Biography
As a PhD candidate at the Department of Obstetrics and Gynaecology at The Chinese University of Hong Kong, I am passionate about advancing the understanding and treatment of gynaecological disorders, especially endometriosis-related infertility. I have published 7 papers in this area, covering topics such as molecular and signaling pathways, hormonal and nonhormonal pharmaceuticals, and natural products.
I was also a visiting PhD student at the Institute of In-vivo und In-vitro Model at the Veterinärmedizinische Universität Wien, where I received the prestigious Ernst Mach Grant from Eurasia-Pacific Uninet. Under the supervision of Prof. Kolbe Thomas and Prof. Boersma Auke, I conducted research on the comparison of different embryo transfer technologies and their application in reproductive studies. I aim to contribute to the improvement of animal welfare and the development of new reproductive techniques.
Research Interest
Reproductive medicine, endometriosis, in vitro fertilization, ovarian aging, stem cells, oncology
Reviewer
Work Details
Doctor
The Chinese University of Hong Kong
Department of Obstetrics and Gynaecology
Hong Kong
Contribution by Topic Area
Why publish with us?
Global Visibility – Indexed in major databases
Fast Peer Review – Decision within 14–21 days
Open Access – Maximize readership and citation
Multidisciplinary Scope – Biology, Medicine and Engineering
Editorial Board Excellence – Global experts involved
University Library Indexing – Via OCLC
Permanent Archiving – CrossRef DOI
APC – Affordable APCs with discounts
Citation – High Citation Potential
Which articles are now trending?
Research Articles
- Evaluating Digital Imaging Technologies for Anogenital Injury Documentation in Sexual Assault Cases
- Wishful Thinking or Valuable Forecasts? The Value of Policy Rate Predictions in Sweden
- Efficacy of Alternative Insecticides against Dusky Cotton Bug (Oxycarenus laetus) to Improve Yield Losses in Cotton Crops through Residue-based Bioassay
- Prevalence of Diabetic Retinopathy among Self-reported Newly Diagnosed Diabetics
- Clustering of Three-dimensional (3-D) Objects by Means of Phase- only Digital Holographic Information using Machine Learning
- Homologous Series of Chemical Compounds in Three-component Systems (Aa+ – Bb+ – Cc–) and (Zn2+ - Ge4+ - P3-) in Generalized Form
Advertisement


