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Title: DETERMINING CELL STIFFNESS USING MICROFLUIDICS
Creator: Penumarthy, Vineet Shyam
Date: 2019
Description: Sorting healthy cells from diseased cells is critical to detecting diseases and treating them before damage is done to a patient. These...
Show moreSorting healthy cells from diseased cells is critical to detecting diseases and treating them before damage is done to a patient. These diseases can be characterized based upon proteins, cytokines, DNA, pathology, blood tests, etc. However, another way of detecting them is using the mechanical properties of a cell, specifically the cell stiffness. In this study, a long microfluidic channel was designed, fabricated, and tested for flow using 6.7 µm polystyrene beads. Following this, Caco-2 cells and preadipocytes were flown through the channel and the travel time each cell took to flow through the channel was recorded, along with its cell diameter. The cells were then treated with blebbistatin, a myosin-II inhibitor, in order to soften the cell actin cytoskeleton and reduce the cell stiffness and were then flown through the channel again and the times taken to flow through were again recorded. We hypothesized that the stiffer a cell, the longer it would take to flow through the channel. From the results obtained using Caco-2 cells, we found that the blebbistatin treated cell times were much lower than the untreated cells, thus indicating that our hypothesis is true.
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Title: EXPRESSION OF PPAR-γ AND PGC-1α TO INFLAMMATION IN HEPATOCYTES
Creator: HE, QIFAN
Date: 2019
Description: In this study, we examined that Proliferator-Activated Receptor γ (PPAR-γ) and Peroxisome Proliferator-Activated Receptor γ Coactivator 1-α ...
Show moreIn this study, we examined that Proliferator-Activated Receptor γ (PPAR-γ) and Peroxisome Proliferator-Activated Receptor γ Coactivator 1-α (PGC1α) protein expression in hepatocytes have different degrees of expression to Tumor Necrosis Factor α (TNFα) and rosiglitazone. To verify this objective, we employed lentivirus, instead of traditional plasmids, to transfect human hepatocytes (HepG2). Fluorescence-related protein of PPAR-γ and PGC-1α were delivered to hepatocytes, and inflammation was induced by adding TNFα and rosiglitazone to the medium. We successfully designed and created the lentivirus with high delivery efficiency, and determined that the test was true, indicating that PPAR-γ and PGC-1α proteins have different expression to inflammation in human hepatocytes.
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Title: Ultrasensitive protein quantification using Rolling Circle Amplification
Creator: Hetzel, Laura Ann
Date: 2019
Description: There are several protein biomarkers that can aid in diagnosing and evaluating the progression of Alzheimer’s Disease (AD), including Amyloid...
Show moreThere are several protein biomarkers that can aid in diagnosing and evaluating the progression of Alzheimer’s Disease (AD), including Amyloid Beta-42 (Aβ42), Amyloid Beta-40 (Aβ40), and Tau proteins. The proteins are most prevalent in the brain and cerebral spinal fluid, becoming more diluted in the bloodstream. Since diagnosis and progression would require evaluating and comparing protein levels over time and identifying miniscule changes, an assay with high sensitivity is paramount. Similarly, evaluating how a drug treatment affects the levels of protein requires a highly sensitive assay. Currently, enzyme-linked immunosorbent assay (ELISA) is accepted as the most sensitive assay for protein detection and quantification. However, in the case of Aβ40 and Aβ42 proteins, oftentimes the levels of the proteins in patients are very close to the sensitivity of the commercial ELISA. The uncertainty in these measurements is very high, which results in reporting of conflicting outcomes. One of the challenges of quantifying proteins is that proteins, unlike nucleic acids, cannot be amplified. To overcome this limitation, we have cleverly pseudo-amplified proteins using rolling circle amplification (RCA). By doing so, we have demonstrated a ten to forty times improvement in sensitivity over ELISA and radioimmunoassays. In previous experiments, C-peptide has been used as the protein of interest, and ELISA reports the smallest detectable quantity is 0.01 ng in buffer. Using RCA, we have found that as little as 0.00075 ng C-peptide in buffer could be quantified, and 0.004 ng in 10% serum could be quantified. The same process can be applied to other proteins such as Aβ40 and Aβ42, and the results are expected to be similar. In fact, we have measured Type I Diabetes autoantibodies with approximately forty times improvement over the gold standard radioimmunoassay. With excellent results in buffer and 10% serum, expansion to human samples holds great potential. If the human experiments are as successful as anticipated, RCA could be used to precisely evaluate the effect of a drug on protein levels, contributing to the overall evaluation of the success of the drug.
