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Wound healing is an intricate multistage process that includes inflammation, cell proliferation, matrix deposition and remodeling phases. It is often associated with oxidative stress and consequent prolonged inflammation, resulting in impaired wound healing. Curcumin has been reported to improve wound healing in different animal models. In order to increase the efficacy of curcumin in the healing arena a curcumin loaded oleic acid based polymeric (COP) bandage was formulated. The in vivo wound healing potency was compared with void bandage and control (cotton gauze treatment) in a rat model. Biochemical parameters and histological analysis revealed increased wound reduction and enhanced cell proliferation in COP bandage treated groups due to its efficient free radical scavenging properties. Comparative acceleration in wound healing was due to early implementation of fibroblasts and its differentiation (increased level of α-smooth muscle actin). Western blotting and semiquantitative PCR analysis clearly indicate that COP bandage can efficiently quench free radicals leading to reduced antioxidative enzyme activity. Further evidence at mRNA and protein level indicates that our system is potent enough to reduce the inflammatory response mediated by the NFκB pathway during wound healing. With this background, we anticipate that such a versatile approach may seed new arena for topical wound healing in the near future.

The discovery of targeting ligand to cancer cells and the development of ligand-targeted therapy will help us to improve therapeutic efficacy and reduce side effects. Unlike other forms of therapy, it will allow us to maintain quality of life for patients, while efficiently attacking the cancer tissue. It indicates that ligands have a pivotal role in cancer cell targeting.

Background and aim: In the age of advanced digital technology, smart healthcare based on the Internet of Things (IoT) is gaining importance to deal with the current COVID-19 pandemic. In this paper, the novel application of cognitive radio (CR) based IoT specific for the medical domain referred to as Cognitive Internet of Medical Things (CIoMT) is explored to tackle the global challenge. This concept of CIoT is best suited to this pandemic as every person is to be connected and monitored through a massive network that requires efficient spectrum management. Methods: An extensive literature survey is conducted in the Google Scholar, Scopus, PubMed, Research Gate, and IEEE Xplore databases using the terms "COVID-19" and "Cognitive IoT" or "Corona virus" and "IoMT". The latest data and inputs from official websites and reports are used for further investigation and analysis of the application areas. Results: This review encompasses different novel applications of CIoMT for fighting the ongoing COVID-19 health crisis. The CR based dynamic spectrum allocation technique is the solution for accommodating a massive number of devices and a wide number of applications. The CIoMT platform enables real-time tracking, remote health monitoring, rapid diagnosis of the cases, contact tracking, clustering, screening, and surveillance thus, reducing the workload on the medical industry for prevention and control of the infection. The challenges and future research directions are also identified. Conclusions: CIoMT is a promising technology for rapid diagnosis, dynamic monitoring and tracking, better treatment and control without spreading the virus to others.

Background and aim: COVID-19 outbreak has created havoc and a quick cure for the disease will be a therapeutic medicine that has usage history in patients to resolve the current pandemic. With technological advancements in Artificial Intelligence (AI) coupled with increased computational power, the AIempowered drug repurposing can prove beneficial in the COVID-19 scenario. Methods: The recent literature is studied and analyzed from various sources such as Scopus, Google Scholar, PubMed, and IEEE Xplore databases. The search terms used are 'COVID-19 0 , ' AI 0 , and 'Drug Repurposing'. Results: AI is implemented in the field design through the generation of the learning-prediction model and performs a quick virtual screening to accurately display the output. With a drug-repositioning strategy, AI can quickly detect drugs that can fight against emerging diseases such as COVID-19. This technology has the potential to improve the drug discovery, planning, treatment, and reported outcomes of the COVID-19 patient, being an evidence-based medical tool. Conclusions: Thus, there are chances that the application of the AI approach in drug discovery is feasible. With prior usage experiences in patients, few of the old drugs, if shown active against SARS-CoV-2, can be readily applied to treat the COVID-19 patients. With the collaboration of AI with pharmacology, the efficiency of drug repurposing can improve significantly.