Cervical cancer remains the third most commonly diagnosed gynecological malignancy in the United States and throughout the world despite being potentially preventable. Patients diagnosed with cervical cancer may develop local recurrence in the cervix and surrounding structures (vaginal apex, parametrial, or paracervical), regional recurrence in pelvic lymph nodes, distant metastasis, or a combination of all. The management of such treatment outcomes has not been subject to rigorous investigation. Therefore, there is a need for studies and clinical trials that focus on decision making to support the choice of the best treatment modality that leads to the minimal number of adverse treatment outcomes. Medical imaging plays a vital role in the initial diagnosis, staging, and guiding treatment decisions for cancer patients. Positron Emission Tomography-Computed Tomography (PET/CT) hybrid scanner has proven to be a primary functional imaging modality in the oncology clinic. A typical oncological application of PET/CT aims to examine the whole body for high tracer uptake as a sign of tumorous lesions or metastasis using 18F-Fluoro-2-deoxy-D-glucose (18F-FDG). This radiopharmaceutical has been proven to be useful for the quantitative determination of regional glucose metabolism localized in the brain, heart, bladder, and, fortunately, in tumors. Currently, 18F-FDG measured on PET is the prominent radiotracer in cancer staging and follow-up imaging. In the –omics1 era, mining data to derive inherent information about a system has influenced the medical field, especially oncological imaging. The process of radiomics involves high throughput analysis of medical images to extract a large number of quantified features that are presented as a decision supporting tool for clinicians in terms of various clinical tasks such as staging, prediction, and prognosis. In recent studies, the focus of radiomics has exceeded the whole-tumor analysis to include the quantification of habitats, sub-regions within the tumor volume defined based on specific criteria, with the intent to investigate the diversity extent of the intratumor heterogeneity as robust descriptors and predictors of clinicopathological factors. The presented work is a retrospective analysis of a cohort consisting of pretreatment Positron Emission Tomography and Computed Tomography (PET/CT) hybrid scans of cervical cancer patients consecutively treated with radiochemotherapy. We extracted radiomic features from the primary cervical tumor volumes, and voxel intensity-based features from tumor habitats to analyze the tumors’ heterogeneity based on 18Flourodeoxyglocuse (18F-FDG) uptake of PET, and Hounsfield Units (HU) of CT to obtain useful tumor information, which might be associated with treatment outcomes. To our knowledge, a limited number of studies have focused on investigating the potential role of radiomic features on cervical cancer PET/CT images. Briefly, the workflow of this study consisted of investigating parameters that might affect radiomic features predictive performance by evaluating the reproducibility of radiomic features extracted from 18F-FDG PET images for segmentation methods, gray levels discretization, and PET reconstruction algorithms. Afterward, we used these features to predict cervical treatment outcomes after radiochemotherapy. Due to the use of human data, this research study acquired the approval of the institutional review board (IRB) at the University of South Florida.

