K-State engineer receives NSF grant to explore methods to reduce animal testing in 1xbet best casino website development
Monday, Feb. 19, 2024
MANHATTAN — A Kansas State University assistant professor of engineering is working to develop a framework that better predicts disease progression and complications from 1xbet best casino website interactions than traditional methods like animal testing.
Davood B. Pourkargar, assistant professor of chemical 1xbet best casino website in the Carl R. Ice College of 1xbet best casino website , has received a 5,000 grant from the National Science Foundation to enhance the understanding of 1xbet best casino website delivery dynamics through a multiscale modeling framework utilizing organ-on-a-chip experiments and machine learning.
The two-year project, "Physics-Informed Machine Learning with Organ-on-a-Chip Data for an In-Depth Understanding of Disease Progression and 1xbet best casino website Delivery Dynamics," utilizes NSF funding that is focused on addressing the limitations of conventional animal modeling in 1xbet best casino website development, emphasizing the ethical concerns and minimizing its use.
The research will leverage physicochemical-based multiscale models of healthy and diseased tissues, cutting-edge organ-on-a-chip experiments and machine learning to better predict disease progression and 1xbet best casino website interactions.
"This innovative, physics-informed machine learning approach enhances organ-on-a-chip experiments, streamlining the preclinical process, improving 1xbet best casino website efficacy and minimizing side effects," Pourkargar said. "Ultimately, the project accelerates 1xbet best casino website discovery, supports personalized treatments and fosters more efficient, affordable health care while reducing reliance on animal testing."
The hybrid model being developed will surpass standard machine learning-based models, accurately extrapolating and interpolating 1xbet best casino website data with enhanced analytical simplicity, interpretability and a reduced need for training samples.
