Immunisation coverage in Australian Indigenous children : time to move the goal posts

Childhood immunisation coverage reported at 12 to <15 months and 2 years of age, may mask deficiencies in the timeliness of vaccines designed to protect against diseases in infancy. This study aimed to evaluate immunisation timeliness in Indigenous infants in the Northern Territory, Australia. Coverage was analysed at the date children turned 7, 13 and 18 months of age. By 7 months of age, 45.2% of children had completed the recommended schedule, increasing to 49.5% and 81.2% at 13 and 18 months of age, respectively. Immunisation performance benchmarks must focus on improving the timeliness in these children in the first year of life.

The epidemiology of invasive group A streptococcal disease in Victoria, Australia

Objective To estimate the incidence and severity of invasive group A streptococcal infection in Victoria, Australia. Design Prospective active surveillance study. Setting Public and private laboratories, hospitals and general practitioners throughout Victoria. Patients eople in Victoria diagnosed with group A streptococcal disease notified to the surveillance system between 1 March 2002 and 31 August 2004. Main outcome measure Confirmed invasive group A streptococcal disease. Results We identified 333 confirmed cases: an average annualised incidence rate of 2.7 (95% CI, 2.3-3.2) per 100000 population per year. Rates were highest in people aged 65 years and older and those younger than 5 years. The case-fatality rate was 7.8%. Streptococcal toxic shock syndrome occurred in 48 patients (14.4%), with a case-fatality rate of 23%. Thirty cases of necrotising fasciitis were reported; five (17%) of these patients died. Type 1 (23%) was the most frequently identified emm sequence type in all, age groups. All tested isolates were susceptible to penicillin and clindamycin. Two isolates (4%) were resistant to erythromycin. Conclusion The incidence of invasive group A streptococcal disease in temperate Australia is greater than previously appreciated and warrants greater public health attention, including its designation as a notifiable disease.

Increased risk of hospitalization for acute lower respiratory tract infection among Australian Indigenous infants 5-23 months of age following pneumococcal vaccination : a cohort study

Background Australian Indigenous children are the only population worldwide to receive the 7-valent pneumococcal conjugate vaccine (7vPCV) at 2, 4, and 6 months of age and the 23-valent pneumococcal polysaccharide vaccine (23vPPV) at 18 months of age. We evaluated this program's effectiveness in reducing the risk of hospitalization for acute lower respiratory tract infection (ALRI) in Northern Territory (NT) Indigenous children aged 5-23 months. Methods We conducted a retrospective cohort study involving all NT Indigenous children born from 1 April 2000 through 31 October 2004. Person-time at-risk after 0, 1, 2, and 3 doses of 7vPCV and after 0 and 1 dose of 23vPPV and the number of ALRI following each dose were used to calculate dose-specific rates of ALRI for children 5-23 months of age. Rates were compared using Cox proportional hazards models, with the number of doses of each vaccine serving as time-dependent covariates. Results There were 5482 children and 8315 child-years at risk, with 2174 episodes of ALRI requiring hospitalization (overall incidence, 261 episodes per 1000 child-years at risk). Elevated risk of ALRI requiring hospitalization was observed after each dose of the 7vPCV vaccine, compared with that for children who received no doses, and an even greater elevation in risk was observed after each dose of the 23vPPV ( adjusted hazard ratio [HR] vs no dose, 1.39; 95% confidence interval [CI], 1.12-1.71;). Risk was highest among children Pp. 002 vaccinated with the 23vPPV who had received < 3 doses of the 7vPCV (adjusted HR, 1.81; 95% CI, 1.32-2.48). Conclusions Our results suggest an increased risk of ALRI requiring hospitalization after pneumococcal vaccination, particularly after receipt of the 23vPPV booster. The use of the 23vPPV booster should be reevaluated.

