Characterization and identification of in vitro metabolites of (-)-epicatechin using ultra-high performance liquid chromatography-mass spectrometry

Purpose: To characterize and identify metabolites of (-)-epicatechin in microsomal fraction of rat hepatocytes (MFRHs). Methods: A single incubation of (-)-epicatechin (1 mL, 50 µg/mL) in MFRH (0.5 mg/mL) was used for the generation of metabolites. Thereafter, the sample was subjected to protein precipitation prior to analysis with ultra-high performance liquid chromatography coupled to linear ion-trap orbitrap mass spectrometry (UHPLC-LTQ-Orbitap MS). Results: Nine metabolites of (-)-epicatechin were characterized on the basis of high resolution mass measurement, MS spectra and literature data. Based on their structures, the major metabolic routes of (-)-epicatechin in MFRHs were identified as hydroxylation, dihydroxylation and glycosylation. Conclusion: This is the first report on metabolites of (-)-epicatechin in MFRHs, and it is helpful in gaining deeper insight into the metabolism of (-)-epicatechin in vivo. The results will also provide guidance in research on the pharmacokinetics of new drugs. Keywords: (-)-Epicatechin, Metabolites, Hydroxylation, Dihydroxylation, Glycosylation, Rat liver microsomes, Pharmacokinetic studie

(E)-N′-(3,4-Dimethoxy­benzyl­idene)-2,4-dihydroxy­benzohydrazide methanol solvate

The title compound, C16H16N2O5·CH3OH, was obtained from a condensation reaction of 3,4-dimethoxy­benzaldehyde and 2,4-dihydroxy­benzohydrazide. The non-H atoms of the Schiff base mol­ecule are approximately coplanar (r.m.s. deviation = 0.043 Å) and the dihedral angle between the two benzene rings is 1.6 (1)°. The mol­ecule adopts an E configuration with respect to the C=N double bond. An intra­molecular O—H⋯O hydrogen bond is observed. The Schiff base and methanol mol­ecules are linked into a two-dimensional network parallel to (10) by inter­molecular N—H⋯O, O—H⋯N and O—H⋯O hydrogen bonds

Baseline elevated Lp-PLA2 is associated with increased risk for re-stenosis after stent placement

BACKGROUND: Lipoprotein associated phospholipase A2 (Lp-PLA2) is a novel biomarker for cardiovascular risk prediction. Whether increased Lp-PLA2 level is associated with re-stenosis after stent-placement is unclear. METHODS: Totally 326 participants eligible for stent-placement were enrolled and divided into two groups according to baseline Lp-PLA2 levels (named normal and elevated groups). Baseline characteristics and clinical outcomes were compared between normal and elevated groups. The relationships between Lp-PLA2 and other risk factors with re-stenosis were evaluated. RESULTS: Only the between-group difference of Lp-PLA2 was significant (123.2 ± 33.6 ng/mL vs 336.8 ± 85.4 ng/mL, P < 0.001) while other demographic and clinical characteristics between these two groups were comparable. Approximately 55.1% and 58.5% of participants in normal and elevated groups presented with acute coronary syndrome, and the percentage of tri-vessels stenoses was significantly higher in elevated group (40.8% vs 32.1%, P = 0.016). Nearly 96.0% and 94.0% of participants in normal and elevated Lp-PLA2 groups were placed with drug-eluting stents, and the others were with bare-metal stents. After 1 year’s follow-up, the incidence of clinical end-points was comparable (13.3% vs 15.4%, P = 0.172). Nevertheless, the incidence of re-stenosis was marginally higher in elevated Lp-PLA2 group (8.5% versus 4.6%, P = 0.047). With multivariate analysis, after adjustment for other risk factors, Lp-PLA2 remained an independent predictor for re-stenosis with a hazard ratio of 1.140. No synergistic effect between Lp-PLA2 and other risk factors for re-stenosis was found. CONCLUSION: Increased Lp-PLA2 level is associated with an increased risk of re-stenosis. Lp-PLA2 assessment may be useful in predicting subjects who are at increased risk for re-stenosis

