Competition and parasitism in the native White Clawed Crayfish Austropotamobius pallipes and the invasive Signal Crayfish Pacifastacus leniusculus in the UK

Many crayfish species have been introduced to novel habitats worldwide, often threatening extinction of native species. Here we investigate competitive interactions and parasite infections in the native Austropotamobius pallipes and the invasive Pacifastacus leniusculus from single and mixed species populations in theUK. We found A. pallipes individuals to be significantly smaller in mixed compared to single species populations; conversely P. leniusculus individuals were larger in mixed than in single species populations. Our data provide no support for reproductive interference as a mechanism of competitive displacement and instead suggest competitive exclusion of A. pallipes from refuges by P. leniusculus leading to differential predation. We screened 52 P. leniusculus and 12 A. pallipes for microsporidian infection using PCR. We present the first molecular confirmation of Thelohania contejeani in the native A. pallipes; in addition, we provide the first evidence for T. contejeani in the invasive P. leniusculus. Three novel parasite sequenceswere also isolated fromP. leniusculus with an overall prevalence of microsporidian infection of 38% within this species; we discuss the identity of and the similarity between these three novel sequences. We also screened a subset of fifteen P. leniusculus and three A. pallipes for Aphanomyces astaci, the causative agent of crayfish plague and for the protistan crayfish parasite Psorospermium haeckeli. We found no evidence for infection by either agent in any of the crayfish screened. The high prevalence of microsporidian parasites and occurrence of shared T. contejeani infection lead us to propose that future studies should consider the impact of these parasites on native and invasive host fitness and their potential effects upon the dynamics of native-invader systems

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Alarm variables for dengue outbreaks: a multi-centre study in Asia and Latin America

BACKGROUND: Worldwide, dengue is an unrelenting economic and health burden. Dengue outbreaks have become increasingly common, which place great strain on health infrastructure and services. Early warning models could allow health systems and vector control programmes to respond more cost-effectively and efficiently. METHODOLOGY/PRINCIPAL FINDINGS: The Shewhart method and Endemic Channel were used to identify alarm variables that may predict dengue outbreaks. Five country datasets were compiled by epidemiological week over the years 2007-2013. These data were split between the years 2007-2011 (historic period) and 2012-2013 (evaluation period). Associations between alarm/ outbreak variables were analysed using logistic regression during the historic period while alarm and outbreak signals were captured during the evaluation period. These signals were combined to form alarm/ outbreak periods, where 2 signals were equal to 1 period. Alarm periods were quantified and used to predict subsequent outbreak periods. Across Mexico and Dominican Republic, an increase in probable cases predicted outbreaks of hospitalised cases with sensitivities and positive predictive values (PPV) of 93%/ 83% and 97%/ 86% respectively, at a lag of 1-12 weeks. An increase in mean temperature ably predicted outbreaks of hospitalised cases in Mexico and Brazil, with sensitivities and PPVs of 79%/ 73% and 81%/ 46% respectively, also at a lag of 1-12 weeks. Mean age was predictive of hospitalised cases at sensitivities and PPVs of 72%/ 74% and 96%/ 45% in Mexico and Malaysia respectively, at a lag of 4-16 weeks. CONCLUSIONS/SIGNIFICANCE: An increase in probable cases was predictive of outbreaks, while meteorological variables, particularly mean temperature, demonstrated predictive potential in some countries, but not all. While it is difficult to define uniform variables applicable in every country context, the use of probable cases and meteorological variables in tailored early warning systems could be used to highlight the occurrence of dengue outbreaks or indicate increased risk of dengue transmission