Designing a Light Inducible Protein Crystal

Overview

Cells are known as the building blocks of life which also makes them the building blocks of disease. Cells degrade foreign material that could be dangerous or unnecessary on a daily basis. Organelles, known as lysosomes, are responsible for degrading this material into macromolecules that may be recycled. This is the process for most materials, however lysosomes can fail to dissolve crystals. As a result lysosomes get stressed out, and there is evidence that they leak lysosomal contents that, in turn, act as a distress signal to cells. This signal can then amplify into an inflammatory response, manifesting as chronic inflammation within tissues. Currently, it is not well understood how lysosomes tell cells they are struggling to dissolve a material. To investigate this process, I wanted t o develop a protein crystal into a tool that can form on cue. Using iPak4 and FC-1, I added a photocleavable protein to disrupt the formation of the crystals. Then when the photocleavable protein is exposed to light it should break into small parts and allow the crystals to form. The end goal was to design a light inducible protein crystal that could be used to study inflammatory pathways as a model for crystal -induced damage to lysosomes. Future research could use this work as a basis or a tool for working with and understanding lysosomes and autophagy better. Reducing Mortality Risk in Veterans: Employing Oxidative Stress Genomic Data and Patient Records to Develop a Machine Learning Model to Supplement Current Diagnostic Tools Dhruv Veda Centennial High School, Ellicott City, MD Blast-Induced Traumatic Brain Injury (bTBI) presents a major risk for veterans due to its extremely rapid progression, with mild cases often advancing to severe cases with a 22% mortality rate. Current diagnostic technology lacks specificity and sensitivity, detecting only 50% of cases on the first attempt, failing to identify subtle symptoms. A long chain of communication between specialists further delays intervention, leaving veterans vulnerable to neurological deterioration. This study developed an innovative late fusion machine learning (ML) diagnostic tool that integrates the results of genomic and clinical data ML models, presented in an Explainable AI (XAI) dashboard improving bTBI diagnosis. Genomic research at Walter Reed Army Institute of Research examined reactive oxygen species (ROS)-related gene expression in blast -exposed ferrets using qRT -PCR, revealing significant increases in ROS-breakdown enzymes (SOD1, GPX1, CAT; p < 0.05) and trends in mitochondrial genes (OPA1, FIS1), supporting the viability of ROS-related genes in bTBI diagnosis. Two ML models were developed: a PCA -optimized K-Nearest Neighbors model achieving 97% accuracy in gene expression pattern analysis, and a Random Forest classifier reaching 98% accuracy in clinical data (patient history and symptoms) analysis. A late -fusion model combines these results in an XAI dashboard offering transparency, empowering physicians to make informed decisions. This modular framework: (1) improves sensitivity and specificity by integrating genomic and clinical data with ML, achieving up to 40% higher accuracy; (2) enhances early bTBI detection potentially saving veterans' lives; (3) provides a scalable platform f or diagnosing diverse neurological conditions, extending its potential impact beyond bTBI. Michigan Advance Phosphor Applications: Integrating Red Phosphorus - Polypropylene Based Sealant into Electric Vehicles Paul Garrison Renaissance High School, Detroit, MI The aim of this research project is to combine red phosphorus - polypropylene sealant with electric vehicle battery casings to help prevent fire from spreading in electric vehicles. This project came out of a need for increased fire safety in electric vehicles due to their increased probability to ignite in flames compared to traditional internal combustion engine vehicles. Red phosphorus is an excellent solution to this problem due to it being used in forest fires and how easily it can be implemented in polyolefins. My hypothesis was that the red phosphorus - polypropylene sealant used would be able to be used in electric vehicle battery casings to help reduce the spread of fire. Through thorough experimentation, I was able to see the effectiveness of the r ed phosphorus - polypropylene sealant in preventing the ignition of polyolefins. The sealant was proven to be very effective in preventing ignition in polyolefins by stopping the fire from reaching the object and producing a char layer upon reacting to oxy gen. The sealant is most likely able to be effectively used in an electric vehicle battery casing to prevent fires, although in the future more testing will be required.

Competition history

• JSHS 2025 Category not listed

Resources

• National JSHS abstract book ↗

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