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Full-Text Articles in Skin and Connective Tissue Diseases
The Role Of Age Ligand And Rap1a In Myofibroblast Signaling Cascade In Type 2 Diabetes Mellitus-Induced Cardiomyopathic Conditions, Camilla Puglia
The Role Of Age Ligand And Rap1a In Myofibroblast Signaling Cascade In Type 2 Diabetes Mellitus-Induced Cardiomyopathic Conditions, Camilla Puglia
Honors Theses
Diabetes is one of the leading causes of death in the United States, taking more than 90,000 lives annually. Type 2 diabetes mellitus-induced cardiac fibrosis is a common condition seen in diabetic patients. The AGE/RAGE signaling cascade has been strongly associated with this supposed cardiac fibrosis along with hyperactive myofibroblasts which excessively remodel extracellular matrix tissue, leading to damaged and enlarged heart muscle tissue. In diabetic patients, Advanced Glycation End Products (AGEs) are produced in abundance and accumulate in the extracellular matrix of heart tissue. To investigate the role of of the AGE ligand on fibroblast behavior, increasing amounts of …
Content Analysis Of Vitiligo Portrayal On Social Media, Mary Miller
Content Analysis Of Vitiligo Portrayal On Social Media, Mary Miller
Honors Theses
Given that vitiligo affects a small percentage of the world population, and its affects are not widely known, there is an increasing need to learn of its effects on those with vitiligo, both physical and psychological. Social media has become an influential platform to spread awareness and build confidence in those with vitiligo. This study aims to find how vitiligo is portrayed through social media using content analysis procedures. This is a cross-sectional content analysis that utilized a codebook to analyze Instagram posts. The results were analyzed by interrater reliability and frequency for each coder. The findings of this content …
Using Deep Learning To Automate The Diagnosis Of Skin Melanoma, Akhil Reddy Alasandagutti
Using Deep Learning To Automate The Diagnosis Of Skin Melanoma, Akhil Reddy Alasandagutti
Honors Theses
Machine learning and image processing techniques have been widely implemented in the field of medicine to help accurately diagnose a multitude of medical conditions. The automated diagnosis of skin melanoma is one such instance. However, a majority of the successful machine learning models that have been implemented in the past have used deep learning approaches where only raw image data has been utilized to train machine learning models, such as neural networks. While they have been quite effective at predicting the condition of these lesions, they lack key information about the images, such as clinical data, and features that medical …