upper limit of h for acceptable simulation accuracy (hul) gradually decreased. However, the difference in hulamong the methods was small. As the h or the tissue dimension increased, the EE method showed greater computational error compared to other methods. The experiment results also showed that the amplitude of the external stimulation pulse current, the size of the spatial range of tissue receiving stimuli, the spatial dimension of the tissue models, as well as the diffusion coefficient of the model, also affected simulation accuracy. The present study discusses the influence of numerical methods on the accuracy of cardiac simulations, providing insights for choosing optimal numerical methods for cardiac simulations.

Recently, self-training strategies have shown outstanding performance in the unsupervised medical image registration field. These strategies use their own network to generate pseudo-displacement fields (PFs) to supervise network training. However, limited diversity and accuracy of these PFs hinder their effectiveness. To address these limitations, we propose a novel mutual learning registration paradigm (MutualReg), where knowledge is distilled mutually between teacher and student networks for alternate improvement via recursive training. This involves two fundamental challenges: 1) how to generate more diverse and accurate PFs; and 2) how to effectively integrate knowledge distillation from the teacher network and learning from the student network. For the former, we employ a different and powerful teacher network thanks to the decoupling nature of MutualReg. For the latter, we introduce a Voxel-wise Reliability Criterion (VRC) module to retain reliable voxel locations of knowledge distillation. In the abdominal CT registration task, MutualReg outperforms state-of-the-art competitors, demonstrating its effectiveness. Code is available from https://github.com/PerceptionComputingLab/MutualReg/.

Arsenic trioxide $(As2\mathrm{O}_{3}$), an antileukemia drug, has been used to treat acute promyelocytic leukemia (APL) for more than fifty years, and its therapeutic effect has been elucidated at the molecular level. However, several side effects were observed in APL patients administrated with $As2\mathrm{O}_{3}$, such as long QT (LQT) syndrome, torsade de pointes tachycardia, and even sudden cardiac death. This means that the clinically relevant dosage may induce severe cardiotoxicity. Accordingly, it is essential to determine the underlying mechanisms of arrhythmia induced by $\mathrm{As}2\mathrm{O}_{3}$. Some biological experiments indicated that $\mathrm{As}2\mathrm{O}_{3}$ can impair the human ether-à-go-gorelated gene (hERG), thus inhibiting rapid delayed rectifier potassium current $(I_{Kr})$ and prolonging action potential duration (APD), which was regarded as the reason for LQT syndrome. However, previous experiments did not illuminate the deep mechanisms of $\mathrm{As}2\mathrm{O}_{3}$-induced side effects, which is important in clinical treatment. In addition, the experimental data were restricted to animal studies, so human cellular data were lacking. In this study, we investigated $\mathrm{As}2\mathrm{O}_{3}$-related cardiotoxicity through a human ventricular model study. Based on the current experimental data, the effects of $\mathrm{As}2\mathrm{O}_{3}$ on ventricular myocytes (VMs) were predicted at various $\mathrm{As}2\mathrm{O}_{3}$ concentrations. In addition, the potential hazard of $\mathrm{As}2\mathrm{O}_{3}$ was simulated and illustrated under different stimulation protocols. Moreover, electrocardiograms (ECGs) were estimated in heterogeneous ventricular cables, by which the clinical phenomenon was verified and explained. Based on the present modeling study, deep reasons for arrhythmia caused by $\mathrm{As}2\mathrm{O}_{3}$ were uncovered. $\mathrm{As}2\mathrm{O}_{3}$ not only led to a prolonged APD but also alternated action potentials and exacerbated heterogeneity among VMs. Moreover, the degree of arrhythmia risk was susceptible to $\mathrm{As}2\mathrm{O}_{3}$ dosage. These new findings may provide targets for attenuating $\mathrm{As}2\mathrm{O}_{3}$ toxicity and may help to improve the APL therapeutic regimen.

Biological pacemaker is a therapy for cardiac rhythm disease, which can be transformed from ventricular myocytes (VMs) by overexpressing HCN gene which codes the expression of hyperpolarization-activated current (${\mathrm {I}}_{\mathrm{f}}$) and knocking off Kir2.1 gene which codes inward-rectifier potassium current (${\mathrm {I}}_{\mathrm{K1}}$). Our previous study built a biological pacemaker single cell model and clarified the underlying mechanisms of how gene expressing levels influence the pacemaking activity of single pacemaker cell. But the pacemaking ability of pacemaker tissue has not been researched systematically. And what factors may have effects on pacemaker’s synchronization and spontaneous beating propagation are not clear. Biological research indicated that both sinoatrial node and pacemaker cells has less expression of connexin than unexcitable cardiac cells, which provides a possibility that improve pacemaking ability of pacemaker by decreasing its cell coupling. Another possible factor is the number of pacemaker cells. According to the common sense, increasing cell number can promote pacemaking behaviours, but overmuch pacemaker cells is unreasonable in clinic. As a result, the balance between pacemaker number and cell coupling is important when applying biological pacemaker. In this study, we constructed a two-dimensional cardiac tissue model with the description of electrophysiology to illustrate the relationship between gap junction and cell number. Based on this model, we modified the cell coupling between pacemaker cells by adjusting the diffusion coefficient of tissue with different pacemaker number. In different condition, the synchronization, pacemaking cycle length and electrical signal propagation were evaluated. It can be concluded that weakening cell coupling among pacemaker cells can lift the efficiency of bio-pacemaker therapy. This study may contribute to produce effective pacemaker in clinic.

