In this paper, we propose hybrid consensus algorithms that combine machine learning (ML) techniques to address the challenges and vulnerabilities in blockchain networks. Consensus Protocols make ensuring agreement among the applicants in the distributed systems difficult. However, existing mechanisms are more vulnerable to cyber-attacks. Previous studies extensively explore the influence of cyber attacks and highlight the necessity for effective preventive measures. This research presents the integration of ML techniques with the proposed hybrid consensus algorithms and advantages over predicting cyber-attacks, anomaly detection, and feature extraction. Our hybrid approaches leverage and optimize the proposed consensus protocols' security, trust, and robustness. However, this research also explores the various ML techniques with hybrid consensus algorithms, such as Delegated Proof of Stake Work (DPoSW), Proof of Stake and Work (PoSW), Proof of CASBFT (PoCASBFT), Delegated Byzantine Proof of Stake (DBPoS) for security enhancement and intelligent decision making in consensus protocols. Here, we also demonstrate the effectiveness of the proposed methodology within the decentralized networks using the ProximaX blockchain platform. This study shows that the proposed research framework is an energy-efficient mechanism that maintains security and adapts to dynamic conditions. It also integrates privacy-enhancing features, robust consensus mechanisms, and ML approaches to detect and prevent security threats. Furthermore, the practical implementation of these ML-based hybrid consensus models faces significant challenges, such as scalability, latency, throughput, resource requirements, and potential adversarial attacks. These challenges must be addressed to ensure the successful implementation of the blockchain network for real-world scenarios.
The past couple of decades in particular have seen a rapid increase in the prevalence of type 2 diabetes mellitus (T2DM), a debilitating metabolic disorder characterised by insulin resistance. The insufficient efficacy of current management strategies for insulin resistance calls for additional therapeutic options. The preponderance of evidence suggests potential beneficial effects of curcumin on insulin resistance, while modern science provides a scientific basis for its potential applications against the disease. Curcumin combats insulin resistance by increasing the levels of circulating irisin and adiponectin, activating PPARγ, suppressing Notch1 signalling, and regulating SREBP target genes, among others. In this review, we bring together the diverse areas pertaining to our current understanding of the potential benefits of curcumin on insulin resistance, associated mechanistic insights, and new therapeutic possibilities.
Loratadine (LoR) is a highly lipophilic and practically insoluble in water, hence having a low oral bioavailability. As it is formulated as topical gel, it competitively binds with the receptors, thus reducing the side-effects. The objective of this study was to prepare LoR loaded nanosponge (LoR-NS) in gel for topical delivery. Nine different formulations of emulsion were prepared by solvent evaporation method with polyvinyl alcohol (PVA), ethyl cellulose (EC), and dichloromethane (DCM). Based on 32 Full Factorial Design (FFD), optimization was carried out by varying the concentration of LOR:EC ratio and stirring rate. The preparations were subjected for the evaluation of particle size (PS), in vitro release, zeta potential (ZP) and entrapment efficiency (EE). The results revealed that the NS dispersion was nanosized with sustained release profiles and significant PS. The optimised formulation was formulated and incorporated into carbopol 934P hydrogel. The formulation was then examined to surface morphological characterizations using scanning electron microscopy (SEM) which depicted spherical NS. Stability studies, undertaken for 2 months at 40 ± 2 °C/75 ± 5% RH, concluded to the stability of the formulation. The formulation did not cause skin irritation. Therefore, the prepared NS hydrogel proved to be a promising applicant for LoR as a novel drug delivery system (NDDS) for safe, sustained and controlled topical application.