2022 | 2023
2022 Cancer Innovation Scholars
Project Title: Design and Preliminary Assessment of a Self-Assembling Peptide Hydrogel for Drug Delivery
REU Scholar: Anne Nong (Rowan University), Advisor: Vivek Kumar, Mentor: Joseph Dodd-o
Abstract: Triple-negative breast cancer accounts for about 10-15% of all breast cancer cases. Triple-negative breast cancers are highly common in younger women and women of African descent. Triple-negative breast cancer is a type of breast cancer that is clinically negative for expression of estrogen receptors (ER), progesterone receptors (PR), and the hormone epidermal growth factor receptor 2 (HER2) protein. The absence of these specific receptors limits the treatment options for patients of this subtype, as antibody and hormone therapy approaches lack a target. Consequently, the treatment options for clinicians in locally advanced Triple Negative Breast Cancer are limited.. To address this clinical need, we have designed shear-thinning, thixotropic multidomain peptide hydrogels containing an apoptosis signaling domain to examine as a carrier for sustained taxane, platinum-based, or anthracycline chemotherapeutics. Hydrogels of the apoptotic peptides E2WEWT, SL(APOP2), ans SL(APOP3) were characterized using rheology, circular dichroism, fourier transform infrared spectroscopy, and cytocompatibility assays to determine the injectability and cytocompatibility of the hydrogels. Using a hydrogel for drug delivery can both fill the space left in the tissue after surgery and coat the surrounding tissue to deliver the apoptotic peptides triggering apoptosis in the remaining malignant cells in the surrounding tissue.
Project Title: Using Convolutional Neural Networks to Classify and Predict Pneumonia in Pediatric Chest X-Ray Images
REU Scholar: Debbie-Ann Spence (NJIT), Advisor: Dr. Joshua Young, Mentors: Daniel Mottern and Mo Li
Abstract: There is an immense need for efficiency in the realm of medical diagnostics and image interpretation. We focused on using machine learning models to expedite the process of diagnosing cases of pneumonia in children from medical images. We utilized a data set containing chest X-rays from pediatric patients from a facility in Guangzhou, China. We sought to create a machine learning model that accepted chest X-ray images as an input to determine its ability to depict pneumonia with high accuracy. The model used for this study was the Convolutional Neural Network (CNN). Using Python 3 through the computing platform Jupyter Notebooks, we developed an algorithm that read in chest X-ray images and transformed them into numerical data points representing pixel color. We then divided the data into three different categories: 1) training set; 2) validation set; and 3) test set. We then trained a basic CNN, that served as a baseline and changed the architecture and hyperparameters to optimize the accuracy of the network. The networks built performed with accuracies greater than 90% on the training data and 70% on the test data. Our results show that CNNs are capable of accurately identifying pneumonia, and could have practical applications such as shortening the time it takes to diagnose pneumonia and leading to quicker treatments.
Project Title: Point-Of-Care Clinical Device To Screen Microcystin-LR, Anatoxin-a, and Cylindrospermopsin Found In Freshwater
REU Scholar: Halexandra Alvarenga (California Baptist University), Advisor: Sagnik Basuray, Mentor: Yu Cheng
Abstract: The lack of portable devices to detect microtoxins in freshwater has encouraged researchers to improve a device that can save patients time, money, and possible health complications. ESSENCE (Electrochemical Sensor that uses a Shear-Enhanced, flow-through Nanoporous Capacitive Electrode), is a sensor platform that overcomes electrochemical sensor limitations, specifically selectivity and sensitivity limitations. The creation of the device includes a microfluidic, affinity-based electrochemical sensor platform for rapid detection that offers unprecedented sensitivity through a combination of receptor probe design, electrode configuration, and their combination within the microfluidic lab-on-a-chip platform. The device consists of a microfluidic channel sandwiched between two sets of interdigitated microelectrodes. One microelectrode is packed with carbon-based transducer material, such as carboxylated single-walled carbon nanotube, and another electrode acts as the reference electrode. The device also shifts signals to a higher frequency range, making the sensor very sensitive and rapid with a high signal-to-noise ratio. This study focused on three different microtoxins – Microcystins, Anatoxin-A, and Cylindrospermopsin in freshwater in 0.01, 0.1, 1, 10 ppb and redox probe pbs. Test runs consisted of three steps: Step 1 redox probe, Step 2 mictoxin and Step 3 redox probe. The results depended on the difference in Step 1 and Step 3. It is concluded that results fall under the accurate and speedy diagnosis for the device. The results will either show a result of a microtoxin detection. Throughout the duration of the research, different ppbs in the microtoxins had detection, no detection or an assembling error resulting in peculiar graphs. The future work will be to input the data into a Calibration Curve and analyze for the three different microtoxins detection.
