The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has killed over 5 million people to date. Despite the introduction of population-wide vaccination drives, countries such as Austria and Germany are witnessing the re-emergence of infections and deaths. Scientists, administrators and clinicians are scrambling to find solutions that include vaccines, and active therapeutic agents. So, there is an urgent requirement for new and effective medications that can treat the disease caused by SARS-CoV-2. Artificial intelligence (AI) enabled drug repurposing, has the potential to shorten the time and reduce the cost compared to de novo drug discovery.

Covid19 pandemic caused by infection with the severe acute respiratory syndrome corona virus2 is continuously spreading all over the world. The impact of covid19 has been fallen on almost all sectors of development including retail which plays a major role in day to day life. In this paper, we proposed an efficient methodology to create a safe environment of people in retail that contributes to public safety. The proposed smart retail system is focused on the real time monitoring of shopping malls which includes grocery, departmental stores, clothing shops, jewellery shops and shops with food essential products. At first a Mobile Application is developed using Android Studio for prebooking of shopping by users/customers. Using Anaconda Navigator a deep learning trained architecture is developed on distinguishing people with and without face mask. A wearable device is developed using ultra wide band radio technology to ensure safe social distancing which would alert customers as soon as the violation of social distancing is detected. The smart retail system also includes IoT based smart shopping cart with RFID sensors for the customers to check for the availability of items in the web server via Nodemcu and for automatic bill payment. The proposed methodology is also suitable for Religious places, Cinema Theatre, Training centres and Browsing centres.

The outbreak of the novel coronavirus disease COVID-19, caused by the SARS-CoV-2 virus has killed over 5 million people to date. So, there is an urgent requirement for new and effective medications that can treat the disease caused by SARS-CoV-2. To find new drugs, identification of drug targets is necessary (Chen et al., 2016). Number of research studies have identified therapeutic targets such as helicases, transmembrane serine protease 2, cathepsin L, cyclin G-associated kinase, adaptor associated kinase 1, two-pore channel, viral virulence factors, 3-chymotrypsin-like protease, suppression of excessive inflammatory response, inhibition of viral membrane, nucleocapsid, envelope, and accessory proteins, and inhibition of endocytosis. Here we present a web enabled tool which helps in ranking the COVID-19 drugs based upon underlying molecular targets. The users are allowed to give drugs in SMILE format and the tools will provide the list of relevant targets related to COVID-19.

MotivationDespite mass level vaccinations and the launch of several repurposed drugs, the emergence ofCOVID-19 reinfection has posed a key challenge in front of health authorities across the world.There is an urgent need to find new drugs and the understanding of the COVID-19 target–ligandinteractions will play an important role in this direction. Here, we present COV-Dock Server, aweb server that predicts the binding modes between COVID-19 targets and the small drugmolecules.ResultsWe collected experimentally solved structures of proteins of SARS-CoV-2. Further, we used thepredicted structure of experimentally unsolved proteins that were also collected. These structureswere prepared for the docking. Next, 257 candidate drugs were docked against these targetsusing the meta-platform to understand the binding energy distributions. This server provides afree and interactive tool for the prediction of COVID-19 target–ligand interactions and enablesdrug discovery for COVID-19.

Paris polyphylla Smith (family: Melanthiaceae), a high-value medicinal herb endemic to the Himalayan region, has drawn much attention recently due to its immense use in the traditional healthcare system since ancient times. In the present review, an extensive database on P. polyphylla was systematically searched from databases such as Medline/PubMed, Scopus, the Web of Science, and the online service E-library.ru and SCImago (https://www.scimagojr.com/). Information on species, ecology, distribution, trade, ethnopharmacology, pharmacology, biotechnology, and molecular biology was gathered from 1979 to 2023 using 116 research publications. Major steroidal saponins such as Paris saponin I, V, VI, VII, and H have been found substantially effective in anticancer activity, abnormal uterine bleeding, dysfunctional uterine bleeding, and menorrhagia. Traditional breeding and propagation techniques cannot keep up with the world’s growing demand for herbal drugs. Therefore, it is critically necessary to take conservation measures and develop novel techniques for growing and cultivating this economically significant and highly valuable therapeutic herb. The advanced biotechnological approaches like micropropagation and genetic analysis introduced long back are either rare or lacking in the case of P. polyphylla. It contains a wealth of information that will serve as a baseline data source for various stakeholders, researchers working on various research aspects, and policymakers to define appropriate utilization and conservation plans for a high-value commercial medicinal plant called P. polyphylla. The review provides an updated overview and critical assessment of secondary data regarding the past and recent applications and interventions of P. polyphylla.