Radiomics treats images as quantitative data and promises to improve cancer prediction in radiology and therapy response assessment in radiation oncology. However, there are a number of fundamental problems that need to be solved in order to potentially apply radiomic features in clinic. The first basic step in computed tomography (CT) radiomic analysis is the acquisition of images using selectable image acquisition and reconstruction parameters. Radiomic features have shown large variability due to variation of these parameters. Therefore, it is important to develop methods to address these variability issues in radiomic features due to each CT parameter. To this end, texture phantoms provide a stable geometry and Hounsfield Units (HU) to characterize the radiomic features with respect to image acquisition and reconstruction parameters. In this project, normalization methods were developed to address the variability issues in CT Radiomics using texture phantoms. In the first part of this project, variability in radiomic features due to voxel size variation was addressed. A voxel size resampling method is presented as a preprocessing step for imaging data acquired with variable voxel sizes. After resampling, variability due to variable voxel size in 42 radiomic features was reduced significantly. Voxel size normalization is presented to address the intrinsic dependence of some key radiomic features. After normalization, 10 features became robust as a function of voxel size. Some of these features were identified as predictive biomarkers in diagnostic imaging or useful in response assessment in radiation therapy. However, these key features were found to be intrinsically dependent on voxel size (which also implies dependence on lesion volume). The normalization factors are also developed to address the intrinsic dependence of texture features on the number of gray levels. After normalization, the variability due to gray levels in 17 texture features was reduced significantly. In the second part of the project, voxel size and gray level (GL) normalizations developed based on phantom studies, were tested on the actual lung cancer tumors. Eighteen patients with non-small cell lung cancer of varying tumor volumes were studied and compared with phantom scans acquired on 8 different CT scanners. Eight out of 10 features showed high (Rs > 0.9) and low (Rs < 0.5) Spearman rank correlations with voxel size before and after normalizations, respectively. Likewise, texture features were unstable (ICC < 0.6) and highly stable (ICC > 0.9) before and after gray level normalizations, respectively. This work showed that voxel size and GL normalizations derived from texture phantom also apply to lung cancer tumors. This work highlights the importance and utility of investigating the robustness of CT radiomic features using CT texture phantoms. Another contribution of this work is to develop correction factors to address the variability issues in radiomic features due to reconstruction kernels. Reconstruction kernels and tube current contribute to noise texture in CT. Most of texture features were sensitive to correlated noise texture due to reconstruction kernels. In this work, noise power spectra (NPS) was measured on 5 CT scanners using standard ACR phantom to quantify the correlated noise texture. The variability in texture features due to different kernels was reduced by applying the NPS peak frequency and the region of interest (ROI) maximum intensity as correction factors. Most texture features were radiation dose independent but were strongly kernel dependent, which is demonstrated by a significant shift in NPS peak frequency among kernels. Percent improvements in robustness of 19 features were in the range of 30% to 78% after corrections. In conclusion, most texture features are sensitive to imaging parameters such as reconstruction kernels, reconstruction Field of View (FOV), and slice thickness. All reconstruction parameters contribute to inherent noise in CT images. The problem can be partly solved by quantifying noise texture in CT radiomics using a texture phantom and an ACR phantom. Texture phantoms should be a pre-requisite to patient studies as they provide stable geometry and HU distribution to characterize the radiomic features and provide ground truths for multi-institutional validation studies.

Colloidal nanocrystals with optical properties in the near-infrared (NIR) are of interest for many applications such as photovoltaic (PV) energy conversion, bioimaging, and therapeutics. For PVs and other electronic devices, challenges in using colloidal nanomaterials often deal with the surfaces. Because of the high surface-to-volume ratio of small nanocrystals, surfaces and interfaces play an enhanced role in the properties of nanocrystal films and devices. Organic ligand-capped CuInSe2 (CIS) and Cu(InXGa1-X)Se2 (CIGS) nanocrystals were synthesized and used as the absorber layer in prototype solar cells. By fabricating devices from spray-coated CuInSe nanocrystals under ambient conditions, solar-to-electric power conversion efficiencies as high as 3.1% were achieved. Many treatments of the nanocrystal films were explored. Although some treatments increased the conductivity of the nanocrystal films, the best devices were from untreated CIS films. By modifying the reaction chemistry, quantum-confined CuInSeXS2-X (CISS) nanocrystals were produced. The potential of the CISS nanocrystals for targeted bioimaging was demonstrated via oral delivery to mice and imaging of nanocrystal fluorescence. The size-dependent photoluminescence of Si nanocrystals was measured. Si nanocrystals supported on graphene were characterized by conventional transmission electron microscopy and spherical aberration (Cs)-corrected scanning transmission electron microscopy (STEM). Enhanced imaging contrast and resolution was achieved by using Cs-corrected STEM with a graphene support. In addition, clear imaging of defects and the organic-inorganic interface was enabled by utilizing this technique. / text

Magnetic resonance imaging (MRI) is an imaging technique to visualize internal structures of the body. Diffusion MRI is an MRI modality that measures overall diffusion effect of molecules in vivo and non-invasively. Diffusion tensor imaging (DTI) is an extended technique of diffusion MRI. The major application of DTI is to measure the location, orientation and anisotropy of fiber tracts in white matter. It enables non-invasive investigation of major neural pathways of human brain, namely tractography. As spatial resolution of MRI is limited, it is possible that there are multiple fiber bundles within the same voxel. However, diffusion tensor model is only capable of resolving a single direction. The goal of this dissertation is to investigate complex anatomical structures using high angular resolution diffusion imaging (HARDI) data without any assumption on the parameters. The dissertation starts with a study of the noise distribution of truncated MRI data. The noise is often not an issue in diffusion tensor model. However, in HARDI studies, with many more gradient directions being scanned, the number of repetitions of each gradient direction is often small to restrict total acquisition time, making signal-to-noise ratio (SNR) lower. Fitting complex diffusion models to data with reduced SNR is a major interest of this study. We focus on fitting diffusion models to data using maximum likelihood estimation (MLE) method, in which the noise distribution is used to maximize the likelihood. In addition to the parameters being estimated, we use likelihood values for model selection when multiple models are fit to the same data. The advantage of carrying out model selection after fitting the models is that both the quality of data and the quality of fitting results are taken into account. When it comes to tractography, we extend streamline method by using covariance of the estimated parameters to generate probabilistic tracts according to the uncertainty of local tract orientations.

