Design and implementation of the Exercise for Health trial – a pragmatic exercise intervention for women with breast cancer

Background Exercise for Health was a pragmatic, randomised, controlled trial comparing the effect of an eight-month exercise intervention on function, treatment-related side effects and quality of life following breast cancer, compared with usual care. The intervention commenced six weeks post-surgery, and two modes of delivering the same intervention was compared with usual care. The purpose of this paper is to describe the study design, along with outcomes related to recruitment, retention and representativeness, and intervention participation. Methods: Women newly diagnosed with breast cancer and residing in a major metropolitan city of Queensland, Australia, were eligible to participate. Consenting women were randomised to a face-to-face-delivered exercise group (FtF, n=67), telephone-delivered exercise group (Tel, n=67) or usual care group (UC, n=60) and were assessed pre-intervention (5-weeks post-surgery), mid-intervention (6 months post-surgery) and 10 weeks post-intervention (12 months post-surgery). Each intervention arm entailed 16 sessions with an Exercise Physiologist. Results: Of 318 potentially eligible women, 63% (n=200) agreed to participate, with a 12-month retention rate of 93%. Participants were similar to the Queensland breast cancer population with respect to disease characteristics, and the randomisation procedure was mostly successful at attaining group balance, with the few minor imbalances observed unlikely to influence intervention effects given balance in other related characteristics. Median participation was 14 (min, max: 0, 16) and 13 (min, max: 3, 16) intervention sessions for the FtF and Tel, respectively, with 68% of those in Tel and 82% in FtF participating in at least 75% of sessions. Discussion: Participation in both intervention arms during and following treatment for breast cancer was feasible and acceptable to women. Future work, designed to inform translation into practice, will evaluate the quality of life, clinical, psychosocial and behavioural outcomes associated with each mode of delivery.

Gaddafi and Libya : a case for just intervention?

Full militaristic intervention cannot be justified on the grounds that this is a ‘just war’. We are then left with the option to intervene militarily in a smaller way or not to intervene militarily at all.

Youth Homelessness and Early Intervention: Key Characteristics and Challenges

The key requirements for effective early intervention into youth homelessness are discussed. Young people are exposed to the risk of homelessness from a constellation of factors at the structural through to the individual level and early intervention has generally been conceptualized as part of a continuum of responses.

Developing the Practice of Early Intervention into Youth Homelessness

The Australian government has found that early intervention services related to youth homelessness expanded through the Reconnect program have been found to be quite effective and successful. The main things that a good early intervention practice requires are highlighted.

Multiscale analysis of protein functions and stochastic modelling of gene transcriptional regulatory networks

Genomic and proteomic analyses have attracted a great deal of interests in biological research in recent years. Many methods have been applied to discover useful information contained in the enormous databases of genomic sequences and amino acid sequences. The results of these investigations inspire further research in biological fields in return. These biological sequences, which may be considered as multiscale sequences, have some specific features which need further efforts to characterise using more refined methods. This project aims to study some of these biological challenges with multiscale analysis methods and stochastic modelling approach. The first part of the thesis aims to cluster some unknown proteins, and classify their families as well as their structural classes. A development in proteomic analysis is concerned with the determination of protein functions. The first step in this development is to classify proteins and predict their families. This motives us to study some unknown proteins from specific families, and to cluster them into families and structural classes. We select a large number of proteins from the same families or superfamilies, and link them to simulate some unknown large proteins from these families. We use multifractal analysis and the wavelet method to capture the characteristics of these linked proteins. The simulation results show that the method is valid for the classification of large proteins. The second part of the thesis aims to explore the relationship of proteins based on a layered comparison with their components. Many methods are based on homology of proteins because the resemblance at the protein sequence level normally indicates the similarity of functions and structures. However, some proteins may have similar functions with low sequential identity. We consider protein sequences at detail level to investigate the problem of comparison of proteins. The comparison is based on the empirical mode decomposition (EMD), and protein sequences are detected with the intrinsic mode functions. A measure of similarity is introduced with a new cross-correlation formula. The similarity results show that the EMD is useful for detection of functional relationships of proteins. The third part of the thesis aims to investigate the transcriptional regulatory network of yeast cell cycle via stochastic differential equations. As the investigation of genome-wide gene expressions has become a focus in genomic analysis, researchers have tried to understand the mechanisms of the yeast genome for many years. How cells control gene expressions still needs further investigation. We use a stochastic differential equation to model the expression profile of a target gene. We modify the model with a Gaussian membership function. For each target gene, a transcriptional rate is obtained, and the estimated transcriptional rate is also calculated with the information from five possible transcriptional regulators. Some regulators of these target genes are verified with the related references. With these results, we construct a transcriptional regulatory network for the genes from the yeast Saccharomyces cerevisiae. The construction of transcriptional regulatory network is useful for detecting more mechanisms of the yeast cell cycle.