Ross River virus arthritis

“Epidemics” of a benign disease causing polyarthralgia and rash were first described in Australia in 1927.63 Following the recovery of the causative agent and the advent of serologic tests able to diagnose Ross River virus infection, epidemic polyarthritis has been recognized as endemic in Australia and has occurred as epidemics in numerous Pacific nations. Approximately 4000 cases of epidemic polyarthritis are reported in Australia each year, with a peak of 7800 cases in 1996. Some confusion has been generated recently by use of the term Ross River fever to describe clinical Ross River virus infections because fever does not develop in more than half of those with clinical disease.59 Additional confusion has been generated by efforts to describe any polyarthritis caused by an Australian arbovirus as epidemic polyarthritis. The term epidemic polyarthritis should be used to describe only clinical disease caused by Ross River virus.

Crizotinib versus chemotherapy in advanced ALK-positive lung cancer

BACKGROUND: In single-group studies, chromosomal rearrangements of the anaplastic lymphoma kinase gene (ALK ) have been associated with marked clinical responses to crizotinib, an oral tyrosine kinase inhibitor targeting ALK. Whether crizotinib is superior to standard chemotherapy with respect to efficacy is unknown. METHODS: We conducted a phase 3, open-label trial comparing crizotinib with chemotherapy in 347 patients with locally advanced or metastatic ALK-positive lung cancer who had received one prior platinum-based regimen. Patients were randomly assigned to receive oral treatment with crizotinib (250 mg) twice daily or intravenous chemotherapy with either pemetrexed (500 mg per square meter of body-surface area) or docetaxel (75 mg per square meter) every 3 weeks. Patients in the chemotherapy group who had disease progression were permitted to cross over to crizotinib as part of a separate study. The primary end point was progression-free survival. RESULTS: The median progression-free survival was 7.7 months in the crizotinib group and 3.0 months in the chemotherapy group (hazard ratio for progression or death with crizotinib, 0.49; 95% confidence interval [CI], 0.37 to 0.64; P<0.001). The response rates were 65% (95% CI, 58 to 72) with crizotinib, as compared with 20% (95% CI, 14 to 26) with chemotherapy (P<0.001). An interim analysis of overall survival showed no significant improvement with crizotinib as compared with chemotherapy (hazard ratio for death in the crizotinib group, 1.02; 95% CI, 0.68 to 1.54; P=0.54). Common adverse events associated with crizotinib were visual disorder, gastrointestinal side effects, and elevated liver aminotransferase levels, whereas common adverse events with chemotherapy were fatigue, alopecia, and dyspnea. Patients reported greater reductions in symptoms of lung cancer and greater improvement in global quality of life with crizotinib than with chemotherapy. CONCLUSIONS: Crizotinib is superior to standard chemotherapy in patients with previously treated, advanced non-small-cell lung cancer with ALK rearrangement. (Funded by Pfizer; ClinicalTrials.gov number, NCT00932893.) Copyright © 2013 Massachusetts Medical Society.

An Artificial Neutral Network (ANN) model for predicting biodiesel kinetic viscosity as a function of temperature and chemical compositions

An Artificial Neural Network (ANN) is a computational modeling tool which has found extensive acceptance in many disciplines for modeling complex real world problems. An ANN can model problems through learning by example, rather than by fully understanding the detailed characteristics and physics of the system. In the present study, the accuracy and predictive power of an ANN was evaluated in predicting kinetic viscosity of biodiesels over a wide range of temperatures typically encountered in diesel engine operation. In this model, temperature and chemical composition of biodiesel were used as input variables. In order to obtain the necessary data for model development, the chemical composition and temperature dependent fuel properties of ten different types of biodiesels were measured experimentally using laboratory standard testing equipments following internationally recognized testing procedures. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture was optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the absolute fraction of variance (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found that ANN is highly accurate in predicting the viscosity of biodiesel and demonstrates the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties at different temperature levels. Therefore the model developed in this study can be a useful tool in accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Estimation of biodiesel properties from chemical composition – An artificial neural network (ANN) approach