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Title: IMPACT OF DATA SHAPE, FIDELITY, AND INTER-OBSERVER REPRODUCIBILITY ON CARDIAC MAGNETIC RESONANCE IMAGE PIPELINES
Creator: Obioma, Blessing Ngozi
Date: 2020
Description: Artificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical...
Show moreArtificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical diagnosis, disease prediction, and treatment, with such interests intensifying in the medical image field. AI can automate various cumbersome data processing techniques in medical imaging such as segmentation of left ventricular chambers and image-based classification of diseases. However, full clinical implementation and adaptation of emerging AI-based tools face challenges due to the inherently opaque nature of such AI algorithms based on Deep Neural Networks (DNN), for which computer-trained bias is not only difficult to detect by physician users but is also difficult to safely design in software development. In this work, we examine AI application in Cardiac Magnetic Resonance (CMR) using an automated image classification task, and thereby propose an AI quality control framework design that differentially evaluates the black-box DNN via carefully prepared input data with shape and fidelity variations to probe system responses to these variations. Two variants of the Visual Geometric Graphics with 19 neural layers (VGG19) was used for classification, with a total of 60,000 CMR images. Findings from this work provides insights on the importance of quality training data preparation and demonstrates the importance of data shape variability. It also provides gateway for computation performance optimization in training and validation time.
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Title: SUSTAINED RELEASE OF PHOSPHATE-BASED THERAPEUTICS FOR ATTENUATION OF PATHOGEN-INDUCED PROTEOLYTIC MATRIX DEGRADATION
Creator: Bittencourt Pimentel, Marja
Date: 2019
Description: Loss of the normal intestinal microbiome community structure and its replacement by pathogenic microbes contributes to severe persistent...
Show moreLoss of the normal intestinal microbiome community structure and its replacement by pathogenic microbes contributes to severe persistent inflammation in diseases such as ulcerative colitis and inflammatory bowel disease. While host-derived proteases are known to contribute to this pathogenesis, the role of increased production of microbial-secreted proteases due to virulent phenotypes remains unclear. Following surgical removal of diseased intestinal tract, increased bacterial protease expression is a key phenotype involved in intestinal healing impairment. Antibiotic administration is ineffective for treating these complications as it inadvertently eliminates normal flora while allowing pathogenic bacteria to acquire antibiotic resistance. Prior research has shown that intestinal phosphate depletion in the surgically stressed host triggers bacterial virulence which is suppressed under phosphate abundant conditions. To address this issue our previous work has demonstrated that the use of free monophosphate (-Pi) and polyphosphate (-PPi), as well as post-loaded PPi nanoparticles (NP-PPi) attenuate collagenase production of gram-negative (Pseudomonas aeruginosa and Serratia marcescens) but not gram-positive (Enterococcus faecalis) pathogens expressing high collagenolytic activity. Due to the variation in phosphate metabolism among microbial species we investigated the in vitro efficacy of a combination treatment of phosphates delivered in a sustained release format using NP-PPi and NP-Pi on collagenase and biofilm attenuation across gram-positive and gram-negative test pathogens.Collagenase screening was assessed using two in vitro models. The first in vitro assay involved culturing pathogens in the presence and absence of NP-Pi and/or NP-PPi treatment using two-dimensional (2D) commercially available fluorogenic protease-sensitive peptide substrates. Although these substrates are among the most commonly used for screening protease activity and inhibition in vitro, their application does not translate to three-dimensional (3D) matrix degradation. Additionally, the addition of drug-loaded nanoparticles directly in bacterial culture does not recapitulate the in vivo sustained release of phosphates due to nanoparticles embedded within tissue. Thus, the second model involved the development of a novel cell culture platform which utilized a proteolytically degradable hydrogel scaffold and a non-degradable nanocomposite hydrogel scaffold. In this assay NP-Pi and NP-PPi were entrapped in a non-degradable poly(ethylene) glycol (PEG) hydrogel to form of a nanocomposite matrix which served as a reservoir for sustained release of phosphates. Bacteria producing high levels of proteases were cultured in the presence of the nanocomposite phosphate releasing reservoir and the proteolytically degradable PEG hydrogel scaffold to determine the efficacy of sustained release of phosphates in attenuating proteolytic hydrogel degradation. To correlate matrix degradation with bacterial enzymes secreted in the culture medium, we also developed a method to efficiently measure hydrogel degradation rate until complete material degradation with a greater degree of accuracy compared to the commonly employed method utilizing gravimetric measurements in gel wet weight. Combined, the in vitro platform and our proposed degradation