Bioimaging and Biomedical Optics Commons^™

Analyzing Pseudomonas Aeruginosa With Bacteriophage Tags Using Photoacoustic Flow Cytometry, Jennifer C. Schinke

Electronic Theses and Dissertations

The number of daily bacterial infections is climbing and the CDC explains that this is due to the antibiotic-resistant threat in the United States. Finding a faster way of bacterial identification is necessary as it currently takes 1-4 days for a medical lab to culture and identify bacteria. Photoacoustic flow cytometry (PAFC) can be used as an alternative method resulting in swift identification within an hour (Edgar, 2019). Pseudomonas aeruginosa, cell line PA01, will be coated in up to a few hundred red dyed phages making it detectible by the photoacoustic flow cytometry system. Bacteriophages (phages) are viruses that …

Novel Magnetic Resonance Imaging-Compatible Mechatronic Needle Guidance System For Prostate Focal Laser Ablation Therapy, Eric R. Knull

Electronic Thesis and Dissertation Repository

Advances in prostate cancer (PCa) screening techniques have led to diagnosis of many cases of low-grade and highly localized disease. Conventional whole-gland therapies often result in overtreatment in such cases and debate still surrounds the optimal method of oncologic control. MRI-guided prostate focal laser ablation (FLA) is a minimally invasive treatment option, which has demonstrated potential to destroy localized lesions while sparing healthy prostatic tissue, thereby reducing treatment-related side effects. Many challenges still exist in the development of FLA, including patient selection; tumour localization, visualization, and characterization; needle guidance; and evaluation of treatment efficacy. The objective of this thesis work …

Predicting Accurate Heart Attacks Using Logistic Regression, Vishal Baral, Pranati Palai, Soumen Nayak

International Journal of Computer and Communication Technology

A heart attack is one of the leading causes of death today. According to a large data population used as a training set for the algorithm for machine learning, classification is a technique for predicting the target class from input data. A difficulty in clinical data analytics is predicting heart attacks with greater precision. The focal point of this work is to analyze the heart attack dataset (Kaggle repository) to find a Machine learning classifier technique that predicts if a person is prone to a heart attack with maximum accuracy based on various health factors. The efficacy of the three …

List Of 121 Papers Citing One Or More Skin Lesion Image Datasets, Neda Alipour

Other resources

No abstract provided.

Mechanisms Of Theta Burst Transcranial Ultrasound Induced Plasticity In The Human Motor Cortex, Yazan Shamli Oghli, Talyta Grippe, Tarun Arora, Tasnuva Hoque, Ghazaleh Darmani, Robert Chen

Department of Medicine Faculty Papers

BACKGROUND: Transcranial ultrasound stimulation (TUS) is a novel non-invasive brain stimulation technique with high depth penetrance and spatial resolution. Theta-burst TUS (tbTUS) is a plasticity-inducing protocol which increases motor cortical excitability for up to 30 min following 80s of sonication. While this protocol may have therapeutic potential for the treatment of psychiatric and neurological disorders, the mechanisms of action of TUS remain unclear.

OBJECTIVE: We conducted the first pharmacological study to examine the mechanisms of TUS in human primary motor cortex. By administering brain-active drugs with known mechanisms of action, we aimed to elucidate the mechanisms of tbTUS.

METHODS: Fourteen …

Methodology For Formalin Fixed Paraffin Embedded Cardiac Tissue Analysis, Leah G. Gutzwiller, Colleen Crouch

Haslam Scholars Projects

No abstract provided.

Noninvasively Monitoring Of Cerebral Blood Flow In Piglet Models Of Graded Hemorrhage And Hypoxic Ischemic Brain Injury Using Diffuse Correlation Spectroscopy And Near-Infrared Spectroscopy, Randolph Sinahon, Danielle Shoshany, Shadi Malaeb, Mert Deniz Polat, Meltem Izzetoglu, Kurtulus Izzetoglu

St. Chris Research Day

No abstract provided.

Cerebral Blood Flow Measured By Diffuse Correlation Spectroscopy For Monitoring Depth Of Anesthesia In Piglets, Mert Deniz Polat, Kurtulus Izzetoglu, Randolph Sinahon, Meltem Izzetoglu, Shadi Malaeb

St. Chris Research Day

No abstract provided.