Fluorescent carbon dots (C-dots) are well known for their low cell-cytotoxicity, biocompatibility, low preparation cost, excitation dependent photoluminescence, and excellent photostability. Typically, raw C-dots have low quantum efficiency and thus researchers have been utilizing biocompatible polymers such as polyethylene glycol (PEG) as a passivation agent in order to increase fluorescence signal. In this work, we report fluorescent self-passivated carbon nanodots (CNDs) synthesized from PEG by using it as a carbon source as well as a passivating agent. Importantly, the addition of graphene quantum dots (GQDs) during the synthesis of self-passivated CNDs can tune photoluminescence property. The results of bioimaging and cytotoxicity test of self-passivated CNDs hold promises for biomedicine applications.

As large-scale techniques for studying and measuring gene expressions have been developed, automatically inferring gene interaction networks from expression data has emerged as a popular technique to advance our understanding of cellular systems. Accurate prediction of gene interactions, especially in multicellular organisms such as Drosophila or humans, requires temporal and spatial analysis of gene expressions, which is not easily obtainable from microarray data. New image based techniques using in-sit hybridization(ISH) have recently been developed to allowlarge-scale spatial-temporal profiling of whole body mRNA expression. However, analysis of such data for discovering new gene interactions still remains an open challenge. This thesis studies the question of predicting gene interaction networks from ISH data in three parts. First, we present SPEX2, a computer vision pipeline to extract informative features from ISH data. Next, we present an algorithm, GINI, for learning spatial gene interaction networks from embryonic ISH images at a single time step. GINI combines multi-instance kernels with recent work in learning sparse undirected graphical models to predict interactions between genes. Finally, we propose NP-MuScL (nonparanormal multi source learning) to estimate a gene interaction network that is consistent with multiple sources of data, having the same underlying relationships between the nodes. NP-MuScL casts the network estimation problem as estimating the structure of a sparse undirected graphical model. We use the semiparametric Gaussian copula to model the distribution of the different data sources, with the different copulas sharing the same covariance matrix, and show how to estimate such a model in the high dimensional scenario. We apply our algorithms on more than 100,000 Drosophila embryonic ISH images from the Berkeley Drosophila Genome Project. Each of the 6 time steps in Drosophila embryonic development is treated as a separate data source. With spatial gene interactions predicted via GINI, and temporal predictions combined via NP-MuScL, we are finally able to predict spatiotemporal gene networks from these images.

