Background: To explore the cytotoxic and apoptotic activity of the pierisin-6 protein in HPV HeLa and HepG2 cell lines. Methods: In this study, isolation, and purification of cytotoxic Prierisin-6 from the larvae of Pieris napi by affinity column chromatography techniques. Characterization of full-length mRNA of pierisin-6 gene was performed using 3’/5’ RACE PCR. The quantitative RT-PCR used to study the developmental stage-specific expression of pierisin-6 mRNA. The most effective concentration of Pierisin-6 protein was determined by measuring cell proliferation. Apoptosis was assessed using AO/Et-Br, Propidium Iodide, and Rhodamine 123 assays, whereas protein levels of caspase 3, cytochrome C were evaluated by ELISA method. Pierisin-6 induced cell cycle arrest was determined using Propidium iodide by FACS. Results: In this study, Pierisin-6, a novel apoptotic protein was found to have cytotoxicity against HeLa, HepG2 human cancer cell lines and L-132 human lung epithelial cell line. Among the target cells, HeLa was the most sensitive to Pierisin-6. Flow cytometry analysis confirms an increased percentage of apoptotic cells in sub G1 phase and cell cycle arrest at S phase. Alteration in the transmembrane potential of mitochondria, Cytochrome c released from the mitochondrial membrane, and caspase substrate assay demonstrated the cleavage of Ac- DEVD-pNA signifying the activation of Caspase-3. These findings suggested that Pierisin-6 significantly induce apoptosis in HeLa and HepG2 cells and is attributed mainly through a mitochondrial pathway by activation of caspases. The developmental and stage-specific expression of pierisin-6 mRNA was one thousand-fold increased from second to third instar larvae and gradually declined before pupation. Conclusion: Pierisin-6 represents a promising therapeutic approach for liver cancer patients.
In the inevitably hi‐tech universe of cybercrimes, one of the major still prevailing methods is the usage of the malicious URLs and creating a Phishing link to obtain user credentials from the people. This method is highly subtle and has more effect on people's lives as well as corporate loss. To identify the malicious URLs, the security community has listed blacklists and benign links online. Still, as the technology is being developed day by day, the attackers try to create new phishing URLs using new social engineering methods that could be easily forged into the user's account. To improve the generality of malicious URL detectors, machine‐learning techniques have been explored with increasing attention in recent years. This article addresses the detection of malicious URLs combining the intelligence of both the heuristic‐based method and the machine learning process. It has been found that there are many possibilities for detecting zero‐day attacks and spear‐phishing attacks when incorporating both lexical features and machine learning methods. Out of the six‐batch learning process analyzed, we implement a decision tree algorithm in our framework with 99.47% accuracy during evaluation. The true positive values gained in our proposed hybrid framework is 99.2% and indicate <1% of the false‐positive values. The implementation shows a precision level higher than the previous model developed and by other antiphishing techniques. A high detection rate on zero‐day and spear‐phishing attacks and overall results reveal that our system outclasses the current approach to detecting phishing scams.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.