abstract: Early detection and treatment of disease is paramount for improving human health and wellness. Micro-scale devices promote new opportunities for the rapid, cost-effective, and accurate identification of altered biological states indicative of disease early-onset; these devices function at a scale more sensitive to numerous biological processes. The application of Micro-Electro-Mechanical Systems (MEMS) in biomedical settings has recently emerged and flourished over course of the last two decades, requiring a deep understanding of material biocompatibility, biosensing sensitively/selectively, biological constraints for artificial tissue/organ replacement, and the regulations in place to ensure device safety. Capitalizing on the inherent physical differences between cancerous and healthy cells, our ultra-thin silicone membrane enables earlier identification of bladder cancer—with a 70% recurrence rate. Building on this breakthrough, we have devised an array to multiplex this sample-analysis in real-time as well as expanding beyond bladder cancer. The introduction of new materials—with novel properties—to augment current and create innovative medical implants requires the careful analysis of material impact on cellular toxicity, mutagenicity, reactivity, and stability. Finally, the achievement of replacing defective biological systems with implanted artificial equivalents that must function within the same biological constraints, have consistent reliability, and ultimately show the promise of improving human health as demonstrated by our hydrogel check valve. The ongoing proliferation, expanding prevalence, and persistent improvement in MEMS devices through greater sensitivity, specificity, and integration with biological processes will undoubtedly bolster medical science with novel MEMS-based diagnostics and therapeutics. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2018

Bladder Cancer is the tenth most common malignancy globally, and is the thirteenth most common cause of tumor associate morbidity. Bladder cancer is largely stratified into two categories: Non-Muscle Invasive Bladder Cancer (NMIBC) and Muscle Invasive Bladder Cancer (MIBC). NMIBC represents disease localized to the urinary bladder, and can be stratified into low and high-grade disease. MIBC represents an aggressive class of bladder cancer, with invasion into the underlying muscle layers of the bladder. MIBC can be classified as either non-metastatic MIBC, with disease localized to the bladder corpus, or metastatic MIBC, with disease spreading to sites beyond the bladder corpus. High grade NMIBC presents significant risk for progression to MIBC, and collectively both high grade NMIBC and MIBC bladder cancers demonstrate poor prognostic outcomes in clinical settings in terms of responses to therapy, recurrence risks, and overall survival. Hexaminolevulinate is a precursor of Protoporphyrin IX (PpIX) in the heme biosynthetic pathway. Hexaminolevulinate has been FDA approved under the trade name Cysview for diagnostic usage in blue light cystoscopies for fluorescence mediated visualization of disease along the bladder wall. I demonstrate that in addition to its diagnostic utility, Cysview and blue light irradiation can be utilized clinical as a potential therapeutic modality. I demonstrate the significant selective cytotoxicity of Cysview in combination with blue light against patient derived organoids (PDOs) from primary bladder cancers. My results determine that Cysview and blue light induce a rapid cell death program mediated by an influx in Reactive Oxygen Species (ROS) production, resulting in less than 5% viability within 24 hrs of treatment. This massive loss in viability is observed in low and high grade NMIBC, as well as MIBC derived PDOs with diverse mutational profiles. The results of this work demonstrate that PDOs are a significant platform for assessing therapy responses for correlation with the large patient population. Furthermore, the work identifies photodynamic therapy with Cysview and blue light irradiation as a putative therapeutic modality for localized bladder cancers, with the potential for significant improvement in patient outcomes. The identification and characterization of the therapeutic effects of Cysview come at a critical time during a global shortage of conventional therapeutics for localized bladder cancer, and presents a pathway for patients affected by these shortages. Progression in bladder cancer has been understood to be driven by processes governing subpopulation and cell state and lineage transformation. Previous studies identify phenotypic plasticity within a subset of bladder cancers and have correlated this phenomenon with an increased risk for disease progression from NMIBC to MIBC. In previous work, a subset of PDOs derived from luminal primary tumors demonstrated significant degrees of luminal to basal plasticity in vitro. In my analysis of these PDOs using transcriptomic and chromatin accessibility data, I identified a transcriptomic and epigenetic signature unique to plastic PDOs. Furthermore, I identified HNF1B, GRHL2, GATA6, and SNAI2 as putative regulators of luminal to basal plasticity in bladder cancer. Using these molecular profiles, I correlated the plasticity phenotype with reduction in overall survival using data from published bladder cancer patient cohorts. Finally, I developed a novel transcriptomic subtypes classification scheme and an accompanying R package to classify epithelial heterogeneity in bladder cancer, based on the transcriptomic subtypes I identified in bladder cancer PDOs.

L'objectif de cette thèse est de faciliter le diagnostic du cancer de la vessie. Durant une cystoscopie, un endoscope est introduit dans la vessie pour explorer la paroi interne de l'organe qui est visualisée sur un écran. Cependant, le faible champ de vue de l'instrument complique le diagnostic et le suivi des lésions. Cette thèse présente des algorithmes pour la création de cartes bi- et tridimensionnelles à large champ de vue à partir de vidéo-séquences cystoscopiques. En utilisant les avancées récentes dans le domaine de la minimisation d'énergies discrètes, nous proposons des fonctions coût indépendantes des transformations géométriques requises pour recaler de façon robuste et précise des paires d'images avec un faible recouvrement spatial. Ces transformations sont requises pour construire des cartes lorsque des trajectoires d'images se croisent ou se superposent. Nos algorithmes détectent automatiquement de telles trajectoires et réalisent une correction globale de la position des images dans la carte. Finalement, un algorithme de minimisation d'énergie compense les faibles discontinuités de textures restantes et atténue les fortes variations d'illuminations de la scène. Ainsi, les cartes texturées sont uniquement construites avec les meilleures informations (couleurs et textures) pouvant être extraites des données redondantes des vidéo-séquences. Les algorithmes sont évalués quantitativement et qualitativement avec des fantômes réalistes et des données cliniques. Ces tests mettent en lumière la robustesse et la précision de nos algorithmes. La cohérence visuelle des cartes obtenues dépassent celles des méthodes de cartographie de la vessie de la littérature / The aim of this thesis is to facilitate bladder cancer diagnosis. The reference clinical examination is cystoscopy, where an endoscope, inserted into the bladder, allows to visually explore the organ's internal walls on a monitor. The main restriction is the small field of view (FOV) of the instrument, which complicates lesion diagnosis, follow-up and treatment traceability.In this thesis, we propose robust and accurate algorithms to create two- and three-dimensional large FOV maps from cystoscopic video-sequences. Based on recent advances in the field of discrete energy minimization, we propose transformation-invariant cost functions, which allow to robustly register image pairs, related by large viewpoint changes, with sub-pixel accuracy. The transformations linking such image pairs, which current state-of-the-art bladder image registration techniques are unable to robustly estimate, are required to construct maps with several overlapping image trajectories. We detect such overlapping trajectories automatically and perform non-linear global map correction. Finally, the proposed energy minimization based map compositing algorithm compensates small texture misalignments and attenuates strong exposure differences. The obtained textured maps are composed by a maximum of information/quality available from the redundant data of the video-sequence. We evaluate the proposed methods both quantitatively and qualitatively on realistic phantom and clinical data sets. The results demonstrate the robustness of the algorithms, and the obtained maps outperform state-of-the-art approaches in registration accuracy and global map coherence