2025 AIChE Annual Meeting

(181i) Development of a Website Platform to Enable Visualization of Antimicrobial Resistance Data for U.S. Foodborne Pathogens

Authors

Zuyi Huang - Presenter, Villanova University
Kimberly Luu, Villanova University
Joseph Evans, Villanova University
Venkat Margapuri, Villanova University
Antimicrobial resistance (AMR) occurs when microorganisms such as bacteria, fungi, and viruses evolve to withstand the drugs that prevent their growth (1). This growing phenomenon has become a major global health crisis due to its severe impact on public health and rapid spread. The CDC reported that more than 2.8 million infections were caused by AMR pathogens every year (2). The mechanisms of AMR are complex, but the four main mechanisms include: limiting the drug uptake, modifying drug targets, inactivating drugs, and active efflux of the drugs (3). Fortunately, AMR data of pathogens are monitored and collected in various tools and databases such as the NCBI Pathogen Isolates Browser, National Antimicrobial Resistance Monitoring System, (NARMS), and the FDA’s National Database of Antibiotic-Resistant Organisms (NDARO). The NCBI Pathogen Isolates Browser specifically tracks pathogen data, including AMR detection in foodborne pathogens such as Salmonella, Campylobacter, Escherichia coli, and others.

While numerous databases and tools offer valuable insights, their complexity limits accessibility for nonexpert users, such as, farmers and the general public. This project aims to bridge that gap by developing a user-friendly website platform to visualize AMR data in the United States, specifically in food animals, from the NCBI Pathogen Isolates Browser. The data were extracted from the available database and analyzed using multivariate statistics analysis methods, such as principal component analysis (PCA). This method was used to visualize the high-dimensional data in a two-dimensional space (4). Based on the PCA-projected data, the hierarchical clustering approach was implemented to study the similarities of AMR patterns within the pathogen dataset (5).

The website framework integrates Javascript, Python, React, and Google Firebase. Each of these plays an important role in creating an interactive and accessible user interface. Javascript controls the website functionality and links the operations to the events as well as Cascading Style Sheets (CSS) to manage the appearance. Python connects to Google Firebase, a cloud-based storage and database system, by using the Pyrebase library. Firebase Storage is used to upload various image files created from pathogen AMR data analysis, while ensuring the secure authentication. The React program forms the foundation of the website’s interface, serving as the frontend and linking all the components together. Overall, the framework connects the backend (data processing and storage) with the frontend (user interaction and visualization). With this approach, dropdown lists were implemented which allowed users to select states, pathogens, antimicrobials, AMR genes, and years for visualization. For instance, the selected data is retrieved from Google Firebase, enabling users to analyze the presence of a specific pathogen in a chose state and year within the U.S. By combining data analysis and website development, the platform ensures that users can explore trends in AMR.

Reference
1. Antimicrobial Resistance. World Health Organization. [Online] July 27, 2017. https://www.who.int/news-room/questions-and-answers/item/antimicrobial-….
2. Antimicrobial Resistance Facts and Stats. Centers for Disease Control and Prevention. [Online] [Cited: April 1, 2025.] https://www.cdc.gov/antimicrobial-resistance/data-research/facts-stats/….
3. Reygaert, Wanda C. An Overview of the Antimicrobial Resistance Mechanisms of Bacteria. National Library of Medicine . [Online] 2018. https://pubmed.ncbi.nlm.nih.gov/31294229/.
4. Wold, Svante, Esbensen, Kim and Geladi, Paul. Principal Component Analysis. 1987. Vol. 2, 1-3. 0169-7439.
5. Chumwatana, Todsanai. A Comparative Study of Clustering Techniques for Non-Segmented Language Documents . 2017. Vol. 7, 1. ISSN 2392-554X.