Narrow Results

Phytochemical flavonoids have been proven to be effective in treating various disorders, including cardiovascular diseases. Acacetin is a natural flavone with diverse pharmacological effects, uniquely including atrial-selective anti-atrial fibrillation (AF) via the inhibition of the atrial specific potassium channel currents $I_{\mathrm{\text{Kur}}}$ (ultra-rapidly delayed rectifier potassium current), $I_{\mathrm{\text{KACh}}}$ (acetylcholine-activated potassium current), $I_{\mathrm{\text{sKCa}}}$ (calcium-activated small conductance potassium current), and $I_{\mathrm{\text{to}}}$ (transient outward potassium current). $I_{\mathrm{\text{to}}}$ inhibition by acacetin, notably, suppresses experimental J-wave syndromes. In addition, acacetin provides extensive cardiovascular protection against ischemia/reperfusion injury, cardiomyopathies/heart failure, autoimmune myocarditis, pulmonary artery hypertension, vascular remodeling, and atherosclerosis by restoring the downregulated intracellular signaling pathway of Sirt1/AMPK/PGC-1α followed by increasing Nrf2/HO-1/SOD thereby inhibiting oxidation, inflammation, and apoptosis. This review provides an integrated insight into the capabilities of acacetin as a drug candidate for treating cardiovascular diseases, especially atrial fibrillation and cardiomyopathies/heart failure.

Our group has described previously the identification of arrhythmogenic pulmonary veins by rapid local electrical activations during atrial fibrillation. We have now investigated an algorithm for automated computer detection of this phenomenon from catheter electrodes in the upper pulmonary veins and assessed its performance in identifying arrhymogenic veins. Ten patients with persistent atrial fibrillation scheduled for pulmonary vein isolation at this hospital were studied. Electrogram recordings in the upper pulmonary veins were recorded and analyzed. Arrhythmogenic veins were identified by focal activity during sinus rhythm at electrophysiological studies. Recordings were visually assessed by a cardiologist for the presence of rapid repetitive electrical activations during atrial fibrillation. An index of rapid repetitive electrical activity (RREA index), the ratio of the number of activations with cycle lengths in the range 50 ms to 100 ms to the number of activations with cycle lengths in the range 100 ms to 200 ms, was devised to describe the extent of such activity automatically. The index was assessed as a predictor of arrhythmogenic veins. Electrograms from 19 upper pulmonary veins were recorded. Rapid activity was evident in 15 veins by visual manual assessment. The mean (range) automatic RREA index was 0.07 (0 to 0.16) for those identified as having no such activity manually, and 0.83 (0.22 to 1.68) for those identified with rapid activity (p<0.0001). With a threshold of RREA index in the range 0.17 to 0.21, the identification of veins with rapid firing was exactly the same as for manual assessment. Eleven upper pulmonary veins were identified as arrhythmogenic during electrophysiological study, and the identification of these veins by both manual and automatic assessment of rapid repetitive electrical activations gave a sensitivity of 100% (11/11) and specificity of 50% (4/8). A technique for automatic characterization of electrogram cycle length has been demonstrated and could be used online as a tool for identifying candidate sites for pulmonary vein isolation in patients despite persistent atrial fibrillation.

Multicellular models of homogeneous and isotropic human atria have been developed by incorporating cellular models of membrane electrical activity of single human atrial myocyte into a parabolic partial differential equation. These models are used to study the rate dependent conduction velocity of excitation wave, vulnerability of tissue to reentry and dynamical behaviors of reentry. Bidomain models were also developed to study the actions of a large and brief external electrical stimulus on wave propagation in human atria. These studies provide basic insights to understand the onset and termination of atrial arrhythmias in the human heart.

