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The occurrence of enhanced concentration of the radium triplet 226Ra, 228Ra and 224Ra is a frequently observed property of highly saline anoxic deep water as used e.g. in geothermal plants. In the present study we develop a model to explain the observed activity levels in the brines. The model considers processes at the rock-fluid interface of the aquifer like alpha recoil, sorption and surface precipitation and is implemented by means of a Monte Carlo simulation. The outcomes of the simulations indicate the dominating role of fine-grained constituents of the reservoir rock, e.g. claystone with enhanced specific activities of the natural decay chains. Mass fractions of such material in the order of a few percent are sufficient to result in radium fluid concentrations >1 Bq l-1. Also a generally valid relation between the Th/U ratio in the aquifer rock and the 228Ra/226Ra activity ratio in the fluid was found. This link improves the agreement between radium fluid data and the mean Th/U ratio of the Earth's crust. The 224Ra/228Ra fluid ratios reflect the transport time from the location of last radium release to the sampling point. The model findings were applied to a well investigated aquifer used in a geothermal plant in the North German Basin. An eight component system of the aquifer rock was established as the basis for the simulation of the radium concentrations in the deep fluid. The comparison between simulation and fluid analyses revealed a degree of radium sorption of about 50 %, which is necessary to match the model's results with the measurements. On the other hand, the 228Ra/226Ra fluid ratio of the brine was well reproduced by the simulation, showing the suitability of the model even in complex heterogeneous reservoirs. From the 224Ra/228Ra fluid ratios a transition from pore-to fracture-guided transport < 10 m distance from the production well is suggested. Precipitates from such deep fluids occurring after changes of the thermodynamic conditions are able to accumulate radium isotopes in Ba/Sr-sulphate phases. The time dependence of the radioactive disequilibrium between 226Ra, 228Ra and its child 228Th in such scales is described by a mathematical model and is applied to two different uptake models. Based on this approach, age determinations on precipitates found in different components of a geothermal plant are conducted. They reveal the triggering of scale formation due to modifications in the plant. The results are suitable for drawing conclusions about the operation of the system, which result in a reduction in the amount of scale and a reduction in downtimes.

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PURPOSE: To research and evaluate the performance of broadband tailored kT-point pulses (TP) and universal pulses (UP) for homogeneous excitation of the human heart at 7T. METHODS: Relative 3D B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps of the thorax were acquired from 29 healthy volunteers. TP and UP were designed using the small-tip-angle approximation for a different composition of up to seven resonance frequencies. TP were computed for each of the 29 B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps, and UPs were calculated using 22 B 1 + $$ {\mathrm{B}}_1^{+} $$ -maps and tested in seven testcases. The performance of the pulses was analyzed using the coefficient of variation (CV) in the 3D heart volumes. The 3D gradient-echo (GRE) scans were acquired for the seven testcases to qualitatively validate the B 1 + $$ {\mathrm{B}}_1^{+} $$ -predictions. RESULTS: Single- and double-frequency optimized pulses achieved homogeneity in flip angle (FA) for the frequencies they were optimized for, while the broadband pulses achieved uniformity in FA across a 1300 Hz frequency range. CONCLUSION: Broadband TP and UP can be used for homogeneous excitation of the heart volume across a 1300 Hz frequency range, including the water and the main six fat peaks, or with longer pulse durations and higher FAs for a smaller transmit bandwidth. Moreover, despite large inter-volunteer variations, broadband UP can be used for calibration-free 3D heart FA homogenization in time-critical situations.

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Increased economization in the German health care system may have an impact on medical decisions. A selective literature search presents an overview of the current evidence on the influence of financial incentives on inpatient healthcare in Germany. Due to the current economic pressure, physicians increasingly feel subjected to financial constraints concerning indication and treatment decisions. There is evidence for financially initiated upcoding and volume expansion. Little is known about the extent, the impact on quality of care, nor on vulnerable groups. The literature clearly documents effects of financial pressure on job satisfaction, perceived stress, and the health of attending physicians. The current discussion on the economization of physician practice is important, especially with regard to disincentives and job satisfaction. Little is known about the risks to patient health.

