Influence of genetic mutations to atria vulnerability to atrial fibrillation: An in-silico 3D human atria study.

BACKGROUND AND OBJECTIVE: Personalized 3D computer models of atria have been extensively implemented in the last yearsas a tool to facilitate the understanding of the mechanisms underlying different forms of arrhythmia, such as atrial fibrillation (AF). Meanwhile, genetic mutations acting on potassium channel dynamics were demonstrated to induce fibrillatory episodes in asymptomatic patients. This research study aims at assessing the effects and the atrial susceptibility to AF of three gain-of-function mutations - namely, KCNH2 T895M, KCNH2 T436M, and KCNE3-V17M - associated with AF outbreaks, using highly detailed 3D atrial models with realistic wall thickness and heterogenous histological properties. METHODS: The 3D atrial model was generated by reconstructing segmented anatomical structures from CT scans of an AF patient. Modified versions of the Courtemanche human atrial myocyte model were used to reproduce the electrophysiological activity of the WT and of the three mutant cells. Ectopic foci (EF) were simulated in sixteen locations across the atrial mesh using an S1-S2 protocol with two S2 basic cycle lengths (BCL) and eleven coupling intervals in order to induce arrhythmias. RESULTS: The three genetic mutations at 3D level reduced the APD90. The KCNE3-V17M mutation provoked the highest shortening (55 % in RA and LA with respect to WT), followed by KCNH2 T895M (14 % in RA and 18 % LA with respect to WT)and KCNH2 T436M (7 % in RA and 9 % LA with respect to WT). The KCNE3-V17M mutation led to arrhythmia in 67 % of the cases simulated and in 94 % of ectopic foci considered, at S2 BCL equal to 100 ms. The KCNH2 T436M and KCNH2 T895M mutations increased the vulnerability to AF in a similar way, leading to arrhythmic episodes in 7 % of the simulated conditions, at S2 BCL set to 160 ms. Overall, 60 % of the arrhythmic events generated arise in the left atrium. Spiral waves, multiple rotors and disordered electrical pattern were elicited in the presence of the KCNE3-V17M mutation, exhibiting an instantaneous mean frequency of 7.6 Hz with a mean standard deviation of 1.12 Hz. The scroll waves induced in the presence of the KCNH2 T436M and KCNH2 T895M mutations showed steadiness and regularity with an instantaneous mean frequencies in the range of 4.9 - 5.1 Hz and a mean standard deviation within 0.19 - 0.53 Hz. CONCLUSIONS: The pro-arrhythmogenicity of the KCNE3-V17M, KCNH2 T895M and KCNH2 T436M mutations was studied and proved on personalized 3D cardiac models. The three genetic mutations were demonstrated to increase the predisposition of atrial tissue to the formation of AF-susceptible substrate in different ways based on their effects on electrophysiological properties of the atria.

Experimentally validated modeling of dynamic drug-hERG channel interactions reproducing the binding mechanisms and its importance in action potential duration.

BACKGROUND AND OBJECTIVE: Assessment of drug cardiotoxicity is critical in the development of new compounds and modeling of drug-binding dynamics to hERG can improve early cardiotoxicity assessment. We previously developed a methodology to generate Markovian models reproducing preferential state-dependent binding properties, trapping dynamics and the onset of IKr block using simple voltage clamp protocols. Here, we test this methodology with real IKr blockers and investigate the impact of drug dynamics on action potential prolongation. METHODS: Experiments were performed on HEK cells stably transfected with hERG and using the Nanion SyncroPatch 384i. Three protocols, P-80, P0 and P 40, were applied to obtain the experimental data from the drugs and the Markovian models were generated using our pipeline. The corresponding static models were also generated and a modified version of the O´Hara-Rudy action potential model was used to simulate the action potential duration. RESULTS: The experimental Hill plots and the onset of IKr block of ten compounds were obtained using our voltage clamp protocols and the models generated successfully mimicked these experimental data, unlike the CiPA dynamic models. Marked differences in APD prolongation were observed when drug effects were simulated using the dynamic models and the static models. CONCLUSIONS: These new dynamic models of ten well-known IKr blockers constitute a validation of our methodology to model dynamic drug-hERG channel interactions and highlight the importance of state-dependent binding, trapping dynamics and the time-course of IKr block to assess drug effects even at the steady-state.

Air Pollution and Cardiac Diseases: A Review of Experimental Studies.

