Illinois Mathematics and Science Academy
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Analysis of Inflammatory Cells in Rat Bladders and the Replacement of Bladder Using Bioprinting Methods
No full textBladder-related conditions, such as interstitial cystitis/bladder pain syndrome (IC/BPS), are significant medical concerns affecting patient quality of life. IC/BPS patients experience pain, pressure, or discomfort localized in the bladder region. This discomfort often originates from bladder pressure, which can lead to an influx of inflammatory cells, resulting in inflammation, infections, and cystitis. Bladder inflammation may cause pain, increased urinary frequency, and decreased bladder capacity. Addressing these issues necessitates exploring new therapeutic interventions to mitigate bladder inflammation and prevent its recurrence. One emerging solution is the use of 3D bioprinting technology, a process that utilizes 3D printing to fabricate living tissues and organs. Bioprinting has demonstrated potential in various medical applications, including repairing damaged tissues, creating functional replacements, and testing pharmaceutical compounds. We used the bioprinter to create a rat-size bladder and allowed it to culture. We stained and analyzed hundreds of rat bladders to further monitor the effects of surgical implantation of inflammation detectors. Further studies will allow us to see the effect of the 3D printed bladder in the rat
Multi-Scale Additive Manufacturing Of Ultra High-Performance Fiber
No full textThis study investigates the mechanical behavior of 3D-printed Ultra High Performance Concrete (UHPC) and Ultra High Performance Fiber-Reinforced Concrete (UHPFRC). It intends to contribute to the research regarding detailed experimental assurance in concrete 3D printing. Current methods are based on coring samples out of 3D printed elements, eliminating the 3D printing features. This study bridges these approaches by proposing a process to evaluate its mechanical properties that account for layered concrete\u27s unique geometric features. A nanomodified mixture is developed to fulfill the fresh-state properties necessary for 3D printing. A flow table test validates this before printing with a piston extruder and a 3-axis gantry printer. A highly flowable mix is used as capping material to maintain surface geometry, providing a smooth, parallel loading surface for mechanical tests. Mechanical properties of the printed UHPC and UHPFRC are assessed by uniaxial compression, tensile splitting, and notched three-point bending tests. Properties of the printed samples are studied by comparing compressive and tensile strengths to cast samples from the same batch. Documentation of dimension and cause of failure included image analyses supported by 3D scanning. The test results provide valuable insight into the effects of the geometric features of 3D printed UHPC samples
Effect of Prosthetic Foot-Ankle Stiffness on Standing and Walking Performance in Transfemoral Prosthesis Users
No full textThe stiffness of a prosthetic foot-ankle component is typically prescribed based on a patient\u27s weight and activity level, which may not be optimal for a particular individual. This study investigates how systematically varying prosthetic foot-ankle stiffness affects standing and walking performance. The aim is to determine an optimal prosthetic foot-ankle stiffness that will enhance standing and walking abilities for transfemoral prosthesis users. The study mechanically characterized the keel and bumper components on the College Park Venture Foot, which revealed an increasing stiffness from soft to hard configuration. Kinematic and kinetic data were collected in a motion analysis research laboratory to determine the roll-over shape (ROS) radii of the different prosthetic foot-ankle combinations. A novel method was developed to create a best-fit circle of the ROS and can be applied in future studies. Utilizing gradient descent for determining the ROS allows for a streamlined procedure while maintaining the outcome precision. The radii created by the different prosthetic keel-bumper combinations in the ROS experiment were directly proportional to their measured stiffness from the mechanical testing. These results indicate that prosthetists can prospectively prescribe a prosthetic foot-ankle stiffness to simultaneously optimize both standing and walking performance in their patients
Understanding Adversarial Attacks on Discrete Generative AI
No full textData attribution methods have been commonly used on generative AI as a way to evaluate the usefulness of data samples. In the future, it may be used to compensate copyright holders for their data. However, these methods have proven to be susceptible to adversarial attacks through various optimization processes. We seek to show that these adversarial attacks can not only be successful, but also understandable to the general public. In particular, we target discrete generative AI in the symbolic music domain, as music has various patterns such as chords and arpeggios that can be easily identified. By highlighting these vulnerabilities, the knowledge can be leveraged to understand other adversarial attacks in different fields, such as text generation and image generation. Our work underscores the need for more robust data attribution methods for the use of generative AI. We attempt various heuristic methods to alter a data sample, including copying influential pieces to another one. We show that simple heuristic methods can also be used to generate adversarial data samples to data attribution
Trajectory Prediction for Autonomous Vehicles in Construction Zones
No full textAutonomous driving technologies have the potential to revolutionize transportation, reducing accidents, improving traffic efficiency, decreasing fuel consumption, and increasing mobility. An autonomous vehicle’s ability to generate a safe future trajectory is dependent on the outputs of many subsystems such as environment perception, intention prediction, trajectory prediction, and finally, planning. However, many of these subsystems are compromised in construction zones, as these environments are complex, cluttered, and often highly uncertain. Traffic laws do not entirely apply, road markings may be missing or misleading, lanes may have varying widths, and unknown objects may be found. This study filters the construction zones in the nuScenes dataset for the purpose of improving the AutoBots trajectory prediction architecture. The encoder architecture of the AutoBot-Ego model was modified to account for the objects in construction zones. The improved model uses nuScenes object annotations to encode theconstruction scene objects
Evaluation of nNOS-mediated AβO Neuropathology as Potential Therapeutic Applications in Alzheimer\u27s Disease