A total of 53 atrial electrograms were recorded from 12 human patients diagnosed with different degrees of atrial arrhythmias and fibrillation, but not atrial flutter. The atrial waves were highly complex, noisy, nonuniform, nonlinear, and nonstationary in time and well suited for recurrence quantification analysis (RQA), spectral analysis (FFT) and atrial rate (AR) measurements. Differing degrees of atrial arrhythmias were quantified by measuring singularities in the electrograms. Singularities were defined as the maximum periods of relative isopotential squared (msec²) and presented as unfilled squares along the central line of identity (LOI) on recurrence plots. These nonsolid (unfilled) squares indicate that most singularities were unstable with noisy baselines. All measured variables were plotted against their corresponding unstable singularities. The best correlations were found for variables V_max and Laminar over the full range of log₁₀(singularity). That is, the higher the degree of fibrillation the smaller the size of the singularity and the shorter V_max and Laminar. The shorter singularities are associated with faster spiral waves. However, since V_max and Laminar are direct derivatives of Singularity, this variable remains the sole best quantifier of choice to identify aberrant pacemaker regions.

We use the formalism of wave-packet propagation in passive media to characterize the spread of the electrical excitation in excitable media, namely the cardiac myocardium. We introduce equivalent concepts of group and phase velocities, attenuation coefficient and refraction index to describe the myocardial excitation wave and apply the wavelet approach to construct an analogue of the classical dispersion dependence for active media — the "equivalent dispersion dependence". Using the wavelet decomposition we develop a method for the reconstruction of the equivalent dispersion dependence for the myocardium on the basis of electrical intracardiac signals that are measured in two spatially separated points. The novel method is applied to two different sets of experimental data and to data obtained from a numerical simulation of the atrial myocardium. We show that the introduced equivalent dispersion dependence under physiological conditions is similar to the one that is obtained for resonant wave-medium interaction. The analysis of both experimental data sets clearly shows that the number of cardiac cycles with a resonant form of the equivalent dispersion dependence predominates in the normal state of the myocardium while it decreases early before the onset of atrial fibrillation. We set up the hypothesis that an increasing number of non-resonant cardiac cycles is a precursor of atrial fibrillation and thus can serve to predict fibrillation already at an early stage before its onset. The proposed conception can be applied to investigate the properties of the atrial as well as the ventricular myocardium.

Predicting termination of atrial fibrillation (AF), based on noninvasive techniques, can be invaluable in order to avoid useless therapeutic interventions and to minimize the risks for the patients. Currently, no reliable method exists to predict the termination of AF. We propose an algorithm for predicting termination of AF using higher order statistical moments of R-R interval signal calculated in both time and empirical mode decomposition (EMD) domains. In the proposed method, R-R interval signal is decomposed into a set of intrinsic mode functions (IMF) and higher order moments including skewness, and kurtosis, as well as mean and variance, are calculated from the first four IMFs. The appropriateness of these features in predicting the termination of AF is studied using atrial fibrillation termination database (AFTDB) which consists of three types of AF episodes: N-type (non-terminated AF episode), S-type (terminated 1'min after the end of the record), and T-type (terminated immediately after the end of the record). By using a support vector machine (SVM) classifier for classification of AF episodes, we obtained sensitivity, specificity, and positive predictivity 92.47%, 95.29%, and 92.80%, respectively. The important advantage of the proposed method compared to the other existing approaches is that our algorithm can simultaneously discriminate the three types of AF episodes with high accuracy. The results demonstrate that the EMD domain is a particularly well-suited domain for analyzing nonstationary and nonlinear R-R interval signal in AF termination prediction application.

Cardiac tissue is characterized by structural and cellular heterogeneities that play an important role in the cardiac conduction system. Under persistent atrial fibrillation (persAF), electrical and structural remodeling occur simultaneously. The classical mathematical models of cardiac electrophysiological showed remarkable progress during recent years. Among those models, it is of relevance the standard diffusion mathematical equation, that considers the myocardium as a continuum. However, the modeling of structural properties and their influence on electrical propagation still reveal several limitations. In this paper, a model of cardiac electrical propagation is proposed based on complex order derivatives. By assuming that the myocardium has an underlying fractal process, the complex order dynamics emerges as an important modeling option. In this perspective, the real part of the order corresponds to the fractal dimension, while the imaginary part represents the log-periodic corrections of the fractal dimension. Indeed, the imaginary part in the derivative implies characteristic scales within the cardiac tissue. The analytical and numerical procedures for solving the related equation are presented. The sinus rhythm and persAF conditions are implemented using the Courtemanche formalism. The electrophysiological properties are measured and analyzed on different scales of observation. The results indicate that the complex order modulates the electrophysiology of the atrial system, through the variation of its real and imaginary parts. The combined effect of the two components yields a broad range of electrophysiological conditions. Therefore, the proposed model can be a useful tool for modeling electrical and structural properties during cardiac conduction.

