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Nagaraja SreeHarsha,1 Rahul Maheshwari,2,3 Bandar E Al-Dhubiab,1 Muktika Tekade,4 Mukesh Chandra Sharma,4 Katharigatta N Venugopala,1,5 Rakesh Kumar Tekade,2,6–7 Abdullah M Alzahrani8 1Department of Pharmaceutical Sciences, College of Clinical Pharmacy, King Faisal University, Al-Ahsa, Saudi Arabia; 2National Institute of Pharmaceutical Education and Research (NIPER) – Ahmedabad, Gandhinagar, Gujarat 382355, India; 3School of Pharmacy and Technology Management, SVKM’s NMIMS, Hyderabad Campus, Hyderabad, Telangana 509 301, India; 4School of Pharmacy, Devi Ahilya Vishwavidyalaya, Takshila Campus, Indore, MP 452001, India; 5Department of Biotechnology and Food Technology, Durban University of Technology, Durban 4001, South Africa; 6Department of Pharmaceutical Technology, The International Medical University, School of Pharmacy, Kuala Lumpur 57000, Malaysia; 7Department of Materials Engineering, Indian Institute of Technology-Jammu, Jammu - 181 221, J&K, India; 8Department of Biological Sciences, College of Science, King Faisal University, Al-Ahsa, Saudi ArabiaCorrespondence: Nagaraja SreeHarshaDepartment of Pharmaceutical Sciences, College of Clinical Pharmacy, King Faisal University, PO Box 380, Al-Ahsa 31982, Saudi ArabiaEmail sharsha@kfu.edu.saBackground: Prostate cancer (PC) has the highest prevalence in men and accounts for a high rate of neoplasia-related death. Doxorubicin (DOX) is one of the most widely used anti-neoplastic drugs for prostate cancer among others. However, it has low specificity and many side effects and affects normal cells. More recently, there have been newly developed drug delivery tools which are graphene or graphene-based, used to increase the specificity of the delivered drug molecules. The graphene derivatives possess both π-π stacking and increased hydrophobicity, factors that increase the likelihood of drug delivery. Despite this, the hydrophilicity of graphene remains problematic, as it induced problems with stability. For this reason, the use of a chitosan coating remains one way to modify the surface features of graphene.Method: In this investigation, a hybrid nanoparticle that consisted of a DOX-loaded reduced graphene oxide that is stabilized with chitosan (rGOD-HNP) was developed.Result: The newly developed rGOD-HNP demonstrated high biocompatibility and efficiency in entrapping DOX (∼65%) and releasing it in a controlled manner (∼50% release in 48 h). Furthermore, it was also demonstrated that rGOD-HNP can intracellularly deliver DOX and more specifically in PC-3 prostate cancer cells.Conclusion: This delivery tool offers a feasible and viable method to deliver DOX photo-thermally in the treatment of prostate cancer.Keywords: graphene, photothermal, chitosan, hybrid nanoparticles, HNP, prostate cancer

Some new boron complexes have been synthesized by the reaction of triisopropohxyborane with the mixed azines, prepared by the condensation of salicylaldehyde and hydrazine with aldehydes/ketones in a 1:1:1 mole ratio to give a new series of (OPri)2B(NO) type of complexes. Their structures were confirmed on the basis of elemental analyses, ultraviolet, infrared, 1H-NMR and 11B-NMR spectral studies. The ligands and their boron complexes were also screened for their antifungal activity. Several of these complexes were found to be quite active in this respect. .

As deepfake technology becomes increasingly sophisticated, the proliferation of manipulated images presents a significant threat to online integrity, requiring advanced detection and mitigation strategies. Addressing this critical challenge, our study introduces a pioneering approach that integrates Explainable AI (XAI) with Adversarial Robustness Training (ART) to enhance the detection and removal of deepfake content. The proposed methodology, termed XAI-ART, begins with the creation of a diverse dataset that includes both authentic and manipulated images, followed by comprehensive preprocessing and augmentation. We then employ Adversarial Robustness Training to fortify the deep learning model against adversarial manipulations. By incorporating Explainable AI techniques, our approach not only improves detection accuracy but also provides transparency in model decision-making, offering clear insights into how deepfake content is identified. Our experimental results underscore the effectiveness of XAI-ART, with the model achieving an impressive accuracy of 97.5% in distinguishing between genuine and manipulated images. The recall rate of 96.8% indicates that our model effectively captures the majority of deepfake instances, while the F1-Score of 97.5% demonstrates a well-balanced performance in precision and recall. Importantly, the model maintains high robustness against adversarial attacks, with a minimal accuracy reduction to 96.7% under perturbations. [ABSTRACT FROM AUTHOR]

DT digital twin technology has become an essential tool in hydraulic systems. It not only offers a virtual representation of the actual plant, but also real-time monitoring and optimization of that same machinery. Digital Twin (DT) technology has become a cornerstone in the optimization of industrial processes, particularly in the domain of hydraulic systems. For example, this research aims to use digital twin technology to detect and fix leaks in hydraulic systems. By integrating advanced simulation algorithms for accurate leak detection and performance enhancement, this study presents a comprehensive framework. Combining techniques developed from both data-driven and state-of-the-art optimization methods our approach looks to change how leaks are detected in hydraulics. Our test introduces a comprehensive framework that not only accurately identifies leaks but also employs advanced simulation algorithms for subsequent performance enhancement. By bringing together data-driven insights and cutting-edge optimization methods, our work at the frontier of revolutionizing leak detection in hydraulic systems. [ABSTRACT FROM AUTHOR]