Colorectal carcinoma progression is not influenced by the pseudokinase PEAK1.

The scaffold protein PEAK1 acts downstream of integrin adhesion complexes and the epidermal growth factor receptor, orchestrating signaling events that control cell proliferation and cytoskeletal remodeling. In this study we investigated the role of PEAK1 in colorectal carcinoma (CRC) progression using various in vitro and in vivo models to replicate the stepwise pathogenesis of CRC. While we observed a cell-type specific role for PEAK1 in the proliferation and in human CRC cell lines in vitro, our in vivo experiments using different CRC mouse models driven by loss of Apc, with or without oncogenic Kras or Pten loss suggest that PEAK1 does not significantly contribute to tumor formation in vivo.

The evaluation of an artificial intelligence system for estrus detection in sows.

Background: Good estrus detection in sows is essential to predict the best moment of insemination. Nowadays, a technological innovation is available that detects the estrus of the sow via connected sensors and cameras. The collected data are subsequently analyzed by an artificial intelligence (AI) system.

Activity Sensing of Coagulation and Fibrinolytic Proteases.

The blood coagulation cascade is a complex physiological process involving the action of multiple coupled enzymes, cofactors, and substrates, ultimately leading to clot formation. Serine proteases have a crucial role, and aberrations in their activity can lead to life-threatening bleeding disorders and thrombosis. This review summarizes the essential proteases involved in blood coagulation and fibrinolysis, the endogenous peptide sequences they recognize and hydrolyze, and synthetic peptide probes based on these sequences to measure their activity.

Osteopenia is associated with inferior survival in patients undergoing partial hepatectomy for hepatocellular carcinoma.

Osteopenia is known to be associated with clinical frailty which is linked to inferior outcomes in various clinical scenarios. However, the exact prognostic value of osteopenia in patients undergoing curative intent-surgery for hepatocellular carcinoma (HCC) is not completely understood. This retrospective study was conducted in a cohort of 151 patients who underwent partial hepatectomy for HCC in curative intent at a German university medical center (05/2008-12/2019).

The Electronic Spectrum of Si.

The optical spectrum of Si is presented. The two electronic band systems observed near 430 and 270 nm correspond to the two lowest optically allowed transitions of Si assigned to Σ(I) ← XΣ and Σ(II) ← XΣ. The spectra were measured via photodissociation spectroscopy of mass-selected ions at the level of vibrational resolution, and the determined spectroscopic constants provide detailed information about the geometric and electronic structure, establishing molecular constants of this fundamental diatomic cation that enable astrophysical detection on, for example, hot rocky super-Earth-like exoplanets.

The Role of Sarcopenia and Myosteatosis in Short- and Long-Term Outcomes Following Curative-Intent Surgery for Hepatocellular Carcinoma in a European Cohort.

Alterations of body composition, especially decreased muscle mass (sarcopenia) and impaired muscle quality (myosteatosis), are associated with inferior outcomes in various clinical conditions. The data of 100 consecutive patients who underwent partial hepatectomy for hepatocellular carcinoma (HCC) at a German university medical centre were retrospectively analysed (May 2008-December 2019). Myosteatosis and sarcopenia were evaluated using preoperative computed-tomography-based segmentation.

A community resource for paired genomic and metabolomic data mining.

Genomics and metabolomics are widely used to explore specialized metabolite diversity. The Paired Omics Data Platform is a community initiative to systematically document links between metabolome and (meta)genome data, aiding identification of natural product biosynthetic origins and metabolite structures.

sv-callers: a highly portable parallel workflow for structural variant detection in whole-genome sequence data.

Structural variants (SVs) are an important class of genetic variation implicated in a wide array of genetic diseases including cancer. Despite the advances in whole genome sequencing, comprehensive and accurate detection of SVs in short-read data still poses some practical and computational challenges. We present , a highly portable workflow that enables parallel execution of multiple SV detection tools, as well as provide users with example analyses of detected SV callsets in a Jupyter Notebook.

