Health Sciences – Page 26 – UROP Spring Symposium 2021

Health Sciences

Health and Sexual Education for Adolescents with Spina Bifida

The dearth and unsatisfactory delivery of disability-specific sexual health knowledge and credences discounting the disabled population’s sexuality have precipitated a concerning health disparity between able-bodied and disabled patients. The increased life expectancy and relatively higher rates of child-bearing among females with Spina Bifida (SB) necessitates a sexual health intervention to mitigate instances of STI transmission and unintended pregnancy. Collated data from surveys of females aged 10-19 with SB and their parents suggested an accessible, online platform is optimal for transmission of sexual education respective of SB’s distinctive physical and cognitive features. The project is currently in the phase of intervention prototype formulation, after which a clinical trial will be conducted to assess effectiveness. Further trajectory includes monitoring long-term effects and circulation of the resource on media platforms. Conclusive results have not yet been drawn; however, we hypothesize this intervention will enhance sexual health efficacy and frequency of medically recommended practices within the population of females with SB. In turn, this will decrease risk of STIs and unintended pregnancy, diminishing the aforementioned disparity, and fostering a greater number of ideal health outcomes.

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The influence of COVID-19 related racism on the behaviors amongst Asian Americanss

Since the start of the COVID-19 Pandemic, many instances of anti-asian sentiment (discrimination, hate crimes, etc.) have been experienced by Asians in the United States. Such experiences can influence one’s well being (the state of being comfortable, healthy, or happy): a huge and overlooked aspect of health. Due to the novelty of this issue, little is known about the depth of the implications these experiences have on one’s well-being, and their inherently behavior; this study aims at looking at the influence of experiencing or viewing such racial instances on protective behaviors carried out by Asians Americans. Protective behaviors can be operationalized as behaviors that ensure one’s feeling of safety. The study utilized an online survey in a convenience sample. The final study results will provide data to verify hypotheses that if there are positive correlations between experiencing or viewing COVID-19 related discrimination and the likelihood of carrying out protective behaviors. The information will provide insight about racism and its association on protective behaviors. The results can better assist in developing mitigation strategies that are tailored to individual characteristics in hopes of improving one’s overall well-being and ultimately, improving their health.

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Promoting Flu Vaccination among Chinese Americans; Providing Flu Education through Trusted Messengers

Objective: The flu is a severe respiratory disease that can cause severe complications, including hospitalization, and is responsible for 290,000-650,000 [6] global deaths per year. The flu vaccine is safe and the best way to prevent the spread of the virus. However, there remains a lot of hesitancy and misinformation regarding the vaccine and the virus itself especially within the Asian community [2]. This study sought to examine reasons for flu vaccination hesitancy as well as to increase influenza knowledge, specifically among the Chinese American community. Methods: Eastern Michigan University Center for Health Disparities Innovations & Studies held 11 CDC funded mobile flu clinics in Michigan in Fall 2020 and provided vaccinations to 337 individuals; during the clinic, bilingual trusted messengers provided flu education. This project reported the results from pre and post flu vaccination surveys with 11 questions about influenza knowledge and demographic information. The results were based on data from 39 Chinese participants. Statistical analyses were conducted in order to measure the efficacy of the flu informational sessions for overall and Chinese participants. Results: The survey data showed that the main reason why the Chinese sub-population had not gotten vaccinated in the past was due to lack of access to the vaccine. Paired t-tests showed that the flu education resulted in significant improvements in overall participants’ knowledge on various aspects about flu, for example, timing of the flu season, vaccination timeline, ways to prevent flu, and others. Conclusion: This study indicated that education on flu and flu vaccination is necessary in order to promote increased vaccination, specifically among Asian communities. Increased education and vaccination will ultimately slow the spread of the virus and improve health outcomes of underserved communities. However, due to the small sample size of the Chinese participants, further research with a larger sample will provide more information about this specific Asian American population. Future intervention efforts can be focused on addressing flu vaccination hesitancy which will provide additional insights for effective strategies to promote COVID-19 vaccination in this population.

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Food Security and Inactivity During COVID-19: Perspectives from the MOTION Coalition.

This presentation will focus on hearing the voices of various stakeholders in the MOTION coalition about Food Security in Detroit. Stakeholders include Dr. William Dietz, Director of Sumner M. Redstone Global Center for Prevention and Wellness, Chef Kevin Frank, Detroit Public Schools Community Assistant Director of Food and Nutrition, and Mrs. Lonias Gillmore, Senior Public Health Consultant at the Michigan Department of Health and Human Services(MDHHS). Topics will include how COVID has impacted food security; legislation proposals/changes that have been made through the 2020-2021 calendar year; and what changes need to be made moving forward to have a more food-secure community in Detroit.

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Coronary artery segmentation for automatic stenosis detection

One type of coronary artery disease (CAD), stenosis, is characterized by the narrowing of the coronary arteries, which is the leading cause of death in the United States. One of the most popular ways to diagnose stenosis is the coronary angiogram, from which a doctor can diagnose stenosis by finding places of narrowing in the arteries with the video or the pictures acquired. A lot of studies are currently focusing on automatic coronary artery segmentation, a critical step of a computer-aided system that assists doctors in detecting coronary stenosis. Here we propose a deep learning pipeline using DenseNet-backbone U-Net for coronary artery segmentation in angiogram images, which could be combined with pre-processing and post-processing steps for stenosis detection.

