Matching Items (31)
Description
An imaging measurement technique is developed using surface plasmon resonance. Plasmonic-based electrochemical current imaging (P-ECi) method has been developed to image the local electrochemical current optically, it allows us to measure the current density quickly and non-invasively [1, 2]. In this thesis, we solve the problems when we extand the P-ECi technique to the field of thin film system. The P-ECi signal in thin film structure was found to be directly proportional to the electrochemical current. The upper-limit of thin film thickness to use the proportional relationship between P-ECi signal and EC current was discussed by experiment and simulation. Furthermore, a new algorithm which can calculate the current density from P-ECi signal without any thickness limitation is developed and tested. Besides, surface plasmon resonance is useful phenomenon which can be used to detect the changes in the refractive index near the gold sensing surface. With the assistance of pH indicator, by applied EC potential on the gold film as the working electrode, the detection of H2 evolution reaction can be enhanced. This measurement technique is useful in analyzing local EC information and H2 evolution. References [1] S. Wang, et al., "Electrochemical Surface Plasmon Resonance: Basic Formalism and Experimental Validation," Analytical Chemistry, vol. 82, pp. 935-941, 2010/02/01 2010. [2] X. Shan, et al., "Imaging Local Electrochemical Current via Surface Plasmon Resonance," Science, vol. 327, pp. 1363-1366, March 12, 2010 2010.
ContributorsZhao, Yanjun (Author) / Tao, Nongjian (Thesis advisor) / Wang, Shaopeng (Committee member) / Tsow, Tsing (Committee member) / Arizona State University (Publisher)
Created2013
Description
Surface plasmon resonance (SPR) has emerged as a popular technique for elucidating subtle signals from biological events in a label-free, high throughput environment. The efficacy of conventional SPR sensors, whose signals are mass-sensitive, diminishes rapidly with the size of the observed target molecules. The following work advances the current SPR sensor paradigm for the purpose of small molecule detection. The detection limits of two orthogonal components of SPR measurement are targeted: speed and sensitivity. In the context of this report, speed refers to the dynamic range of measured kinetic rate constants, while sensitivity refers to the target molecule mass limitation of conventional SPR measurement. A simple device for high-speed microfluidic delivery of liquid samples to a sensor surface is presented to address the temporal limitations of conventional SPR measurement. The time scale of buffer/sample switching is on the order of milliseconds, thereby minimizing the opportunity for sample plug dispersion. The high rates of mass transport to and from the central microfluidic sensing region allow for SPR-based kinetic analysis of binding events with dissociation rate constants (kd) up to 130 s-1. The required sample volume is only 1 μL, allowing for minimal sample consumption during high-speed kinetic binding measurement. Charge-based detection of small molecules is demonstrated by plasmonic-based electrochemical impedance microscopy (P-EIM). The dependence of surface plasmon resonance (SPR) on surface charge density is used to detect small molecules (60-120 Da) printed on a dextran-modified sensor surface. The SPR response to an applied ac potential is a function of the surface charge density. This optical signal is comprised of a dc and an ac component, and is measured with high spatial resolution. The amplitude and phase of local surface impedance is provided by the ac component. The phase signal of the small molecules is a function of their charge status, which is manipulated by the pH of a solution. This technique is used to detect and distinguish small molecules based on their charge status, thereby circumventing the mass limitation (~100 Da) of conventional SPR measurement.
ContributorsMacGriff, Christopher Assiff (Author) / Tao, Nongjian (Thesis advisor) / Wang, Shaopeng (Committee member) / LaBaer, Joshua (Committee member) / Chae, Junseok (Committee member) / Arizona State University (Publisher)
Created2013
Description
Evolving knowledge about the tumor microenvironment (TME) is driving innovation in designing novel therapies against hard-to-treat breast cancer. Addressing the immune elements within the tumor microenvironment (TME) has emerged as a highly encouraging strategy for treating cancer. Although current immunotherapies have made advancements in reinstating the body's ability to fight tumors, the search for effective cancer treatments to combat tumor evasion remains a formidable challenge. In line with this objective, there is a pressing need to better understand the complex tumor-immune dynamics and crosstalk within the TME. To evaluate the cancer-immune interaction, this study aimed at investigating the crosstalk between naïve macrophages and cytotoxic T cells in driving tumor progression using an organotypic 3D ex vivo tumor on-a-chip model. The presented microfluidic platform consists of two distinct regions namely: The tumor region and the stroma region separated by trapezoidal microposts to ensure interconnectivity between regions thereby incorporating high spatial organization. In the established triculture platform, the complex Tumor Immune Microenvironment was successfully recapitulated by incorporating naïve macrophage and T cells within an appropriate 3D matrix. Through invasion and morphometric analyses, definitive outcomes were obtained that underscore the significant contribution of macrophages in facilitating tumor progression. Furthermore, the inclusion of T cells led to a notable decrease in the migratory speed of cancer cells and macrophages, underscoring the reciprocal communication between these two immune cell populations in the regulation of tumor advancement. Overall, this study highlights the complexity of TME and underscores the critical role of immune cells in regulating cancer progression.
