Positive Association between Degenerative Cervical Myelopathy and Trigeminal Neuralgia

Overview

Trigeminal neuralgia (TN) is an idiopathic pain disorder, classified by paroxysmal pain in the face. The spinal trigeminal tract extends into the spinal cord as far as the fourth cervical vertebrae, and limited research suggests that cervical spine compres sion may be a risk factor for TN. We hypothesized that adults with TN would have a greater likelihood of concurrent degenerative cervical myelopathy (DCM) compared to matched adults without TN. The data utilized in this study spanned the past 20 years and were obtained from TriNetX, a national database with de-identified medical records from 113 million patients across 79 million healthcare institutions. Two groups of adults (≥18 years of age) were created: patients with (1) TN and (2) No-TN excluding predisposing conditions for TN (e.g multiple sclerosis, ophthalmic and oral/maxillofacial surgery), then groups were propensity matched (e.g., age, sex, body mass index, diabetes mellitus, hypertensive diseases, migraine, osteoporosis) to minimize between group differences. After matching, both groups consisted of 37,163 patients and the mean point prevalence was 0.55% in the TN group (95% CI: 0.47–0.63%) and 0.04% (95% CI: 0.03–0.06%) in the No-TN group, resulting in an odds ratio of 12.94 (95% CI: 7.78–21.53; p<0.0001). The present data show that adults with DCM are over 12 times as likely to have concurrent TN. These findings support our hypothesis and suggest that DCM may be a risk factor for TN. Oregon Polynomial Time Algorithms for Covering Problems Using Extraction Theorems with Small Extraction Numbers Arjun Agarwal Jesuit High School, Portland, OR Covering problems, a class of combinatorial optimization problems, play a crucial role in addressing various challenges in computational biology, wireless and sensor networks, VLSI design, robotics and image processing. For instance, identifying a minimal gene set that satisfies certain biological requirements such as enabling essential functions or covering key pathways can be abstracted as a covering problem as each gene “covers” a subset of these necessary processes. In a typical covering problem, given a set of points and a set of geometric objects covering these points, the goal is to pick a subset of objects whose union contains the input points (i.e., cover them) while optimizing a certain objective function. In this work, we develop Extraction Theorems for classes of geometric objects with small extraction numbers. These classes include intervals, axis-parallel segments, axis parallel rays, and octants. We investigate these classes of objects and prove small bounds on the extraction numbers. The extraction number is determined from a proper κ-coloring of the corresponding hypergraph. The tightness of these bounds is demonstrated by examples with matching lower bounds. Polynomial -time algorithms are developed to determine the proper κ-coloring, and therefore the minimum covering. These algorithms can form an essential component in addressing coverage -based strategies for large datasets, thereby efficiently providing optimal solutions. Helios-X: A Novel Low-Cost Sensorless Solar Tracking and Forecasting System with Astrophysics-Based Positioning, Sky Image Cloud Detection, and Deep Learning Andrew Ma Jesuit High School, Portland, OR The rapid growth of artificial intelligence, particularly generative AI models, has significantly increased energy consumption due to high computational power and continuous electricity demand in data centers. As global energy demands rise, the climate cri sis pushes for a shift toward clean, sustainable energy. Solar energy is pivotal, yet maximizing photovoltaic (PV) efficiency remains challenging due to widely adopted fixed-angle panels and costly sensor-based tracking systems. This work introduces Helios -X, a low -cost, dual -axis solar tracking and forecasting system designed to enhance solar energy capture. Helios-Xutilizes astrophysical algorithms, calculating the sun’s azimuth and elevation. To improve accuracy under cloudy conditions, a sky image processing module leverages normalized red-blue ratio (NRBR), Clear Sky Libraries (CSL), and Removal of Atmospheric Scattering (RAS) Channels to detect cloud patches and estimate real -time cloudiness. Additionally, cloudiness helps determine what algorithm is used. Running on a Raspberry Pi, the hybrid control system dynamically adjusts solar panel orientation —clear, partly cloudy, or overcast —by controlling the motor with different algorithms. The system monitors the sky and tracking angles, and stepper motors make real-time adjustments as conditions change. Additionally, sky images are used by a trained Convolutional Neural Network (CNN) model to predict short-term solar output, improving adaptive energy management and optimizing PV performance. Being cost-effective without sensors, Helios -Xkeeps tracking deviations under 1.5° and boosts energy efficiency by up to 30% over fixed panels. In -field testing across seasons will validate reliability and scalability, making it a viable solution for small- and large-scale PV installations and smart grid integration. FoGDTECT: A Novel Non-Invasive Freezing of Gait (FoG) Monitoring Solution Integrating Machine Learning and Mobile App-Generated Triaxial Accelerometer Data Akash Ragam Jesuit High School, Portland, OR This revolutionary solution uses multivariate machine -learning time -series models to detect freezing of gait (FoG ) instances in individuals with Parkinson's disease (PD) based on accelerometer data from a smartphone. One of the most profound symptoms of PD, FoG manifests as abrupt episodes of walking hesitation or immobility and impairs a patient’s balance, increases falls, and reduces overall quality of life. This study compares the performance of various machine learning models on a FoG -accelerometer dataset and optimizes a model to accurately detect and plot instances of FoG in real individuals based on mobile -app-generated accelerometer data. Performance was evaluated using standard ML model metrics, and graphs of detected FoG instances were validated by Balance Disorder Lab Engineers at OHSU. Results highlight that the LSTM with Attention Mechanism had the best performance, with an accuracy of 0.875, precision of 0.602, recall of 0.495, and F1 score of 0.818. The uniqueness of this solution involves the ease of implementation of these models by fitting them to accelerometer data collected from a smartphone, achievi ng an average accuracy of 0.809. After optimizing the data collection and processing by creating an Android app, the model successfully detected and graphed FoG events corresponding to timestamps with an industry -grade average accuracy of 0.809. The effica cy of this model highlights the potential to become a non -invasive long -term monitoring solution for individuals with PD. This can lead to proactive management, personalized treatment plans, and enhanced safety through real -time feedback, to minimize the risk of falls associated with FoG.

Competition history

• JSHS 2025 Category not listed

Resources

• National JSHS abstract book ↗

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