BC-75 Investigating the Effects of Chaotropic Salts on Protein Corona Formation in Polyethylene (PEG)-b-polylactic acid (PLA) Polymersomes

In recent years, polymersomes (PS) have emerged as highly promising drug delivery systems due to their tunable membrane properties, high colloidal stability, and ability to encapsulate both hydrophilic and hydrophobic drugs. These nanoscale vesicles, composed of bilayer membranes formed by self-assembling amphiphilic copolymers, offer a unique platform for versatile therapeutic applications. However, despite their potential, a significant barrier to their clinical use is the formation of the protein corona—a layer of proteins that adsorbs onto the surface of nanoparticles upon exposure to biological fluids. The composition of the protein corona plays a critical role in determining the PS interactions with biological systems, including circulation time, cellular uptake, and biodistribution. However, it is highly dynamic and influenced by factors such as the PS size, surface properties, and incubation conditions. Protein adsorption on the PS surface is inevitable, and due to its large impact on biological fate, researchers have begun to explore methods to engineer and control the protein corona to optimize therapeutic efficacy. In this study, we investigate the effects of various ionic environments on the protein corona composition of PEG-b-PLA PS in bovine and human serum. Specifically, we have used chaotropic salts Na+\u3e Ca2+\u3e Mg2+ in order of Hofmeister Series strength to enable specific protein binding. We have identified that incubation with divalent chaotropic cations leads to significant changes in the protein corona composition, identified through mass spectroscopy. Specifically, we see a decrease in albumin adsorption and an increase in novel proteins within the corona. Particularly, the addition of Ca2+ dramatically changes the top 20 most abundant proteins due to the prevalence of calcium-binding proteins in serum. Early work using human serum indicates similar findings. By systematically analyzing how ionic strength and composition influence corona formation, we aim to identify key parameters for tailoring protein-nanoparticle interactions. This work not only advances fundamental knowledge of corona dynamics but also lays the groundwork for designing polymersomes that can be customized for individual patients

SS-16 Using political affiliation to study in-group and out-group behavior

Social conformity occurs when many people change their beliefs or behaviors to better fit with the characteristics and beliefs of the group. This conformity can take many forms, it could be a person going along with the group decision even if they disagree, pretending to like something to fit into the group, or coming to believe something is true because their group believes it. The consistent finding in the literature is that people are more likely to conform to the norms and behaviors of their own group (in-group) than to a group to which they do not belong (out-group). While other studies have shown that cultural background, age, gender, and personality can influence conformity, the current study wanted to determine if stated political affiliation (Democrat/Republican) would also influence one’s willingness to either conform or not to the group. Participants were presented with a series of 50/50 green and blue color distributed pixelated squares on a computer. They were first asked to identify if the image was more green or more blue, and then they were asked to submit their confidence rating of their decision on a Likert scale from 1 (not confident) to 5 (very confident). After each presentation, the participants were prompted with a statement that either stated the opposing party or their political party picked the opposite color that they previously selected (e.g., if they selected green, they were told the group had selected blue). The participants were then asked again about the distribution of blue and green in the image and asked if they wanted to change their response. It was hypothesized that the participants would be more likely to change their response (i.e., conform) for the in-group (same political party) and less likely to change their response for the outgroup (opposite political party)

Green Luxury Hotels: How Sustainability Can Succeed with Better Governance

Vacations are universal. People from all around the world use trips as a getaway from their routine lives. They could be long-distance or short, budget or luxury, but they all share the purpose of a new and different experience. As described by the United Nations, tourism “enhances the well-being of individuals, safeguards the natural environment, stimulates economic advancement, and fosters international harmony.” (Pololikashvili, UN Tourism, 2024). However, as the world has grown to become more interconnected than ever, tourism has also grown exponentially. With the post-COVID-19 boom, there is more worry today than ever before regarding the negative impacts travel has on both the environment and humans. These impacts can be grouped into three main observable categories. The first is depletion of natural resources, for example, immense water usage for pools, wasting food that locals may require, or burning excess fossil fuels to power transportation or facilities. This contributes to the second group, creating air and noise pollution, which lowers the quality of living in the area. Solid waste and litter also grow from the high amount of food and disposable products used throughout the experience. Lastly, there are physical impacts on the land caused by increased construction, deforestation, and alteration of the natural ecosystem by tourists (Global Development Research Center). All three of these impacts can especially be seen in the hotel sector, a permanent part of tourism

Focal Adhesion Genes and Proteins Are Differentially Expressed Across Cell Types in Down Syndrome

