A thesis submitted to the Faculty of Health Sciences, University of the Witwatersrand, in fulfilment of the requirements for the degree of Doctor of Philosophy Department of Pharmacy and Pharmacology, Faculty of Health Sciences, University of the Witwatersrand, South Africa Department of Pharmacy and Pharmacology, Faculty of Health Sciences, University of the Witwatersrand, South Africa Johannesburg 2017. / Psoriasis vulgaris is a chronic, hyper-proliferative skin condition which affects the patient’s quality of life. The treatment strategy involves long term use of drugs that maintain the condition, however; playing a pivotal negative role in patient compliance. A constructive development in the design of treatment addressing the disease should focus on the challenges faced by current designs. Hence, cellular internalization and trans-barrier transport of nanoparticles can be manipulated on the basis of the physicochemical and mechanical characteristics of nanoparticles to enhance the treatment options of the condition by reducing dosing and increasing the healing due to intracellular drug delivery. Dictating these characteristics allows for the control of the rate and extent of cellular uptake, as well as delivering the drug-loaded nanosystem intra-cellularly which is imperative for drugs that require a specific cellular level to exert their effects, as is with psoriasis. Additionally, physicochemical characteristics of the nanoparticles should be optimal for the nanosystem to bypass the natural restricting phenomena of the body and act therapeutically at the targeted site. Neo-geometric copper nanoparticles (CuNPs) in the biomedical application ascertained skin permeation and retention of the CuNPs as a drug delivery system. The approach to the use of the nanocrystal exploited the shape properties as a function of enhanced cellular uptake and the copper in the inflamed psoriatic environment acted as a cytotoxic agent against hyper-proliferating keratinocytes. A Self-Oscillating Polymeric Network (SOPN) served as a vehicle for the topical delivery of the geometric CuNPs in addition to its oscillating phenomenon to promote the permeation of the active nanoparticles across the rate limiting barrier of the skin, the stratum corneum. This twofold system adequately targets the key limitations in addressing psoriasis. A statistical experimental design comprising a full factorial model for the optimization of the geometric CuNPs and Box-Behnken design applied on the SOPN served as a refining factor to achieve stable, homogenous, geometric nanoparticles using a one-pot method for the systematic optimization of the geometric CuNPs. The optimization of the SOPN involved amplitude and duration of the oscillations, permeation kinetics and cytotoxicity. After optimization of the nano-shapes and oscillations of the SOPN, extensive ex vivo cellular internalization studies were conducted to elucidate the effect of geometric CuNPs on uptake rates; in addition to the vital toxicity assays to further understand the cellular effect of geometric CuNPs as a drug delivery system. Complementing the geometry analysis; volume, surface area, orientation to the cell membrane and colloidal stability were also addressed. The SOPN was also investigated ex vivo for its biocompatibility to determine the LD50 and permeation kinetics. The in vivo study probed the nanosystem embedded in the innovative SOPN to stimulate the permeation of the CuNPs across the stratum corneum of the induced psoriasiform-plaque in a BALB/c mouse model. The results confirmed an optimized CuNPs-loaded SOPN topical system with promising plaque thickness reduction when compared with a commercial gold standard in the treatment of the skin condition. This novel system can be safely used with less frequent, lower dosing and no odour, therefore promoting patient compliance. / MT2017

Skin cancer is a major medical problem. If not detected early enough, skin cancer like melanoma can turn fatal. As a result, early detection of skin cancer, like other types of cancer, is key for survival. In recent times, deep learning methods have been explored to create improved skin lesion diagnosis tools. In some cases, the accuracy of these methods has reached dermatologist level of accuracy. For this thesis, a full-fledged cloud-based diagnosis system powered by convolutional neural networks (CNNs) with near dermatologist level accuracy has been designed and implemented in part to increase early detection of skin cancer. A large range of client devices can connect to the system to upload digital lesion images and request diagnosis results from the diagnosis pipeline. The diagnosis is handled by a two-stage CNN pipeline hosted on a server where a preliminary CNN performs quality check on user requests, and a diagnosis CNN that outputs lesion predictions. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2018. / FAU Electronic Theses and Dissertations Collection

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