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A Novel Cancer Treatment Platform Utilizing HER2-Immunoliposomes and Ultrasound
No full textA Master of Science thesis in Chemical Engineering by Saniha Aysha Ajith entitled, “A Novel Cancer Treatment Platform Utilizing Her2- Immunoliposomes And Ultrasound”, submitted in May 2020. Thesis advisor is Dr. Ghaleb Husseini. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form).Cancer is defined as the uncontrolled growth of cells in the body. It is one of the leading causes of death worldwide. One of the most common approaches to destroy cancer cells is via chemotherapy treatment, in which anti-cancer therapeutics are administered to the body. However, chemotherapy causes various adverse effects, including cardiotoxicity, nausea, anemia, and many more. To counteract these undesired effects, different smart drug delivery systems have been researched, in which nanocarriers can be used to exploit the enhanced permeability and retention (EPR) effect of cancerous tumors. Once the nanocarrier reaches the desired tumor site, external triggers can be applied to control the release of anti-neoplastic agents in that specific area. This study focuses on the use of liposomes conjugated with the monoclonal antibody, Trastuzumab, which is loaded with the chemotherapeutic drug, Doxorubicin, to target the overexpressed HER2 receptors on the surface of many breast cancer cells. Dynamic light scattering (DLS) was used to determine the liposome size, which was found to be 94.9 ± 1.29 nm for the immunoliposomes and 91.2 ± 1.47 nm for the NH2-terminated control liposomes. The concentration of lipids in the liposomes was determined using the Stewart Assay and the protein content using the BCA Assay. The external trigger used for the controlled release of the drug was low-frequency ultrasound. The release profiles of the control liposomes and immunoliposomes were studied at three different power densities, namely 7.46, 9.85, and 17.31 mW/cm2. Results showed that the immunoliposomes were slightly more sensitive to ultrasound and released a higher amount of drug in comparison to the control liposomes. Finally, drug release was modeled using nine kinetic models, namely: Zero-order, First-order, Higuchi, Hixon-Crowell, Korsmeyer-Peppas, Baker-Lonsdale, Weibull, Hopfenberg, and Gompertz. After linearizing the release data, the Baker-Lonsdale model provided the best fit. The future scope of this thesis involves using high-frequency ultrasound (HFUS) to release the drug, as well as in vitro and in vivo studies to determine the feasibility of using this drug delivery platform in hospitals and clinics around the globe.College of EngineeringDepartment of Chemical EngineeringMaster of Science in Chemical Engineering (MSChE
EEG-Based Semantic Vigilance Level Classification Using Directed Connectivity Patterns and Graph Theory Analysis
No full textThis paper proposes two novel methods to classify semantic vigilance levels by utilizing EEG directed connectivity patterns with their corresponding graphical network measures. We estimate the directed connectivity using relative wavelet transform entropy (RWTE) and partial directed coherence (PDC) and the graphical network measures by graph theory analysis (GTA) at four frequency bands. The RWTE and PDC quantify the strength and directionality of information flow between EEG nodes. On the other hand, the GTA of the complex network measures summarizes the topological structure of the network. We then evaluate the proposed methods using machine learning classifiers. We carried out an experiment on nine subjects performing semantic vigilance task (Stroop color word test (SCWT)) for approximately 45 minutes. Behaviorally, all subjects demonstrated vigilance decrement as reflected by the significant increase in response time and reduced accuracy. The strength and directionality of information flow in the connectivity network by RWTE/PDC and the GTA measures significantly decrease with vigilance decrement, p<0.05. The classification results show that the proposed methods outperform other related and competitive methods available in the literature and achieve 100% accuracy in subject-dependent and above 89% in subject-independent level in each of the four frequency bands. The overall results indicate that the proposed methods of directed connectivity patterns and GTA provide a complementary aspect of functional connectivity. Our study suggests directed functional connectivity with GTA as informative features and highlight Support Vector Machine as the suitable classifier for classifying semantic vigilance levels.American University of Sharja
Chemical disinfectants of COVID-19: an overview
