Abstract

Cardiovascular diseases (CVDs) are one of the leading causes of death in the United States. However, the treatment of myocardial infarction (MI) driven by cardiovascular diseases (CVDs) is limited to heart transplantation. Tissue engineering is an alternative solution as the availability of heart transplantation largely depends on the availability of donor organs. While synthetic materials may trigger anti-inflammatory responses after implantation, natural biomaterials such as silk have a high potential as a material for building scaffolds due to its high biocompatibility and biodegradability. Spider silk is a material composed of fibroin proteins. When the proteins are spun, they are called recombinant spider silk, which can be used itself or combined with other biomaterials for surface modification. Especially in relation to cardiovascular tissue engineering, spider silk’s biocompatibility has proven to resemble the native cardiac tissue. Spider silk’s potential for cardiovascular tissue engineering application is investigated through reliable literature reviews and comparisons with other biomaterials including collagen, PCL, PLA, silkworm silk, alginate, chitosan. The growth of the field in research for each biomaterial in relation to cardiovascular tissue engineering was statistically evaluated. The statistical results indicated that there is an urgent need for more research of spider silk and cardiovascular tissue engineering. The mechanical properties of the biomaterials including ultimate tensile strength (UTS) and elastic modulus (EM) were analyzed corresponding to those of native cardiac tissue. The results suggested spider silk’s promising ability to be used as a biomaterial for scaffolds. The applicability of spider silk to electrospinning and combination with poly (3-hydroxybutyrate-co-3-hydroxyhexanoate) were discussed.

Abstract

This study rigorously examines the effects of career-oriented programs on public high school students’ academic motivation, a crucial factor in educational achievement and persistence. Employing a mixed-methods approach with a sample of 61 students from diverse backgrounds, this research assesses the roles of elective courses geared toward career pathways and their intersections with supportive school environments and extracurricular involvement. Findings reveal that when students engage in elective programs aligned with their career aspirations, they display significantly higher motivation, increased engagement, and a deeper investment in learning processes. Further, these programs cultivate autonomy, foster a sense of community, and allow students to draw relevant connections between academic activities and real-world applications. By emphasizing the value of interest-based, student-centered learning experiences, this study contributes to the discourse on career and technical education and advocates for the inclusion of career-oriented academies as a means to bolster motivation and positively influence educational outcomes in public high schools.

Abstract

Psychotherapists in training lack a standardized and formalized method of patient interaction for proper development of empathy, communication, and experience. Currently, training involves residents practicing with each other, where one acts as the patient and one as the psychotherapist, or with simulated patients -actors who replicate patient scenarios. Both methods have shortcomings in availability, reliability, and the accuracy in replicating real scenarios. This project attempted to create virtual patients by utilizing online patient transcripts through the fine-tuning of three modern Artificial Intelligence models, ChatGPT-4o, LlaMa-3.1v-405B, and Gemini 1.5 Pro; as well as their miniature versions where applicable. A website interface was created to interact with the fine-tuned models for evaluation. The accuracy of the models was determined using cosine similarities to measure semantic relation between data and model outputs, ranging from 93.3% to 83.11% , with ChatGPT-4o Mini achieving the highest accuracy. These findings highlight the potential for virtual patients to serve as a more accessible, reliable, and effective training method for residents. Further evaluation and continual refinement remain necessary to address current limitations.  

Abstract

The prevalence of Parkinson's Disease (PD) has doubled in the last 25 years, making it the second most common neurodegenerative disease in the US. Central to PD pathology is the degeneration of dopaminergic neurons within the substantia nigra pars compacta. Levodopa, a primary therapeutic agent, aids in dopamine production to alleviate symptoms of PD. However, it has side effects due to premature conversion of L-dopa into dopamine before passing the blood-brain barrier. Compounds such as sinapic acid, characterized by their methoxy and hydroxyl groups, possess antioxidant properties that can mitigate oxidative stress damaging mitochondrial DNA. This study aimed to mitigate parkinsonism effects from ɑ-synuclein mutation using sinapic acid (10, 20, 30 µM), levodopa 30µM, and sinapic acid 30µM + levodopa 30µM, assessing its impact on lifespan, dopamine concentration, movement speed, and memory, comparing it with Levodopa. 30125, 8848, and 8146 strained drosophila were crossed to obtain Mutant ɑ-synuclein Drosophila expressing GFP in dopaminergic neurons. Drosophila (wild and PD) were exposed to treatments, with assessments at 5 and 35 days old. Two-way ANOVA and post hoc Tukey/Scheffe analyses revealed that all 30µM treatment groups significantly reduced PD symptoms of PD strain drosophila (p<0.05). Sinapic acid 30µM + Levodopa 30µM exhibited the best results, attributed to sinapic acid's antioxidant abilities. It had a 43.7% increase in movement speed, a 56% increase in lifespan, and a 62.5% increase in memory. Long-term Levodopa showed increased detrimental effects due to its adverse impacts during consumption. Limitations included the absence of various chemicals (carbidopa and paraquat), suggesting the need for future studies with these chemicals and other model organisms (rats and C.elegans).

