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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: Three-Dimensional Co-Culture Systems for Vascularization of Cardiac Tissue
Creator: Rodriguez Arias, Jessica A.
Date: 2023
Description: Myocardial Infarction (MI) is the partial or complete blockage of blood flow to the myocardial tissue resulting in damage and therefore loss...
Show moreMyocardial Infarction (MI) is the partial or complete blockage of blood flow to the myocardial tissue resulting in damage and therefore loss of heart function. In the U.S. every 40 seconds, someone will suffer from MI and the only available treatment is medication to treat the symptoms of heart function loss, but do not treat the underlying cause. Some attempts to treat the underlying cause have arisen in the last decades including cell-based therapies or tissue engineering therapies such as spheroid-based cardiac patches that have shown to be promising. Improvement in the mechanical properties to create suturable engineered tissues remain to be improved for ease of implantation purposes. Cell-laden hydrogel scaffolds can provide improved mechanical properties compared to biomaterial free cell-based therapies but need to allow for vascularization of the engineered tissue. Thus, the goal of this thesis is to provide preliminary studies for the use of a cell adhesive, proteolytically degradable PEG hydrogel scaffold that eventually would be used as an invitro model to evaluate engineered tissue vascularization for cardiac tissue engineering. To construct this model, important cell spheroid parameters on vascular invasion in 3D culture were investigated including the total number of cells/spheroid, the supporting cell for endothelial cells. In order to scale-up scaffolds to size of clinically relevant dimensions, a multilayered hydrogel construct visible light free-radical polymerization approach encapsulating vascular spheroids in multiple layers was also investigated. Results indicate that a total cell number of 5000 cells/spheroid aggregate were feasible due to cell sourcing. In addition, co-cultures of endothelial and mesenchymal stem cells led to maximized vascular invasion of the spheroids compared to fibroblast/endothelial co-culture and endothelial monoculture of spheroids in the hydrogel. Finally, the extent of vascularization of spheroids in each layer of the multilayered hydrogel constructs varied due to the observed differences in mechanical properties and swelling ratio of each layer due to incomplete polymerization of layers. This study demonstrated the importance of support cells and hydrogel mechanical properties in promoting vascularization of spheroid which serves as basis for building cell-laden hydrogel scaffolds for vascularization for cardiac tissues.
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Title: Intraoperative Assessment of Surgical Margins in Head And Neck Cancer Resection Using Time-Domain Fluorescence Imaging
Creator: Cleary, Brandon M.
Date: 2023
Description: Rapid and accurate determination of surgical margin depth in fluorescence guided surgery has been a difficult issue to overcome, leading to...
Show moreRapid and accurate determination of surgical margin depth in fluorescence guided surgery has been a difficult issue to overcome, leading to over- or under-resection of cancerous tissues and follow-up treatments such as ‘call-back’ surgery and chemotherapy. Current techniques utilizing direct measurement of tumor margins in frozen section pathology are slow, which can prevent surgeons from acting on information before a patient is sent home. Other fluorescence techniques require the measurement of margins via captured images that are overlayed with fluorescent data. This method is flawed, as measuring depth from captured images loses spatial information. Intensity-based fluorescence techniques utilizing tumor-to-background ratios do not decouple the effects of concentration from the depth information acquired. Thus, it is necessary to perform an objective measurement to determine depths of surgical margins. This thesis focuses on the theory, device design, simulation development, and overall viability of time-domain fluorescence imaging as an alternative method of determining surgical margin depths. Characteristic regressions were generated using a thresholding method on acquired time-domain fluorescence signals, which were used to convert time-domain data to a depth value. These were applied to an image space to generate a depth map of a modelled tissue sample. All modeling was performed on homogeneous media using Monte Carlo simulations, providing high accuracy at the cost of increased computational time. In practice, the imaging process should be completed within a span of under 20 minutes for a full tissue sample, rather than 20 minutes for a single slice of the sample. This thesis also explores the effects of different thresholding levels on the accuracy of depth determination, as well as the precautions to be taken regarding hardware limitations and signal noise.
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Title: Retrospective Quantitative T1 Imaging to Examine Characteristics of Multiple Sclerosis Lesions
Creator: Young, Griffin James
Date: 2024
Description: Quantitative MRI plays an essential role in assessing tissue abnormality and diseaseprogression in multiple sclerosis (MS). Specifically, T1...