No full textThe outbreak of coronavirus (COVID-19) has led to a broad use of chemical disinfectants in order to sterilize public spaces and prevent contamination. This paper surveys the chemicals that are effective in deactivating the virus and their mode of action. It presents the different chemical classes of disinfectants and identifies the chemical features of these compounds that pertain to their biocidal activity, relevant to surface/water disinfection
Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning
No full textA Master of Science thesis in Biomedical Engineering by Mostafa Mohamed Moussa entitled, “Distinguishing Between Fake and Real Smiles Using EEG Signals and Deep Learning”, submitted in April 2020. Thesis advisors are Dr. Usman Tariq and Dr. Hasan Al Nashash. Soft copy is available (Thesis, Approval Signatures, Completion Certificate, and AUS Archives Consent Form).Genuineness of smiles is one aspect of the field of deception recognition, one that is prevalent in myriad social situations, and it is not easy to tell when a person’s smile is genuine or not for the average person. Machine learning techniques, such as support vector machines or artificial neural networks, can allow better distinction between fake and real smiles by making use of electroencephalograms (EEG) from subjects with a simple experimental protocol, in which the subject’s response is known by the experimenters. Machine learning techniques were previously used in affect recognition, though not for distinguishing real and fake smiles through EEG signals. The objective of this study is to distinguish between fake and real smiles using deep learning techniques, more specifically shallow neural networks, convolutional neural networks, and support vector machines (SVMs) as a baseline from EEG signals. The experimental approach involved presenting subjects with visual stimuli and recording their physical response and their EEG, which was used with the aforementioned algorithms. The SVM classifier used the radial basis function kernel, with optimized parameters, the simple neural network was a three-layer pattern recognition network with 150 hidden units using scaled conjugate gradient as the training function, the convolutional neural networks used stochastic gradient descent with a momentum of 0.95 for all the different architectures, and the optimal one was selected based on the results. The accuracies of the simple neural network, convolutional neural network, and SVM are 88.879 %, 90.446 %, and 48.387 % respectively for subject-dependent classification, and the convolutional neural network yielded 53.418 % for subject-independent classification.College of EngineeringMultidisciplinary ProgramsMaster of Science in Biomedical Engineering (MSBME
Enhancement of SAR Speckle Denoising Using the Improved Iterative Filter
No full textThe recent advancement in synthetic aperture radar (SAR) technology has enabled high-resolution imaging capability that calls for efficient speckle filtering algorithms to preprocess radar imagery. Since the introduction of the Lee sigma filter in 1980, the various versions of the minimum mean square error (MMSE) filter were developed, focusing essentially on how to estimate the processed pixels. For instance, the iterative MMSE (IMMSE) filter that is commonly initialized by the boxcar filter maintains the initially filtered homogeneous areas and corrects the initially blurred spatial details after a few iterations. In this article, an effort is made to enhance the performance of the IMMSE filter in terms of speckle reduction and spatial detail preservation by refining the choice of the initial filter, optimizing its parameters, and improving the estimation of local statistics. Compared with the basic version, results showed that the improved iterative filter considerably enhanced the filtering criteria. When the improved iterative filtering process was initialized by the nonlocal mean filter, for few iterations, the filtering performances were improved. Simulated, airborne (ESAR, Oberpfaffenhofen Germany) and spaceborne (Sentinel 1, Palm Jumeirah Dubai UAE) SAR data were used to assess the filtering performances of the studied filters.American University of Sharja
Experimental Investigation of Sewage Sludge Pyrolysis In An Auger Reactor
