It is well established that microglial form and function are inextricably linked. In recent years, the traditional view that microglial form ranges between “ramified resting” and “activated amoeboid” has been emphasized through advancing imaging techniques that point to microglial form being highly dynamic even within the currently accepted morphological categories. Moreover, microglia adopt meaningful intermediate forms between categories, with considerable crossover in function and varying morphologies as they cycle, migrate, wave, phagocytose, and extend and retract fine and gross processes. From a quantitative perspective, it is problematic to measure such variability using traditional methods, but one way of quantitating such detail is through fractal analysis. The techniques of fractal analysis have been used for quantitating microglial morphology, to categorize gross differences but also to differentiate subtle differences (e.g., amongst ramified cells). Multifractal analysis in particular is one technique of fractal analysis that may be useful for identifying intermediate forms. Here we review current trends and methods of fractal analysis, focusing on box counting analysis, including lacunarity and multifractal analysis, as applied to microglial morphology.

ContextThere is evidence that heart rate variability (HRV) is reduced in major depressive disorder (MDD), although there is debate about whether this effect is caused by medication or the disorder per se. MDD is associated with a two to fourfold increase in the risk of cardiac mortality, and HRV is a robust predictor of cardiac mortality; determining a direct link between HRV and not only MDD, but common comorbid anxiety disorders, will point to psychiatric indicators for cardiovascular risk reduction.ObjectiveTo determine in physically healthy, unmedicated patients whether (1) HRV is reduced in MDD relative to controls, and (2) HRV reductions are driven by MDD alone, comorbid generalized anxiety disorder (GAD, characterized by anxious anticipation), or comorbid panic and posttraumatic stress disorders (PD/PTSD, characterized by anxious arousal).Design, Setting, and PatientsA case-control study in 2006 and 2007 on 73 MDD patients, including 24 without anxiety comorbidity, 24 with GAD, and 14 with PD/PTSD. Seventy-three MDD and 94 healthy age- and sex-matched control participants were recruited from the general community. Participants had no history of drug addiction, alcoholism, brain injury, loss of consciousness, stroke, neurological disorder, or serious medical conditions. There were no significant differences between the four groups in age, gender, BMI, or alcohol use.Main Outcome MeasuresHRV was calculated from electrocardiography under a standardized short-term resting state condition.ResultsHRV was reduced in MDD relative to controls, an effect associated with a medium effect size. MDD participants with comorbid generalized anxiety disorder displayed the greatest reductions in HRV relative to controls, an effect associated with a large effect size.ConclusionsUnmedicated, physically healthy MDD patients with and without comorbid anxiety had reduced HRV. Those with comorbid GAD showed the greatest reductions. Implications for cardiovascular risk reduction strategies in otherwise healthy patients with psychiatric illness are discussed.

The natural complexity of the brain, its hierarchical structure, and the sophisticated topological architecture of the neurons organized in micronetworks and macronetworks are all factors contributing to the limits of the application of Euclidean geometry and linear dynamics to the neurosciences. The introduction of fractal geometry for the quantitative analysis and description of the geometric complexity of natural systems has been a major paradigm shift in the last decades. Nowadays, modern neurosciences admit the prevalence of fractal properties such as self-similarity in the brain at various levels of observation, from the microscale to the macroscale, in molecular, anatomic, functional, and pathological perspectives. Fractal geometry is a mathematical model that offers a universal language for the quantitative description of neurons and glial cells as well as the brain as a whole, with its complex three-dimensional structure, in all its physiopathological spectrums. For a holistic view of fractal geometry of the brain, we review here the basic concepts of fractal analysis and its main applications to the basic neurosciences.

Objective: This study illustrates the relationship between oxidative DNA damage and obesity in patients with prediabetes and type 2 diabetes compared with controls. Design and methods: Participants attended the School of Community Health, Diabetes Screening Clinic, Charles Sturt University, Australia, between February 2006 and June 2008. A total of 162 participants (35 type 2 diabetic patients; eight prediabetic subjects; and 119 age-, gender-, and weight-matched controls) were investigated. All patients were selected on clinical grounds. Results: Serum 8-hydroxy 2 0 -deoxy-guanosine (8-OHdG) level was significantly greater in the prediabetic subjects (671.3G140 pg/ml) compared with controls (210.1G166 pg/ml; P!0.01). The diabetic group (1979.6G1209 pg/ml) had the highest level of 8-OHdG. There was a significant increase in serum 8-OHdG in obese subjects (848.5G103 pg/ml; P!0.001) and overweight subjects (724G102 pg/ml; PZ0.005) compared with the lean subjects (196.5G327 pg/ml). Conclusion: Our results indicate that serum 8-OHdG is increased already in prediabetes suggesting oxidative DNA damage to be present with minor elevation of blood glucose levels (BGLs). The statistically significant positive correlation between serum 8-OHdG and body mass index in the diabetic group indicates that obesity has an additive effect to increased BGL contributing to oxidative DNA damage. European Journal of Endocrinology 164 899-904