Les nanoparticules inorganiques luminescentes ont suscité un intérêt croissant au cours des dernières décennies, notamment pour leur application en imagerie biologique. Un certain nombre d’entre elles présentent toutefois des limitations telles que toxicité, absence de biodégradabilité, faible brillance, clignotements…. Dans cette optique, les nanoparticules fluorescentes à base de petites molécules organiques (FONs) offrent une solution alternative prometteuse aux nanoparticules inorganiques pour l'imagerie biologique. Le principal défi réside dans l'élaboration des nanoparticules organiques possédant une brillance élevée, une bonne stabilité dans l'eau (y compris en milieu biologique), une bonne biocompatibilité ainsi qu'une émission accordable dans le visible et au-delà dans le proche infrarouge (pour une détection plus aisée en milieu diffusant). Dans cette optique, nous avons utilisé une stratégie basée sur l’utilisation de chromophores dipolaires de type "push pull" « adaptés ». Au cours du travail, la synthèse de séries de chromophores homologues bâtis sur le même système conjugué et ayant en commun un groupe donneur de type triphénylamine (destiné à préserver les propriétés de luminescence) présentant ou non des motifs encombrants positionnés a été réalisée. Les nanoparticules correspondantes ont été préparées selon un protocole classique, simple et rapide à mettre en oeuvre (précipitation). L’étude des propriétés photophysiques des nanoparticules organiques fluorescentes ainsi obtenues a été réalisée et mise en perspective avec celles des chromophores en solution dans des solvants organiques de polarité variable. Une étude systématique de l’évolution dans le temps des propriétés optiques des nanoparticules organiques a été réalisée permettant de mettre en lumière des relations entre la structure des sous-unités chromophoriques et la stabilité colloïdale et « optique » des nanoparticules. Ces études ont permis d’identifier des nanoparticules émettant dans le proche infrarouge extrêmement brillantes et présentant une stabilité colloïdale remarquable dans l’eau, une photostabilité accrue et une très bonne biocompatibilité. De ce fait, ces nanoparticules ont pu être utilisées avec succès dans l'imagerie biologique des cellules et le suivi (tracking) à l'échelle de la particule unique, démontrant l'intérêt de la démarche d'ingénierie mise en oeuvre. / During the last decades, luminescent inorganic nanoparticles have attracted a large interest in different fields including biological imaging. However, a number of them have drawbacks such as toxicity and absence of biodegradability. Recently, molecular-based fluorescent organic nanoparticles (FONs) have emerged as a promising alternative to inorganic nanoparticles for bioimaging. The main challenge lies in the elaboration of organic nanoparticles that combine large brightness, good colloidal stability in biological environments) and biocompatibility as well as NIR emission (to allow improved detection in thick tissues). To achieve this objective, we have implemented a molecular engineering strategy based on dedicated polar and polarizable "push pull" chromophore built from a triphenylamine donor moiety and a specific pi-conjugated system. The corresponding nanoparticles were readily prepared by the reprecipitation method. In the present manuscript, the synthesis of the chromophores and the preparation and characterization of the organic fluorescent nanoparticles is described. A comprehensive investigation of their photophysical properties and study of their colloidal stability is presented allowing to derive structure-property relationships. The implemented study led to innovative NIR-emitting nanoparticles combining large brightness (superior to those of QDs and NIR-emitting organic dyes), remarkable colloid stability and suitable photostability. These nanoparticles have been successfully used for single particle tracking and imaging in cells, while no toxic effect was observed.

Magister Scientiae - MSc (Biotechnology) / The expansion of nanotechnology, resulting in multitudes of consumer and industrial products, causes concern amongst the scientific community regarding the risks associated with the release of nanomaterials into the environment and its subsequent effects on plants. Therefore, the focus of this study was aimed at investigating the effects of MPA-capped CdTe and carbon QDs on legumes plants namely P. vulgaris and G. max. Fluorescent imaging revealed that QDs were translocated from the roots to the aerial parts of the plant and accumulated in the edible parts of P. vulgaris. Subsequent physiological and biochemical tests revealed that both QD types induced oxidative stress as biological markers for stress including lipid peroxidation and cell death were elevated. In addition, carbon QDs displayed lower toxicity in comparison to MPA-CdTe QDs, but still possessed the ability to induce oxidative stress in plant cells. However, the effects were more pronounced in G. max in comparison to P. vulgaris; and more so with MPA-CdTe QDs than carbon QDs. Furthermore, MPA-CdTe and carbon QDs altered the concentrations and translocation of essential macro and microelements that are required for plant growth and development. This may have detrimental effects on crop productivity and yield, with negative implications on food quality and food security. / 2021-08-31

abstract: Gold nanoparticles have emerged as promising nanomaterials for biosensing, imaging, photothermal treatment and therapeutic delivery for several diseases, including cancer. We have generated poly(amino ether)-functionalized gold nanorods (PAE-GNRs) using a layer-by-layer deposition approach. Sub-toxic concentrations of PAE-GNRs were employed to deliver plasmid DNA to prostate cancer cells in vitro. PAE-GNRs generated using 1,4C-1,4Bis, a cationic polymer from our laboratory demonstrated significantly higher transgene expression and exhibited lower cytotoxicities when compared to similar assemblies generated using 25 kDa poly(ethylene imine) (PEI25k-GNRs), a current standard for polymer-mediated gene delivery. Additionally, sub-toxic concentrations of 1,4C-1,4Bis-GNR nanoassemblies were employed to deliver expression vectors that express shRNA ('shRNA plasmid') against firefly luciferase gene in order to knock down expression of the protein constitutively expressed in prostate cancer cells. The roles of poly(amino ether) chemistry and zeta-potential in determining transgene expression efficacies of PAE-GNR assemblies were investigated. The theranostic potential of 1,4C-1,4Bis-GNR nanoassemblies was demonstrated using live cell two-photon induced luminescence bioimaging. The PAE class of polymers was also investigated for the one pot synthesis of both gold and silver nanoparticles using a small library poly(amino ethers) derived from linear-like polyamines. Efficient nanoparticle synthesis dependent on concentration of polymers as well as polymer chemical composition is demonstrated. Additionally, the application of poly(amino ether)-gold nanoparticles for transgene delivery is demonstrated in 22Rv1 and MB49 cancer cell lines. Base polymer, 1,4C-1,4Bis and 1,4C-1,4Bis templated and modified gold nanoparticles were compared for transgene delivery efficacies. Differences in morphology and physiochemical properties were investigated as they relate to differences in transgene delivery efficacy. There were found to be minimal differences suggestion that 1,4C-1,4Bis efficacy is not lost following use for nanoparticle modification. These results indicate that poly(amino ether)-gold nanoassemblies are a promising theranostic platform for delivery of therapeutic payloads capable of simultaneous gene silencing and bioimaging. / Dissertation/Thesis / Doctoral Dissertation Bioengineering 2014