The development of an intervention to improve the safety of community care nurses while driving and a qualitative investigation of its preliminary effects

This paper details the development of, and perceived role and effectiveness of an innovative intervention designed to ultimately improve the safety of a group of community care (CC) nurses while driving. Recruiting participants from an Australian CC nursing car fleet, qualitative responses from a series of open-ended questions were obtained from drivers (n = 36), supervisors (n = 22), and managers (n = 6). The findings supported the effectiveness of the intervention in reducing self-reported speeding and promoting greater insight into one’s behaviour on the road. This research has important practical implications in that it highlights the value of developing an intervention based on a sound theoretical framework and which is aligned with the needs and beliefs of personnel within a particular organisation.

Living well with diabetes: a randomized controlled trial of a telephone-delivered intervention for maintenance of weight loss, physical activity and glycaemic control in adults with type 2 diabetes

Background By 2025, it is estimated that approximately 1.8 million Australian adults (approximately 8.4% of the adult population) will have diabetes, with the majority having type 2 diabetes. Weight management via improved physical activity and diet is the cornerstone of type 2 diabetes management. However, the majority of weight loss trials in diabetes have evaluated short-term, intensive clinic-based interventions that, while producing short-term outcomes, have failed to address issues of maintenance and broad population reach. Telephone-delivered interventions have the potential to address these gaps. Methods/Design Using a two-arm randomised controlled design, this study will evaluate an 18-month, telephone-delivered, behavioural weight loss intervention focussing on physical activity, diet and behavioural therapy, versus usual care, with follow-up at 24 months. Three-hundred adult participants, aged 20-75 years, with type 2 diabetes, will be recruited from 10 general practices via electronic medical records search. The Social-Cognitive Theory driven intervention involves a six-month intensive phase (4 weekly calls and 11 fortnightly calls) and a 12-month maintenance phase (one call per month). Primary outcomes, assessed at 6, 18 and 24 months, are: weight loss, physical activity, and glycaemic control (HbA1c), with weight loss and physical activity also measured at 12 months. Incremental cost-effectiveness will also be examined. Study recruitment began in February 2009, with final data collection expected by February 2013. Discussion This is the first study to evaluate the telephone as the primary method of delivering a behavioural weight loss intervention in type 2 diabetes. The evaluation of maintenance outcomes (6 months following the end of intervention), the use of accelerometers to objectively measure physical activity, and the inclusion of a cost-effectiveness analysis will advance the science of broad reach approaches to weight control and health behaviour change, and will build the evidence base needed to advocate for the translation of this work into population health practice.

Streptococcus pneumoniae infection suppresses allergic airways disease by inducing regulatory T cells

An inverse association exists between some bacterial infections and the prevalence of asthma. We investigated whether Streptococcus pneumoniae infection protects against asthma using mouse models of ovalbumin (OVA)-induced allergic airway disease (AAD). Mice were intratracheally infected or treated with killed S. pneumoniae before, during or after OVA sensitisation and subsequent challenge. The effects of S. pneumoniae on AAD were assessed. Infection or treatment with killed S. pneumoniae suppressed hallmark features of AAD, including antigen-specific T-helper cell (Th) type 2 cytokine and antibody responses, peripheral and pulmonary eosinophil accumulation, goblet cell hyperplasia, and airway hyperresponsiveness. The effect of infection on the development of specific features of AAD depended on the timing of infection relative to allergic sensitisation and challenge. Infection induced significant increases in regulatory T-cell (Treg) numbers in lymph nodes, which correlated with the degree of suppression of AAD. Tregs reduced T-cell proliferation and Th2 cytokine release. The suppressive effects of infection were reversed by anti-CD25 treatment. Respiratory infection or treatment with S. pneumoniae attenuates allergic immune responses and suppresses AAD. These effects may be mediated by S. pneumoniae-induced Tregs. This identifies the potential for the development of therapeutic agents for asthma from S. pneumoniae.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the λ phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. In this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.