Biomedical Engineering and Bioengineering Commons^™

Understanding The Surface Fouling Mechanism Of Ultrananocrystalline Diamond Microelectrodes Using Microfluidics For Neurochemical Detection, An-Yi Chang

Doctoral Dissertations

Electrochemical methods are widely used for chronic neurochemical sensing, but thus far, the organic solution redox reactions fouled the electrodes' surface. It caused the reduction of sensitivity and the electrodes' lifetime.

Here, we present the boron-doped nanocrystalline diamond microelectrodes (BDUNCD) as the next generation electrode material for neurochemical sensor development. To aid in long-term chronic monitoring of neurochemicals, they have a wide window of electrochemical potential, extremely low background current, and excellent chemical inertness. The main research goal is to reduce the rate of electrode fouling due to the reaction by-products, and significantly extend their useful lifetime.

We systematically characterize …

Anxiolytic Effects Of Propranolol And Diphenoxylate On Mice And Automated Stretch-Attend Posture Analysis, Kevin Scott Holly

Doctoral Dissertations

The prevention of social anxiety, performance anxiety, and social phobia via the combination of two generic drugs, diphenoxylate HC1 (opioid) plus atropine sulfate (anticholinergic) and propranolol HCl (beta blocker) was evaluated in mice through behavioral studies. A patent published on a September 8, 2011 by Benjamin D. Holly, US 2011/0218215 Al, prompted the research. The drug combination of diphenoxylate and atropine plus propranolol could be an immediate treatment for patients suffering from acute phobic and social anxiety disorders. Demonstrating the anxiolytic effects of the treatment on mice would validate a mouse model for neuroscientist to be used to detect the …

Generalized Partial Directed Coherence And Centrality Measures In Brain Networks For Epileptogenic Focus Localization, Joshua Aaron Adkinson

Doctoral Dissertations

Accurate epileptogenic focus localization is required prior to surgical resection of brain tissue for treatment of patients with intractable temporal lobe epilepsy, a clinical need that is partially fulfilled to date through a subjective, and at times inconclusive, evaluation of the recorded electroencephalogram (EEG). Using brain connectivity analysis, patterns of causal interactions between brain regions were derived from multichannel EEG of 127 seizures in nine patients with focal, temporal lobe epilepsy (TLE). The statistically significant directed interactions in the reconstructed brain networks were estimated from three second intracranial multi-electrode EEG segments using the Generalized Partial Directed Coherence (GPDC) and validated …

Mutlifunctional Platforms For Gene And Drug Delivery For Cancer Therapy, Jeffery J. Ambrose Jr.

Doctoral Dissertations

The National Cancer Institute and the American Cancer Society estimate that 1.6 million new cancer incidences and over half a million cancer related deaths occur annually [1][2]. Cancer the second most common cause of death in the United States [1], [2]. Although the causes of cancer can vary depending on cell type, all or almost all instances of cancer arise from a mutation or from an abnormal activation of the cellular genes that control cell growth and mitosis [3].

Treatment of a given cancer type depends on the subtype, stage and progression of the cancer. Varieties of cancer therapy include …

Modeling And Control Of Nanoparticle Bloodstream Concentration For Cancer Therapies, Scarlett S. Bracey

Doctoral Dissertations

Currently, the most commonly used treatments for cancerous tumors (chemotherapy, radiation, etc.) have almost no method of monitoring the administration of the treatment for adverse effects in real time. Without any real time feedback or control, treatment becomes a "guess and check" method with no way of predicting the effects of the drugs based on the actual bioavailability to the patient's body. One particular drug may be effective for one patient, yet provide no benefit to another. Doctors and scientists do not routinely attempt to quantifiably explain this discrepancy. In this work, mathematical modeling and analysis techniques are joined together …

A Novel Microfluidic Enrichment Technique For Carbonylated Proteins, Bryant C. Hollins

Doctoral Dissertations

Proteins are the building blocks of cells in living organisms, and are composed of amino acids. The expression of proteins is regulated by the processes of transcription and translation. Proteins undergo post-translational modifications in order to dictate their role physiologically within a cell.

Not all post-translational modifications are beneficial for the protein or the cell. One type of post-translational modification, called carbonylation, irreversibly places a carbonyl group onto an amino acid residue, most commonly proline, lysine, arginine, and threonine. This modification can have severe consequences physiologically, including loss of solubility, loss of function, and protein aggregation.

Carbonylated proteins have commonly …

Modular Machine Learning Methods For Computer-Aided Diagnosis Of Breast Cancer, Mia Kathleen Markey '94

Doctoral Dissertations

The purpose of this study was to improve breast cancer diagnosis by reducing the number of benign biopsies performed. To this end, we investigated modular and ensemble systems of machine learning methods for computer-aided diagnosis (CAD) of breast cancer. A modular system partitions the input space into smaller domains, each of which is handled by a local model. An ensemble system uses multiple models for the same cases and combines the models' predictions.

Five supervised machine learning techniques (LDA, SVM, BP-ANN, CBR, CART) were trained to predict the biopsy outcome from mammographic findings (BIRADS™) and patient age based on a …

Bottom-Up Design Of Artificial Neural Network For Single-Lead Electrocardiogram Beat And Rhythm Classification, Srikanth Thiagarajan

Doctoral Dissertations

Performance improvement in computerized Electrocardiogram (ECG) classification is vital to improve reliability in this life-saving technology. The non-linearly overlapping nature of the ECG classification task prevents the statistical and the syntactic procedures from reaching the maximum performance. A new approach, a neural network-based classification scheme, has been implemented in clinical ECG problems with much success. The focus, however, has been on narrow clinical problem domains and the implementations lacked engineering precision. An optimal utilization of frequency information was missing. This dissertation attempts to improve the accuracy of neural network-based single-lead (lead-II) ECG beat and rhythm classification. A bottom-up approach defined …