Predicting the job satisfaction of female sex workers in Queensland, Australia

This study used a cross-sectional survey to examine job satisfaction and its correlates among 247 female sex workers working as private service providers, in licensed brothels and in illegal sectors of the industry (mainly street-based workers). Overall, most sex workers reported positive job satisfaction. Satisfaction was higher in women working legally and was generally comparable with women from the general population. Multivariate analyses revealed that job satisfaction was significantly linked to women’s reasons for initially entering the industry. Sex workers’ age, education, marital status, length of time in the industry and current working conditions were apparently less important for satisfaction.

Characterisation and source identification of selected pollutants in house dust

House dust is a heterogeneous matrix, which contains a number of biological materials and particulate matter gathered from several sources. It is the accumulation of a number of semi-volatile and non-volatile contaminants. The contaminants are trapped and preserved. Therefore, house dust can be viewed as an archive of both the indoor and outdoor air pollution. There is evidence to show that on average, people tend to stay indoors most of the time and this increases exposure to house dust. The aims of this investigation were to: " assess the levels of Polycyclic Aromatic Hydrocarbons (PAHs), elements and pesticides in the indoor environment of the Brisbane area; " identify and characterise the possible sources of elemental constituents (inorganic elements), PAHs and pesticides by means of Positive Matrix Factorisation (PMF); and " establish the correlations between the levels of indoor air pollutants (PAHs, elements and pesticides) with the external and internal characteristics or attributes of the buildings and indoor activities by means of multivariate data analysis techniques. The dust samples were collected during the period of 2005-2007 from homes located in different suburbs of Brisbane, Ipswich and Toowoomba, in South East Queensland, Australia. A vacuum cleaner fitted with a paper bag was used as a sampler for collecting the house dust. A survey questionnaire was filled by the house residents which contained information about the indoor and outdoor characteristics of their residences. House dust samples were analysed for three different pollutants: Pesticides, Elements and PAHs. The analyses were carried-out for samples of particle size less than 250 µm. The chemical analyses for both pesticides and PAHs were performed using a Gas Chromatography Mass Spectrometry (GC-MS), while elemental analysis was carried-out by using Inductively-Coupled Plasma-Mass Spectroscopy (ICP-MS). The data was subjected to multivariate data analysis techniques such as multi-criteria decision-making procedures, Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE), coupled with Geometrical Analysis for Interactive Aid (GAIA) in order to rank the samples and to examine data display. This study showed that compared to the results from previous works, which were carried-out in Australia and overseas, the concentrations of pollutants in house dusts in Brisbane and the surrounding areas were relatively very high. The results of this work also showed significant correlations between some of the physical parameters (types of building material, floor level, distance from industrial areas and major road, and smoking) and the concentrations of pollutants. Types of building materials and the age of houses were found to be two of the primary factors that affect the concentrations of pesticides and elements in house dust. The concentrations of these two types of pollutant appear to be higher in old houses (timber houses) than in the brick ones. In contrast, the concentrations of PAHs were noticed to be higher in brick houses than in the timber ones. Other factors such as floor level, and distance from the main street and industrial area, also affected the concentrations of pollutants in the house dust samples. To apportion the sources and to understand mechanisms of pollutants, Positive Matrix Factorisation (PMF) receptor model was applied. The results showed that there were significant correlations between the degree of concentration of contaminants in house dust and the physical characteristics of houses, such as the age and the type of the house, the distance from the main road and industrial areas, and smoking. Sources of pollutants were identified. For PAHs, the sources were cooking activities, vehicle emissions, smoking, oil fumes, natural gas combustion and traces of diesel exhaust emissions; for pesticides the sources were application of pesticides for controlling termites in buildings and fences, treating indoor furniture and in gardens for controlling pests attacking horticultural and ornamental plants; for elements the sources were soil, cooking, smoking, paints, pesticides, combustion of motor fuels, residual fuel oil, motor vehicle emissions, wearing down of brake linings and industrial activities.

