Challenges to environmental toxicology and epidemiology : where do we stand and which way do we go?

Modern toxicology investigates a wide array of both old and new health hazards. Priority setting is needed to select agents for research from the plethora of exposure circumstances. The changing societies and a growing fraction of the aged have to be taken into consideration. A precise exposure assessment is of importance for risk estimation and regulation. Toxicology contributes to the exploration of pathomechanisms to specify the exposure metrics for risk estimation. Combined effects of co-existing agents are not yet sufficiently understood. Animal experiments allow a separate administration of agents which can not be disentangled by epidemiological means, but their value is limited for low exposure levels in many of today’s settings. As an experimental science, toxicology has to keep pace with the rapidly growing knowledge about the language of the genome and the changing paradigms in cancer development. During the pioneer era of assembling a working draft of the human genome, toxicogenomics has been developed. Gene and pathway complexity have to be considered when investigating gene–environment interactions. For a best conduct of studies, modern toxicology needs a close liaison with many other disciplines like epidemiology and bioinformatics.

A multilevel investigation of inequalities in clinical and psychosocial outcomes for women after breast cancer

Background In Australia, breast cancer is the most common cancer affecting Australian women. Inequalities in clinical and psychosocial outcomes have existed for some time, affecting particularly women from rural areas and from areas of disadvantage. We have a limited understanding of how individual and area-level factors are related to each other, and their associations with survival and other clinical and psychosocial outcomes. Methods/Design This study will examine associations between breast cancer recurrence, survival and psychosocial outcomes (e.g. distress, unmet supportive care needs, quality of life). The study will use an innovative multilevel approach using area-level factors simultaneously with detailed individual-level factors to assess the relative importance of remoteness, socioeconomic and demographic factors, diagnostic and treatment pathways and processes, and supportive care utilization to clinical and psychosocial outcomes. The study will use telephone and self-administered questionnaires to collect individual-level data from approximately 3, 300 women ascertained from the Queensland Cancer Registry diagnosed with invasive breast cancer residing in 478 Statistical Local Areas Queensland in 2011 and 2012. Area-level data will be sourced from the Australian Bureau of Statistics census data. Geo-coding and spatial technology will be used to calculate road travel distances from patients' residence to diagnostic and treatment centres. Data analysis will include a combination of standard empirical procedures and multilevel modelling. Discussion The study will address the critical question of: what are the individual- or area-level factors associated with inequalities in outcomes from breast cancer? The findings will provide health care providers and policy makers with targeted information to improve the management of women with breast cancer, and inform the development of strategies to improve psychosocial care for women with breast cancer.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Hospital to home outreach for malnourished elders (HHOME) : a feasibility pilot

Objectives: Malnutrition is common in older hospitalised patients, and barriers to adequate intake in hospital limit the effectiveness of hospital-based nutrition interventions. This pilot study was undertaken to determine whether nutrition-focussed care at discharge and in the early post-hospital period is feasible and acceptable to patients and carers, and improves nutritional status. Design: Prospective cohort study Setting: Internal medicine wards of a tertiary teaching hospital in Brisbane, Australia Participants: Patients aged 65 and older admitted for at least 3 days, identified as malnourished or at risk of malnutrition using Mini Nutritional Assessment (MNA). Interventions: An interdisciplinary discharge team (specialist discharge planning nurse and accredited practicing dietitian) provided nutrition-focussed education, advice, service coordination and follow-up (home visits and telephone) for 6 weeks following hospitalisation Measurements: Nutritional intake, weight, functional status and MNA were recorded 6 and 12 weeks after discharge. Service intensity and changes to care were noted, and hospital readmissions recorded. Service feedback from patients and carers was sought using a brief questionnaire. Results: 12 participants were enrolled during the 6 week pilot (mean age 82 years, 50% male). All received 1-2 home visits, and 3-8 telephone calls. Four participants had new community services arranged, 4 were commenced on oral nutritional supplements, and 7 were referred to community dietetics services for follow-up. Two participants had a decline in MNA score of more than 10% at 12 week follow-up, while the remainder improved by at least 10%. Individualised care including community service coordination was valued by participants. Conclusion: The proposed model of care for older adults was feasible, acceptable to patients and carers, and associated with improved nutritional status at 12 weeks for most participants. The pilot data will be useful for design of intervention trials.

Is the iPhone a useful clinical tool to monitor spinal deformity patients?

The progression of spinal deformity is traditionally monitored on hard copy radiographs using the Cobb method with a protractor and pencil. The rotation of the spine and ribcage (rib hump) in scoliosis is measured with a hand-held inclinometer/Scoliometer. The iPhone and other smart phones, can accurately sense inclination, and can therefore be used to measure Cobb angles and rib hump angulation. The purpose of this study was to quantify the performance of the iPhone compared to the standard protractor (Cobb angles) and the Scoliometer (rib hump).

The identification of therapeutic targets in metastatic melanoma

Metastatic melanoma, a cancer historically refractory to chemotherapeutic strategies, has a poor prognosis and accounts for the majority of skin cancer related mortality. Although the recent approval of two new drugs combating this disease, Ipilimumab and Vemurafenib (PLX4032), has demonstrated for the first time in decades an improvement in overall survival; the clinical efficacy of these drugs has been marred by severe adverse immune reactions and acquired drug resistance in patients, respectively. Thus, understanding the etiology of metastatic melanoma will contribute to the improvement of current therapeutic strategies while leading to the development of novel drug approaches. In order to identify recurrently mutated genes of therapeutic relevance in metastatic melanoma, a panel of stage III local lymph node melanomas were extensively characterised using high-throughput genomic technologies. This led to the identification of mutations in TFG in 5% of melanomas from a candidate gene sequencing approach using SNP array analysis, 24% of melanomas with mutations in MAP3K5 or MAP3K9 though unbiased whole-exome sequencing strategies, and inactivating mutations in NF1 in BRAF/NRAS wild type tumours though pathway analysis. Lastly, this thesis describes the development of a melanoma specific mutation panel that can rapidly identify clinically relevant mutation profiles that could guide effective treatment strategies through a personalised therapeutic approach. These findings are discussed in respect to a number of important issues raised by this study including the current limitation of next-generation sequencing technology, the difficulty in identifying ‘driver’ mutations critical to the development of melanoma due to high carcinogenic exposure by UV radiation, and the ultimate application of mutation screening in a personalised therapeutic setting. In summary, a number novel genes involved in metastatic melanoma have been identified that may have relevance for current therapeutic strategies in treating this disease.