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Fluid mechanics driven bioreactor design has entered a new phase of development in the last 15 years: the scalable expansion and differentiation of human pluripotent stem cells (hPSCs) for translational therapies, drug screening and discovery, developmental biology research, and disease pathology. Demand for hPSC-derived resources is expected to increase due to the evolving global human health initiatives. Bioreactors are a suitable alternative to tissue culture dishes for the purposes of mass production. Stirred spinner flasks are a common bench-scale bioreactor for testing hPSC culture outcomes including the efficient production of undifferentiated cells, intermediate progenitor cells and terminally differentiated end-products. To continuously supply nutrients and oxygen, and homogeneously distribute cellular metabolic waste, culture medium in spinner flasks is stirred; however, this generates heterogenous fluidic shear stress on hPSCs due to the laws of fluid mechanics. As hPSCs are sensitive to physiochemical and bioenergetic cues, this poses a unique challenge. Exposure to shear stress or other forces has been known to kill the cells, or counterproductively direct them to an undesired phenotype. For these reasons, fluidic shear stress is often avoided in bioreactor design and parameterization, but much is unknown about the nuanced effect of fluidic shear on hPSCs. It is unclear if appropriate application of fluidic shear could productively direct lineage specification, maintain pluripotency, or enhance differentiation efficiency or functional maturity. Thus, the development of novel devices and methods capable of integrating refined engineering control of the bulk hydrodynamic conditions and the fluidic microenvironments in which the cells are grown is warranted. This work seeks to uncover the role of fluidic shear in dynamic suspension in bioreactors. This was achieved in part by observing the cellular and molecular expression of hPSC aggregates cultured in st

Open-Angle Glaucoma (OAG) is the leading cause of irreversible blindness. The cause of OAG is an increase in intraocular pressure, thought to be driven by fibrosis and other changes in the trabecular meshwork (TM). This accumulation is often the result of changes in the behavior of TM cells, which are responsible for maintaining the TM. In OAG, the cells are fewer in number, produce more extracellular matrix (ECM) proteins, and fail to remove ECM proteins and debris accumulation in the TM. Another change is mitochondrial damage, which takes the form of a decrease in mitochondrial DNA (mtDNA) per cell, an increase in the mitochondrial common deletion, and an increase in the damaged nucleotide 8-Oxo-2'-deoxyguanosine in mtDNA. This work sought to investigate the extent to which existing glaucoma models in TM cells affect mitochondrial performance and mtDNA damage. Further, this work also aimed to examine how mitochondrial DNA damage and depletion impacted levels of OAG-related transcripts. Dexamethasone (Dex) has been used to model glaucoma-like changes in TM cells. Specifically, it induces fibrotic changes in TM cells. In vivo, it can lead to glaucoma in humans and animal models. Our work shows that Dex leads to a decrease in ATP being produced by mitochondria in TM cells, as well as a decrease in mitochondrial performance. However, Dex treated TM cells did not show any evidence of the mtDNA damage found in OAG. For this reason, we next aimed to directly induce mtDNA damage and depletion in TM cells. Damage and depletion of mtDNA was induced by two methods, to account for potential off-target effects. One method was via treatment with ethidium bromide. In addition, another method involving doxycycline inducible expression of a mutant DNA repair enzyme was used as well. To allow for transduction and selection of cells with this construct, an immortalized version of TM cells was selected. Both of these methods yield a decrease in mtDNA per cell, as well a decrease in c

PurposeThis study aimed to compare a new Artificial Intelligence (AI) method to conventional mathematical warping in accurately overlaying peripheral retinal vessels from two different imaging devices: confocal scanning laser ophthalmoscope (cSLO) wide-field images and SLO ultra-wide field images.MethodsImages were captured using the Heidelberg Spectralis 55-degree field-of-view and Optos ultra-wide field. The conventional mathematical warping was performed using Random Sample Consensus-Sample and Consensus sets (RANSAC-SC). This was compared to an AI alignment algorithm based on a one-way forward registration procedure consisting of full Convolutional Neural Networks (CNNs) with Outlier Rejection (OR CNN), as well as an iterative 3D camera pose optimization process (OR CNN + Distortion Correction [DC]). Images were provided in a checkerboard pattern, and peripheral vessels were graded in four quadrants based on alignment to the adjacent box.ResultsA total of 660 boxes were analysed from 55 eyes. Dice scores were compared between the three methods (RANSAC-SC/OR CNN/OR CNN + DC): 0.3341/0.4665/4784 for fold 1-2 and 0.3315/0.4494/4596 for fold 2-1 in composite images. The images composed using the OR CNN + DC have a median rating of 4 (out of 5) versus 2 using RANSAC-SC. The odds of getting a higher grading level are 4.8 times higher using our OR CNN + DC than RANSAC-SC (p < 0.0001).ConclusionPeripheral retinal vessel alignment performed better using our AI algorithm than RANSAC-SC. This may help improve co-localizing retinal anatomy and pathology with our algorithm.

Opinion statementPSMA-PET has been a practice-changing imaging biomarker for the management of men with PCa. Research suggests improved accuracy over conventional imaging and other PET radiotracers in many contexts. With multiple approved PSMA-targeting radiotracers, PSMA PET will become even more available in clinical practice. Its increased use requires an understanding of the prospective data available and caution when extrapolating from prior trial data that utilized other imaging modalities. Future trials leveraging PSMA PET for treatment optimization and management decision-making will ultimately drive its clinical utility.

Hypertrophy Cardiomyopathy (HCM) is the most prevalent hereditary cardiovascular disease – affecting >1:500 individuals. Advanced forms of HCM clinically present with hypercontractility, hypertrophy and fibrosis. Several single-point mutations in Beta-Myosin Heavy Chain (MYH7) have been associated with HCM and increased contractility at the organ level. Different MYH7 mutations have resulted in increased, decreased, or unchanged force production at the molecular level. Yet, how these molecular kinetics link to cell and tissue pathogenesis remains unclear. The Hippo Pathway, specifically its effector molecule YAP, has been demonstrated to be reactivated in pathological hypertrophic growth. This is intriguing because it signals hypertrophic and proliferative growth during development and is quiescent during cardiac homeostasis. We hypothesized that changes in force production (intrinsically or extrinsically) directly alter the homeostatic mechano-signaling of the Hippo pathway through changes in stresses on the nucleus. Using human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs), we asked whether homeostatic mechanical signaling through the canonical growth regulator, YAP, is altered 1) by changes in the biomechanics of single HCM mutant cardiomyocytes and 2) by alterations in the mechanical environment. We use genetically edited hiPSC-CM with point mutations in MYH7 associated with HCM, and their matched controls, combined with micropatterned traction force microscopy substrates to confirm the hypercontractile phenotype in MYH7 mutants. We next modulate contractility in healthy and disease hiPSC-CMs by treatment with positive and negative inotropic drugs and demonstrate a correlative relationship between contractility and YAP activity. We further demonstrate the activation of YAP in both HCM mutants and healthy hiPSC-CMs treated with contractility modulators correlates with enhanced nuclear deformation. We conclude that the overactivation of YAP

Hydra has a tubular bilayered epithelial body column with a dome-shaped head on one end and a foot on the other. Hydra lacks a permanent mouth: its head epithelium is sealed. Upon neuronal activation, a mouth opens at the apex of the head which can exceed the body column diameter in seconds, allowing Hydra to ingest prey larger than itself. While the kinematics of mouth opening are well characterized, the underlying mechanism is unknown. We show that Hydra mouth opening is generated by independent local contractions that require tissue-level coordination. We model the head epithelium as an active viscoelastic nonlinear spring network. The model reproduces the size, timescale and symmetry of mouth opening. It shows that radial contractions, travelling inwards from the outer boundary of the head, pull the mouth open. Nonlinear elasticity makes mouth opening larger and faster, contrary to expectations. The model correctly predicts changes in mouth shape in response to external forces. By generating innervated : nerve-free chimera in experiments and simulations, we show that nearest-neighbour mechanical signalling suffices to coordinate mouth opening. Hydra mouth opening shows that in the absence of long-range chemical or neuronal signals, short-range mechanical coupling is sufficient to produce long-range order in tissue deformations.

