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The present technological era significantly makes use of Internet-of-Things (IoT) devices for offering and implementing healthcare services. Post COVID-19, the future of the healthcare system is highly reliant upon the inculcation of Artificial-Intelligence (AI) mechanisms in its day-to-day procedures, and this is realized in its implementation using sensor-enabled smart and intelligent IoT devices for providing extensive care to patients relative to the symmetric concept. The offerings of such AI-enabled services include handling the huge amount of data processed and sensed by smart medical sensors without compromising the performance parameters, such as the response time, latency, availability, cost and processing time. This has resulted in a need to balance the load of the smart operational devices to avoid any failure of responsiveness. Thus, in this paper, a fog-based framework is proposed that can balance the load among fog nodes for handling the challenging communication and processing requirements of intelligent real-time applications.

Exploration of the dual and opposing facets of estrogen (E2) necessitate a clear understanding to diminish the controversy of estrogen regulation in averting the systemic, autoimmune, joint degrading disorder, rheumatoid arthritis (RA). Experimental evidences consider estrogen as a pivotal enzyme to modulate the disease progression via managing several cellular mechanisms targeting inflammatory markers such as TNF, ILs, NF-κB and other regulatory proteins like MMPs impeding joint erosion and cartilage degradation. E2 modulates cellular signalling associated with inflammation, oxidative stress, related cardiovascular risk and miRNA regulation during RA progression. Studies determining E2 regulation in RA complicate the resemblance of the outcome as they represent both hyper and hypo level of E2 linked to the disease. Although some reports deliver E2 as malign, there is now increasing evidence of rendering protection dose dependently. Variation in estrogen level cause differential expression of certain proteins and their related signalling which directly or indirectly linked to RA pathogenesis. This review summarizes the variations in protein expression levels by focusing on the in vitro, in vivo and clinical studies of E2 deficiency and treatment. Construction of PPI network, GO and KEGG pathway enrichment analysis of the differentially expressed proteins assist in hypothesizing a potential molecular mechanism of E2 in RA via in silico studies. Targeting these differential proteins can emerge a new path for developing advanced therapeutic strategies.

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Fog computing is a distributed technology which performs computations at the edge of the network. IoT devices are generating large amount of data that too Heterogeneous in nature. In order to handle applications which, require time sensitive data and quick decision making, a reliable platform is required which can handle all the tasks in a robust manner considering fault tolerance and state migration. To process request originating from end user, an efficacious and pragmatic approach is to get the request served at the edge of the network rather than cloud envisioned with the aim to minimize the latency. Quick decision-making characteristic of fog computing makes it a smart choice for smoother processing and execution of smart city applications. As utilization of internet and IoT devices has boomed the industry, it is important to have reliable applications for day to day activities like payment of bills, traffic light management, health support system etc. The research study conducted explores multi-dimensional abstractions that can extend technological contributions in current epidemic situation.