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.

Inhibitory and excitatory axon terminals share a common nano-architecture of their Cav2.1 (P/Q-type) Ca(2+) channels.

Tuning of the time course and strength of inhibitory and excitatory neurotransmitter release is fundamental for the precise operation of cortical network activity and is controlled by Ca(2+) influx into presynaptic terminals through the high voltage-activated P/Q-type Ca(2+) (Cav2.1) channels. Proper channel-mediated Ca(2+)-signaling critically depends on the topographical arrangement of the channels in the presynaptic membrane.

Regional diversity and developmental dynamics of the AMPA-receptor proteome in the mammalian brain.

Native AMPA receptors (AMPARs) in the mammalian brain are macromolecular complexes whose functional characteristics vary across the different brain regions and change during postnatal development or in response to neuronal activity. The structural and functional properties of the AMPARs are determined by their proteome, the ensemble of their protein building blocks. Here we use high-resolution quantitative mass spectrometry to analyze the entire pool of AMPARs affinity-isolated from distinct brain regions, selected sets of neurons, and whole brains at distinct stages of postnatal development.

High-resolution proteomics unravel architecture and molecular diversity of native AMPA receptor complexes.

AMPA-type glutamate receptors (AMPARs) are responsible for a variety of processes in the mammalian brain including fast excitatory neurotransmission, postsynaptic plasticity, or synapse development. Here, with comprehensive and quantitative proteomic analyses, we demonstrate that native AMPARs are macromolecular complexes with a large molecular diversity. This diversity results from coassembly of the known AMPAR subunits, pore-forming GluA and three types of auxiliary proteins, with 21 additional constituents, mostly secreted proteins or transmembrane proteins of different classes.

Visual Interpretation of Kernel-Based Prediction Models.

Statistical models are frequently used to estimate molecular properties, e.g., to establish quantitative structure-activity and structure-property relationships.