Analytical solutions for the multi-term time-space Caputo-Riesz fractional advection-diffusion equations on a finite domain

Generalized fractional partial differential equations have now found wide application for describing important physical phenomena, such as subdiffusive and superdiffusive processes. However, studies of generalized multi-term time and space fractional partial differential equations are still under development. In this paper, the multi-term time-space Caputo-Riesz fractional advection diffusion equations (MT-TSCR-FADE) with Dirichlet nonhomogeneous boundary conditions are considered. The multi-term time-fractional derivatives are defined in the Caputo sense, whose orders belong to the intervals [0, 1], [1, 2] and [0, 2], respectively. These are called respectively the multi-term time-fractional diffusion terms, the multi-term time-fractional wave terms and the multi-term time-fractional mixed diffusion-wave terms. The space fractional derivatives are defined as Riesz fractional derivatives. Analytical solutions of three types of the MT-TSCR-FADE are derived with Dirichlet boundary conditions. By using Luchko's Theorem (Acta Math. Vietnam., 1999), we proposed some new techniques, such as a spectral representation of the fractional Laplacian operator and the equivalent relationship between fractional Laplacian operator and Riesz fractional derivative, that enabled the derivation of the analytical solutions for the multi-term time-space Caputo-Riesz fractional advection-diffusion equations. © 2012.

Analytical solutions for the multi-term time-fractional diffusion-wave/diffusion equations in a finite domain

Multi-term time-fractional differential equations have been used for describing important physical phenomena. However, studies of the multi-term time-fractional partial differential equations with three kinds of nonhomogeneous boundary conditions are still limited. In this paper, a method of separating variables is used to solve the multi-term time-fractional diffusion-wave equation and the multi-term time-fractional diffusion equation in a finite domain. In the two equations, the time-fractional derivative is defined in the Caputo sense. We discuss and derive the analytical solutions of the two equations with three kinds of nonhomogeneous boundary conditions, namely, Dirichlet, Neumann and Robin conditions, respectively.

FGFR2 point mutations in 466 Endometrioid Endometrial Tumors : relationship with MSI, KRAS, PIK3CA, CTNNB1 mutations and Clinicopathological features

Mutations in multiple oncogenes including KRAS, CTNNB1, PIK3CA and FGFR2 have been identified in endometrial cancer. The aim of this study was to provide insight into the clinicopathological features associated with patterns of mutation in these genes, a necessary step in planning targeted therapies for endometrial cancer. 466 endometrioid endometrial tumors were tested for mutations in FGFR2, KRAS, CTNNB1, and PIK3CA. The relationships between mutation status, tumor microsatellite instability (MSI) and clinicopathological features including overall survival (OS) and disease-free survival (DFS) were evaluated using Kaplan-Meier survival analysis and Cox proportional hazard models. Mutations were identified in FGFR2 (48/466); KRAS (87/464); CTNNB1 (88/454) and PIK3CA (104/464). KRAS and FGFR2 mutations were significantly more common, and CTNNB1 mutations less common, in MSI positive tumors. KRAS and FGFR2 occurred in a near mutually exclusive pattern (p = 0.05) and, surprisingly, mutations in KRAS and CTNNB1 also occurred in a near mutually exclusive pattern (p = 0.0002). Multivariate analysis revealed that mutation in KRAS and FGFR2 showed a trend (p = 0.06) towards longer and shorter DFS, respectively. In the 386 patients with early stage disease (stage I and II), FGFR2 mutation was significantly associated with shorter DFS (HR = 3.24; 95% confidence interval, CI, 1.35-7.77; p = 0.008) and OS (HR = 2.00; 95% CI 1.09-3.65; p = 0.025) and KRAS was associated with longer DFS (HR = 0.23; 95% CI 0.05-0.97; p = 0.045). In conclusion, although KRAS and FGFR2 mutations share similar activation of the MAPK pathway, our data suggest very different roles in tumor biology. This has implications for the implementation of anti-FGFR or anti-MEK biologic therapies.

Non-destructive evaluation of articular cartilage defects using near-infrared (NIR) spectroscopy in osteoarthritic rat models and its direct relation to Mankin Score