Air pollution is associated with around 6.5 million premature deaths annually, which are directly related to cardiovascular diseases, and the most dangerous atmospheric pollutants to health are as follows: NO2, SO2, CO, and PM. The mechanisms underlying the observed effects have not yet been clearly defined. This work aims to conduct a narrative review of experimental studies to provide a more comprehensive and multiperspective assessment of how the effect of atmospheric pollutants on cardiac activity can result in the development of cardiac diseases. For this purpose, a review was carried out in databases of experimental studies, excluding clinical trials, and epidemiological and simulation studies. After analyzing the available information, the existence of pathophysiological effects of the different pollutants on cardiac activity from exposure during both short-term and long-term is evident. This narrative review based on experimental studies is a basis for the development of recommendations for public health.

Combining pharmacokinetic and electrophysiological models for early prediction of drug-induced arrhythmogenicity.

BACKGROUND AND OBJECTIVE: In silico methods are gaining attention for predicting drug-induced Torsade de Pointes (TdP) in different stages of drug development. However, many computational models tended not to account for inter-individual response variability due to demographic covariates, such as sex, or physiologic covariates, such as renal function, which may be crucial when predicting TdP. This study aims to compare the effects of drugs in male and female populations with normal and impaired renal function using in silico methods. METHODS: Pharmacokinetic models considering sex and renal function as covariates were implemented from data published in pharmacokinetic studies. Drug effects were simulated using an electrophysiologically calibrated population of cellular models of 300 males and 300 females. The population of models was built by modifying the endocardial action potential model published by O'Hara et al. (2011) according to the experimentally measured gene expression levels of 12 ion channels. RESULTS: Fifteen pharmacokinetic models for CiPA drugs were implemented and validated in this study. Eight pharmacokinetic models included the effect of renal function and four the effect of sex. The mean difference in action potential duration (APD) between male and female populations was 24.9 ms (p<0.05). Our simulations indicated that women with impaired renal function were particularly susceptible to drug-induced arrhythmias, whereas healthy men were less prone to TdP. Differences between patient groups were more pronounced for high TdP-risk drugs. The proposed in silico tool also revealed that individuals with impaired renal function, electrophysiologically simulated with hyperkalemia (extracellular potassium concentration [K+]o = 7 mM) exhibited less pronounced APD prolongation than individuals with normal potassium levels. The pharmacokinetic/electrophysiological framework was used to determine the maximum safe dose of dofetilide in different patient groups. As a proof of concept, 3D simulations were also run for dofetilide obtaining QT prolongation in accordance with previously reported clinical values. CONCLUSIONS: This study presents a novel methodology that combines pharmacokinetic and electrophysiological models to incorporate the effects of sex and renal function into in silico drug simulations and highlights their impact on TdP-risk assessment. Furthermore, it may also help inform maximum dose regimens that ensure TdP-related safety in a specific sub-population of patients.

Uncertainty assessment of proarrhythmia predictions derived from multi-level in silico models.

In silico methods can be used for an early assessment of arrhythmogenic properties of drug candidates. However, their use for decision-making is conditioned by the possibility to estimate the predictions' uncertainty. This work describes our efforts to develop uncertainty quantification methods for the predictions produced by multi-level proarrhythmia models. In silico models used in this field usually start with experimental or predicted IC50 values that describe drug-induced ion channel blockade. Using such inputs, an electrophysiological model computes how the ion channel inhibition, exerted by a drug in a certain concentration, translates to an altered shape and duration of the action potential in cardiac cells, which can be represented as arrhythmogenic risk biomarkers such as the APD90. Using this framework, we identify the main sources of aleatory and epistemic uncertainties and propose a method based on probabilistic simulations that replaces single-point estimates predicted using multiple input values, including the IC50s and the electrophysiological parameters, by distributions of values. Two selected variability types associated with these inputs are then propagated through the multi-level model to estimate their impact on the uncertainty levels in the output, expressed by means of intervals. The proposed approach yields single predictions of arrhythmogenic risk biomarkers together with value intervals, providing a more comprehensive and realistic description of drug effects on a human population. The methodology was tested by predicting arrhythmogenic biomarkers on a series of twelve well-characterised marketed drugs, belonging to different arrhythmogenic risk classes.

Characterization of airborne particulate matter and its toxic and proarrhythmic effects: A case study in Aburrá Valley, Colombia.

