Health Sciences – Page 27 – UROP Spring Symposium 2021

Health Sciences

Exploring Feeding Cues in Older Hospitalized Infants: A Survey Among Hospital Staff and Feeding Specialists

Some hospitals feed infants on a provider-driven schedule, which emphasizes quantity of the oral feed over the quality and frequently ignores infant oral-feeding engagement and disengagement cues to quickly meet oral feeding goals. Infants who are fed using this type of approach quickly learn that eating is a scary process that should be limited or avoided entirely. Alternatively, a cue-based feeding approach focuses on responding to behaviors infants use to communicate oral-feeding readiness and disengagement. Cue-based feeding promotes safe, efficient oral feeding skill acquisition and avoids negative feeding experiences that result in oral aversions. Currently, the majority of the cue-based feeding literature focuses on premature infants and newborns, and there is a lack of research for implementation with older infants (6-12 months). The research that does exist primarily focuses on behaviors observed in typically developing, healthy infants; however, behaviors of older hospitalized infants can differ dramatically from healthy infants. Consequently, cue-based feeding guidelines for older hospitalized infants do not exist. To address this gap in the literature, we used a brief 15-question online Qualtrics survey to gather information about (1) the use of cue-based feeding in older hospitalized infants and (2) specific feeding behaviors observed in older hospitalized infants. Participants included nurses, feeding therapists, and unit techs. Participation was incentivized with the chance to win a gift card. Data analysis will include frequency counts and chi-square tests to identify relationships between hospital position, length of employment, and cue-based feeding beliefs. The information gleaned from this study, in combination with existing literature on cue-based feeding, will add critical knowledge to enhance the implementation of a cue-based feeding protocol hospital wide, ensuring that even older infants will be protected from scary and overwhelming feeding experiences that place them at risk for long-term feeding issues.

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De novo Design of Protein Based HER2 Inhibitor

Protein function is determined by protein structure, which is in turn determined by the corresponding protein sequence. Because we understand how a protein adopts a particular structure, it is possible to redefine the function of a protein by working backward from the desired structure to the sequence. De novo protein design is used to design new protein structures from scratch based on loose constraints supplied by the user. The ultimate goal is to develop inhibitors to the HER2 receptor to prevent ERK and AKT cancer pathways from being activated. The hope in doing this is to prevent unnecessary cell signals from replicating that ultimately lead to breast cancer. To design mini protein inhibitors, we will first extract the native interfaces from experimentally solved structures of known HER2 binding proteins. The next step will be determining the proposed topologies for the designs. These topologies will be fed into FoldDesign to generate the inhibitor structures. The resulting scaffolds will then be fed into EvoDesign to design sequences for these structures. The results can be validated in either a wet lab or using computations. The designs will be tested for their binding affinity and folding stability. We expect to be able to design an inhibitor to HER2 with high affinity and demonstrate stability with computations.

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The role of CD39 in vascular aging

Arterial calcification involves crystallization of calcium and phosphate in the extracellular matrix of the arterial wall. Vascular aging, the gradual hardening and stiffening of the arteries over time through arterial calcification and atherosclerosis, and the subsequent consequences of it including myocardial infarction and heart disease are leading causes of death in the United States. As a result, the mitigation of these illnesses in a clinical setting is increasingly important. This research looks to find the link between the influence of ectonucleotidases CD39 and CD73 on inflammation and arterial calcification using two target genes (RUNX2 and GAPDH) of human coronary artery smooth muscle cells treated with ticagrelor (a P2Y12 inhibitor), CGS21680 (an adenosine A2A subtype receptor antagonist), and an ossifying medium. The osteogenic medium is expected to amplify the expression of the RUNX2 gene which would cause calcification in the human coronary artery smooth muscle cells, while the treatment of ticagrelor and the adenosine receptor antagonist should reverse it. Preliminary evidence demonstrates that although the ossifying medium increased the number of transcription factors that aid in calcification, neither ticagrelor nor the CGS21680 reversed this effect.

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Role of the MYRF transcription factor in retinal development

Vision is critical to quality of life. Diseases of the retina, which senses light and sends signals to the brain, can lead to significant vision impairment. One such disease called nanophthalmos affects the overall growth of the eye and can be associated with genetic mutations in the myelin regulatory factor (MYRF) gene. In this project, we use a mouse model to examine the role of the MYRF as a regulator of eye development and disease in the RPE, and to define new genes that could be implicated in retinal disease. Myrf is a transcription factor expressed in the retinal pigment epithelium (RPE), a supporting cell in the retina. We utilized single cell RNA sequencing, scRNA, from mouse eye cups, at varying stages of development to compare gene expression profiles with wild-type eye cups to those loss of Myrf. We used Seurat software for analysis to identify genes that are differently expressed in these two genotypes among different cell types to provide clues into how MYRF impairs eve development. We hypothesize that genes involved in pigmentation and structure of the RPE will be most affected by loss of MYRF, given that mice lacking MYRF have pigmentary defects and develop retina degeneration. To identify which of the genes regulated by MYRF could be implicated in retinal disease, we have also used Clinvar, an online database to search for diseases associated with genes and their allele variants. We have found approximately seventy-seven genes that are likely regulated by MYRF, but not yet linked to human disease, which will now be screened for variants in disease populations. This project will further our understanding of eye disease caused by MYRF mutations, discover genes regulated by mouse Myrf in eye development, and may lead to new targets for treatments.