Project Title: Estimation of dermal absorption of chemical agents using physiologically-based pharmacokinetic models
REU Scholar: Luster Harris (Alcorn State University), Advisor: Laurent Simon, Mentor: Fiyinfoluwa Fasina
Abstract: Chemical Warfare Agents are weapons of mass destruction mainly utilized in early wars to cause death and incapacitation by using the toxic properties of chemicals. These toxic agents have been dispersed in gaseous or liquid states that impose many different lethal effects to humans such as cancer. The skin is the most common route by which harmful chemicals enter the body. Deem it necessary, a framework that models the dermal absorption of chemicals is very important. Physiologically based pharmacokinetic (PBPK) is a computer modeling approach that assesses the risk posed by toxic chemicals and explains their mechanisms as the chemicals are transported through tissues and organs. The transportation between the tissues and organs can be recognized as a partial differential equation(PDE). This complex (PDE) can be broken down into ordinary differential equations (ODEs) by orthogonal Collection techniques. These complex equations are solved using the computational system Mathematica. This system was used to evaluate various liquid chemical agents, as they are diffused through the different layers of the skin.
Project Title: Investigating platinum nanoparticles for cancer treatment
REU Scholar: Noshin Siddiq (New York University), Advisor: Kathleen McEnnis, Mentor: Aida López Ruiz
Abstract: According to the National Cancer Institute, cancer is among the leading causes of death worldwide and the number of both cases and deaths is expected to rise by millions by 2040. Platinum nanoparticles (PtNPs), however, have shown potent anticancer effects against various types of cancer cells while maintaining low toxicity in healthy cells. To further investigate PtNPs as a promising cancer treatment option, a platinum ion release study was performed to test if PtNPs work against cancer cells by releasing Pt2+ instead of increasing reactive oxygen species (ROS) in the cells, which is the current accepted hypothesis for PtNPs’ mechanism of action. Therefore, the amount of Pt2+ released from PtNPs in PBS (pH 7) and MES (pH 5) buffers at several time points was recorded using an inductively coupled mass spectrometer (ICP-MS). PBS simulates a healthy cell environment since it has a neutral pH while MES simulates a cancer cell environment since it has a more acidic pH. Furthermore, PtNPs’ protein corona formation and aggregation behavior over 24 hours in bovine blood plasma were observed with nanoparticle tracking analysis (NTA). Protein corona refers to the set of proteins that attach to a nanoparticle’s surface and is suspected to signal for a nanoparticle’s removal from the body. Preliminary results of the release study indicate that more Pt2+ is released in MES for the first 24 hours, after which more ions are released in PBS and by 120 hours, there are cumulatively more Pt2+ in PBS than MES. Currently, the control and two more trials of the study are being tested. From the protein corona studies, by comparing PtNP size in water versus in blood, the protein corona was estimated to be 46.85 nm. Aggregation study results show that PtNPs form the most and largest aggregates at 14 hours (Figure 2). Overall, these experiments provide valuable information relevant to designing a safe and effective PtNP drug delivery system for cancer treatment. Future studies may include testing PtNPs with poly(lactic-co-glycolic acid) (PLGA), a common drug delivery system used to improve nanoparticles’ circulation time, in blood and performing in vivo animal experiments with PtNPs.
Project Title: Wearable Piezoelectric Cancer Detection Device using Electrospun Nanofibers
REU Scholar: Olivia Dyke (Calfornia Baptist University), Advisor: Lin Dong, Mentor: Sun Kwong
Abstract: Abstract: The Center for Disease Control and Prevention (CDC) reports that cancer is the second leading cause of death in the United States. More so, in 2019, a little under 2 million people were diagnosed with cancer and over a quarter of them died. Invasive biopsies are required to determine if a patient has cancer when suspected. Electrospun piezoelectric materials are readily used in biomedical engineering as they are biocompatible and have the ability to create electrical energy from mechanical energy. These electrospun piezoelectric nanofibers have the potential to be applied to cancer detection via a biosensor in which materials are layered together. This project layers polydimethylsiloxane (PDMS), carbon nanotube (CNT), electrospun nanofibers, phosphate buffer solution (PBS), and a linker solution to fabricate a device that hasa the potential to detect prostate cancer in the patients’ blood. Prostate cancer will be detected through (PSA) antibody or α-fetoprotein antibody interaction with PSA antigen. A prostate cancer detection biosensor that supplies its energy by turning mechanical stress/biological vibrations from the human body into an electrical charge eliminates the need for lithium-ion batteries, which are commonly used and frequently replaced. The biosensor will convey antibody-antigen interaction as a change in frequency and thus indicate whether carcinogenic antigens are present. Live data of biomarker detection in the bloodstream also is a potential component of future work, in which healthcare providers can easily monitor PSA levels. This introduction of wearable non-invasive piezoelectric biosensors, without a resonator or transducer, to detect carcinogenic biomarkers in the bloodstream would be a massive breakthrough in healthcare as there are few similar applications, none of which are yet widespread in the realm of oncology.
