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- Status: Published
Scaled Formulations of Succinate based Polymeric particles for Eventual Testing in Clinical Settings
With an estimated 19.3 million cases and nearly 10 million deaths from cancer in a year worldwide, immunotherapies, which stimulate the immune system so that it can attack and kill cancer cells, are of interest. Tumors are produced from the uncontrolled and rapid proliferation of cells in the body. Cancer cells rely heavily on glutamine for proliferation due to its contribution of nitrogen for nucleotides and amino acids. Glutamine enters the tricarboxylic acid (TCA) cycle as α-ketoglutarate via glutaminolysis, in which glutamine is converted into glutamate by the enzyme glutaminase (GLS). Cancer cell proliferation may be limited by using glutaminase inhibitor CB-839. However, immune cells also rely on these metabolic pathways. Thus, a method for restarting the metabolic pathways in the presence of inhibitors is attractive. Succinate, a key metabolite in the TCA cycle, has been shown to stimulate the immune system despite the presence of metabolic inhibitors, such as CB-839. A delivery method of succinate is through microparticles (MPs) or nanoparticles (NPs) which may be coated in polyethylene glycol (PEG) for improved hydrophilicity. Polyethylene glycol succinate (PEGS) MPs were generated and tested in vivo and were shown to reduce tumor growth and prolong mouse survival. With the success in stimulating the immune system with MPs, NPs were investigated for an improved immune response due to their smaller size. These PES NPs were generated in this study. For clinical settings, it is necessary to scale-up the production of particles. Two methods of scale-up were proposed: (1) a combination of multiple small batches into a mixed batch, and (2) a singular, big batch. Size and release properties were compared to a small batch of PES NPs, and it was concluded that the big batch more closely resembled the small batch compared to the mixed batch. Thus, it was concluded that batch-to-batch variability plays a larger role than volume changes when scaling-up. In clinical settings, it is recommended to produce the particles in a big batch rather than a mixed batch.
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.
This thesis scrutinizes CLE technology for its ability to provide real-time intraoperative in vivo and ex vivo visualization of histopathological features of the normal and tumor brain tissues. First, the optimal settings for CLE imaging are studied in an animal model along with a generational comparison of CLE performance. Second, the ability of CLE to discriminate uninjured normal brain, injured normal brain and tumor tissues is demonstrated. Third, CLE was used to investigate cerebral microvasculature and blood flow in normal and pathological conditions. Fourth, the feasibility of CLE for providing optical biopsies of brain tumors was established during the fluorescence-guided neurosurgical procedures. This study established the optimal workflow and confirmed the high specificity of the CLE optical biopsies. Fifth, the feasibility of CLE was established for endoscopic endonasal approaches and interrogation of pituitary tumor tissue. Finally, improved and prolonged near wide-field fluorescent visualization of brain tumor margins was demonstrated with a scanning fiber endoscopy and 5-aminolevulinic acid.
These studies suggested a novel paradigm for neurosurgery-pathology workflow when the noninvasive intraoperative optical biopsies are used to interrogate the tissue and augment intraoperative decision making. Such optical biopsies could shorten the time for obtaining preliminary information on the histological composition of the tissue of interest and may lead to improved diagnostics and tumor resection. This work establishes a basis for future in vivo optical biopsy use in neurosurgery and planning of patient-related outcome studies. Future studies would lead to refinement and development of new confocal scanning technologies making noninvasive optical biopsy faster, convenient and more accurate.
In this work, plasmonic nanocomposites have been synthesized and used in laser tissue welding for ruptured porcine intestine ex vivo and incised murine skin in vivo. These laser-responsive nanocomposites improved tissue strength and healing, respectively. Additionally, a spatiotemporal model has been developed for laser tissue welding of porcine and mouse cadaver intestine sections using near-infrared laser irradiation. This mathematical model can be employed to identify optimal conditions for minimizing healthy cell death while still achieving a strong seal of the ruptured tissue using laser welding. Finally, in a model of surgical site infection, laser-responsive nanomaterials were shown to be efficacious in inhibiting bacterial growth. By incorporating an anti-microbial functionality to laser-responsive nanocomposites, these materials will serve as a treatment modality in sealing tissue, healing tissue, and protecting tissue in surgery.