Antiinflammatory Efficacy of Extracts of Latex of Calotropis procera Against Different Mediators of Inflammation

The latex of the plant Calotropis procera has been reported to exhibit potent antiinflammatory activity against carrageenin and formalin that are known to release various mediators. In the present study, we have evaluated the efficacy of extracts prepared from the latex of C procera against inflammation induced by histamine, serotonin, compound 48/80, bradykinin (BK), and prostaglandin E(2)(PGE(2)) in the rat paw oedema model. The paw oedema was induced by the subplantar injection of various inflammagens and oedema volume was recorded using a plethysmometer. The aqueous and methanol extracts of the dried latex (DL) and standard antiinflammatory drugs were administered orally 1 hour before inducing inflammation. The inhibitory effect of the extracts was also evaluated against cellular influx induced by carrageenin. The antiinflammatory effect of aqueous and methanolic extracts of DL was more pronounced than phenylbutazone (PBZ) against carrageenin while it was comparable to chlorpheniramine and PBZ against histamine and PGE(2), respectively. Both extracts produced about 80%, 40%, and 30% inhibition of inflammation induced by BK, compound 48/80, and serotonin. The histological analysis revealed that the extracts were more potent than PBZ in inhibiting cellular infiltration and subcutaneous oedema induced by carrageenin. The extracts of DL exert their antiinflammatory effects mainly by inhibiting histamine and BK and partly by inhibiting PGE(2)

Dealing with Receiver Misbehavior in Multicast Congestion Control

In multicast congestion control, receivers can misbehave by maliciously causing congestion to steal network bandwidth from well behaved flows. In source driven congestion control protocols (SDCC), receivers misbehave by sending a wrong feedback to the source. In receiver-driven congestion control protocols(RDCC), receivers misbehave by inflating their subscriptions. In this report, we present techniques to deal with these misbehaviours. Firstly, we show that when network tomography tools such as MINC are used in conjunction with SDCC protocols, they can aid in misbehavior detection. But in order to use MINC for misbehaviour detection, MINC itself must be made immune to misbehaviour. we analyze the effect of misbehavior within MINC and propose two techniques to detect and prevent misbehavior in MINC. For RDCC protocols, a misbehaviour prevention mechanism based on in-band distribution of keys was recently proposed for FLID-DL protocol. In this report, we show how we can design keys which can prevent misbehaviour in most RDCC protocols

MINC and Misbehavior

Network tomography tools such as MINC can be used to infer important internal properties of a network underlying a multicast tree. This inference is made by analyzing receiver feedbacks to the measurement probes sent from the source and can be utilized to make significant decisions concerning the network. However, certain misbehaving receivers can return incorrect feedback and mislead the MINC inference resulting in an erroneous decision. Hence it is required to verify if the feedbacks collected from the receivers can be utilized to make a trustworthy MINC inference. In this report, the MINC inference procedure which computes the loss probabilities of various paths in the multicast tree is investigated. Firstly, it is shown how the loss probabilities computed by MINC change when receivers alter their feedback. Then, a statistical detection procedure is presented, which searches for loss probability inconsistencies in the feedback data. Some preliminary results b! ased on old mbone loss traces are presented

SUPPORTIVE THERAPY: AN OPTION TO ENHANCE HOST IMMUNITY AGAINST COVID-19

The threat posed by COVID 19 outbreak, which is considered to be a global pandemic, is immeasurably affecting all the communities worldwide. COVID-19 is a zoonotic disease, which can affect birds, humans and, other animals. The emergence of this pandemic has been creating a tragic situation worldwide by affecting more people through human-human transmission. The burden (both healthwise and economic) placed by the disease is so huge that any measures to improve the current situation, to fasten up the recovery of already affected patients and, to reduce the risk of death and health deterioration should be considered. Vaccination, being the hope in the scenario, helps in preventing the condition to an extent, but in the absence of availability of a proper drug regimen to fight off COVID 19, the requirement of the need to find a system to control the severity of the infection is a necessity Nutritional supplementation helps in boosting up the immune system especially, vitamins like vitamin C, Vitamin D, Zinc, Omega 3 fatty acids, etc. They also exhibit established immunomodulatory, antiviral as well as anti-inflammatory effects. Pieces of evidence have also highlighted the importance of supportive therapy using nutrient supplements in covid patients as it helps in prominent decreasing of SARS CoV2 load of the virus and also significantly reduces the hospitalization period. Hence the nutritional levels of each of the infected person must be assessed before initiating the anti-viral therapy. The search criteria used were PubMed, Medscape, google scholar, etc. The keywords used to search were COVID 19 Supportive therapy, Vitamin D, Vitamin C, Nutrient supplementation, Host immunity, etc. The range of years is between 1978 and 2021