Myocardial ischemia, injury and infarction (MI) are the three stages of acute coronary syndrome (ACS). In the past two decades, a great number of studies focused on myocardial ischemia and MI individually, and showed that the occurrence of reentrant arrhythmias is often associated with myocardial ischemia or MI. However, arrhythmogenic mechanisms in the tissue with various degrees of remodeling in the ischemic heart have not been fully understood. In this study, biophysical detailed single-cell models of ischemia 1a, 1b, and MI were developed to mimic the electrophysiological remodeling at different stages of ACS. 2D tissue models with different distributions of ischemia and MI areas were constructed to investigate the mechanisms of the initiation of reentrant waves during the progression of ischemia. Simulation results in 2D tissues showed that the vulnerable windows (VWs) in simultaneous presence of multiple ischemic conditions were associated with the dynamics of wave propagation in the tissues with each single pathological condition. In the tissue with multiple pathological conditions, reentrant waves were mainly induced by two different mechanisms: one is the heterogeneity along the excitation wavefront, especially the abrupt variation in conduction velocity (CV) across the border of ischemia 1b and MI, and the other is the decreased safe factor (SF) for conduction at the edge of the tissue in MI region which is attributed to the increased excitation threshold of MI region. Finally, the reentrant wave was observed in a 3D model with a scar reconstructed from MRI images of a MI patient. These comprehensive findings provide novel insights for understanding the arrhythmic risk during the progression of myocardial ischemia and highlight the importance of the multiple pathological stages in designing medical therapies for arrhythmias in ischemia.

Pacemaking dysfunction (PD) may result in heart rhythm disorders, syncope or even death. Current treatment of PD using implanted electronic pacemakers has some limitations, such as finite battery life and the risk of repeated surgery. As such, the biological pacemaker has been proposed as a potential alternative to the electronic pacemaker for PD treatment. Experimentally and computationally, it has been shown that bio-engineered pacemaker cells can be generated from non-rhythmic ventricular myocytes (VMs) by knocking out genes related to the inward rectifier potassium channel current (IK1) or by overexpressing hyperpolarization-activated cyclic nucleotide gated channel genes responsible for the "funny" current (If). However, it is unclear if a bio-engineered pacemaker based on the modification of IK1- and If-related channels simultaneously would enhance the ability and stability of bio-engineered pacemaking action potentials. In this study, the possible mechanism(s) responsible for VMs to generate spontaneous pacemaking activity by regulating IK1 and If density were investigated by a computational approach. Our results showed that there was a reciprocal interaction between IK1 and If in ventricular pacemaker model. The effect of IK1 depression on generating ventricular pacemaker was mono-phasic while that of If augmentation was bi-phasic. A moderate increase of If promoted pacemaking activity but excessive increase of If resulted in a slowdown in the pacemaking rate and even an unstable pacemaking state. The dedicated interplay between IK1 and If in generating stable pacemaking and dysrhythmias was evaluated. Finally, a theoretical analysis in the IK1/If parameter space for generating pacemaking action potentials in different states was provided. In conclusion, to the best of our knowledge, this study provides a wide theoretical insight into understandings for generating stable and robust pacemaker cells from non-pacemaking VMs by the interplay of IK1 and If, which may be helpful in designing engineered biological pacemakers for application purposes.

OBJECTIVE: Detecting changes in the QRS complexes in ECG signals is regarded as a straightforward, noninvasive, inexpensive, and preliminary diagnosis approach for evaluating the cardiac health of patients. Therefore, detecting QRS complexes in ECG signals must be accurate over short times. However, the reliability of automatic QRS detection is restricted by all kinds of noise and complex signal morphologies. The objective of this paper is to address automatic detection of QRS complexes. METHODS: In this paper, we proposed a new algorithm for automatic detection of QRS complexes using dual channels based on U-Net and bidirectional long short-term memory. First, a proposed preprocessor with mean filtering and discrete wavelet transform was initially applied to remove different types of noise. Next the signal was transformed and annotations were relabeled. Finally, a method combining U-Net and bidirectional long short-term memory with dual channels was used for the automatic detection of QRS complexes. RESULTS: The proposed algorithm was trained and tested using 44 ECG records from the MIT-BIH arrhythmia database and CPSC2019 dataset, which achieved 99.06% and 95.13% for sensitivity, 99.22% and 82.03% for positive predictivity, and 98.29% and 78.73% accuracy on the two datasets respectively. CONCLUSION: Experimental results prove that the proposed method may be useful for automatic detection of QRS complex task. SIGNIFICANCE: The proposed method not only has application potential for QRS complex detecting for large ECG data, but also can be extended to other medical signal research fields.

