Heart cancer results from a heart tumor or another cancer that spreads to the heart. Heart failure is a vital sign of heart cancer; however, most patients cannot identify or properly self-assess the symptoms when they arise. This project aims to develop a new heart monitoring strategy using a nanomaterial-based sensing platform to effectively diagnose and timely intervention for heart cancers. We will design and fabricate new nanomaterials and measure heart signals to perform decision-making and real-time optimal control in heart cancer studies. The expected outcomes of this research are to (1) design advanced nanomaterials to measure heart signals; (2) extract physiological insights of the sensing data for assessment of heart status as an intervention tool for heart cancers. The scientific outcomes of this research will be a new sensing platform that is potentially revolutionizing cardiac cancer diagnosis solutions. Participants will learn knowledge of engineering fundamentals as they apply to materials science and biomedical engineering. Diverse students will involve in multidisciplinary research training and learning through this REU program.
The two significant limitations of current analytical/diagnostic systems are: 1) Sensitivity or the species of interest are too low in the measured sample, thus leading to false-negative(s) 2) Selectivity or the sample may contain species of interest intermixed with many similar species, thus leading to false positives(s). The design must facilitate rapid analysis for Point-Of-Care (POC) diagnostic devices without using expensive or bulky equipment. The project aims to investigate a new POC electrochemical platform that combines shear force with a nanoporous and capacitive electrode. It meets the ASSURED criteria set by World Health Organization for POC diagnosis. Here the platform detects cancer biomarkers. The proposed research will integrate technologies for 1) a flow-through, nanoporous and capacitive electrode, 2) electrochemical sensor capable of label-free, multiplexed, rapid, portable, sensitive, and selective detection; and 3) integrates multiple electrochemical sensing techniques in one chip.The student participating in this research will rapidly develop and optimize the chip to detect breast cancer biomarkers. This includes the proteins p53, tumor suppression protein, human epidermal growth factor receptor 2 (HER2), breast cancer type 1 susceptibility proteins (BRCA1), and interleukin 6 (IL-6). The graduate student will train the undergraduate student to run devices to develop preliminary detection limits (LOD’s) for p53 and HER2. The best LODs are 53 ng/L using enzyme-based amperometry for p53 70, 37 pg/L using square wave stripping voltammetry (SWV) for HER2. The undergraduate student will also investigate p53 and HER2 diagnosis selectivity by spiking the blood plasma with other proteins, DNA, and biomaterials like lysed ecoli cells. The other biomarkers for breast cancer and other cancers will also be similarly explored in successive years. The primary research outcome will be to validate the selectivity and sensitivity of the device. The student researcher will develop a calibration plot and get the LOD. The student is expected to show the effect of the interference matrix on the LOD. The undergraduate student will be able to integrate a chip, run a chip and analyze the results from the chip by the end of the REU project. During the research, the undergraduate student will get valuable insights into statistical analysis like p-test, student t-test, electrochemical spectroscopy like cyclic voltammetry, electrochemical impedance spectroscopy, and handling sensitive biological samples. We expect the undergraduate student to show their findings in an appropriate research conference like ACS, BMES, or AIChE.