POLYMER -LIPID HYBRID NANOPARTICLES FOR BRAIN TARGETING THROUGH INTRANASAL DELIVERY

Brain targeting is a difficult task due to various factors; those factors can restrict the entry of drugs into the brain, in the present study polymer-lipid hybrid nanoparticles were prepared for targeting carbamazepine into the brain through the intranasal route. Five formulations were successfully prepared using chitosan, stearic acid and glyceryl mono stearate in different ratio. The particles size were found between 78.88-790nm, the poly dispersibility index were found in the range of 0.273-0.531, the zeta potential were found to be -7.1, -11.6, 22.3 for HN1, HN2, HN3 respectively and for formulation HN4 and HN5 it was found as +12.1 and +22.3. The entrapment efficiency of all the formulations was found between 62.66-88.31%, the in-vitro releases were found in the range of 40-72%. The in-vivo studies were performed on Wister rats. Formulation HN5 containing higher conc. of chitosan has shown high drug targeting efficiency. The lipid-polymer hybrid nanoparticles have shown the possibility of targeting the brain through intranasal delivery. Keywords: polymer-lipid hybrid Nanoparticles, carbamazepine, brain targeting, chitosanÂ

An axiomatic framework for ex-ante dynamic pricing mechanisms in smart grid

In electricity markets, the choice of the right pricing regime is crucial for the utilities because the price they charge to their consumers, in anticipation of their demand in real-time, is a key determinant of their profits and ultimately their survival in competitive energy markets. Among the existing pricing regimes, in this paper, we consider ex-ante dynamic pricing schemes as (i) they help to address the peak demand problem (a crucial problem in smart grids), and (ii) they are transparent and fair to consumers as the cost of electricity can be calculated before the actual consumption. In particular, we propose an axiomatic framework that establishes the conceptual underpinnings of the class of ex-ante dynamic pricing schemes. We first propose five key axioms that reflect the criteria that are vital for energy utilities and their relationship with consumers. We then prove an impossibility theorem to show that there is no pricing regime that satisfies all the five axioms simultaneously. We also study multiple cost functions arising from various pricing regimes to examine the subset of axioms that they satisfy. We believe that our proposed framework in this paper is first of its kind to evaluate the class of ex-ante dynamic pricing schemes in a manner that can be operationalised by energy utilities

Ownership preserving AI Market Places using Blockchain

We present a blockchain based system that allows data owners, cloud vendors, and AI developers to collaboratively train machine learning models in a trustless AI marketplace. Data is a highly valued digital asset and central to deriving business insights. Our system enables data owners to retain ownership and privacy of their data, while still allowing AI developers to leverage the data for training. Similarly, AI developers can utilize compute resources from cloud vendors without loosing ownership or privacy of their trained models. Our system protocols are set up to incentivize all three entities - data owners, cloud vendors, and AI developers to truthfully record their actions on the distributed ledger, so that the blockchain system provides verifiable evidence of wrongdoing and dispute resolution. Our system is implemented on the Hyperledger Fabric and can provide a viable alternative to centralized AI systems that do not guarantee data or model privacy. We present experimental performance results that demonstrate the latency and throughput of its transactions under different network configurations where peers on the blockchain may be spread across different datacenters and geographies. Our results indicate that the proposed solution scales well to large number of data and model owners and can train up to 70 models per second on a 12-peer non optimized blockchain network and roughly 30 models per second in a 24 peer network