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False news stories cause welfare losses and fatal health consequences. To limit its dissemination, it is essential to know what determines the ability to distinguish between true and false news stories. In our experimental study, we present subjects corona-related stories taken from the media from various categories (e.g. social isolation, economic consequences, direct health consequences, and strong exaggeration). The subject's task is to evaluate the stories as true or false. Besides students with and without healthcare background, we recruit healthcare professionals to increase the external validity of our study. Our main findings are: (i) Healthcare professionals perform similar to students in correctly distinguishing between true and false news stories. (ii) The propensity to engage in analytical thinking and actively open-minded thinking is positively associated with the ability to distinguish between true and false. (iii) We find that the residence of the subjects (East- or West-Germany) plays only a minor role. (iv) If news stories are in line with existing narratives, subjects tend to think that the stories are true.

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Lack of efficacy in the intended disease indication is the major cause of clinical phase drug development failure. Explanations could include the poor external validity of pre-clinical (cell, tissue, and animal) models of human disease and the high false discovery rate (FDR) in preclinical science. FDR is related to the proportion of true relationships available for discovery (γ), and the type 1 (false-positive) and type 2 (false negative) error rates of the experiments designed to uncover them. We estimated the FDR in preclinical science, its effect on drug development success rates, and improvements expected from use of human genomics rather than preclinical studies as the primary source of evidence for drug target identification. Calculations were based on a sample space defined by all human diseases - the 'disease-ome' - represented as columns; and all protein coding genes - 'the protein-coding genome'- represented as rows, producing a matrix of unique gene- (or protein-) disease pairings. We parameterised the space based on 10,000 diseases, 20,000 protein-coding genes, 100 causal genes per disease and 4000 genes encoding druggable targets, examining the effect of varying the parameters and a range of underlying assumptions, on the inferences drawn. We estimated γ, defined mathematical relationships between preclinical FDR and drug development success rates, and estimated improvements in success rates based on human genomics (rather than orthodox preclinical studies). Around one in every 200 protein-disease pairings was estimated to be causal (γ = 0.005) giving an FDR in preclinical research of 92.6%, which likely makes a major contribution to the reported drug development failure rate of 96%. Observed success rate was only slightly greater than expected for a random pick from the sample space. Values for γ back-calculated from reported preclinical and clinical drug development success rates were also close to the a priori estimates. Substituting genome wide (or druggable genome wide) association studies for preclinical studies as the major information source for drug target identification was estimated to reverse the probability of late stage failure because of the more stringent type 1 error rate employed and the ability to interrogate every potential druggable target in the same experiment. Genetic studies conducted at much larger scale, with greater resolution of disease end-points, e.g. by connecting genomics and electronic health record data within healthcare systems has the potential to produce radical improvement in drug development success rate.

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Mutual coupling and injection locking of semiconductor lasers is of great interest in non-linear dynamics and its applications for instance in secure data communication and photonic reservoir computing. Despite its importance, it has hardly been studied in microlasers operating at µW light levels. In this context, vertically emitting quantum dot micropillar lasers are of high interest. Usually, their light emission is bimodal, and the gain competition of the associated linearly polarized fundamental emission modes results in complex switching dynamics. We report on selective optical injection into either one of the two fundamental mode components of a bimodal micropillar laser. Both modes can lock to the master laser and influence the non-injected mode by reducing the available gain. We demonstrate that the switching dynamics can be tailored externally via optical injection in very good agreement with our theory based on semi-classical rate equations.

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Protein domains mediate drug-protein interactions and this principle can guide the design of multi-target drugs i.e. polypharmacology. In this study, we associate multi-target drugs with CATH functional families through the overrepresentation of targets of those drugs in CATH functional families. Thus, we identify CATH functional families that are currently enriched in drugs (druggable CATH functional families) and we use the network properties of these druggable protein families to analyse their association with drug side effects. Analysis of selected druggable CATH functional families, enriched in drug targets, show that relatives exhibit highly conserved drug binding sites. Furthermore, relatives within druggable CATH functional families occupy central positions in a human protein functional network, cluster together forming network neighbourhoods and are less likely to be within proteins associated with drug side effects. Our results demonstrate that CATH functional families can be used to identify drug-target interactions, opening a new research direction in target identification.

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Target identification (determining the correct drug targets for a disease) and target validation (demonstrating an effect of target perturbation on disease biomarkers and disease end points) are important steps in drug development. Clinically relevant associations of variants in genes encoding drug targets model the effect of modifying the same targets pharmacologically. To delineate drug development (including repurposing) opportunities arising from this paradigm, we connected complex disease- and biomarker-associated loci from genome-wide association studies to an updated set of genes encoding druggable human proteins, to agents with bioactivity against these targets, and, where there were licensed drugs, to clinical indications. We used this set of genes to inform the design of a new genotyping array, which will enable association studies of druggable genes for drug target selection and validation in human disease.

