Healthcare acquired infections (HAIs) contribute substantially to the burden of morbidity and mortality among hospitalized patients in the United States and are a significant economic burden on the healthcare system. Approximately 3-5% of patients in the United States contract a HAI during the course of their treatment, and an estimated $7-14 billion are spent annually on treating these infections. HAIs also contribute to development of antimicrobial resistance (AMR), placing additional burden on the healthcare system and substantially raising the costs of treatment. Novel microbiome engineering technologies that address the HAI problem are currently being developed by researchers. We will conduct a qualitative study to assess the factors influencing the adoption of novel microbiome engineering among hospital nurses in the United States.

We are assessing clinician and breast cancer survivors' perceptions of our interactive, artificial intelligence-based decision aid, designed to help inform patients who are considering breast reconstruction after mastectomy.

This study seeks to build novel understanding of the household-level impacts of extreme weather events (like flash floods) on motor vehicle ownership and travel patterns.

This research study aims to improve the resolution of cone beam computed tomography (CBCT) imaging to reduce radiation exposure using software.

This study is about how young adults use generative AI, which can create text, images, and more. We want to learn why they use these tools, how they feel about them, and whether using them is connected to their personality and mental health. Understanding this can help us know more about how these technologies affect people's lives.

To test the safety of the trial drug, MK-8527 compared to emtricitabine/tenofovir (FTC/TDF) and to see how well MK-8527 works to reduce the chance of getting HIV-1 infection compared to FTC/TDF.

The study is designed to look at Crohn's disease over a period time, from before a surgery to 12 months after the surgery has taken place. The role bacteria in the gut plays in Crohn's disease (CD) is not well understood. Which particular microbes contribute to disease remain unknown. In CD, ~70% of patients will end up requiring surgery due to chronic unrelenting complications, and ~50% require additional surgery. We hope to identify key microbes at the time of surgery in stool and tissue and correlate it over time with data collected at timepoints after surgery. We will use this data with clinical information to determine if specific microbes are associated with disease recurrence.

The United States is facing a worsening physician shortage that particularly affects people living in rural or underserved communities. One strategy used to address this problem is clinical exposure programs for students in high school, college, and/or medical school. Research currently suggests that these programs could influence participants' interest in medicine, specialty choice, and practice location preferences. Mentoring in Medicine is a summer clinical exposure program that targets college students in the Johnstown area of Pennsylvania. This region of the state is socioeconomically disadvantaged and medically underserved. No formal follow-up has been conducted on the participants since the program began in 2004. We plan on surveying these past participants to examine their academic and career interests and outcomes. We will also use physician databases to determine career outcomes. This data will help contribute to the body of evidence regarding educational interventions to improve medical shortages in the United States.

The purpose of this research study is to collect data about attitudes towards social topics in the news.

We are studying how to make medical test results easier for patients to understand. Pathology reports, which explain what doctors find in tissue samples, are often written for medical professionals and can be hard for patients to read. In this study, we are testing whether artificial intelligence (AI) can help explain these reports in plain language. Patients having a routine screening procedure will read a sample report with or without an AI-generated explanation. Then they will answer questions about how well they understood the report and how they felt about it. We want to learn if using AI helps people better understand their health information and feel more confident making decisions. This will help improve the way test results are shared in the future.