Inducible cardiomyocyte injury within the atrioventricular conduction system uncovers latent regenerative capacity in mice

Elastomeric platform with surface wrinkling patterns to control cardiac cell alignment

There is a growing interest in creating 2D cardiac tissue models that display native extracellular matrix (ECM) cues of the heart tissue. Cellular alignment alone is known to be a crucial cue for cardiac tissue development by regulating cell-cell and cell-ECM interactions. In this study, we report a simple and robust approach to create lamellar surface wrinkling patterns enabling spatial control of pattern dimensions with a wide range of pattern amplitude (A ≈ 2-55 μm) and wavelength (λ ≈ 35-100 μm). For human cardiomyocytes (hCMs) and human cardiac fibroblasts (hCFs), our results indicate that the degree of cellular alignment and pattern recognition are correlated with pattern A and λ. We also demonstrate fabrication of devices composed of micro-well arrays with user-defined lamellar patterns on the bottom surface of each well for high-throughput screening studies. Results from a screening study indicate that cellular alignment is strongly diminished with increasing seeding density. In another study, we show our ability to vary hCM/hCF seeding ratio for each well to create co-culture systems where seeding ratio is independent of cellular alignment.

Protocol for inducing cellular ablation in the mouse atrioventricular conduction system

Damage to the atrioventricular conduction system (AVCS), the main electrical connection between the atrial and ventricular chambers, can result in a variety of cardiac conduction disorders. Here, we provide a protocol for selective damage of the mouse AVCS to study its response during injury. We describe tamoxifen-induced cellular ablation, detection of AV block through electrocardiography, and quantification of histological and immunofluorescence markers to analyze the AVCS. This protocol can be used to study mechanisms associated with AVCS injury repair and regeneration. For complete details on the use and execution of this protocol, please refer to Wang et al. (2021).1.

Hypoxia modeling techniques: A review

Hypoxia is the main cause and effect of a large number of diseases, including the most recent one facing the world, the coronavirus disease (COVID-19). Hypoxia is divided into short-term, long-term, and periodic, it can be the result of diseases, climate change, or living and traveling in the high mountain regions of the world. Since each type of hypoxia can be a cause and a consequence of various physiological changes, the methods for modeling these hypoxias are also different. There are many techniques for modeling hypoxia under experimental conditions. The most common animal for modeling hypoxia is a rat. Hypoxia models (hypoxia simulations) in rats are a tool to study the effect of various conditions on the oxygen supply of the body. These models can provide a necessary information to understand hypoxia and also provide effective treatment, highlighting the importance of various reactions of the body to hypoxia. The main parameters when choosing a model should be reproducibility and the goal that the scientist wants to achieve. Hypoxia in rats can be reproduced both ways exogenously and endogenously. The reason for writing this review was the aim to systematize the models of rats available in the literature in order to facilitate their selection by scientists. The relative strengths and limitations of each model need to be identified and understood in order to evaluate the information obtained from these models and extrapolate these results to humans to develop the necessary generalizations. Despite these problems, animal models have been and remain vital to understanding the mechanisms involved in the development and progression of hypoxia. The eligibility criteria for the selected studies was a comprehensive review of the methods and results obtained from the studies. This made it possible to make generalizations and give recommendations on the application of these methods. The review will assist scientists in choosing an appropriate hypoxia simulation method, as well as assist in interpreting the results obtained with these methods.

Visualization deep learning model for automatic arrhythmias classification

With the improvement of living standards, heart disease has become one of the common diseases that threaten human health. As a widely used reliable, non-invasive technology, the Electrocardiography (ECG) data has been increasingly used for diagnosing cardiovascular diseases. With the rapid growth of ECG examinations and the shortage of cardiologists, accurate and automatic recognition of ECG patterns has become a research hotspot. In order to improve the accuracy in detecting abnormal ECG patterns, this paper proposes a hybrid 1D Resnet-GRU consisting of the Resnet and gated recurrent unit (GRU) modules to implement classification of arrhythmias from 12-lead ECG recordings. In addition, the focal Loss function is used to solve the problem of unbalanced datasets. Based on the proposed 1D Resnet-GRU model, the Grad-CAM++ mechanism, one of class-discriminative visualization methods, has been employed to the trained network model and generate thermal images superimposed on raw signals, so as to improve interpretability and transparency for arrhythmia classification. Experimental results show the proposed Resnet-GRU method can improve the F_1 score and accuracy effectively, and Grad-CAM++ can provide interpretability and clinical information for arrhythmia classification.