School of Engineering Department of Chemical and Biological Engineering 66 Department of Chemical and Biological Engineering Generation of Adversarial Chemical Reactions Supervisor: GAO Hanyu / CBE Student: CHEUNG Chi Shan Jennifer / CPEG Course: UROP 1100, Summer The extraction of chemical data from literature has been one of the focuses in collecting chemical information. Previous works have developed computer vision systems, such as rule-based and machinelearning systems, to identify molecules and their relationships in reaction diagrams. However, there are still limitations to these methods, as they are still unable to extract data from images completely with great accuracy, especially when the information is represented in schemes and tables. This project focuses on extracting reaction data from table images, using previous works, particularly RxnScribe and GPT-4o, as a starting point. This article is a report on the progress of this project. Skin-adherent Bioelectronic ECG Patch for Ambulatory Care Supervisor: HSING I-ming / CBE Co-supervisor: NYEIN Hnin Yin Yin / CBE Student: FONG Sin Ning / CPEG Course: UROP 1100, Fall The demand for ongoing electrocardiography (ECG) monitoring, aimed at preventing sudden cardiac events and premature death, has spurred the creation of wearable bioelectronics. These devices offer non-invasive, portable monitoring options for the detection and diagnosis of cardiovascular disease (CVD). Here, a 12-lead ECG patch is introduced, which improves conventional ECG sensors in terms of compliance, conformity, and reduced skin damage. The utilization of flexible, skin-friendly, and biocompatible materials in its construction allows for the recording of clear and consistent ECG signals. The patch is further enhanced by its distinctive 3-bridge patch structure, embedded liquid metal circuitry, and the use of medical-grade, skin-safe hydrogels. These improvements resulted in the collection of more stable and continuous signals, which could be subsequently processed. Engineering of Next Generation mRNA Vaccine Construct Supervisor: KUANG Becki Yi / CBE Student: FU Lala / BIOT Course: UROP 1100, Fall UROP 3200, Spring The COVID-19 vaccine has brought to light synthetic mRNA as a promising therapeutic modality, while current mRNA vaccines are still limited to low stability and protein productivity. In our previous studies, we have optimized a cytidine-containing tail sequence that can overcome these limitations with a high pharmacokinetics (PK) value. As a follow-up study on cytidine-containing tails, we aim to 1) adapt our tail constructs to fit plasmid-based production in industry, and 2) performance pharmacodynamics (PD) study on the tail and its protein targets. We found our optimized tails can 1) reduce the recombination rate during bacteria amplification of the plasmid thus facilitating scale-up of mRNA production, and 2) protect mRNA from CNOT6L/CNOT7- mediated deadenylation. We believe our findings will serve as a valuable resource for the development of the next-generation mRNA vaccines.
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