Biodiesel, produced from renewable feedstock represents a more sustainable source of energy and will therefore play a significant role in providing the energy requirements for transportation in the near future. Chemically, all biodiesels are fatty acid methyl esters (FAME), produced from raw vegetable oil and animal fat. However, clear differences in chemical structure are apparent from one feedstock to the next in terms of chain length, degree of unsaturation, number of double bonds and double bond configuration-which all determine the fuel properties of biodiesel. In this study, prediction models were developed to estimate kinematic viscosity of biodiesel using an Artificial Neural Network (ANN) modelling technique. While developing the model, 27 parameters based on chemical composition commonly found in biodiesel were used as the input variables and kinematic viscosity of biodiesel was used as output variable. Necessary data to develop and simulate the network were collected from more than 120 published peer reviewed papers. The Neural Networks Toolbox of MatLab R2012a software was used to train, validate and simulate the ANN model on a personal computer. The network architecture and learning algorithm were optimised following a trial and error method to obtain the best prediction of the kinematic viscosity. The predictive performance of the model was determined by calculating the coefficient of determination (R2), root mean squared (RMS) and maximum average error percentage (MAEP) between predicted and experimental results. This study found high predictive accuracy of the ANN in predicting fuel properties of biodiesel and has demonstrated the ability of the ANN model to find a meaningful relationship between biodiesel chemical composition and fuel properties. Therefore the model developed in this study can be a useful tool to accurately predict biodiesel fuel properties instead of undertaking costly and time consuming experimental tests.

Projecting the impact of climate change on the transmission of Ross River virus : methodological challenges and research needs

Ross River virus (RRV) is the most common vector-borne disease in Australia. It is vitally important to make appropriate projections on the future spread of RRV under various climate change scenarios because such information is essential for policy-makers to identify vulnerable communities and to better manage RRV epidemics. However, there are many methodological challenges in projecting the impact of climate change on the transmission of RRV disease. This study critically examined the methodological issues and proposed possible solutions. A literature search was conducted between January and October 2012, using the electronic databases Medline, Web of Science and PubMed. Nineteen relevant papers were identified. These studies demonstrate that key challenges for projecting future climate change on RRV disease include: (1) a complex ecology (e.g. many mosquito vectors, immunity, heterogeneous in both time and space); (2) unclear interactions between social and environmental factors; and (3) uncertainty in climate change modelling and socioeconomic development scenarios. Future risk assessments of climate change will ultimately need to better understand the ecology of RRV disease and to integrate climate change scenarios with local socioeconomic and environmental factors, in order to develop effective adaptation strategies to prevent or reduce RRV transmission.

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

The Brain Research Institute (BRI) uses various types of indirect measurements, including EEG and fMRI, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the BRI asked the MISG 2010 participants to examine how the available EEG and fMRI data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was made with the following three aspects. (1) The science behind brain activity investigations depends crucially on the quality of the analysis and interpretation of, as well as the recovery of information from, EEG and fMRI measurements. A number of specific methodologies were discussed and formalised, including independent component analysis, principal component analysis, profile monitoring and change point analysis (hidden Markov modelling, time series analysis, discontinuity identification). (2) Even though EEG measurements accurately and very sensitively record the onset of an epileptic event or seizure, they are, from the perspective of understanding the internal initiation and localisation, of limited utility. They only record neuronal activity in the cortical (surface layer) neurons of the brain, which is a direct reflection of the type of electrical activity they have been designed to record. Because fMRI records, through the monitoring of blood flow activity, the location of localised brain activity within the brain, the possibility of combining fMRI measurements with EEG, as a joint inversion activity, was discussed and examined in detail. (3) A major goal for the BRI is to improve understanding about ``when'' (at what time) an epileptic seizure actually commenced before it is identified on an eeg recording, ``where'' the source of this initiation is located in the brain, and ``what'' is the initiator. Because of the general agreement in the literature that, in one way or another, epileptic events and seizures represent abnormal synchronisations of localised and/or global brain activity the modelling of synchronisations was examined in some detail. References C. M. Michel, G. Thut, S. Morand, A. Khateb, A. J. Pegna, R. Grave de Peralta, S. Gonzalez, M. Seeck and T. Landis, Electric source imaging of human brain functions, Brain Res. 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Epidemiological patterns of Ross River virus disease in Queensland, Australia, 2001-2011