Non-target side effects of systemic therapeutics have provided the impetus for developing next-generation carriers for in situ drug delivery. We have developed a (self-assembling) peptide platform tailorable with receptor binding domains – e.g., EGF-receptor highly expressed in breast and ovarian cancers. This consequent death can be addressed by novel biomaterials-based drugs that address 3 vital aspects: i) de novo design and targeting of promiscuous receptors, ii) in vitro cytocompatibility and receptor binding, iii) in vivo localization and targeting of receptor-positive cells. Here we seek to computationally develop EGF-receptor binding peptides and assay theirs in vitro and Vivo efficacy in abrogating proliferative malignant tissue disease. The goals are to i) Design an EGFr binding (self-assembling) peptide; ii) Development of peptide vehicle to carry anti-neoplastic payloads; iii) Evaluation of peptide receptor binding in vitro and in vivo. Students participating in this REU as a mentee of Dr. Kumar will be responsible for the initial design, fabrication, and characterization of peptides. Students will utilize a computational peptide modeling (GROMACS+Rosetta) to design EGFr binding peptides; solid-phase peptide synthesis to make peptides and characterize them (LC/MS, circular dichroism, FTIR, AFM, SEM); evaluate in vitro binding (SPR and fluorescent-tagged peptide with EGFr+ cells) and in vivo (time and funds permitting).The targeted outcome will be to develop a novel approach for the targeting of EGFr+ cancers, their potential for diagnosis and treatment. The results will provide critical data to prove the feasibility of our approach and allow further evaluation of the abilities of rational peptide design for the treatment of cancer. In addition, participants will learn basic methods of computational design, synthesis, characterization, as performed by UG in the lab regularly.
A significant hurdle for drug delivery nanoparticles is the circulation of the nanoparticles. Many drug delivery nanoparticles fail at this step and are quickly shunted by the immune system to the liver. One theory for removing particles is that the formation of a protein corona on the particle signals for its removal. Therefore, accurately measuring the protein corona of nanoparticles in blood plasma is crucial to understanding how the particle will behave in vivo. This project will synthesize PLGA nanoparticles with a targeting ligand (an antibody for EGFR, a receptor overexpressed in triple-negative breast cancer) and PEG ligands. Particles with different ratios of these ligands will be synthesized. The particles will be analyzed in blood plasma using nanoparticle tracking analysis (NTA) to determine the size of the complex and soft protein corona on the particles and the aggregation behavior over a 24 hour incubation time in blood plasma. Analysis of the particles with different ligand expressions will determine the optimal ligands for particle circulation in blood. Students participating in this REU as a mentee of Dr. McEnnis will be responsible for the NTA measurements in saline and blood plasma to determine particle size, centrifugation and washing steps of the particles to determine hard and soft corona size, and viscosity measurements of the blood plasma for accurate size analysis. The student will assist with particle synthesis and surface chemistry modifications. Additionally, the student will assist in a study observing the particles in plasma over 24 hrs and will analyze the resulting videos for the presence of multicomponent aggregates. The outcome will determine the optimal ligand presentation for enhanced circulation time of targeted drug delivery nanoparticles for triple-negative breast cancer. The results will be coupled with corresponding cellular uptake data of the identical particles with triple-negative breast cancer cells and healthy breast tissue. Together these results will provide preliminary data of the most promising nanoparticle design to test in future in vivo studies. In addition, student participants will learn particle characterization techniques and polymer-drug delivery synthesis and modification basics.
Neurotoxic organophosphorus compounds (NOPCs) are chemical warfare substances that constitute a major threat to public health. They are readily produced and can be absorbed by inhalation, dermal and oral routes. Investigators have focused on percutaneous penetration since the skin is the most common route for harmful chemicals to enter the body. After dermal contact, decontamination to reduce systemic toxicity aims at achieving an immediate removal of the agents. Current protocols involve water only or soap and water solutions, adsorption/ absorption of the NOPC and degradation/neutralization by an active ingredient. While showers, adsorbing powders and absorbents are standard and intuitive methods, increasing attention is being paid to discovering new generations of decontaminants designed to degrade NOPCs that have penetrated the skin layers. Scientists are urged to i) develop numerical methods to help select candidates for decontaminating chemical agents, ii) provide a simulation platform to elucidate the transport mechanism and iii) identify the physicochemical properties of an efficient deactivation. This research is intended to provide a mathematical description of NOPC transport and inactivation of chemicals in the skin. Specific preventive measures target the breakdown of toxic agents within the dermal layers, in addition to their immediate removal from the surface. These efforts are in response to reports of the significant amount of low volatile hazardous substanaces, such as VX, remaining in the deeper layers for an extended period. Such initiatives can help researchers indentify promising candidates and evaluate their abilities to degrade organophosphorus compounds. The ideal decontaminant would follow a path, similar to the NOPC, and neutralize the toxic chemical before its absorption into the bloodstream. The influence of physicochemical characteristics on the degradation and diffusion mechanism needs to be studied.