Project Title: 3D Bioprinting of Soft Tissue Sarcoma Spheroids-Laden GelMA for Tumor Modeling
REU Scholar: Raylynn Thompson (Alcorn State University), Advisor: Amir Miri, Mentor: Elvan Dogan
Abstract: Bioprinted hydrogel-based microfluidic models are suspected to be able to mimic the cellular composition of the extracellular matrix properties in that of tumor tissue. The completion of this study can lead to the development of personalized medicine through rapid drug screening. With personalized medicine, the risk of adverse effects as it relates to cancer treatments can be limited seeing as how the drugs will have the ability to target the abnormal cells while leaving the unwarranted cells alone. Whereas, in current cancer drug screenings, the only way to test the effectiveness and dosage is to directly deliver the medication to a living organism. This method can lead to both inhumane practices and minacious health developments. The 3D Bioprinted Microfluidic Chips approach will grant medical practitioners the ability to harvest the cancerous cells from their patients and structure a device that allows researchers to understand how the medication or treatment will directly affect them without bringing about any unwarranted complications. The methodology of the experiment is to form spheroids in low attachment plates and then encapsulate them in uncrosslinked 5% GelMA hydrogels. After UV Crosslinking through a light-based bioprinting technique and bioprinted spheroid-laden GelMA on a glass slide, we then observe the cell behavior for 14 days while they’re still in the GelMA chip. Cancerous cells have known hallmarks that allow them to continuously divide. In order, for this study to be successful, the cancer cells ought to be able to replicate these same hallmarks inside of the chip. Careful monitoring of the hallmarks drives a large portion of the experiment. Seeding the Soft Tissue Sarcoma allows for one to track their behavior for the frequent hallmarks of migration and metastasis as well as others. As expected, in the presence of a protein-rich serum buffer the cell spheroids were viable on the periphery, and they formed a hypoxic core similar to in-vivo conditions. Therefore, this rendered the Gelatin Methacrylate of being capable of providing a biomimicking Extracellular Matrix. Future work will include assessing the Ki-67 expression of Soft Tissue Sarcoma spheroids with and without the presence of fibroblast and to deliver the drug NVP-TAE684 to detect and examine how cell migration and metastasis anticancer properties are affected on a tumor and it’s microenvironment.
Project Title: Detection of PFOA through an Electrochemical EIS Microfluidics Platform
REU Scholar: Stella Makuza (NCAT), Advisor: Nellone Reid, Mentor: Li Zhenglong
Abstract: Public vulnerability to biochemical threats posed by widespread and extensive anthropological uses of per/polyfluoroalkyl (PFAS) water contaminants establishes urgency for rapid, ultrasensitive, and selective technology for chemical/biological/radiological/nuclear/environmental (CBRNE) forensics and detection. These contaminants that are widely abundant in our day to day lives for decades and weaponize against an individual’s body to develop cancer with prolonged exposure and/or when consumed. Perfluorooctanoic acid (PFOA) is one of the most dominant environmental contributors, and its half-life in water has been estimated to be longer than 92 years. Therefore, the monitoring of PFOA level in the water source is needed. To date, PFAS analysis is predominately based on high-performance liquid or gas chromatography-mass spectroscopy. However, these methods suffer some limitations in practice, such as ex-situ analyses (not adaptable for field-deployment), time-consuming, high cost. Our electrochemical impedance spectroscopy (EIS) platform technology is a rapid, sensitive, and selective detection technique for rapid screening of PFOA in the field in the source water. With current limitations of electrochemical sensors, the objective of the designed metal-organic framework (MOF) microfluidic impedance sensor platform model is to expand the device’s sensitivity capacity with defined calibration curves using Zirconium based MOFs (UiO-66 and its derivative (UiO-66 NH2)). This application aids to the development of a microfluidic, affinity-based electrochemical sensor platform for a sensitive and analytical impedance detection of PFOA. The high frequency operation of the UiO-66 and UiO-66-NH2-based MOF receptor probes in the microfluidic channels coupled with a non-planar interdigitated microelectrode design and ground water solutions through an impedance analyzer enables proof-of-concept by demonstrating the presence of minute quantities of PFOA, then ultimately other environmental contaminants with appropriate receptor probes and design. It may also be synergized with other approaches to enhance CBRNE detection entirely to develop electrode/probe/target interaction at the molecular level.
Project Title: Effect of Molecular Weight on the Curing of PEGDA Hydrogels
REU Scholar: Yorquiria Maldonado Mejia (NJIT), Advisor: Amir Miri, Mentor: Hoda Fattel
Abstract: Digital light processing (DLP) 3D printing technology has been advancing and is widely used to make medical devices, implants, chips, tissues, and drug delivery systems. The main hydrogels that are used in this additive manufacturing technology are gelatin methacryloyl (GelMA) and polyethylene glycol diacrylate (PEGDA) because of their photopolymerization effect and good biocompatibility with cells. In this project, we analyze the impact of molecular weight, in PEGDA solutions, on curing properties under a DLP printer. Solutions containing 20% PEGDA and 0.5% Lithium phenyl-2,4,6-trimethyl-benzoyl phosphinate (LAP) were made for the varying molecular weights of 575, 700, 4000, and 6000 Mn. As the molecular weight increases, the curing time also increases, however, the resolution decreases. Higher molecular weights cause the density per volume of the acrylate in the ink to decrease which reduces the resolution of the product. In the future, this data could be used to determine the appropriate PEGDA molecular weight for different applications; In tissue engineering, the most appropriate PEGDA to use would be 575 Mn because it will provide high-resolution prints.