O trabalho apresentado nessa dissertação concentra-se na área de espectroscopia de íons terras raras com aplicação em biofotônica e foi desenvolvido através da síntese de partículas de fluoreto de bário e gadolínio dopada com o par Yb3+/Tm3+ para estudos conversão ascendente de energia, além da tentativa de crescimento desse material nos poros de uma nanoesfera de sílica. Os materiais a base de fluoretos foram sintetizados através de metodologia hidrotermal com agente complexante citrato de sódio variando a concentração de dopagem do par Yb3+/Tm3+ produzindo três diferentes materiais luminescentes que apresentou conversão ascendente em energia e emissões características do íon túlio a partir da excitação em 980 nm (BaGdF5YbTm nos quais a razão (porcentagem mol/mol) Yb/Tm foi igual à 18/0,5; 20/1 e 10/0,5), além de um quarto material dopado com o íon Eu3+ para estudos estruturais e espectroscópicos acerca da matriz (BaGdF5Eu Gd/Eu 1% mol/mol). Todos os materiais obtidos pela metodologia são cristalinos e apresentaram fase cúbica da solução sólida de BaGdF5. Ademais, dois materiais híbridos, contendo sílica e fluoreto, foram preparados. O primeiro, recobrimento das partículas de fluoreto com sílica (SiO2), apresentou resultados diferentes do que foi previamente reportado por esse grupo de pesquisa, direcionando os estudos ao segundo material, que consistiu no crescimento de partículas de fluoreto dentro dos poros de uma nanoesfera de sílica (SiO2). Neste último, os materiais apresentaram manutenção da morfologia esférica precursora das nanoesferas de sílica além de, para uma amostra sintetiza hidrotermicamente na ausência de citrato de sódio, promover o aparecimento da banda 1D4 5H6 e serão conduzidas para testes biológicos subsequentes como citotoxicidade. / The work presented herein focuses on the area of rare earth spectroscopy with application in biophotonics and it was developed through the synthesis of barium and gadolinium fluoride particles doped with the Yb3+/Tm3+ pair for upconversion studies and the attempt to grow the material in the pores of a silica nanosphere. Biophotonic has been an area of growing interest for the last 30 years, it studies the interaction between light and living matter seeking new technologies in order to characterize, image, diagnose and treat a complexity of dysfunctions in biological material. The search and development of new materials for bioimaging is a need that involves several areas of knowledge. In this work, fluoride-based materials were synthesized by hydrothermal method by varying the doping concentration of Yb3+/Tm3+ pair producing 3 different luminescent materials (BaGdF5YbTm - in which the ratio (mole/mole percentage) Yb/Tm was equal to 18/0,5; 20/1 and 10/0,5). In addition, a fourth material doped with Eu3+ was synthetized for structural and spectroscopic studies on the matrix (BaGdF5Eu - Gd / Eu 1 mol% mol). All materials obtained by this method were crystalline and presented cubic phase of BaGdF5 solid solution. Moreover, two multifunctional materials were prepared. The first was obtained by coating the fluoride particles with silica (SiO2) presented different results than previously reported by this research group, directing the studies to the second material, which consisted of the growth of fluoride particles inside the pores of a nanosphere of silica (SiO 2). In the latter, the materials showed maintenance of the precursor spherical morphology of silica nanospheres and, for a hydrothermally synthesized sample in the absence of sodium citrate, promote the appearance of the 1D4 5H6 band and will be conducted for subsequent biological tests as cytotoxicity.