Risk factors for oesophageal, lung, oral and laryngeal cancers in black South Africans

The authors used data collected from 1995 to 1999, from an on-going cancer case–control study in greater Johannesburg, to estimate the importance of tobacco and alcohol consumption and other suspected risk factors with respect to cancer of the oesophagus (267 men and 138 women), lung (105 men and 41 women), oral cavity (87 men and 37 women), and larynx (51 men). Cancers not associated with tobacco or alcohol consumption were used as controls (804 men and 1370 women). Tobacco smoking was found to be the major risk factor for all of these cancers with odds ratios ranging from 2.6 (95% CI 1.5–4.5) for oesophageal cancer in female ex-smokers to 50.9 (95% CI 12.6–204.6) for lung cancer in women, and 23.9 (95% CI 9.5–60.3) for lung cancer and 23.6 (95% CI 4.6–121.2) for laryngeal cancer in men who smoked 15 or more grams of tobacco a day. This is the first time an association between smoking and oral and laryngeal cancers has been shown in sub-Saharan Africa. Long-term residence in the Transkei region in the southeast of the country continues to be a risk factor for oesophageal cancer, especially in women (odds ratio=14.7, 95% CI 4.7–46.0), possibly due to nutritional factors. There was a slight increase in lung cancer (odds ratio=2.9, 95% CI 1.1–7.5) in men working in ‘potentially noxious’ industries. ‘Frequent’ alcohol consumption, on its own, caused a marginally elevated risk for oesophageal cancer (odds ratio=1.7, 95% CI 1.0–2.9, for women and odds ratio=1.8, 95% CI 1.2–2.8, for men). The risks for oesophageal cancer in relation to alcohol consumption increased significantly in male and female smokers (odds ratio=4.7, 95% CI=2.8–7.9 in males and odds ratio=4.8, 95% CI 3.2–6.1 in females). The above results are broadly in line with international findings.

The BAD project: data mining, database and prediction of protein adsorption on surfaces

Protein adsorption at solid-liquid interfaces is critical to many applications, including biomaterials, protein microarrays and lab-on-a-chip devices. Despite this general interest, and a large amount of research in the last half a century, protein adsorption cannot be predicted with an engineering level, design-orientated accuracy. Here we describe a Biomolecular Adsorption Database (BAD), freely available online, which archives the published protein adsorption data. Piecewise linear regression with breakpoint applied to the data in the BAD suggests that the input variables to protein adsorption, i.e., protein concentration in solution; protein descriptors derived from primary structure (number of residues, global protein hydrophobicity and range of amino acid hydrophobicity, isoelectric point); surface descriptors (contact angle); and fluid environment descriptors (pH, ionic strength), correlate well with the output variable-the protein concentration on the surface. Furthermore, neural network analysis revealed that the size of the BAD makes it sufficiently representative, with a neural network-based predictive error of 5% or less. Interestingly, a consistently better fit is obtained if the BAD is divided in two separate sub-sets representing protein adsorption on hydrophilic and hydrophobic surfaces, respectively. Based on these findings, selected entries from the BAD have been used to construct neural network-based estimation routines, which predict the amount of adsorbed protein, the thickness of the adsorbed layer and the surface tension of the protein-covered surface. While the BAD is of general interest, the prediction of the thickness and the surface tension of the protein-covered layers are of particular relevance to the design of microfluidics devices.

Lung sickness in Murri kids : a cohort study in urban Indigenous children

QLD, 4Murri Health Group, Caboolture, QLD Introduction Respiratory illnesses with cough as a symptom are predominant causes of morbidity in young Australian Indigenous children. With the exception of ear disease, there are limited studies that have addressed burden and outcome. Also, there are no studies that are specific to urban Indigenous children. Aim: We aim to comprehensively investigate the incidence, aetiology, risk factors for and outcomes of acute respiratory illnesses (ARIs) in this population. Methods A cohort study of Indigenous children aged less than 5 years registered with an urban Indigenous primary health care service. Comprehensive baseline data are collected and children are followed monthly for 12 months to capture ARI events. ARI events are subsequently followed weekly for 4 weeks to determine cough outcomes, with review by a paediatric respiratory physician if cough has not resolved within 28 days. Results To date, 58 children (57% female) have been enrolled and 46 ARIs have been captured over 907 child weeks of observation (5.1 events per 100 child weeks, 95%CI 3.7–6.8). 13 ARIs (28.3%) have resulted in persistent cough for >28 days following onset. Conclusion Our early findings suggest an excess incidence of ARI in this population. The proportion of ARIs resulting in persistent cough for more than 4 weeks is the highest yet reported. Key Words: Indigenous, acute respiratory illness, paediatric.

