Pseudotumour in Christmas disease

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Autumn microhabitat breadth differs between family groups of Atlantic salmon parr (Salmo salar) in a small chalk stream

The effect of family traits on the microhabitat use by six genetically distinct groups (three in each year of study) of juvenile Atlantic salmon tagged with passive integrated transponder (PIT) tags was studied via PIT-tag detectors installed on the river bed in a small chalk stream of southern England, during Autumn in 2006 and 2007. Canonical correspondence analysis of the molecular and microhabitat data revealed considerable overlap in the microhabitat use of the family groups and notable differences in microhabitat breadth, which was partly influenced by sample size. The data suggest that microhabitat breadth and preferences of wild salmon are influenced by family of origin

Synthesis of mono and Bis[60]fullerene-based dicationic peptoids

Increasing numbers of biological applications of fullerenyl amino acids and their derivatives encouraged us to synthesise [60]fullerenyldihydropyrrole peptides, prepared from the coupling of mono- and bis[60]fullerenyldihydropyrrolecarboxylic acids 4, 5 and 41 with presynthesised peptides 13, 16, 24, 28, 29 and 46. The resulting hydrophobic scaffolded di- and tetra-cationic derivatives were tested against Staphylococcus aureus NCTC 6571 and Escherichia coli NCTC 10418. The synthesis, characterisation and biological results are discussed in this paper

Binaphthyl-1,2,3-triazole peptidomimetics with activity against Clostridium difficile and other pathogenic bacteria

Clostridium difficile (C. difficile) is a problematic Gram positive bacterial pathogen causing moderate to severe gastrointestinal infections. Based on a lead binaphthyl-tripeptide dicationic antimicrobial, novel mono-, di- and tri-peptidomimetic analogues targeting C. difficile were designed and synthesized incorporating one, two or three d-configured cationic amino acid residues, with a common 1,2,3-triazole ester isostere at the C-terminus. Copper- and ruthenium-click chemistry facilitated the generation of a 46 compound library for in vitro bioactivity assays, with structure-activity trends over the largest compound subset revealing a clear advantage to triazole-substitution with a linear or branched hydrophobic group. The most active compounds were dicationic-dipeptides where the triazole was substituted with a 4- or 5-cyclohexylmethyl or 4,5-diphenyl moiety, providing MICs of 4 μg mL-1 against three human isolates of C. difficile. Further biological screening revealed significant antimicrobial activity for several compounds against other common bacterial pathogens, both Gram positive and negative, including S. aureus (MICs ≥2 μg mL-1), S. pneumoniae (MICs ≥1 μg mL-1), E. coli (MICs ≥4 μg mL-1), A. baumannii (MICs ≥4 μg mL-1) and vancomycin-resistant E. faecalis (MICs ≥4 μg mL-1)

Does relatedness influence migratory timing and behaviour in Atlantic salmon smolts?

Aggregating and moving with relatives may enable animals to increase opportunities for kin selection to occur. To gain group-living benefits, animals must coordinate their behaviour. Atlantic salmon, Salmo salar, demonstrate both territoriality and schooling: the two key social behaviours performed by fish. In this investigation we compared the migratory timing and behaviour of six distinct full-sibling groups of tagged S. salar smolts with a large control sample from the same wild population. The results clearly demonstrate that the incidence of schooling and diel migratory timing is not significantly influenced by relatedness, and this adds further support to the hypothesis that S. salar smolt migration is primarily an adaptive response to environmental conditions, rather than a behaviour based solely on genetics or kin-biased behaviour. Used in conjunction with the results of two previous investigations, this is the first study to illustrate that kin discrimination among full-sibling groups of parr does not lead to kin-biased schooling in smolts. Thus, even within the same full-sibling groups, the extent of kin-biased behaviour in fish can both differ within a life history stage under varying ecological conditions and shift from one life history stage to the next

Multivariate meta-analysis of individual participant data helped externally validate the performance and implementation of a prediction model