Sinus node ischemia is mainly characterized by slow heart rate, which is caused by ischemia-induced changes in electrophysiological properties and ion homeostasis leading to prolonged pacing cycle length. According to the available data, research on the mechanism of sinus node ischemia has been reported, but the report on the drug treatment of the disease is scarce. In order to reveal the effect of Chinese medicine (Yiqi Tongyang) in sinus node ischemia, this paper uses rabbit sinus node center and periphery models to simulate the effects of medium and high doses of the Chinese medicine in ischemia at the sub-cellular, cellular and tissue levels, and to simulate the changes in cellular action potentials and tissue pacing functions to predict the drug efficacy. Simulation results showed that 1) the Chinese medicine can effectively shorten the pacing cycle, with negligible effect on the duration of cellular action potential and maximal diastolic potential, but can cause a decrease in the maximal depolarization velocity; 2) it accelerated the activation of ischemic sinus node-atrum tissue, so that the electrical excitatory activity of the tissue return to the normal state; 3) by comparing the simulation results of medium and high doses of the Chinese medicine, it was shown that the high dose group did not increase the heart rate significantly, while the medium dose group had a significant effect on the regulation of heart rate, indicating that the Chinese medicine (Yiqi Tongyang) can effectively treat sinus node ischemic disease.

Sinus node ischemia is mainly characterized by slow heart rate, which is caused by ischemia-induced changes in electrophysiological properties and ion homeostasis leading to prolonged pacing cycle length. According to the available data, research on the mechanism of sinus node ischemia has been reported, but the report on the drug treatment of the disease is scarce. In order to reveal the effect of Chinese medicine (Yiqi Tongyang) in sinus node ischemia, this paper uses rabbit sinus node center and periphery models to simulate the effects of medium and high doses of the Chinese medicine in ischemia at the sub-cellular, cellular and tissue levels, and to simulate the changes in cellular action potentials and tissue pacing functions to predict the drug efficacy. Simulation results showed that 1) the Chinese medicine can effectively shorten the pacing cycle, with negligible effect on the duration of cellular action potential and maximal diastolic potential, but can cause a decrease in the maximal depolarization velocity; 2) it accelerated the activation of ischemic sinus node-atrum tissue, so that the electrical excitatory activity of the tissue return to the normal state; 3) by comparing the simulation results of medium and high doses of the Chinese medicine, it was shown that the high dose group did not increase the heart rate significantly, while the medium dose group had a significant effect on the regulation of heart rate, indicating that the Chinese medicine (Yiqi Tongyang) can effectively treat sinus node ischemic disease.

The biological pacemaker was a promising therapy for cardiac diseases such as sick sinus syndrome and atrioventricular block. A lot of experiments showed that pacemaker cells can be transformed from non-rhythmic cardiac cells or stem cells by gene therapy. However, at the tissue level, the electrophysiological properties between rhythmic and non-rhythmic regions are different. For example, the expression of connexin (such as Cx43) reduced in the induced-pacemaker cells which means that the pacemaker cells may have a less electrical coupling with adjacent cells. In addition, some researches indicated that the spatial distribution of pacemaker cells influenced the excitability of cardiac tissue. To the best of our knowledge, it is still unclear how the spatial distribution of bio-pacemaker cells affects the pacemaking behaviour in biological pacemaker tissue. In this study, we constructed a series of two-dimensional pacemaker-ventricle models containing different distributions of pacemaker cells to investigate the effect of spatial distribution on the pacemaking behaviour. Three kinds of models were designed in our simulations: (1) Tight model; (2) Embedded model; (3) Electrically isolated model. The pacemaking ability of cardiac tissue was measured by the least ratio of pacemaker cells needed to drive the whole tissue. Simulation results showed that electrically isolated model was the optimal model as it showed the best pacemaking ability among these three models. This study may guide the clinical use of bio-pacemaker.

The biological pacemaker was a promising therapy for cardiac diseases such as sick sinus syndrome and atrioventricular block. A lot of experiments showed that pacemaker cells can be transformed from non-rhythmic cardiac cells or stem cells by gene therapy. However, at the tissue level, the electrophysiological properties between rhythmic and non-rhythmic regions are different. For example, the expression of connexin (such as Cx43) reduced in the induced-pacemaker cells which means that the pacemaker cells may have a less electrical coupling with adjacent cells. In addition, some researches indicated that the spatial distribution of pacemaker cells influenced the excitability of cardiac tissue. To the best of our knowledge, it is still unclear how the spatial distribution of bio-pacemaker cells affects the pacemaking behaviour in biological pacemaker tissue. In this study, we constructed a series of two-dimensional pacemakerventricle models containing different distributions of pacemaker cells to investigate the effect of spatial distribution on the pacemaking behaviour. Three kinds of models were designed in our simulations: (1) Tight model; (2) Embedded model; (3) Electrically isolated model. The pacemaking ability of cardiac tissue was measured by the least ratio of pacemaker cells needed to drive the whole tissue. Simulation results showed that electrically isolated model was the optimal model as it showed the best pacemaking ability among these three models. This study may guide the clinical use of biopacemaker.

Air pollution has been long recognized as a hazardous factor for the human cardiovascular system. Sulfur dioxide (SO2) is a common ambient air pollutant that is able to cause detrimental effects on hearts. Though the cardiotoxicity effects by sulfur dioxide were well documented in epidemiological reports, however, the underlying mechanisms remain unclear owing to the technical limitations that exist in traditional experimental measures. In this article, we developed a multi-scale virtual ventricular tissue, which incorporated electrophysiological activities from subcellular to tissue levels and could provide comprehensive records and insightful mechanisms of the SO2induced ventricular arrhythmias. Based on the available cellular and molecular experimental data, our findings provide a rationale at tissue level in support of epidemiologic studies pointing to the deleterious effects of SO2pollution on cardiac function.

