Team 2

Jiapeng Han

Track: Therapy

Establishment of an Optimized Assay Platform to Discover Novel MPC Inhibitor Compounds

Alopecia, or hair loss, can have significant psychosocial impacts. The stages of hair follicle growth—rest (telogen), regeneration (anagen), and degeneration (catagen)—are maintained by hair follicle stem cells (HFSCs). Although normally dormant, HFSCs can quickly become activated to initiate a new hair cycle. Glycolytic metabolism, and particularly the activity of lactate dehydrogenase has been shown to be favored by HFSCs. Increased lactate production by mitochondrial pyruvate carrier (MPC) inhibition results in HFSCs activation and hair cycle acceleration. Therefore, it is crucial to screen potential MPC inhibitor compounds for successful drug discovery to treat alopecia. In this study, we aim to set up an assay platform using either live cells and / or skin lysates to identify MPC inhibition for the establishment of compound screening project in the future. 

Team 3

Kyoka Ono, Anusha Puri

Track: Public Health

Our brief project idea is to apply machine learning models on ovarian cancer data set, not only comparing different set of machine learning models but hopefully able to find some clinical outputs from the data as well. 

Team 5

Sonya Hu, Alexander Chen, Joseph Hu

Track: Biomedical Engineering

Machine Learning-Powered Brain Tumor Detection in MRI Scan Images

One idea is AI medical imaging. AI in medical imaging is crucial as it enhances diagnostic accuracy and enables early disease detection by identifying patterns in images that might be missed by humans. This precision leads to better treatment planning and outcomes.

Team 6

Tony Li, David Gao, Katherine Li

Track: Therapy

Quantitative Machine Learning Approach for Gene Expression Profiling in Idiopathic Pulmonary Fibrosis Severity Assessment

This project aims to employ supervised and unsupervised machine learning models to elucidate the relationship between gene expression profiles and the multifaceted development of IPF severity. By integrating Expression profiling by high throughput sequencing, the method seeks to quantify the severity of IPF in a continuous manner, providing a refined understanding of the disease’s molecular underpinnings as they relate to clinical phenotypes.

Team 7

Christopher Korban, Aryan Kohli, Christian Chung

Our team will be presenting our drug discovery AI platform that has been in development through Nexabio Solutions, an established startup founded and run at UCLA. We leverage machine learning to find drug targets and repurpose protein drugs to bring them back into the market for re-commercialization opportunities. The technology overall mitigates the cost and risk of reinvestigating drugs that otherwise may have great potential to patients.

Team 8

Katrina Lam, Andrew La

Track: Therapy

Transforming human gut bacteria to utilize PET micro and nano plastics as energy

Background: Micro and nano plastics are everywhere in our bodies. A bacteria called I. sakaiensis has a pathway to completely turn PET plastic into compounds that can be used as energy by the bacteria. Proposed solution: transform human gut bacteria to produce enzymes capable of biodegrading PET plastic found in our bodies through viral transduction of the gene signaling pathway. This will not only reduce the amount of microplastics in our body, but also turn PET plastic into compounds that our bodies can use as an energy source.

Team 9

Felisha Kuo, Kehan Jiao, Kao Kawasumi

Track: Mechanism

Directed evolution and applications towards biomedical research

the application of directed evolution to improve the target specificity and stability of agalsidase beta for the treatment of Fabry disease in enzyme replacement therapy

Team 10

Sarala Sharma, Bella, Siting Lu

Track: Public Health

The application is used for serve the women with postnatal depression, which include several functions such as chat-bot with well-trained professional LLM that can used to answer questions in post-natal depression and get advice, it also include course/product recommendation function, the calendar for tracking the customer’s daily mood and medication routine, send notification to them, and the function of community/friends recommendation as well.

Team 12

Samuel Choi, Noor Alhammad, Tianze Zhao, Hassan Tariq

Track: Mechanism

CRISPR screening and optimization

We’d like to present a literary review on the current state of the CRISPR screening technology and propose how we can optimize it on different cell lines. 

Team 13

Aarya Pandit, Morrow Zhang, Rachel Kwan, Christopher Zhong

Track: Therapy

Nutrition, Cell Senescence, and Colorectal Cancer

Using literature review, synthesized data analysis, and proposed mechanisms, propose a potential nutrition recommendation/therapy that could alleviate cell senescence and imbalances in the microbiome. Good nutrition –> A balanced microbiome could lead to decreased cell senescence specific to colorectal cancer, and may decrease inflammation and risks for developing the cancer.

Team 16

Wing Yu, Yuqi Zhang, Yanishka Gahlot, Jessica Yao

Track: Biomedical Engineering

Leveraging a machine learning algorithm to predict potential cardiovascular disease using bio marker data

Welcome to a world of limitless possibilities, where the journey is as exhilarating as the We’re hoping to create a machine learning model to predict potential cardiovascular diseases using biomarkers such as blood pressure, cholesterol levels, and etc.