Particle matter (PM) is a complex mixture of particles suspended in the air, mainly caused by fuel combustion from vehicles and industry, and has been related to pulmonary and cardiovascular diseases. The Metropolitan Area of Aburrá Valley in Colombia is the second most populous urban agglomeration in the country and the third densest in the world, composed of ten municipalities. Examining the physicochemical properties of PM is crucial in comprehending its composition and its effects on human health, as it varies based on the socioeconomic dynamics specific to each city. This study characterized the PM collected from the north, south, and central zones to evaluate its chemical composition and morphology. Different elements such as silicon, carbon, aluminum, potassium, calcium, sodium, iron, magnesium, and copper and the presence of unburned fuel, motor oil, and silicon fibers were identified. In vitro and in silico studies were conducted to evaluate the toxicity of the PM, and it was found that the PM collected from the central zone had the greatest impact on cell viability and caused DNA damage. The in silico study demonstrated that PM has concentration-dependent proarrhythmic effects, reflected in an action potential duration shortening and an increased number of reentries, which may contribute to the development of cardiac arrhythmias. Overall, the results suggest that the size and chemical composition of ambient PM can induce toxicity and play an important role in the generation of arrhythmias.

Factores predictores de resultado en tratamiento de tabaquismo en población de adultos mayores de la Comunidad Valenciana / Predictors of smoking treatment outcome in a population of older adults in the Valencian Community

El consumo de tabaco es uno de los más importantes factores de riesgo de enfermedad y muerte en España. Por ello, el objetivo de este estudio fue investigar las características epidemiológicas de 239pacientes fumadores mayores de 50 años en una unidad hospitalaria de deshabituación de tabaco, así como analizar los principales factores predictores que pueden influir en sus intentos de cese a losdoce meses. Es un estudio analítico transversal con tratamiento multicomponente combinando terapia psicológica e intervención farmacológica. Para comparar el éxito de abandono de los pacientes yconocer los posibles factores predictores, se llevó a cabo un análisis multivariante y de regresión logística binaria. De todos los pacientes, el 49,7% estableció el día D y la tasa de abandono final fue de 41,4%. Los predictores de intentos de abandono significativos fueron: índice paquetes-año entre 30 y 60, valor de cooximetría ≤ 10ppm, con una o más veces de intentos previos, tiempo máximo de cese superior a tres meses y grado alto en el test de Richmond. La escala de Minnesota con un valor inferior a 5 puntos fue el único predictor de abstinencia puntual a los doce meses. Los pacientes que consumieron menos tabaco y manifestaron intentos previos y alto grado de motivación tuvieron mayor posibilidad de tomar la decisión con éxito para dejar de fumar. Además, controlar el síndrome de abstinencia fue el aspecto más importante para tratar y reducir la tasa de recaída. (AU)

Tobacco consumption is one of the most important risk factors for disease and death in Spain. Therefore, the aim of this study was to investigate the epidemiological characteristics of 239 smoking patients over 50 years of age in a hospital smoking cessation unit, as well as to analyse the main predictors that may influence their cessation attempts at 12 months. It is a cross-sectional analytical study with multicomponent treatment combining psychological therapy and pharmacological intervention. A multivariate and binary logistic regression analysis was carried out to compare patients’ cessation success and to identify possible predictors. Of all patients, 49.7% established D-day and the final quit rate was 41.4%. Significant predictors of quit attempts were: pack-year index between 30 and 60, cooximetry value ≤ 10ppm, with one or more times of previous attempts, maximum cessation time greater than three months and high grade on the Richmond test. The Minnesota scale with a value of less than 5 points was the only predictor of timely abstinence at 12 months. Patients who used less tobacco and reported previous attempts and high motivation were more likely to make a successful decision to quit smoking. In addition, controlling the withdrawal syndrome was the most important aspect to treat and reduce the relapse rate. (AU)

Predictors of Health Satisfaction in Spanish Physically Active Older Adults: A Cross-Sectional Observational Study.