INDIA – South Asia waters harbor drug-resistant bugs.

JAPAN – Daiichi Sankyo initiates phase 3 ENSURE-AF study, investigating patients with atrial fibrillation undergoing cardioversion.

SINGAPORE – Scientists discover a number of novel genetic defects which cause esophageal cancer.

SINGAPORE – NUS researchers develop world's first fluorescent sensor to detect common illicit date rape drug.

SINGAPORE – Genome Institute of Singapore partners Nutricia Research to investigate human gut microbiome.

SINGAPORE – Singapore-developed drug coated balloon enrolls its first patients in clinical study.

THE PHILIPPINES – Biodiversity loss linked to outbreaks of diseases.

VIETNAM – Mosquito genes could be key to halting dengue spread.

EUROPE – Curetis obtains key patent for Unyvero™ technology.

NEPAL – Fly ash waste used as water purifier.

UNITED STATES – Researchers discover genetic markers that may predict when people with heart disease are likely to have heart attacks.

UNITED STATES – Celluar ‘counting’ of rhythmic signals synchronizes changes in cell fate.

UNITED STATES – Potential lung cancer vaccine shows renewed promise.

UNITED STATES – Shrink wrap used to enhance detection of infectious disease biomarkers.

UNITED STATES – Triple negative breast cancer's progression and relapse pinned to a gene.

UNITED STATES – Seaweed forests could help power tropical islands.

Vascular Dynamics receives FDA approval to begin conclusive and pivotal trial experimentation for treatment of chronic hypertension.

Orchard Therapeutics’ OTL-101 granted Promising Innovative Medicine Designation for treatment of ADA-SCID.

Concept Life Sciences appoints John Handley as chief operating officer.

Oak Therapeutics completes Phase 1 of NIH grant to develop formulation for tuberculosis.

Relvar Ellipta significantly improves asthma control compared with usual care medicines.

Hematological disorders to benefit from significant level of first-in-class innovation.

Boehringer Ingelheim initiates Phase IIa study of Pharmaxis compound for treating NASH.

Ambrosus partners with Trek Therapeutics to develop blockchain method to track quality in pharmaceutical manufacturing.

Bosch subsidiary Valicare offers consulting in GMP-compliant development of cancer therapies.

Korea-Singapore Healthcare Incubator to support Korean firms in Singapore and Southeast Asia.

Newer oral anticoagulant recommended for reducing risk of stroke in patients with irregular heartbeats.

Korean start-up Sky Labs selected as one of the winners for Bayer Grants4Apps Accelerator.

Supplement: Could COMPASS be a game changer in managing stable artery disease?

Supplement: Global study confirms positive benefit-risk profile of rivaroxaban for stroke prevention.

The following topics are under this section:

• AUM Biosciences obtains global rights to novel targeted cancer therapy
• Vitafoods Asia Conference to present top 5 APAC nutraceutical trends
• Weight Loss Made Simple – MyDoc Launches Medically supervised LivingLite™, in Singapore and the region
• Taiwan boosts development of biomedical industry through memorandums promoting a next-generation biomedical research ecosystem
• Mount Alvernia Hospital appoints ICON SOC to build new state-of-theart integrated cancer centre
• Healthy Hearts, Healthy Aging Asia Pacific report calls for specific policy actions and focus in managing cardiovascular disease
• Anticancer agent Halaven approved for treatment of locally advanced or metastatic breast cancer in China
• Advent Access receives CE Mark certificate for pioneering av-Guardian™ vascular access system
• ACT Genomics opens third laboratory in Asia at Hong Kong Science Park
• GSK opens new state-of-the-art pharmaceutical manufacturing facility in Singapore
• Taiwan Protein Project successfully enhances Taiwan' biotechnology R&D industry
• S$6.4 million boost for NUHS to push Artificial Intelligence (AI) in Healthcare Efficiency and Outcome
• HKUST Researchers Co-Discover a Novel Function of an Enzyme Offering Insight into the Pathology of Hereditary Spastic Paraplegia

