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Our team has a keen interest in developing non-invasive markers for predicting preterm birth, studying natural conception and infertility.
Preterm birth, a major contributor to neonatal morbidity and mortality. Globally, over 13 million babies were born preterm (birth before 37 gestational weeks) each year. It is estimated that 10 neonates die per minute world-wide as a result of preterm birth. Better prediction of preterm birth in women without preterm labor (asymptomatic) and with preterm labor (symptomatic) is a vital step to improving the management of this condition.
We have leveraged on the technological breakthrough brought forth by massively parallel sequencing (MPS). MPS has enabled us to explore the microbiomes and transcriptomes at an unprecedentedly high resolution and wide scope for markers. Clinically useful markers in maternal peripheral blood or in the female reproductive tract swab samples have been identified with promising potential to predict:
spontaneous preterm birth after clinical intervention on women who are asymptomatic but at high-risk for preterm birth;
spontaneous preterm birth among the symptomatic women.
Multi-centre studies pertinent to the further characterisation and development of these markers in different ethnic groups are underway.
Infertility is an agonizing problem that troubled one in every 6 couples. Chronic endometritis is associated with women with reproductive failures, including recurrent miscarriage and repeated implantation failure after in vitro fertilization (IVF) treatment. Using also MPS, we identified panels of bacteria that exist in the uterus at higher levels in women with chronic endometritis than those without. We are also studying the microbiomes and transcriptomes of other subtypes of infertile women with a view to understanding the factors that may hamper them from natural conception.
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Papers共 87 篇Author StatisticsCo-AuthorSimilar Experts
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Yingyu Liu,Karen Ka-Wing Wong, Elaine Yee-Ling Ko,Xiaoyan Chen,Jin Huang,Stephen Kwok-Wing Tsui,Tin Chiu Li,Stephen Siu-Chung Chim
user-5bd69975530c70d56f390249(2017)
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#Papers: 87
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