Objective The aim of this study was to demonstrate the potential of near-infrared (NIR) spectroscopy for categorizing cartilage degeneration induced in animal models. Method Three models of osteoarthritic degeneration were induced in laboratory rats via one of the following methods: (i) menisectomy (MSX); (ii) anterior cruciate ligament transaction (ACLT); and (iii) intra-articular injection of mono-ido-acetete (1 mg) (MIA), in the right knee joint, with 12 rats per model group. After 8 weeks, the animals were sacrificed and tibial knee joints were collected. A custom-made nearinfrared (NIR) probe of diameter 5 mm was placed on the cartilage surface and spectral data were acquired from each specimen in the wavenumber range 4 000 – 12 500 cm−1. Following spectral data acquisition, the specimens were fixed and Safranin–O staining was performed to assess disease severity based on the Mankin scoring system. Using multivariate statistical analysis based on principal component analysis and partial least squares regression, the spectral data were then related to the Mankinscores of the samples tested. Results Mild to severe degenerative cartilage changes were observed in the subject animals. The ACLT models showed mild cartilage degeneration, MSX models moderate, and MIA severe cartilage degenerative changes both morphologically and histologically. Our result demonstrate that NIR spectroscopic information is capable of separating the cartilage samples into different groups relative to the severity of degeneration, with NIR correlating significantly with their Mankinscore (R2 = 88.85%). Conclusion We conclude that NIR is a viable tool for evaluating articularcartilage health and physical properties such as change in thickness with degeneration.

Semi-parametric forecasting of realized volatility

Forecasts generated by time series models traditionally place greater weight on more recent observations. This paper develops an alternative semi-parametric method for forecasting that does not rely on this convention and applies it to the problem of forecasting asset return volatility. In this approach, a forecast is a weighted average of historical volatility, with the greatest weight given to periods that exhibit similar market conditions to the time at which the forecast is being formed. Weighting is determined by comparing short-term trends in volatility across time (as a measure of market conditions) by means of a multivariate kernel scheme. It is found that the semi-parametric method produces forecasts that are significantly more accurate than a number of competing approaches at both short and long forecast horizons.

Spatio-temporal patterns of Barmah Forest virus disease in Queensland, Australia

Background Barmah Forest virus (BFV) disease is a common and wide-spread mosquito-borne disease in Australia. This study investigated the spatio-temporal patterns of BFV disease in Queensland, Australia using geographical information system (GIS) tools and geostatistical analysis. Methods/Principal Findings We calculated the incidence rates and standardised incidence rates of BFV disease. Moran's I statistic was used to assess the spatial autocorrelation of BFV incidences. Spatial dynamics of BFV disease was examined using semi-variogram analysis. Interpolation techniques were applied to visualise and display the spatial distribution of BFV disease in statistical local areas (SLAs) throughout Queensland. Mapping of BFV disease by SLAs reveals the presence of substantial spatio-temporal variation over time. Statistically significant differences in BFV incidence rates were identified among age groups (χ2 = 7587, df = 7327,p<0.01). There was a significant positive spatial autocorrelation of BFV incidence for all four periods, with the Moran's I statistic ranging from 0.1506 to 0.2901 (p<0.01). Semi-variogram analysis and smoothed maps created from interpolation techniques indicate that the pattern of spatial autocorrelation was not homogeneous across the state. Conclusions/Significance This is the first study to examine spatial and temporal variation in the incidence rates of BFV disease across Queensland using GIS and geostatistics. The BFV transmission varied with age and gender, which may be due to exposure rates or behavioural risk factors. There are differences in the spatio-temporal patterns of BFV disease which may be related to local socio-ecological and environmental factors. These research findings may have implications in the BFV disease control and prevention programs in Queensland.

Helping understand nutritional gaps in the elderly (HUNGER) : a prospective study of patient factors associated with inadequate nutritional intake in older medical inpatients

BACKGROUND: Malnutrition, and poor intake during hospitalisation, are common in older medical patients. Better understanding of patient-specific factors associated with poor intake may inform nutritional interventions. AIMS: To measure the proportion of older medical patients with inadequate nutritional intake, and identify patient-related factors associated with this outcome. METHODS: Prospective cohort study enrolling consecutive consenting medical inpatients aged 65 years or older. Primary outcome was energy intake less than resting energy expenditure estimated using weight-based equations. Energy intake was calculated for a single day using direct observation of plate waste. Explanatory variables included age, gender, number of co-morbidities, number of medications, diagnosis, usual residence, nutritional status, functional and cognitive impairment, depressive symptoms, poor appetite, poor dentition, and dysphagia. RESULTS: Of 134 participants (mean age 80 years, 51% female), only 41% met estimated resting energy requirements. Mean energy intake was 1220 kcal/day (SD 440), or 18.1 kcal/kg/day. Factors associated with inadequate energy intake in multivariate analysis were poor appetite, higher BMI, diagnosis of infection or cancer, delirium and need for assistance with feeding. CONCLUSIONS: Inadequate nutritional intake is common, and patient factors contributing to poor intake need to be considered in nutritional interventions.

