Endocrine Abstracts

Background: Infertility affects 1 in 6 couples causing devastating psychological impact. In vitro fertilisation (IVF) treatment can aid couples to conceive, but personalisation of treatment is needed to optimise patient outcomes. Machine learning can aid in the analysis of large complex datasets such as those encountered during IVF treatment. One example is in determining the optimal follicle size on the day of trigger to maximise the number of oocytes collected. Both follicle...

The pathophysiology of Polycystic Ovary Syndrome (PCOS) is multifactorial, therefore discovering effective treatments is challenging. Bioactive food molecules are a potential avenue for PCOS treatment; however, they often lack robust evidence. Applying machine learning (ML) to a genomic dataset may provide accelerated discovery of molecules and drugs that potentially alleviate symptoms through interactions with PCOS-related genes. 17,600 genes, 2,100 bioactive molecules found ...

Background: Due to practical limitations, the diagnosis of hypogonadism is predominantly based on a single measure of reproductive hormones, often with confirmation on a second occasion. Factors associated with reproductive hormone variation include: pulsatile secretion, diurnal rhythm, and food intake, which can affect the accuracy of diagnosis of reproductive disorders. There is limited data quantitatively estimating the variability of reproductive hormone levels over the da...