This paper concerns the validation of automatic retinal image analysis (ARIA) algorithms. For reasons of space and consistency, we concentrate on the validation of algorithms processing color fundus camera images, currently the largest section of the ARIA literature. We sketch the context (imaging instruments and target tasks) of ARIA validation, summarizing the main image analysis and validation techniques. We then present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks, and capable of running submitted software automatically on the data stored, with clear and widely agreed-on performance criteria, to provide a fair comparison.

ObjectiveTo assess clinical profiles of patients with type 2 diabetes in the United Arab Emirates (UAE), including patterns, frequencies, and risk factors of microvascular and macrovascular complications.Research design and methodsFour hundred and ninety patients with type 2 diabetes were enrolled from two major hospitals in Abu Dhabi. The presence of microvascular and macrovascular complications was assessed using logistic regression, and demographic, clinical and laboratory data were collected. Significance was set at p<0.05.ResultsHypertension (83.40%), obesity (90.49%) and dyslipidemia (93.43%) were common type 2 diabetes comorbidities. Most of the patients had relatively poor glycemic control and presented with multiple complications (83.47% of patients had one or more complication), with frequent renal involvement. The most frequent complication was retinopathy (13.26%). However, the pattern of complications varied based on age, where in patients <65 years, a single pattern presented, usually retinopathy, while multiple complications was typically seen in patients >65 years old. Low estimated glomerular filtration rate in combination with disease duration was the most significant risk factor in the development of a diabetic-associated complication especially for coronary artery disease, whereas age, lipid values and waist circumference were significantly associated with the development of diabetic retinopathy.ConclusionsPatients with type 2 diabetes mellitus in the UAE frequently present with comorbidities and complications. Renal disease was found to be the most common comorbidity, while retinopathy was noted as the most common diabetic complication. This emphasizes the need for screening and prevention program toward early, asymptomatic identification of comorbidities and commence treatment, especially for longer disease duration.

Abstract-In this paper, we present an algorithm to detect the presence of diabetic retinopathy (DR) related lesions from fundus images based on a common analytical approach that is capable of identifying both red and bright lesions without requiring specific pre-or post-processing. Our solution constructs a visual word dictionary representing points of interest (PoIs) located within regions marked by specialists that contain lesions associated with DR and classifies the fundus images based on the presence or absence of these PoIs as normal or DRrelated pathology. The novelty of our approach is in locating DR lesions in the optic fundus images using visual words that combines feature information contained within the images in a framework easily extendible to different types of retinal lesions or pathologies and builds a specific projection space for each class of interest (e.g. white lesions such as exudates or normal regions) instead of a common dictionary for all classes. The visual words dictionary was applied to classifying bright and red lesions with classical cross-validation and cross dataset validation to indicate the robustness of this approach. We obtained an AUC of 95.3% for white lesion detection and an AUC of 93.3% for red lesion detection using 5-fold cross-validation and our own data consisting of 687 images of normal retinae, 245 images with bright lesions, 191 with red lesions and 109 with signs of both bright and red lesions. For cross dataset analysis, the visual dictionary also achieves compelling results using our images as the training set and the RetiDB and Messidor images as test sets. In this case, the image classification resulted in an AUC of 88.1% when classifying the RetiDB dataset and in an AUC of 89.3% when classifying the Messidor dataset, both cases for bright lesion detection. The results indicate the potential for training with different acquisition images under different setup conditions with a high accuracy of referral based on the presence of either red or bright lesions or both. The robustness of the visual dictionary against image quality (blurring), resolution, and retinal background, makes it a strong candidate for diabetic retinopathy screening of large, diverse communities with varying cameras and settings and levels of expertise for image capture.

The small elevation of blood glucose levels in the prediabetic state may have a detectable influence on endothelial function as indicated by changes to 8-OHdG, indicating an increased DNA-damage and homocysteine release from endothelial cells. Increased oxidative stress as indicated by the reduced GSH/GSSG ratio is likely to be the link between the moderate hyperglycaemia in prediabetes and pathological changes in endothelial function, which in the long-term may promote atherogenesis and result in the development of cardiovascular disease. Early detection of prediabetes is essential to avoid diabetes development and the associated complications like cardiovascular disease. The GSH/GSSG ratio and biomarkers like urinary 8-OHdG and plasma homocysteine offer a possible tool for the assessment of prediabetes in prevention screenings.