COL5A1 gene variants previously associated with reduced soft tissue injury risk are associated with elite athlete status in rugby.

BACKGROUND: Two common single nucleotide polymorphisms within the COL5A1 gene (SNPs; rs12722 C/T and rs3196378 C/A) have previously been associated with tendon and ligament pathologies. Given the high incidence of tendon and ligament injuries in elite rugby athletes, we hypothesised that both SNPs would be associated with career success. RESULTS: In 1105 participants (RugbyGene project), comprising 460 elite rugby union (RU), 88 elite rugby league athletes and 565 non-athlete controls, DNA was collected and genotyped for the COL5A1 rs12722 and rs3196378 variants using real-time PCR. For rs12722, the injury-protective CC genotype and C allele were more common in all athletes (21% and 47%, respectively) and RU athletes (22% and 48%) than in controls (16% and 41%, P ≤ 0.01). For rs3196378, the CC genotype and C allele were overrepresented in all athletes (23% and 48%) and RU athletes (24% and 49%) compared with controls (16% and 41%, P ≤ 0.02). The CC genotype in particular was overrepresented in the back and centres (24%) compared with controls, with more than twice the odds (OR = 2.25, P = 0.006) of possessing the injury-protective CC genotype. Furthermore, when considering both SNPs simultaneously, the CC-CC SNP-SNP combination and C-C inferred allele combination were higher in all the athlete groups (≥18% and ≥43%) compared with controls (13% and 40%; P = 0.01). However, no genotype differences were identified for either SNP when RU playing positions were compared directly with each other. CONCLUSION: It appears that the C alleles, CC genotypes and resulting combinations of both rs12722 and rs3196378 are beneficial for rugby athletes to achieve elite status and carriage of these variants may impart an inherited resistance against soft tissue injury, despite exposure to the high-risk environment of elite rugby. These data have implications for the management of inter-individual differences in injury risk amongst elite athletes

Simulation of an SEIR infectious disease model on the dynamic contact network of conference attendees

The spread of infectious diseases crucially depends on the pattern of contacts among individuals. Knowledge of these patterns is thus essential to inform models and computational efforts. Few empirical studies are however available that provide estimates of the number and duration of contacts among social groups. Moreover, their space and time resolution are limited, so that data is not explicit at the person-to-person level, and the dynamical aspect of the contacts is disregarded. Here, we want to assess the role of data-driven dynamic contact patterns among individuals, and in particular of their temporal aspects, in shaping the spread of a simulated epidemic in the population. We consider high resolution data of face-to-face interactions between the attendees of a conference, obtained from the deployment of an infrastructure based on Radio Frequency Identification (RFID) devices that assess mutual face-to-face proximity. The spread of epidemics along these interactions is simulated through an SEIR model, using both the dynamical network of contacts defined by the collected data, and two aggregated versions of such network, in order to assess the role of the data temporal aspects. We show that, on the timescales considered, an aggregated network taking into account the daily duration of contacts is a good approximation to the full resolution network, whereas a homogeneous representation which retains only the topology of the contact network fails in reproducing the size of the epidemic. These results have important implications in understanding the level of detail needed to correctly inform computational models for the study and management of real epidemics

Potential for early warning of viral influenza activity in the community by monitoring clinical diagnoses of influenza in hospital emergency departments

<p>Abstract</p> <p>Background</p> <p>Although syndromic surveillance systems are gaining acceptance as useful tools in public health, doubts remain about whether the anticipated early warning benefits exist. Many assessments of this question do not adequately account for the confounding effects of autocorrelation and trend when comparing surveillance time series and few compare the syndromic data stream against a continuous laboratory-based standard. We used time series methods to assess whether monitoring of daily counts of Emergency Department (ED) visits assigned a clinical diagnosis of influenza could offer earlier warning of increased incidence of viral influenza in the population compared with surveillance of daily counts of positive influenza test results from laboratories.</p> <p>Methods</p> <p>For the five-year period 2001 to 2005, time series were assembled of ED visits assigned a provisional ED diagnosis of influenza and of laboratory-confirmed influenza cases in New South Wales (NSW), Australia. Poisson regression models were fitted to both time series to minimise the confounding effects of trend and autocorrelation and to control for other calendar influences. To assess the relative timeliness of the two series, cross-correlation analysis was performed on the model residuals. Modelling and cross-correlation analysis were repeated for each individual year.</p> <p>Results</p> <p>Using the full five-year time series, short-term changes in the ED time series were estimated to precede changes in the laboratory series by three days. For individual years, the estimate was between three and 18 days. The time advantage estimated for the individual years 2003–2005 was consistently between three and four days.</p> <p>Conclusion</p> <p>Monitoring time series of ED visits clinically diagnosed with influenza could potentially provide three days early warning compared with surveillance of laboratory-confirmed influenza. When current laboratory processing and reporting delays are taken into account this time advantage is even greater.</p