Our sense of hearing is mediated by cochlear hair cells, of which there are two types organized in one row of inner hair cells and three rows of outer hair cells. Each cochlea contains 5-15 thousand terminally differentiated hair cells, and their survival is essential for hearing as they do not regenerate after insult. It is often desirable in hearing research to quantify the number of hair cells within cochlear samples, in both pathological conditions, and in response to treatment. Machine learning can be used to automate the quantification process but requires a vast and diverse dataset for effective training. In this study, we present a large collection of annotated cochlear hair-cell datasets, labeled with commonly used hair-cell markers and imaged using various fluorescence microscopy techniques. The collection includes samples from mouse, rat, guinea pig, pig, primate, and human cochlear tissue, from normal conditions and following in-vivo and in-vitro ototoxic drug application. The dataset includes over 107,000 hair cells which have been identified and annotated as either inner or outer hair cells. This dataset is the result of a collaborative effort from multiple laboratories and has been carefully curated to represent a variety of imaging techniques. With suggested usage parameters and a well-described annotation procedure, this collection can facilitate the development of generalizable cochlear hair-cell detection models or serve as a starting point for fine-tuning models for other analysis tasks. By providing this dataset, we aim to give other hearing research groups the opportunity to develop their own tools with which to analyze cochlear imaging data more fully, accurately, and with greater ease.

Six decades after its conception, proton computed tomography (pCT) and proton radiography have yet to be used in medical clinics. However, good progress has been made on relevant detector technologies in the past two decades, and a few prototype pCT systems now exist that approach the performance needed for a clinical device. The tracking and energy-measurement technologies in common use are described, as are the few pCT scanners that are in routine operation at this time. Most of these devices still look like detector R\&D efforts as opposed to medical devices, are difficult to use, are at least factor of five slower than desired for clinical use, and are too small to image many parts of the human body. Recommendations are made for what to consider when engineering a pre-clinical pCT scanner that is designed to meet clinical needs in terms of performance, cost, and ease of use.

Objective. In image-guided radiotherapy (IGRT), off-by-one vertebral body misalignments are rare but potentially catastrophic. In this study, a novel detection method for such misalignments in IGRT was investigated using densely-connected convolutional networks (DenseNets) for applications towards real-time error prevention and retrospective error auditing.Approach. A total of 4213 images acquired from 527 radiotherapy patients aligned with planar kV or MV radiographs were used to develop and test error-detection software modules. Digitally reconstructed radiographs (DRRs) and setup images were retrieved and co-registered according to the clinically applied alignment contained in the DICOM REG files. A semi-automated algorithm was developed to simulate patient positioning errors on the anterior-posterior (AP) and lateral (LAT) images shifted by one vertebral body. A DenseNet architecture was designed to classify either AP images individually or AP and LAT image pairs. Receiver-operator characteristic curves (ROC) and areas under the curves (AUC) were computed to evaluate the classifiers on test subsets. Subsequently, the algorithm was applied to the entire dataset in order to retrospectively determine the absolute off-by-one vertebral body error rate for planar radiograph guided RT at our institution from 2011-2021.Main results. The AUCs for the kV models were 0.98 for unpaired AP and 0.99 for paired AP-LAT. The AUC for the MV AP model was 0.92. For a specificity of 95%, the paired kV model achieved a sensitivity of 99%. Application of the model to the entire dataset yielded a per-fraction off-by-one vertebral body error rate of 0.044% [0.0022%, 0.21%] for paired kV IGRT including one previously unreported error.Significance. Our error detection algorithm was successful in classifying vertebral body positioning errors with sufficient accuracy for retrospective quality control and real-time error prevention. The reported positioning error rate for planar radiograph I

Thick, fully depleted charge-coupled devices are known to exhibit nonlinear behavior at high signal levels due to the dynamic behavior of charges collecting in the potential wells of pixels, called the brighter-fatter effect (BFE). The effect results in distorted images of bright calibration stars, creating a flux-dependent point-spread function that if left unmitigated, could make up a large fraction of the error budget in Stage IV weak-lensing (WL) surveys such as the Legacy Survey of Space and Time (LSST). In this paper, we analyze image measurements of flat fields and artificial stars taken at different illumination levels with the LSST Camera (LSSTCam) at SLAC National Accelerator Laboratory in order to quantify this effect in the LSSTCam before and after a previously introduced correction technique. We observe that the BFE evolves anisotropically as a function of flux due to higher-order BFEs, which violates the fundamental assumption of this correction method. We then introduce a new method based on a physically motivated model to account for these higher-order terms in the correction, and then we test the modified correction on both data sets. We find that the new method corrects the effect in flat fields better than it corrects the effect in artificial stars, which we suggest is the result of sub-pixel physics not included in this correction model. We use these results to define a new metric for the full-well capacity of our sensors and advise image processing strategies to further limit the impact of the effect on LSST WL science pathways.

The dynamic response of surface ligands on nanoparticles (NPs) to external stimuli critically determines the functionality of NP–ligand systems. For example, in electrocatalysis the collective dissociation of ligands on NP surfaces can lead to the creation of an NP/ordered-ligand interlayer, a microenvironment that is highly active and selective for CO2-to-CO conversion. However, the lack of in situ characterization techniques with high spatial resolution hampers a comprehensive molecular-level understanding of the mechanism of interlayer formation. Here we utilize in situ infrared nanospectroscopy and surface-enhanced Raman spectroscopy, unveiling an electrochemical bias-induced consecutive bond cleavage mechanism of surface ligands leading to formation of the NP/ordered-ligand interlayer. This real-time molecular insight could influence the design of confined localized fields in multiple catalytic systems. Moreover, the demonstrated capability of capturing nanometre-resolved, dynamic molecular-scale events holds promise for the advancement of using controlled local molecular behaviour to achieve desired functionalities across multiple research domains in nanoscience. (Figure presented.)