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Measurement of Coronary Artery Diameter using Image Processing and Geometric Modeling

While doctors use their eyes to understand and interpret coronary artery data provided by Magnetic Resonance Angiography (MRA) or Computed Tomography Angiography (CTA), researchers have been looking for ways to use technology to automatically track coronary arteries. This document looks to combine two methods of interpreting angiograms, Kalman filtering and a geometric vessel model in order to glean a fuller estimate of a coronary artery’s radius. So far, I have created an algorithm in MATLAB to detect radii across different artery connections. The next step is to compare these radii results to formulae referenced in other research papers and apply the two aforementioned methods to improve the accuracy of my radii measurements. A better estimate of a coronary artery’s radius will prove vital to doctors, who, in relying on their eyes to interpret coronary artery data, might struggle to differentiate noise from weak stenosis requiring treatment. Further, these doctors, though they differ from each other on how they treat varying levels of stenosis, would love to have a standardized way of understanding how severe the stenosis is, and having a more reliable radius will provide exactly that.

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Identifying Brain Edema in CT Scans Using Machine Learning

Brain edema is the swelling of the brain as a result of traumatic brain injuries, strokes, tumors, and infections. This affects the patients cognitive and motor function and can lead to lasting adverse health risks and death. Early and accurate identification of edema can prevent these hazards. Studies have found that brain edema is difficult for clinicians to accurately identify, as it often blends in with other brain matter. Additionally finding a link between the volume of edema and the effect on the patient is considered valuable, but there is currently no standard software in place for this end. Even when clinicians are able to identify edema, they are not able to quantify the volume present. Convolutional neural networks were used for training the model to segment the edema region. Images from the PROTECT III collection at the University of Michigan hospital were used for this research. Some images were previously annotated by clinicians and these images were subsequently used in the process of training the machine learning model. The performance of the model was evaluated using quantitative techniques such as dice, sensitivity, specificity, accuracy, and AUC. The goal of this software is to decrease adverse effects and death related to brain edema by creating a system to quantitatively measure edema and make informed decisions on how to treat the patient based on the information collected.

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Head CT image analysis for detecting edema

Cerebral edema, which is swelling in the brain, is commonly found in patients suffering from head trauma, injuries, or other diseases and can be fatal. The purpose of this research project was to develop a method for automatically detecting and segmenting edema in head CT scans in order to make it faster and easier for clinicians to diagnose and treat traumatic brain injury (TBI) patients. However, edema is difficult to segment due to its unclear boundaries and its similarity in pixel value to other brain tissue. In previous research, most methods for segmenting edema have either been semi-automated or for MRI scans. More accurate methods require MRI scans, but even though an MRI scan is more detailed and can make it easier to segment edema, CT scan is the gold standard for evaluating brain injuries and is faster and more widely available. Therefore, automatic segmentation of CT scans will be very beneficial. In this project, the active contours without edges method, developed by Chan and Vese, is used with manually segmented hematoma as the initial contour. The method was developed in MATLAB. The segmented edema was then compared with the manually segmented images, and the DICE score was used to measure the accuracy. Currently, this method successfully segments select CT scans. However, it needs improvement in order to be more generalizable. In the future, I will look further into other techniques such as deep learning that can help improve accuracy and generalizability in automatic edema segmentation.

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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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Experiences of uncertainty among patients with rheumatic illnesses during Covid-19

Despite the fact that Covid-19 is known to be more severe in patients with other comorbidities, the experiences of people with rheumatic illnesses during the pandemic are seldom discussed. In reality, their experiences are important to understanding both the virus’ physical and emotional effects on at-risk populations. Specific questions were in regards to the main sources of anxiety and uncertainty felt by rheumatic patients, and how many felt impacts to mental health. Using Nvivo analysis software, our research team qualitatively coded deidentified patient transcripts and case reports from FORWARD, the National Databank for Rheumatic Diseases. The software allowed us to compare the data presented in order to find common issues between patient experiences. These comparisons revealed that many of the patients, whether diagnosed or not with Covid-19, felt great anxiety and uncertainty during the pandemic. Further analysis showed that these feelings came from deaths of friends or family members, worsened symptoms in conjunction with their illness, or general lack of knowledge of the effects the virus could have. Although each patient as an individual had their own experiences, the qualitative coding allowed us to find that uncertainty was a major issue that still affects the mental well-being of these patients, their family members, and their friends. This project will allow health care providers and other stakeholders to understand the rheumatic patient experience with viruses from an emotional level, as well as a physiological one.

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