Transdermal absorption studies of Nirgundi (Vitex Negundo Linn) taila and Kashaya (Decoction) with the aid of gas chromatography-mass spectrometry

Introduction: Transdermal route is one of the routes of drug administration where the drug is applied over the skin in different forms and procedures. References highlight absorption of the veerya of the drug through the skin. In this study, the dermal absorption of the drug was assessed using diffusion cell apparatus. For easy evaluation, a single drug Nirgundi (Vitex negundo) was selected. To assess the influence of media, the drug was tested in oil and water media. Methods: Experimental study was performed using diffusion cell apparatus with Strata-M synthetic membrane. The transdermally absorbed compounds were analyzed by gas chromatography-mass spectrometry (GCMS) in comparison to the original sample. The study was repeated at 30°C and 42°C to assess the influence of temperature. Results: In the absorption study at 30°C, four compounds were detected in Tila Taila (TT), one compound in Nirgundi taila (NT), and 13 compounds were detected in Nirgundi kashaya (NK). At 42°C, TT showed one compound, two compounds in NT, and no absorption was evident in NK. Conclusion: The comparative GCMS showed the passage of only a few chemical constituents. The compounds detected after absorption were chemically different, which may be due to the oxygenation of the compounds or interaction with the buffer solution. More constituents passed in water media. When the temperature of the liquid is raised, less number o constituents was detected and their chemical nature also changed, which may be due to thermal damage to the membrane. This method, with alternative analytical techniques, can be used as an efficient method for the analysis of transdermal absorption of Ayurvedic medicines

S-Nav: Safety-Aware IoT Navigation Tool for Avoiding COVID-19 Hotspots

In this article, we present a Q -learning-enabled safe navigation system - S-Nav - that recommends routes in a road network by minimizing traveling through categorically demarcated COVID-19 hotspots. S-Nav takes the source and destination as inputs from the commuters and recommends a safe path for traveling. The S-Nav system dodges hotspots and ensures minimal passage through them in unavoidable situations. This feature of S-Nav reduces the commuter's risk of getting exposed to these contaminated zones and contracting the virus. To achieve this, we formulate the reward function for the reinforcement learning model by imposing zone-based penalties and demonstrate that S-Nav achieves convergence under all conditions. To ensure real-time results, we propose an Internet of Things (IoT)-based architecture by incorporating the cloud and fog computing paradigms. While the cloud is responsible for training on large road networks, the geographically aware fog nodes take the results from the cloud and retrain them based on smaller road networks. Through extensive implementation and experiments, we observe that S-Nav recommends reliable paths in near real time. In contrast to state-of-the-art techniques, S-Nav limits passage through red/orange zones to almost 2% and close to 100% through green zones. However, we observe 18% additional travel distances compared to precarious shortest paths