Response to comments on "Productivity is a poor predictor of plant species richness"

Pan et al. claim that our results actually support a strong linear positive relationship between productivity and richness, whereas Fridley et al. contend that the data support a strong humped relationship. These responses illustrate how preoccupation with bivariate patterns distracts from a deeper understanding of the multivariate mechanisms that control these important ecosystem properties.

Near infrared spectroscopy for non-destructive evaluation of articular cartilage

There is a need for an accurate real-time quantitative system that would enhance decision-making in the treatment of osteoarthritis. To achieve this objective, significant research is required that will enable articular cartilage properties to be measured and categorized for health and functionality without the need for laboratory tests involving biopsies for pathological evaluation. Such a system would provide the capability of access to the internal condition of the cartilage matrix and thus extend the vision-based arthroscopy that is currently used beyond the subjective evaluation of surgeons. The system required must be able to non-destructively probe the entire thickness of the cartilage and its immediate subchondral bone layer. In this thesis, near infrared spectroscopy is investigated for the purpose mentioned above. The aim is to relate it to the structure and load bearing properties of the cartilage matrix to the near infrared absorption spectrum and establish functional relationships that will provide objective, quantitative and repeatable categorization of cartilage condition outside the area of visible degradation in a joint. Based on results from traditional mechanical testing, their innovative interpretation and relationship with spectroscopic data, new parameters were developed. These were then evaluated for their consistency in discriminating between healthy viable and degraded cartilage. The mechanical and physico-chemical properties were related to specific regions of the near infrared absorption spectrum that were identified as part of the research conducted for this thesis. The relationships between the tissue's near infrared spectral response and the new parameters were modeled using multivariate statistical techniques based on partial least squares regression (PLSR). With significantly high levels of statistical correlation, the modeled relationships were demonstrated to possess considerable potential in predicting the properties of unknown tissue samples in a quick and non-destructive manner. In order to adapt near infrared spectroscopy for clinical applications, a balance between probe diameter and the number of active transmit-receive optic fibres must be optimized. This was achieved in the course of this research, resulting in an optimal probe configuration that could be adapted for joint tissue evaluation. Furthermore, as a proof-of-concept, a protocol for obtaining the new parameters from the near infrared absorption spectra of cartilage was developed and implemented in a graphical user interface (GUI)-based software, and used to assess cartilage-on-bone samples in vitro. This conceptual implementation has been demonstrated, in part by the individual parametric relationship with the near infrared absorption spectrum, the capacity of the proposed system to facilitate real-time, non-destructive evaluation of cartilage matrix integrity. In summary, the potential of the optical near infrared spectroscopy for evaluating articular cartilage and bone laminate has been demonstrated in this thesis. The approach could have a spin-off for other soft tissues and organs of the body. It builds on the earlier work of the group at QUT, enhancing the near infrared component of the ongoing research on developing a tool for cartilage evaluation that goes beyond visual and subjective methods.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

