The Danish Veterinary and Food Administration’s Fight against Fake Nutrition News on Digital Media

This article examines fake nutrition news on digital media and the steps the Danish Veterinary and Food Administration (DVFA) are taking to counteract this development. Through a critical discourse analysis of four cases, we aim to shed light on the DVFA’s tactics to counteract the fake nutrition news phenomenon and discuss how the development of digital media has affected the spreading of fake news and alternate methods to combat fake news. The article concludes that the DVFA use discourses of responsibility and credibility to combat fake news and promote their strategy by establishing themselves as a credible source who consumers should trust.Denne artikel undersøger fake nutrition news på digitale medier, og hvordan Fødevarestyrelsen modarbejder denne udvikling. Gennem en kritisk diskursanalyse af fire cases ønsker vi at belyse Fødevarestyrelsens taktik til at modvirke fake news fænomenet og diskutere, hvordan udviklingen af digitale medier har påvirket spredningen af fake news samt alternative metoder til at bekæmpe fake news. Artiklen konkluderer, at Fødevarestyrelsen bruger ansvars- og troværdighedsdiskurser til at bekæmpe fake news og promovere deres strategi ved at fastsætte dem selv som en troværdig kilde, som forbrugere bør stole på

Structure-Based Rationale for Selectivity in the Asymmetric Allylic Alkylation of Cycloalkenyl Esters Employing the Trost ‘Standard Ligand’ (TSL): Isolation, Analysis and Alkylation of the Monomeric form of the Cationic η3-Cyclohexenyl Complex [(η3-c-C6H9)Pd(TSL)]+

The solution-phase structures of the monomeric forms of the cationic Pd-η3-allyl and Pd-η3-cyclohexenyl complexes [Pd(R,R)-1(η3-C3H5)]+ (7+) and [Pd(R,R)-1(η3-C6H9)]+ (8+) bearing the trans-cyclohexylenediamine-based Trost ‘Standard Ligand’ (R,R)-1 have been elucidated by NMR, isotopic labeling and computation. In both complexes, (R,R)-1 is found to adopt a C1-symmetric conformation, leading to a concave shape in the 13-membered chelate in which one amide group in the chiral scaffold projects its NH unit out of the concave surface in close vicinity to one allyl terminus. The adjacent amide has a reversed orientation and projects its carbonyl group out of the concave face in the vicinity of the opposite allyl terminus. Stoichiometric and catalytic asymmetric alkylations of [8+][X−] by MCHE2 (E = ester, M = ‘escort’ counterion, X = Pd allyl counterion) show the same selectivities and trends as have been reported for in situ-generated catalysts, and a new model for the enantioselectivity has been explored computationally. Three factors are found to govern the regioselectivity (pro-S vs pro-R) of attack of nucleophiles on the η3-C6H9 ring in 8+ and thus the ee of the alkylation product: (i) a pro-R torquoselective bias is induced by steric interaction of the η3-C6H9 moiety with one phenyl ring of the ligand; (ii) pro-S delivery of the nucleophile can be facilitated by hydrogen-bonding with the concave orientated amide N−H; and (iii) pro-R delivery of the nucleophile can be facilitated by escort ion (M) binding to the concave orientated amide carbonyl. The latter two opposing interactions lead to the selectivity of the alkylation being sensitive to the identities of X− and M+. The generation of 8+ from cyclohexenyl ester substrate has also been explored computationally. The concave orientated amide N−H is able to activate the leaving group of the allylic ester by hydrogen bonding to its carbonyl group. However, this interaction is only feasible for the (S)-enantiomer of substrate, leading to the prediction of a powerful kinetic resolution (kS kR), as is found experimentally. This new model involving two regiochemically distinct (NH) and (CO) locations for nucleofuge or nucleophile binding, may prove of broad utility for the interpretation of the selectivity in asymmetric allylic alkylation reactions catalyzed by Pd complexes of (R,R)-1 and related ligands.<br/

Pharmacophore and receptor models for neurokinin receptors

Three neurokinin (NK) antagonist pharmacophore models (Models 1–3) accounting for hydrogen bonding groups in the `head' and `tail' of NK receptor ligands have been developed by use of a new procedure for treatment of hydrogen bonds during superimposition. Instead of modelling the hydrogen bond acceptor vector in the strict direction of the lone pair, an angle is allowed between the hydrogen bond acceptor direction and the ideal lone pair direction. This approach adds flexibility to hydrogen bond directions and produces more realistic RMS values. By using this approach, two novel pharmacophore models were derived (Models 2 and 3) and a hydrogen bond acceptor was added to a previously published NK2 pharmacophore model [Poulsen et al., J. Comput.-Aided Mol. Design, 16 (2002) 273] (Model 1). Model 2 as well as Model 3 are described by seven pharmacophore elements: three hydrophobic groups, three hydrogen bond acceptors and a hydrogen bond donor. Model 1 contains the same hydrophobic groups and hydrogen bond donor as Models 2 and 3, but only one hydrogen bond acceptor. The hydrogen bond acceptors and donor are represented as vectors. Two of the hydrophobic groups are always aromatic rings whereas the other hydrophobic group can be either aromatic or aliphatic. In Model 1 the antagonists bind in an extended conformation with two aromatic rings in a parallel displaced and tilted conformation. Model 2 has the same two aromatic rings in a parallel displaced conformation whereas Model 3 has the rings in an edge to face conformation. The pharmacophore models were evaluated using both a structure (NK receptor homology models) and a ligand based approach. By use of exhaustive conformational analysis (MMFFs force field and the GB/SA hydration model) and least-squares molecular superimposition studies, 21 non-peptide antagonists from several structurally diverse classes were fitted to the pharmacophore models. More antagonists could be fitted to Model 2 with a low RMS and a low conformational energy penalty than to Models 1 and 3. Pharmacophore Model 2 was also able to explain the NK1, NK2 and NK3 subtype selectivity of the compounds fitted to the model. Three NK 7TM receptor models were constructed, one for each receptor subtype. The location of the antagonist binding site in the three NK receptor models is identical. Compounds fitted to pharmacophore Model 2 could be docked into the NK1, NK2 and NK3 receptor models after adjustment of the conformation of the flexible linker connecting the head and tail. Models 1 and 3 are not compatible with the receptor models.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42969/1/10822_2004_Article_5257316.pd

Improved ligand geometries in crystallographic refinement using AFITT

Modern crystal structure refinement programs rely on geometry restraints to overcome the challenge of a low data-to-parameter ratio. While the classical Engh and Huber restraints work well for standard amino-acid residues, the chemical complexity of small-molecule ligands presents a particular challenge. Most current approaches either limit ligand restraints to those that can be readily described in the Crystallographic Information File (CIF) format, thus sacrificing chemical flexibility and energetic accuracy, or they employ protocols that substantially lengthen the refinement time, potentially hindering rapid automated refinement workflows. PHENIX–AFITT refinement uses a full molecular-mechanics force field for user-selected small-molecule ligands during refinement, eliminating the potentially difficult problem of finding or generating high-quality geometry restraints. It is fully integrated with a standard refinement protocol and requires practically no additional steps from the user, making it ideal for high-throughput workflows. PHENIX–AFITT refinements also handle multiple ligands in a single model, alternate conformations and covalently bound ligands. Here, the results of combining AFITT and the PHENIX software suite on a data set of 189 protein–ligand PDB structures are presented. Refinements using PHENIX–AFITT significantly reduce ligand conformational energy and lead to improved geometries without detriment to the fit to the experimental data. For the data presented, PHENIX–AFITT refinements result in more chemically accurate models for small-molecule ligands