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BACKGROUND: Increased circulating plasma urate concentration is associated with an increased risk of coronary heart disease, but the extent of any causative effect of urate on risk of coronary heart disease is still unclear. In this study, we aimed to clarify any causal role of urate on coronary heart disease risk using Mendelian randomisation analysis. METHODS: We first did a fixed-effects meta-analysis of the observational association of plasma urate and risk of coronary heart disease. We then used a conventional Mendelian randomisation approach to investigate the causal relevance using a genetic instrument based on 31 urate-associated single nucleotide polymorphisms (SNPs). To account for potential pleiotropic associations of certain SNPs with risk factors other than urate, we additionally did both a multivariable Mendelian randomisation analysis, in which the genetic associations of SNPs with systolic and diastolic blood pressure, HDL cholesterol, and triglycerides were included as covariates, and an Egger Mendelian randomisation (MR-Egger) analysis to estimate a causal effect accounting for unmeasured pleiotropy. FINDINGS: In the meta-analysis of 17 prospective observational studies (166 486 individuals; 9784 coronary heart disease events) a 1 SD higher urate concentration was associated with an odds ratio (OR) for coronary heart disease of 1·07 (95% CI 1·04-1·10). The corresponding OR estimates from the conventional, multivariable adjusted, and Egger Mendelian randomisation analysis (58 studies; 198 598 individuals; 65 877 events) were 1·18 (95% CI 1·08-1·29), 1·10 (1·00-1·22), and 1·05 (0·92-1·20), respectively, per 1 SD increment in plasma urate. INTERPRETATION: Conventional and multivariate Mendelian randomisation analysis implicates a causal role for urate in the development of coronary heart disease, but these estimates might be inflated by hidden pleiotropy. Egger Mendelian randomisation analysis, which accounts for pleiotropy but has less statistical power, suggests there might be no causal effect. These results might help investigators to determine the priority of trials of urate lowering for the prevention of coronary heart disease compared with other potential interventions. FUNDING: UK National Institute for Health Research, British Heart Foundation, and UK Medical Research Council.

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UNLABELLED: PPDMs is a resource that maps small molecule bioactivities to protein domains from the Pfam-A collection of protein families. Small molecule bioactivities mapped to protein domains add important precision to approaches that use protein sequence searches alignments to assist applications in computational drug discovery and systems and chemical biology. We have previously proposed a mapping heuristic for a subset of bioactivities stored in ChEMBL with the Pfam-A domain most likely to mediate small molecule binding. We have since refined this mapping using a manual procedure. Here, we present a resource that provides up-to-date mappings and the possibility to review assigned mappings as well as to participate in their assignment and curation. We also describe how mappings provided through the PPDMs resource are made accessible through the main schema of the ChEMBL database. AVAILABILITY AND IMPLEMENTATION: The PPDMs resource and curation interface is available at https://www.ebi.ac.uk/chembl/research/ppdms/pfam_maps. The source-code for PPDMs is available under the Apache license at https://github.com/chembl/pfam_maps. Source code is available at https://github.com/chembl/pfam_map_loader to demonstrate the integration process with the main schema of ChEMBL.

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ChEMBL is an open large-scale bioactivity database (https://www.ebi.ac.uk/chembl), previously described in the 2012 Nucleic Acids Research Database Issue. Since then, a variety of new data sources and improvements in functionality have contributed to the growth and utility of the resource. In particular, more comprehensive tracking of compounds from research stages through clinical development to market is provided through the inclusion of data from United States Adopted Name applications; a new richer data model for representing drug targets has been developed; and a number of methods have been put in place to allow users to more easily identify reliable data. Finally, access to ChEMBL is now available via a new Resource Description Framework format, in addition to the web-based interface, data downloads and web services.

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We report on the integration of pharmacological data and homology information for a large scale analysis of small molecule binding to related targets. Differences in small molecule binding have been assessed for curated pairs of human to rat orthologs and also for recently diverged human paralogs. Our analysis shows that in general, small molecule binding is conserved for pairs of human to rat orthologs. Using statistical tests, we identified a small number of cases where small molecule binding is different between human and rat, some of which had previously been reported in the literature. Knowledge of species specific pharmacology can be advantageous for drug discovery, where rats are frequently used as a model system. For human paralogs, we demonstrate a global correlation between sequence identity and the binding of small molecules with equivalent affinity. Our findings provide an initial general model relating small molecule binding and sequence divergence, containing the foundations for a general model to anticipate and predict within-target-family selectivity.