Point defects in two-dimensional materials are of key interest for quantum information science. However, the parameter space of possible defects is immense, making the identification of high-performance quantum defects very challenging. Here, we perform high-throughput (HT) first-principles computational screening to search for promising quantum defects within WS2, which present localized levels in the band gap that can lead to bright optical transitions in the visible or telecom regime. Our computed database spans more than 700 charged defects formed through substitution on the tungsten or sulfur site. We found that sulfur substitutions enable the most promising quantum defects. We computationally identify the neutral cobalt substitution to sulfur (Co S0 ) and fabricate it with scanning tunneling microscopy (STM). The Co S0 electronic structure measured by STM agrees with first principles and showcases an attractive quantum defect. Our work shows how HT computational screening and nanoscale synthesis routes can be combined to design promising quantum defects.

Compartments are a fundamental feature of life, based variously on lipid membranes, protein shells, or biopolymer phase separation. Here, this combines self-assembling bacterial microcompartment (BMC) shell proteins and liquid-liquid phase separation (LLPS) to develop new forms of compartmentalization. It is found that BMC shell proteins assemble at the liquid-liquid interfaces between either 1) the dextran-rich droplets and PEG-rich continuous phase of a poly(ethyleneglycol)(PEG)/dextran aqueous two-phase system, or 2) the polypeptide-rich coacervate droplets and continuous dilute phase of a polylysine/polyaspartate complex coacervate system. Interfacial protein assemblies in the coacervate system are sensitive to the ratio of cationic to anionic polypeptides, consistent with electrostatically-driven assembly. In both systems, interfacial protein assembly competes with aggregation, with protein concentration and polycation availability impacting coating. These two LLPS systems are then combined to form a three-phase system wherein coacervate droplets are contained within dextran-rich phase droplets. Interfacial localization of BMC hexameric shell proteins is tunable in a three-phase system by changing the polyelectrolyte charge ratio. The tens-of-micron scale BMC shell protein-coated droplets introduced here can accommodate bioactive cargo such as enzymes or RNA and represent a new synthetic cell strategy for organizing biomimetic functionality.

Modeling tools aim to predict potential drug side effects, although they suffer from imperfect performance. Specifically, protein-protein interaction models predict drug effects from proteins surrounding drug targets, but they tend to overpredict drug phenotypes and require well-defined pathway phenotypes. In this study, we used PathFX, a protein-protein interaction tool, to predict side effects for active ingredient-side effect pairs extracted from drug labels. We observed limited performance and defined new pathway phenotypes using pathway engineering strategies. We defined new pathway phenotypes using a network-based and gene expression-based approach. Overall, we discovered a trade-off between sensitivity and specificity values and demonstrated a way to limit overprediction for side effects with sufficient true positive examples. We compared our predictions to animal models and demonstrated similar performance metrics, suggesting that protein-protein interaction models do not need perfect evaluation metrics to be useful. Pathway engineering, through the inclusion of true positive examples and omics measurements, emerges as a promising approach to enhance the utility of protein interaction network models for drug effect prediction.

Different from extensively reported carbon nanotubes‐polyamide (PA) composite membranes, this work reports the scalable, low‐cost manufacturing of metallic single‐walled carbon nanotubes network supported PA reverse osmotic desalination membranes. Metallic single‐walled carbon nanotubes (SWCNTs) separated from high‐pressure carbon monoxide conversion (HiPCO) SWCNTs with a diameter ranging from 0.6 to 1.2 nm are deposited on polyethersulfone (PES) films by replacing surfactants around SWCNTs dispersed in aqueous solution, for supporting uniform and smooth sub‐10 nm PA nanofilm formed through aqueous‐organic bilayer interfacial polymerization. The industrial scale manufactured PA/SWCNTs/PES desalination membranes (508 mm × 915 mm) are rolled into spiral wound elements without damage through cutting, gluing, and rolling. The desalination tests on flat PA/SWCNTs/PES membranes exhibits 30 l m−2 h−1 (LMH) under 17.2 bar with 98.45% salt injection when fed with 2000 ppm NaCl at pH = 9. The normalized permeate flux of PA/SWCNTs/PES spiral wound elements is ≈17 LMH under 13.8 bar with 90% salt injection underfed with 2000 ppm MgSO4 at pH = 9–11 condition. The high‐performance sub‐10 nm smooth PA/SWCNTs/PES desalination membranes are fabricated with materials savings and can promote low operation costs compared to conventional 50–100 nm crumpled PA films on any supporting substrates.

Antibiotic resistant skin infections are an emerging threat to public health. This is driven by the ubiquitous nature of bacteria, their ability to thrive in hostile environments, and their molecular machinery that allows them to quickly develop mechanisms to render antibiotics ineffective. As a result, there is an urgent need to develop therapeutic approaches to treat bacterial infections without relying on antibiotics. Instead, there should be an emphasis on emerging treatments that rapidly and directly enhance immunity against pathogenic infections. Polymorphonuclear leukocytes (PMN) are the most abundant and important effector cells of the innate immune system and should be a primary target for strategies aimed at enhancing innate immunity. Three PMN-centered approaches are presented in this dissertation. The first one revolves around the development and testing of a designed host defense peptide (dHDP) that acts synergistically with PMN to tackle multi-drug resistant S. aureus infection in diabetic mouse wounds. The second approach involves optimizing PMN production and their antibacterial capacity in vitro with the objective of subsequently transferring these cells directly into a site of infection to enhance pathogen clearance. The third strategy aims at investigating signaling pathways that govern PMN functions against S. aureus and P. aeruginosa, with an emphasis on Toll-like receptor signaling and activation of the inflammasome. Two pathogens of clinical interest are Methicillin-Resistant Staphylococcus aureus (MRSA) and Multi-Drug Resistant Pseudomonas aeruginosa (MDRPA). These are common sources of hospital and community-acquired skin infections in immunocompromised patients, including the elderly and people with diabetes. S. aureus is a gram-positive bacterium capable of forming biofilm and secreting virulence factors that can limit the ability of PMN to be recruited into sites of infection. As a result, therapeutic strategies should amplify PMN quantit

In vat polymerization (VP) 3D printing, there is an urgent need to expand characterization efforts for resins derived from natural resources to counter the increasing consumption of fossil fuels required to synthesize conventional monomers. Here, we apply multiscale mechanical characterization techniques to interrogate a 3D printed biobased copolymer along a controlled range of monomer ratios. We varied the concentration of two dissimilar monomers to derive structural information about the polymer networks. Current research primarily considers the macroscale, but recent understanding of the process-induced anisotropy in 3D printed layers suggests a multiscale approach is critical. By combining typical macroscopic techniques with micro- and nanoscale analogues, clear correlations in the processing-structure-property relationships appeared. We observed that measured moduli were always greater via surface-localized methods, but property differences between formulations were easier to identify. As researchers continue to develop novel sustainable biopolymers that match or exceed the performance of commercial resins, it is vital to understand the multiscale relationships between the VP process, the structure of the formed polymer networks, and the resultant properties.

Live microorganisms can be manipulated and engineered for colorectal cancer detection and treatment through methods such as faecal microbiota transplantation, native bacteria engineering and synthetic circuit engineering. Although promising, substantial effort is required to translate these approaches for clinical use.