Studies that analyze the predictors of satisfaction with the health of the elderly are scarce. That is the reason why the objective of this study is to analyze whether the physical-psychological state, sports practice, and the use of socio-health resources are factors that predict satisfaction with health status in physically active elderly people. The Physical Activity and Quality of Life questionnaires were applied to a sample of 397 elderly people in this cross-sectional observational study. The data have been analyzed using Student's t-test chi-square test, Cohen's d, Phi Coefficient and Cramer's V. The results have shown that the lack of physical illnesses (OR = 3.920; p < 0.001) and psychological problems (OR = 1.940; p = 0.032), practicing a high level of physical activity (OR = 2.049; p = 0.001), having high scores in functional skills (OR = 8.059; p < 0.001) and using little social and health services (OR = 2.595; p < 0.001) are all predictors of being highly satisfied with one's health. In conclusion, predictors associated with high health satisfaction of active older people have been found, such as functional abilities, the existence of physical illness, psychological problems, level of physical activity, frequency of use of health and social services and satisfaction with health and social services; but it is not associated with gender or age of participants.

Manifold analysis of the P-wave changes induced by pulmonary vein isolation during cryoballoon procedure.

BACKGROUND/AIM: In atrial fibrillation (AF) ablation procedures, it is desirable to know whether a proper disconnection of the pulmonary veins (PVs) was achieved. We hypothesize that information about their isolation could be provided by analyzing changes in P-wave after ablation. Thus, we present a method to detect PV disconnection using P-wave signal analysis. METHODS: Conventional P-wave feature extraction was compared to an automatic feature extraction procedure based on creating low-dimensional latent spaces for cardiac signals with the Uniform Manifold Approximation and Projection (UMAP) method. A database of patients (19 controls and 16 AF individuals who underwent a PV ablation procedure) was collected. Standard 12-lead ECG was recorded, and P-waves were segmented and averaged to extract conventional features (duration, amplitude, and area) and their manifold representations provided by UMAP on a 3-dimensional latent space. A virtual patient was used to validate these results further and study the spatial distribution of the extracted characteristics over the whole torso surface. RESULTS: Both methods showed differences between P-wave before and after ablation. Conventional methods were more prone to noise, P-wave delineation errors, and inter-patient variability. P-wave differences were observed in the standard leads recordings. However, higher differences appeared in the torso region over the precordial leads. Recordings near the left scapula also yielded noticeable differences. CONCLUSIONS: P-wave analysis based on UMAP parameters detects PV disconnection after ablation in AF patients and is more robust than heuristic parameterization. Moreover, additional leads different from the standard 12-lead ECG should be used to detect PV isolation and possible future reconnections better.

Application of machine learning to improve the efficiency of electrophysiological simulations used for the prediction of drug-induced ventricular arrhythmia.

BACKGROUND AND OBJECTIVE: In silico prediction of drug-induced ventricular arrhythmia often requires computationally intensive simulations, making its application tedious and non-interactive. This inconvenience can be mitigated using matrices of precomputed simulation results, allowing instantaneous computation of biomarkers such as action potential duration at 90% of the repolarisation (APD90). However, preparing such matrices can be computationally intensive for the method developers, limiting the range of simulated conditions. In this work, we aim to optimise the generation of these matrices so that they can be obtained with less effort and for a broader range of input values. METHODS: Machine learning methods were applied, building models trained with only a small fraction of the originally simulated results. The predictive performances of the models were assessed by comparing their predicted values with the actual simulation results, using percentual mean absolute error and mean relative error, as well as the percentage of data with a relative error below 5%. RESULTS: Our method obtained highly accurate estimations of the original values, leading to a nearly one hundred-fold decrease in computation time. This method also allows precomputing more complex matrices, describing the effect of more ion channels on the APD90. The best results were obtained by applying Support Vector Machine models, which yielded errors below 1% in most cases. This approach was further validated by predicting the APD90 of a set of 12 CiPA compounds and exporting the optimal settings for predicting APD90 using a different set of ion channels, always with satisfactory results. CONCLUSIONS: The proposed method effectively reduces the computational effort required to generate matrices of precomputed electrophysiological simulation values. The same approach can be applied in other fields where computationally costly simulations are applied repeatedly using slightly different input values.

Automatic modeling of dynamic drug-hERG channel interactions using three voltage protocols and machine learning techniques: A simulation study.

