Dr. Abigail Takyi

I am a DPhil student at the Nuffield Department of Women's & Reproductive Health. I graduated as a Medical Doctor from Leicester Medical School in 2017. During my time in medical school, I completed an intercalated Bsc in Pharmacological Science. As part of my intercalated BSc, I co-authored a study investigating Low Dose Chronic Prenatal Alcohol Exposure Abolishes the Pro-Cognitive Effects of Angiotensin IV and successfully submitted my work to the Behavioural Brain and Research Journal. I was awarded the 'Medical Student Runner-Up Researcher of the Year' award. I am currently a Histopathology trainee who wants to build a career in perinatal pathology research with particular reference to the placenta. I recently completed my Master Public Health at University of Warwick. My dissertation explored How do we improve information on causes of maternal deaths in Ghana through autopsy? My findings were successfully disseminated to the Ministry of Health Ghana. As part of Digital Pathology Fellow, I co-authored a study on Digital pathology for reporting histopathology samples, including cancer screening samples – definitive evidence from a multi-site study and successfully submitted my work to the Lancet Journal. My research is focused on dissecting the pathological phenotype in preterm labour, Small for Gestational Age (SGA) syndromes and Fetal Growth Restriction(FGR), by understanding the role of the placenta. The INTERBIO-21st Study has collected over 6000 placental specimens (frozen, paraffin-embedded and RNA later) and other biological samples from extremely well-characterised pregnancies in Brazil, Kenya, Pakistan, South Africa, Thailand, and the UK. The ultimate aim is to improve the functional classification of these highly heterogenous syndromes through a better understanding of how environmental exposures, clinical conditions, and nutrition influence patterns of growth and neurodevelopment from conception to early childhood. I aim to achieve this by collaborating with the Prof Neil Sebire at Great Ormand Street and contribute to the development of their computationally tractable deep learning pipeline to automate the analysis of placenta histology at the level of the cell, by helping to establish ‘ground truth’ relating to cell size, type and morphology. Furthermore, I will utilise AI enabled image analysis platforms to integrate all the INTERBIO-21st fetal ultrasound and placental histological images with the clinical data. This will enable me, for the first time in this field, to: i) explore novel interactions and effect modifiers from early pregnancy to 2 years of age and ii) improve the deep phenotyping of the preterm birth, FGR and SGA syndromes.