In this paper, an electrocardiogram (ECG)-based pattern analysis methodology is presented for the detection of artrial flutter and atrial fibrillation using fractal dimension (FD) of continuous wavelet transform (CWT) coefficients of raw ECG signals, sample entropy of heart beat interval time series, and mean heart beat interval features. Accurate diagnosis of atrial tachyarrhythmias is important, as they have different therapeutic options and clinical decisions. In view of this, we have made an attempt to develop a discrimination mechanism between artrial flutter and atrial fibrillation. The methodology consists of mean heart beat interval detection using Pan Tompkins algorithm, calculation of sample entropy of heart beat interval time series, computation of box counting FD from CWT coefficients of raw ECG, statistical significance test, and subsequent pattern classification using different classifiers. Different wavelet basis functions like Daubechies-4, Daubechies-6, Symlet-2, Symlet-4, Symlet-6, Symlet-8, Coiflet-2, Coiflet-5, Biorthogonal-1.3, Biorthogonal-3.1, and Mayer wavelet have been used to compute CWT coefficients. Features are evaluated using statistical analysis and subsequently two-class pattern classification is done using unsupervised (k-means, fuzzy c-means, and Gaussian mixture model) and supervised (error back propagation neural network and support vector machine) techniques. In order to reduce the bias in choosing the training and testing set, k-fold cross validation is used. The obtained results are compared and discussed. It is found that the supervised classifiers provide higher accuracy in comparison to the set of unsupervised classifiers.

Atrial fibrillation (AF) is a common atrial arrhythmia occurring in clinical practice and can be diagnosed using electrocardiogram (ECG) signal. The conventional diagnostic features of ECG signal are not enough to quantify the pathological variations during AF. Therefore, an automated detection of AF pathology using the new diagnostic features of ECG signal is required. This paper proposes a novel method for the detection of AF using ECG signals. In this work, we are using a novel nonlinear method namely, the two-stage variational mode decomposition (VMD) to analyze ECG and deep belief network (DBN) for automated AF detection. First, the ECG signals of both normal sinus rhythm (NSR) and AF classes are decomposed into different modes using VMD. The first mode of VMD is decomposed in the second stage as this mode captures the atrial activity (AA) information during AF. The remaining modes of ECG captures the ventricular activity information. The sample entropy (SE) and the VMD estimated center frequency features are extracted from the sub-modes of AA mode and ventricular activity modes. These extracted features coupled with DBN classifier is able to classify normal and AF ECG signals with an accuracy, sensitivity and specificity values of 98.27%, 97.77% and 98.67%, respectively. We have developed an atrial fibrillation diagnosis index (AFDI) using selected SE and center frequency features to detect AF with a single number. The system is ready to be tested on huge database and can be used in main hospitals to detect AF ECG classes.

In this work, a scalable hybrid model is proposed for the purpose of screening and continuous monitoring of atrial fibrillation (AF) using electrocardiogram (ECG) signals collected from wearable ECG devices. The time series of RR intervals (with units in seconds) extracted from the ECG signal is fed into a recurrent neural network (RNN), and the bandpass filtered and scaled signal itself is fed into a convolutional neural network (CNN). At the post-processing stage, these two predictions are merged. An additional logistic regression model using statistical features of “pseudo” PR interval sequence is applied to aid making the final prediction. The proposed model is trained and validated on several datasets from PhysioNet and achieves a precision of 98.28% and a specificity of 99.82% on a dataset collected from several PhysioNet databases. This hybrid model has already been deployed through a WeChat applet, providing services those using wearable ECG devices, thus helping the screening and continuous out-of-hospital monitoring of the disease of AF.

Atrial fibrillation (AF) is the most common arrhythmia, and its incidence is constantly increasing. It is associated with higher stroke risk and the presence of sleep disorders and dementia. The choice between rhythm and rate control in AF patients remains a debated topic, and it should be tailored on specific patient characteristics. In specific situations, electrical cardioversion (ECV) for rhythm control represents the preferred choice; in particular, in patients affected by cardiopathy and/or heart failure. Because of relevant AF social costs, there is a growing interest in developing new devices for large-scale screening and monitoring programs in patients affected or at risk of AF, to reduce the incidence of disabling events.

