We attempted to apply a novel approach to stock market predictions. The Logistic Regression machine learning algorithm (Joseph Berkson) was applied to analyze news article headlines as represented by a bag-of-words (tri-gram and single-gram) representation in an attempt to predict the trends of stock prices based on the Dow Jones Industrial Average. The results showed that a tri-gram bag led to a 49% trend accuracy, a 1% increase when compared to the single-gram representation’s accuracy of 48%.

Maternal morbidity and mortality rates in the United States continues to rise, with a wide range of contributing factors such as mental illness, cardiovascular disease and systemic inequality. This metastudy provides a holistic view of the research that has been published on the issue of U.S. maternal healthcare from 2000-2022. The patterns of publications on specific topics over time can tell us what is perceived as a current major cause by physicians, public leaders, researchers, and the public. A deeper dive into systemic inequality as a cause of maternal morbidity and mortality highlights it as a major contributor to these high rates, but that progress is slowly being made through the implementation of detection and prevention tactics, as well as accessible prenatal programs and care.

The field of veterinary medicine can be rewarding, but also very demanding. Research has shown that many practicing veterinarians struggle with mental illness, and the profession has one of the highest suicide rates in the United States. Research has also shown that many veterinary students struggle with mental illness. It is important to further research the mental health of veterinary students and how that can correlate with one's mental health as a practicing veterinarian. The purpose of this project is to summarize findings of the literature concerning the mental health of veterinary students and to present a new resource, the Wisdom Vet app, that can potentially support the well-being of veterinary students.