Matching Items (15)
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- All Subjects: electrochemistry
- All Subjects: MRI
- Creators: Harrington Bioengineering Program
- Resource Type: Text
Description
Glioblastoma multiforme (GBM) is a malignant, aggressive and infiltrative cancer of the central nervous system with a median survival of 14.6 months with standard care. Diagnosis of GBM is made using medical imaging such as magnetic resonance imaging (MRI) or computed tomography (CT). Treatment is informed by medical images and includes chemotherapy, radiation therapy, and surgical removal if the tumor is surgically accessible. Treatment seldom results in a significant increase in longevity, partly due to the lack of precise information regarding tumor size and location. This lack of information arises from the physical limitations of MR and CT imaging coupled with the diffusive nature of glioblastoma tumors. GBM tumor cells can migrate far beyond the visible boundaries of the tumor and will result in a recurring tumor if not killed or removed. Since medical images are the only readily available information about the tumor, we aim to improve mathematical models of tumor growth to better estimate the missing information. Particularly, we investigate the effect of random variation in tumor cell behavior (anisotropy) using stochastic parameterizations of an established proliferation-diffusion model of tumor growth. To evaluate the performance of our mathematical model, we use MR images from an animal model consisting of Murine GL261 tumors implanted in immunocompetent mice, which provides consistency in tumor initiation and location, immune response, genetic variation, and treatment. Compared to non-stochastic simulations, stochastic simulations showed improved volume accuracy when proliferation variability was high, but diffusion variability was found to only marginally affect tumor volume estimates. Neither proliferation nor diffusion variability significantly affected the spatial distribution accuracy of the simulations. While certain cases of stochastic parameterizations improved volume accuracy, they failed to significantly improve simulation accuracy overall. Both the non-stochastic and stochastic simulations failed to achieve over 75% spatial distribution accuracy, suggesting that the underlying structure of the model fails to capture one or more biological processes that affect tumor growth. Two biological features that are candidates for further investigation are angiogenesis and anisotropy resulting from differences between white and gray matter. Time-dependent proliferation and diffusion terms could be introduced to model angiogenesis, and diffusion weighed imaging (DTI) could be used to differentiate between white and gray matter, which might allow for improved estimates brain anisotropy.
ContributorsAnderies, Barrett James (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Stepien, Tracy (Committee member) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The purpose of this project was to examine the viability of protein biomarkers in pre-symptomatic detection of lung cancer. Regular screening has been shown to vastly improve patient survival outcome. Lung cancer currently has the highest occurrence and mortality of all cancers and so a means of screening would be highly beneficial. In this research, the biomarker neuron-specific enolase (Enolase-2, eno2), a marker of small-cell lung cancer, was detected at varying concentrations using electrochemical impedance spectroscopy in order to develop a mathematical model of predicting protein expression based on a measured impedance value at a determined optimum frequency. The extent of protein expression would indicate the possibility of the patient having small-cell lung cancer. The optimum frequency was found to be 459 Hz, and the mathematical model to determine eno2 concentration based on impedance was found to be y = 40.246x + 719.5 with an R2 value of 0.82237. These results suggest that this approach could provide an option for the development of small-cell lung cancer screening utilizing electrochemical technology.
ContributorsEvans, William Ian (Author) / LaBelle, Jeffrey (Thesis director) / Spano, Mark (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
In this paper, β-estradiol was characterized utilizing electrochemical impedance spectroscopy (EIS) techniques for the purpose of developing a multi-marker fertility sensor. β-estradiol was immobilized onto the surface of gold disk electrodes to find the optimal binding frequency of estradiol and its respective antibody, anti-17β-estradiol, which was determined to be 37.46Hz. At this frequency a logarithmic relationship between concentration and impedance (Z/ohm) was established creating a concentration calibration curve with a slope of 211 ohm/ln(pg mL-1), an R-squared value of 0.986 and a lower limit of detection of 742 fg mL-1. The specificity and cross-reactivity of the antibody with other hormones was tested through interferent and non-target experiments. Signal-to-noise ratio analysis verified that anti-17β-estradiol exhibited minimal chemical reactions with other hormones (SNR< 3) in non-target experiments. Additionally, there were minimal changes in the amount of signal collected during interferent testing, with albumin and follicle stimulating hormone having SNR values greater than 3. These results, along with the unique frequency response of the antibody-target binding reaction, allow for the possibility of using anti-17β-estradiol and β-estradiol for detecting multiple fertility biomarkers on a single sensor.