No full textA Master of Science thesis in Chemical Engineering by Omar Mohamed Moussa entitled, “Experimental Investigation of Sewage Sludge Pyrolysis In An Auger Reactor”, submitted in November 2020. Thesis advisor is Dr. Yassir Makkawi. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Waste-to-energy technology is fast growing around the world in order to satisfy the growing demand for renewable energies and to avoid the negative impact of the current practice of waste disposal into landfills. This thesis aims at carrying out an experimental study of the thermochemical conversion of sewage sludge, to bio-oil, fuel gas and bio-char. As with many biomass-based materials, sewage sludge, when heated to a high temperature (> 300 ºC) in the absence of oxygen (i.e. pyrolysis), produces a gas and a solid phase. Upon rapid condensation, the effluent pyrolysis gas produces a liquid oil and a non-condensable gas. The gas and bio-oil can be used for energy generation, with the former being more attractive for application in the transportation sector. Generally, bio-char has great potential in soil amendment, especially in desert soil such as in the UAE. In this thesis, two different sewage sludge samples collected from water treatment plants in Abu Dhabi (post-anaerobic digestion) and Sharjah (pre-anaerobic digestion) were subjected to thermal conversion at 450 ˚C, 550 ˚C, and 600 ˚C to assess their suitability for biofuel and bio-char production and investigate the effect of temperature on the pyrolysis products. The maximum bio-oil yield from the two samples was 35.82 wt.% (Abu Dhabi sludge) and 44.22 wt.% (Sharjah sludge), both at the pyrolysis temperature of 550 ˚C. The high heating values of the bio-oils from the Abu Dhabi sample were found to be in the range of 32.70-32.96 MJ/kg, while the same from the Sharjah sewage sample were in the range of 25.5-29.8 MJ/kg. The difference in the bio-oil yield between the samples was attributed to the higher volatile matter content in the Sharjah sludge, and the higher ash in the Abu Dhabi sludge. These results are important to shed light on the effect of sludge treatment on the quality of the pyrolysis products. For the UAE, this study presents evidence that sewage sludge can be used to produce valuable products and counter the negative environmental impact caused by the current practice of dumping the sewage sludge into landfills.College of EngineeringDepartment of Chemical EngineeringMaster of Science in Chemical Engineering (MSChE
Communication Network for Ultrasonic Acoustic Water Leakage Detectors
No full textWater leaks in the distribution network produce significant losses and cause serious economic inconvenience especially in areas with water shortage. In this paper, the operational aspects of the most popular offline detection technologies, ground penetrating radars (GPR’s), infrared (IR) cameras, and acoustic detectors, were compared. The authors also studied the potential of using the recent Terahertz imaging technology for the same application. Acoustic detectors were found the most suitable technology for the atmosphere in UAE, where the levels of humidity and, consequently, soil moisture are high, because both of GPRs and IR cameras operational capability to detect leaks tend to decrease sharply as soil moisture increases. On the other side, a conventional acoustic detector has very limited scope of detection. This paper presents a method of expanding the sensing component of acoustic detectors by connecting acoustic sensors through a digital communication system using the 3G/4G networks to a monitoring center with an acoustic spectrum analyzer. The novelty of this system is its ability to provide offline detection of leakages in the underground water pipelines remotely without deforming the surrounding environment or adjusting the acoustic detector's analyzing system. Simulation results proves the ability of the system to reconstruct the input noise signal at the end of the proposed network which is to be connected to the acoustic analyzer
The Photocatalytic Degradation of 2,3,7,8-Tetrachlorodibenzo-p-Dioxin in the Presence of Silver–Titanium Based Catalysts
No full textPolychlorinated dibenzo-p-dioxins (PCDD) are persistent toxic compounds that are ubiquitous in the environment. The photodegradation of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in the presence of silver titanium oxide (AgTi) and silver titanium doped into the Y-zeolite (AgTiY) was tested using high (254 nm) and mid (302 nm) energy UV irradiation sources. AgTi and AgTiY, both showed success in the photodegradation of 2,3,7,8-TCDD dissolved in methanol/tetrahydrofuran solution. Both catalysts were found to effectively decompose TCDD at 302 nm (lower energy) reaching in between 98–99% degradation after five hours, but AgTiY showed better performance than AgTi at 60 min reaching 91% removal. Byproducts of degradation were evaluated using Gas chromatography/mass spectrometry (GC–MS), resulting in 2,3,7-trichlorodibenzo-p-dioxin, a lower chlorinated congener and less toxic, as the main degradation product. Enzyme Linked Immunosorbent Assay (ELISA) was used to evaluate the relative toxicity of the degradation byproducts were a decrease in optical density indicated that some products of degradation could be potentially more toxic than the parent TCDD. On the other hand, a decrease in toxicity was observed for the samples with the highest 2,3,7,8-TCDD degradation, confirming that AgTiY irradiated at 302 nm is an excellent choice for degrading TCDD. This is the first study to report on the efficiency of silver titanium doped zeolites for the removal of toxic organic contaminants such as dioxins and furans from aquatic ecosystems.American University of Sharja