2-Amino-4-tert-butyl-5-(2,4-dichloro­benz­yl)thia­zol-3-ium bromide

The asymmetric unit of the title compound, C14H17Cl2N2S+·Br−, contains one cation and two Br− ions with site symmetry . The dihedral angle between the planes of the thia­zol and the dichloro­phenyl rings is 77.8 (6)°. In the crystal, the ions are connected by N–H⋯Br hydrogen bonds

A yolk-albumen-shell structure of mixed Ni-Co oxide with an ultrathin carbon shell for high-sensitivity glucose sensors

Altres ajuts: ICN2 is funded by the CERCA Programme/Generalitat de Catalunya. Part of the present work has been performed in the framework of Universitat Autònoma de Barcelona Materials Science PhD program. TZ has received funding from the CSC-UAB PhD scholarship program.Non-enzymatic glucose sensors based on different Co-Ni-C composite materials were developed by pyrolysis of bimetallic or single metal based metal-organic frameworks (MOFs). The structure and composition of the resulting materials were explored by XRD, nitrogen adsorption/desorption isotherms, SEM, HRTEM and STEM-EELS. The electrochemical performance of the bimetallic MOF derived novel yolk-albumen-shell structure of Ni-Co@C (YASNiCo@C) stands out from these materials. The YASNiCo@C electrode exhibited a sensitivity of 1964 μA cm-2 mM-1 with the detection limit of 0.75 μM, a linear range from 5 μM to 1000 μM and good stability for the detection of glucose. These promising electrochemical performances prove that YASNiCo@C is a promising material for glucose sensors. Moreover, the strategy outlined in this work for the design of MOF based nanomaterials can also be used beyond glucose sensors

Structure activity related, mechanistic, and modeling studies of gallotannins containing a glucitol-core and α-glucosidase

Gallotannins containing a glucitol core, which are only produced by members of the maple (Acer) genus, are more potent α-glucosidase inhibitors than the clinical drug, acarbose. While this activity is influenced by the number of substituents on the glucitol core (e.g. more galloyl groups leads to increased activity), the mechanisms of inhibitory action are not known. Herein, we investigated ligand–enzyme interactions and binding mechanisms of a series of ‘glucitol-core containing gallotannins (GCGs)’ against the α-glucosidase enzyme. The GCGs included ginnalins A, B and C (containing two, one, and one galloyl/s, respectively), maplexin F (containing 3 galloyls) and maplexin J (containing 4 galloyls). All of the GCGs were noncompetitive inhibitors of α-glucosidase and their interactions with the enzyme were further explored using biophysical and spectroscopic measurements. Thermodynamic parameters (by isothermal titration calorimetry) revealed a 1 : 1 binding ratio between GCGs and α-glucosidase. The binding regions between the GCGs and α-glucosidase, probed by a fluorescent tag, 1,1′-bis(4-anilino-5-naphthalenesulfonic acid), revealed that the GCGs decreased the hydrophobic surface of the enzyme. In addition, circular dichroism analyses showed that the GCGs bind to α-glucosidase and lead to loss of the secondary α-helix structure of the protein. Also, molecular modeling was used to predict the binding site between the GCGs and the α-glucosidase enzyme. This is the first study to evaluate the mechanisms of inhibitory activities of gallotannins containing a glucitol core on α-glucosidase

Deep quantum neural networks equipped with backpropagation on a superconducting processor

Deep learning and quantum computing have achieved dramatic progresses in recent years. The interplay between these two fast-growing fields gives rise to a new research frontier of quantum machine learning. In this work, we report the first experimental demonstration of training deep quantum neural networks via the backpropagation algorithm with a six-qubit programmable superconducting processor. In particular, we show that three-layer deep quantum neural networks can be trained efficiently to learn two-qubit quantum channels with a mean fidelity up to 96.0% and the ground state energy of molecular hydrogen with an accuracy up to 93.3% compared to the theoretical value. In addition, six-layer deep quantum neural networks can be trained in a similar fashion to achieve a mean fidelity up to 94.8% for learning single-qubit quantum channels. Our experimental results explicitly showcase the advantages of deep quantum neural networks, including quantum analogue of the backpropagation algorithm and less stringent coherence-time requirement for their constituting physical qubits, thus providing a valuable guide for quantum machine learning applications with both near-term and future quantum devices.Comment: 7 pages (main text) + 11 pages (Supplementary Information), 10 figure