Down syndrome (DS), caused by an extra copy of chromosome 21, leads to widespread gene expression changes through mechanisms such as transcriptional dysregulation and altered protein interactions. These disruptions contribute to a range of clinical features, including impaired wound healing, immune dysfunction, and neurodevelopmental abnormalities. This study focused on how DS affects fibroblast morphology and motility—processes critical for tissue repair and brain development. Using quantitative immunocytochemistry and image analysis, we found that DS fibroblasts displayed a broader, less polarized shape, with increased cell perimeter and reduced aspect ratio. However, levels of key adhesion proteins like vinculin, FAK, and β-actin were not significantly different from controls. To investigate upstream causes, we performed a meta-analysis of RNA sequencing datasets across multiple DS models. While results for focal adhesion genes were variable, we observed consistent upregulation of ECM components such as laminins and integrins. These findings suggest that altered fibroblast morphology in DS may stem from changes in ECM composition and adhesion signaling rather than core protein abundance. Such changes may impair cell polarization and mechanosensing, contributing to clinical symptoms. Because similar mechanisms regulate neuronal migration, these disruptions could also affect brain development. Future studies should integrate transcriptomics with spatial and functional analyses to fully understand DS cellular pathology

Opioid Treatment Matters: an Analysis of District Attorney Non-Prosecution

Drug use and drug overdose have become an overriding concern both for civic order and due to the raw death toll from the opioid epidemic. One reform push in the late 2010s/early 2020s, county-level District Attorneys signalling that they would not prose- cute drug use and related crimes, aimed to reduce overdose by reducing criminalization and stigma as a barrier to opioid treatment. At its peak, the policy covered between twenty and thirty million Americans. Using hand-collected data on District Attorney policies and Augmented Synthetic Control for causal identification, I estimate null ef- fects of non-prosecution policies on an urban area’s opioid overdose rates. I hypothesize my findings reflect that non-prosecution policies likely did not affect drug overdose once controlling for the increase in drug overdose in the late 2010s

CH-4 MENTAL HEALTH PREDICTION IN CHILDREN USING THE SOCIAL MEDIA CONTENT

Introduction: Mental health is now a crucial part of leading a fulfilling life. People without proper mental health maintenance cannot live well. It\u27s not just adults who face this issue; nowadays, these cases are also visible in young people, especially those using social media or visiting specific types of online content. There is a high chance that we can identify this behavior early and prevent them from accessing harmful content or help them get the necessary support from adults. Research question: Is it possible to find the early stage of mental health, especially depression or anxiety, in children or young people based on the social media content they are watching or the type of content they are visiting? Methods: Developing a model that helps to flag the type of content they are watching or visiting and flag them of different levels of affect it has on the people or what way it is affecting them and then based on the data, we will train the model for predicting and also protection model Biggest question: The data privacy. No one wants to share the type of content they are visiting or watching, even if we track them How much concern is it that some one is seeing what you are going through? So we have to go through a vigorous process so that data is protected and not used against certain individuals Basically, it’s a kids YouTube how can parents see what type of content the kids are watching? Results: We can identify the people who are facing mental issues beforehand, help them out and also protect children from the dangerous content they are watchin

BS-3 Generative AI in Real Estate: Leveraging Generative AI for Smarter Property Investment

Traditionally, real estate investment strategies relied on approaches such as buy-and-hold, fix-and-flip, and rental income, each with inherent risks related to market fluctuations, interest rate changes, and other limitations. These challenges can make it difficult to identify profitable opportunities in a competitive market, as well as present limitations on an investor\u27s knowledge and ability to analyze large datasets related to an investment area. With the evolving landscape in today’s world with the emergence of more sophisticated Artificial Intelligence (AI), generative AI can revolutionize the world of real estate by revolutionizing market analysis, property valuation, risk assessment, and investment strategies. This research explores the different ways generative AI and real estate investments intersect, and how generative AI can be used as an investment strategy for smarter property investments. Generative AI aids in addressing previous challenges revolving around real estate investments by enabling inventors to make informed decisions based on historical data. AI also has the ability to analyze market trends, property valuations, and analyze neighborhood dynamics to identify undervalued properties with high-return opportunities. Additionally, AI can be used to help those renting out properties by assessing the financial stability and rental history of potential renters, thus reducing turnover rates and evictions. Overall, with predictive models that assess economic indicators and market conditions, coupling current data with historical data, generative AI helps investors reduce bad investment risks and increase the odds of maximizing returns. This research paper aims to discuss the various applications of generative AI in real estate by providing insights into how generative AI can improve decision-making, reduce costs, and increase profitable returns for investors in both residential and commercial markets

SS-1 Understanding Stigma; How Community Members and College Students View and Respond to People Experiencing Homelessness

The purpose of this study is to better understand how perceptions of bias and discrimination regarding race and gender influence responses to people experiencing homelessness (PEH). Prior research has examined the understanding of the effects of race and gender biases regarding the perceptions of PEH (Markowitz & Syverson, 2021). Results from the initial study revealed that the materials may not have been salient enough to identify differences in perceptions of PEH (Graves, Gray, & Ruppel, 2024). The current study uses AI generated pictures to manipulate race and gender in a more meaningful way. We measured bias around avoidance, dangerousness, pity, and help constructs (Snow-Hill, 2019) by using a community and college sample. Due to the increasing number of PEH, and the lack of federal and state resources available to PEH, we hope to better understand how the local community will perceive and interact with PEH (Budescu et al., 2021)