Publications by authors named "T A Bamford"
Article Synopsis
- Morphokinetic models, specifically the PREFER and LB models, show better ability to prioritize euploid embryos for transfer compared to traditional embryologist selection.
- Study data was collected from 1958 biopsied blastocysts across nine IVF clinics in the UK over two years, analyzing various selection methods' effectiveness.
- Results indicate that the morphokinetic models had higher ranking success for euploid embryos (47% for PREFER) compared to embryologist selection (39%) and arbitrary selection (37%), with significant statistical differences.
Objective: To determine whether the aneuploidy risk score from a morphokinetic ploidy prediction model, Predicting Euploidy for Embryos in Reproductive Medicine (PREFER), is associated with miscarriage and live birth outcomes.
Design: Multicentre cohort study.
Setting: Nine in vitro fertilization clinics in the United Kingdom.
Article Synopsis
- The study compared machine learning methods to traditional mixed effects logistic regression in predicting blastocyst ploidy status using a dataset of 8147 embryos.
- Mixed effects logistic regression outperformed all machine learning models, indicating its effectiveness in this context.
- The study found significant delays in morphokinetic timings for aneuploid embryos, highlighting key predictors such as blastocyst expansion and trophectoderm grade.
Background: A time lapse system (TLS) is utilized in some fertility clinics with the aim of predicting embryo viability and chance of live birth during IVF. It has been hypothesized that aneuploid embryos display altered morphokinetics as a consequence of their abnormal chromosome complement. Since aneuploidy is one of the fundamental reasons for IVF failure and miscarriage, attention has focused on utilizing morphokinetics to develop models to non-invasively risk stratify embryos for ploidy status.
View Article and Find Full Text PDFCadmium sulphide can be templated on λ-DNA molecules to form an aqueous dispersion of CdS/λ-DNA nanowires. Subsequent addition of ethylene glycol to 50% v/v is sufficient to formulate an ink suitable for printing using piezoelectric drop-on-demand technology. Printed droplet arrays show a coffee-ring morphology of individual deposits by fluorescence and Raman microscopy, but upon increasing the number of layers of printed material by repeated printing over each droplet, the dry deposit approaches closer to a disc shape.
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