IntroductionDigital tomosynthesis (DT) has emerged as a potential imaging modality for evaluating anatomic structures in veterinary medicine. This study aims to validate the diagnostic yield of DT in identifying predefined anatomic structures in feline cadaver heads, comparing it with conventional intraoral dental radiography (DR).MethodsA total of 16 feline cadaver heads were utilized to evaluate 19 predefined clinically relevant anatomic structures using both DR and DT. A semi-quantitative scoring system was employed to characterize the ability of each imaging method to identify these structures.ResultsDT demonstrated a significantly higher diagnostic yield compared to DR for all evaluated anatomic structures. Orthogonal DT imaging identified 13 additional anatomic landmarks compared to a standard 10-view feline set obtained via DR. Moreover, DT achieved statistically significant higher scores for each of these landmarks, indicating improved visualization over DR.DiscussionThese findings validate the utility of DT technology in reliably identifying clinically relevant anatomic structures in the cat skull. This validation serves as a foundation for further exploration of DT imaging in detecting dentoalveolar and other maxillofacial bony lesions and pathologies in cats.

Complex tissue models aim to recapitulate features of native tissues to model development, disease pathogenesis, and drug treatments. While techniques have been developed to improve the complexity of engineered tissues, it remains challenging to coordinate and spatially control multicellular phenotypes during differentiation processes. The work presented in this thesis describes the coordinated development of engineered cells and materials to enable communication via synthetic notch receptor pathways to control multi-fate differentiation. First, I will highlight the need to incorporate synthetic biology techniques to improve complex tissue engineering. Next, I will demonstrate the methods of engineering cells with synthetic pathways. Following, I will describe the development of a library of synthetic ligand-presenting material substrates with capacity to spatially activate synthetic pathways in 2D and 3D microenvironments. In the next chapter, I summarize how we utilize ligand-patterned substrates to spatially control myogenic and endothelial differentiation in the same culture. Subsequently, I detail our investigations into how substrate mechanical properties influence activation of synthetic receptors. Finally, I present the fabrication of a RNAi dual-gradient hydrogel for interfacial gene silencing of encapsulated cells. This work contributes to improving the complexity of tissue models and therapeutics by enabling engineered microenvironments to directly influence gene expression and control cell phenotypes.

This dissertation represents a broad exploration of polymer chemistry, anchored in the principles of the Circular Economy and sustainability. The work presented herein places a strong emphasis on utilizing monomers and polymers sourced from nature. We endeavor to enhance the utility of lignin-derived polymers for commercial applications by improving solubility and thermal properties, while also delving into the synthesis of aromatic-aliphatic polyesters from renewable resources. Lignin-derived poly(ether-amide)s are targeted initially, with the aim of overcoming their solubility limitations through thiol-ene reactions. By introducing small thiol-tagged molecules, the repeating double bonds are reduced, leading to increased chain flexibility and in turn, improved solubility and glass transition temperatures. We then explored the copolymerization of lignin-derived components with lactones for the synthesis of aromatic-aliphatic polyesters. Lactones, such as lactide and glycolide, are natural building blocks pervasive across nature. They were used in ring-opening polymerizations to produce aliphatic alcohol likers. The lactone linkers underwent condensation polymerizations with hydroxycinnamate dimers, creating sustainable, high molecular weight polyesters with desirable thermal properties. These approaches not only enhance the viability of polymers from biomass but also align with the ethos of sustainability by repurposing natural materials.We then pivot to focus on the development of hydrogel wound dressings, as a biomimetic extracellular matrix. Bioinspired polymers were used to initiate redox hydrogel crosslinking, leading to hydrogels with improved biocompatibility compared to the gold standard approach (APS and TEMED). Finally, this was expanded to the development of in situ forming hydrogel wound dressings that sequester and release antibiotics, analgesics, and hemostatic agents. These hydrogels exhibited rapid formation, controlled release of therapeutic agents

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a routine method to noninvasively quantify perfusion dynamics in tissues. The standard practice for analyzing DCE-MRI data is to fit an ordinary differential equation to each voxel. Recent advances in data science provide an opportunity to move beyond existing methods to obtain more accurate measurements of fluid properties. Here, we developed a localized convolutional function regression that enables simultaneous measurement of interstitial fluid velocity, diffusion, and perfusion in 3D. We validated the method computationally and experimentally, demonstrating accurate measurement of fluid dynamics in situ and in vivo. Applying the method to human MRIs, we observed tissue-specific differences in fluid dynamics, with an increased fluid velocity in breast cancer as compared to brain cancer. Overall, our method represents an improved strategy for studying interstitial flows and interstitial transport in tumors and patients. We expect that our method will contribute to the better understanding of cancer progression and therapeutic response.

Magnetic resonance imaging (MRI) is a valuable tool for the clinical management and scientific investigation of patients diagnosed with brain tumors. Continued efforts in brain tumor imaging research are critical because of the ever-evolving landscape of MRI technological advancements and new insights into brain tumor biology. For example, while historically, tumor tissue diagnoses relied on histopathological features, today, tumor molecular genetic features are the foundational component of tumor classification and patient management. Isocitrate dehydrogenase (IDH)-mutant gliomas are a specific brain tumor molecular subtype that particularly impact younger adults and remain incurable. Within IDH-mutant gliomas, there are also now molecularly defined IDH-mutant astrocytomas (1p/19q intact) and IDH-mutant oligodendrogliomas (1p/19q-codeleted). Although IDH-mutant gliomas often present as indolent, low-grade tumors, these tumors eventually become more aggressive in a process clinically described as “malignant transformation” into high-grade tumors, which are more resistant to therapy and have worse prognosis. This dissertation focuses on developing novel MRI tools to characterize molecular subtypes of IDH-mutant gliomas and malignant transformation. Specifically, this dissertation introduces four new tools for IDH-mutant glioma imaging research and clinical care: (1) optimal normal appearing white matter-normalization for diffusion and perfusion MRI analyses, (2) T2-FLAIR subtraction maps for quantitative T2-FLAIR mismatch analyses, (3) pseudo-resting-state functional MRI for functional connectivity analyses using DSC perfusion MRI (provisional patent filed), and (4) digital flipbooks of patient MRI scans to visually assess brain tumors. This dissertation utilized these tools to study IDH-mutant gliomas, particularly focusing on classifying glioma molecular subtypes, assessing cognitive impairment in patients, characterizing IDH inhibitor targeted therapy treatment re

Cardiovascular organ-on-a-chip (OoC) devices are composed of engineered or native functional tissues that are cultured under controlled microenvironments inside microchips. These systems employ microfabrication and tissue engineering techniques to recapitulate human physiology. This review focuses on human OoC systems to model cardiovascular diseases, to perform drug screening, and to advance personalized medicine. We also address the challenges in the generation of organ chips that can revolutionize the large-scale application of these systems for drug development and personalized therapy.