The aim of this study was to evaluate the feasibility of the use of a set-up for multi-parametric monitoring of candidates to AF ECV. In particular, new technologies were exploited for photoplethysmographic (PPG) and electroencephalographic (EEG) signal registration, integrated with clinical and instrumental data. We analyzed the effect of AF ECV on heart rate variability (HRV) and vascular age parameters derived from PPG signals registered with Empatica (CE 1876/MDD 93/42/EEC; Empatica S.r.l, Milan, Italy), and on EEG sleep pattern registered with Neurosteer (IEC 60601-1-2; Neurosteer Inc., Herzliya, Israel).

24 patients were enrolled, 75% males, mean age $65.6\pm 8.5$ years. HRV analyses considering time frames registered before and after ECV showed a significant reduction of most variables ($p<$0.001), only LF/HF ratio did not differ significantly. Considering HRV parameters, comparisons between PPG signals registered during day or night before and after ECV showed a significant difference in SD1/SD2 ratio (p=0.035) and HF ($p=0.002$). Regarding vascular age parameters, a significant reduction was observed in both turning point ratio (TPR) and a wave after ECV ($p<0.001$). Moreover, we observed that patients with Mini-Mental State Examination (MMSE) ≤28 presented higher values of TPR ($65.9\pm 1.6$ versus $64.2\pm 1.4$, $p=0.035$) and CHA₂DS₂-VASc score (congestive heart failure, hypertension, age, diabetes mellitus, prior stroke or transient ischemic attack or thromboembolism, vascular disease, age, sex category) ($2.9\pm 0.9$ versus $1.7\pm 1.2$, p=0.022). Considering sleep patterns, a tendency to higher coherence was observed in registrations acquired during AF than in presence of sinus rhythm, or considering signals registered before and after ECV for each patient.

In conclusion, the use of this new setup of multiparametric monitoring of candidates to ECV showed significant modifications on vascular age parameters derived from PPG signals measured before and after ECV. Moreover, a possible AF effect on sleep pattern registered with Neurosteer was noticed, but more data are necessary to confirm these preliminary results.

In this paper, a novel multi-scale deep residual shrinkage network (MS-DRSN) is proposed for signal denoising and atrial fibrillation (AF) recognition. Signal denoising is done by multi-scale threshold denoising module (MS-TDM), which consists of two parts: threshold acquisition and threshold denoising. The thresholds are automatically obtained through the global attention module constructed by the neural network. Threshold denoising chooses Garrote as the threshold function, which combines the advantages of soft and hard thresholding. The multi-scale features consist of global attention module and local attention module, and then the multi-scale features are denoised using the acquired thresholds and threshold functions, and the AF recognition task is finally completed in the Softmax layer after the superposition of multiple MS-TDMs. An adaptive synthetic sampling (ADASYN) algorithm is also used to oversample the dataset and achieve data category balancing by generating new samples, which improves the accuracy of AF recognition and alleviates the overfitting of the neural network. This method was experimented and validated on the PhysioNet2017 dataset. The experimental results show that the approach achieves an accuracy of 0.894 and an $F_1$ score of 0.881, which is better than current machine learning and deep learning models.

Cardiac arrhythmia affects a large portion of the population, particularly the elderly. As the population ages, the number of patients suffering from this condition is expected to rise, underlining the importance of more efficient treatment methods. Radio-frequency (RF) catheter ablation is the treatment of choice for many cardiac arrhythmias. To recover a normal heartbeat, the abnormal pacemakers inside the heart are first identified and then isolated or destroyed by applying RF energy through the use of an ablation catheter. While the catheter’s design and precise navigation are topics of significant interest, there is currently no standardized evaluation setup to compare different catheter ablation systems. This paper addresses this problem by introducing an evaluation platform including an in vitro anatomical model, a catheter tracking system, and a dedicated graphical user interface. This platform enables performance assessment of catheter ablation systems using various metrics, such as procedure duration, contact stability, ablation angle and user satisfaction. A simulation environment is also presented, allowing for rapid assessment of usability-related parameters. We demonstrate the efficacy of the evaluation platform through a proof-of-concept study, and the efficacy of the simulation environment through evaluating different control gains.

