Health Sciences – Page 10 – UROP Spring Symposium 2021

Health Sciences

Scoping review on the impact of type 2 diabetes self management programs in Asian-Americans

Type 2 diabetes (T2D) is among the top reasons for deaths in Asian-Americans (AA). While this group consists of people from over 20 different countries, existing research often does not reflect the diversity and differences among AA, including many existing diabetes self-management programs (DSMP). No previous studies have compared existing DSMPs targeting AA. This scoping review includes adult AA participants and T2 DSMPs. DSMPs were defined as managing an individual’s condition, including self-care, symptom management, family management, medical-management, emotional management, or resource utilization. Keywords used to search for relevant articles included type 2 diabetes, Asian Americans (and its different subpopulations), and self-management. We searched for relevant articles with no data range in six databases, yielding 2581 results. DistillerSR was used to screen and extract data. After screening for titles and abstracts, 311 articles were included in the full-text screening. After screening the full-text articles, 31 articles were included in the final analysis. Articles were excluded if they were non-primary research studies, did not focus on T2D or AA adults, and did not include DSMPs. Data on different interventions and outcomes were extracted and analyzed using a charting form. We hypothesized that there is a need for more research focusing on minority subgroups. We also hypothesized that there is a need for more specialization of DSMPs to overcome many barriers AA face in healthcare, so that specific cultural and ethnic needs are addressed. Improved understanding of existing research may improve and guide future research in developing T2D self-management interventions for AA.

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Bioinformatics study on genetic diseases

Proteins are responsible for much of the functions and regulation of living organisms and their body systems. Understanding protein function has much to do with protein structure, thus, knowing what form proteins take is very useful. There are millions of different types of proteins, many of which are essential for human survival. Current ways to determine protein structure require knowledge of the genetic DNA sequence, which can later be translated into an amino acid sequence, thus revealing the shape through chemical properties. However, this study aims to create a program that can accurately predict protein structure without knowledge of the amino acid sequence. Instead, cryo-EM density maps are utilized to map the protein structure. The density maps are created utilizing convolutional neural networks (CNN), which are used to predict key atoms. These deep learning techniques are applied to create the program that can accurately predict a protein structure. This research is valuable because other scientists who require knowledge of protein structure could use it, especially if they do not have access to a specific genetic sequence they need. Next steps include perfecting the program such that the predictions reach the threshold for accuracy.

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Understanding how liver metastases regulate cancer trajectory

Moniah Almaweri Pronouns: She, Her, Hers Research Mentor(s): Michael Green, Assistant Professor Research Mentor School/College/Department: Radiation Oncology, Michigan Medicine Presentation Date: Thursday, April 22, 2021 Session: Session 5 (3pm-3:50pm) Breakout Room: Room 10 Presenter: 4 Event Link Abstract For privacy concerns this abstract cannot be published at this time. Authors: Moniah Almaweri, Priyal Bajaj, Long

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Defining the Impact of Liver Metastases on Disease Trajectory

Priyal Bajaj Pronouns: She/Her/Hers Research Mentor(s): Michael Green, Assistant Professor Research Mentor School/College/Department: Radiation Oncology, Michigan Medicine Presentation Date: Thursday, April 22, 2021 Session: Session 5 (3pm-3:50pm) Breakout Room: Room 10 Presenter: 5 Event Link Abstract For privacy concerns this abstract cannot be published at this time. Authors: Priyal Bajaj, Moniah Almaweri, Long Jiang, Amanda

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Modeling Molecular Pathogenesis of Idiopathic Pulmonary Fibrosis associated Lung Cancer (IPF-LC) in mice

Lung diseases are prolific killers in the United States each year. Of the major respiratory illnesses and cancers, lung cancer (LC) and idiopathic pulmonary fibrosis (IPF) are especially notable. In the US alone, there is estimated to be 250,000 new cases of lung cancer diagnosed annually, and around 130,000 patients die from the disease as well (Cancer.org). Meanwhile, IPF affects close to 200,000 people in the United States with a 3-5 year survival rate of 50% (National Heart, Lung, and Blood Institute). For each of the two diseases, treatment options are very different, making it especially difficult to provide sufficient care. In addition, around 22% of IPF patients will go on further to develop non-small cell lung carcinoma (NSCLC), or lung cancer (The Lancet). While it is not completely clear as to why this happens, it is likely due to increased inflammation in the lungs, providing an optimal ground for tumor growth.

