Software platform virtualization in chemistry research and university teaching

<p>Abstract</p> <p>Background</p> <p>Modern chemistry laboratories operate with a wide range of software applications under different operating systems, such as Windows, LINUX or Mac OS X. Instead of installing software on different computers it is possible to install those applications on a single computer using Virtual Machine software. Software platform virtualization allows a single guest operating system to execute multiple other operating systems on the same computer. We apply and discuss the use of virtual machines in chemistry research and teaching laboratories.</p> <p>Results</p> <p>Virtual machines are commonly used for cheminformatics software development and testing. Benchmarking multiple chemistry software packages we have confirmed that the computational speed penalty for using virtual machines is low and around 5% to 10%. Software virtualization in a teaching environment allows faster deployment and easy use of commercial and open source software in hands-on computer teaching labs.</p> <p>Conclusion</p> <p>Software virtualization in chemistry, mass spectrometry and cheminformatics is needed for software testing and development of software for different operating systems. In order to obtain maximum performance the virtualization software should be multi-core enabled and allow the use of multiprocessor configurations in the virtual machine environment. Server consolidation, by running multiple tasks and operating systems on a single physical machine, can lead to lower maintenance and hardware costs especially in small research labs. The use of virtual machines can prevent software virus infections and security breaches when used as a sandbox system for internet access and software testing. Complex software setups can be created with virtual machines and are easily deployed later to multiple computers for hands-on teaching classes. We discuss the popularity of bioinformatics compared to cheminformatics as well as the missing cheminformatics education at universities worldwide.</p

A 6 Week Randomized Double-Blind Placebo-Controlled Trial of Ziprasidone for the Acute Depressive Mixed State

OBJECTIVE: To examine the efficacy of ziprasidone vs. placebo for the depressive mixed state in patients with bipolar disorder type II or major depressive disorder (MDD). METHODS: 73 patients were randomized in a double-blinded, placebo-controlled study to ziprasidone (40-160 mg/d) or placebo for 6 weeks. They met DSM-IV criteria for a major depressive episode (MDE), while also meeting 2 or 3 (but not more nor less) DSM-IV manic criteria. They did not meet DSM-IV criteria for a mixed or manic episode. Baseline psychotropic drugs were continued unchanged. The primary endpoint measured was Montgomery-Åsberg Depression Rating Scale (MADRS) scores over time. The mean dose of ziprasidone was 129.7±45.3 mg/day and 126.1±47.1 mg/day for placebo. RESULTS: The primary outcome analysis indicated efficacy of ziprasidone versus placebo (p = 0.0038). Efficacy was more pronounced in type II bipolar disorder than in MDD (p = 0.036). Overall ziprasidone was well tolerated, without notable worsening of weight or extrapyramidal symptoms. CONCLUSIONS: There was a statistically significant benefit with ziprasidone versus placebo in this first RCT of any medication for the provisional diagnostic concept of the depressive mixed state. TRIAL REGISTRATION: Clinicaltrials.gov NCT00490542

Perspectives on the future of multi-dimensional platforms

Two-dimensional liquid chromatography (2D-LC) formats have emerged to help address separation problems that are too complex for conventional one-dimensional LC. There are a number of obstacles to the proliferation of 2D-LC that are gradually being removed. Reliable commercial instrumentation has become available and data analysis software is being improved. Detector-sensitivity and phase-system compatibility issues can largely be solved by using active-modulation strategies. The remaining challenge, developing good and fast 2D-LC methods within a reasonable time, may be solved with smart algorithms. The technology platform that has been developed for 2D-LC also creates a number of other possibilities. Between the two separation stages, all kinds of physical (e.g. dissolution) or chemical (e.g. enzymatic or light-induced degradation) processes can be made to take place, allowing a wide variety of experiments to be performed within a single, efficient and automated analysis. All these developments are discussed in this paper and a number of critical issues are identified. A practical example, the characterization of polysorbates by high-resolution comprehensive two-dimensional liquid chromatography in combination with high-resolution mass spectrometry, is described as a culmination of recent developments in 2D-LC and as an illustration of the current state of the art

Clinical Metabolomics: Expanding the Metabolome Coverage Using Advanced Analytical Techniques

Metabolomics, the comprehensive analysis of all metabolites and intermediate products of reactions present within a biological system, is a promising field to enable precision medicine. Clinical metabolomics faces two main challenges at the bioanalytical level. The first is the need for high resolution to obtain maximum metabolome coverage. This is exemplified by the latest version of the Human Metabolome Database (HMDB), which reports more than 110,000 metabolites and endogenous compounds. The second is the high-throughput needed to enable the analysis of a large number of samples typically encountered in large-scale cohort studies. Reversed-phase liquid chromatography (LC) at regular or ultrahigh pressures combined with high-resolution mass spectrometry (HRMS) has long been considered the “gold standard” in metabolomics. However, these conventional reversed-phase LC–MS approaches are no longer sufficient to analyze the vast variety of polar compounds, as well as discriminate closely related compounds such as isomers or enantiomers. This review article discusses the novel separation and detection strategies that are considered promising in clinical metabolomics to enhance the metabolome coverage. It includes hydrophilic interaction chromatography (HILIC), supercritical fluid chromatography (SFC), multidimensional LC approaches, as well as ion-mobility mass spectrometry (IM-MS) and data-independent acquisition (DIA) analysis methods