assay provide a novel approach for screening the effect of therapeutics for attenuation of bacterial protease-induced matrix degradation.The 2D in vitro study demonstrated that the combination treatment (NP-PPi + NP-Pi) confers broad spectrum efficacy for suppression of collagenase and biofilm production across test pathogens. Conversely, the 3D in vitro model demonstrated that the combination treatment (NP-PPi + NP-Pi) attenuated protease production for gram-negative pathogens, while the gram-positive test pathogen exhibited significant decreases in protease levels only in the presence of NP-Pi. Finally, our novel Sirius red absorbance assay for quantifying hydrogel degradation was found to provide greater accuracy when compared to gravimetric measurements in gel wet weight. It also enabled real-time monitoring of 3D matrix degradation kinetics as well as the time required for complete material dissolution in the presence of bacterial proteases and active human MMP-9 enzyme solutions. These findings highlight the importance of designing relevant in vitro platforms for screening therapeutic efficacy in the presence of cells and nanomaterials.
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Title: Wireless Body Sensor Network for Tracking Human Mobility using Long Short-Term Memory Neural Network for Classification
Creator: Gupta, Saumya
Date: 2019
Description: A large number of sensors are used without justification of the number chosen or placement choice. Many papers about body sensor networks...
Show moreA large number of sensors are used without justification of the number chosen or placement choice. Many papers about body sensor networks explore how to capture a type or types of motion, but all their sensors are placed in different locations; making their algorithms very specific to that movement. In this research, we explore the enhancement of human activity classification algorithm using long short-term memory (LSTM) neural network and wearable sensor network. There are five identical nodes used in the body sensor network to collect data. Each node incorporates an ESP8266 Microcontroller with Wi-Fi which is connected to an inertial measurement unit consisting of triple axis accelerometer and gyroscope sensor board. An analysis on the accuracy that each sensor node provides separately and in different combinations has been conducted to allow future research to focus their positioning in optimal positions. A Robot Operating System (ROS) central node is used to illustrate the in-built multi-threading capability. For demonstration, the positions chosen are waist, ankles and wrists. The raw sensor data can be observed on screen while it is being labelled live to create fitting dataset for developing an artificial neural network. Expectation is that increasing the number of sensors should raise the overall accuracy of the output but that isn’t the case observed, positioning of the sensor is pertinent to improvement. These platforms can be further extended to understand different motions and different sensor positions, also expanded to include other sensors.
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Title: FUNCTIONAL CONNECTIVITY LABELS FOR THE MULTICHANNEL IIT AND RUSH UNIVERSITY AGING (MIITRA) ATLAS
Creator: Badhon, Rashadul Hasan
Date: 2022
Description: In the field of medical imaging, a brain atlas refers to a specific model of the brain of a population where different parts of the atlas...
Show moreIn the field of medical imaging, a brain atlas refers to a specific model of the brain of a population where different parts of the atlas correspond to different anatomical parts of the average brain of the population. A brain atlas is composed of MRI templates and semantic labels and is a crucial component of neuroscience for its critical role in facilitating spatial normalization, temporal characterization and automated segmentation for the purposes of voxel-wise, region of interest and network analyses. Building a brain atlas requires registering multi-dimensional brain datasets from a population into a reference space and, during the last decade, the advent of new technologies and computational modeling approaches has made it possible to build high-quality, detailed brain atlases. At the same time developments in data acquisition now allow the construction of comprehensive brain atlases containing a variety of information about the brain. The Multichannel Illinois Institute of Technology and Rush university Aging (MIITRA) atlas project is developing a high-quality comprehensive atlas of the older adult brain containing a multitude of templates and labels. These templates are constructed with state-of-the-art spatial normalization of high-quality data and as a result, they are characterized by higher image quality, are more representative of the brain of non-demented older adults and provide higher inter-subject spatial normalization accuracy of older adult data compared to other available templates. The methodology used in the development of the MIITRA templates facilitates the construction of accurate structural and connectivity labels. Functional connectivity MRI reveals sets of functionally connected brain regions, forming networks, by investigating synchronous fluctuations in MRI signal over time across these brain regions during rest. The purpose of this work was to generate functional connectivity labels for several brain networks in MIITRA space.
