The objective of this study was to investigate the use of complementary and alternative medicines (CAM) in a group of older rural Australian attending a multi-disciplinary health screening clinic. The average age of all participants (n ¼ 102) was 66 AE 10 years (range 49-89) and 61% were female. Three-quarters (78%) of respondents had used at least one CAM product within the past 12 months and 66% had visited a CAM practitioner. The most frequently used CAM were vitamin/mineral supplements (54%) followed by herbal supplements (28%). Among products named by respondents that they were currently using the most frequently cited were omega-3/fish oils (28%) and glucosamine (24%). The main source of information for most respondents (53%) was doctors and pharmacists followed by family and friends (28%). Almost half (46%) had not discussed their use of CAM with their doctor and only 15% had discussed their CAM use with a pharmacist. Respondents were not found to hold strongly pro-CAM or anti-CAM health beliefs. This study demonstrates that while older, rural Australians do not have significant pro-CAM beliefs they do have high use of CAM. Based on the types of products used it is suggested that CAM use forms part of these individuals' self-management strategies.

Background. It has been hypothesized that a link exists between erythrocyte metabolism (particularly redox metabolism) and erythrocyte shape and that both are related to erythrocyte deformability. The aim of this research is to confirm the results of earlier studies and to investigate a correlation between erythrocyte morphology and erythrocyte oxidative damage in chronic fatigue syndrome (CFS). Methods. Reduced glutathione (GSH), malondialdehyde (MDA), methemoglobin (metHb) and 2,3-diphosphoglyceric acid (2,3-DPG) were measured in 31 patients suffering from CFS and 41 healthy control subjects. Scanning electron microscopic studies of the erythrocytes from both groups were also carried out.

This paper describes an innovative multi-disciplinary pilot project for people living in rural north-east Victoria and southern New South Wales that focused on screening for diabetes complications. Participants enrolled for the diabetes screening through media announcements. A total of 91 participants were screened, 14 with diabetes. Screening tests included 12lead ECG, ankle-brachial index, monofilament sensitivity, retinal photography as well as blood biochemistry. Twenty-six participants were referred to their general practitioners with cardiovascular anomalies, eight had retinal vasculature changes indicating diabetes, and 26 had foot problems that warranted podiatric assessment. This project illustrates that a universitybased screening clinic is feasible, can be operated in conjunction with major research projects and results in significant community benefit. Serious health issues were identified in 31% of the study population. Without involvement in the screening, these people's medical conditions may have otherwise remained hidden, potentially leading to significant health problems.

cells to a high degree of precision (3, 4). One particu-An image analysis method called two-dimensional wavelet larly interesting technique involves measurement of packet analysis (2D WPA) is introduced to quantify branching the fractal dimension, D f (5-8). Fractal geometry ofcomplexity of neurons. Both binary silhouettes and contour fers a method for describing complex patterns using profiles of neurons were analyzed to determine accuracy and precision of the fractal dimension in cell classification tasks. noninteger dimensions. It provides an objective, Two-dimensional WPA plotted the slope of decay for a sorted mathematical formalism necessary to characterize list of discrete wavelet packet coefficients belonging to the such stochastic (fractal) images (10). In contrast, Euadapted wavelet best basis to obtain the fractal dimension clidean geometry deals with ideal objects and integer for test images and binary representations of neurons. Twodimensions similar to the topological dimension. A dimensional WPA was compared with box counting and massmethod to measure the fractal dimension of

Terms such as 'self-similarity', 'space filling' 'fractal dimension', and associated concepts have different meanings to different people depending on their background. We examine how methodology in fractal analysis is influenced by diverse definitions of fundamental concepts that lead to difficulties in understanding fundamental issues. The meaning of terms associated with fractal analysis need to be clarified if this method is to be useful in diverse disciplines. It is our premise that communications that are result focused constitute a danger in perpetuating misconceptions of terms due to the concise nature of the writing and the reliance on references to fill in the procedural and conceptual gaps. Communicating effectively requires a sound understanding of the terminology and a clear and meaningful presentation. We address here communication and the nature of scientific discourse, 'fractal linguistics'

Myofibre disarray in progressive hypertrophic cardiomyopathy (HCM) is a well established pathological cardiac tissue change and thereby represents a biomarker for that condition. On the other hand, in diabetic cardiomyopathy, myofibre disarray historically has been reported not to occur.This is surprising given that many of the pathological, remodelling and mechanical changes that present in the diabetic ventricle are also present in HCM, for example, myocardial stiffness, myocardial hypertrophy, apoptosis, cell slippage, extensive collagen expression and fibrosis.The question therefore begs is the absence of myocyte disarray in the diabetic heart a paradox or simply an oversight?

Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes.

We present a method for automated segmentation of the vasculature in retinal images. The method produces segmentations by classifying each image pixel as vessel or nonvessel, based on the pixel's feature vector. Feature vectors are composed of the pixel's intensity and continuous twodimensional Morlet wavelet transform responses taken at multiple scales. The Morlet wavelet is capable of tuning to specific frequencies, thus allowing noise filtering and vessel enhancement in a single step. We use a Bayesian classifier with class-conditional probability density functions (likelihoods) described as gaussian mixtures, yielding a fast classification, while being able to model complex decision surfaces. The probability distributions are estimated based on a training set of labeled pixels obtained from manual segmentations. The method's performance is evaluated on publicly available DRIVE [34] and STARE [16] databases of manually labeled non-mydriatic images. On the DRIVE database, it achieves an area under the receiver operating characteristic (ROC) curve of 0.9598 and an accuracy of 0.9467 versus 0.9473 for a second human observer.

Proliferative diabetic retinopathy is a complication of diabetes that can eventually lead to blindness. Early identification of this complication reduces the risk of blindness by initiating timely treatment. We report the utility of pattern analysis tools linked with a simple linear discriminant analysis that not only identifies new vessel growth in the retinal fundus but also localises the area of pathology. Ten fluorescein images were analysed using seven feature descriptors including area, perimeter, circularity, curvature, entropy, wavelet second moment and the correlation dimension. Our results indicate that traditional features such as area or perimeter measures of neovascularisation associated with proliferative retinopathy were not sensitive enough to detect early proliferative retinopathy (SNR = 0.76, 0.75 respectively). The wavelet second moment provided the best discrimination with a SNR of 1.17. Combining second moment, curvature and global correlation dimension provided a 100% discrimination (SNR = ∞).

Cognition or higher brain activity is sometimes seen as a phenomenon greater than the sum of its parts. This viewpoint however is largely dependent on the state of the art of experimental techniques that endeavor to characterize morphology and its association to function. Retinal ganglion cells are readily accessible for this work and we discuss recent advances in computational techniques in identifying novel parameters that describe structural attributes possibly associated with specific function. These parameters are based on calculating wavelet gradients from cell images followed by the extraction of meaningful measures including 2nd wavelet moment, entropy of orientation, and curvature. For the three cell types analyzed, the mean 2nd wavelet moment, which relates to the field of influence of the dendritic-tree segments was significantly different. β cells had the highest mean 2nd wavelet moment, followed by the α and δ cells (134 ± 22, 93 ± 19 and 63 ± 12, respectively). There was no significant difference between cells for entropy of orientation, indicating no class with a preferential orientation of their dendritic tree. Curvature provided similar results to the 2nd wavelet moment with β cells having the highest curvature followed by α and the δ cells (mean ± SD: 161 ± 15; 134 ± 22; 121 ± 15). Our feature space analysis also indicated a difference between these cell types. No difference was found between the α and β cell types and their physiological counterparts the Y and X cells based on wavelet analysis. Both the X and Y cells can be divided into two subtypes, the ON-and OFF-center cells based on the stratification level of the dendritic tree within the retina. Using 2nd wavelet moment, a difference in their morphological attributes, not reported previously, was noted for these subtypes. The 2nd wavelet moment and curvature are further discussed with respect to explaining regularity of spacing and coverage associated with retinal ganglion cell mosaics.

Automated image processing has the potential to assist in the early detection of diabetes, by detecting changes in blood vessel diameter and patterns in the retina. This paper describes the development of segmentation methodology in the processing of retinal blood vessel images obtained using non-mydriatic colour photography. The methods used include wavelet analysis, supervised classifier probabilities and adaptive threshold procedures, as well as morphology-based techniques. We show highly accurate identification of blood vessels for the purpose of studying changes in the vessel network that can be utilized for detecting blood vessel diameter changes associated with the pathophysiology of diabetes. In conjunction with suitable feature extraction and automated classification methods, our segmentation method could form the basis of a quick and accurate test for diabetic retinopathy, which would have huge benefits in terms of improved access to screening people for risk or presence of diabetes.