Unveiling the potential of machine learning in cost-effective degradation of pharmaceutically active compounds: A stirred photo-reactor study.

In this study, neural networks and support vector regression (SVR) were employed to predict the degradation over three pharmaceutically active compounds (PhACs): Ibuprofen (IBP), diclofenac (DCF), and caffeine (CAF) within a stirred reactor featuring a flotation cell with two non-concentric ultraviolet lamps. A total of 438 datapoints were collected from published works and distributed into 70% training and 30% test datasets while cross-validation was utilized to assess the training reliability. The models incorporated 15 input variables concerning reaction kinetics, molecular properties, hydrodynamic information, presence of radiation, and catalytic properties.

The ability of donkey sperm to induce oocyte activation and mule embryo development after ICSI.

Members of the Equus genus exhibit a fascinating capacity for hybridization, giving rise to healthy offspring. Mules, resulting from the mating of a mare with a jack, represent the most prevalent equid hybrid, serving diverse roles in our society. While in vitro embryo production, particularly through Intracytoplasmic Sperm Injection (ICSI), has rapidly gained significance in domestic horses, the in vitro production in other equids remains largely unexplored.