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Title: RADIAL MAP ASSESSMENT APPROACH FOR DEEP LEARNING DENOISED CARDIAC MAGNETIC RESONANCE RECONSTRUCTION SHARPNESS
Creator: Mo, Fei
Date: 2021
Description: Deep Learning (DL) and Artificial Intelligence (AI) play important roles in the computer-aided medical diagnostics and precision medicine...
Show moreDeep Learning (DL) and Artificial Intelligence (AI) play important roles in the computer-aided medical diagnostics and precision medicine fields, capable of complementing human operators in disease diagnosis and treatment but optimizing and streamlining medical image display. While incredibly powerful, images produced via Deep Learning or Artificial Intelligence should be analyzed critically in order to be cognizant of how the algorithms are producing the new image and what the new imagine is. One such opportunity arose in the form of a unique collaborative project: the technical development of an image assessment tool that would analyze outputs between DL-based and non DL-based Magnetic Resonance Imaging reconstruction methods.More specifically, we examine the operator input dependence of the existing reference method in terms of accuracy and precision performance, and subsequently propose a new metric approach that preserves the heuristics of the intended quantification, overcomes operator dependence, and provides a relative comparative scoring approach that may normalize for angular dependence of examined images. In chapter 2 of this thesis, we provide a background description pertaining to the two imaging science principles that yielded our proposed method description and study design. First, if treated naively, the examined linear measurement approach exhibits potential bias with respect to the coordinate lattice space of the examined image. Second, the examined DL-based image reconstruction methods used in this thesis warrants an elaborate and explicit description of the measured noise and signal present in the reconstructed images. This specific reconstruction approach employs an iterative scheme with an embedded DL-based substep or filter to which we are blinded. In chapters 3 and 4 of this thesis, the imaging and DL-based image reconstruction experiments are described. These experiments employ cardiac MRI datasets from multiple clinical centers. We first outline the clinical and technical background for this approach, and then examine the quality of DL-based reconstructed image sharpness by two alternative methods: 1) by employing the gold-standard method that addresses the lattice point irregularity using a ‘re-gridding’ method, and 2) by applying our novel proposed method inspired by radial MRI k-space sampling, which exploits the mathematical properties of uniform radial sampling to yield the target voxel counts in the ‘gridded’ polar coordinate system. This new measure of voxel counts is shown to overcome the limitation due to the operator-dependence for the conventional approach. Furthermore, we propose this metric as a relative and comparative index between two alternative reconstruction methods from the same MRI k-space.
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Title: IMPACT OF DATA SHAPE, FIDELITY, AND INTER-OBSERVER REPRODUCIBILITY ON CARDIAC MAGNETIC RESONANCE IMAGE PIPELINES
Creator: Obioma, Blessing Ngozi
Date: 2020
Description: Artificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical...
Show moreArtificial Intelligence (AI) holds a great promise in the healthcare. It provides a variety of advantages with its application in clinical diagnosis, disease prediction, and treatment, with such interests intensifying in the medical image field. AI can automate various cumbersome data processing techniques in medical imaging such as segmentation of left ventricular chambers and image-based classification of diseases. However, full clinical implementation and adaptation of emerging AI-based tools face challenges due to the inherently opaque nature of such AI algorithms based on Deep Neural Networks (DNN), for which computer-trained bias is not only difficult to detect by physician users but is also difficult to safely design in software development. In this work, we examine AI application in Cardiac Magnetic Resonance (CMR) using an automated image classification task, and thereby propose an AI quality control framework design that differentially evaluates the black-box DNN via carefully prepared input data with shape and fidelity variations to probe system responses to these variations. Two variants of the Visual Geometric Graphics with 19 neural layers (VGG19) was used for classification, with a total of 60,000 CMR images. Findings from this work provides insights on the importance of quality training data preparation and demonstrates the importance of data shape variability. It also provides gateway for computation performance optimization in training and validation time.
Show less

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