Social determinants of health and diabetic foot disease

Background Diabetic foot disease (DFD) is the leading cause of hospitalisation and lower extremity amputation (LEA) in people with diabetes. Many studies have established the relationship between DFD and clinical risk factors, such as peripheral neuropathy and peripheral arterial disease. Other studies have identified the relationship between diabetes and non-clinical risk factors termed social determinants of health (SDoH), such as socioeconomic status. However, it appears very few studies have investigated the relationship between DFD and SDoH. This paper aims to review the existing literature investigating the relationship between DFD and the SDoH factors socioeconomic status (SES), race and geographical remoteness (remoteness). Process Electronic databases (MEDLINE, CINAHL, and PubMed) were searched for studies reporting SES, race (including Aboriginal and Torres Strait Islander in Australia) and remoteness and their relationship to DFD and LEA. Exclusion criteria were studies conducted in developing countries and studies published prior to 2000. Findings Forty-eight studies met the inclusion criteria and were reviewed; 10 in Australia. Overall, 28 (58%) studies investigated LEA, 10 (21%) DFD, and 10 (21%) DFD and LEA as the DFD-related outcome. Thirty-six (75%) studies investigated the SDoH risk factor of race, 22 (46%) SES, and 20 (42%) remoteness. SES, race and remoteness were found to be individually associated with LEA and DFD in the majority of studies. Only four studies investigated interactions between SES, race and remoteness and DFD with contrasting findings. All four studies used only LEA as their investigated outcome. No Australian studies investigate the interaction of all three SDoH risk factors on DFD outcomes. Conclusions The SDoH risk factors of SES, race and GR appear to be individually associated with DFD. However, only few studies investigated the interaction of these three major SDoH risk factors and DFD outcomes with contrasting results. There is a clear gap in this area of DFD research and particularly in Australia. Until urgent future research is performed, current practice and policy does not adequately take into consideration the implication of SDoH on DFD.

Predicting the temporal response of seagrass meadows to dredging using Dynamic Bayesian Networks

Predicting temporal responses of ecosystems to disturbances associated with industrial activities is critical for their management and conservation. However, prediction of ecosystem responses is challenging due to the complexity and potential non-linearities stemming from interactions between system components and multiple environmental drivers. Prediction is particularly difficult for marine ecosystems due to their often highly variable and complex natures and large uncertainties surrounding their dynamic responses. Consequently, current management of such systems often rely on expert judgement and/or complex quantitative models that consider only a subset of the relevant ecological processes. Hence there exists an urgent need for the development of whole-of-systems predictive models to support decision and policy makers in managing complex marine systems in the context of industry based disturbances. This paper presents Dynamic Bayesian Networks (DBNs) for predicting the temporal response of a marine ecosystem to anthropogenic disturbances. The DBN provides a visual representation of the problem domain in terms of factors (parts of the ecosystem) and their relationships. These relationships are quantified via Conditional Probability Tables (CPTs), which estimate the variability and uncertainty in the distribution of each factor. The combination of qualitative visual and quantitative elements in a DBN facilitates the integration of a wide array of data, published and expert knowledge and other models. Such multiple sources are often essential as one single source of information is rarely sufficient to cover the diverse range of factors relevant to a management task. Here, a DBN model is developed for tropical, annual Halophila and temperate, persistent Amphibolis seagrass meadows to inform dredging management and help meet environmental guidelines. Specifically, the impacts of capital (e.g. new port development) and maintenance (e.g. maintaining channel depths in established ports) dredging is evaluated with respect to the risk of permanent loss, defined as no recovery within 5 years (Environmental Protection Agency guidelines). The model is developed using expert knowledge, existing literature, statistical models of environmental light, and experimental data. The model is then demonstrated in a case study through the analysis of a variety of dredging, environmental and seagrass ecosystem recovery scenarios. In spatial zones significantly affected by dredging, such as the zone of moderate impact, shoot density has a very high probability of being driven to zero by capital dredging due to the duration of such dredging. Here, fast growing Halophila species can recover, however, the probability of recovery depends on the presence of seed banks. On the other hand, slow growing Amphibolis meadows have a high probability of suffering permanent loss. However, in the maintenance dredging scenario, due to the shorter duration of dredging, Amphibolis is better able to resist the impacts of dredging. For both types of seagrass meadows, the probability of loss was strongly dependent on the biological and ecological status of the meadow, as well as environmental conditions post-dredging. The ability to predict the ecosystem response under cumulative, non-linear interactions across a complex ecosystem highlights the utility of DBNs for decision support and environmental management.