Osteoarthritis is characterized by enzymatic breakdown of the articular cartilage via the disruption of chondrocyte homeostasis, ultimately resulting in the destruction of the articular surface. Decades of research have highlighted the importance of inflammation in osteoarthritis progression, with inflammatory cytokines shifting resident chondrocytes into a pro-catabolic state. Inflammation can result in poor outcomes for cells implanted for cartilage regeneration. Therefore, a method to promote the growth of new cartilage and protect the implanted cells from the pro-inflammatory cytokines found in the joint space is required. In this study, we fabricate two gel types: polymer network hydrogels composed of chondroitin sulfate and hyaluronic acid, glycosaminoglycans (GAGs) known for their anti-inflammatory and prochondrogenic activity, and interpenetrating networks of GAGs and collagen I. Compared to a collagen-only hydrogel, which does not provide an anti-inflammatory stimulus, chondrocytes in GAG hydrogels result in reduced production of pro-inflammatory cytokines and enzymes as well as preservation of collagen II and aggrecan expression. Overall, GAG-based hydrogels have the potential to promote cartilage regeneration under pro-inflammatory conditions. Further, the data have implications for the use of GAGs to generally support tissue engineering in pro-inflammatory environments.

Peripheral artery disease is an atherosclerotic disease associated with limb ischemia that necessitates limb amputation in severe cases. Cell therapies comprised of adult mononuclear or stromal cells have been clinically tested and show moderate benefits. Bioengineering strategies can be applied to modify cell behavior and function in a controllable fashion. Using mechanically tunable or spatially controllable biomaterials, we highlight examples in which biomaterials can increase the survival and function of the transplanted cells to improve their revascularization efficacy in preclinical models. Biomaterials can be used in conjunction with soluble factors or genetic approaches to further modulate the behavior of transplanted cells and the locally implanted tissue environment in vivo. We critically assess the advances in bioengineering strategies such as 3-dimensional bioprinting and immunomodulatory biomaterials that can be applied to the treatment of peripheral artery disease and then discuss the current challenges and future directions in the implementation of bioengineering strategies.

We present performance studies of the Time-of-Flight (ToF) subdetector of the ATLAS Forward Proton (AFP) detector at the LHC. Efficiencies and resolutions are measured using high-statistics data samples collected at low and moderate pile-up in 2017, the first year when the detectors were installed on both sides of the interaction region. While low efficiencies are observed, of the order of a few percent, the resolutions of the two ToF detectors measured individually are 21 ps and 28 ps, yielding an expected resolution of the longitudinal position of the interaction, z vtx, in the central ATLAS detector of 5.3 ± 0.6 mm. This is in agreement with the observed width of the distribution of the difference between z vtx, measured independently by the central ATLAS tracker and by the ToF detector, of 6.0 ± 2.0 mm.

Magnetic resonance imaging (MRI) is widely adopted to assess anatomy and function of human bodies. Current clinical MRI mainly relies on contrast-weighted images and the clinicians make diagnosis by observing the relative intensity differences between tissues. In contrast, quantitative MRI directly measures the tissue physical parameters, thus providing biomarkers with enhanced objectivity, reproducibility, and sensitivity. Considering the clinicians’ reliance on conventional weighted MRI and the advantages of quantitative MRI, obtaining both is promising to provide a comprehensive tissue characterization and to benefit patient care. However, the acquisition of a full array of contrast-weighted images and parameter maps demands a prolonged scan time, which has been a major challenge.In this dissertation, we developed deep learning approaches to obtain both multi-contrast weighted images and multi-parametric maps within short scan times. Based on the physical relationships between the contrast-weighted images and the tissue parameter maps, we proposed to only acquire one of them (i.e., either weighted images or quantitative maps) and to retrospectively derive the other from the same acquired data. Both directions were explored. First, an efficient multi-parametric quantitative MRI technique was employed for acquisition, and multiple conventional T1-weighted and T2-weighted images were synthesized from the quantitative MRI data using deep learning. The method was evaluated on healthy volunteers and multiple sclerosis patients, achieving comparable image quality with true acquired images and better performance than the traditional Bloch-equation-based synthesis. In the second direction, multi-contrast weighted images were acquired following current clinical practices and deep learning approaches were developed to retrospectively estimate T1 and T2 maps from them. Both supervised and physics-guided self-supervised learning methods were developed. The supervised learning

Type-I hypersensitivity is a mast cell-mediated degranulation process induced by IgE-antigen recognition. Inspired by this feature, we genetically modified mouse mast cell line MC/9 to overexpress perforin and granzyme B (PG-MCs) with lentiviral particles. In our melanoma model utilizing B16 cells expressing membrane-bound OVA, we observed a polarized degranulation of mast cells towards cancer cells upon the recognition of antigens by anti-OVA IgE. PG-MCs could selectively kill cancer cells via IgE-antigen recognition. The IgE-sensitized PG-MCs administered through an intertumoral route successfully delivered perforin and granzyme B toward target cells to induce anti-tumor efficacy. Moreover, we demonstrated the tuning effect of tumor microenvironment by mast cell-induced hypersensitivity, skewing the tumor milieu toward a ‘hot’ state with more lymphocyte infiltration. In addition, this mast cell therapy, in combination with 5- fluorouracil (5-Fu), induced a more pronounced inhibition of tumor growth, which is likely associated with the depletion of myeloid-derived suppressor cells (MDSCs).

Measuring the three-dimensional (3D) distribution of chemistry in nanoscale matter is a longstanding challenge for metrological science. The inelastic scattering events required for 3D chemical imaging are too rare, requiring high beam exposure that destroys the specimen before an experiment is completed. Even larger doses are required to achieve high resolution. Thus, chemical mapping in 3D has been unachievable except at lower resolution with the most radiation-hard materials. Here, high-resolution 3D chemical imaging is achieved near or below one-nanometer resolution in an Au-Fe3O4 metamaterial within an organic ligand matrix, Co3O4-Mn3O4 core-shell nanocrystals, and ZnS-Cu0.64S0.36 nanomaterial using fused multi-modal electron tomography. Multi-modal data fusion enables high-resolution chemical tomography often with 99% less dose by linking information encoded within both elastic (HAADF) and inelastic (EDX/EELS) signals. We thus demonstrate that sub-nanometer 3D resolution of chemistry is measurable for a broad class of geometrically and compositionally complex materials.

The conventional magnetoelastic effect describes the variation in magnetic flux density of certain materials under mechanical pressure within the megapascal range. In this study, we introduce porous microstructures into a soft magnetic system, resulting in an enhanced magnetoelasticity, characterized by a counterintuitive increase in magnetic flux density with the application of subtle mechanical pressure. This phenomenon, termed the structure-induced positive magnetoelastic effect, is attributed to the stress-induced redistribution of micromagnets within the porous, soft system, which exhibits a low Young’s modulus and near-zero Poisson’s ratio behavior. The technological impact of this discovery is demonstrated in its application to acoustic sensing and voice recognition, overcoming the low-pressure detection limits of current platform technologies. This breakthrough holds substantial potential for ultrasensitive pressure detection, with wide-ranging applications in extracting subtle physiological information.

The current paradigm of medicine is mostly designed to block or prevent pathological events. Once the disease-led tissue damage occurs, the limited endogenous regeneration may lead to depletion or loss of function for cells in the tissues. Cell therapy is rapidly evolving and influencing the field of medicine, where in some instances attempts to address cell loss in the body. Due to their biological function, engineerability, and their responsiveness to stimuli, cells are ideal candidates for therapeutic applications in many cases. Such promise is yet to be fully obtained as delivery of cells that functionally integrate with the desired tissues upon transplantation is still a topic of scientific research and development. Main known impediments for cell therapy include mechanical insults, cell viability, host's immune response, and lack of required nutrients for the transplanted cells. These challenges could be divided into three different steps: 1) Prior to, 2) during the and 3) after the transplantation procedure. In this review, we attempt to briefly summarize published approaches employing biomaterials to mitigate the above technical challenges. Biomaterials are offering an engineerable platform that could be tuned for different classes of cell transplantation to potentially enhance and lengthen the pharmacodynamics of cell therapies.

