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Full-Text Articles in Biomedical Engineering and Bioengineering

Metabolomic Differentiation Of Tumor Core And Edge In Glioma., Mary E. Baxter Apr 2023

Metabolomic Differentiation Of Tumor Core And Edge In Glioma., Mary E. Baxter

Electronic Theses and Dissertations

Glioma is one of the most aggressive forms of brain cancer. It has been shown that the microenvironments differ significantly between the core and edge regions of glioma tumors. This study obtained metabolomic profiles of glioma core and edge regions using paired glioma core and edge tissue samples from 27 human patients. Data was acquired by performing liquid-liquid metabolite extraction and 2DLC-MS/MS on the tissue samples. In addition, a boosted generalized linear machine learning model was employed to predict the metabolomic profiles associated with O-6-methylguanine-DNA methyltransferase (MGMT) promoter methylation.

A panel of 66 metabolites was found to be statistically significant …


Gradient Generating Microfluidic Coculture System For Disease Modeling And Neural Development, Phaneendra Chennampally Dec 2021

Gradient Generating Microfluidic Coculture System For Disease Modeling And Neural Development, Phaneendra Chennampally

Electronic Theses and Dissertations

Cellular microenvironment or cell niche plays an important role in developmental biology and disease pathophysiology. Physical or chemical signals in microenvironment drive the cellular activity. These signaling molecules are generated from the surrounding cells/tissues as part of intercellular communication; a fundamental property of a cell. Dynamic profile of these signaling molecules in the microenvironment plays a pivotal role in transfer of molecular information from cell to cell in disease proliferation or fate determination. Recapitulating these signaling cues in an in vitro study is difficult to achieve using standard cell culture techniques. However microfluidic systems are capable of addressing these issues, …


Bio-Inspired Materials For Electrochemical Sensors, Matthew Joseph Hummel Jan 2021

Bio-Inspired Materials For Electrochemical Sensors, Matthew Joseph Hummel

Electronic Theses and Dissertations

Electrochemical biosensors are a rapidly growing research area that has greatly improved its specificity, accuracy, and precision in the detection of biomolecules in contemporary literature and industry alike. Typically, these systems exist in a three-electrode conformation with a working electrode functioning as the anode, a counter electrode functioning as the cathode, and a reference electrode allowing for the control of potential in the system. The method by which these sensors work is through the sharing of electrons via redox reactions with the target molecule and the working electrode or modifications on its surface. By exploiting the function of biomaterials that …


Modeling And Simulation Methodologies For Spinal Cord Stimulation., Saliya Kumara Kirigeeganage Dec 2018

Modeling And Simulation Methodologies For Spinal Cord Stimulation., Saliya Kumara Kirigeeganage

Electronic Theses and Dissertations

The use of neural prostheses to improve health of paraplegics has been a prime interest of neuroscientists over the last few decades. Scientists have performed experiments with spinal cord stimulation (SCS) to enable voluntary motor function of paralyzed patients. However, the experimentation on the human spinal cord is not a trivial task. Therefore, modeling and simulation techniques play a significant role in understanding the underlying concepts and mechanics of the spinal cord stimulation. In this work, simulation and modeling techniques related to spinal cord stimulation were investigated. The initial work was intended to visualize the electric field distribution patterns in …


Longitudinal Tracking Of Physiological State With Electromyographic Signals., Robert Warren Stallard May 2018

Longitudinal Tracking Of Physiological State With Electromyographic Signals., Robert Warren Stallard

Electronic Theses and Dissertations

Electrophysiological measurements have been used in recent history to classify instantaneous physiological configurations, e.g., hand gestures. This work investigates the feasibility of working with changes in physiological configurations over time (i.e., longitudinally) using a variety of algorithms from the machine learning domain. We demonstrate a high degree of classification accuracy for a binary classification problem derived from electromyography measurements before and after a 35-day bedrest. The problem difficulty is increased with a more dynamic experiment testing for changes in astronaut sensorimotor performance by taking electromyography and force plate measurements before, during, and after a jump from a small platform. A …


A Comprehensive Analysis On Eeg Signal Classification Using Advanced Computational Analysis, Kaushik Bhimraj Jan 2017

A Comprehensive Analysis On Eeg Signal Classification Using Advanced Computational Analysis, Kaushik Bhimraj

Electronic Theses and Dissertations

Electroencephalogram (EEG) has been used in a wide array of applications to study mental disorders. Due to its non-invasive and low-cost features, EEG has become a viable instrument in Brain-Computer Interfaces (BCI). These BCI systems integrate user's neural features with robotic machines to perform tasks. However, due to EEG signals being highly dynamic in nature, BCI systems are still unstable and prone to unanticipated noise interference. An important application of this technology is to help facilitate the lives of the tetraplegic through assimilating human brain impulses and converting them into mechanical motion. However, BCI systems are remarkably challenging to implement …


Neuromodulation Based Control Of Autonomous Robots On A Cloud Computing Platform, Cameron Muhammad Jan 2014

Neuromodulation Based Control Of Autonomous Robots On A Cloud Computing Platform, Cameron Muhammad

Electronic Theses and Dissertations

In recent years, the advancement of neurobiologically plausible models and computer networking has resulted in new ways of implementing control systems on robotic platforms. The work presents a control approach based on vertebrate neuromodulation and its implementation on autonomous robots in the open-source, open-access environment of robot operating system (ROS). A spiking neural network (SNN) is used to model the neuromodulatory function for generating context based behavioral responses of the robots to sensory input signals. The neural network incorporates three types of neurons- cholinergic and noradrenergic (ACh/NE) neurons for attention focusing and action selection, dopaminergic (DA) neurons for rewards- and …