Problem-based learning in an e-learning environment: A case study at Griffith University School of Medicine

Increasing numbers of medical schools in Australia and overseas have moved away from didactic teaching methodologies and embraced problem-based learning (PBL) to improve clinical reasoning skills and communication skills as well as to encourage self-directed lifelong learning. In January 2005, the first cohort of students entered the new MBBS program at the Griffith University School of Medicine, Gold Coast, to embark upon an exciting, fully integrated curriculum using PBL, combining electronic delivery, communication and evaluation systems incorporating cognitive principles that underpin the PBL process. This chapter examines the educational philosophies and design of the e-learning environment underpinning the processes developed to deliver, monitor and evaluate the curriculum. Key initiatives taken to promote student engagement and innovative and distinctive approaches to student learning at Griffith promoted within the conceptual model for the curriculum are (a) Student engagement, (b) Pastoral care, (c) Staff engagement, (d) Monitoring and (e) Curriculum/Program Review. © 2007 Springer-Verlag Berlin Heidelberg.

Assessment of crop insect damage using unmanned aerial systems: A machine learning approach

Agricultural pests are responsible for millions of dollars in crop losses and management costs every year. In order to implement optimal site-specific treatments and reduce control costs, new methods to accurately monitor and assess pest damage need to be investigated. In this paper we explore the combination of unmanned aerial vehicles (UAV), remote sensing and machine learning techniques as a promising technology to address this challenge. The deployment of UAVs as a sensor platform is a rapidly growing field of study for biosecurity and precision agriculture applications. In this experiment, a data collection campaign is performed over a sorghum crop severely damaged by white grubs (Coleoptera: Scarabaeidae). The larvae of these scarab beetles feed on the roots of plants, which in turn impairs root exploration of the soil profile. In the field, crop health status could be classified according to three levels: bare soil where plants were decimated, transition zones of reduced plant density and healthy canopy areas. In this study, we describe the UAV platform deployed to collect high-resolution RGB imagery as well as the image processing pipeline implemented to create an orthoimage. An unsupervised machine learning approach is formulated in order to create a meaningful partition of the image into each of the crop levels. The aim of the approach is to simplify the image analysis step by minimizing user input requirements and avoiding the manual data labeling necessary in supervised learning approaches. The implemented algorithm is based on the K-means clustering algorithm. In order to control high-frequency components present in the feature space, a neighbourhood-oriented parameter is introduced by applying Gaussian convolution kernels prior to K-means. The outcome of this approach is a soft K-means algorithm similar to the EM algorithm for Gaussian mixture models. The results show the algorithm delivers decision boundaries that consistently classify the field into three clusters, one for each crop health level. The methodology presented in this paper represents a venue for further research towards automated crop damage assessments and biosecurity surveillance.

Caution, helping may be hazardous: Client abuse, threats and assaults

The shooting of a social worker by a client on the Gold Coast in 1991 graphically illustrated the issue of physical assaults and violence by service users against social workers. In this article we look at the incidence of physical assault, threats of violence, abuse of agency property and verbal abuse to social and other welfare workers by clients, using data from a survey in Melbourne. We then look at probable causes of menacing behaviour, such as issues involved in work with involuntary clients' and we discuss options for preventing and coping with violence and abuse in the welfare work place.

Trip to Sunshine Coast Queensland, September 2004 visit to bridge and foreshore

A trip was undertaken to look at concerns of Public Works and Main Roads Departments of Queensland. David Paterson and Wayne Ganther from CSIRO travelled to the Sunshine Coast with Alan Carse of Queensland Department of Main Roads and Michael Ball of Queensland Department of Public Works. We were also joined for part of the visits by Ed Bowers of QBuild which is a commercial unit of Public Works responsible for maintenance of Public Works. During the trip we visited a bridge on the David Low Way at Sunrise Beach near Noosa. This bridge was in a serve marine environment with high salt content in the concrete and corrosion of the galvanised guardrails and barriers. Also the foreshore at Coolum was visited and the use of stainless steel was examined. This is discussed in this report. Most problems stemmed from incorrect specification due to lack of awareness of the severity of the environment. The companion report Visit to Schools Report 2002-059-B No 7. covers the visit to four schools north of Caloundra.