ContributorsSmith, Victoria Ann (Author) / LaBelle, Jeffrey (Thesis director) / Spano, Mark (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
Diabetes mellitus is a disease characterized by many chronic and acute conditions. With the prevalence and cost quickly increasing, we seek to improve on the current standard of care and create a rapid, label free sensor for glycated albumin (GA) index using electrochemical impedance spectroscopy (EIS). The antibody, anti-HA, was fixed to gold electrodes and a sine wave of sweeping frequencies was induced with a range of HA, GA, and GA with HA concentrations. Each frequency in the impedance sweep was analyzed for highest response and R-squared value. The frequency with both factors optimized is specific for both the antibody-antigen binding interactions with HA and GA and was determined to be 1476 Hz and 1.18 Hz respectively in purified solutions. The correlation slope between the impedance response and concentration for albumin (0 \u2014 5400 mg/dL of albumin) was determined to be 72.28 ohm/ln(mg/dL) with an R-square value of 0.89 with a 2.27 lower limit of detection. The correlation slope between the impedance response and concentration for glycated albumin (0 \u2014 108 mg/dL) was determined to be -876.96 ohm/ln(mg/dL) with an R-squared value of 0.70 with a 0.92 mg/dL lower limit of detection (LLD). The above data confirms that EIS offers a new method of GA detection by providing unique correlation with albumin as well as glycated albumin. The unique frequency response of GA and HA allows for modulation of alternating current signals so that several other markers important in the management of diabetes could be measured with a single sensor. Future work will be necessary to establish multimarker sensing on one electrode.
ContributorsEusebio, Francis Ang (Author) / LaBelle, Jeffrey (Thesis director) / Pizziconi, Vincent (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
Acetaminophen, commonly found in Tylenol and other over the counter (OTC) pharmaceuticals, was electrochemically characterized on custom made, flexible, screen printed electrodes (SPEs) to serve as a model target pharmaceutical found in flowing water lines. Carbon, silver/silver chloride, and insulator paste inks were printed onto polyethylene naphthalateolyester (PEN) using custom made stencils for a 4x1 array of 3-electrode electrochemical cells. Cyclic voltammetry was performed to find the electrical potential corresponding to the greatest current response and the experiments were conducted using amperometric current-time mode (AMP*i-t). The physical limitations of SPEs as well as the detection limitations of the target, such as pH and temperature were tested. A concentration gradient of the target was fitted with a linear curve (R2 0.99), and a lower limit of detection of 14.5 μM. It was also found that both pH and temperature affect the current produced by acetaminophen at a fixed concentration, and that the sensors can detect target in a continuous flow. A flow apparatus consisting of an inlet and effluent pipe served as the flow model into which a rolled up flexible electrode array was inserted. The broader goal of this research is to develop a highly sensitive electrode array on flexible substrates which can detect multiple targets simultaneously. Acetaminophen was chosen due to its electro-active properties and its presence in most public water lines in the United States.
ContributorsMaxwell, Stephanie Ann (Author) / LaBelle, Jeffrey (Thesis director) / Allee, David (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
Smart contrast agents allow for noninvasive study of specific events or tissue conditions inside of a patient's body using Magnetic Resonance Imaging (MRI). This research aims to develop and characterize novel smart contrast agents for MRI that respond to temperature changes in tissue microenvironments. Transmission Electron Microscopy, Nuclear Magnetic Resonance, and cell culture growth assays were used to characterize the physical, magnetic, and cytotoxic properties of candidate nanoprobes. The nanoprobes displayed thermosensitve MR properties with decreasing relaxivity with temperature. Future work will be focused on generating and characterizing photo-active analogues of the nanoprobes that could be used for both treatment of tissues and assessment of therapy.
ContributorsHussain, Khateeb Hyder (Author) / Kodibagkar, Vikram (Thesis director) / Stabenfeldt, Sarah (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor)
Created2014-05
Description
Glioblastoma Multiforme (GBM) is an aggressive and deadly form of brain cancer with a median survival time of about a year with treatment. Due to the aggressive nature of these tumors and the tendency of gliomas to follow white matter tracks in the brain, each tumor mass has a unique growth pattern. Consequently it is difficult for neurosurgeons to anticipate where the tumor will spread in the brain, making treatment planning difficult. Archival patient data including MRI scans depicting the progress of tumors have been helpful in developing a model to predict Glioblastoma proliferation, but limited scans per patient make the tumor growth rate difficult to determine. Furthermore, patient treatment between scan points can significantly compound the challenge of accurately predicting the tumor growth. A partnership with Barrow Neurological Institute has allowed murine studies to be conducted in order to closely observe tumor growth and potentially improve the current model to more closely resemble intermittent stages of GBM growth without treatment effects.