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BACKGROUND: Large-scale bioactivity/SAR Open Data has recently become available, and this has allowed new analyses and approaches to be developed to help address the productivity and translational gaps of current drug discovery. One of the current limitations of these data is the relative sparsity of reported interactions per protein target, and complexities in establishing clear relationships between bioactivity and targets using bioinformatics tools. We detail in this paper the indexing of targets by the structural domains that bind (or are likely to bind) the ligand within a full-length protein. Specifically, we present a simple heuristic to map small molecule binding to Pfam domains. This profiling can be applied to all proteins within a genome to give some indications of the potential pharmacological modulation and regulation of all proteins. RESULTS: In this implementation of our heuristic, ligand binding to protein targets from the ChEMBL database was mapped to structural domains as defined by profiles contained within the Pfam-A database. Our mapping suggests that the majority of assay targets within the current version of the ChEMBL database bind ligands through a small number of highly prevalent domains, and conversely the majority of Pfam domains sampled by our data play no currently established role in ligand binding. Validation studies, carried out firstly against Uniprot entries with expert binding-site annotation and secondly against entries in the wwPDB repository of crystallographic protein structures, demonstrate that our simple heuristic maps ligand binding to the correct domain in about 90 percent of all assessed cases. Using the mappings obtained with our heuristic, we have assembled ligand sets associated with each Pfam domain. CONCLUSIONS: Small molecule binding has been mapped to Pfam-A domains of protein targets in the ChEMBL bioactivity database. The result of this mapping is an enriched annotation of small molecule bioactivity data and a grouping of activity classes following the Pfam-A specifications of protein domains. This is valuable for data-focused approaches in drug discovery, for example when extrapolating potential targets of a small molecule with known activity against one or few targets, or in the assessment of a potential target for drug discovery or screening studies.

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The challenge of translating the huge amount of genomic and biochemical data into new drugs is a costly and challenging task. Historically, there has been comparatively little focus on linking the biochemical and chemical worlds. To address this need, we have developed ChEMBL, an online resource of small-molecule SAR (structure-activity relationship) data, which can be used to support chemical biology, lead discovery and target selection in drug discovery. The database contains the abstracted structures, properties and biological activities for over 700000 distinct compounds and in excess of more than 3 million bioactivity records abstracted from over 40000 publications. Additional public domain resources can be readily integrated into the same data model (e.g. PubChem BioAssay data). The compounds in ChEMBL are largely extracted from the primary medicinal chemistry literature, and are therefore usually 'drug-like' or 'lead-like' small molecules with full experimental context. The data cover a significant fraction of the discovery of modern drugs, and are useful in a wide range of drug design and discovery tasks. In addition to the compound data, ChEMBL also contains information for over 8000 protein, cell line and whole-organism 'targets', with over 4000 of those being proteins linked to their underlying genes. The database is searchable both chemically, using an interactive compound sketch tool, protein sequences, family hierarchies, SMILES strings, compound research codes and key words, and biologically, using a variety of gene identifiers, protein sequence similarity and protein families. The information retrieved can then be readily filtered and downloaded into various formats. ChEMBL can be accessed online at https://www.ebi.ac.uk/chembldb.

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Fragment formal concept analysis (FragFCA) for compound classification: Signature fragment combinations for compound classes with closely related biological activity were identified using FragFCA. These combinations are used to accurately classify active test compounds on the basis of fragment mapping. FragFCA can extract class-specific fragment combinations from compounds active against different target families that have signature character and practical utility in compound classification and database searching.Formal concept analysis (FCA), originally developed in information science, has been adapted to identify relationships between fragments of compounds and their biological activity. Here applications of the FragFCA approach with practical utility for medicinal chemistry are explored. Hierarchically derived fragment populations of 24 classes of compounds active against eight target families were subjected to FragFCA analysis, and fragment combinations were identified that distinguished compounds with closely related biological activity from each other. Mapping of signature fragment combinations was carried out to classify active compounds for different target families with high accuracy. The results indicate that compound-class-specific structural information and selectivity determinants are predominantly encoded by fragment combinations, rather than individual fragments. Furthermore, class-specific fragment combinations were successfully applied in similarity searching. The results demonstrate that FragFCA is capable of identifying fragment combinations that differentiate between compound sets with closely related biological activity and that can be used to predict structure-activity relationships.

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