ContributorsManoharan, Twinkle Jina Minette (Author) / Nikkhah, Mehdi (Thesis advisor) / Acharya, Abhinav P (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2023
Description
Detection technologies and physical methods used for separation of complex molecules can be effective tools in research when applied to bioparticles including, but not limited to,
bacteria, viruses, and proteins. Dielectrophoresis (DEP) is a technique that has been used in
microfluidics for separation and concentration of bioparticles, with the benefits of not requiring
custom primers, utilizing small sample sizes, and relatively quick separation times for rapid
identification of pathogens such as viruses. As demonstrated in this study, a DEP device using
polydimethylsiloxane (PDMS) as an insulator was used for the identification and separation of a
mouse hepatitis coronavirus (MHV), a model coronavirus that only infects mice. Results indicate
that, using 10 microliters of MHV test sample diluted in buffer, the virus can be identified and
separated within 30 seconds using DC voltage of 800 V.
Contributorsmcfadden, matthew (Author) / Hogue, Brenda G (Thesis advisor) / Hayes, Mark (Thesis advisor) / Christen, Jennifer B (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2023
Description
Obesity has consistently presented a significant challenge, with excess body fat contributing to the development of numerous severe conditions such as diabetes, cardiovascular disease, cancer, and various musculoskeletal disorders. In this study, different methods are proposed to study substrate utilization (carbohydrates, proteins, and fats) in the human body and validate the biomarkers enabling to investigation of weight management and monitor metabolic health. The first technique to study was Indirect calorimetry, which assessed Resting Energy Expenditure (REE) and measured parameters like oxygen consumption (VO2) and carbon dioxide production (VCO2). A validation study was conducted to study the effectiveness of the medical device Breezing Med determining REE, VO2, and VCO2. The results were compared with correlation slopes and regression coefficients close to 1. Indirect Calorimetry can be used to determine carbohydrate and fat utilization but it requires additional correction for protein utilization. Protein utilization can be studied by analyzing urinary nitrogen. Therefore, a secondary technique was studied for identifying urea and ammonia concentration in human urine samples. Along this line two methods for detecting urea were explored, a colorimetric technique and it was validated against the Ion-Selective method. The results were then compared by correlation analysis of urine samples measured with both methods simultaneously curves. The equations for fat, carb, and protein oxidation, involving VO2, VCO2 consumption, and urinary nitrogen were implemented and validated, using the above-described methods in a human subject study with 16 subjects. The measurements included diverse diets (normal vs. high fat/protein) in normal energy balance and pre-/post interventions of exercise, fasting, and a high-fat meal. It can be concluded that the indirect calorimetry portable method in conjunction with urine urea methods are important to help the understanding of substrate utilization in human subjects, and therefore, excellent tools to contribute to the treatments and interventions of obesity and overweighted populations.
ContributorsPradhan, Ayushi (Author) / Forzani, Erica (Thesis advisor) / Lind, Mary Laura (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2023
Description
The development of biosensing platforms not only has an immediate lifesaving effect but also has a significant socio-economic impact. In this dissertation, three very important biomarkers with immense importance were chosen for further investigation, reducing the technological gap and improving their sensing platform.Firstly, gold nanoparticles (AuNP) aggregation and sedimentation-based assays were developed for the sensitive, specific, and rapid detection of Ebola virus secreted glycoprotein (sGP)and severe acute respiratory syndrome coronavirus 2 (SARS-COV2) receptor-binding domain (RBD) antigens. An extensive study was done to develop a complete assay workflow from critical nanobody generation to optimization of AuNP size for rapid detection. A rapid portable electronic reader costing (<$5, <100 cm3), and digital data output was developed. Together with the developed workflow, this portable electronic reader showed a high sensitivity (limit of detection of ~10 pg/mL, or 0.13 pM for sGP and ~40 pg/mL, or ~1.3 pM for RBD in diluted human serum), a high specificity, a large dynamic range (~7 logs), and accelerated readout within minutes. Secondly, A general framework was established for small molecule detection using plasmonic metal nanoparticles through wide-ranging investigation and optimization of assay parameters with demonstrated detection of Cannabidiol (CBD). An unfiltered assay suitable for personalized dosage monitoring was developed and demonstrated. A portable electronic reader demonstrated optoelectronic detection of CBD with a limit of detection (LOD) of <100 pM in urine and saliva, a large dynamic range (5 logs), and a high specificity that differentiates closely related Tetrahydrocannabinol (THC). Finally, with careful biomolecular design and expansion of the portable reader to a dual-wavelength detector the classification of antibodies based on their affinity to SARS-COV2 RBD and their ability to neutralize the RBD from binding to the human Angiotensin-Converting Enzyme 2 (ACE2) was demonstrated with the capability to detect antibody concentration as low as 1 pM and observed neutralization starting as low as 10 pM with different viral load and variant. This portable, low-cost, and versatile readout system holds great promise for rapid, digital, and portable data collection in the field of biosensing.