Heart failure (HF) is associated with an increased propensity for atrial fibrillation (AF), causing higher mortality than AF or HF alone. It is hypothesized that HF-induced remodelling of atrial cellular and tissue properties promotes the genesis of atrial action potential (AP) alternans and conduction alternans that perpetuate AF. However, the mechanism underlying the increased susceptibility to atrial alternans in HF remains incompletely elucidated. In this study, we investigated the effects of how HF-induced atrial cellular electrophysiological (with prolonged AP duration) and tissue structural (reduced cell-to-cell coupling caused by atrial fibrosis) remodelling can have an effect on the generation of atrial AP alternans and their conduction at the cellular and one-dimensional (1D) tissue levels. Simulation results showed that HF-induced atrial electrical remodelling prolonged AP duration, which was accompanied by an increased sarcoplasmic reticulum (SR) Ca2+ content and Ca2+ transient amplitude. Further analysis demonstrated that HF-induced atrial electrical remodelling increased susceptibility to atrial alternans mainly due to the increased sarcoplasmic reticulum Ca2+-ATPase (SERCA) Ca2+ reuptake, modulated by increased phospholamban (PLB) phosphorylation, and the decreased transient outward K+ current (Ito). The underlying mechanism has been suggested that the increased SR Ca2+ content and prolonged AP did not fully recover to their previous levels at the end of diastole, resulting in a smaller SR Ca2+ release and AP in the next beat. These produced Ca2+ transient alternans and AP alternans, and further caused AP alternans and Ca2+ transient alternans through Ca2+→AP coupling and AP→Ca2+ coupling, respectively. Simulation of a 1D tissue model showed that the combined action of HF-induced ion channel remodelling and a decrease in cell-to-cell coupling due to fibrosis increased the heart tissue's susceptibility to the formation of spatially discordant alternans, resulting in an increased functional AP propagation dispersion, which is pro-arrhythmic. These findings provide insights into how HF promotes atrial arrhythmia in association with atrial alternans.

It has been verified that biological pacemaker could be produced based on ventricular myocytes (VMs) by overexpressing HCN gene which codes the expression of hyperpolarization-activated current (If). Clinically, xenograft is in common use by which one specie' stem cell is infected with another specie's HCN gene so that the stem cell could transfer into cardiac pacemaker cell. The difference of HCN gene between species affects Ifproperties, but how the Ifproperties influence pacemaker creation is not easy to be qualified in biological experiments. In this study, we build an engineered biological pacemaker model based on a ventricular myocyte model by incorporating Ifformulation and simulated the membrane potential of biological pacemaker. The Ifof different species is simulated by modifying average half-maximal activation voltage (V1/2) of Ifactivation gate and Ifconductance (Gf). Based on the modified pacemaker model, the effect of Ifproperties on pacemaking stability and frequency is evaluated. Simulation results indicate that pacemaking ability is influenced dramatically by Ifproperties. In addition, the spontaneous pacemaking mechanism showed both membrane-clock and Ca2+-clock and its deep reason is analyzed in this paper. This study may provide a subcellular perspective for the clinical use of biological pacemaker.

Cardiac electrical alternans (CEA), manifested as T-wave alternans in ECG, is a clinical biomarker for predicting cardiac arrhythmias and sudden death. However, the mechanism underlying the spontaneous transition from CEA to arrhythmias remains incompletely elucidated. In this study, multiscale rabbit ventricular models were used to study the transition and a potential role of INa in perpetuating such a transition. It was shown CEA evolved into either concordant or discordant action potential (AP) conduction alternans in a homogeneous one-dimensional tissue model, depending on tissue AP duration and conduction velocity (CV) restitution properties. Discordant alternans was able to cause conduction failure in the model, which was promoted by impaired sodium channel with either a reduced or increased channel current. In a two-dimensional homogeneous tissue model, a combined effect of rate- and curvature-dependent CV broke-up alternating wavefronts at localised points, facilitating a spontaneous transition from CEA to re-entry. Tissue inhomogeneity or anisotropy further promoted break-up of re-entry, leading to multiple wavelets. Similar observations have also been seen in human atrial cellular and tissue models. In conclusion, our results identify a mechanism by which CEA spontaneously evolves into re-entry without a requirement for premature ventricular complexes or pre-existing tissue heterogeneities, and demonstrated the important pro-arrhythmic role of impaired sodium channel activity. These findings are model-independent and have potential human relevance.

Atrial tachy-arrhytmias, such as atrial fibrillation (AF), are characterised by irregular electrical activity in the atria, generally associated with erratic excitation underlain by re-entrant scroll waves, fibrillatory conduction of multiple wavelets or rapid focal activity. Epidemiological studies have shown an increase in AF prevalence in the developed world associated with an ageing society, highlighting the need for effective treatment options. Catheter ablation therapy, commonly used in the treatment of AF, requires spatial information on atrial electrical excitation. The standard 12-lead electrocardiogram (ECG) provides a method for non-invasive identification of the presence of arrhythmia, due to irregularity in the ECG signal associated with atrial activation compared to sinus rhythm, but has limitations in providing specific spatial information. There is therefore a pressing need to develop novel methods to identify and locate the origin of arrhythmic excitation. Invasive methods provide direct information on atrial activity, but may induce clinical complications. Non-invasive methods avoid such complications, but their development presents a greater challenge due to the non-direct nature of monitoring. Algorithms based on the ECG signals in multiple leads (e.g. a 64-lead vest) may provide a viable approach. In this study, we used a biophysically detailed model of the human atria and torso to investigate the correlation between the morphology of the ECG signals from a 64-lead vest and the location of the origin of rapid atrial excitation arising from rapid focal activity and/or re-entrant scroll waves. A focus-location algorithm was then constructed from this correlation. The algorithm had success rates of 93% and 76% for correctly identifying the origin of focal and re-entrant excitation with a spatial resolution of 40 mm, respectively. The general approach allows its application to any multi-lead ECG system. This represents a significant extension to our previously developed algorithms to predict the AF origins in association with focal activities.