Interactions of phenyldithioesters with gold nanoparticles (AuNPs): Implications for AuNP functionalization and molecular barcoding of AuNP assemblies

The interactions of phenyldithioesters with gold nanoparticles (AuNPs) have been studied by monitoring changes in the surface plasmon resonance (SPR), depolarised light scattering, and surface enhanced Raman spectroscopy (SERS). Changes in the SPR indicated that an AuNP-phenyldithioester charge transfer complex forms in equilibrium with free AuNPs and phenyldithioester. Analysis of the Langmuir binding isotherms indicated that the equilibrium adsorption constant, Kads, was 2.3 ± 0.1 × 106 M−1, which corresponded to a free energy of adsorption of 36 ± 1 kJ mol−1. These values are comparable to those reported for interactions of aryl thiols with gold and are of a similar order of magnitude to moderate hydrogen bonding interactions. This has significant implications in the application of phenyldithioesters for the functionalization of AuNPs. The SERS results indicated that the phenyldithioesters interact with AuNPs through the C═S bond, and the molecules do not disassociate upon adsorption to the AuNPs. The SERS spectra are dominated by the portions of the molecule that dominate the charge transfer complex with the AuNPs. The significance of this in relation to the use of phenyldithioesters for molecular barcoding of nanoparticle assemblies is discussed.

The encapsulation of gold nanoparticles using RAFT, ATRP and miniemulsion polymerisation techniques

The investigation into the encapsulation of gold nanoparticles (AuNPs) by poly(methyl methacrylate) (PMMA) was undertaken. This was performed by three polymerisation techniques including: grafting PMMA synthesised by reversible addition-fragmentation chain transfer (RAFT) polymerisation to AuNPs, grafting PMMA synthesised by atom transfer radical polymerisation (ATRP) from the surface of functionalised AuNPs and by encapsulation of AuNPs within PMMA latexes produced through photo-initiated oil-in-water (o/w) miniemulsion polymerisation. The grafting of RAFT PMMA to AuNPs was performed by the addition of the RAFT functionalised PMMA to citrate stabilised AuNPs. This was conducted with a range of PMMA of varying molecular weight distribution (MWD) as either the dithioester or thiol end-group functionalities. The RAFT PMMA polymers were characterised by gel permeation chromatography (GPC), ultraviolet-visible (UV-vis), Fourier transform infrared-attenuated total reflectance (FTIR-ATR), Fourier transform Raman (FT-Raman) and proton nuclear magnetic resonance (1H NMR) spectroscopies. The attachment of PMMA to AuNPs showed a tendency for AuNPs to associate with the PMMA structures formed, though significant aggregation occurred. Interestingly, thiol functionalised end-group PMMA showed very little aggregation of AuNPs. The spherical polymer-AuNP structures did not vary in size with variations in PMMA MWD. The PMMA-AuNP structures were characterised using scanning electron microscopy (SEM), transition electron microscopy (TEM), energy dispersive X-ray analysis (EDAX) and UV-vis spectroscopy. The surface confined ATRP grafting of PMMA from initiator functionalised AuNPs was polymerised in both homogeneous and heterogeneous media. 11,11’- dithiobis[1-(2-bromo-2-methylpropionyloxy)undecane] (DSBr) was used as the surface-confined initiator and was synthesised in a three step procedure from mercaptoundecanol (MUD). All compounds were characterised by 1H NMR, FTIR-ATR and Raman spectroscopies. The grafting in homogeneous media resulted in amorphous PMMA with significant AuNP aggregation. Individually grafted AuNPs were difficult to separate and characterise, though SEM, TEM, EDAX and UV-vis spectroscopy was used. The heterogeneous polymerisation did not produce grafted AuNPs as characterised by SEM and EDAX. The encapsulation of AuNPs within PMMA latexes through the process of photoinitiated miniemulsion polymerisation was successfully achieved. Initially, photoinitiated miniemulsion polymerisation was conducted as a viable low temperature method of miniemulsion initiation. This proved successful producing a stable PMMA with good conversion efficiency and narrow particle size distribution (PSD). This is the first report of such a system. The photo-initiated technique was further optimised and AuNPs were included into the miniemulsion. AuNP encapsulation was very effective, producing reproducible AuNP encapsulated PMMA latexes. Again, this is the first reported case of this. The latexes were characterised by TEM, SEM, GPC, gravimetric analysis and dynamic light scattering (DLS).