Photocatalytic demulsification of oil/water emulsions containing nonionic surfactant
No full textSeparation of oil-water (OW) emulsions is investigated using a photocatalytic demulsification approach. Experiments were conducted using two types of photocatalysts, namely, ZnO and TiO2. The emulsion samples were prepared with oil to water ratios of 1:3, 1:1, and 3:1 and using nonionic surfactant Tween 20 as an emulsifier. The demulsification efficiency was determined using a direct time varying phase separation measurement, while dynamic light scattering (DLS) and microscope imaging (MI) were used to determine the change in emulsion droplets size. The investigation results showed that all the emulsions were destabilized and separated within 30–90 min with demulsification efficiency that ranged from 38 to 90%. On the other hand, untreated control samples remained stable with no phase separation for more than 24 h. For most of the studied experimental conditions, TiO2 nanoparticles gave better demulsification results than ZnO. Modeling of the batch demulsification kinetics for both systems agreed satisfactorily with the experimental measurements. This could allow its further extension towards design of continuous processes for potential implementation in treatment of industrial oily wastewaters
In Vitro Cell Work of Two Types of Targeted Nanoparticles with Ultrasound Triggering
No full textA Master of Science thesis in Chemical Engineering by Waad Hussein Abuwatfa entitled, “In Vitro Cell Work of Two Types of Targeted Nanoparticles with Ultrasound Triggering”, submitted in November 2020. Thesis advisor is Dr. Ghaleb Husseini. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Targeted liposomes have shown promising potential as effective chemotherapeutics delivery vehicles in Smart Drug Delivery Systems (SDDSs). Upon accumulation and internalization at the tumor site, the liposomes need to be potentiated by an external trigger to effectively and controllably release their contents. In this study, synthesis, characterization and in vitro cell work of two types of targeted liposomes with ultrasound (US) triggering were considered. The MTT assays of PEGylated liposomes, encapsulating Doxorubicin (DOX), conjugated to human serum albumin (HSA), and Herceptin (HER) were carried out on different cell lines. The size of these nanocarriers was measured using dynamic light scattering (DLS). All three nanoparticles were found to be large unilamellar vesicles (LUVs), with radii of 83.5 ± 0.734 nm, 101 ± 1.56 nm, and 103 ± 1.86 nm for the control, HSA-conjugated, and HER-conjugated liposomes, respectively. The lipid content of the different liposomes was determined using the spectrophotometrical Stewart assay, and the confirmation of the moiety-conjugation was established using the bicinchoninic acid (BCA) assay. The MTT results revealed that functionalizing the liposomes with HSA coupled with US exposure for 20 seconds in a 40-kHz sonicating bath significantly enhanced the nanocarrier’s cellular uptake by the MCF-7 (HSA+ breast cancer) cells compared to HeLa (HSA- cervical cancer) cells, with cell viabilities of 15.7 ± 0.613 % and 47.3 ± 0.621% (p-value=8.05x10⁻⁷), respectively. As for the liposomes functionalized with HER under the same experimental conditions, the cell viabilities in SKBR-3 (HER+ breast cancer) cells and MDA-MB-231 (triple-negative breast cancer) were 27.4 ± 0.260 % and 40.1 ± 0.216% (p-value= 7.53x10⁻⁷), respectively. Thus, it is suggested that coupling US with active targeting elicits synergistic effects and enhanced drug uptake by the cells. Also, liposomal treatments, i.e., control and targeted, along with US exposure, showed more pronounced effects in both cell lines, as the observed cell viabilities were significantly less than in control non-sonicated cells. The results presented in this thesis show promise of utilizing targeted liposomal delivery and ultrasound in the treatment of cancer.College of EngineeringDepartment of Chemical EngineeringMaster of Science in Chemical Engineering (MSChE