A recent experimental study investigating patients with lone atrial fibrillation identified six novel mutations in the KCNA5 gene. The mutants exhibited both gain- and loss-of-function of the atrial specific ultra-rapid delayed rectifier K+ current, IKur. The aim of this study is to elucidate and quantify the functional impact of these KCNA5 mutations on atrial electrical activity. A multi-scale model of the human atria was updated to incorporate detailed experimental data on IKur from both wild-type and mutants. The effects of the mutations on human atrial action potential and rate dependence were investigated at the cellular level. In tissue, we assessed the effects of the mutations on the vulnerability to unidirectional conduction patterns and dynamics of re-entrant excitation waves. Gain-of-function mutations shortened the action potential duration in single cells, and stabilised and accelerated re-entrant excitation in tissue. Loss-of-function mutations had heterogeneous effects on action potential duration and promoted early-after-depolarisations following beta-adrenergic stimulation. In the tissue model, loss-of-function mutations facilitated breakdown of excitation waves at more physiological excitation rates than the wild-type, and the generation of early-after-depolarisations promoted unidirectional patterns of excitation. Gain- and loss-of-function IKur mutations produced multiple mechanisms of atrial arrhythmogenesis, with significant differences between the two groups of mutations. This study provides new insights into understanding the mechanisms by which mutant IKur contributes to atrial arrhythmias. In addition, as IKur is an atrial-specific channel and a number of IKur-selective blockers have been developed as anti-AF agents, this study also helps to understand some contradictory results on both pro- and anti-arrhythmic effects of blocking IKur.

Intracellular calcium cycling is a vital component of cardiac excitation-contraction coupling. The key structures responsible for controlling calcium dynamics are the cell membrane (comprising the surface sarcolemma and transverse-tubules), the intracellular calcium store (the sarcoplasmic reticulum), and the co-localisation of these two structures to form dyads within which calcium-induced-calcium-release occurs. The organisation of these structures tightly controls intracellular calcium dynamics. In this study, we present a computational model of intracellular calcium cycling in three-dimensions (3-D), which incorporates high resolution reconstructions of these key regulatory structures, attained through imaging of tissue taken from the sheep left ventricle using serial block face scanning electron microscopy. An approach was developed to model the sarcoplasmic reticulum structure at the whole-cell scale, by reducing its full 3-D structure to a 3-D network of one-dimensional strands. The model reproduces intracellular calcium dynamics during control pacing and reveals the high-resolution 3-D spatial structure of calcium gradients and intracellular fluxes in both the cytoplasm and sarcoplasmic reticulum. We also demonstrated the capability of the model to reproduce potentially pro-arrhythmic dynamics under perturbed conditions, pertaining to calcium-transient alternans and spontaneous release events. Comparison with idealised cell models emphasised the importance of structure in determining calcium gradients and controlling the spatial dynamics associated with calcium-transient alternans, wherein the probabilistic nature of dyad activation and recruitment was constrained. The model was further used to highlight the criticality in calcium spark propagation in relation to inter-dyad distances. The model presented provides a powerful tool for future investigation of structure-function relationships underlying physiological and pathophysiological intracellular calcium handling phenomena at the whole-cell. The approach allows for the first time direct integration of high-resolution images of 3-D intracellular structures with models of calcium cycling, presenting the possibility to directly assess the functional impact of structural remodelling at the cellular scale.

Gain-of-function mutations in KCNJ2-encoded Kir2.1 channels underlie variant 3 (SQT3) of the short QT syndrome, which is associated with atrial fibrillation (AF). Using biophysically-detailed human atria computer models, this study investigated the mechanistic link between SQT3 mutations and atrial arrhythmogenesis, and potential ion channel targets for treatment of SQT3. A contemporary model of the human atrial action potential (AP) was modified to recapitulate functional changes in IK1 due to heterozygous and homozygous forms of the D172N and E299V Kir2.1 mutations. Wild-type (WT) and mutant formulations were incorporated into multi-scale homogeneous and heterogeneous tissue models. Effects of mutations on AP duration (APD), conduction velocity (CV), effective refractory period (ERP), tissue excitation threshold and their rate-dependence, as well as the wavelength of re-entry (WL) were quantified. The D172N and E299V Kir2.1 mutations produced distinct effects on IK1 and APD shortening. Both mutations decreased WL for re-entry through a reduction in ERP and CV. Stability of re-entrant excitation waves in 2D and 3D tissue models was mediated by changes to tissue excitability and dispersion of APD in mutation conditions. Combined block of IK1 and IKr was effective in terminating re-entry associated with heterozygous D172N conditions, whereas IKr block alone may be a safer alternative for the E299V mutation. Combined inhibition of IKr and IKur produced a synergistic anti-arrhythmic effect in both forms of SQT3. In conclusion, this study provides mechanistic insights into atrial proarrhythmia with SQT3 Kir2.1 mutations and highlights possible pharmacological strategies for management of SQT3-linked AF.