Show moreQuantitative MRI plays an essential role in assessing tissue abnormality and diseaseprogression in multiple sclerosis (MS). Specifically, T1 relaxometry is gaining popularity as elevated T1 values have been shown to correlate with increased inflammation, demyelination, and gliosis. The predominant issue is that relaxometry requires parametric mapping through advanced imaging techniques not commonly included in standard clinical protocols. This leaves an information gap in large clinical datasets from which quantitative mapping could have been performed. We introduce T1-REQUIRE, a retrospective T1 mapping method that approximates T1 values from a single T1-weighted MR image. This method has already been shown to be accurate within 10% of a clinically available reference standard in healthy controls but will be further validated in MS cohorts. We also further aim to determine T1-REQUIRE’s statistical significance as a unique biomarker for the assessment of MS lesions as they relate to clinical disability and disease burden. A 14-subject comparison between T1-REQUIRE maps derived from 3D T1 weighted turbo field echoes (3D T1w TFE) and an inversion-recovery fast field echo (IRFFE) revealed a whole-brain voxel-wise Pearson’s correlation of r = 0.89 (p < 0.001) and mean bias of 3.99%. In MS white matter lesions, r = 0.81, R2 = 0.65 (p < 0.001, N = 159), bias = 10.07%, and in normal appearing white matter (NAWM), r = 0.82, R 2 = 0.67 (p < 0.001), bias = 9.48%. Mean lesional T1-REQUIRE and MTR correlated significantly (r = -0.68, p < 0.001, N = 587) similar to previously published literature. Median lesional MTR correlated significantly with EDSS (rho = -0.34, p = 0.037), and lesional T1-REQUIRE exhibited xiii significant correlations with global brain tissue atrophy as measured by brain parenchymal fraction (BPF) (r = -0.41, p = 0.010, N = 38). Multivariate linear regressions between T1- REQUIRE NAWM provided meaningful statistical relationships with EDSS (β = 0.03, p = 0.027, N = 38), as well as did mean MTR values in the Thalamus (β = -0.27, p = 0.037, N = 38). A new spoiled gradient echo variation of T1-REQUIRE was assessed as a proof of concept in a small 5-subject MS cohort compared with IR-FFE T1 maps, with a whole brain voxel-wise correlation of r = 0.88, R2 = 0.77 (p < 0.001), and Bias = 0.19%. Lesional T1 comparisons reached a correlation of r = 0.75, R2 = 0.56 (p < 0.001, N = 42), and Bias = 10.81%. The significance of these findings means that there is the potential to provide supplementary quantitative information in clinical datasets where quantitative protocols were not implemented. Large MS data repositories previously only containing structural T1 weighted images now may be used in big data relaxometric studies with the potential to lead to new findings in newly uncovered datasets. Furthermore, T1-REQUIRE has the potential for immediate use in clinics where standard T1 mapping sequences aren’t able to be readily implemented.
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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: Cardiolipin Modulates the Insertion of Adsorbed Helical Amyloid Beta Peptide Into Model Mitochondrial Membranes
Creator: Kaczmarek, Julia A.
Date: 2023
Description: The loss of mitochondrial phospholipid cardiolipin (CL) may play a role in both the pathogenesis of Alzheimer's Disease (AD) and its treatment...
Show moreThe loss of mitochondrial phospholipid cardiolipin (CL) may play a role in both the pathogenesis of Alzheimer's Disease (AD) and its treatment. An effector molecule of the disease, amyloid-beta (Aβ), has been observed to interact with lipid membranes, but its relevance to mitochondrial membranes containing CL remained elusive. The present study investigated if the presence of CL modulated the insertion of adsorbed helical amyloid beta (Aβ14-40) into model mitochondrial membranes, and if this effect was more pronounced for its N-terminus or C-terminus. I conducted a coarse-grained computer simulation using well-tempered metadynamics to traverse the free energy landscape that maps the translocation of Aβ14-40. Insertion into CL-containing bilayers created larger local membrane deformations and modulated the location of the transition path but had an inconclusive impact on the free energy cost of translocation. Since the generation of toxic calcium-permeable pores depends on the insertion of Aβ into the bilayer, the loss of CL seen in AD may prime the inner mitochondrial membrane for pore formation, but more research is needed to pursue this hypothesis.
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