Wound healing is a complex physiological process that requires precise control and modulation of many parameters. Therapeutic ion and biomolecule delivery has the capability to regulate the wound healing process beneficially. However, achieving controlled delivery through a compact device with the ability to deliver multiple therapeutic species can be a challenge. Bioelectronic devices have emerged as a promising approach for therapeutic delivery. Here, we present a pro-reparative bioelectronic device designed to deliver ions and biomolecules for wound healing applications. The device incorporates ion pumps for the targeted delivery of H+ and zolmitriptan to the wound site. In vivo studies using a mouse model further validated the device's potential for modulating the wound environment via H+ delivery that decreased M1/M2 macrophage ratios. Overall, this bioelectronic ion pump demonstrates potential for accelerating wound healing via targeted and controlled delivery of therapeutic agents to wounds. Continued optimization and development of this device could not only lead to significant advancements in tissue repair and wound healing strategies but also reveal new physiological information about the dynamic wound environment.

ObjectivesTo investigate image-guided volumetric hyperthermia strategies using the ExAblate Body MR-guided focused ultrasound ablation system, involving mechanical transducer movement and sector-vortex beamforming.Materials and methodsAcoustic and thermal simulations were performed to investigate volumetric hyperthermia using mechanical transducer movement combined with sector-vortex beamforming, specifically for the ExAblate Body transducer. The system control in the ExAblate Body system was modified to achieve fast transducer movement and MR thermometry-based hyperthermia control, mechanical transducer movements and electronic sector-vortex beamforming were combined to optimize hyperthermia delivery. The experimental validation was performed using a tissue-mimicking phantom.ResultsThe developed simulation framework allowed for a parametric study with varying numbers of heating spots, sonication durations, and transducer movement times to evaluate the hyperthermia characteristics for mechanical transducer movement and sector-vortex beamforming. Hyperthermic patterns involving 2-4 sequential focal spots were analyzed. To demonstrate the feasibility of volumetric hyperthermia in the system, a tissue-mimicking phantom was sonicated with two distinct spots through mechanical transducer movement and sector-vortex beamforming. During hyperthermia, the average values of Tmax, T10, Tavg, T90, and Tmin over 200 s were measured within a circular ROI with a diameter of 10 pixels. These values were found to be 8.6, 7.9, 6.6, 5.2, and 4.5 °C, respectively, compared to the baseline temperature.ConclusionsThis study demonstrated the volumetric hyperthermia capabilities of the ExAblate Body system. The simulation framework developed in this study allowed for the evaluation of hyperthermia characteristics that could be implemented with the ExAblate MRgFUS system.

Cryogenic electron tomography (cryoET) is capable of determining in situ biological structures of molecular complexes at near-atomic resolution by averaging half a million subtomograms. While abundant complexes/particles are often clustered in arrays, precisely locating and seamlessly averaging such particles across many tomograms present major challenges. Here, we developed TomoNet, a software package with a modern graphical user interface to carry out the entire pipeline of cryoET and subtomogram averaging to achieve high resolution. TomoNet features built-in automatic particle picking and three-dimensional (3D) classification functions and integrates commonly used packages to streamline high-resolution subtomogram averaging for structures in 1D, 2D, or 3D arrays. Automatic particle picking is accomplished in two complementary ways: one based on template matching and the other using deep learning. TomoNet's hierarchical file organization and visual display facilitate efficient data management as required for large cryoET datasets. Applications of TomoNet to three types of datasets demonstrate its capability of efficient and accurate particle picking on flexible and imperfect lattices to obtain high-resolution 3D biological structures: virus-like particles, bacterial surface layers within cellular lamellae, and membranes decorated with nuclear egress protein complexes. These results demonstrate TomoNet's potential for broad applications to various cryoET projects targeting high-resolution in situ structures.

Tissues comprise a multiplicity of specialized cell types that must coordinate state changes in order to function collectively. These state changes are orchestrated by coordinated direct cellular interactions and indirect responses to microenvironmental and systemic cues. Consequently, chronic perturbations to this collective behavior can result in disease states that are difficult to reprogram such as autoimmunity and cancer. As such, studying the self-reinforced dynamics of tissue function can benefit from a systems biology approach where the aim is to understand how individual components of biological systems interact to give rise to emergent properties. The recent growth in the availability of single-cell resolution genomics platforms has further expanded biologists’ ability to do this kind of unbiased inquiry. However, despite the increasing ease of generating these high-dimensional datasets, analyzing these data still presents significant computational challenges because of their noise and sparsity, which are further exacerbated on the level of individual cells and genes. As such, there is a need to develop computational methods that enable scientists to extract systems-level biological insight from noisy high dimensional data. This dissertation introduces DECIPHER, a machine learning framework tailored for network inference in systems biology, with a focus on applications to single-cell RNA sequencing data. Chapter 2 details the DECIPHER algorithm and its implementation for the R computing environment, deciphR, that is designed to reconstruct cell state networks from high-dimensional molecular profiles. Chapter 3 applies DECIPHER to unveil cell-cell interaction networks in the human breast, elucidating how state changes on the cell-level propagate throughout tissue in response to hormonal fluctuations. Chapter 4 extends DECIPHER's application to investigate peripheral immune dysregulation in a rare pediatric autoimmune disease, revealing underlying immune imb

Aging is a complex biological process that remains one of the greatest risk factors for numerous chronic diseases, including cancer, cardiovascular disorders, and neurodegenerative conditions. As global demographics shift towards an aging population, there is an urgent need to develop interventions that can delay the onset of age-related declines and promote healthy longevity. This thesis presents a groundbreaking high-throughput screening platform for identifying genetic and pharmacological interventions that extend lifespan in the model organism Saccharomyces cerevisiae (budding yeast).The foundation of this platform is the development of a novel genetic construct, the Daughter Arrest Program (DAP), which selectively arrests daughter cell division while allowing mother cells to continue dividing. By combining the DAP with innovative microfluidic devices and fluorescence-based assays, we have established a robust system for rapid and efficient measurement of yeast replicative lifespan. This high-throughput approach enables the screening of thousands of compounds and genetic modifications, vastly expanding the potential for discovery in the field of gerontology. Leveraging this platform, we conducted comprehensive screenings of FDA-approved drug libraries and bioactive compound libraries, leading to the identification of several promising anti-aging compounds, including mTOR inhibitors, GSK-3 inhibitors, and topoisomerase inhibitors. Additionally, we performed genetic screenings using non-essential gene deletion mutants and the Decreased Abundance by mRNA Perturbation (DAmP) library, revealing the diverse roles of various genes in regulating aging processes, such as nutrient sensing, stress response, and cellular repair mechanisms. The findings from this research provide valuable insights into the genetic and molecular pathways underlying aging and longevity, paving the way for the development of targeted interventions to promote healthy aging.

