The study aimed to analyze body weight changes in women undergoing IVF-ET with an antagonist protocol across up to three treatment cycles.
A cohort of patients was assessed for weight changes at different stages of treatment, including before the cycle, during hormonal stimulation, and at the end, tracking a total of 519 cycles.
Results showed that weight changes were minimal (less than 1% of initial weight) and clinically insignificant, debunking the myth that hormone therapy for IVF leads to significant weight gain, potentially easing patient concerns.
Autonomous experimentation systems utilize algorithms and past data to intelligently select and conduct new experiments to achieve specific goals, particularly in experimental chemistry where data can be scarce and costly to obtain.
Active learning methods focus on choosing experiments based on machine learning predictions and uncertainties, while meta-learning techniques aim to quickly adapt models to new tasks with limited data.
This study applied the MAML and PLATIPUS approaches to investigate halide perovskite growth, using a dataset of 1870 reactions to optimize the use of historical data in predicting successful reaction compositions, with PLATIPUS outperforming other algorithms in experiments.