POPULATION GENETIC STRUCTURE OF \u3cem\u3eNECTURUS MACULOSUS\u3c/em\u3e IN CENTRAL AND EASTERN KENTUCKY

Population structure is influenced by extrinsic factors, such as landscape architecture and dispersal barriers. Lotic network architecture is known to constrain ecological, demographic and evolutionary processes, including population genetic structure. I assessed the population structure of a widespread aquatic salamander, Necturus maculosus, across three river basins in central and eastern Kentucky. I examined the role of network architecture, anthropogenic barriers, and spatial scale on patterns of population structure. I also provided a review of N. maculosus capture methods and offer an improved trap design. I identified significant structuring between the combined Licking/Kinniconick basin and the Kentucky River basin, with further structure within each basin. I found evidence for both hierarchically organized populations structure (e.g. Stream Hierarchy Model), as well as population structure unaffected by network hierarchy (e.g. Death Valley Model). These results highlight the importance of scale when examining population structure. Whereas one model may suffice to explain population structure at a local scale, a second model may be necessary to accurately describe the population structure across larger spatial scales. These results suggest that local factors affect population structure uniquely across a species’ range, and support a multi-model approach for assessing population structure

Using Real-World Data to Guide Ustekinumab Dosing Strategies for Psoriasis: A Prospective Pharmacokinetic-Pharmacodynamic Study.

Variation in response to biologic therapy for inflammatory diseases, such as psoriasis, is partly driven by variation in drug exposure. Real-world psoriasis data were used to develop a pharmacokinetic/pharmacodynamic (PK/PD) model for the first-line therapeutic antibody ustekinumab. The impact of differing dosing strategies on response was explored. Data were collected from a UK prospective multicenter observational cohort (491 patients on ustekinumab monotherapy, drug levels, and anti-drug antibody measurements on 797 serum samples, 1,590 measurements of Psoriasis Area Severity Index (PASI)). Ustekinumab PKs were described with a linear one-compartment model. A maximum effect (Emax ) model inhibited progression of psoriatic skin lesions in the turnover PD mechanism describing PASI evolution while on treatment. A mixture model on half-maximal effective concentration identified a potential nonresponder group, with simulations suggesting that, in future, the model could be incorporated into a Bayesian therapeutic drug monitoring "dashboard" to individualize dosing and improve treatment outcomes

Risk of major cardiovascular events in patients with psoriasis receiving biologic therapies: a prospective cohort study

Background: The cardiovascular safety profile of biologic therapies used for psoriasis is unclear. Objectives: To compare the risk of major cardiovascular events (CVEs; acute coronary syndrome, unstable angina, myocardial infarction and stroke) in patients with chronic plaque psoriasis treated with adalimumab, etanercept or ustekinumab in a large prospective cohort. Methods: Prospective cohort study examining the comparative risk of major CVEs was conducted using the British Association of Dermatologists Biologics and Immunomodulators Register. The main analysis compared adults with chronic plaque psoriasis receiving ustekinumab with tumour necrosis‐α inhibitors (TNFi: etanercept and adalimumab), whilst the secondary analyses compared ustekinumab, etanercept or methotrexate against adalimumab. Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated using overlap weights by propensity score to balance baseline covariates among comparison groups. Results: We included 5468 biologic‐naïve patients subsequently exposed (951 ustekinumab; 1313 etanercept; and 3204 adalimumab) in the main analysis. The secondary analyses also included 2189 patients receiving methotrexate. The median (p25–p75) follow‐up times for patients using ustekinumab, TNFi, adalimumab, etanercept and methotrexate were as follows: 2.01 (1.16–3.21), 1.93 (1.05–3.34), 1.94 (1.09–3.32), 1.92 (0.93–3.45) and 1.43 (0.84–2.53) years, respectively. Ustekinumab, TNFi, adalimumab, etanercept and methotrexate groups had 7, 29, 23, 6 and 9 patients experiencing major CVEs, respectively. No differences in the risk of major CVEs were observed between biologic therapies [adjusted HR for ustekinumab vs. TNFi: 0.96 (95% CI 0.41–2.22); ustekinumab vs. adalimumab: 0.81 (0.30–2.17); etanercept vs. adalimumab: 0.81 (0.28–2.30)] and methotrexate against adalimumab [1.05 (0.34–3.28)]. Conclusions: In this large prospective cohort study, we found no significant differences in the risk of major CVEs between three different biologic therapies and methotrexate. Additional studies, with longer term follow‐up, are needed to investigate the potential effects of biologic therapies on incidence of major CVEs

Development and validation of a multivariable risk prediction model for serious infection in patients with psoriasis receiving systemic therapy