The student participating in this research will develop and optimize a graph neural network (GNN) capable of differentiating between healthy and cancerous cells in breast tomosynthesis images. The diagnosis of cancer from medical images is a difficult task that most often relies on analysis by medical professionals with years of specialized training. Over the past few years, advances in artificial intelligence and deep learning have allowed for the development of tools that, when used as a complement to such analysis, increase the likelihood of a correct diagnosis. However, challenges remain in the application of these techniques, especially in medical image analysis; in particular, traditional deep learning techniques such as convolutional neural networks can have issues with segmenting images effectively and identifying distinct structures therein (e.g., between different cells and their surroundings), making it difficult to identify abnormalities. In this project, students will develop and apply a GNN that will analyze breast tomosynthesis images and detect abnormalities by (1) processing the images by accurately identifying separate spatial regions and (2) checking for abnormalities within these regions to determine whether the image indicates a status of healthy or cancerous. The eventual outcome of this research will be a model capable of processing medical images and outputting a diagnosis.
Due to high surface area to volume ratio, low cost of production, and ease of complex fluid handling, microfluidics provide a potential pathway to cancer diagnosis. Quantitative analysis of cancer cell metabolic kinetics is of great importance in characterizing cancer cell behavior and unraveling the role of cell metabolism in cancer progression and transformation. The goals of this research are to 1) study and compare the effects of an EMF through imaging of non-deuterated and deuterated lipids in healthy and cancer cells on a 3-D printed microfluidic device with planar electrodes; 2) develop quantitative models that accurately describe the effect of EMF on lipid metabolism of cancer cells. Students participating will be responsible for the initial fabrication and characterization of microfluidic devices. Students will utilize a stereolithography 3-D printer, 3-D optical profilometer, and optical microscopes for fabrication, characterization, and visualization of microfluidic devices, respectively. Specifically, students will 1) fabricate series of electrode-equipped, 3-D printed microfluidic devices; 2) perform characterization and biocompatibility studies; and if time permits, 3) image healthy and breast cancer cells in order to establish proof of concept. The targeted outcome will be to develop a novel approach to diagnosing and treating cancer cells using non-invasive methods. The results will provide critical data to prove the feasibility of our approach and allow further evaluation of the abilities of non-linear microscopy and the effectiveness of electromagnetic fields on cancer cells. In addition, participants will learn basic methods of fabrication and characterization of 3-D printed microfluidic devices for biophotonic studies.
The success of tumor models depends on the collection of cell bioparticles encapsulated in an extracellular matrix (ECM)-like system. Conventional collections of cell biomarkers require harvesting and digestion of ECM, which has hampered our ability to efficiently monitor the tumor cells, and they suffer from undesired regulation of cellular behavior by our physical handling. PROPOSED SOLUTION: As a label-free, contactless, and high-throughput approach, acoustofluidic separation of biological bioparticles can be used in cell-laden hydrogels to enhance rapid screening of tumor cells. However, acoustic-induced streaming and radiation forces in a fluid-saturated media may affect the tumor cell behavior and create undesired responses in a cell-laden hydrogel. METHODS: We will design and fabricate a novel 3D bioprinted breast cancer-cell-laden microfluidic platform equipped by an interdigital transducer surface acoustic wave (IDT-SAW) module while controlling and tabulating the microstructure of the ECM-like hydrogel. Then, we will induce a wide range of acoustic fields to isolate bioparticles from breast cancer cells within the pores of a gelatin-based hydrogel and will characterize the biophysical and biological characteristics of the cells (such as ROS signal and Ki67). STUDENT RESPONSIBILITY: The student participating in this research will fabricate different designs for the microfluidic chip, based on the ink composition and geometrical features of the device (Miri Lab will provide the IDT module). The undergraduate student will encapsulate cancer cells into the ink precursors and will measure the cell response to the acoustic field through PCR measurements of stress-related factors (control: no acoustic modulation). The other biomarkers such as Ki67 will be similarly explored. The primary research outcome will be to validate the sensitivity of the device in isolating bioparticles. The student is expected to show the effect of the interference acoustic loads on the cell response. The undergraduate student will be able to integrate a chip, run a chip and analyze the results from the chip by the end of the REU project. During the research, the undergraduate student will get valuable insights into statistical analysis like p-test, student t-test, electrochemical spectroscopy like cyclic voltammetry, electrochemical impedance spectroscopy, and handling sensitive biological samples. We expect the results will be published in a peer-reviewed journal such as Lab-on-Chip.
