Matching Items (130)
Description
Surgery as a profession requires significant training to improve both clinical decision making and psychomotor proficiency. In the medical knowledge domain, tools have been developed, validated, and accepted for evaluation of surgeons' competencies. However, assessment of the psychomotor skills still relies on the Halstedian model of apprenticeship, wherein surgeons are observed during residency for judgment of their skills. Although the value of this method of skills assessment cannot be ignored, novel methodologies of objective skills assessment need to be designed, developed, and evaluated that augment the traditional approach. Several sensor-based systems have been developed to measure a user's skill quantitatively, but use of sensors could interfere with skill execution and thus limit the potential for evaluating real-life surgery. However, having a method to judge skills automatically in real-life conditions should be the ultimate goal, since only with such features that a system would be widely adopted. This research proposes a novel video-based approach for observing surgeons' hand and surgical tool movements in minimally invasive surgical training exercises as well as during laparoscopic surgery. Because our system does not require surgeons to wear special sensors, it has the distinct advantage over alternatives of offering skills assessment in both learning and real-life environments. The system automatically detects major skill-measuring features from surgical task videos using a computing system composed of a series of computer vision algorithms and provides on-screen real-time performance feedback for more efficient skill learning. Finally, the machine-learning approach is used to develop an observer-independent composite scoring model through objective and quantitative measurement of surgical skills. To increase effectiveness and usability of the developed system, it is integrated with a cloud-based tool, which automatically assesses surgical videos upload to the cloud.
ContributorsIslam, Gazi (Author) / Li, Baoxin (Thesis advisor) / Liang, Jianming (Thesis advisor) / Dinu, Valentin (Committee member) / Greenes, Robert (Committee member) / Smith, Marshall (Committee member) / Kahol, Kanav (Committee member) / Patel, Vimla L. (Committee member) / Arizona State University (Publisher)
Created2013
Description
This dissertation investigates the condition of skeletal muscle insulin resistance using bioinformatics and computational biology approaches. Drawing from several studies and numerous data sources, I have attempted to uncover molecular mechanisms at multiple levels. From the detailed atomistic simulations of a single protein, to datamining approaches applied at the systems biology level, I provide new targets to explore for the research community. Furthermore I present a new online web resource that unifies various bioinformatics databases to enable discovery of relevant features in 3D protein structures.
ContributorsMielke, Clinton (Author) / Mandarino, Lawrence (Committee member) / LaBaer, Joshua (Committee member) / Magee, D. Mitchell (Committee member) / Dinu, Valentin (Committee member) / Willis, Wayne (Committee member) / Arizona State University (Publisher)
Created2013
Description
The living world we inhabit and observe is extraordinarily complex. From the perspective of a person analyzing data about the living world, complexity is most commonly encountered in two forms: 1) in the sheer size of the datasets that must be analyzed and the physical number of mathematical computations necessary to obtain an answer and 2) in the underlying structure of the data, which does not conform to classical normal theory statistical assumptions and includes clustering and unobserved latent constructs. Until recently, the methods and tools necessary to effectively address the complexity of biomedical data were not ordinarily available. The utility of four methods--High Performance Computing, Monte Carlo Simulations, Multi-Level Modeling and Structural Equation Modeling--designed to help make sense of complex biomedical data are presented here.
ContributorsBrown, Justin Reed (Author) / Dinu, Valentin (Thesis advisor) / Johnson, William (Committee member) / Petitti, Diana (Committee member) / Arizona State University (Publisher)
Created2012
Description
Over the past decade, several high-value proteins have been produced using plant-based transient expression systems. However, these studies exposed some limitations that must be overcome to allow plant expression systems to reach their full potential. These limitations are the low level of recombinant protein accumulation achieved in some cases, and lack of efficient co-expression vectors for the production of multi-protein complexes. This study report that tobacco Extensin (Ext) gene 3' untranslated region (UTR) can be broadly used to enhance recombinant protein expression in plants. Extensin is the hydroxyproline-rich glycoprotein that constitutes the major protein component of cell walls. Using transient expression, it was found that the Ext 3' UTR increases recombinant protein expression up to 13.5- and 6-fold in non-replicating and replicating vector systems, respectively, compared to previously established terminators. Enhanced protein accumulation was correlated with increased mRNA levels associated with reduction in read-through transcription. Regions of Ext 3' UTR essential for maximum gene expression included a poly-purine sequence used as a major poly-adenylation site. Furthermore, modified bean yellow dwarf virus (BeYDV)-based vectors designed to allow co-expression of multiple recombinant genes were constructed and tested for their performance in driving transient expression in plants. Robust co-expression and assembly of heavy and light chains of the anti-Ebola virus monoclonal antibody 6D8, as well as E. coli heat-labile toxin (LT) were achieved with the modified vectors. The simultaneous co-expression of three fluoroproteins using the single replicon, triple cassette is demonstrated by confocal microscopy. In conclusion, this study provides an excellent tool for rapid, cost-effective, large-scale manufacturing of recombinant proteins for use in medicine and industry.