A potential HIV epidemic among male street laborers in urban Vietnam

Background: Mass migration to Asian cities is a defining phenomenon of the present age, as hundreds of millions of people move from rural areas or between cities in search of economic prosperity. Although many do prosper, large numbers of people experience significant social disadvantage. This is especially the case among poorly educated, migrant unskilled unregistered male laborers who do much of the manual work throughout the cities. These men are at significant risk for many health problems, including HIV infection. However, to date there has been little research in developing countries to explain the determinants of this risk, and thereby to suggest feasible preventive strategies. Objectives and Methodology: Using combined qualitative and quantitative methods, the aim of this study was to explore the social contexts that affect health vulnerabilities and to develop conceptual models to predict risk behaviors for HIV [illicit drug use, unsafe sex, and non-testing for HIV] among male street laborers in Hanoi, Vietnam. Qualitative Research: Sixteen qualitative interviews revealed a complex variety of life experiences, beliefs and knowledge deficits that render these mostly poor and minimally educated men vulnerable to health problems including HIV infection. This study formed a conceptual model of numerous stressors related to migrants’ life experiences in urban space, including physical, financial and social factors. A wide range of coping strategies were adopted to deal with stressors – including problem-focused coping (PFC) and emotion-focused coping (EFC), pro-social and anti-social, active and passive. These men reported difficulty in coping with stressors because they had weak social networks and lacked support from formal systems. A second conceptual model emerged that highlighted equivalent influences of individual psychological factors, social integration, social barriers, and accessibility regarding drug use and sexual risk behavior. Psychological dimensions such as tedium, distress, fatalism and revenge, were important. There were strong effects of collective decision-making and fear of social isolation on shaping risk behaviors. These exploratory qualitative interviews helped to develop a culturally appropriate instrument for the quantitative survey and informed theoretical models of the factors that affect risk behaviors for HIV infection. Quantitative Research: The Information-Motivation-Behavioral Skills (IMB) model was adopted as the theoretical framework for a large-scale survey. It was modified to suit the contexts of these Vietnamese men. By doing a social mapping technique, 450 male street laborers were interviewed in Hanoi, Vietnam. The survey revealed that the risk of acquiring and transmitting HIV was high among these men. One in every 12 men reported homosexual or bisexual behavior. These men on average had 3 partners within the preceding year, and condom use was inconsistent. One third had had sex with commercial sex workers (CSW) and only 30% of them reported condom use; 17% used illicit drugs sometimes, with 66.7% of them frequently sharing injecting equipment with peers. Despite the risks, only 19.8% of men had been tested for HIV during the previous 12 months. These men have limited HIV knowledge and only moderate motivation and perceived behavioral skills for protective behavior. Although rural-to-urban migration was not associated with sexual risk behavior, three elements of the IMB model and depression associated with the process of mobility were significant determinants of sexual behavior. A modified model that incorporated IMB elements and psychosocial stress was found to be a better fit than the original IMB model alone in predicting protected sex behavior among the men. Men who were less psychologically and socially stressed, better informed and motivated for HIV prevention were more likely to demonstrate behavioral skills, and in turn were more likely to engage in safer sexual behavior. With regard to drug use, although the conventional model accounted for slightly less variance than the modified IMB model, data were of better fit for the conventional model. Multivariate analyses revealed that men who originated from urban areas, those who were homo- or bi-sexually identified and had better knowledge and skills for HIV prevention were more likely to access HIV testing, while men who had more sexual partners and those who did not use a condom for sex with CSW were least likely to take a test. The modified IMB model provided a better fit than the conventional model, as it explained a greater variance in HIV testing. Conclusions and Implications: This research helps to highlight a potential hidden HIV epidemic among street male, unskilled, unregistered laborers. This group has multiple vulnerabilities to HIV infection through both their partners and peers. However, most do not know their HIV status and have limited knowledge about preventing infection. This is the first application of a modified IMB model of risk behaviors for HIV such as drug use, condom use, and uptake of HIV testing to research with male street laborers in urban settings. The study demonstrated that while the extended IMB model had better fit than the conventional version in explaining the behaviors of safe sex and HIV testing, it was not so for drug use. The results provide interesting directions for future research and suggest ways to effectively design intervention strategies. The findings should shed light on culturally appropriate HIV preventive education and support programs for these men. As Vietnam has much in common with other developing countries in Southeast Asia, this research provides evidence for policy and practice that may be useful for public health systems in similar countries.

Prognostic significance of IGF and ECM induced signalling proteins in breast cancer patients