The detection of volatile organic compounds (VOCs) is of great importance in the field of breath diagnostics, especially for early cancer detection. Lung cancer stands as the foremost contributor to cancer-related fatalities on a global scale. In individuals with lung cancer, volatile organic compounds (VOCs) such as propane, carbon disulfide, 2-propenal, ethylbenzene, isopropyl alcohol, benzene, and hexane are detected in exhaled breath of lung cancer patients in parts per billion (ppb). The prevailing approach for identifying VOCs with high sensitivity and selectivity in exhaled breath involves gathering the sample using collection cartridges or containers, followed by laboratory-based analysis utilizing Gas Chromatography–Mass spectrometry (GC-MS). Nevertheless, this approach necessitates sample preparation, transportation, and the desorption of gases, all contributing to substantial turnaround times and augmenting the overall expenses of the procedure. Due to these, researchers have tried developing portable, low-cost sensors that detect multiple VOCs. However, the current sensors face two main limitations: 1) Low sensitivity and 2) poor selectivity. The limit of detection(LOD) for VOC sensors needs to be in ppb or at a low ppm level to be used in the applications stated above. The VOC sensor should also be able to distinguish between different VOCs even of the same class (alcohols, ketones, and alkanes) to be useful in the above applications. To achieve this goal, our lab is developing a microfluidic electrochemical gas sensor. We believe that combining microfluidic architecture with an electrochemical detection technique can significantly enhance the sensitivity of the sensor. The microfluidic architecture also allows the use of a very low volume of sensing material/electrolyte thereby reducing the cost of the sensor and allowing the sampling of small amounts of gases. To enhance the selectivity of the sensor in detecting multiple VOCs, multiple electrochemical detection techniques like electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), differential pulse voltammetry(DPV), current-voltage(IV), and amperometry can be used in tandem to gain as much information and use ML and AI techniques to differentiate between different VOCs confidently. Currently, for this project, our lab is developing and optimizing sensor architectures that can facilitate in-situ gas-liquid contact using carbon dioxide as the model gas and ionic liquid as the sensing material/electrolyte. The project will have two goals: 1) To get insights into the underlying physics of the microfluidic gas sensor system and 2) To develop a calibration curve for the sensor to understand important parameters like the linear range, LOD, and limit of quantification. The undergraduate student involved in the project will be directed to focus on one of these goals based on their interests, knowledge, and the project's progression. To achieve the first goal the student will be using COMSOL Multiphysics software to model the gas sensor system using Navier-Stokes equation and diffusion-convection equation. To achieve the second goal the student will be performing different electrochemical experiments and analyzing the results using different ML/AI techniques. The training provided to the undergraduate student will encompass assembling the microfluidic gas sensor device and conducting various electrochemical experiments such as EIS, CV, DPV, and IV measurements. Additionally, the student will be instructed in developing a comprehensive model using COMSOL Multiphysics software and applying various ML/AI techniques using Python..