ContributorsRosenthal, Sun Hee (Author) / Mason, Hugh (Thesis advisor) / Mor, Tsafrir (Committee member) / Chang, Yung (Committee member) / Arntzen, Charles (Committee member) / Arizona State University (Publisher)
Created2012
Description
Critical care environments are complex in nature. Fluctuating team dynamics and the plethora of technology and equipment create unforeseen demands on clinicians. Such environments become chaotic very quickly due to the chronic exposure to unpredictable clusters of events. In order to cope with this complexity, clinicians tend to develop ad-hoc adaptations to function in an effective manner. It is these adaptations or "deviations" from expected behaviors that provide insight into the processes that shape the overall behavior of the complex system. The research described in this manuscript examines the cognitive basis of clinicians' adaptive mechanisms and presents a methodology for studying the same. Examining interactions in complex systems is difficult due to the disassociation between the nature of the environment and the tools available to analyze underlying processes. In this work, the use of a mixed methodology framework to study trauma critical care, a complex environment, is presented. The hybrid framework supplements existing methods of data collection (qualitative observations) with quantitative methods (use of electronic tags) to capture activities in the complex system. Quantitative models of activities (using Hidden Markov Modeling) and theoretical models of deviations were developed to support this mixed methodology framework. The quantitative activity models developed were tested with a set of fifteen simulated activities that represent workflow in trauma care. A mean recognition rate of 87.5% was obtained in automatically recognizing activities. Theoretical models, on the other hand, were developed using field observations of 30 trauma cases. The analysis of the classification schema (with substantial inter-rater reliability) and 161 deviations identified shows that expertise and role played by the clinician in the trauma team influences the nature of deviations made (p<0.01). The results shows that while expert clinicians deviate to innovate, deviations of novices often result in errors. Experts' flexibility and adaptiveness allow their deviations to generate innovative ideas, in particular when dynamic adjustments are required in complex situations. The findings suggest that while adherence to protocols and standards is important for novice practitioners to reduce medical errors and ensure patient safety, there is strong need for training novices in coping with complex situations as well.
ContributorsVankipuram, Mithra (Author) / Greenes, Robert A (Thesis advisor) / Patel, Vimla L. (Thesis advisor) / Petitti, Diana B. (Committee member) / Dinu, Valentin (Committee member) / Smith, Marshall L. (Committee member) / Arizona State University (Publisher)
Created2012
Description
This work involved the analysis of a public health system, and the design, development and deployment of enterprise informatics architecture, and sustainable community methods to address problems with the current public health system. Specifically, assessment of the Nationally Notifiable Disease Surveillance System (NNDSS) was instrumental in forming the design of the current implementation at the Southern Nevada Health District (SNHD). The result of the system deployment at SNHD was considered as a basis for projecting the practical application and benefits of an enterprise architecture. This approach has resulted in a sustainable platform to enhance the practice of public health by improving the quality and timeliness of data, effectiveness of an investigation, and reporting across the continuum.
ContributorsKriseman, Jeffrey Michael (Author) / Dinu, Valentin (Thesis advisor) / Greenes, Robert (Committee member) / Johnson, William (Committee member) / Arizona State University (Publisher)
Created2012
Description
Immunosignaturing is a technology that allows the humoral immune response to be observed through the binding of antibodies to random sequence peptides. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides in a multiplexed fashion. There are computational and statistical challenges to the analysis of immunosignaturing data. The overall aim of my dissertation is to develop novel computational and statistical methods for immunosignaturing data to access its potential for diagnostics and drug discovery. Firstly, I discovered that a classification algorithm Naive Bayes which leverages the biological independence of the probes on our array in such a way as to gather more information outperforms other classification algorithms due to speed and accuracy. Secondly, using this classifier, I then tested the specificity and sensitivity of immunosignaturing platform for its ability to resolve four different diseases (pancreatic cancer, pancreatitis, type 2 diabetes and panIN) that target the same organ (pancreas). These diseases were separated with >90% specificity from controls and from each other. Thirdly, I observed that the immunosignature of type 2 diabetes and cardiovascular complications are unique, consistent, and reproducible and can be separated by 100% accuracy from controls. But when these two complications arise in the same person, the resultant immunosignature is quite different in that of individuals with only one disease. I developed a method to trace back from informative random peptides in disease signatures to the potential antigen(s). Hence, I built a decipher system to trace random peptides in type 1 diabetes immunosignature to known antigens. Immunosignaturing, unlike the ELISA, has the ability to not only detect the presence of response but also absence of response during a disease. I observed, not only higher but also lower peptides intensities can be mapped to antigens in type 1 diabetes. To study immunosignaturing potential for population diagnostics, I studied effect of age, gender and geographical location on immunosignaturing data. For its potential to be a health monitoring technology, I proposed a single metric Coefficient of Variation that has shown potential to change significantly when a person enters a disease state.