ContributorsIkbal, Md Ashif (Author) / Wang, Chao (Thesis advisor) / Goryll, Michael (Committee member) / Zhao, Yuji (Committee member) / Wang, Shaopeng (Committee member) / Arizona State University (Publisher)
Created2022
Description
Drug delivery has made a significant contribution to cancer immunotherapy and can have a tremendous impact on modulating immunometabolism, thereby affecting cancer outcomes. Notably, the science of delivery of cancer vaccines and immunotherapeutics, modulating immune cell functions has inspired development of several successful companies and clinical products. For example, cancer vaccines require activation of dendritic cells (DCs) and tumour associated Mɸs (TAMs) through modulation of their energy metabolism (e.g., glycolysis, glutaminolysis, Krebs cycle). Similar to activated immune cells, cancer cells also upregulate glucose and glutamine transporters for proliferation and survival. Cancer cells having accelerated energy metabolism, which has been exploited as a target for various therapeutic studies. In the first strategy, an immunometabolism strategy based on sustained release of succinate from biomaterials, which incorporate succinate in the backbone of the polymer was developed. This study demonstrates that succinate-based polymeric microparticles act as alarmins by modulating the immunometabolism of DCs and Mɸs to generate robust pro-inflammatory responses for melanoma treatment in immunocompetent young as well as aging mice. In the second strategy, a biomaterial-based strategy was developed to deliver metabolites one-step downstream of the node where the glycolytic pathway is inhibited, to specifically rescue DCs from glycolysis inhibition. The study successfully demonstrated for the first time that the glycolysis of DCs can be rescued both in vitro and in vivo using a biomaterial strategy of delivering metabolites downstream of the inhibitory node. Overall, it is believed that advanced drug delivery strategies will play an important role in marrying the fields of immunometabolism and immunotherapy to generate translatable anti-cancer treatments.
ContributorsInamdar, Sahil (Author) / Acharya, Abhinav P (Thesis advisor) / Rege, Kaushal (Committee member) / Green, Matthew (Committee member) / Curtis, Marion (Committee member) / Seetharam, Mahesh (Committee member) / Arizona State University (Publisher)
Created2022
Description
Autoimmunity develops when the immune system targets self-antigens within the body. Rheumatoid arthritis (RA) is a common autoimmune disease, and its progression is characterized by pro-inflammatory immune cells rapidly proliferating, migrating, and infiltrating joint tissue to provoke inflammation. In order to fulfill this taxing autoreactive response, an increase in energy metabolism is required by immune cells, such as dendritic cells (DCs). Therefore, a shift in DC energy reliance from the Krebs cycle toward glycolysis occurs. This metabolic shift phenotypically transitions DCs from anti-inflammatory properties toward an aggressive pro-inflammatory phenotype, in turn activating pro-inflammatory T cells and promoting RA pathogenesis. If the disease persists uncontrollably, further complications and eventual joint dysfunction can occur. Although, clinically approved drugs can prevent RA progression, they require frequent administration for temporary symptom relief. Furthermore, current approved biological products for RA are not known to have a direct modulatory effect on immunometabolism. Given that cellular metabolism controls immune cell function, this work aims to harness perturbations within RA immune cell energy metabolism and utilizes it as a therapeutic target by reprogramming immune cell metabolism via the delivery of metabolite-based particles. The two-time delivery of these particles reduced RA inflammation in a RA collagen-induced arthritis (CIA) mouse model and generated desired responses with long-term effects. Specifically, this work was achieved by:
Aim 1 – developing and delivering metabolite-based polymeric microparticles synthesized from the Krebs cycle metabolite, alpha-ketoglutarate (aKG; termed paKG MPs) to DCs to modulate their energy metabolism and promote anti-inflammatory properties (in context of RA).
