Towards Multi-Functional ECG Smart System Based on a Client-Edge-Cloud Architecture

This paper presents a novel client-edge-cloud-based framework that integrates the learning of task-invariant ECG feature representations from ultra-short ECG segments (&lt;10 sec) and subsequent training of task-specific machine learning (ML) classifiers for different applications. Our proposed framework removes the need for application-specific ECG processing by training a general ECG representation learner in a self-supervised manner. The ECG representation learner is then used for generating feature inputs for the different task-specific applications. The proposed framework distributes the computation across cloud, edge, and client components depending on the resource requirement and time criticality. We demonstrate the feasibility and promise of the proposed approach on two different applications, that is, acute stress type classification, and biometric user identification and authentication. The use cases were analyzed using the computational parameters for the different models and computational tasks along with the overall performance. Our analyses show that the application-specific ML models can perform real-time inference in less than a second and the training time of the ML classifiers at the edge devices are in the order of 10-20 seconds. In the future, the proposed framework can be utilized for developing reliable, secure, and multi-functional ECG-based smart systems.</p

Lentivirus vector-mediated genetic manipulation of oncogenic pathways induces tumor formation in rabbit brain

Translation of promising experimental therapies from rodent models to clinical success has been complicated as the novel therapies often fail in clinical trials. Existing rodent glioma models generally do not allow for preclinical evaluation of the efficiency of novel therapies in combination with surgical resection. Therefore, the aim of the present study was to develop a larger animal model utilizing lentivirus vector-mediated oncogenic transformation in the rabbit brain. Lentiviruses carrying constitutively active AKT and H-Ras oncogenes, and p53 small interfering (si)RNA were introduced into newborn rabbit neural stem cells (NSCs) and intracranially implanted into rabbits' brains to initiate tumor formation. In one of the ten rabbits a tumor was detected 48 days after the implantation of transduced NSCs. Histological features of the tumor mimic was similar to a benign Grade II ganglioglioma. Immunostaining demonstrated that the tissues were positive for AKT and H-Ras. Strong expression of GFAP and Ki-67 was also detected. Additionally, p53 expression was notably lower in the tumor area. The implantation of AKT, H-Ras and p53 siRNA transduced NSCs for tumor induction resulted in ganglioglioma formation. Despite the low frequency of tumor formation, this preliminary data provided a proof of principle that lentivirus vectors carrying oncogenes can be used for the generation of brain tumors in rabbits. Moreover, these results offer noteworthy insights into the pathogenesis of a rare brain tumor, ganglioglioma.Peer reviewe