This study investigates wound healing differences between full-thickness excisional wounds and scald burns in adult mice, highlighting the challenges of burn injury regeneration. Despite timely debridement, burns consistently exhibit delayed healing and increased scarring compared to excisional wounds. We develop a mouse model that recapitulates the clinical features of burn wounds in humans. We use this model to explore the potential of D-peptide and L-peptide crosslinked microporous annealed particle (MAP) hydrogels to enhance burn wound regeneration. Our findings underscore the critical influence of injury mechanisms on tissue regenerative potential, suggesting that MAP hydrogels, particularly those with peptide crosslinking would improve burn wounds. These insights deepen our understanding of wound healing dynamics. Our findings reveal the therapeutic strategies to enhance tissue repair and regeneration in burn injuries, ultimately advancing clinical interventions and improving patient care.

The design of protein-protein interfaces using physics-based design methods such as Rosetta requires substantial computational resources and manual refinement by expert structural biologists. Deep learning methods promise to simplify protein-protein interface design and enable its application to a wide variety of problems by researchers from various scientific disciplines. Here, we test the ability of a deep learning method for protein sequence design, ProteinMPNN, to design two-component tetrahedral protein nanomaterials and benchmark its performance against Rosetta. ProteinMPNN had a similar success rate to Rosetta, yielding 13 new experimentally confirmed assemblies, but required orders of magnitude less computation and no manual refinement. The interfaces designed by ProteinMPNN were substantially more polar than those designed by Rosetta, which facilitated in vitro assembly of the designed nanomaterials from independently purified components. Crystal structures of several of the assemblies confirmed the accuracy of the design method at high resolution. Our results showcase the potential of deep learning-based methods to unlock the widespread application of designed protein-protein interfaces and self-assembling protein nanomaterials in biotechnology.

Developing vascular cells have been shown to self-organize into unique structures in both two and three dimensions. Depending on the conditions, these cells may develop micropatterns with spatial segregation of different cell types in 2D or develop into perfusable vascular vessels in 3D. This self-organization arises from the interplay of motility, proliferation, differentiation, and cellular signaling; with the relative importance of these factors remaining unclear. In this dissertation, I report the development and use of a computational model to explore how motility, proliferation, and differentiation rates affect the emergence of micropatterns from differentiating vascular cells in a 2D in silico environment. Later, I explore the in vitro vascular development, via a microfluidic platform, of vascular networks that are functional, perfusable, and stable for more than two months. Firstly, I developed a stochastic on-lattice population-based model to study the emergence of vascular patterns from a starting distribution of stem cell induced vascular progenitor cells capable of differentiating into both endothelial cells and smooth muscle cells that are motile and proliferative. Our model yielded patterns that were qualitatively and quantitatively consistent with our experimental observations, for physiologically reasonable parameters. Our results suggest that, for such parameter values, it is the post-differentiation motility and proliferation rates that drive the formation of vascular patterns more than differentiation alone. This was shown to be true even when higher order effects like density dependent adhesions and paracrine signaling were considered. Secondly, microfluidic devices and organ-on-a-chip models have become good solutions for studying 3D cell cultures that more closely mimic physiologically relevant lengths and timescales. These devices allow for the incorporation of height into cultures by suspending cells in extracellular matrices, such as fibrin, that more closely mimic in vivo microenvironments. Here I also report the use of endothelial cells in culture with mural cells, smooth muscle cells and pericyte cell cultures, as ideal conditions for the successful development of perfusable vasculature within a three-channel microfluidic device. We found the use of these cells, in tri-culture, to lead to the development of physiologically narrow vessels that were functional and perfusable for more than two months. These findings hint at methods that could be employed for directing specific micropatterns or 3D structures that focus on controlling the motility and proliferation rates of differentiating stem cells. Furthermore, these studies aim to advance the field of organoid development, by providing a reliable method for developing fully vascularized organoids and organs that are stable for long time-scales.

Joint absence in people with upper-limb-difference leads to compensatory motions. Such compensation has long been a topic of study, but typically only for a single object/user layout, which is unlikely to spatially generalize. We seek to understand how motion varies over a planar workspace for different target orientations and wrist mobility conditions. We therefore present a study that records arm and torso pose during grasping of 49 equally spaced cylindrical targets. Furthermore, we seek to validate the research practice of using wrist-immobilizing bypass sockets on able-bodied participants to simulate prostheses without wrists. Participants were 2 transradial amputees and 7 able-bodied individuals who conducted the study with and without wrist braces, generating 2450 trajectories. Heat-maps illustrate variation over the workspace in Mean Joint Angle, Range of Joint Motion and Distance Travelled by Body Segment. Results indicate that greater wrist restriction primarily exacerbated shoulder internal rotation and elbow flexion, not the trunk. We observed that bypass sockets do not fully simulate amputee behavior. Furthermore, amputee reaching with their intact limb is different to the reaching motion of normative participants, implying that transradial limb-difference affects both sides of the body. Differences in participant behavior were also observed between horizontal and vertical target orientations.

SignificanceIt has long been hypothesized that naturally occurring electric fields (EFs) aid wound healing by guiding cell migration. Consequently, the application of EFs has significant potential for promoting wound healing. However, the mechanisms underlying the cellular response to EFs remain unclear. Recent Advances： Although the directed migration of isolated single cells under EFs has been studied for decades, only recently has experimental evidence demonstrated the distinct collective migration of large sheets of keratinocytes and corneal epithelial cells in response to applied EFs. Accumulating evidence suggests that the emergent properties of cell groups in response to EF guidance offer new opportunities for EF-assisted directional migration.Critical issuesIn this review, we provide an overview of the field of collective electrotaxis, highlighting key advances made in recent years. We also discuss advanced engineering strategies utilized to manipulate collective electrotaxis.Future directionsWe outline a series of unanswered questions in this field and propose potential applications of collective electrotaxis in developing electrical stimulation technologies for wound healing.

Imaging systems capture light rays contain rich information, which can be described by the plenoptic function, ?(?, ?, ?, ?, ?, ?, ?)—where ?, ?, ? represent spatial coordinates; ?, ? denote emittance angles; ? signifies wavelength; and ? denotes time. Given a finite photon budget, it is crucial for an imaging system to maximize the information yield from each captured image. Yet, traditional cameras only capture two-dimensional spatial data (?, ?), neglecting the wealth of information.Capturing multi-dimensional information presents significant challenges, primarily due to the complexity of mapping high-dimensional datacubes onto a two-dimensional detector array. This mapping process introduces a fundamental trade-off among various axes of information, such as spatial, angular, and spectral dimensions, which can adversely affect imaging speed, resolution, and other critical parameters. Balancing of these factors often leads to compromises in one aspect to enhance another, pose a significant challenge in designing and implementing a multi-dimensional imaging system.In response to these challenges, this dissertation presents three innovative multi-dimensional optical imaging systems. The first system, snapshot hyperspectral light field imaging utilizing image mapping spectrometer (LF-IMS), represents a five-dimensional (?, ?, ?, ?, ?) imaging system. It is uniquely designed to maintain full light throughput, enabling the capture of detailed three-dimensional spatial and spectral information without sacrificing efficiency. The second system, snapshot hyperspectral light field tomography (Hyper-LIFT), leverages compressed sensing to facilitate five-dimensional (?, ?, ?, ?, ?) imaging. This approach significantly alleviates the tradeoff between different dimensions of information, allowing for capturing an input scene with a more compact sensor, thereby greatly reducing the volume of data generated during image acquisition. The third system, the tunable image projection

