AUS Repository

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    Review of Gold Nanoparticles: Synthesis, Properties, Shapes, Cellular Uptake, Targeting, Release Mechanisms and Applications in Drug Delivery and Therapy
    (MDPI, 2024-10-16) Georgeous, Joel; AlSawaftah, Nour; Abuwatfa, Waad; Husseini, Ghaleb
    The remarkable versatility of gold nanoparticles (AuNPs) makes them innovative agents across various fields, including drug delivery, biosensing, catalysis, bioimaging, and vaccine development. This paper provides a detailed review of the important role of AuNPs in drug delivery and therapeutics. We begin by exploring traditional drug delivery systems (DDS), highlighting the role of nanoparticles in revolutionizing drug delivery techniques. We then describe the unique and intriguing properties of AuNPs that make them exceptional for drug delivery. Their shapes, functionalization, drug-loading bonds, targeting mechanisms, release mechanisms, therapeutic effects, and cellular uptake methods are discussed, along with relevant examples from the literature. Lastly, we present the drug delivery applications of AuNPs across various medical domains, including cancer, cardiovascular diseases, ocular diseases, and diabetes, with a focus on in vitro and in vivo cancer research.
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    Digital Twins of Biological Systems: A Narrative Review
    (IEEE, 2024) Alsalloum, Ghufran A.; AlSawaftah, Nour Majdi; Percival, Kelly M.; Husseini, Ghaleb
    The concept of Digital Twins (DTs), software models that mimic the behavior and interactions of physical or conceptual objects within their environments, has gained traction in recent years, particularly in medicine and healthcare research. DTs technology emerges as a pivotal tool in disease modeling, integrating diverse data sources to computationally model dynamic biological systems. This narrative review explores potential DT applications in medicine, from defining DTs and their history to constructing DTs, modeling biologically relevant systems, as well as discussing the benefits, risks, and challenges in their application. The influence of DTs extends beyond healthcare and can revolutionize healthcare management, drug development, clinical trials, and various biomedical research fields.
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    Drug Release via Ultrasound-Activated Nanocarriers for Cancer Treatment: A Review
    (MDPI, 2024-10-27) Al Refaai, Khaled Armouch; AlSawaftah, Nour; Abuwatfa, Waad; Husseini, Ghaleb
    Conventional cancer chemotherapy often struggles with safely and effectively delivering anticancer therapeutics to target tissues, frequently leading to dose-limiting toxicity and suboptimal therapeutic outcomes. This has created a need for novel therapies that offer greater efficacy, enhanced safety, and improved toxicological profiles. Nanocarriers are nanosized particles specifically designed to enhance the selectivity and effectiveness of chemotherapy drugs while reducing their toxicity. A subset of drug delivery systems utilizes stimuli-responsive nanocarriers, which enable on-demand drug release, prevent premature release, and offer spatial and temporal control over drug delivery. These stimuli can be internal (such as pH and enzymes) or external (such as ultrasound, magnetic fields, and light). This review focuses on the mechanics of ultrasound-induced drug delivery and the various nanocarriers used in conjunction with ultrasound. It will also provide a comprehensive overview of key aspects related to ultrasound-induced drug delivery, including ultrasound parameters and the biological effects of ultrasound waves.
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    Advances in Liposomal Nanotechnology: From Concept to Clinics
    (Royal Society of Chemistry (RSC), 2024-10-28) Senjab, Reem M.; AlSawaftah, Nour; Abuwatfa, Waad H.; Husseini, Ghaleb
    Liposomes, spherical phospholipid vesicles with a unique morphology mimicking that of body cells, have emerged as versatile nanoparticles for drug delivery. Their biocompatibility, low cytotoxicity, targeted delivery, and hydrophobic and hydrophilic characteristics make them stand out over traditional drug delivery systems. Liposomes can be tailored in size, composition, lamellarity, and surface charge, offering a unique level of customization for various applications. Extensive research in liposome technology has led to the development of a wide range of liposomal formulations with enhanced functionalities, such as PEGylated liposomes, ligand-targeted liposomes, and stimuli-responsive liposomes. Beyond their crucial role in cancer treatment, liposomes play a significant role in influenza, COVID-19, cancer, and hepatitis A vaccines. They are also utilized in pain management, fungal treatment, brain targeting, and topical and ocular drug delivery. This review offers insight into the types of liposomes, their composition, preparation methods, characterization methods, and clinical applications. Additionally, it discusses challenges and highlights potential future directions in liposome-based drug delivery.
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    AI-Aided Robotic Wide-Range Water Quality Monitoring System
    (MDPI, 2024-10-31) Awwad, Ameen; Husseini, Ghaleb; Albasha, Lutfi
    Waterborne illnesses lead to millions of fatalities worldwide each year, particularly in developing nations. In this paper, we introduce a comprehensive system designed for the autonomous early detection of viral outbreaks transmitted through water to ensure sustainable access to healthy water resources, especially in remote areas. The system utilizes an autonomous water quality monitoring setup consisting of an airborne water sample collector, an autonomous sample processor, and an artificial intelligence-aided microscopic detector for risk assessment. The proposed system replaces the time-consuming conventional monitoring protocol by automating sample collection, sample processing, and pathogen detection. Furthermore, it provides a safer processing method against the spillage of contaminated liquids and potential resultant aerosols during the heat fixation of specimens. A morphological image processing technique of light microscopic images is used to segment images, assisting in selecting a unified appropriate input segment size based on individual blob areas of different bacterial cultures. The dataset included harmful pathogenic bacteria (A. baumanii, E. coli, and P. aeruginosa) and harmless ones found in drinking water and wastewater (E. faecium, L. paracasei, and Micrococcus spp.). The segmented labeled dataset was used to train deep convolutional neural networks to automatically detect pathogens in microscopic images. To minimize prediction error, Bayesian optimization was applied to tune the hyperparameters of the networks’ architecture and training settings. Different convolutional networks were tested in accordance with different required output labels. The neural network used to classify bacterial cultures as harmful or harmless achieved an accuracy of 99.7%. The neural network used to identify the specific types of bacteria achieved a cumulative accuracy of 93.65%.

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