No full textAlzheimer\u27s disease (AD) is a progressive neurodegenerative disease characterized by pathogenic accumulation of amyloid beta (Aβ) oligomers in a mortality-inducing cognitive decline self-reinforcing feedback loop. A key driver of AD pathology is Aβ oligomer (AβO) neurotoxicity, precipitating dysfunction in synaptic transmission and ultimately resulting in neuronal death. Recent studies show that in both death signaling pathways activated by AβO involve neuronal nitric oxide synthase (nNOS) and thus presence of nNOS may complicate AD progression in two avenues: 1. Complication via association with hyperphosphorylated tau, and 2. Complication via pro-neuroinflammatory signaling. The purpose of this study is to assess whether nNOS inhibition could be a therapeutic target for AD if nNOS activity increases AβO-induced pathological signaling. Assessment of nNOS will be conducted in both Alzheimer\u27s disease models, MC65 and HT22, via tau phosphorylation and neuronal death. Treating cells with nNOS inhibitors will determine the potential of nNOS inhibition to alleviate AβO-induced neurotoxic results. AβO exposure elevates nNOS, tau phosphorylation, and neuronal death; however, preliminary results indicate treatment with the selective nNOS inhibitor significantly reduced all three. This study seeks to determine the role of nNOS in Aβ-driven AD pathologyand support nNOS inhibition as a viable therapeutic strategy for Alzheimer\u27s disease
Assessing Pim-1 Kinase Inhibition as an Effective Treatment for Acute Myeloid Leukemia
No full textProviral integration site for malignancy-1 (PIM-1) is a serine/threonine kinase proto-oncogene that has many important functions, namely cell proliferation and signal transduction, in the context of Acute Myeloid Leukemia (AML). Generally, PIM-1 kinase, which arises from the expression of the PIM-1 oncogene, can cause poor prognosis of AML. Additionally, PIM-1 kinase, also a substrate of Triad1, stabilizes as expression of Triad1 decreases during the prognosis of AML. Thus, this project looks at PIM-1 kinase inhibition and aims to determine its effectiveness in treatment for AML and how it can affect potential therapeutic interventions. AZD1208, a potent pan-PIM inhibitor, is theorized to be an alternative treatment for AML instead of chemotherapy. This project involves a luciferase reporter assay, which is used to track PIM-1’s gene activation. The assay allows for analyzing PIM-1s impact on signaling pathways and helping determine its oncogenic potential through measuring the fluorescent activity in the Pim-1 kinase promoter
OsteoPredict: An AI-Driven Application for Osteoarthritis Risk Assessment, Progression Monitoring, and Treatment Optimization
No full textOsteoarthritis (OA) is a lead cause of chronic pain and disability, yet early detection and personalized treatment remain challenging. OsteoPredict is an AI-driven application designed to improve OA management by integrating predictive risk modeling, patient monitoring, and evidence-based treatment recommendations.
The application employs a machine learning-driven risk stratification model utilizing demographic, clinical, and lifestyle factors to predict an individual’s likelihood of OA onset and progression. Through a structured patient survey and physician-inputted data, OsteoPredict categorizes users into different risk levels and provides preventative care recommendations. Additionally, the platform incorporates advanced imaging analysis to detect early structural changes indicative of OA helping clinicians in objective diagnostics.
For healthcare professionals, OsteoPredict serves as a decision-support tool linking patients to officially approved treatment protocols from organizations such as the FDA, ACR, and AAOS. Patients can access a curated database of conservative therapies, pharmacologic interventions, and emerging clinical trials that are aligned with current medical standards. Furthermore, the app features progress tracking capabilities, allowing patients to log symptoms, and treatment efficacy over time.
OsteoPredict integrates AI, musculoskeletal research, and telemedicine to enhance early intervention and optimize osteoarthritis treatment. This study details its development and potential impact on patient care and clinical decision-making
Developing a cheaper, capable Direct Metal Laser Sintering 3D printer
No full textThis independent research project focuses on the development of a small-form Direct Metal Laser Sintering (DMLS) 3D printer capable of producing high-quality metal parts. The research involved consulting with professionals from DMG MORI and Scientists and conducting on in-depth study of loser Systems, motion systems, and powder-based additive manufacturing. Over the course of the project, various aspects of DMLS technology were explored, including optimizing laser parameters for precise sintering. designing compact motion control systems, and ensuring material compatibility for industrial- grade part production. The project also examined existing metal 3D printing solutions and identified key challenges related to cost, size, and accessibility. Through this research, a preliminary design framework was established for a compact and efficient DMLS system for small-scale applications. The most significant outcome of this project was the development of a technical roadmap for prototyping a small-form DMLS printer, which could contribute to broader advancements in affordable metal additive manufacturing
Punishment Versus Rehabilitation Study Under the Department of Homeland Security
No full textNew York\u27s historical reliance on punitive drug policies has created tension between the goals of retribution and rehabilitation, leading to significant social and economic consequences.
I will form a research paper studying the hypothesis that rehabilitation-focused approaches to addressing drug offenses are more effective than punitive measures because they address the root causes of addiction, reduce recidivism rates, prevent the disproportionate impact on minority communities, and utilize public resources more efficiently than the failed Rockefeller Drug Laws demonstrated over decades of implementation in New York.
Through a qualitative methodology, I intend to discover how evidence-based
rehabilitation approaches in New York correlate with improved post-intervention outcomes compared to purely punitive measures. I expect to find that participants in comprehensive treatment programs show not only lower formal recidivism rates but also qualitatively different recovery trajectories marked by improved coping strategies, stronger support networks, and more sustainable community reintegration.
I will consider my hypothesis supported if rehabilitation participants that I read personal reviews of and who are represented in the data sets I look at consistently demonstrate better outcomes across multiple dimensions of recovery and lower rates of return to substance use and criminal behavior