The Effectiveness of a Standardized Mixture of Antioxidants as a Preventative Treatment for PTSD and its Symptoms in C. elegans

Overview

Antioxidants can be used to help prevent neurodegeneration and reduce reactive oxygen species (ROS) associated with Post traumatic Stress Disorder (PTSD) in a C.elegans model. 1/11 people are diagnosed with PTSD and this number is increasing. PTSD affects everyday life, since flashbacks and nightmares may occur at any time, causing a fight or flight or avoidance reaction. Previous studies have shown a link between oxidative stress and PTSD, as well as that antioxidants can reduce oxidative stress caused by reactive oxygen species. It was predicted that if the natural substances work, then the ROS of the test group and their avoidance behavior (movement backwards) caused by a trigger molecule should be similar to non-PTSD worms. The N2 C. elegans strain was put through an avoidance assay to simulate PTSD by having a trigger molecule: diacetyl. The antioxidant mixture was mixed with LB and E. coli, and then plated as a food source on growth medium. ROS was measured using the LD1171 strain put through the same conditioning, a fluorescent microscope, and Image Jsoftware. The 32˚C & diacetyl group that had antioxidants in their food source avoided a statistically smaller average amount of diacetyl drops compared to either control group without diacetyl present in the conditioning, and the LD1171 worms conditioned and treated also expressed less GFP . This study lays the groundwork for investigating the relationship between associations and PTSD, and future actions include investigating epigenetics to see if PTSD behaviors and ROS expression will reoccur in the offspring. Artificial Intelligence Image Detection with GIS Multi-Layer Topological Analysis: A Twofold Approach to Lung Cancer Diagnosis Jaeyi Song Cambridge Rindge and Latin School, Cambridge, MA CT and MRI scans inaccurately identify 5 out of 100 lung tumor boundaries that potentially result in metastatic disease. Late diagnosis hinders treatment and survival. This research examined the use of image detection and Geographic Information System (GIS) techniques to differentiate benign versus malignant lung tissue. A novel model was implemented to utilize both image detection and GIS techniques. MRI & CT scans of the thoracic cavity from the Lung Image Database Consortium and Image Database Resource Initiative (n=1,223) were used and round truth annotations were included. T o build the R-CNN model, Matplotlib, numpy, opencv, Pillow, and scipy were used to perform image preprocessing and AI model training. A 3D GIS model and common coordinate framework was built in order to improve the convenience of diagnosis and build a topology. Noise reduction and topological analysis were performed by implementing spatial autocorrelation and Anselin Local Moran's I through cluster and outlier analysis. The GIS model helped to detect clusters, representing the tumors and vasculature. The image detection model achieved 68.6% accuracy and 72.7% recall score for detection of malignant lung tissue. With the inclusion of the GIS approach, the model resulted in 80% accuracy, an improvement of 11.4% from the AI model. The GIS model analyzed the relationship between tumor formation and vasculature, which may increase the percentage of patients diagnosed at an earlier, more treatable stage and also have applications for research in fields such as angiogenesis.

Competition history

JSHS 2023 Category not listed

Resources

National JSHS abstract book

Related projects

ISEF · 2023