Genetic Diversity in the SIR Model of Pathogen Evolution

We introduce a model for assessing the levels and patterns of genetic diversity in pathogen populations, whose epidemiology follows a susceptible-infected-recovered model (SIR). We model the population of pathogens as a metapopulation composed of subpopulations (infected hosts), where pathogens replicate and mutate. Hosts transmit pathogens to uninfected hosts. We show that the level of pathogen variation is well predicted by analytical expressions, such that pathogen neutral molecular variation is bounded by the level of infection and increases with the duration of infection. We then introduce selection in the model and study the invasion probability of a new pathogenic strain whose fitness (R0(1+s)) is higher than the fitness of the resident strain (R0). We show that this invasion probability is given by the relative increment in R0 of the new pathogen (s). By analyzing the patterns of genetic diversity in this framework, we identify the molecular signatures during the replacement and compare these with those observed in sequences of influenza A

Early changes within the lymphocyte population are associated with the development of multiple organ dysfunction syndrome in trauma patients

2016 The Author(s). Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.JM was funded, in part, by the Royal College of Surgeons of England, The Phillip King Charitable Trust Research Fellowship and The National Institute of Health Research (NIHR)

A Hidden Markov Model for Analysis of Frontline Veterinary Data for Emerging Zoonotic Disease Surveillance

Surveillance systems tracking health patterns in animals have potential for early warning of infectious disease in humans, yet there are many challenges that remain before this can be realized. Specifically, there remains the challenge of detecting early warning signals for diseases that are not known or are not part of routine surveillance for named diseases. This paper reports on the development of a hidden Markov model for analysis of frontline veterinary sentinel surveillance data from Sri Lanka. Field veterinarians collected data on syndromes and diagnoses using mobile phones. A model for submission patterns accounts for both sentinel-related and disease-related variability. Models for commonly reported cattle diagnoses were estimated separately. Region-specific weekly average prevalence was estimated for each diagnoses and partitioned into normal and abnormal periods. Visualization of state probabilities was used to indicate areas and times of unusual disease prevalence. The analysis suggests that hidden Markov modelling is a useful approach for surveillance datasets from novel populations and/or having little historical baselines

SdiA, an N-Acylhomoserine Lactone Receptor, Becomes Active during the Transit of Salmonella enterica through the Gastrointestinal Tract of Turtles

encode a LuxR-type AHL receptor, SdiA, they cannot synthesize AHLs. In vitro, it is known that SdiA can detect AHLs produced by other bacterial species..We conclude that the normal gastrointestinal microbiota of most animal species do not produce AHLs of the correct type, in an appropriate location, or in sufficient quantities to activate SdiA. However, the results obtained with turtles represent the first demonstration of SdiA activity in animals

Emergency department visits, ambulance calls, and mortality associated with an exceptional heat wave in Sydney, Australia, 2011: a time-series analysis

<p>Abstract</p> <p>Background</p> <p>From January 30-February 6, 2011, New South Wales was affected by an exceptional heat wave, which broke numerous records. Near real-time Emergency Department (ED) and ambulance surveillance allowed rapid detection of an increase in the number of heat-related ED visits and ambulance calls during this period. The purpose of this study was to quantify the excess heat-related and all-cause ED visits and ambulance calls, and excess all-cause mortality, associated with the heat wave.</p> <p>Methods</p> <p>ED and ambulance data were obtained from surveillance and administrative databases, while mortality data were obtained from the state death registry. The observed counts were compared with the average counts from the same period from 2006/07 through 2009/10, and a Poisson regression model was constructed to calculate the number of excess ED visits, ambulance and deaths after adjusting for calendar and lag effects.</p> <p>Results</p> <p>During the heat wave there were 104 and 236 ED visits for heat effects and dehydration respectively, and 116 ambulance calls for heat exposure. From the regression model, all-cause ED visits increased by 2% (95% CI 1.01-1.03), all-cause ambulance calls increased by 14% (95% CI 1.11-1.16), and all-cause mortality increased by 13% (95% CI 1.06-1.22). Those aged 75 years and older had the highest excess rates of all outcomes.</p> <p>Conclusions</p> <p>The 2011 heat wave resulted in an increase in the number of ED visits and ambulance calls, especially in older persons, as well as an increase in all-cause mortality. Rapid surveillance systems provide markers of heat wave impacts that have fatal outcomes.</p

Evolutionary Epidemiology of Drug-Resistance in Space

The spread of drug-resistant parasites erodes the efficacy of therapeutic treatments against many infectious diseases and is a major threat of the 21st century. The evolution of drug-resistance depends, among other things, on how the treatments are administered at the population level. “Resistance management” consists of finding optimal treatment strategies that both reduce the consequence of an infection at the individual host level, and limit the spread of drug-resistance in the pathogen population. Several studies have focused on the effect of mixing different treatments, or of alternating them in time. Here, we analyze another strategy, where the use of the drug varies spatially: there are places where no one receives any treatment. We find that such a spatial heterogeneity can totally prevent the rise of drug-resistance, provided that the size of treated patches is below a critical threshold. The range of parasite dispersal, the relative costs and benefits of being drug-resistant compared to being drug-sensitive, and the duration of an infection with drug-resistant parasites are the main factors determining the value of this threshold. Our analysis thus provides some general guidance regarding the optimal spatial use of drugs to prevent or limit the evolution of drug-resistance