3D-e-Chem: Structural Cheminformatics Workflows for Computer-Aided Drug Discovery.

eScience technologies are needed to process the information available in many heterogeneous types of protein-ligand interaction data and to capture these data into models that enable the design of efficacious and safe medicines. Here we present scientific KNIME tools and workflows that enable the integration of chemical, pharmacological, and structural information for: i) structure-based bioactivity data mapping, ii) structure-based identification of scaffold replacement strategies for ligand design, iii) ligand-based target prediction, iv) protein sequence-based binding site identification and ligand repurposing, and v) structure-based pharmacophore comparison for ligand repurposing across protein families. The modular setup of the workflows and the use of well-established standards allows the re-use of these protocols and facilitates the design of customized computer-aided drug discovery workflows.

A Structural Framework for GPCR Chemogenomics: What's In a Residue Number?

The recent surge of crystal structures of G protein-coupled receptors (GPCRs), as well as comprehensive collections of sequence, structural, ligand bioactivity, and mutation data, has enabled the development of integrated chemogenomics workflows for this important target family. This chapter will focus on cross-family and cross-class studies of GPCRs that have pinpointed the need for, and the implementation of, a generic numbering scheme for referring to specific structural elements of GPCRs. Sequence- and structure-based numbering schemes for different receptor classes will be introduced and the remaining caveats will be discussed.

3D-e-Chem-VM: Structural Cheminformatics Research Infrastructure in a Freely Available Virtual Machine.

3D-e-Chem-VM is an open source, freely available Virtual Machine ( http://3d-e-chem.github.io/3D-e-Chem-VM/ ) that integrates cheminformatics and bioinformatics tools for the analysis of protein-ligand interaction data.

Distributed Cognition and Process Management Enabling Individualized Translational Research: The NIH Undiagnosed Diseases Program Experience.

The National Institutes of Health Undiagnosed Diseases Program (NIH UDP) applies translational research systematically to diagnose patients with undiagnosed diseases. The challenge is to implement an information system enabling scalable translational research. The authors hypothesized that similar complex problems are resolvable through process management and the distributed cognition of communities.

Type and severity of septal deviation are not related with the degree of subjective nasal obstruction.

Background: Septoplasty is a frequently performed operation by otolaryngologists to relieve nasal obstruction complaints. When objective measurements tools are not available, preoperative decision-making is based on careful clinical examination. Our aim was to evaluate the relationship between type and severity of septal deviation and patient-reported nasal obstruction.

Designer aminoglycosides prevent cochlear hair cell loss and hearing loss.

Bacterial infections represent a rapidly growing challenge to human health. Aminoglycosides are widely used broad-spectrum antibiotics, but they inflict permanent hearing loss in up to ~50% of patients by causing selective sensory hair cell loss. Here, we hypothesized that reducing aminoglycoside entry into hair cells via mechanotransducer channels would reduce ototoxicity, and therefore we synthesized 9 aminoglycosides with modifications based on biophysical properties of the hair cell mechanotransducer channel and interactions between aminoglycosides and the bacterial ribosome.

In silico prediction and automatic LC-MS(n) annotation of green tea metabolites in urine.

The colonic breakdown and human biotransformation of small molecules present in food can give rise to a large variety of potentially bioactive metabolites in the human body. However, the absence of reference data for many of these components limits their identification in complex biological samples, such as plasma and urine. We present an in silico workflow for automatic chemical annotation of metabolite profiling data from liquid chromatography coupled with multistage accurate mass spectrometry (LC-MS(n)), which we used to systematically screen for the presence of tea-derived metabolites in human urine samples after green tea consumption.

Automatic Compound Annotation from Mass Spectrometry Data Using MAGMa.