BACKGROUND: Assessment of drug cardiac safety is critical in the development of new compounds and is commonly addressed by evaluating the half-maximal blocking concentration of the potassium human ether-à-go-go related gene (hERG) channels. However, recent works have evidenced that the modelling of drug-binding dynamics to hERG can help to improve early cardiac safety assessment. Our goal is to develop a methodology to automatically generate Markovian models of the drug-hERG channel interactions. METHODS: The training and the test sets consisted of 20800 and 5200 virtual drugs, respectively, distributed into 104 groups with different affinities and kinetics to the conformational states of the channel. In our system, drugs may bind to any state (individually or simultaneously), with different degrees of preference for a conformational state and the change of the conformational state of the drug bound channels may be restricted or allowed. To model such a wide range of possibilities, 12 Markovian chains are considered. Our approach uses the response of the drugs to our three previously developed voltage clamp protocols, which enhance the differences in the probabilities of occupying a certain conformational state of the channel (open, closed and inactivated). The computing tool is comprised of a classifier and a parameter optimizer and uses linear interpolation, support vector machines and a simplex method for function minimization. RESULTS: We propose a novel methodology that automatically generates dynamic drug models using Markov model formulations and that elucidates the states where the drug binds and unbinds and the preferential binding state using data obtained from simple voltage clamp protocols that captures the preferential state-dependent binding properties, the relative affinities, trapping and non-trapping dynamics and the onset of IKr block. Overall, the tool correctly predicted the class of 92.04% of the drugs and the model provided by the tool accurately fitted the response of the target compound, the mean accuracy being 97.53%. Moreover, generation of the dynamic model of an IKr blocker from its response to our voltage clamp protocols usually takes less than an hour on a common desktop computer. CONCLUSION: Our methodology could be very useful to model and simulate dynamic drug-hERG channel interactions. It would contribute to the improvement of the preclinical assessment of the proarrhythmic risk of drugs that inhibit IKr and the efficacy of antiarrhythmic IKr blockers.

Atrial fibrosis identification with unipolar electrogram eigenvalue distribution analysis in multi-electrode arrays.

Atrial fibrosis plays a key role in the initiation and progression of atrial fibrillation (AF). Atrial fibrosis is typically identified by a peak-to-peak amplitude of bipolar electrograms (b-EGMs) lower than 0.5 mV, which may be considered as ablation targets. Nevertheless, this approach disregards signal spatiotemporal information and b-EGM sensitivity to catheter orientation. To overcome these limitations, we propose the dominant-to-remaining eigenvalue dominance ratio (EIGDR) of unipolar electrograms (u-EGMs) within neighbor electrode cliques as a waveform dispersion measure, hypothesizing that it is correlated with the presence of fibrosis. A simulated 2D tissue with a fibrosis patch was used for validation. We computed EIGDR maps from both original and time-aligned u-EGMs, denoted as [Formula: see text] and [Formula: see text], respectively, also mapping the gain in eigenvalue concentration obtained by the alignment, [Formula: see text]. The performance of each map in detecting fibrosis was evaluated in scenarios including noise and variable electrode-tissue distance. Best results were achieved by [Formula: see text], reaching 94% detection accuracy, versus the 86% of b-EGMs voltage maps. The proposed strategy was also tested in real u-EGMs from fibrotic and non-fibrotic areas over 3D electroanatomical maps, supporting the ability of the EIGDRs as fibrosis markers, encouraging further studies to confirm their translation to clinical settings. Upper panels: map of [Formula: see text] from 3×3 cliques for Ψ= 0∘ and bipolar voltage map Vb-m, performed assuming a variable electrode-to-tissue distance and noisy u-EGMs (noise level σv = 46.4 µV ). Lower panels: detected fibrotic areas (brown), using the thresholds that maximize detection accuracy of each map.

Considering population variability of electrophysiological models improves the in silico assessment of drug-induced torsadogenic risk.

BACKGROUND AND OBJECTIVE: In silico tools are known to aid in drug cardiotoxicity assessment. However, computational models do not usually consider electrophysiological variability, which may be crucial when predicting rare adverse events such as drug-induced Torsade de Pointes (TdP). In addition, classification tools are usually binary and are not validated using an external data set. Here we analyze the role of incorporating electrophysiological variability in the prediction of drug-induced arrhythmogenic-risk, using a ternary classification and two external validation datasets. METHODS: The effects of the 12 training CiPA drugs were simulated at three different concentrations using a single baseline model and an electrophysiologically calibrated population of models. 9 biomarkers related with action potential (AP), calcium dynamics and net charge were measured for each simulated concentration. These biomarkers were used to build ternary classifiers based on Support Vector Machines (SVM) methodology. Classifiers were validated using two external drug sets: the 16 validation CiPA drugs and 81 drugs from CredibleMeds database. RESULTS: Population of models allowed to obtain different AP responses under the same pharmacological intervention and improve the prediction of drug-induced TdP with respect to the baseline model. The classification tools based on population of models achieve an accuracy higher than 0.8 and a mean classification error (MCE) lower than 0.3 for both validation drug sets and for the two electrophysiological action potential models studied (Tomek et al. 2020 and a modified version of O'Hara et al. 2011). In addition, simulations with population of models allowed the identification of individuals with lower conductances of IKr, IKs, and INaK and higher conductances of ICaL, INaL, and INCX, which are more prone to develop TdP. CONCLUSIONS: The methodology presented here provides new opportunities to assess drug-induced TdP-risk, taking into account electrophysiological variability and may be helpful to improve current cardiac safety screening methods.