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Development and Assessment of an In-Vehicle Cardiac Monitoring and Severe Event Prediction System

Cardiac issues are serious medical issues that affect a large part of the population. Many studies have looked into using programs to automate the identification process and help discover symptoms early without the need for a doctor’s visit. The project focuses on developing machine-learning algorithms that can rapidly detect cardiac issues, specifically arrhythmias, to be implemented directly into motor vehicles. This project involves conducting training and testing of non-parametric machine learning algorithms on the publicly available MIT-BIH Arrhythmia Database. Models are created on MATLAB and tested based on statistics such as accuracy, specificity, and sensitivity through a confusion matrix. So far, decision tree based models have shown accuracies around 90% on smaller datasets after tuning various hyperparameters. Testing is to be continued with different models such as Support Vector Machines (SVM) and Convoluted Neural Networks (CNN) on larger datasets that will ideally result in significant improvements to the final model. These models will be better suited to accurately predict cardiac issues even with the extra signal noise when built into embedded systems within motor vehicles. These systems will add an extra layer of security to vehicles and help identify symptoms earlier than traditionally possible.

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Bioinformatics study on protein structures

The majority of proteins are composed of foldable, stable subunits called domains. The structures of these proteins can be made up of a single domain or multiple domains. Determining structures of multidomain proteins is a crucial step in elucidating their functions and designing new drugs to regulate these functions. However, it has been largely ignored by the mainstream of computational biology due to the difficulty in modeling inter-domain interactions. Therefore, almost all of the advanced protein structure prediction methods are optimized for modeling single domain proteins. In this study, we presented a method to construct a multidomain protein structure library with known full-length structures to assist the multidomain protein structure prediction. We collect all multidomain proteins from the Protein Data Bank based on the DomainParser, and multidomain proteins defined in CATH and SCOPe databases are also included in the library. This resulted in a total of 15,293 multidomain proteins in the library. The completeness of the library is examined by structurally matching a set of non-redundant multidomain proteins through the library using TM-align. The results show that most of the cases can obtain at least 1 template with correct global fold (TM-score >0.5) from the library, which indicates that the constructed multidomain protein library can likely be used to guide the multidomain protein structure modeling.

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Pancreas Segmentation via Image Processing and Machine Learning

Pancreas segmentation is consistently one of the least accurate out of all organ segmentation due to its low contrast in its boundary and high anatomical variability in its geometric properties. Many hospitals and biomedical laboratories therefore do not have accurate methodologies for cancer detection, 3D modeling, etcetera for pancreas scans. This paper serves to improve current pancreas segmentation methods by making use of a type of Convolutional Neural Network (CNN) called a U-Net in addition to image processing techniques such as an Integrated Hausdorff-Sine Loss Function for preprocessing and 3D Gaussian smoothing for post processing. The CNN was trained on Computed Tomography (CT) images in n-fold cross-validation from a public NIH dataset and a private dataset from Michigan Medicine. Data augmentation was performed on the CT scans using Keras, an open-source neural network library. Accuracy and precision of the CNN was tested using a Dice-Sørensen Similarity Coefficient (DSC) and Jaccard Index (JI). The mean DSC and JI achieved for the proposed methods are expected to be at least 70.00%.

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Multidomain protein analogous templates detection based on TM-align

Protein structure prediction is a crucial step to understanding and transforming biological and cellular functions. Most proteins exist with multiple domains in cells for cooperative functionality. However, due to the technical difficulties in structural biology, most of the multidomain proteins have only single domain structures solved. To guide the multidomain protein modeling, we present a two-step procedure method to detect the analogous templates from the multidomain protein structure library which includes the multidomain proteins with known full-length structures through the structural alignment. In the first step, individual domains are used to evaluate each template by TM-align, regardless of the overlap between the alignments of different domains, and the average TM-score of all domains is calculated as the local score of a template. In the second step, the top 500 templates selected from the first step are evaluated by the TM-align again with no overlap allowed in the alignments of different domains, and the average TM-score is defined as the global score of a template. Finally, the template with the best global score is selected as the best template. We test the method over 2,269 non-redundant proteins with 2 domains. With homologous templates with sequence identity >30% to the targets excluded, the results indicated that >80% of target proteins have at least 1 template with a TM-score >0.5 and alignment coverage >90%. The data demonstrate that most interdomain orientations can be inferred from the template library, which probably can be used to assist the multidomain protein structure assembly from the independently determined/predicted domain models.

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Testing requirements for cannabis products vary by US state

As an increasing number of states in the USA legalize medical and recreational cannabis (i.e., marijuana) use, testing of cannabis products (for potency, contaminants, etc.) should be a priority to ensure safe cannabis use by consumers. The present study compiled the testing requirements for cannabis products from the 29 states that currently have legalized medical or recreational cannabis. Results found that the testing requirements for cannabis products varied considerably by state. In order to maximize the benefits and minimize the harms of retail cannabis, policy makers should develop a universal and comprehensive framework for testing requirements. The standardization of cannabis testing requirements would promote safer use for medical and recreational cannabis consumers.

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