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Biomedical Engineering and Bioengineering Commons™
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- Artificial Neural Networks (1)
- Bioinformatics (1)
- Computational simulation (1)
- Dielectrophoresis (1)
- Ehlers-Danlos Syndrome (1)
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- Electrokinetic microfluidic devices (1)
- Faradaic reactions (1)
- Finite element method (1)
- Gene Regulatory Networks (1)
- Lab-on-a-chip devices (1)
- Machine Learning (1)
- Magnesium implant (1)
- Mechanical Properties (1)
- Mechanobiology (1)
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- Phase-field (1)
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Articles 1 - 4 of 4
Full-Text Articles in Biomedical Engineering and Bioengineering
Collagen V Promotes Fibroblast Contractility, And Adhesion Formation, And Stability, Shaina P. Royer-Weeden
Collagen V Promotes Fibroblast Contractility, And Adhesion Formation, And Stability, Shaina P. Royer-Weeden
Dissertations, Master's Theses and Master's Reports
Ehlers-Danlos syndrome, classical type, (cEDS) is a hereditary connective tissue disorder causing excessive elasticity and fragility of the connective tissue and problems with wound healing. Most cases of cEDS are caused by haploinsufficiency for collagen V. Collagen V regulates collagen fibril diameter. In cEDS fibroblast migration is impaired and integrin expression is altered.
The effects of collagen V on collagen gel ultrastructure and how it alters its mechanical properties were measured using scanning electron microscopy (SEM) and rheology respectively. Fibroblast contractility and adhesion dynamics were investigated to better understand the role of fibroblast disfunction in wound healing in cEDS. To …
An Experimentally Validated Computational Model For The Degradation And Fracture Of Magnesium-Based Implants In A Chemically Corrosive Environment, Mark M. Ousdigian
An Experimentally Validated Computational Model For The Degradation And Fracture Of Magnesium-Based Implants In A Chemically Corrosive Environment, Mark M. Ousdigian
Dissertations, Master's Theses and Master's Reports
In the orthopedic and cardiovascular fields there is a growing interest for biodegradable implants, which can be naturally degraded in the body environment over time so that no extraction surgery is required. These implants must be designed to maintain their strength until the fracture has healed in the body, which could be influenced by many factors such as -the patient’s age, activities, body weight, pre-existing conditions etc. Hence, an ideal implant design should be done on a patient-by-patient basis. In the present work, a computational model is developed to predict the degradation and fracture of magnesium-based implants in a stress-coupled …
Exploring Ph Gradient Phenomena In Non-Linear Electrokinetic Microfluidic Devices, Azade Tahmasebi
Exploring Ph Gradient Phenomena In Non-Linear Electrokinetic Microfluidic Devices, Azade Tahmasebi
Dissertations, Master's Theses and Master's Reports
Electrokinetic microfluidics is a versatile technology utilized within lab on a chip (LOC) devices for diagnostic and analytical applications; advantages include reduced resource demands, flexibility, and simplicity of use. Dielectrophoresis (DEP) is a precision nonlinear electrokinetic tool utilized within microfluidic microdevices to induce polarization and control bioparticle motions for applications that range from hemoglobin separations to cancer cell isolation and detection. Despite promising results, undesired side phenomena can occur in electrokinetic systems which impede reproducibility and accuracy. These unfavorable phenomena have not been comprehensively explored in the literature. Prior preliminary research suggests the fundamental phenomena originate from microelectrodes utilized in …
Machine Learning And Deep Learning Approaches For Gene Regulatory Network Inference In Plant Species, Sai Teja Mummadi
Machine Learning And Deep Learning Approaches For Gene Regulatory Network Inference In Plant Species, Sai Teja Mummadi
Dissertations, Master's Theses and Master's Reports
The construction of gene regulatory networks (GRNs) is vital for understanding the regulation of metabolic pathways, biological processes, and complex traits during plant growth and responses to environmental cues and stresses. The increasing availability of public databases has facilitated the development of numerous methods for inferring gene regulatory relationships between transcription factors and their targets. However, there is limited research on supervised learning techniques that utilize available regulatory relationships of plant species in public databases.
This study investigates the potential of machine learning (ML), deep learning (DL), and hybrid approaches for constructing GRNs in plant species, specifically Arabidopsis thaliana, …