Method Of Evanescently Coupling Whispering Gallery Mode Optical Resonators Using Liquids [U.S. Patent Us11650370b2], Hengky Chandrahalim, Kyle T. Bodily

Faculty Publications

The present invention relates to evanescently coupling whispering gallery mode optical resonators having a liquid coupling as well as methods of making and using same. The aforementioned evanescently coupling whispering gallery mode optical resonators having a liquid couplings provide increased tunability and sensing selectivity over current same. The aforementioned. Applicants’ method of making evanescent-wave coupled optical resonators can be achieved while having coupling gap dimensions that can be fabricated using standard photolithography. Thus economic, rapid, and mass production of coupled WGM resonators-based lasers, sensors, and signal processors for a broad range of applications can be realized.

Dual Color Optogenetic Control For Analyzing Cardiac Function In Drosophila, Jiantao Zhu

McKelvey School of Engineering Theses & Dissertations

Prolonged consumption of carbohydrate-rich diets and immobile lifestyles frequently cause metabolic disorders and obesity and, as a result, may lead to progressive heart dysfunction among broad social groups of the population. Drosophila melanogaster serves as an essential model organism in cardiovascular disease research due to conserved physiological and genomic traits shared with humans, its genetic and molecular toolbox versatility, and cost-effective maintenance. Here, we combine optogenetics and optical coherence tomography to study cardiovascular function in D. melanogaster. A new optogenetic pacing system has been developed, employing a transgenic line carrying two opsins: ChR2 and NpHR2.0. A custom-built hardware setup …

A Comparison Of Optical Measurement Methods For The Growth Of S. Cerevisiae, Jackson Black

Chemical Engineering Undergraduate Honors Theses

Genetic engineering of living organisms provides the opportunity to express and harvest different proteins from cell surfaces. Yeast (S. cerevisiae) is one such organism and is capable of being grown on an industrial scale. Cellular concentration is an important parameter to monitor while fermentation processes are underway, in order to control the environment inside the growth medium and maximize yields. Spectrophotometry is a conventional method for measuring concentration, but is limited by a narrow absorbance range, and the need for on-site periodic sampling. A continuous method of measurement, as provided by Bug Labs BE2100 non-invasive biomass monitor, would …

Light Sheet Microscopy Incubation Design And Control Scheme, Venkatanathan Kidambi

Honors Scholar Theses

The focus of the following paper is the development of a light sheet or selective plane illumination microscopy (LSM / SPIM) platform to support long duration three dimensional imaging of various cells and tissues. This paper describes the complete design and fabrication process behind the prototype, including a definition of light sheet microscopy and its differences from confocal microscopy, as well as existing open source selective plane microscopy literature and designs. The complete engineering design process as applied to the microscope design is described in detail. Specifically, this paper will focus on the development of an incubation chamber for light …

Portable Diffuse Reflectance Spectroscopy For Non-Invasive And Quantitative Assessment Of The Parathyroid Glands Viability During Surgery, Mark Romine, Linh Luong, Alex Moazzen, Katie Cho, Paul Lee

Symposium of Student Scholars

Portable Diffuse Reflectance Spectroscopy for Non-invasive and Quantitative Assessment of the Parathyroid Glands Viability During Surgery

Mark Romine, Linh Luong, Alex Moazzen, Katie Cho and Paul Lee

The parathyroid glands (PTGs) are responsible for the regulation of calcium levels in the blood by secreting a parathyroid hormone. This parathyroid hormone then regulates the body’s absorption, storage, and secretion of calcium, which can directly affect the way muscles and nerves operate. PTGs are often at risk of damage, or accidental removal during thyroid surgeries, because it is challenging to identify PTGs and to determine their viability. Current methods of visual inspections …

Wireless, Handheld Diffuse Reflectance Spectroscopy To Quantify Tissue Microvascular Hemodynamics, Linh Luong, Alex Moazzen, Mark Romine, Katie Cho, Paul Lee

Symposium of Student Scholars

Diffuse Reflectance Spectroscopy (DRS) is a non-invasive optical method to characterize tissue optical properties for disease diagnosis and health monitoring. Two optical fibers are often used in a DRS system: one to deliver light to the tissue and the other to gather diffuse reflectance spectra, which provide quantitative details about the structure and composition of the tissue. The conventional DRS system, however, is expensive, bulky, and composed of fragile optical fibers and multiple electrical connections. Here we propose to build a wireless, handheld, and fiber-less diffuse optical spectroscopy system. Unfortunately, the diffusion approximation utilized for data analysis of the conventional …

Enhancement Of Deep Learning Protein Structure Prediction, Ruoming Shen

Modeling, Simulation and Visualization Student Capstone Conference

Protein modeling is a rapidly expanding field with valuable applications in the pharmaceutical industry. Accurate protein structure prediction facilitates drug design, as extensive knowledge about the atomic structure of a given protein enables scientists to target that protein in the human body. However, protein structure identification in certain types of protein images remains challenging, with medium resolution cryogenic electron microscopy (cryo-EM) protein density maps particularly difficult to analyze. Recent advancements in computational methods, namely deep learning, have improved protein modeling. To maximize its accuracy, a deep learning model requires copious amounts of up-to-date training data.

