Background: e-Mental health is an established field of exploiting information and communication technologies for mental health care. It offers different solutions and has shown effectiveness in managing many psychological issues. Introduction: The coronavirus disease 2019 (COVID-19) pandemic has critically influenced health care systems and health care workers (HCWs). HCWs are working under hard conditions, and are suffering from different psychological issues, including anxiety, stress, and depression. Consequently, there is an undeniable need of mental care interventions for HCWs. Under the circumstances caused by COVID-19, e-health interventions can be used as tools to assist HCWs with their mental health. These solutions can provide mental health care support remotely, respecting the recommended safety measures. Materials and Methods: This study aims to identify e-mental health interventions, reported in the literature, that are developed for HCWs during the COVID-19 pandemic. A systematic literature review was conducted following the PRISMA protocol by searching the following digital libraries: IEEE, ACM, ScienceDirect, Scopus, and PubMed. Results and Discussion: Eleven publications were selected. The identified e-mental health interventions consisted of social media platforms, e-learning content, online resources and mobile applications. Only 27% of the studies included empirical evaluation of the reported interventions, 55% listed challenges and limitations related to the adoption of the reported interventions. And 45% presented interventions developed specifically for HCWs in China. The overall feedback on the identified interventions was positive, yet a lack of empirical evaluation was identified, especially regarding qualitative evidence. Conclusions: The COVID-19 pandemic has highlighted the importance and need for e-mental health solutions for HCWs.
The inability of the heart to recover from an ischemic insult leads to the formation of fibrotic scar tissue and heart failure. From the therapeutic strategies under investigation, cardiac regeneration holds the promise of restoring the full functionality of a damaged heart. Taking into consideration the presence of vast numbers of fibroblasts and myofibroblasts in the injured heart, direct fibroblast reprogramming into cardiomyocytes using small drug molecules is an attractive therapeutic option to replenish the lost cardiomyocytes. Here, a spermine-acetalated dextran-based functional nanoparticle is developed for pH-triggered drug delivery of two poorly water soluble small molecules, CHIR99021 and SB431542, both capable of increasing the efficiency of direct reprogramming of fibroblast into cardiomyocytes. Upon functionalization with polyethylene glycol and atrial natriuretic peptide, the biocompatibility of the nanosystem is improved, and the cellular interactions with the cardiac nonmyocytes are specifically augmented. The dual delivery of the compounds is verified in vitro, and the compounds exerted concomitantly anticipate biological effects by stabilizing β-catenin (CHIR99021) and by preventing translocation of Smad3 to the nucleus of (myo)fibroblasts (SB431542). These observations highlight the potential of this nanoparticle-based system toward improved drug delivery and efficient direct reprogramming of fibroblasts into cardiomyocyte-like cells, and thus, potential cardiac regeneration therapy.
In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks’ priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.
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