Ross River virus (RRV) infection is a debilitating disease which has a significant impact on population health, economic productivity and tourism in Australia. This study examined epidemiological patterns of the RRV disease in Queensland, Australia between January 2001 and December 2011 at a statistical local area level. Spatial-temporal analyses were used to identify the patterns of the disease distribution over time stratified by age, sex and space. The results show that the mean annual incidence was 54 per 100,000 people, with a male: female ratio of 1:1.1. Two space-time clusters were identified: the areas adjacent to Townsville, on the eastern coast of Queensland; and the south east areas. Thus, although public health intervention should be considered across all areas in which RRV occurs, it should specifically focus on these high risk regions, particularly during the summer and autumn to reduce the social and economic impacts of RRV.

Formation of blood clot on biomaterial implants influences bone healing

The first step in bone healing is forming a blood clot at injured bones. During bone implantation, biomaterials unavoidably come into direct contact with blood, leading to a blood clot formation on its surface prior to bone regeneration. Despite both situations being similar in forming a blood clot at the defect site, most research in bone tissue engineering virtually ignores the important role of a blood clot in supporting healing. Dental implantology has long demonstrated that the fibrin structure and cellular content of a peri-implant clot can greatly affect osteoconduction and de novo bone formation on implant surfaces. This paper reviews the formation of a blood clot during bone healing in related to the use of platelet-rich plasma (PRP) gels. It is implicated that PRP gels are dramatically altered from a normal clot in healing, resulting conflicting effect on bone regeneration. These results indicate that the effect of clots on bone regeneration depends on how the clots are formed. Factors that influence blood clot structure and properties in related to bone healing are also highlighted. Such knowledge is essential for developing strategies to optimally control blood clot formation, which ultimately alter the healing microenvironment of bone. Of particular interest are modification of surface chemistry of biomaterials, which displays functional groups at varied composition for the purpose of tailoring blood coagulation activation, resultant clot fibrin architecture, rigidity, susceptibility to lysis, and growth factor release. This opens new scope of in situ blood clot modification as a promising approach in accelerating and controlling bone regeneration.

Corner and finger formation in Hele-Shaw flow with kinetic undercooling regularisation

We examine the effect of a kinetic undercooling condition on the evolution of a free boundary in Hele--Shaw flow, in both bubble and channel geometries. We present analytical and numerical evidence that the bubble boundary is unstable and may develop one or more corners in finite time, for both expansion and contraction cases. This loss of regularity is interesting because it occurs regardless of whether the less viscous fluid is displacing the more viscous fluid, or vice versa. We show that small contracting bubbles are described to leading order by a well-studied geometric flow rule. Exact solutions to this asymptotic problem continue past the corner formation until the bubble contracts to a point as a slit in the limit. Lastly, we consider the evolving boundary with kinetic undercooling in a Saffman--Taylor channel geometry. The boundary may either form corners in finite time, or evolve to a single long finger travelling at constant speed, depending on the strength of kinetic undercooling. We demonstrate these two different behaviours numerically. For the travelling finger, we present results of a numerical solution method similar to that used to demonstrate the selection of discrete fingers by surface tension. With kinetic undercooling, a continuum of corner-free travelling fingers exists for any finger width above a critical value, which goes to zero as the kinetic undercooling vanishes. We have not been able to compute the discrete family of analytic solutions, predicted by previous asymptotic analysis, because the numerical scheme cannot distinguish between solutions characterised by analytic fingers and those which are corner-free but non-analytic.

