Elucidation of the metabolites of the novel psychoactive substance 4-methyl-N-ethyl-cathinone (4-MEC) in human urine and pooled liver microsomes by GC-MS & LC-HR-MS/MS techniques and of its detectability by GC-MS or LC-MS(n) standard screening approaches

4-methyl-N-ethcathinone (4-MEC), the N-ethyl homologue of mephedrone, is a novel psychoactive substance of the beta-keto amphetamine (cathinone) group. The aim of the present work was to study the phase I and phase II metabolism of 4-MEC in human urine as well as in pooled human liver microsome (pHLM) incubations. The urine samples were worked up with and without enzymatic cleavage, the pHLM incubations by simple deproteinization. The metabolites were separated and identified by gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-high resolution-tandem mass spectrometry (LC-HR-MS/MS). Based on the metabolites identified in urine and/or pHLM, the following metabolic pathways could be proposed: reduction of the keto group, N-deethylation, hydroxylation of the 4-methyl group followed by further oxidation to the corresponding 4-carboxy metabolite, and combinations of these steps. Glucuronidation could only be observed for the hydroxy metabolite. These pathways were similar to those described for the N-methyl homologue mephedrone and other related drugs. In pHLM, all phase I metabolites with the exception of the N-deethyl-dihydro isomers and the 4-carboxy-dihydro metabolite could be confirmed. Glucuronides could not be formed under the applied conditions. Although the taken dose was not clear, an intake of 4-MEC should be detectable in urine by the GC-MS and LC-MS(n) standard urine screening approaches at least after overdose

Accurate Liability Estimation Improves Power in Ascertained Case Control Studies

Linear mixed models (LMMs) have emerged as the method of choice for confounded genome-wide association studies. However, the performance of LMMs in non-randomly ascertained case-control studies deteriorates with increasing sample size. We propose a framework called LEAP (Liability Estimator As a Phenotype, https://github.com/omerwe/LEAP) that tests for association with estimated latent values corresponding to severity of phenotype, and demonstrate that this can lead to a substantial power increase

Optogenetics and deep brain stimulation neurotechnologies

Brain neural network is composed of densely packed, intricately wired neurons whose activity patterns ultimately give rise to every behavior, thought, or emotion that we experience. Over the past decade, a novel neurotechnique, optogenetics that combines light and genetic methods to control or monitor neural activity patterns, has proven to be revolutionary in understanding the functional role of specific neural circuits. We here briefly describe recent advance in optogenetics and compare optogenetics with deep brain stimulation technology that holds the promise for treating many neurological and psychiatric disorders

Possible pseudogap behavior of electron doped high-temperature superconductors

We have measured the low-energy quasiparticle excitation spectrum of the electron doped high-temperature superconductors (HTS) Nd(1.85)Ce(0.15)CuO(4-y) and Pr(1.85)Ce(0.15)CuO(4-y) as a function of temperature and applied magnetic field using tunneling spectroscopy. At zero magnetic field, for these optimum doped samples no excitation gap is observed in the tunneling spectra above the transition temperature Tc. In contrast, below Tc for applied magnetic fields well above the resistively determined upper critical field, a clear excitation gap at the Fermi level is found which is comparable to the superconducting energy gap below Tc. Possible interpretations of this observation are the existence of a normal state pseudogap in the electron doped HTS or the existence of a spatially non-uniform superconducting state.Comment: 4 pages, 4 ps-figures included, to be published in Phys. Rev. B, Rapid Com

Metabolic fate, mass spectral fragmentation, detectability, and differentiation in urine of the benzofuran designer drugs 6-APB and 6-MAPB in comparison to their 5-isomers using GC-MS and LC-(HR)-MSn techniques

The number of so-called new psychoactive substances (NPS) is still increasing by modification of the chemical structure of known (scheduled) drugs. As analogues of amphetamines, 2-aminopropyl-benzofurans were sold. They were consumed because of their euphoric and empathogenic effects. After the 5-(2-aminopropyl)benzofurans, the 6-(2-aminopropyl)benzofuran isomers appeared. Thus, the question arose whether the metabolic fate, the mass spectral fragmentation, and the detectability in urine are comparable or different and how an intake can be differentiated. In the present study, 6-(2-aminopropyl)benzofuran (6-APB) and its N-methyl derivative 6-MAPB (N-methyl-6-(2-aminopropyl)benzofuran) were investigated to answer these questions. The metabolites of both drugs were identified in rat urine and human liver preparations using GC-MS and/or liquid chromatography-high resolution-mass spectrometry (LC-HR-MSn). Besides the parent drug, the main metabolite of 6-APB was 4-carboxymethyl-3-hydroxy amphetamine and the main metabolites of 6-MAPB were 6-APB (N-demethyl metabolite) and 4-carboxymethyl-3-hydroxy methamphetamine. The cytochrome P450 (CYP) isoenzymes involved in the 6-MAPB N-demethylation were CYP1A2, CYP2D6, and CYP3A4. An intake of a common users’ dose of 6-APB or 6-MAPB could be confirmed in rat urine using the authors’ GC-MS and the LC-MSn standard urine screening approaches with the corresponding parent drugs as major target allowing their differentiation. Furthermore, a differentiation of 6-APB and 6-MAPB in urine from their positional isomers 5-APB and 5-MAPB was successfully performed after solid phase extraction and heptafluorobutyrylation by GC-MS via their retention times

A complete tool set for molecular QTL discovery and analysis

Population scale studies combining genetic information with molecular phenotypes (for example, gene expression) have become a standard to dissect the effects of genetic variants onto organismal phenotypes. These kinds of data sets require powerful, fast and versatile methods able to discover molecular Quantitative Trait Loci (molQTL). Here we propose such a solution, QTLtools, a modular framework that contains multiple new and well-established methods to prepare the data, to discover proximal and distal molQTLs and, finally, to integrate them with GWAS variants and functional annotations of the genome. We demonstrate its utility by performing a complete expression QTL study in a few easy-to-perform steps. QTLtools is open source and available at https://qtltools.github.io/qtltools/.</p