The MAGMa software for automatic annotation of mass spectrometry based fragmentation data was applied to 16 MS/MS datasets of the CASMI 2013 contest. Eight solutions were submitted in category 1 (molecular formula assignments) and twelve in category 2 (molecular structure assignment). The MS/MS peaks of each challenge were matched with in silico generated substructures of candidate molecules from PubChem, resulting in penalty scores that were used for candidate ranking.

The use of portable equipment for the activity concentration index determination of building materials: method validation and survey of building materials on the Belgian market.

The Euratom BSS requires that in the near future (2015) the building materials for application in dwellings or buildings such as offices or workshops are screened for NORM nuclides. The screening tool is the activity concentration index (ACI). Therefore it is expected that a large number of building materials will be screened for NORM and thus require ACI determination.

Automatic chemical structure annotation of an LC-MS(n) based metabolic profile from green tea.

Liquid chromatography coupled with multistage accurate mass spectrometry (LC-MS(n)) can generate comprehensive spectral information of metabolites in crude extracts. To support structural characterization of the many metabolites present in such complex samples, we present a novel method ( http://www.emetabolomics.

CT cisternography for presurgical evaluation of arachnoid cyst: case report.

A 62-year-old man complaining of vertigo and progressive hearing loss was diagnosed with an arachnoid cyst at the right cerebellopontine angle based on magnetic resonance imaging (MRI). In this case-report, we used computed tomography (CT) cisternography to determine whether the arachnoid cyst communicated with the cerebrospinal fluid (CSF) space. Differentiating between a noncommunicating and communicating arachnoid cyst is required for presurgical evaluation.

Identification of new biomarker candidates for glucocorticoid induced insulin resistance using literature mining.

Background: Glucocorticoids are potent anti-inflammatory agents used for the treatment of diseases such as rheumatoid arthritis, asthma, inflammatory bowel disease and psoriasis. Unfortunately, usage is limited because of metabolic side-effects, e.g.

Substructure-based annotation of high-resolution multistage MS(n) spectral trees.

Rationale: High-resolution multistage MS(n) data contains detailed information that can be used for structural elucidation of compounds observed in metabolomics studies. However, full exploitation of this complex data requires significant analysis efforts by human experts. In silico methods currently used to support data annotation by assigning substructures of candidate molecules are limited to a single level of MS fragmentation.

A prospective cross-screening study on G-protein-coupled receptors: lessons learned in virtual compound library design.

We present the systematic prospective evaluation of a protein-based and a ligand-based virtual screening platform against a set of three G-protein-coupled receptors (GPCRs): the β-2 adrenoreceptor (ADRB2), the adenosine A(2A) receptor (AA2AR), and the sphingosine 1-phosphate receptor (S1PR1). Novel bioactive compounds were identified using a consensus scoring procedure combining ligand-based (frequent substructure ranking) and structure-based (Snooker) tools, and all 900 selected compounds were screened against all three receptors. A striking number of ligands showed affinity/activity for GPCRs other than the intended target, which could be partly attributed to the fuzziness and overlap of protein-based pharmacophore models.

Snooker: a structure-based pharmacophore generation tool applied to class A GPCRs.

G-protein coupled receptors (GPCRs) are important drug targets for various diseases and of major interest to pharmaceutical companies. The function of individual members of this protein family can be modulated by the binding of small molecules at the extracellular side of the structurally conserved transmembrane (TM) domain. Here, we present Snooker, a structure-based approach to generate pharmacophore hypotheses for compounds binding to this extracellular side of the TM domain.

ss-TEA: Entropy based identification of receptor specific ligand binding residues from a multiple sequence alignment of class A GPCRs.

Background: G-protein coupled receptors (GPCRs) are involved in many different physiological processes and their function can be modulated by small molecules which bind in the transmembrane (TM) domain. Because of their structural and sequence conservation, the TM domains are often used in bioinformatics approaches to first create a multiple sequence alignment (MSA) and subsequently identify ligand binding positions. So far methods have been developed to predict the common ligand binding residue positions for class A GPCRs.