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Phytochemical Intervention for Neuroinflammation in Modulating Macrophage Dynamics at the Blood-Brain-Barrier

JSHS · 2025

Lorena Pachiardi, Dr. Antonio H Martins and Dr. Yancy Ferrer · Cupeyville School, San Juan, Puerto Rico

Overview

Chronic neuroinflammation and the migration of macrophages to the blood -brain barrier (BBB) are significant characteristics of various neurological diseases. In this context, the adhesion of macrophages plays a crucial role in immune responses. However, lo ng-term anti-inflammatory treatments can lead to serious side effects, such as osteoporosis and cardiovascular problems. This study aims to explore phytochemical alternatives, focusing on quercetin derived from soursop, as a potential inhibitor of macropha ge adhesion in brain endothelial cells. Although the peripheral anti -inflammatory effects of quercetin are well -established, its influence on macrophage adhesion within the neurovascular unit is not yet fully understood. We hypothesize that quercetin will modulate macrophage adhesion on brain endothelial cells. To investigate this, we used RAW 264.7 macrophages together with bEnd.3 brain endothelial cells. Inflammation in the bEnd.3 cells were induced using 100 ng/ml of lipopolysaccharide (LPS), followed by treatment with 5 µM quercetin for 48 hours. A fluorescence-based adhesion assay was employed to quantify the attachment of macrophages. The results indicated that 5 µM quercetin did not decrease macrophage adhesion under inflammatory conditions. In contra st, 50 µM dexamethasone resulted in a notable decrease in adhesion. These findings imply that quercetin does not modify the binding of BBB endothelial cells to macrophages during inflammation. Future studies will focus on the direct effects of quercetin on macrophages to further evaluate its impact on adhesion. This research contributes to a deeper understanding of how phytochemicals can affect inflammation - related cellular functions. Artificial Intelligence Models to Simulate and Assess the Viral Expansion of Influenza A/ H5N1 Cattle Outbreak Sriniketan Sridhar Southwestern Educational Society, Mayaguez, Puerto Rico H5N1 Avian influenza Bird Flu is an infectious disease that has implications for the human health and global economy. Recent infections have spread across the United States, forcing farmers to kill their livestock of ducks and hens, leading to an increase in egg prices. H5N1 is most common with cattle, B3.13 as recent human infections have been seen to be directly linked with cattle. Artificial Intelligence (AI) based approaches are developed to simulate the expansion of B3.13 viral DNA sequences. The pre -trained Generative Pretrained Transformer (GPT -2) which is a Natural Language Processing (NLP), Large Language Model (LLM) has been adapted to generate new B3.13 DNA viral sequences. A second AI Deep Learning (DL) model called the LSTM (Long Short -Term Memory) which uses long short -term dependencies has been used to generate new B3.13 DNA viral sequences. 1300 evolutions and 225 evolut ions have been simulated using Transformer and LSTM, respectively. The new evolved B3.13 cattle virus sequences were evaluated using Convolutional Neural Network (CNN) by classification of the simulated sequences against other Mammal genotype H5N1 A/Influenza original viral sequences. The assessment demonstrated that both Transformer and LSTM were efficient at predicting new expansions of H5N1 B3.13 cattle outbreak virus sequences with a classification accuracy of 96.2% for transformer and 70.8% for LSTM for 5 genotypes. The transformer shows a better performance in simulation than LSTM model. Five-fold cross validation was done to test the CNN model. South Carolina

Competition history

  • JSHS 2025 Category not listed

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Source: Junior Science and Humanities Symposium

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