Aim 2 – exploiting the encapsulation ability of paKG MPs to inhibit DC glycolysis in the presence of the CIA self-antigen (collagen type II (bc2)) for the treatment of RA in CIA mice. Herein, paKG MPs encapsulating a glycolytic inhibitor and bc2 induce an anti-inflammatory DC phenotype in vitro and generate suppressive bc2-specific T cell responses and reduce paw inflammation in CIA mice.
ContributorsMangal, Joslyn Lata (Author) / Acharya, Abhinav P (Thesis advisor) / Florsheim, Esther B (Committee member) / Wu, Hsin-Jung Joyce (Committee member) / Anderson, Karen (Committee member) / Arizona State University (Publisher)
Created2022
Description
In this dissertation, new data-driven techniques are developed to solve three problems related to generating predictive models of the immune system. These problems and their solutions are summarized as follows. The first problem is that, while cellular characteristics can be measured using flow cytometry, immune system cells are often analyzed only after they are sorted into groups by those characteristics. In Chapter 3 a method of analyzing the cellular characteristics of the immune system cells by generating Probability Density Functions (PDFs) to model the flow cytometry data is proposed. To generate a PDF to model the distribution of immune cell characteristics a new class of random variable called Sliced-Distributions (SDs) is developed. It is shown that the SDs can outperform other state-of-the-art methods on a set of benchmarks and can be used to differentiate between immune cells taken from healthy patients and those with Rheumatoid Arthritis. The second problem is that while immune system cells can be broken into different subpopulations, it is unclear which subpopulations are most significant. In Chapter 4 a new machine learning algorithm is formulated and used to identify subpopulations that can best predict disease severity or the populations of other immune cells. The proposed machine learning algorithm performs well when compared to other state-of-the-art methods and is applied to an immunological dataset to identify disease-relevant subpopulations of immune cells denoted immune states. Finally, while immunotherapies have been effectively used to treat cancer, selecting an optimal drug dose and period of treatment administration is still an open problem. In Chapter 5 a method to estimate Lyapunov functions of a system with unknown dynamics is proposed. This method is applied to generate a semialgebraic set containing immunotherapy doses and period of treatment that is predicted to eliminate a patient's tumor. The problem of selecting an optimal pulsed immunotherapy treatment from this semialgebraic set is formulated as a Global Polynomial Optimization (GPO) problem. In Chapter 6 a new method to solve GPO problems is proposed and optimal pulsed immunotherapy treatments are identified for this system.
ContributorsColbert, Brendon (Author) / Peet, Matthew M (Thesis advisor) / Acharya, Abhinav P (Committee member) / Berman, Spring M (Committee member) / Crespo, Luis G (Committee member) / Yong, Sze Z (Committee member) / Arizona State University (Publisher)
Created2021
Description
Energy Expenditure (EE) (kcal/day) is a key parameter used to guide obesity treatment, and it is often measured from CO2 production, VCO2 (mL/min), and/or O2 consumption, VO2 (mL/min) through the principles of indirect calorimetry. Current EE measurement technologies are limited due to the requirement of wearable facial accessories, which can introduce errors as measurements are not taken under free-living conditions. A novel contactless system, the SmartPad, which measures EE via VCO2 from a room’s ambient CO2 concentration transients was evaluated. First, SmartPad accuracy was validated by comparing the SmartPad’s EE and VCO2 measurements with the measurements of a reference instrument, the MGC Ultima CPXTM, in a cross-sectional study consisting of 20 subjects. A high correlation between the SmartPad’s EE and VCO2 measurements and the MGC Ultima CPX’s EE and VCO2 measurements was found, and the Bland-Altman plots contained a low mean bias for EE and VCO2 measurements. Thus, the SmartPad was validated as being accurate for VCO2 and EE measurements. Next, resting EE (REE) and exercise VCO2 measurements were recorded using the SmartPad and the MGC Ultima CPXTM at different operating CO2 threshold ranges to investigate the influence of measurement duration on system accuracy in an effort to optimize the SmartPad system. The SmartPad displayed 90% accuracy (±1 SD) for 14–19 min of REE measurement and for 4.8–7.0 min of exercise, using a known room’s air exchange rate. Additionally, the SmartPad was validated by accurately measuring subjects’ REE across a wide range of body mass indexes (BMI = 18.8 to 31.4 kg/m^2) with REEs ranging from ~1200 to ~3000 kcal/day. Lastly, the SmartPad has been used to assess the physical fitness of subjects via the “Contactless Thermodynamic Efficiency Test” (CTET).
ContributorsVictor, Shaun (Author) / Forzani, Erica (Thesis director) / Wang, Shaopeng (Committee member) / Barrett, The Honors College (Contributor) / Watts College of Public Service & Community Solut (Contributor) / Harrington Bioengineering Program (Contributor)
Created2022-05