Abstract

Ethanol, commonly known as drinking alcohol, is a psychoactive drug that gives the stimulative effect of alcoholic intoxication. Addiction to ethanol is difficult to overcome due to the withdrawal symptoms present after discontinuing exposure. Planaria, scientifically, Dugesia dorotocephala, is a species of flatworm, commonly used as a model organism for humans; planaria show withdrawal symptoms such as low dopamine levels and changes in movement from addictive drugs, making them a suitable organism to test the effectiveness of white mulberry on withdrawal. Given that previous studies show that white mulberries can revert the movements of planarians after addictive substances, it is hypothesized that white mulberries can help planarians recover from alcohol withdrawal. This study investigates how white mulberry extract may affect the behavior and locomotion of ethanol-withdrawn planarians. Planaria were put in a 1% ethanol solution for 60 minutes and given either post or pretreated with 0%, 3%, 6%, and 9% mulberry in beef for 15 minutes. A vehicle group that received no ethanol exposure or mulberry treatment was also observed. After the treatment, the number of gridlines crossed, head bops, and C-shapes were counted for 20 minutes, followed by a Conditional Preference Test (CPT) for 10 minutes. It was observed that the 6% and 9% white mulberry pre-treatments were able to completely reverse the ethanol’s impacts on light preference and motility slightly. The post-treatment on the other hand was shown to worsen ethanol’s impact, significantly decreasing motility from the control.  

Abstract

Invasive lionfish (''Pterois volitans'' and ''Pterois miles'') have become a significant threat to Atlantic marine ecosystems, outcompeting native species through rapid reproduction and predation. This bibliometric study of contemporary research examines the growing research interest in lionfish since their introduction to the Atlantic in the late 20th century through an analysis of academic publications from 1980 to 2020 comparing lionfish studies with research on seven different Atlantic grouper species (Epinephelinae), which served as controls due to their shared ecological trophic level. While lionfish-focused research has steadily increased, statistical analysis (paired t-tests) showed no significant difference in publication rates compared to grouper studies. Additionally, an analysis of five observational studies on lionfish stomach contents revealed a preference for small, schooling, pelagic fish, such as grunts (Haemulidae), particularly those active during twilight hours. Lionfish consume prey at alarming rates in Atlantic reef ecosystems, uhindered by the biotic factors of native predators or parasites. Their diet overlaps with native mesopredators, including the economically valuable groupers, further threatening these species by depleting juvenile populations and monopolizing resources.The findings highlight lionfish as a growing ecological and economic concern for Atlantic marine ecosystems. Their unprecedented proliferation underscores the urgent need for targeted interventions to mitigate their impact on biodiversity and support the resilience of native marine communities.

Abstract

Eastern migratory monarch butterflies have declined by over 80% since the 1990s and have a 56–74% chance of extinction by 2080, which has been attributed to climate change and habitat loss. As a native migratory insect widely distributed across North America, the monarch butterfly serves as a valuable bioindicator for environmental change and conservation needs. However, there is still a lack of understanding of the spatiotemporal nature, relative importance, and future risks of individual threats due to the monarch’s multi-generational and vast annual cycle. Given the monarch's distinct ecological niche and sensitivity to environment conditions for migration, this study used convolutional neural network species distribution models (CNN-SDMs), which enhance occurrence predictions by capturing the surrounding environmental neighborhood, to analyze suitability predictors of monarch butterflies and explain their contributions at each step of the migratory route. The models were projected onto future climate and land cover scenarios in 2061–2080. Monthly maximum and minimum temperature ranked highest in feature importance across the migratory cycle, while vegetative land cover became ranked high in importance for the overwintering monarch population and future breeding habitat forecasted to shift northward. This niche-switching suggests that conservation efforts to facilitate the northward expansion of suitable habitat and the cultivation of nectar sources near hibernating colonies will be critical with growing climate impact and emissions. This study was the first to establish a CNN-based predictive spatiotemporal model of monarch butterflies and incorporate a comprehensive set of environmental predictors to evaluate potential threats to the monarch decline.