Toward making inroads in reducing the disparity of lung health in Australian Indigenous and New Zealand Maori children

Globally, Indigenous populations, which include Aboriginal and Torres Strait islanders in Australia and Māori people in New Zealand (NZ), have poorer health than their non-Indigenous counterparts. Indigenous peoples worldwide face substantial challenges in poverty, education, employment, housing and disconnection from ancestral lands. While addressing social determinants of health is a priority, solving clinical issues is equally important. Indeed, ignoring the latter until social issues improve risks further disparity as this may take generations. A systematic overview of interventions addressing social determinants of health found a striking lack of reliable evaluations.Where evidence was available, health improvement associated with interventions was modest or uncertain. 10 Thus advances in healthcare remain essential and these require the best evidence available in 11 preventing and managing common illnesses, including respiratory illnesses.

Potential role of bioactive lipids in the development of non-antibody mediated transfusion-related acute lung injury (TRALI)

Transfusion-related acute lung injury (TRALI) has been the leading cause of transfusion-related morbidity and mortality in the UK and the USA in recent years. A threshold mechanism of TRALI has been proposed in which both patient factors (type and/or severity of clinical insult) and blood product factors (strength and/or concentration of antibodies or biological response modifiers) interact to surpass a threshold for TRALI development (Bux et al. Br J Haematol; 2007; 136: 788-99). The risk of developing antibody-mediated TRALI has been minimised by the introduction of risk-reduction strategies such as limiting the use of plasma from female donors. In contrast, there are no strategies currently in place to mitigate the development of non-antibody mediated TRALI as the mechanisms remain largely undefined. Previous studies have implicated non-polar lipids such as arachidonic acid and various species of hydroxyeicosatetranoic acid (HETE) in the development of non-antibody mediated TRALI (Silliman et al. Transfusion; 2011; 51: 2549-54), however the contribution of these lipids to the development of an inflammatory response in TRALI is poorly understood.

Provision of survivorship care for patients with haematological malignancy at completion of treatment: a cancer nursing practice survey study

Purpose Many haematological cancer survivors report long-term physiological and psychosocial effects, which persist far beyond treatment completion. Cancer services have been required to extend care to the post-treatment phase to implement survivorship care strategies into routine practice. As key members of the multidisciplinary team, cancer nurses’ perspectives are essential to inform future developments in survivorship care provision. Methods This is a pilot survey study, involving 119 nurses caring for patients with haematological malignancy in an Australian tertiary cancer care centre. The participants completed an investigator developed survey designed to assess cancer care nurses’ perspectives on their attitudes, confidence levels, and practice in relation to post-treatment survivorship care for patients with a haematological malignancy. Results Overall, the majority of participants agreed that all of the survivorship interventions included in the survey should be within the scope of the nursing role. Nurses reported being least confident in discussing fertility and employment/financial issues with patients and conducting psychosocial distress screening. The interventions performed least often included, discussing fertility, intimacy and sexuality issues and communicating survivorship care with the patient’s primary health care providers. Nurses identified lack of time, limited educational resources, lack of dedicated end-of-treatment consultation and insufficient skills/knowledge as the key barriers to survivorship care provision. Conclusion Cancer centres should implement an appropriate model of survivorship care and provide improved training and educational resources for nurses to enable them to deliver quality survivorship care and meet the needs of haematological cancer survivors.

Learning the learning rate for prediction with expert advice

Most standard algorithms for prediction with expert advice depend on a parameter called the learning rate. This learning rate needs to be large enough to fit the data well, but small enough to prevent overfitting. For the exponential weights algorithm, a sequence of prior work has established theoretical guarantees for higher and higher data-dependent tunings of the learning rate, which allow for increasingly aggressive learning. But in practice such theoretical tunings often still perform worse (as measured by their regret) than ad hoc tuning with an even higher learning rate. To close the gap between theory and practice we introduce an approach to learn the learning rate. Up to a factor that is at most (poly)logarithmic in the number of experts and the inverse of the learning rate, our method performs as well as if we would know the empirically best learning rate from a large range that includes both conservative small values and values that are much higher than those for which formal guarantees were previously available. Our method employs a grid of learning rates, yet runs in linear time regardless of the size of the grid.

