Biology subjects

Chae, D.

Publications and source records attributed to Chae, D..

2 recordsLinked to original sources

bioRxiv2024

Bridging in vitro and in vivo insights: A PKPD model for effective phage therapy against multidrug-resistant Pseudomonas aeruginosa

Bacteriophage therapy provides a promising solution for multidrug-resistant Pseudomonas aeruginosa. However, optimizing phage combinations and dosages remains challenging. Here, we developed and validated a pharmacokinetic-pharmacodynamic (PKPD) model integrating in vitro and in vivo efficacy data using three phage strains (MP-A, PP-A, and PP-B), administered both alone and in combination. In vitro phage resistance was substantially delayed by combining phages that exhibit collateral sensitivity. However, multiple phage resistance eventually emerged. In vivo, growth of phage-resistant mutants was minimal. Our model suggests that this outcome was due to an initial phage-induced reduction in bacterial load, followed by immune clearance of the phage-resistant bacteria. Simulations indicated that higher phage exposure at the site of infection caused a faster decline in bacterial levels below the immune clearance threshold and prevented excessive immune activation. Overall, this study offers crucial guidance for treatment strategies and highlights the importance of mathematical modeling in advancing phage therapy.

microbiology↗

bioRxiv2019

Fast and robust animal pose estimation

Quantitative behavioral measurements are important for answering questions across scientific disciplines--from neuroscience to ecology. State-of-the-art deep-learning methods offer major advances in data quality and detail by allowing researchers to automatically estimate locations of an animals body parts directly from images or videos. However, currently-available animal pose estimation methods have limitations in speed and robustness. Here we introduce a new easy-to-use software toolkit, DeepPoseKit, that addresses these problems using an eZcient multi-scale deep-learning model, called Stacked DenseNet, and a fast GPU-based peak-detection algorithm for estimating keypoint locations with subpixel precision. These advances improve processing speed >2x with no loss in accuracy compared to currently-available methods. We demonstrate the versatility of our methods with multiple challenging animal pose estimation tasks in laboratory and field settings--including groups of interacting individuals. Our work reduces barriers to using advanced tools for measuring behavior and has broad applicability across the behavioral sciences.

animal behavior and cognition↗