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Skilled Reaching Performance in a Model of DYT1 Dystonia

DYT1 Dystonia is a neurological condition that causes abnormal, sustained twisting postures, resulting from a Delta-E mutation in the torsinA gene. Mice with this genotype have few motor abnormalities, making it difficult to test treatments in this model. However, previous attempts to phenotype these mice used simple motor assays. The project in which I am assisting is the first project aiming to find the relationship between mice with a DYT1 Dystonia mutation and their ability to perform skilled reaching, a demanding task that requires fine motor coordination. To gather results, I am scoring hundreds of videos of mice reaching for pellets on a pedestal to see how the DYT1 genotype impacts a mouse’s ability to reach. In scoring videos, I watch each video numerous times and give it a score 1-10 based on how/if the mouse reaches for and grasps the sugar pellet. I put these results in a spreadsheet, and will analyze them to determine if DYT1 mice are significantly impaired compared to wildtype mice. Because this is a brand new project, there have been no results. I just started scoring videos this week, however, the lab suspects that these will have issues obtaining the pellet This would suggest that they have subtle motor abnormalities that could be used to test therapies. The results of this experiment are important because they can be the first steps in finding treatment for humans with DYT1 Dystonia.

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Protein Structure Prediction Using CryoEM Density Maps

The goal of my research was to prepare my own dataset, which includes a training set and a test set, and build my own neural network in order to predict protein backbone with cryo-EM density map as my input. In order to get to know more about deep learning and neural networks, I completed 2 coursera courses recommended by my mentor to gain the skills needed to start building my own neural network by using python software. I also read one major literature that focused on cryo-EM technologies to further learn about how incorporating density maps of proteins can provide us with important structural information of proteins. A density map can be obtained using cryo-electron microscopy in order to obtain atomic-resolution models of the protein, including the coordinates of the backbone protein atoms. However, the index/connection of these atoms are lost during the process of obtaining the density map. Predicting the protein backbone will allow us to restore the connectivity of the backbone atoms in the protein and improve the CR-I-TASSER, which incorporates both deep learning and I-TASSER force fields in order to provide accurate structures of the protein backbone.

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Development of protein property prediction methods from sequence based on deep learning

Although proteins have become increasingly easier to sequence, experimental determination of a protein’s structure remains difficult and time-consuming. Therefore, the prediction of a protein’s structure and properties based on its sequence is a key challenge in making better use of the vast amount of sequencing data. Our project seeks to develop a deep-learning-based method that uses a protein’s sequence to predict its properties, such as phi/psi angles and solvent accessibility. The initial goal of the project was to design and write the deep-learning program using the PyTorch library. After completion of the program, we assembled a training and testing set based on existing data from the Protein Data Bank and used the training data to train the model. We then ran the testing dataset and analyzed the results by comparing the predicted properties to the experimentally determined ones. While we do not have any results yet, we hope to be able to make conclusions about the relative effectiveness of the model we design compared to existing models for prediction. The results we obtain could help us determine which prediction techniques or algorithms are well-suited to this task, or which ones lead to errors and thus may need to be avoided in future research. The results could also contribute to improving the accuracy and efficiency of computational protein structure prediction, allowing scientists to make better use of the available sequencing data without the difficulties of experimental determination.

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De novo design of PD-L1 protein-based inhibitor

Certain types of proteins, such as PD-L1, enable a cancer cell to bypass T-cell immune checkpoints. This results in cancer cells being undetected in the body and time for cancer cells to grow and multiply. This investigation attempts to create a favorable PD-L1 protein inhibitor so that the cancer cells that it resides on will be detected by the body’s immune response. Through the usage of PERL programming, EvoDesign, and FoldDesign, the best protein inhibitor will be determined by its binding affinity to the protein and its stability and will be tested in a wet lab in the future. The results of this study will eventually be used and tested to see whether the designed inhibitor is suitable for usage.

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Bioinformatics study on genetic diseases

Missense single nucleotide polymorphisms (SNPs) are single point mutations that alter the amino acid produced. By changing the amino acid, protein stability, pathogenicity, or chemical properties of the protein can change. These mutations can also cause various diseases, specifically diabetes, intellectual disability, and speech-language disorder. However, not all mutations are pathogenic. This study’s objective is to research the properties of these mutations by looking at the change in free energy, amino acid change, and pathogenicity and to determine whether or not it’s harmful to humans. The study specifically focused on the forkhead transcription factors, a set of DNA-binding proteins involved in regulating gene expression. Data was gathered from the Uniprot database, and PyMOL was used to determine protein to DNA contact. EvoEF was used to calculate the change in free energy, and SIFT and Polyphen-2 were used to predict pathogenicity. The results showed common trends among most pathogenic mutations such as high ??G values, no DNA contact, and a high damaging score. Therefore, the data collected is useful for making predictions about mutations.

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