BACKGROUND: Patients with psoriasis are often concerned about the risk of serious infection associated with systemic psoriasis treatments. OBJECTIVES: To develop and externally validate a prediction model for serious infection in patients with psoriasis within 1 year of starting systemic therapies. METHODS: The risk prediction model was developed using the British Association of Dermatologists Biologic Interventions Register (BADBIR), and the German Psoriasis Registry PsoBest was used as the validation dataset. Model discrimination and calibration were assessed internally and externally using the C-statistic, the calibration slope and the calibration in the large. RESULTS: Overall 175 (1·7%) out of 10 033 participants from BADBIR and 41 (1·7%) out of 2423 participants from PsoBest developed a serious infection within 1 year of therapy initiation. Selected predictors in a multiple logistic regression model included nine baseline covariates, and starting infliximab was the strongest predictor. Evaluation of model performance showed a bootstrap optimism-corrected C-statistic of 0·64 [95% confidence interval (CI) 0·60-0·69], calibration in the large of 0·02 (95% CI -0·14 to 0·17) and a calibration slope of 0·88 (95% CI 0·70-1·07), while external validation performance was poor, with C-statistic 0·52 (95% CI 0·42-0·62), calibration in the large 0·06 (95% CI -0·25 to 0·37) and calibration slope 0·36 (95% CI -0·24 to 0·97). CONCLUSIONS: We present the first results of the development of a multivariable prediction model. This model may help patients and dermatologists in the U.K. and the Republic of Ireland to identify modifiable risk factors and inform therapy choice in a shared decision-making process

Erratum: Author Correction: Identification of genes required for eye development by high-throughput screening of mouse knockouts.

[This corrects the article DOI: 10.1038/s42003-018-0226-0.]

Writing in Britain and Ireland, c. 400 to c. 800

No abstract available

Intentional and unintentional medication non-adherence in psoriasis: The role of patients’ medication beliefs and habit strength

Medication non-adherence is a missed opportunity for therapeutic benefit. We assessed “real-world” levels of self-reported non-adherence to conventional and biologic systemic therapies used for psoriasis and evaluated psychological and biomedical factors associated with non-adherence using multivariable analyses. Latent profile analysis was used to investigate whether patients can be categorized into groups with similar medication beliefs. Latent profile analysis categorizes individuals with similar profiles on a set of continuous variables into discrete groups represented by a categorical latent variable. Eight hundred and eleven patients enrolled in the British Association of Dermatologists Biologic Interventions Register were included. Six hundred and seventeen patients were using a self-administered systemic therapy; 22.4% were classified as “non-adherent” (12% intentionally and 10.9% unintentionally). Patients using an oral conventional systemic agent were more likely to be non-adherent compared to those using etanercept or adalimumab (29.2% vs. 16.4%; P ≤ 0.001). Latent profile analysis supported a three-group model; all groups held strong beliefs about their need for systemic therapy but differed in levels of medication concerns. Group 1 (26.4% of the sample) reported the strongest concerns, followed by Group 2 (61%), with Group 3 (12.6%) reporting the weakest concerns. Group 1 membership was associated with intentional non-adherence (odds ratio = 2.27, 95% confidence interval = 1.16−4.47) and weaker medication-taking routine or habit strength was associated with unintentional non-adherence (odds ratio = 0.92, 95% confidence interval = 0.89−0.96). Medication beliefs and habit strength are modifiable targets for strategies to improve adherence in psoriasis

Implications For The Origin Of GRB 051103 From LIGO Observations

We present the results of a LIGO search for gravitational waves (GWs) associated with GRB 051103, a short-duration hard-spectrum gamma-ray burst (GRB) whose electromagnetically determined sky position is coincident with the spiral galaxy M81, which is 3.6 Mpc from Earth. Possible progenitors for short-hard GRBs include compact object mergers and soft gamma repeater (SGR) giant flares. A merger progenitor would produce a characteristic GW signal that should be detectable at the distance of M81, while GW emission from an SGR is not expected to be detectable at that distance. We found no evidence of a GW signal associated with GRB 051103. Assuming weakly beamed gamma-ray emission with a jet semi-angle of 30 deg we exclude a binary neutron star merger in M81 as the progenitor with a confidence of 98%. Neutron star-black hole mergers are excluded with > 99% confidence. If the event occurred in M81 our findings support the the hypothesis that GRB 051103 was due to an SGR giant flare, making it the most distant extragalactic magnetar observed to date.Comment: 8 pages, 3 figures. For a repository of data used in the publication, go to: https://dcc.ligo.org/cgi-bin/DocDB/ShowDocument?docid=15166 . Also see the announcement for this paper on ligo.org at: http://www.ligo.org/science/Publication-GRB051103/index.ph