Spatial-temporal epidemiological analyses of two sympatric, co-endemic alphaviral diseases in Queensland, Australia

Background: The two most reported mosquito-borne diseases in Queensland, a northern state of Australia, are Ross River virus (RRV) disease and Barmah Forest virus (BFV) disease. Both diseases are endemic in Queensland and have similar clinical symptoms and comparable transmission cycles involving a complex inter-relationship between human hosts, various mosquito vectors, and a range of nonhuman vertebrate hosts, including marsupial mammals that are unique to the Australasian region. Although these viruses are thought to share similar vectors and vertebrate hosts, RRV is four times more prevalent than BFV in Queensland. Methods: We performed a retrospective analysis of BFV and RRV human disease notification data collected from 1995 to 2007 in Queensland to ascertain whether there were differences in the incidence patterns of RRV and BFV disease. In particular, we compared the temporal incidence and spatial distribution of both diseases and considered the relationship between their disease dynamics. We also investigated whether a peak in BFV incidence during spring was indicative of the following RRV and BFV transmission season incidence levels. Results: Although there were large differences in the notification rates of the two diseases, they had similar annual temporal patterns, but there were regional variations between the length and magnitude of the transmission seasons. During periods of increased disease activity, however, there was no association between the dynamics of the two diseases. Conclusions: The results from this study suggest that while RRV and BFV share similar mosquito vectors, there are significant differences in the ecology of these viruses that result in different epidemic patterns of disease incidence. Further investigation is required into the ecology of each virus to determine which factors are important in promoting RRV and BFV disease outbreaks.

Outbreak detection algorithms for seasonal disease data : a case study using Ross River virus disease

Background Detection of outbreaks is an important part of disease surveillance. Although many algorithms have been designed for detecting outbreaks, few have been specifically assessed against diseases that have distinct seasonal incidence patterns, such as those caused by vector-borne pathogens. Methods We applied five previously reported outbreak detection algorithms to Ross River virus (RRV) disease data (1991-2007) for the four local government areas (LGAs) of Brisbane, Emerald, Redland and Townsville in Queensland, Australia. The methods used were the Early Aberration Reporting System (EARS) C1, C2 and C3 methods, negative binomial cusum (NBC), historical limits method (HLM), Poisson outbreak detection (POD) method and the purely temporal SaTScan analysis. Seasonally-adjusted variants of the NBC and SaTScan methods were developed. Some of the algorithms were applied using a range of parameter values, resulting in 17 variants of the five algorithms. Results The 9,188 RRV disease notifications that occurred in the four selected regions over the study period showed marked seasonality, which adversely affected the performance of some of the outbreak detection algorithms. Most of the methods examined were able to detect the same major events. The exception was the seasonally-adjusted NBC methods that detected an excess of short signals. The NBC, POD and temporal SaTScan algorithms were the only methods that consistently had high true positive rates and low false positive and false negative rates across the four study areas. The timeliness of outbreak signals generated by each method was also compared but there was no consistency across outbreaks and LGAs. Conclusions This study has highlighted several issues associated with applying outbreak detection algorithms to seasonal disease data. In lieu of a true gold standard, a quantitative comparison is difficult and caution should be taken when interpreting the true positives, false positives, sensitivity and specificity.

Subclinical psychotic experiences in healthy young adults : associations with stress and genetic predisposition

Stress has been identified as a common trigger for psychosis. Dopamine pathways are suggested to be affected by chronic and severe stress and to play an important role in psychosis. This pilot study investigates the potential relationship of stress and psychosis in subclinical psychotic experiences. It was hypothesized that single-nucleotide polymorphisms (SNPs) previously found to be associated with psychiatric disorders would be associated with both stress and subclinical psychotic experiences. University students (N=182) were genotyped for 17 SNPs across 11 genes. Higher stress reporting was associated with rs4680 COMT, rs13211507 HLA region, and rs13107325 SLC39A8. Reports of higher subclinical psychotic experiences were associated with DRD2 SNPs rs17601612 and rs658986 and an AKT1 SNP rs2494732. Replication studies are recommended to further pursue this line of research for identification of markers of psychosis for early diagnosis and intervention.