Effect of Lead Position and Polarity on Paresthesia Coverage in Spinal Cord Stimulation Therapy: A Computational Study.

OBJECTIVES: The effect of lead placement and programming strategies on spinal cord stimulation (SCS) therapy has been widely studied; however, there is a need to optimize these parameters to favor dorsal column (DC) over dorsal root (DR) stimulation in complex pain treatment. This study aimed to determine the optimal lateral distance between two leads and the effect of transverse stimulation using a mathematical model. MATERIALS AND METHODS: A three-dimensional computational SCS and a nerve fiber model were used to determine the effect of the lateral distance between two leads at the same vertebral level T8 and the effect of the addition of anodes with two parallel leads at T8 and three different lateral distances on the model-based results (perception thresholds, activated DC fiber area and depth, and position of the first stimulated fiber). RESULTS: With two parallel leads programmed with symmetrical polarities, the maximal DC fiber area stimulated was found for a lateral distance of 5 mm. The results also show a higher preference for DR stimulation as the lateral distance increased. The addition of positive contacts at the same level of active contacts in the second lead produces a displacement of the first stimulated fiber laterally. CONCLUSIONS: A lateral distance of 5 mm shows a DC stimulated fiber area greater than when leads are placed contiguously. The addition of anodes creates an effect whereby the area of paresthesia is not displaced to the midline, but in the opposite direction. This may be useful when the leads are too close and stimulation of one of the sides is compromised.

Rotors Drift Toward and Stabilize in Low Power Regions in Heterogeneous Models of Atrial Fibrillation.

Atrial fibrillation (AF) afflicts more than 33 million people worldwide. Success of therapy strategies remains poor and better understanding of the arrhythmia and how to device more effective therapies are needed. The aim of this work is to study the role of electric power distributions in rotors and AF dynamics. For this purpose, single cell and tissue simulations were performed to study the effect of ionic currents gradients and fibrosis in rotor's drifting. The root mean square of the ionic (Pion), capacitance (Pc) and electrotonic (Pele) power was computed over action potentials. Single cell simulations were performed for different values of IK1 and ICaL and number of coupled myofibroblasts. Tissue simulations were performed in presence of IK1 and ICaL gradients and diffused fibrosis. Single cell simulations showed that Pion and Pc increased with IK1, while decreased by increasing ICaL. Increasing the number of coupled myofibroblasts reduced Pion and Pc, whereas Pele increased. Finally, in tissue simulations rotors drifted to regions with low power and anchored in regions with higher density of blunted ionic induced power gradients.

Directed Network Mapping Approach to Rotor Localization in Atrial Fibrillation Simulation.

Catheter ablation for atrial fibrillation (AF) is one of the most commonly performed electrophysiology procedures. Despite significant advances in our understanding of AF mechanisms in the last years, ablation outcomes remain suboptimal for many patients, particularly those with persistent or long-standing AF. A possible reason is that ablation techniques mainly focus on anatomic, rather than patient-specific functional targets for ablation. The identification of such ablation targets remains challenging. The purpose of this study is to investigate a novel approach based on directed networks, which allow the automatic detection of important arrhythmia mechanisms, that can be convenient for guiding the ablation strategy. The networks are generated by processing unipolar electrograms (EGMs) collected by the catheters positioned at the different regions of the atria. Network vertices represent the locations of the recordings and edges are determined using cross-covariance time-delay estimation method. The algorithm identifies rotational activity, spreading from vertex to vertex creating a cycle. This work is a simulation study and it uses a highly detailed computational 3D model of human atria in which sustained rotor activation of the atria was achieved. Virtual electrodes were placed on the endocardial surface, and EGMs were calculated at each of these electrodes. The propagation of the electric wave fronts in the atrial myocardium during AF is very complex, so in order to properly capture wave propagation patterns, we split EGMs into multiple short time frames. Then, a specific network for each of these time frames was generated, and the cycles repeating in consecutive networks point us to the stable rotor's location. The respective atrial voltage map served as reference. By detecting a cycle between the same 3 nodes in 19 out of 58 networks, where 10 of these networks were in consecutive time frames, a stable rotor was successfully located.