With timely and proper treatment, an estimated 70% of the 65 million people with epilepsy worldwide could be seizure-free. For the 30-40% of epilepsy patients who do not respond to anti-seizure drugs, detection of pathological interictal activity can speed up treatment procedures. However, analysis of brain activity is a manual process today, which is tedious, time-consuming, and unscalable. An automated approach tackles these issues. That said, interictal detection algorithms rely on supervised learning, which requires labeled training data and human intervention for patient-specific tuning. An unsupervised approach overcomes these limitations with an automated and personalized solution that requires no labels or intervention from medical staff. The algorithm presented in this thesis is a fully unsupervised model that classifies intracranial electroencephalography (iEEG) interictal activity as pathological or physiological. 170 features were extracted from three domains: time (19 features), frequency (7 features), and time-frequency (144 features). Four unsupervised dimensionality reduction techniques were then combined with four unsupervised classification methods. Out of these 16 combinations, Principal Component Analysis (PCA) paired with a K-Means model achieved the best performance. Across 15 epilepsy patients, it yielded an average 92.6% F-2 score, 93.5% precision, and 93.0% recall with standard deviations of 12.0%, 13.8%, and 12.5%, respectively. There was no statistically significant difference between the performance of this unsupervised model and that of two supervised models, Support Vector Machine and Random Forest. The proposed method is a promising approach to enhancing the efficiency of treatment for drug-resistant epilepsy.

Tuberculosis (TB) remains as the second leading infectious killer worldwide. In 2022 alone, 1.3 million people died from TB, with the disease being most prevalent in low-resource countries. In these settings with high TB prevalence, access to expensive diagnostic tools is limited, making the development of fast, sensitive, specific and low-cost diagnostic methods crucial. Here, we employ the 4-N,Ndimethylamino-1,8-naphthalimide–conjugated trehalose (DMN-Tre) and 3-Hydroxychromoneconjugated trehalose (3HC-Tre) solvatochromic trehalose conjugated fluorogenic dyes to probe the unique properties of mycobacteria and other Actinomycetales. These conjugates are incorporated into the mycomembrane as trehalose dimycolates, where they fluoresce upon contact with the hydrophobic environment of the mycomembrane. For these probes to facilitate faster diagnostics, they must specifically target mycobacteria, eliminating the requirement of extensive pre-processing of clinical samples. This thesis presents the results of specificity testing for both DMN-Tre and 3HC-Tre. Using flow cytometry, plate reader and microscopy, this research demonstrates that DMN-Tre exhibits specificity for mycobacteria. In contrast, 3HC-Tre demonstrated limited specificity. These findings suggest that DMN-Tre has the potential to be developed into a rapid, low-cost diagnostic tool for TB, tailored to the needs of resource-limited settings.

With the global population projected to reach 9.8 billion by 2050, there is a critical need for sustainable and innovative food sources. Cultured meat, an innovative and sustainable approach to food production that allows the production of meat by cultivating animal cells in vitro, exhibits the potential to circumvent the challenges associated with traditional animal agriculture. A key to the success of cultured meat is developing adipose tissue, crucial for achieving the desired taste, texture, and nutritional quality. Accelerating adipogenesis has the potential to improve production efficiency, making large-scale cultured meat manufacturing more viable and cost-effective. This study aims to identify small molecules that accelerate adipogenesis for edible fat tissue production using a high-throughput screen. Our findings identified magnolol and dicoumarol as effective natural compounds that promote lipid accumulation in differentiating mouse, rabbit, and porcine preadipocytes. These compounds have also demonstrated a synergetic effect in accelerating adipogenesis, which highlights the potential of novel treatments in effectively promoting adipocyte differentiation across various species.

In order to more effectively diagnose and treat both communicable and non-communicable diseases, it is crucial to develop diagnostic tests that are accessible at the point of care. This is especially pertinent in resource-limited settings areas, where there is a need for affordable, rapid, and robust diagnostic tests due to limited access to healthcare services and laboratory-based tests. One diagnostic modality that is well-suited for these settings is the lateral-flow immunoassay (LFA), which saw widespread use during the COVID-19 pandemic. However, the applicability of the conventional LFA to other diseases and conditions is limited by its inability to provide a quantitative readout, which this thesis aims to address.First, we sought to improve the quantitative capabilities of the LFA by utilizing the multicolor etching of gold nanorods (GNRs). As a proof-of-concept, our group had previously combined the LFA with GNR etching in suspension to produce a multicolor readout that quantifies the concentration of digoxin in human serum. To make the assay more point-of-care friendly, we expanded upon this work in this thesis by adapting the workflow to be fully paper-based, which involved designing an all-in-one 3D-printed casing that combined both LFA detection and GNR etching via novel color-changing GNR pads. The resulting color hues from the GNR pads are easily distinguishable by the naked eye, thereby enabling a workflow that easily and quickly quantifies target biomarker concentrations in point-of-care settings.Next, we turned to utilizing the chemiluminescence reaction of luminol as another method to improve the quantitative capabilities of the LFA. After detecting the target biomarker using the LFA, the light output generated from the chemiluminescence reaction can be quantified using a smartphone-based reader to determine the concentration of the target biomarker in the patient sample. To assist with the development of this assay, a mathematical model of the che

Metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), is the most common chronic liver disease with a current global prevalence of 25% to 40%. MASLD is associated with the metabolic syndrome and cardiovascular morbidity, and can progress to fibrosis and cirrhosis. Chronic liver diseases such as viral hepatitis and MASLD can also lead to hepatic iron overload. Magnetic resonance imaging (MRI) provides non-invasive evaluation of hepatic steatosis and iron overload by quantifying proton-density fat fraction (PDFF) and R2*. Conventional MRI techniques for liver PDFF and R2* quantification require breath-holding, which can be challenging for children and elderly patients. 3D stack-of-radial MRI techniques have been proposed for self-gated free-breathing liver PDFF and R2* quantification. However, several challenges remain, including residual streaking artifacts from system imperfections, long scan acquisition times, computationally expensive reconstructions, and insufficient modelling of non-rigid liver motion during free-breathing. Techniques to overcome these challenges are important for a wide clinical adoption of free-breathing MRI techniques for liver PDFF and R2* quantification. Additionally, in recent years, there has been an increased interest in lower-field MRI systems. A less expensive lower-field MRI system with a larger bore diameter may improve accessibility and comfort for populations with obesity and at risk for fatty liver diseases. However, the low signal-to-noise ratio problem can impact image quality and quantification accuracy. Therefore, noise reduction techniques are important to improve liver PDFF and R2* quantification in lower-field MRI systems. This work focuses on developing MRI reconstruction techniques to improve liver PDFF and R2* quantification. First, this work developed a phase-preserving beamforming-based technique to effectively reduce radial streaking artifacts fr