Yutong (Irina) Zhu

Hi there! I'm glad you made it to my digital portfolio. I hope this platform gives you a comprehensive understanding of who I am :)

I am a first-year Bioengineering PhD student at University of Pennsylvania, advised by Derek A. Oldridge. I previously received my BASc in Engineering Science from the University of Toronto, majoring in Biomedical Systems Engineering and minoring in Engineering Business. I have also obtained a MSE in Chemical and Biomolecular Engineering from Johns Hopkins University.

My research interests lie in the fields of biological engineering and computational biology. I am passionate about developing and leveraging cutting-edge data-driven technologies, to address challenges in both spatial biology and translational solutions.

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The study involves quantitative assessment of human pancreatic intraepithelial neoplasia (PanIN) using CODA, a novel machine-learning pipeline for 3D image analysis that generates quantifiable models of large pieces of human pancreas with single-cell resolution. By leveraging CODA, for the first time, we are able to describe the spatial and genetic multifocality of human PanINs, providing important insights into the initiation and progression of pancreatic neoplasia.

This study reevaluates the conventional clinical classification of precursor lesions in Pancreatic Ductal Adenocarcinoma (PDAC), namely PanIN (Pancreatic Intraepithelial Neoplasia) and IPMN (Intraductal Papillary Mucinous Neoplasms). It reveals the inadequacy of the traditional classification when assessing high-resolution 3D images. By constructing cellular-resolution 3D maps, the research uncovers instances where some PanINs grow to macroscopic sizes in narrow ducts, meeting the criteria for IPMNs.

The study focuses on developing a Scaffold-supported Platform for Organoid-based Tissues (SPOT) platform compatible with generic 96/384 platforms, using 3D design and bioprinting techniques. SPOT enables a 3D heterogeneous environment that mimics the tumor microenvironment, serving as a useful tool to assess the drug sensitivity of patient-derived organoids and can be easily integrated into the drug discovery pipeline.

The study involves the construction of 3D, multi-organ volumes of the normal reproductive system of mice at different age groups using CODA, a novel machine-learning pipeline for 3D image analysis, to qualitatively and quantitatively identify major changes to ovary, fallopian, cervix, and vagina morphology and immune infiltration with age.

The study focuses on the distinct adhesion mechanism employed by freshwater mussels. By quantitatively analyzing localization profiles and post-translational modifications of the adhesive proteins using a staining-based approach, this work provides a well-established role model for developing novel bioadhesives such as self-healing materials and advanced coatings in aqueous environments.

The widely utilized mouse model in pre-clinical testing, due to its shared biological features and over 80 percent identical genetic components with humans, has proven crucial in revealing patterns of cancer cell invasion and host immune responses within the reproductive system.

This study employs the 3D, multi-organ-labeled volumes of mouse reproductive systems constructed in the preliminary study, to qualitatively and quantitatively compare with human tissues of interest. The ultimate objective is to assess the mouse model's efficacy in predicting aging and disease progression in the human model.

Serous tubal intraepithelial carcinoma (STIC) is the predominant precursor lesion for high-grade serous pelvic carcinomas, the most prevalent form of ovarian cancer. It is believed to have originated from the fallopian tube instead of the ovary.

This study aims to explore the progression of STIC lesions in both healthy and diseased models, leveraging computational mapping, genomic analysis, spatial transcriptomics, and mathematical modeling for a comprehensive investigation.

Pancreatic cancer often becomes lethal due to the challenge of early diagnosis and treatment. This study seeks to develop a mathematical model that correlates historical histology slides with patient diagnoses of pancreatic cancer.

The aim is to aid scientists and pathologists in identifying crucial visually identifiable biomarkers. The potential application lies in utilizing histology images for the early detection of pancreatic cancer in future diagnostic processes.

Designed and assessed the efficacy of various Cell-Penetrating Peptide (CPP)-based delivery systems carrying the zinc finger construct for achieving stable transfection. Validation was conducted using the previously constructed landing pad cell library.

Designed and developed a library of landing pad cell lines targeting pSH231 and AAVS1 safe harbor sites. The library provides an efficient approach for recombinase-mediated cassette exchange with a specific promoter and gene of interest.

Designed and simulated a device that prepares the subtalar joint for fusion during tibiotalocaneal arthrodesis, which consists of 2 modular components: a cutting tool and a guide tube.

Discovered great potential in the combination of platelet inhibition and natural killer (NK) cell enhancement, where platelet inhibition sensitizes tumor cells to NK cell activity and NK enhancement increases cytotoxic effects.

Engineered C. albicans to express GFP with specific genes and developed a statistical method for quantifying this gene expression following long-time space mission.

Designed and manufactured a fully functioning prosthetic leg targeted towards pediatric patients. Initial design ideas were generated through AutoCAD & SolidWorks, with the finalized version of the prototype selected by the client.

Designed and fabricated of a fully autonomous mobile robot that can deploy miniature traffic cones based on the detection of "cracks" and "holes". The robot was constructed with omnidirectional mecanum wheels, infrared sensors, as well as an interactive LCD and keyboard user interface.