ContributorsSnyder, Lena Haley (Author) / Kostelich, Eric (Thesis director) / Frakes, David (Committee member) / Barrett, The Honors College (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
In epilepsy, malformations that cause seizures often require surgery. The purpose of this research is to join forces with the Multi-Center Epilepsy Lesion Detection (MELD) project at University College London (UCL) in order to improve the process of detecting lesions in patients with drug-resistant epilepsy. This, in turn, will improve surgical outcomes via more structured surgical planning. It is a global effort, with more than 20 sites across 5 continents. The targeted populations for this study include patients whose epilepsy stems from Focal Cortical Dysplasia. Focal Cortical Dysplasia is an abnormality of cortical development, and causes most of the drug-resistant epilepsy. Currently, the creators of MELD have developed a set of protocols which wrap various
commands designed to streamline post-processing of MRI images. Using this partnership, the Applied Neuroscience and Technology Lab at PCH has been able to complete production of a post-processing pipeline which integrates locally sourced smoothing techniques to help identify lesions in patients with evidence of Focal Cortical Dysplasia. The end result is a system in which a patient with epilepsy may experience more successful post-surgical results due to the
combination of a lesion detection mechanism and the radiologist using their trained eye in the presurgical stages. As one of the main points of this work is the global aspect of it, Barrett thesis funding was dedicated for a trip to London in order to network with other MELD project collaborators. This was a successful trip for the project as a whole in addition to this particular thesis. The ability to troubleshoot problems with one another in a room full of subject matter
experts allowed for a high level of discussion and learning. Future work includes implementing machine learning approaches which consider all morphometry parameters simultaneously.
commands designed to streamline post-processing of MRI images. Using this partnership, the Applied Neuroscience and Technology Lab at PCH has been able to complete production of a post-processing pipeline which integrates locally sourced smoothing techniques to help identify lesions in patients with evidence of Focal Cortical Dysplasia. The end result is a system in which a patient with epilepsy may experience more successful post-surgical results due to the
combination of a lesion detection mechanism and the radiologist using their trained eye in the presurgical stages. As one of the main points of this work is the global aspect of it, Barrett thesis funding was dedicated for a trip to London in order to network with other MELD project collaborators. This was a successful trip for the project as a whole in addition to this particular thesis. The ability to troubleshoot problems with one another in a room full of subject matter
experts allowed for a high level of discussion and learning. Future work includes implementing machine learning approaches which consider all morphometry parameters simultaneously.
ContributorsHumphreys, Zachary William (Author) / Kodibagkar, Vikram (Thesis director) / Foldes, Stephen (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Previous studies have found that the detection of near-threshold stimuli is decreased immediately before movement and throughout movement production. This has been suggested to occur through the use of the internal forward model processing an efferent copy of the motor command and creating a prediction that is used to cancel out the resulting sensory feedback. Currently, there are no published accounts of the perception of tactile signals for motor tasks and contexts related to the lips during both speech planning and production. In this study, we measured the responsiveness of the somatosensory system during speech planning using light electrical stimulation below the lower lip by comparing perception during mixed speaking and silent reading conditions. Participants were asked to judge whether a constant near-threshold electrical stimulation (subject-specific intensity, 85% detected at rest) was present during different time points relative to an initial visual cue. In the speaking condition, participants overtly produced target words shown on a computer monitor. In the reading condition, participants read the same target words silently to themselves without any movement or sound. We found that detection of the stimulus was attenuated during speaking conditions while remaining at a constant level close to the perceptual threshold throughout the silent reading condition. Perceptual modulation was most intense during speech production and showed some attenuation just prior to speech production during the planning period of speech. This demonstrates that there is a significant decrease in the responsiveness of the somatosensory system during speech production as well as milliseconds before speech is even produced which has implications for speech disorders such as stuttering and schizophrenia with pronounced deficits in the somatosensory system.
ContributorsMcguffin, Brianna Jean (Author) / Daliri, Ayoub (Thesis director) / Liss, Julie (Committee member) / Department of Psychology (Contributor) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
The International Dyslexia Association defines dyslexia as a learning disorder that is characterized by poor spelling, decoding, and word recognition abilities. There is still no known cause of dyslexia, although it is a very common disability that affects 1 in 10 people. Previous fMRI and MRI research in dyslexia has explored the neural correlations of hemispheric lateralization and phonemic awareness in dyslexia. The present study investigated the underlying neurobiology of five adults with dyslexia compared to age- and sex-matched control subjects using structural and functional magnetic resonance imaging. All subjects completed a large battery of behavioral tasks as part of a larger study and underwent functional and structural MRI acquisition. This data was collected and preprocessed at the University of Washington. Analyses focused on examining the neural correlates of hemispheric lateralization, letter reversal mistakes, reduced processing speed, and phonemic awareness. There were no significant findings of hemispheric differences between subjects with dyslexia and controls. The subject making the largest amount of letter reversal errors had deactivation in their cerebellum during the fMRI language task. Cerebellar white matter volume and surface area of the premotor cortex was the largest in the individual with the slowest reaction time to tapping. Phonemic decoding efficiency had a high correlation with neural activation in the primary motor cortex during the fMRI motor task (r=0.6). Findings from the present study suggest that brain regions utilized during motor control, such as the cerebellum, premotor cortex, and primary motor cortex, may have a larger role in dyslexia then previously considered. Future studies are needed to further distinguish the role of the cerebellum and other motor regions in relation to motor control and language processing deficits related to dyslexia.
ContributorsHoulihan, Chloe Carissa Prince (Author) / Rogalsky, Corianne (Thesis director) / Peter, Beate (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-12