ContributorsKukreja, Muskan (Author) / Johnston, Stephen Albert (Thesis advisor) / Stafford, Phillip (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Human Immunodeficiency Virus type 1 (HIV-1) causes millions of deaths every year, but a protective vaccine remains elusive. A promising vaccine strategy is to use virus-like particles (VLPs) for HIV-1. To this end, HIV-1 VLPs were produced in Nicotiana benthamiana plants that were stably expressing the HIV-1 Gag protein and transiently expressing a truncated form of gp41. These VLPs were tested to determine their inherent adjuvant effects due to their production in plants in order to dissect the previously observed stimulating activity of these VLPs in a prime-boost vaccine approach. THP1 human monocytes were differentiated using PMA or IL-4 and GM-CSF to form macrophages and dendritic cells, respectively. These cells were treated with purified VLPs or control samples to determine the individual adjuvant effects of the plant, bacterial, and VLP components in the purified VLP samples. It was postulated that the PMA-differentiated THP1 cells were not induced to become macrophages due to the lack of CD11b+ cells in the sample and the lack of increased TNFα expression in response to LPS treatment. It was also determined that the VLPs have inherent adjuvant properties to dendritic cells due to bacterial and VLP components, but not due to plant components.
ContributorsDickey, Rebekah Marie (Author) / Mor, Tsafrir (Thesis director) / Blattman, Joseph (Committee member) / Meador, Lydia (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
The Intercellular Adhesion Molecule-1 (ICAM-1, known as CD54) is a cell surface type I transmembrane glycoprotein with a molecular weight of 85 to 110 kDa. The primary function of ICAM-1 is to provide adhesion between endothelial cells and leukocytes after injury or stress. ICAM-1 is used as a receptor for various pathogens such as rhinoviruses, coxsackievirus A21 and the malaria parasite Plasmodium falciparum. ICAM-1 contains five immunoglobulin (Ig) domains in its long N-terminal extracellular region, a hydrophobic transmembrane domain, and a small C-terminal cytoplasmic domain. The Ig domains 1-2 and Ig domains 3-4-5 have been crystallized separately and their structure solved, however the full ICAM-1 structure has not been solved. Because ICAM-1 appears to be important for the mediation of cell-to-cell communication in physiological and pathological conditions, gaining a structural understanding of the full-length membrane anchored ICAM-1 is desirable. In this context, we have transiently expressed a plant-optimized gene encoding human ICAM-1 in Nicotiana benthamiana plants using the MagnICON expression system. The plant produced ICAM-1 is forming aggregates according to previous data. Thus, the current extraction and purification protocols have been altered to include TCEP, a reducing agent. The protein was purified using TALON metal affinity resin and partially characterized using various biochemical techniques. Our results show that there is a reduction in aggregation formation with the use of TCEP.
ContributorsPatel, Heeral (Author) / Mor, Tsafrir (Thesis director) / Mason, Hugh (Committee member) / Kannan, Latha (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2015-05
Description
Variants of human butyrylcholinesterase (BChE) have been designed to have high cocaine hydrolytic activity. These variants have potential pharmacological applications toward treating cocaine overdose and addiction. These enzymes must be stable in the human body over fairly long periods of time in order to be effective at treating cocaine addiction. Recombinantly expressed BChE, however, tends to be in monomer or dimer oligomeric forms, which are far less stable than the tetramer form of the enzyme. When BChE is transiently expressed in Nicotiana benthamiana, it is produced mainly as monomers and dimers. However, when the protein is expressed through stable transformation, it produces much greater proportions of tetramers. Tetramerization of WT human plasma derived BChE is facilitated by the binding of a proline rich peptide. In this thesis, I investigated if a putative plant-derived analog of the mammalian proline-rich attachment domain caused stably expressed cocaine hydrolase variants of human BChE to undergo tetramerization. I also examined if co-expression of peptides with known proline-rich attachment domains further shifted the monomer-tetramer ratio toward the tetramer.
ContributorsKendle, Robert Player (Author) / Mor, Tsafrir (Thesis director) / Mason, Hugh (Committee member) / Larrimore, Kathy (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor)
Created2015-05