Fibrotic Remodeling during Persistent Atrial Fibrillation: In Silico Investigation of the Role of Calcium for Human Atrial Myofibroblast Electrophysiology.

During atrial fibrillation, cardiac tissue undergoes different remodeling processes at different scales from the molecular level to the tissue level. One central player that contributes to both electrical and structural remodeling is the myofibroblast. Based on recent experimental evidence on myofibroblasts' ability to contract, we extended a biophysical myofibroblast model with Ca2+ handling components and studied the effect on cellular and tissue electrophysiology. Using genetic algorithms, we fitted the myofibroblast model parameters to the existing in vitro data. In silico experiments showed that Ca2+ currents can explain the experimentally observed variability regarding the myofibroblast resting membrane potential. The presence of an L-type Ca2+ current can trigger automaticity in the myofibroblast with a cycle length of 799.9 ms. Myocyte action potentials were prolonged when coupled to myofibroblasts with Ca2+ handling machinery. Different spatial myofibroblast distribution patterns increased the vulnerable window to induce arrhythmia from 12 ms in non-fibrotic tissue to 22 ± 2.5 ms and altered the reentry dynamics. Our findings suggest that Ca2+ handling can considerably affect myofibroblast electrophysiology and alter the electrical propagation in atrial tissue composed of myocytes coupled with myofibroblasts. These findings can inform experimental validation experiments to further elucidate the role of myofibroblast Ca2+ handling in atrial arrhythmogenesis.

Atrial proarrhythmic effect of lead as one of the PM10 metal components of air pollution. An in-silico study.

Particulate matter (PM) is considered the most severe environmental pollution problem due to its serious effects on human health associated with an increased risk of cardiovascular morbidity and mortality. In this work, a physicochemical characterization of PM10 from the city of Medellin was developed. The results evince that lead (Pb) is one of the most abundant elements since it is present in all analyzed samples. Therefore, Pb was chosen to perform an in-silico study to assess its effects on atrial arrhythmias generation. For this purpose, we developed a model representing the Pb2+ blocking effect on the L-type calcium channel. This formulation was incorporated in a human atrial cell mathematical model and in 2D and 3D models of human atria. The simulations showed a proarrhythmic effect at high Pb2+ concentrations, through shortening of action potential duration inducing the generation of reentrant activity and atrial flutter. The results contribute to the knowledge about the cardiac physiopathological processes, triggered by lead as one of the main PM10 metal components of air pollution, that yields the generation of arrhythmias.

Macrophage-Dependent Interleukin-6-Production and Inhibition of IK Contributes to Acquired QT Prolongation in Lipotoxic Guinea Pig Heart.

In the heart, the delayed rectifier K current, IK, composed of the rapid (IKr) and slow (IKs) components contributes prominently to normal cardiac repolarization. In lipotoxicity, chronic elevation of pro-inflammatory cytokines may remodel IK, elevating the risk for ventricular arrythmias and sudden cardiac death. We investigated whether and how the pro-inflammatory interleukin-6 altered IK in the heart, using electrophysiology to evaluate changes in IK in adult guinea pig ventricular myocytes. We found that palmitic acid (a potent inducer of lipotoxicity), induced a rapid (~24 h) and significant increase in IL-6 in RAW264.7 cells. PA-diet fed guinea pigs displayed a severely prolonged QT interval when compared to low-fat diet fed controls. Exposure to isoproterenol induced torsade de pointes, and ventricular fibrillation in lipotoxic guinea pigs. Pre-exposure to IL-6 with the soluble IL-6 receptor produced a profound depression of IKr and IKs densities, prolonged action potential duration, and impaired mitochondrial ATP production. Only with the inhibition of IKr did a proarrhythmic phenotype of IKs depression emerge, manifested as a further prolongation of action potential duration and QT interval. Our data offer unique mechanistic insights with implications for pathological QT interval in patients and vulnerability to fatal arrhythmias.