Motivation: Long QT syndromes (LQTS) are characterized by early after depolarizations (EADs), repolarization dispersion and tachyarrhythmias. However, mechanisms by which these substrates promote tachyarrhythmias remain to be fully elucidated. This study sought to test the hypothesis that EADs induced by abrupt rate accelerations can occur and investigate how this abrupt rate accelerations is related to the mechanisms of reentrant excitations.Methods: The TP06 model for human ventricular cell was modified to model experimental conditions in LQTS. Then, the normal and EADs cell models were incorporated into homogeneous multicellular 1D and 2D tissue models to study the mechanism underlying the generation of reentrant events. Results and conclusions: In single cell simulations, abrupt accelerations in the heart rate prolonged action potential duration and favored to the genesis of EADs. In the ID simulations, an EADs region increased tissue's vulnerability to unidirectional conduction block in response to abrupt rate accelerations. In the 2D idealized tissue simulations, abrupt rate accelerations induced initiation of spiral waves due to an increase in repolarization gradients caused by an EADs region. These computer simulations suggest that abrupt rate accelerations can favor to the genesis of EADs and an EADs region can enhance the susceptibility of arrhythmias by increasing dispersion of repolarization. Thus, the increased regional repolarization dispersion caused by abrupt rate accelerations is a primary factor that may primarily contribute to the genesis of tachyarrhythmias in LQTS.

In this paper, we proposed a novel left ventricular volumes prediction method. This method is a cascade architecture which is based on multi-scale LV atlas location and deep convolutional neural networks (CNN). Firstly, we adopted LV atlas mapping method to achieve accurate location of LV region in cardiac magnetic resonance (CMR) images. And then, the CNN were used to train an end-to-end LV volumes prediction model to achieve the direct prediction. What's more, the large number of CMR images data (1140 subjects, more than 1026000 images) make the proposed deep CNN have relatively better feature representation and robust prediction ability. The experiment results on the large-scale CMR datasets prove that the proposed method has higher accuracy than the state-of-the-art prediction methods in terms of the end-diastole volumes (EDV), the end-systole volumes (ESV), and the ejection fraction (EF). Besides, we make the proposed method open accessible to public for wide application in other biomedical image processing fields.

The identified genetic short QT syndrome (SQTS) is associated with an increased risk of arrhythmia and sudden death. This study was to investigate the potential effects of propafenone on KCNH2-linked short QT syndrome (SQT1) using a multi-scale biophysically detailed model of the heart developed by ten Tusscher and Panfilov. The ion electrical conductivities were reduced by propafenone in order to simulate the pharmacological effects in healthy and SQT1 cells. Based on the experimental data of McPate et al., the pharmacological effect of propafenone was modelled by dose-dependent IKrblocking. Action potential (AP) profiles and 1D tissue level were analyzed to predict the effects of propafenone on SQT1. Both low- and high- dose of propafenone prolonged APD and QT interval in SQT1 cells. It suggests the superior efficacy of high dose of propafenone on SQT1. However, propafenone did not significantly alter the healthy APD or QT interval at low dose, whereas markedly shortened them at high dose. Our simulation data show that propafenone has a dose-dependently anti-arrhythmic effect on SQT1, and a pro-arrhythmic effect on healthy cells. These computer simulations help to better understand the underlying mechanisms responsible for the initiation or termination of arrhythmias in healthy or SQT1 patients using propafenone.

Transcription factor TBX18 could reduce the electrical coupling of ventricular myocytes and slow the electrical propagation, leading to pacemaker activity. In this article, the effect of TBX18 was analyzed by modulating coupling conductance (diffusion coefficient) and we found that with the decreasing of coupling, the pacemaker activity of ventricle increased. The first pacing time decreased with the reduction of coupling. However, when coupling conductance was lower than a critical value, the automatic excitation could not propagate, although the pacemaker worked robustly. Once the working myocytes could be driven, the pacemakers with different coupling conductance made no significant difference. Action potentials (APs) of pacemaker cells and normal cardiac myocytes at the same coordinates were similar for different coupling.

Rapid atrial arrhythmias such as atrial fibrillation (AF) predispose to ventricular arrhythmias, sudden cardiac death and stroke. Identifying the origin of atrial ectopic activity from the electrocardiogram (ECG) can help to diagnose the early onset of AF in a cost-effective manner. The complex and rapid atrial electrical activity during AF makes it difficult to obtain detailed information on atrial activation using the standard 12-lead ECG alone. Compared to conventional 12-lead ECG, more detailed ECG lead configurations may provide further information about spatio-temporal dynamics of the body surface potential (BSP) during atrial excitation. We apply a recently developed 3D human atrial model to simulate electrical activity during normal sinus rhythm and ectopic pacing. The atrial model is placed into a newly developed torso model which considers the presence of the lungs, liver and spinal cord. A boundary element method is used to compute the BSP resulting from atrial excitation. Elements of the torso mesh corresponding to the locations of the placement of the electrodes in the standard 12-lead and a more detailed 64-lead ECG configuration were selected. The ectopic focal activity was simulated at various origins across all the different regions of the atria. Simulated BSP maps during normal atrial excitation (i.e. sinoatrial node excitation) were compared to those observed experimentally (obtained from the 64-lead ECG system), showing a strong agreement between the evolution in time of the simulated and experimental data in the P-wave morphology of the ECG and dipole evolution. An algorithm to obtain the location of the stimulus from a 64-lead ECG system was developed. The algorithm presented had a success rate of 93%, meaning that it correctly identified the origin of atrial focus in 75/80 simulations, and involved a general approach relevant to any multi-lead ECG system. This represents a significant improvement over previously developed algorithms.

