Investigating the Selectivity of Thymoquinone Towards Cancerous Prostate Cells

Overview

Androgen insensitivity in PC-3, a variant of prostate cancer, has been recognized as highly lethal and drug-resistant due to its reduced dependency on substances that are commonly inhibited through 100 modern treatments, such as androgen. As a result, there is a lack of effective treatment options. However, thymoquinone, a substance commonly known for its role in essential oils and readily available, has risen in popularity as a potential treatment option due to its implied anticancer effect from previous studies. Additionally, our previous study demonstrated that thymoquinone lowers PC-3 viability. Despite the implied anticancer effect, whether thymoquinone selectively targets PC-3 only remains largely unstudied. The purpose of this study is to investigate the selectivity of thymoquinone and compare the effects of thymoquinone on PC-3 (cancerous cells) and HPrEC (noncancerous cells) viability. In this study, a monoculture model and a co-culture mode were used. After utilizing an Acid Phosphatase Assay for cell viability, both the cancerous cell monoculture and co-culture model data support the notion that thymoquinone inhibits cancer cell viability, which is consistent with our previous study. However, when comparing the cancerous well plates with the noncancerous cells that were grown in a co-culture setting, the data clearly shows that the noncancerous cell viability values with thymoquinone added were not lower than the control. Early data suggests that thymoquinone selectively targets cancerous cells, revealing thymoquinone as a potential treatment option. Future research should investigate the efficacy of thymoquinone application further. VIRTUAL The Exploration of Machine Learning for Morphological Neuron Classification in the Neocortex Lukas Abraham Suncoast Community High School, Riviera Beach, FL This study discusses the applications of machine learning techniques to classify neurons based on their morphological features. By utilizing these models, neurons can be accurately identified and categorized based on their shape and structure. The study outlines the various deep-learning models used in this classification process, specifically using a supervised artificial neural network. The use of deep learning models allows for more efficient and effective research, as they can analyze large amounts of data faster and more accurately than traditional methods. This improved methodology has the potential to accelerate the development of targeted and novel therapies for a variety of neurological and neurodegenerative diseases, such as Alzheimer's and Huntington’s. The data showed the optimal artificial neural network had 2 hidden layers, consisting of 64 and 64 nodes respectively, with a learning rate of 0.001 and momentum of 0.9. The model produced an MAE of 3.08, RMSE of 1.76, R-Squared value of 0.899, and overall accuracy of 96.10% in classifying neurons over 100 successive trials. With this ability to predict and classify neurons, the neural network proves the viability of machine learning in broader applications within the field of neurodegenerative diseases.