Objectives Our aim was to improve meta-analysis methods for summarizing a prediction model's performance when individual participant data are available from multiple studies for external validation. Study Design and Setting We suggest multivariate meta-analysis for jointly synthesizing calibration and discrimination performance, while accounting for their correlation. The approach estimates a prediction model's average performance, the heterogeneity in performance across populations, and the probability of "good" performance in new populations. This allows different implementation strategies (e.g., recalibration) to be compared. Application is made to a diagnostic model for deep vein thrombosis (DVT) and a prognostic model for breast cancer mortality. Results In both examples, multivariate meta-analysis reveals that calibration performance is excellent on average but highly heterogeneous across populations unless the model's intercept (baseline hazard) is recalibrated. For the cancer model, the probability of "good" performance (defined by C statistic ≥ 0.7 and calibration slope between 0.9 and 1.1) in a new population was 0.67 with recalibration but 0.22 without recalibration. For the DVT model, even with recalibration, there was only a 0.03 probability of "good" performance. Conclusion Multivariate meta-analysis can be used to externally validate a prediction model's calibration and discrimination performance across multiple populations and to evaluate different implementation strategies

Prognosis research strategy (PROGRESS) 1: a framework for researching clinical outcomes.

The PROGRESS series (www.progress-partnership.org) sets out a framework of four interlinked prognosis research themes and provides examples from several disease fields to show why evidence from prognosis research is crucial to inform all points in the translation of biomedical and health related research into better patient outcomes. Recommendations are made in each of the four papers to improve current research standards What is prognosis research? Prognosis research seeks to understand and improve future outcomes in people with a given disease or health condition. However, there is increasing evidence that prognosis research standards need to be improved Why is prognosis research important? More people now live with disease and conditions that impair health than at any other time in history; prognosis research provides crucial evidence for translating findings from the laboratory to humans, and from clinical research to clinical practice This first article introduces the framework of four interlinked prognosis research themes and then focuses on the first of the themes - fundamental prognosis research, studies that aim to describe and explain future outcomes in relation to current diagnostic and treatment practices, often in relation to quality of care Fundamental prognosis research provides evidence informing healthcare and public health policy, the design and interpretation of randomised trials, and the impact of diagnostic tests on future outcome. It can inform new definitions of disease, may identify unanticipated benefits or harms of interventions, and clarify where new interventions are required to improve prognosis

Propensity-based standardization to enhance the validation and interpretation of prediction model discrimination for a target population

External validation of the discriminative ability of prediction models is of key importance. However, the interpretation of such evaluations is challenging, as the ability to discriminate depends on both the sample characteristics (ie, case-mix) and the generalizability of predictor coefficients, but most discrimination indices do not provide any insight into their respective contributions. To disentangle differences in discriminative ability across external validation samples due to a lack of model generalizability from differences in sample characteristics, we propose propensity-weighted measures of discrimination. These weighted metrics, which are derived from propensity scores for sample membership, are standardized for case-mix differences between the model development and validation samples, allowing for a fair comparison of discriminative ability in terms of model characteristics in a target population of interest. We illustrate our methods with the validation of eight prediction models for deep vein thrombosis in 12 external validation data sets and assess our methods in a simulation study. In the illustrative example, propensity score standardization reduced between-study heterogeneity of discrimination, indicating that between-study variability was partially attributable to case-mix. The simulation study showed that only flexible propensity-score methods (allowing for non-linear effects) produced unbiased estimates of model discrimination in the target population, and only when the positivity assumption was met. Propensity score-based standardization may facilitate the interpretation of (heterogeneity in) discriminative ability of a prediction model as observed across multiple studies, and may guide model updating strategies for a particular target population. Careful propensity score modeling with attention for non-linear relations is recommended

Regularized parametric survival modeling to improve risk prediction models

We propose to combine the benefits of flexible parametric survival modeling and regularization to improve risk prediction modeling in the context of time-to-event data. Thereto, we introduce ridge, lasso, elastic net, and group lasso penalties for both log hazard and log cumulative hazard models. The log (cumulative) hazard in these models is represented by a flexible function of time that may depend on the covariates (i.e., covariate effects may be time-varying). We show that the optimization problem for the proposed models can be formulated as a convex optimization problem and provide a user-friendly R implementation for model fitting and penalty parameter selection based on cross-validation. Simulation study results show the advantage of regularization in terms of increased out-of-sample prediction accuracy and improved calibration and discrimination of predicted survival probabilities, especially when sample size was relatively small with respect to model complexity. An applied example illustrates the proposed methods. In summary, our work provides both a foundation for and an easily accessible implementation of regularized parametric survival modeling and suggests that it improves out-of-sample prediction performance