Machine learning approach to identify adverse events in scientific biomedical literature.

Monitoring the occurrence of adverse events in the scientific literature is a mandatory process in drug marketing surveillance. This is a very time-consuming and complex task to fulfill the compliance and, most importantly, to ensure patient safety. Therefore, a machine learning (ML) algorithm has been trained to support this manual intellectual review process, by automatically providing a classification of the literature articles into two types.

Olfaction in three genetic and two MPTP-induced Parkinson's disease mouse models.

Various genetic or toxin-induced mouse models are frequently used for investigation of early PD pathology. Although olfactory impairment is known to precede motor symptoms by years, it is not known whether it is caused by impairments in the brain, the olfactory epithelium, or both. In this study, we investigated the olfactory function in three genetic Parkinson's disease (PD) mouse models and mice treated with MPTP intraperitoneally and intranasally.