Abstract/Overview:
As of 2022, the CDC has found that nearly 14% of pregnant mothers in the US have drank during pregnancy. This roughly translates to about 1 in 7 mothers drinking while pregnant with about 1 in 20 mothers recording binge drinking. The high-risk behavior of alcohol consumption becomes exceedingly more dangerous for pregnant mothers; maternal drinking not only endangers their own life but also the life of the unborn fetus as well. Researchers have found that most of these prenatally ethanol-exposed fetuses that develop Fetal Alcohol Syndrome (FAS) are experiencing premature death due to dilated cardiomyopathy. This project investigated the heart’s l-type calcium channels’ (LCCs) role in the development of the cardiac disorder. The alternative hypothesis states that there will be a difference in the presence of LCCs in FAS offspring versus healthy control (HC) offspring. Archived hearts from vervet monkey subjects were cut, frozen, sliced, then stained with a DNA-specific immunostain for visibility. Stained images of FAS monkey models are age-matched with HC subjects and compared based on the presence of LCCs. This project is ongoing, but the data collected so far has shown a presence of LCCs in FAS models and a lack thereof in HC models; the data allows us to accept the alternative hypothesis. The data collection process will continue to provide more images for a more reliable conclusion.
Abstract/Overview:
For years, the PGCPS Medicaid Office has been documenting student bus route data using paper. Currently, the bus drivers take roll calls and follow other procedures to document data that would be used to bill Medicaid later on. This may have been the norm in the former years, however, the current year is 2022, and many things have now become digital. Thus, the idea of the Medicaid App was formed, this would be an app that would make documentation digitally accessible, which would come with other features such as backups, statistical graphs, and potentially more to come. Many benefits come with an online documentation system such as paper usage reduction which helps the environment, stronger security, and an ability for this system to be integrated into other technological tools at the Medicaid Office if needed. After several iterations and optimizations, a stable version of the app was successfully built using Firebase, Google Sheets, Figma, and Android Studio. Medicaid bus drivers will no longer use paper to gather data, but may soon have access to this app in the forthcoming years. The Medicaid Office in PGCPS should now be able to go paperless, think digital.
Abstract/Overview:
Human-following robots are a significant technological advancement in today's world. They can assist us in carrying items, accompanying us in stores, or accompanying us on a jogging session in the park. There are numerous additional benefits to developing human-following robots. They can, however, be quite expensive, limiting the number of robots that can assist people. Thus, the idea of the Human Following Arduino Robot was formed. This robot would be a relatively low component and low-cost robot the average human could afford. After several iterations, the final prototype was made using an Arduino UNO, motor driver shield, ultrasonic and infrared sensors, TT and servo motors, and custom laser cut and 3D printed parts. The technology used to make the human-following robots can now be manufactured with relatively cheap parts, but may soon work as well as actual cargo robots like the Gitamini.
Abstract/Overview:
Asthma is very prevalent in the D.C. area. About 1 in 6 people in D.C. have been diagnosed and many more suffer without a diagnosis. This growing trend has led hospitals to prioritize asthma detection and treatment. As a result, the Pediatric Surgical Innovation Team at Children’s National Hospital in D.C. aimed to create a computer algorithm/model that would allow worsening asthma to be detected from home. The telltale auditory sound is wheezing, which helped determine the severity of the asthma case. The goal of this model was to train the computer to differentiate between recording with severe asthma (prominent and prolonged wheezing) and recordings with clear lungs. The first goal of this project was to obtain different recordings of pediatric lung sounds, both with and without wheezing. The recordings obtained were labeled and cross-validated between multiple clinicians. Next, a model/algorithm was proposed and created. The algorithm utilized several techniques, including learning algorithms (artificial intelligence, machine learning, neural networks, and deep learning), spectrograms, training data, and the cross-validation of data. The algorithm was extensively researched, trained, tested, and developed. This project has not been completed because of the number of lung recordings currently obtained and labeled. Convolutional neural networks require large amounts of data to be trained and tested. The Children’s National set does not have enough labeled and clean recordings to create an accurate algorithm. Once the data set is expanded and labeled, the algorithm will be closer to being complete.
Abstract/Overview:
Over the past 20 years, initiatives by the World Health Organization (WHO) and UNAIDS have made great strides in reducing mother-to-child transmission of human immunodeficiency virus (HIV). Despite these efforts, there continue to be over 150,000 new pediatric HIV (pHIV) cases annually, remaining a global health crisis. Antiretroviral therapy (ART) has improved childhood survival, but only an estimated 53% of children have access to treatment. Early in the disease progression, pHIV infants display neurological impairments, including executive and cognitive functions, as well as in the motor, sensory, visuospatial and emotional domains. Imaging studies suggest a disruption within the prefrontal cortex network, however the extent of damage remains unknown. Using a pediatric simian immunodeficiency virus (SIV) model, we have previously shown neuronal loss in the hippocampus and dorsolateral prefrontal cortex (dlPFC). Here we expand these studies to investigate the cingulate cortex which is involved in the processing of emotions, movement, and visuospatial orientation. We hypothesize that pediatric SIV-infection reduces the neuronal population in the cingulate cortex. To test this hypothesis, infant macaques were either intravenously (IV) inoculated at ~1 week of age, or orally inoculated at 9-weeks of age with SIVmac25. SIV-infected and uninfected controls were euthanized between 16-30 weeks of age. Brains were serially-sectioned, stained with Nissl to identify neurons, and design-based stereology was used to estimate the neuronal population. Preliminary data suggests a reduced neuronal population. This study provides insights to the potential role that the cingulate cortex hass in the neurological consequences of pHIV.