Breast cancer is a leading contributor to the burden of disease in Australia. Fortunately, the recent introduction of diverse therapeutic strategies have improved the survival outcome for many women. Despite this, the clinical management of breast cancer remains problematic as not all approaches are sufficiently sophisticated to take into account the heterogeneity of this disease and are unable to predict disease progression, in particular, metastasis. As such, women with good prognostic outcomes are exposed to the side effects of therapies without added benefit. Furthermore, women with aggressive disease for whom these advanced treatments would deliver benefit cannot be distinguished and opportunities for more intensive or novel treatment are lost. This study is designed to identify novel factors associated with disease progression, and the potential to inform disease prognosis. Frequently overlooked, yet common mediators of disease are the interactions that take place between the insulin-like growth factor (IGF) system and the extracellular matrix (ECM). Our laboratory has previously demonstrated that multiprotein insulin-like growth factor-I (IGF-I): insulin-like growth factor binding protein (IGFBP): vitronectin (VN) complexes stimulate migration of breast cancer cells in vitro, via the cooperative involvement of the insulin-like growth factor type I receptor (IGF-IR) and VN-binding integrins. However, the effects of IGF and ECM protein interactions on the dissemination and progression of breast cancer in vivo are unknown. It was hypothesised that interactions between proteins required for IGF induced signalling events and those within the ECM contribute to breast cancer metastasis and are prognostic and predictive indicators of patient outcome. To address this hypothesis, semiquantitative immunohistochemistry (IHC) analyses were performed to compare the extracellular and subcellular distribution of IGF and ECM induced signalling proteins between matched normal, primary cancer, and metastatic cancer among archival formalin-fixed paraffin-embedded (FFPE) breast tissue samples collected from women attending the Princess Alexandra Hospital, Brisbane. Multivariate Cox proportional hazards (PH) regression survival models in conjunction with a modified „purposeful selection of covariates. method were applied to determine the prognostic potential of these proteins. This study provides the first in-depth, compartmentalised analysis of the distribution of IGF and ECM induced signalling proteins. As protein function and protein localisation are closely correlated, these findings provide novel insights into IGF signalling and ECM protein function during breast cancer development and progression. Distinct IGF signalling and ECM protein immunoreactivity was observed in the stroma and/or in subcellular locations in normal breast, primary cancer and metastatic cancer tissues. Analysis of the presence and location of stratifin (SFN) suggested a causal relationship in ECM remodelling events during breast cancer development and progression. The results of this study have also suggested that fibronectin (FN) and ¥â1 integrin are important for the formation of invadopodia and epithelial-to-mesenchymal transition (EMT) events. Our data also highlighted the importance of the temporal and spatial distribution of IGF induced signalling proteins in breast cancer metastasis; in particular, SFN, enhancer-of-split and hairy-related protein 2 (SHARP-2), total-akt/protein kinase B 1 (Total-AKT1), phosphorylated-akt/protein kinase B (P-AKT), extracellular signal-related kinase-1 and extracellular signal-related kinase-2 (ERK1/2) and phosphorylated-extracellular signal-related kinase-1 and extracellular signal-related kinase-2 (P-ERK1/2). Multivariate survival models were created from the immunohistochemical data. These models were found to fit well with these data with very high statistical confidence. Numerous prognostic confounding effects and effect modifications were identified among elements of the ECM and IGF signalling cascade and corroborate the survival models. This finding provides further evidence for the prognostic potential of IGF and ECM induced signalling proteins. In addition, the adjusted measures of associations obtained in this study have strengthened the validity and utility of the resulting models. The findings from this study provide insights into the biological interactions that occur during the development of breast tissue and contribute to disease progression. Importantly, these multivariate survival models could provide important prognostic and predictive indicators that assist the clinical management of breast disease, namely in the early identification of cancers with a propensity to metastasise, and/or recur following adjuvant therapy. The outcomes of this study further inform the development of new therapeutics to aid patient recovery. The findings from this study have widespread clinical application in the diagnosis of disease and prognosis of disease progression, and inform the most appropriate clinical management of individuals with breast cancer.

Impact of a government triple zero awareness campaign on emergency department patient characteristics

Objective: To evaluate the impact of a government triple zero community awareness campaign on the characteristics of patients attending an ED. Methods: A study using Emergency Department Information System data was conducted in an adult metropolitan tertiary-referral teaching hospital in Brisbane. The three outcomes measured in the 3 month post-campaign period were arrival mode, Australasian Triage Scale and departure status. These measures reflect ambulance usage, clinical urgency and illness severity, respectively. They were compared with those in the 3 month pre-campaign period. Multivariate logistic regression models were used to investigate the impacts of the campaign on each of the three outcome measures after controlling for age, sex, day and time of arrival, and daily minimum temperature. Results: There were 17 920 visits in the pre- and 17 793 visits in the post-campaign period. After the campaign, fewer patients arrived at the ED by road ambulance (odds ratio [OR] 0.90, 95% confidence interval [CI] 0.80–1.00), although the impact of the campaign on the arrival mode was only close to statistical significance (Wald χ2-test, P= 0.055); and patients were significantly less likely to have higher clinical urgency (OR 0.86, 95% CI 0.79–0.94), while more likely to be admitted (OR 1.68, 95% CI 1.38–2.05) or complete treatment in the ED (OR 1.46, 95% CI 1.23–1.73) instead of leaving without waiting to be seen. Conclusions: The campaign had no significant impact on the arrival mode of the patients. After the campaign, the illness acuity of the patients decreased, whereas the illness severity of the patients increased.