The electrocardiogram F-wave arising from atrial electrical activity is an important global measure for assessment of atrial fibrillation (AF). However, successful F-wave extraction from the ventricular waveform can be problematic. Herein, a new F-wave isolation technique is introduced. For analysis, electrocardiogram lead I (termed unfiltered or UNF-signals) was retrospectively analyzed (39 AF patients, 8.4-s recordings, 8192 sample points, 96 recordings in total). To measure the efficacy of isolation techniques, a synthetic F-wave (7.29 Hz) and an interference were added to each electrocardiogram signal. In the resulting composite signals, the average electrocardiogram QRST complex template was subtracted from each actual QRST (AVG-isolation). The QRST template was also adjusted using a new adaptive least mean-squares (LMS) algorithm, and subtracted from each actual QRST (termed LMS-isolation). Four spectral parameters were measured to assess isolated F-wave quality: the dominant amplitude (DA), dominant frequency (DF) and mean/standard deviation in spectral profile (MP/SP). Significant parameter differences between UNF/LMS and between AVG/LMS were determined. The electrocardiogram F-wave spectral parameters were significantly improved by incorporating LMS-isolation as compared to no isolation (p < 0.001). The F-wave spectral parameters were also significantly improved using LMS-isolation as compared with AVG-isolation (DA/MP/SP: p < 0.001; DF: p < 0.05). The DF was correctly identified as 7.29 ± 0.10 Hz using ensemble spectral analysis with the following percentages (UNF: 24.0%, AVG: 69.8%, LMS: 80.2%), and Fourier spectral analysis with the following percentages (UNF: 15.6%, AVG: 60.4%, LMS: 75.0%). The LMS algorithm is helpful to isolate the electrocardiogram F-wave from the ventricular component as measured by spectral analysis, when compared to the use of an average QRST subtraction template.

Background: Although it is well known that rapid atrial activation causes electrical remodeling, processes of electrical remodeling at different atrial sites are still unclear. In present study, atrial electrophysiologic parameters were monitored at several atrial sites during rapid atrial stimulation for 2weeks to clarify heterogeneity of process of atrial electrical remodeling.

Methods: RAA or LAA was paced with 400bpm for 2weeks. At 4atrial sites of RAA, Bachmann's bundle (BB), IVC and LAA, AERP, AERP dispersion(AERPd) and inducibility of atrial fibrillation were evaluated at several points in pacing and recovery phase.

Results: AERP shortening (Δ AERP)was heterogeneous in 4atrial sites in process of atrial electrical remodeling. In RAA stimulation group, Δ AERP was larger in RAA and LA sites than other sites. In contrast, LAA stimulation group showed larger Δ AERP at BB site than others. Maximal AERPd was larger in LAA than RAA stimulation group. AF inducibility was highest at LA site in both groups, but inducibility was higher in LAA than RAA stimulation group.

Conclusions: In this model, process of the atrial electrical remodeling was heterogeneous in different parts of atria. Δ AERP was largest at LA site regardless of rapid pacing site, but AERPd was larger and AF inducibility was higher in LAA stimulation group than RAA stimulation group. LA seemed to play an important role in causing AF in canine rapid stimulation model of atrial electrical remodeling.

Magnetocardiographic mapping (MCG) was applied in 23 healthy subjects and in 28 patients with lone paroxysmal atrial fibrillation (AF) initiated by P-on-T atrial premature complexes. Magnetic fields in 33 locations over anterior chest were recorded and averaged. Onset and offset of atrial wave were determined automatically after 40 Hz high-pass filtering. Integral maps were created for the initial part (from onset to 50 ms) and the later part (50 ms backwards from offset) of the unfiltered atrial signals. These integrals coincide with right and left atrial depolarization. Spatial orientation of the magnetic field in frontal plane was analyzed. Duration of filtered atrial wave was 106 ± 9 ms in controls and 109 ± 10 ms in patients (p = n.s.). The magnetic field orientation during the initial part was on average 28° in controls and 25° in patients (p = n.s.) The orientation during later part was 69° in controls and 142° in patients (p = 0.019), signifying much greater field rotation in patients. Thus MCG mapping reveals altered wavefront direction during late atrial wave but no prolongation in atrial depolarization time in patients prone to lone paroxysmal AF. This suggests that altered conduction to or within the left atrium may underlie tendency to develop atrial fibrillation.