Research

This project investigates the transcriptional and regulatory signatures of human endometrium during implantation. By comparing profiles between fertile women and those experiencing recurrent implantation failure, we aim to identify key molecular pathways and potential therapeutic targets. The research integrates genome-wide genetic variants to build a comprehensive understanding of implantation success and failure.

This research examines the complex process of trophoblast invasion during early pregnancy. The project focuses on understanding how different trophoblast subtypes interact with maternal tissues, form the syncytium, and establish connections with uterine glands and vessels, crucial steps for successful implantation and placentation.

This project maps the intricate anticoagulant machinery active during early human placentation. Understanding how these factors are controlled in space and time is crucial for successful implantation and the maintenance of pregnancy. The research provides insights into the spatial-temporal regulation of coagulation at the maternal-fetal interface.

This project investigates progesterone concentrations in endometrial tissues during the luteal phase, with a focus on cycle-to-cycle variability and the relationship between tissue and serum levels. The research aims to optimize luteal phase support in assisted reproduction through improved understanding of local progesterone dynamics.

This project examines immune cells of the innate and adaptive endometrial response, investigating their role in implantation and early pregnancy. The research focuses on characterizing different immune cell populations and their contribution to successful implantation or implantation failure.

This project examines transcriptomic signatures of bovine conceptuses derived from different reproductive techniques. RNA sequencing will be used to characterize conceptuses at Day 16 and evaluate molecular mechanisms of pregnancy establishment and embryo loss.

This research focuses on developing an endometrium-on-chip (EoC) device to design and fabricate a microfluidic platform mimicking endometrial cell layers, integrate trophoblast interactions and monitor cellular processes, and validate the system using commercial and primary human cell lines.

This project aims to develop a comprehensive 3D model of human endometrium using light sheet microscopy to elucidate 3D structure during early pregnancy, hydrogel-scaffold containing primary endometrial stromal and glandular epithelial cells, and an air-liquid interface technique to recreate menstrual cycle phases.

This project will develop machine learning approaches to predict endometrial receptivity by creating a curated 3D transvaginal ultrasound database, developing machine-learning-based techniques for uterine receptivity characterization, designing algorithms for embryo implantation success estimation, and exploring machine learning techniques for uterine motion analysis.

This project aims to develop a novel screening platform combining stem-cell based embryos, blastoids with patient-derived endometrial cells to identify potential therapeutic candidates for improving implantation success. The research will establish a high-throughput screening system and validate promising compounds.