Keratin, one kind of intermediate filament, is the key component of hair, nail and skin in vertebrates, including mammals. Trichocyte hard keratin has relatively higher modulus and toughness than other biological materials, and epithelial keratin has larger extensibility. We focus on investigating trichocyte (hard) keratin and epithelial (soft) keratin, which have distinct material properties, including high strength, high modulus, high extensibility and mutability. 

In this study, we aim to provide molecular insight on mechanical behavior of these materials, and further assist applications in engineering biomaterials.

Hydrogels are one most commonly used bio-inks in 3D bio-printing technology due to their tunable mechanical properties and biocompatibility. In order to make hydrogels well-suited for printing applications, researchers must thoroughly control their mechanical and rheological properties. Specifically, the hydrogels must have the right balance of flow and viscosity to be extruded through the printhead nozzle, while also being flexible enough to be squeezed through and retain their shape after printing. 

In this study, we are interested in how modifying polymer or protein segments influences the configuration of polymer chains, and how these changes impact the microscopic network structure and overall mechanical/rheological properties of hydrogels. Ultimately, this work aims to provide critical insights into designing hydrogel bio-inks with optimized functionality for 3D bio-printing. 

Hydrogels are hydrophilic polymer network materials. The materials are thermodynamically stable in nature and have tremendous applications in various fields like biological materials, drug delivery, flexible electronics, etc. The study focuses on discovering the gel formation and network structure in the microstructure of polymerized material, and further to explore the materials’ structure and behavior at the nanoscale to link the mechanical properties observed at the macroscale. 

Compared to adult clinical settings, pediatric care presents unique challenges. Children's physiological structures and functions undergo continuous changes during growth, and they may struggle to articulate symptoms clearly. Additionally, the smaller size of the pediatric population makes it challenging to develop machine learning models due to limited electronic health record data. 

This study aims to establish a database for critically ill children, utilizing machine learning techniques to analyze complex medical information from the pediatric ICUs. The goal is to develop risk models and tools to assist clinical decision-making and enhance the prognosis of pediatric patients.