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In this work, a novel method is developed for making nano- and micro- fibrous hydrogels capable of preventing the rejection of implanted materials. This is achieved by either (1) mimicking the native cellular environment, to exert fine control over the cellular response or (2) acting as a protective barrier, to

In this work, a novel method is developed for making nano- and micro- fibrous hydrogels capable of preventing the rejection of implanted materials. This is achieved by either (1) mimicking the native cellular environment, to exert fine control over the cellular response or (2) acting as a protective barrier, to camouflage the foreign nature of a material and evade recognition by the immune system. Comprehensive characterization and in vitro studies described here provide a foundation for developing substrates for use in clinical applications. Hydrogel dextran and poly(acrylic acid) (PAA) fibers are formed via electrospinning, in sizes ranging from nanometers to microns in diameter. While "as-electrospun" fibers are continuous in length, sonication is used to fragment fibers into short fiber "bristles" and generate nano- and micro- fibrous surface coatings over a wide range of topographies. Dex-PAA fibrous surfaces are chemically modified, and then optimized and characterized for non-fouling and ECM-mimetic properties. The non-fouling nature of fibers is verified, and cell culture studies show differential responses dependent upon chemical, topographical and mechanical properties. Dex-PAA fibers are advantageously unique in that (1) a fine degree of control is possible over three significant parameters critical for modifying cellular response: topography, chemistry and mechanical properties, over a range emulating that of native cellular environments, (2) the innate nature of the material is non-fouling, providing an inert background for adding back specific bioactive functionality, and (3) the fibers can be applied as a surface coating or comprise the scaffold itself. This is the first reported work of dex-PAA hydrogel fibers formed via electrospinning and thermal cross-linking, and unique to this method, no toxic solvents or cross-linking agents are needed to create hydrogels or for surface attachment. This is also the first reported work of using sonication to fragment electrospun hydrogel fibers, and in which surface coatings were made via simple electrostatic interaction and dehydration. These versatile features enable fibrous surface coatings to be applied to virtually any material. Results of this research broadly impact the design of biomaterials which contact cells in the body by directing the consequent cell-material interaction.
ContributorsLouie, Katherine BoYook (Author) / Massia, Stephen P (Thesis advisor) / Bennett, Kevin (Committee member) / Garcia, Antonio (Committee member) / Pauken, Christine (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2011
Description
Myocardial infarction (MI) remains the leading cause of mortality and morbidity in the U.S., accounting for nearly 140,000 deaths per year. Heart transplantation and implantation of mechanical assist devices are the options of last resort for intractable heart failure, but these are limited by lack of organ donors and potential

Myocardial infarction (MI) remains the leading cause of mortality and morbidity in the U.S., accounting for nearly 140,000 deaths per year. Heart transplantation and implantation of mechanical assist devices are the options of last resort for intractable heart failure, but these are limited by lack of organ donors and potential surgical complications. In this regard, there is an urgent need for developing new effective therapeutic strategies to induce regeneration and restore the loss contractility of infarcted myocardium. Over the past decades, regenerative medicine has emerged as a promising strategy to develop scaffold-free cell therapies and scaffold-based cardiac patches as potential approaches for MI treatment. Despite the progress, there are still critical shortcomings associated with these approaches regarding low cell retention, lack of global cardiomyocytes (CMs) synchronicity, as well as poor maturation and engraftment of the transplanted cells within the native myocardium. The overarching objective of this dissertation was to develop two classes of nanoengineered cardiac patches and scaffold-free microtissues with superior electrical, structural, and biological characteristics to address the limitations of previously developed tissue models. An integrated strategy, based on micro- and nanoscale technologies, was utilized to fabricate the proposed tissue models using functionalized gold nanomaterials (GNMs). Furthermore, comprehensive mechanistic studies were carried out to assess the influence of conductive GNMs on the electrophysiology and maturity of the engineered cardiac tissues. Specifically, the role of mechanical stiffness and nano-scale topographies of the scaffold, due to the incorporation of GNMs, on cardiac cells phenotype, contractility, and excitability were dissected from the scaffold’s electrical conductivity. In addition, the influence of GNMs on conduction velocity of CMs was investigated in both coupled and uncoupled gap junctions using microelectrode array technology. Overall, the key contributions of this work were to generate new classes of electrically conductive cardiac patches and scaffold-free microtissues and to mechanistically investigate the influence of conductive GNMs on maturation and electrophysiology of the engineered tissues.
ContributorsNavaei, Ali (Author) / Nikkhah, Mehdi (Thesis advisor) / Brafman, David (Committee member) / Migrino, Raymond Q. (Committee member) / Stabenfeldt, Sarah (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2018
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Description
Rapid intraoperative diagnosis of brain tumors is of great importance for planning treatment and guiding the surgeon about the extent of resection. Currently, the standard for the preliminary intraoperative tissue analysis is frozen section biopsy that has major limitations such as tissue freezing and cutting artifacts, sampling errors, lack of

Rapid intraoperative diagnosis of brain tumors is of great importance for planning treatment and guiding the surgeon about the extent of resection. Currently, the standard for the preliminary intraoperative tissue analysis is frozen section biopsy that has major limitations such as tissue freezing and cutting artifacts, sampling errors, lack of immediate interaction between the pathologist and the surgeon, and time consuming.

Handheld, portable confocal laser endomicroscopy (CLE) is being explored in neurosurgery for its ability to image histopathological features of tissue at cellular resolution in real time during brain tumor surgery. Over the course of examination of the surgical tumor resection, hundreds to thousands of images may be collected. The high number of images requires significant time and storage load for subsequent reviewing, which motivated several research groups to employ deep convolutional neural networks (DCNNs) to improve its utility during surgery. DCNNs have proven to be useful in natural and medical image analysis tasks such as classification, object detection, and image segmentation.

This thesis proposes using DCNNs for analyzing CLE images of brain tumors. Particularly, it explores the practicality of DCNNs in three main tasks. First, off-the shelf DCNNs were used to classify images into diagnostic and non-diagnostic. Further experiments showed that both ensemble modeling and transfer learning improved the classifier’s accuracy in evaluating the diagnostic quality of new images at test stage. Second, a weakly-supervised learning pipeline was developed for localizing key features of diagnostic CLE images from gliomas. Third, image style transfer was used to improve the diagnostic quality of CLE images from glioma tumors by transforming the histology patterns in CLE images of fluorescein sodium-stained tissue into the ones in conventional hematoxylin and eosin-stained tissue slides.

These studies suggest that DCNNs are opted for analysis of CLE images. They may assist surgeons in sorting out the non-diagnostic images, highlighting the key regions and enhancing their appearance through pattern transformation in real time. With recent advances in deep learning such as generative adversarial networks and semi-supervised learning, new research directions need to be followed to discover more promises of DCNNs in CLE image analysis.
ContributorsIzady Yazdanabadi, Mohammadhassan (Author) / Preul, Mark (Thesis advisor) / Yang, Yezhou (Thesis advisor) / Nakaji, Peter (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2019