Celastrol: A Potential Natural Lead Molecule for New Drug Design, Development and Therapy for Memory Impairment

Celastrol is a naturally occurring chemical isolated from Tripterygium wilfordii Hook. f., root extracts widely known for their neuroprotective properties. In this review, we focus on the efficacy of celastrol in mitigating memory impairment (MI) in both in vivo and in vitro models. Scopus, PubMed and Web of Science databases were utilised to locate pertinent literatures that explore the effects of celastrol in the brain, including its pharmacokinetics, bioavailability, behavioral effects and some of the putative mechanisms of action on memory in many MI models. To date, preclinical studies strongly suggest that celastrol is highly effective in enhancing the cognitive performance of MI animal models, particularly in the memory domain, including spatial, recognition, retention and reference memories, via reduction in oxidative stress and attenuation of neuro-inflammation, among others. This review also emphasised the challenges and potential associated enhancement of medication delivery for MI treatment. Additionally, the potential structural alterations and derivatives of celastrol in enhancing its physicochemical and drug-likeness qualities are examined. The current review demonstrated that celastrol can improve cognitive performance and mitigate MI in several preclinical investigations, highlighting its potential as a natural lead molecule for the design and development of a novel neuroprotective medication.

HadoopSec 2.0: Prescriptive analytics-based multi-model sensitivity-aware constraints centric block placement strategy for Hadoop

Like many open-source technologies such as UNIX or TCP/IP, Hadoop was not created with Security in mind. Hadoop however evolved from the other tools over time and got widely adopted across large enterprises. Some of Hadoop’s architectural features present Hadoop its unique security issues. Given this security vulnerability and potential invasion of confidentiality due to malicious attackers or internal customers, organizations face challenges in implementing a strong security framework for Hadoop. Furthermore, given the method in which data is placed in Hadoop Cluster adds to the only growing list of these potential security vulnerabilities. Data privacy is compromised when these critical and data-sensitive blocks are accessed either by unauthorized users or for that matter even misuse by authorized users. In this paper, we intend to address the strategy of data block placement across the allotted DataNodes. Prescriptive analytics algorithms are used to determine the Sensitivity Index of the Data and thereby decide on data placement allocation to provide impenetrable access to an unauthorized user. This data block placement strategy aims to adaptively distribute the data across the cluster using innovative ML techniques to make the data infrastructure extra secured.