Smart biomaterials have been rapidly advancing ever since the concept of tissue engineering was proposed.

Recently, cardiac arrhythmia recognition from electrocardiography (ECG) with deep learning approaches is becoming popular in clinical diagnosis systems due to its good prognosis findings, where expert data preprocessing and feature engineering are not usually required. But a lightweight and effective deep model is highly demanded to face the challenges of deploying the model in real-life applications and diagnosis accurately. In this work, two effective and lightweight deep learning models named Deep-SR and Deep-NSR are proposed to recognize ECG beats, which are based on two-dimensional convolution neural networks (2D CNNs) while using different structural regularizations. First, 97720 ECG beats extracted from all records of a benchmark MIT-BIH arrhythmia dataset have been transformed into 2D RGB (red, green, and blue) images that act as the inputs to the proposed 2D CNN models. Then, the optimization of the proposed models is performed through the proper initialization of model layers, on-the-fly augmentation, regularization techniques, Adam optimizer, and weighted random sampler. Finally, the performance of the proposed models is evaluated by a stratified 5-fold cross-validation strategy along with callback features. The obtained overall accuracy of recognizing normal beat and three arrhythmias (V-ventricular ectopic, S-supraventricular ectopic, and F-fusion) based on the Association for the Advancement of Medical Instrumentation (AAMI) is 99.93%, and 99.96% for the proposed Deep-SR model and Deep-NSR model, which demonstrate that the effectiveness of the proposed models has surpassed the state-of-the-art models and also expresses the higher model generalization. The received results with model size suggest that the proposed CNN models especially Deep-NSR could be more useful in wearable devices such as medical vests, bracelets for long-term monitoring of cardiac conditions, and in telemedicine to accurate diagnose the arrhythmia from ECG automatically. As a result, medical costs of patients and work pressure on physicians in medicals and clinics would be reduced effectively.

Extracellular vesicles (EVs), products released by cells in multiple biological activities, are currently widely accepted as functional particles and intercellular communicators. From the orthodox perspective, EVs derived from apoptotic cells (apoEVs) are responsible for cell debris clearance, while recent studies have demonstrated that apoEVs participate in tissue regeneration. However, the underlying mechanisms and particular functions in tissue regeneration promotion of apoEVs remain ambiguous. Some molecules active during apoptosis also function in tissue regeneration triggered by apoptosis, such as caspases. ApoEVs are generated in the process of apoptosis, carrying cell contents to manifest biological effects and possess biomarkers to target phagocytes. The regenerative effect of apoEVs might be due to their abilities to facilitate cell proliferation and regulate inflammation. Such regenerative effect has been observed in various tissues, including skin, bone, cardiovascular system, and kidneys. Engineered apoEVs are produced to amplify the biological benefits of apoEVs, rendering them optional for drug delivery. Meanwhile, challenges exist in thorough mechanistic exploration and standardization of production. In this review, we discussed the link between apoptosis and regeneration, current comprehension of the origination and investigation strategies of apoEVs, and mechanisms in tissue regeneration of apoEVs and their applications. Challenges and prospects are also addressed here.

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