Subcellular compartmentalization is a key feature of eukaryotic cells. Selectively targeting subcellular compartments, though holding many exciting opportunities for biomedicine, remains rather underdeveloped. Enzyme noncovalent synthesis (ENS), an approach that integrate enzymatic reactions and self-assembly, provides a new way for subcellular targeting. In this talk, we briefly introduce the development of supramolecular self-assemblies for targeting mitochondria, endoplasmic reticulum, Golgi apparatus, and cell membrane. We mainly focus on the use ENS, which spatiotemporally controls the formation of supramolecular assemblies, for subcellular targeting and its applications, such as developing novel cancer therapeutics.

Enzymatic Noncovalent Synthesis for Targeting Subcellular Organelles

COVID-19, or SARS-CoV-2, is a novel strain of coronavirus that has greatly affected societies world-wide. To combat this virus, Vitamin D3, or 1, 25-dihydroxy vitamin D3, is a promising factor to help manage the expression of COVID-19 in patients. The goal of this study is to understand the relationship between COVID-19 and Vitamin D with respect to various people, regardless of ethnic background. Patients selected will be from the St. Louis area and are within the age range of 18-50 years old. While Vitamin D3 is the overarching factor, there are other factors that modulate and enhance its capability, including: T-cells, memory B-cells and interleukin-10. Vitamin D will be analyzed using a 25(OH) Vitamin D ELISA kit. SARS-CoV-2 concentration will be determined by using RT-PCR. Flow cytometry will be used to determine levels of T-cell subset Th2. A memory B-cell isolation kit will be used to isolate memory B-cells by using antibody complexes. A Human IL-10 ELISA kit will be used to analyze IL-10 serum levels within patients. Furthermore, we plan to expand this study for the future by conducting this study in other countries and cities around the United States. In addition, access to healthcare and receiving blood work in marginalized communities will be explored in relation to Vitamin D will be explored for future potential application.

The Impact of Vitamin D on the Immune Response of COVID-19 Positive Patients

Breast cancer is one of the leading cancers worldwide and by 2040, it is expected to
cause more than 1 million deaths. Hence it is important to study the cytology of this
cancer to track disease progression. In this research, we have utilized a dataset from
the Wisconsin breast cancer dataset which contains 699 samples of benign and
malignment tumors. The samples contain clump size, cell size, and shape, single
epithelial cell size, chromatin, nuclei, nucleoli, mitosis, and adhesion. We have
constructed a data science pipeline through data collection, exploratory data analysis,
and building the machine learning model. After collecting the data, we explored the data
to see the relationship between the independent variables by constructing different plots
such as the pair plot, violin plot, and heatmap. Then we built five different supervised
machine learning models such as the decision tree, naive Bayes algorithm, deep
learning, support vector machine, and logistic regression with associated parameters
and hyperparameters. Then the model has been evaluated using different evaluation
metrics, for example, the area under the curve, accuracy, sensitivity, and specificity
scores to compare them. Receiver operating characteristics plots also have been
constructed to investigate the prediction ability of those models. According to the
results, deep learning and support vector machines with all features have the highest
AUC score which is 0.99 and 0.998 and this indicates these models can predict breast
cancer progression more accurately.

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Enzymatic Noncovalent Synthesis for Targeting Subcellular Organelles

The Impact of Vitamin D on the Immune Response of COVID-19 Positive Patients

“Construction of a data science pipeline and comparing different supervised machine learning algorithms to predict breast cancer”

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