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Abstract Background The amount of available biological information is rapidly increasing and the focus of biological research has moved from single components to networks and even larger projects aiming at the analysis, modelling and simulation of biological networks as well as large scale comparison of cellular properties. It is therefore essential that biological knowledge is easily accessible. However, most information is contained in the written literature in an unstructured way, so that methods for the systematic extraction of knowledge directly from the primary literature have to be deployed. Description Here we present a text mining algorithm for the extraction of kinetic information such as KM, Ki, kcat etc. as well as associated information such as enzyme names, EC numbers, ligands, organisms, localisations, pH and temperatures. Using this rule- and dictionary-based approach, it was possible to extract 514,394 kinetic parameters of 13 categories (KM, Ki, kcat, kcat/KM, Vmax, IC50, S0.5, Kd, Ka, t1/2, pI, nH, specific activity, Vmax/KM) from about 17 million PubMed abstracts and combine them with other data in the abstract. A manual verification of approx. 1,000 randomly chosen results yielded a recall between 51% and 84% and a precision ranging from 55% to 96%, depending of the category searched. The results were stored in a database and are available as "KID the KInetic Database" via the internet. Conclusions The presented algorithm delivers a considerable amount of information and therefore may aid to accelerate the research and the automated analysis required for today's systems biology approaches. The database obtained by analysing PubMed abstracts may be a valuable help in the field of chemical and biological kinetics. It is completely based upon text mining and therefore complements manually curated databases. The database is available at http://kid.tu-bs.de. The source code of the algorithm is provided under the GNU General Public Licence and available on request from the author.

Abstract Background Metabolome analysis with GC/MS has meanwhile been established as one of the "omics" techniques. Compound identification is done by comparison of the MS data with compound libraries. Mass spectral libraries in the field of metabolomics ought to connect the relevant mass traces of the metabolites to other relevant data, e.g. formulas, chemical structures, identification numbers to other databases etc. Since existing solutions are either commercial and therefore only available for certain instruments or not capable of storing such information, there is need to provide a software tool for the management of such data. Results Here we present mSpecs, an open source software tool to manage mass spectral data in the field of metabolomics. It provides editing of mass spectra and virtually any associated information, automatic calculation of formulas and masses and is extensible by scripts. The graphical user interface is capable of common techniques such as copy/paste, undo/redo and drag and drop. It owns import and export filters for the major public file formats in order to provide compatibility to commercial instruments. Conclusion mSpecs is a versatile tool for the management and editing of mass spectral libraries in the field of metabolomics. Beyond that it provides capabilities for the automatic management of libraries though its scripting functionality. mSpecs can be used on all major platforms and is licensed under the GNU General Public License and available at http://mspecs.tu-bs.de.

Background: The amount of available biological information is rapidly increasing and the focus of biological research has moved from single components to networks and even larger projects aiming at the analysis, modelling and simulation of biological networks as well as large scale comparison of cellular properties. It is therefore essential that biological knowledge is easily accessible. However, most information is contained in the written literature in an unstructured way, so that methods for the systematic extraction of knowledge directly from the primary literature have to be deployed. Description: Here we present a text mining algorithm for the extraction of kinetic information such as KM, Ki, kcat etc. as well as associated information such as enzyme names, EC numbers, ligands, organisms, localisations, pH and temperatures. Using this rule- and dictionary-based approach, it was possible to extract 514,394 kinetic parameters of 13 categories (KM, Ki, kcat, kcat/KM, Vmax, IC50, S0.5, Kd, Ka, t½, pI, nH, specific activity, Vmax/KM) from about 17 million PubMed abstracts and combine them with other data in the abstract. A manual verification of approx. 1,000 randomly chosen results yielded a recall between 51% and 84% and a precision ranging from 55% to 96%, depending of the category searched. The results were stored in a database and are available as "KID the KInetic Database" via the internet. Conclusions: The presented algorithm delivers a considerable amount of information and therefore may aid to accelerate the research and the automated analysis required for today's systems biology approaches. The database obtained by analysing PubMed abstracts may be a valuable help in the field of chemical and biological kinetics. It is completely based upon text mining and therefore complements manually curated databases. The database is available at http://kid.tu-bs.de. The source code of the algorithm is provided under the GNU General Public Licence and available on request from the author. [ABSTRACT FROM AUTHOR]

Objectives: Previous research suggests that female gender is associated with increased mortality rates after surgery for Stanford A acute aortic dissection (AAD). However, women with AAD usually present with different clinical symptoms that may bias outcomes. Moreover, there is a lack of long-term results regarding overall mortality and freedom from major cerebrovascular events. We analysed the impact of gender on long-term outcomes after surgery for Stanford A AAD by comparing genders with similar risk profiles using propensity score matching., Methods: A total of 240 patients operated for Stanford A AAD were included in this study. To control for selection bias and other confounders, propensity score matching was applied to gender groups., Results: After propensity score matching, the gender groups were well balanced in terms of risk profiles. There were no statistically significant differences regarding duration of cardiopulmonary bypass ( P  = 0.165) and duration of aortic cross-clamp time ( P  = 0.111). Female patients received less fresh frozen plasma ( P  = 0.021), had shorter stays in the intensive care unit ( P  = 0.031), lower incidence of temporary neurological dysfunction ( P  < 0.001) and lower incidence of dialysis ( P  = 0.008). There were no significant differences regarding intraoperative mortality ( P  = 1.000), 30-day mortality ( P  = 0.271), long-term overall cumulative survival ( P  = 0.954) and long-term freedom from cerebrovascular events ( P  = 0.235) with up to a 9-year follow-up., Conclusions: Considering patients with similar risk profiles, female gender per se is not associated with worse long-term survival and freedom from stroke after surgical aortic repair. Moreover, female patients might even benefit from a smoother early postoperative course and lower incidence of early postoperative complications., (© The Author 2017. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.)