Cisapride as a prokinetic drug inhibits rapid delayed rectifier potassium channel current. As producing QT interval prolongation and causes fatal cardiac arrhythmias, it has been withdrawn from clinical uses. However, exact mechanisms for the proarrhythmic effects of cisapride are incompletely unclear. In this study, we implemented a biophysically detailed computational model of the heart to quantify the effects of the cisapride on cardiac electrical activities at cellular and tissue levels, from which we analyzed the proarrhythmic effects of the agent.

Turning non-autonomic ventricular cells into pacemaking cells is believed to hold the key for making a bio-pacemaker that could potentially treat patients with cardiac conduction diseases. In this article, we analyze the effects of various membrane ion channel currents on ventricular automaticity induced by reducing the inward-rectifier K+current (IK1). It was found that the L-type calcium current (ICaL), rather than the fast sodium current (INa), plays a major role in the rapid depolarization phase of the action potential. With a small ICaL, the automaticity of cells failed due to incompletion of the rapid depolarization. However, during the slow depolarization phase of the action potential, the background sodium current (IbNa), background calcium current (IbCa) and Na+/Ca2+exchanger current (INaCa) were playing more important roles. In 2D simulations, the automatic ventricular excitations arising from IK1reduction only couldn't propagate; it required other currents to be modulated at the same time for driving the surrounding cardiac tissues.

Automatic neuron reconstruction is one of the foremost challenging and important problem in the field of neuroscience. However, none of the prevalent algorithms can automatically reconstruct full anatomy structure. All of these make it is essential of developing new method for the tracing task. This paper introduced a novel seeding method for reconstructing neuron structures from 3-D microscopy images stacks. The protocol was initialized with a set of seeds which were detected by our proposed Sliding Volume Filter. And then the open curve snake was applied to the detected seeds to reconstruct the full structural of neuron cells. Results showed the proposed method exhibited excellent performance with its accuracy compared with traditional method. It is worth noting that the seeding method can clearly benefit for 3-D neuron fiber detection and reconstruction.

Hodgkin-Huxley(HH) equation is a classical model in electrophysiology and has been studied by many scholars. Applying stability theory, and taking maximal sodium conductance g̅naand potassium conductance g̅kas variables, in this study we analyze the stability and bifurcations of the model. Bifurcations are found when the variables change, and bifurcation points and boundary are calculated. When g̅nais the variable, there is only one bifurcation point and there are two points when g̅kis variable. The (g̅na, g̅k) plane is partitioned into two regions and the upper bifurcation boundary is similar to a line when both g̅naand g̅kare variables. The results gotten could be a help to control relevant diseases caused by maximal conductance anomaly.

Micro-computed tomography (micro-CT) has been widely used to generate high-resolution 3-D tissue images from small animals nondestructively, especially for mineralized skeletal tissues. However, its application to the analysis of soft cardiovascular tissues has been limited by poor inter-tissue contrast. Recent ex vivo studies have shown that contrast between muscular and connective tissue in micro-CT images can be enhanced by staining with iodine. In the present study, we apply this novel technique for imaging of cardiovascular structures in canine hearts. We optimize the method to obtain high-resolution X-ray micro-CT images of the canine atria and its distinctive regions-including the Bachmann's bundle, atrioventricular node, pulmonary arteries and veins-with clear inter-tissue contrast. The imaging results are used to reconstruct and segment the detailed 3-D geometry of the atria. Structure tensor analysis shows that the arrangement of atrial fibers can also be characterized using the enhanced micro-CT images, as iodine preferentially accumulates within the muscular fibers rather than in connective tissues. This novel technique can be particularly useful in nondestructive imaging of 3-D cardiac architectures from large animals and humans, due to the combination of relatively high speed ( ~ 1 h/per scan of the large canine heart) and high voxel resolution (36 μm) provided. In summary, contrast micro-CT facilitates fast and nondestructive imaging and segmenting of detailed 3-D cardiovascular geometries, as well as measuring fiber orientation, which are crucial in constructing biophysically detailed computational cardiac models.