Superpixel-based appearance change prediction for long-term navigation across seasons

Changing environments pose a serious problem to current robotic systems aiming at long term operation under varying seasons or local weather conditions. This paper is built on our previous work where we propose to learn to predict the changes in an environment. Our key insight is that the occurring scene changes are in part systematic, repeatable and therefore predictable. The goal of our work is to support existing approaches to place recognition by learning how the visual appearance of an environment changes over time and by using this learned knowledge to predict its appearance under different environmental conditions. We describe the general idea of appearance change prediction (ACP) and investigate properties of our novel implementation based on vocabularies of superpixels (SP-ACP). Our previous work showed that the proposed approach significantly improves the performance of SeqSLAM and BRIEF-Gist for place recognition on a subset of the Nordland dataset under extremely different environmental conditions in summer and winter. This paper deepens the understanding of the proposed SP-ACP system and evaluates the influence of its parameters. We present the results of a large-scale experiment on the complete 10 h Nordland dataset and appearance change predictions between different combinations of seasons.

Prediction of GMA welding characteristic parameter by artificial neural network system

Nowadays, demand for automated Gas metal arc welding (GMAW) is growing and consequently need for intelligent systems is increased to ensure the accuracy of the procedure. To date, welding pool geometry has been the most used factor in quality assessment of intelligent welding systems. But, it has recently been found that Mahalanobis Distance (MD) not only can be used for this purpose but also is more efficient. In the present paper, Artificial Neural Networks (ANN) has been used for prediction of MD parameter. However, advantages and disadvantages of other methods have been discussed. The Levenberg–Marquardt algorithm was found to be the most effective algorithm for GMAW process. It is known that the number of neurons plays an important role in optimal network design. In this work, using trial and error method, it has been found that 30 is the optimal number of neurons. The model has been investigated with different number of layers in Multilayer Perceptron (MLP) architecture and has been shown that for the aim of this work the optimal result is obtained when using MLP with one layer. Robustness of the system has been evaluated by adding noise into the input data and studying the effect of the noise in prediction capability of the network. The experiments for this study were conducted in an automated GMAW setup that was integrated with data acquisition system and prepared in a laboratory for welding of steel plate with 12 mm in thickness. The accuracy of the network was evaluated by Root Mean Squared (RMS) error between the measured and the estimated values. The low error value (about 0.008) reflects the good accuracy of the model. Also the comparison of the predicted results by ANN and the test data set showed very good agreement that reveals the predictive power of the model. Therefore, the ANN model offered in here for GMA welding process can be used effectively for prediction goals.

Reduced intensity conditioning for allogeneic hematopoietic stem cell transplant determines the kinetics of acute Graft-Versus-Host Disease

Background Preparative myeloablative conditioning regimens for allogeneic hematopoietic stem-cell transplantation (HSCT) may control malignancy and facilitate engraftment but also contribute to transplant related mortality, cytokine release, and acute graft-versus-host disease (GVHD). Reduced intensity conditioning (RIC) regimens have decreased transplant related mortality but the incidence of acute GVHD, while delayed, remains unchanged. There are currently no in vivo allogeneic models of RIC HSCT, limiting studies into the mechanism behind RIC-associated GVHD. Methods We developed two RIC HSCT models that result in delayed onset GVHD (major histocompatibility complex mismatched (UBI-GFP/BL6 [H-2b]→BALB/c [H-2d]) and major histocompatibility complex matched, minor histocompatibility mismatched (UBI-GFP/BL6 [H-2b]→BALB.B [H-2b])) enabling the effect of RIC on chimerism, dendritic cell (DC) chimerism, and GVHD to be investigated. Results In contrast with myeloablative conditioning, we observed that RIC-associated delayed-onset GVHD is characterized by low production of tumor necrosis factor-α, maintenance of host DC, phenotypic DC activation, increased T-regulatory cell numbers, and a delayed emergence of activated donor DC. Furthermore, changes to the peritransplant milieu in the recipient after RIC lead to the altered activation of DC and the induction of T-regulatory responses. Reduced intensity conditioning recipients suffer less early damage to GVHD target organs. However, as donor cells engraft, activated donor DC and rising levels of tumor necrosis factor-α are associated with a later onset of severe GVHD. Conclusions Delineating the mechanisms underlying delayed onset GVHD in RIC HSCT recipients is vital to improve the prediction of disease onset and allow more targeted interventions for acute GVHD.