This project explores DeepSSETracer, a …

A Device For Measuring Acoustic Output Intensity Of Transcranial Doppler Ultrasound Transducers For Comparison With Fda Regulations, Sarah Altman, Gregory Bashford, Benjamin Hage

Honors Theses, University of Nebraska-Lincoln

Transcranial Doppler Ultrasonography (TCD) is a non-invasive methodology which can evaluate cerebral blood flow velocity in real time. Single-element focused circular transducers placed on the scalp produce ultrasound waves capable of penetrating the skull with minimal aberration, enabling measurement of the Doppler shift. As such, TCD can measure blood flow velocity in the internal carotid and vertebral arteries, as well as the arteries of the Circle of Willis: the Anterior Cerebral (ACA), Posterior Cerebral (PCA), and Middle Cerebral Arteries (MCA). Intracranial aneurysm and ischemic stroke are serious conditions in which ballooning or occlusion of cerebral vessels cause insufficient perfusion, leading …

Computational Design Of Fiber-Optic Probes For Biosensing, Suwarna Karna

Electrical Engineering Theses

This thesis presents a study on the optical characteristics of hollow-core photonic crystal fibers (HC-PCFs) with a band gap cladding structure and their applications in optical fiber sensing. This 800B HC-PCF exhibited excellent optical properties and has a flexible structure, which makes them suitable for a wide range of industrial applications. Finite element simulations and structural optimization designs were conducted using the surface plasmon resonance (SPR) technique to determine the optimal performance parameters of the 800B HC-PCF. The fiber was further modified using the SPR technique to improve its practical detection capabilities. The performance of the modified fiber was observed …

Differentiating Axonal From Demyelinating Neuropathies Using Multiparametric Quantitative Mri Of Peripheral Nerves, Jacob D. Baraz, Stephanie Xuan, Sadaf Saba, Xue Yang, Ryan Castoro, Yang Xuan, Alison Roth, Richard D. Dortch, Jun Li, Yongsheng Chen

Medical Student Research Symposium

Objectives: To develop a multiparametric quantitative MRI (qMRI) method to track pathological changes in the peripheral neuropathies.

Background: Irrespective of the causes or types of polyneuropathies, peripheral nerves are mainly afflicted by two kinds of pathologies – axonal loss and demyelination. It is critical to differentiate between the two as treatments are different for the two conditions. While nerve conduction studies (NCS) have been used to differentiate the two pathologies in the distal nerves, there are no tools to probe the pathologies in the proximal peripheral nerves. This is particularly needed when distal nerves become non-responsive in NCS.

Methods: We …

Efficient Scopeformer: Towards Scalable And Rich Feature Extraction For Intracranial Hemorrhage Detection Using Hybrid Convolution And Vision Transformer Networks, Yassine Barhoumi

Theses and Dissertations

The field of medical imaging has seen significant advancements through the use of artificial intelligence (AI) techniques. The success of deep learning models in this area has led to the need for further research. This study aims to explore the use of various deep learning algorithms and emerging modeling techniques to improve training paradigms in medical imaging. Convolutional neural networks (CNNs) are the go-to architecture for computer vision problems, but they have limitations in mapping long-term dependencies within images. To address these limitations, the study explores the use of techniques such as global average pooling and self-attention mechanisms. Additionally, the …

Session 12: Active Learning To Minimize The Possible Risk From Future Epidemics, Kc Santosh

SDSU Data Science Symposium

In medical imaging informatics, for any future epidemics (e.g., Covid-19), deep learning (DL) models are of no use as they require a large dataset as they take months and even years to collect enough data (with annotations). In such a context, active learning (or human/expert-in-the-loop) is the must, where a machine can learn from the first day with minimum possible labeled data. In unsupervised learning, we propose to build pre-trained DL models that iteratively learn independently over time, where human/expert intervenes only when it makes mistakes and for only a limited data. In our work, deep features are used to …