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. An algorithm has been designed to automatically classify "relevant articles" which are reporting any kind of drug safety relevant information, and those which are not reporting an adverse drug reaction as "not relevant." The review process is consisted of many rules and aspects which needed to be taken into consideration. Therefore, for the training of the algorithm, thousands of documents from previous screenings have been used. After several iterations of adjustments and fine tuning, the ML approach is definitively a great achievement in pre-sorting the articles into "relevant" and "non-relevant" and supporting the intellectual review process.

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. Here, we used high-resolution SDS-digested freeze-fracture replica immunoelectron microscopy together with automatized computational analysis of Cav2.1 immunogold labeling to determine the precise subcellular organization of Cav2.1 channels in both inhibitory and excitatory terminals. Immunoparticles labeling the pore-forming α1 subunit of Cav2.1 channels were enriched over the active zone of the boutons with the number of channels (3-62) correlated with the area of the synaptic membrane. Detailed analysis showed that Cav2.1 channels are non-uniformly distributed over the presynaptic membrane specialization where they are arranged in clusters of an average five channels per cluster covering a mean area with a diameter of about 70 nm. Importantly, clustered arrangement and cluster properties did not show any significant difference between GABAergic and glutamatergic terminals. Our data demonstrate a common nano-architecture of Cav2.1 channels in inhibitory and excitatory boutons in stratum radiatum of the hippocampal CA1 area suggesting that the cluster arrangement is crucial for the precise release of transmitters from the axonal boutons.

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. These analyses show that the AMPAR proteome is dynamic in both space and time: AMPARs exhibit profound region specificity in their architecture and the constituents building their core and periphery. Likewise, AMPARs exchange many of their building blocks during postnatal development. These results provide a unique resource and detailed contextual data sets for the analysis of native AMPAR complexes and their role in excitatory neurotransmission.

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. Their integration at distinct abundance and stability establishes the heteromultimeric architecture of native AMPAR complexes: a defined core with a variable periphery resulting in an apparent molecular mass between 0.6 and 1 MDa. The additional constituents change the gating properties of AMPARs and provide links to the protein dynamics fundamental for the complex role of AMPARs in formation and operation of glutamatergic synapses.

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. For such models, interpretability, knowledge of the domain of applicability, and an estimate of confidence in the predictions are essential. We develop and validate a method for the interpretation of kernel-based prediction models. As a consequence of interpretability, the method helps to assess the domain of applicability of a model, to judge the reliability of a prediction, and to determine relevant molecular features. Increased interpretability also facilitates the acceptance of such models. Our method is based on visualization: For each prediction, the most contributing training samples are computed and visualized. We quantitatively show the effectiveness of our approach by conducting a questionnaire study with 71 participants, resulting in significant improvements of the participants' ability to distinguish between correct and incorrect predictions of a Gaussian process model for Ames mutagenicity.