Computer models provide a powerful platform for investigating mechanisms that underlie atrial rhythm disturbances. We have used novel techniques to build a structurally-detailed, image-based model of 3-D atrial anatomy. A volume image of the atria from a normal sheep heart was acquired using serial surface macroscopy, then smoothed and down-sampled to 50 μm(3) resolution. Atrial surface geometry was identified and myofiber orientations were estimated throughout by eigen-analysis of the 3-D image structure tensor. Sinus node, crista terminalis, pectinate muscle, Bachman's bundle, and pulmonary veins were segmented on the basis of anatomic characteristics. Heterogeneous electrical properties were assigned to this structure and electrical activation was simulated on it at 100 μm(3) resolution, using both biophysically-detailed and reduced-order cell activation models with spatially-varying membrane kinetics. We confirmed that the model reproduced key features of the normal spread of atrial activation. Furthermore, we demonstrate that vulnerability to rhythm disturbance caused by structural heterogeneity in the posterior left atrium is exacerbated by spatial variation of repolarization kinetics across this region. These results provide insight into mechanisms that may sustain paroxysmal atrial fibrillation. We conclude that image-based computer models that incorporate realistic descriptions of atrial myofiber architecture and electrophysiologic properties have the potential to analyse and identify complex substrates for atrial fibrillation.

The short QT syndrome (SQTS) is a genetically heterogeneous condition characterized by abbreviated QT intervals and an increased susceptibility to arrhythmia and sudden death. This simulation study identifies arrhythmogenic mechanisms in the rapid-delayed rectifier K(+) current (I(Kr))-linked SQT1 variant of the SQTS. Markov chain (MC) models were found to be superior to Hodgkin-Huxley (HH) models in reproducing experimental data regarding effects of the N588K mutation on KCNH2-encoded hERG. These ionic channel models were then incorporated into human ventricular action potential (AP) models and into 1D and 2D idealised and realistic transmural ventricular tissue simulations and into a 3D anatomical model. In single cell models, the N588K mutation abbreviated ventricular cell AP duration at 90% repolarization (APD(90)) and decreased the maximal transmural voltage heterogeneity (δV) during APs. This resulted in decreased transmural heterogeneity of APD(90) and of the effective refractory period (ERP): effects that are anticipated to be anti-arrhythmic rather than pro-arrhythmic. However, with consideration of transmural heterogeneity of I(Kr) density in the intact tissue model based on the ten Tusscher-Noble-Noble-Panfilov ventricular model, not only did the N588K mutation lead to QT-shortening and increases in T-wave amplitude, but δV was found to be augmented in some local regions of ventricle tissue, resulting in increased tissue vulnerability for uni-directional conduction block and predisposing to formation of re-entrant excitation waves. In 2D and 3D tissue models, the N588K mutation facilitated and maintained re-entrant excitation waves due to the reduced substrate size necessary for sustaining re-entry. Thus, in SQT1 the N588K-hERG mutation facilitates initiation and maintenance of ventricular re-entry, increasing the lifespan of re-entrant spiral waves and the stability of scroll waves in 3D tissue.

Abstract Atrial fibrillation (AF) is a common cardiac disease of genuine clinical concern with high rates of morbidity, leading to major personal and National Health Service costs. Computer modelling of AF using biophysically detailed cellular models with realistic 3D anatomical geometry allows investigation of the underlying ionic mechanisms in far more detail than with experimental physiology. We have developed a 3D virtual human atrium that combines detailed cellular electrophysiology including ion channel kinetics and homeostasis of ionic concentrations with anatomical details. The segmented anatomical structure and the multivariable nature of the system make the 3D simulations of AF computationally large and intensive. Computational demands are such that a full problem‐solving environment requires access to resources of high‐performance computing (HPC), high‐performance visualization (HPV), remote data repositories and backend infrastructure. This is a classic example of eScience and Grid‐enabled computing. This study was carried out using multiple processor shared memory systems and massively parallel distributed memory systems. With the envisaged increase in anatomical and molecular detail in our cardiac models the requirement for HPC resources is predicted to increase many fold (∼ 1–10 teraflops). Distributed computing is essential, both through massively parallel systems (a single supercomputer) and multiple parallel systems made accessible through the Grid. Analysis and interpretation of results are enhanced by HPV, which in itself is a large data computing aspect of cardiac modelling. Copyright © 2008 John Wiley & Sons, Ltd.

Palm medicine is an important part of Tradition Chinese Medicine (TCM), which has been widely practiced in China and southeast country of Asia. Palmprint is composed of many lines and some special structures which imply a number of diseases. In this paper a fuzzy approach is proposed to recognize one of special structures in palmprint which is a key process in automated palm diagnosis system. Firstly, a palm image is preprocessed and all palmprint lines are extracted. Secondly, the extracted palm-lines are transformed to an undirected graph according to the connection of the points on the palm-lines. Thirdly, three features are extracted from this graph and their membership functions are defined. Finally, these three features are utilized to recognize one special structure which is called mi structure. Applying our approach to 200 palmprint images, the experimental results are encouraging.

Characteristics of tongue pose the most important information for traditional Chinese medicine diagnosis. So far, extensive studies have been made on extracting tongue surface features, but rarely refer to sublingual vein that is also diagnostically important. This paper presents a novel approach to extract spatial characteristics of sublingual vein based on the HSI color space using the H and S components. Sublingual vein structures have been successfully mapped for 113 out of 150 patients and healthy subjects.

The kinetics of proteins involved in ion transfer, sequestration and binding in cardiac cells can be modelled to construct a model of the electrical activity of isolated cardiac cells as a system of ordinary differential equations. These cell models may be incorporated into tissue models, which, when combined with histology and anatomy, form virtual tissues. The effects of changes in specific protein expression, or changes in protein kinetics, produced by mutations or pharmacological agents, can be simulated using these tissue models and used to account for the whole organ effects of changes in specific ion-transport protein activity.