Candesartan Normalizes Changes in Retinal Blood Flow and p22phox in the Diabetic Rat Retina

Angiotensin II has been implicated in the progression of diabetic retinopathy, which is characterized by altered microvasculature, oxidative stress, and neuronal dysfunction. The signaling induced by angiotensin II can occur not only via receptor-mediated calcium release that causes vascular constriction, but also through a pathway whereby angiotensin II activates NADPH oxidase to elicit the formation of reactive oxygen species (ROS). In the current study, we administered the angiotensin II receptor antagonist candesartan (or vehicle, in untreated animals) in a rat model of type 1 diabetes in which hyperglycemia was induced by injection of streptozotocin (STZ). Eight weeks after the STZ injection, untreated diabetic rats were found to have a significant increase in tissue levels of angiotensin converting enzyme (ACE; p < 0.05) compared to non-diabetic controls, a 33% decrease in retinal blood flow rate (p < 0.001), and a dramatic increase in p22phox (a subunit of the NADPH oxidase). The decrease in retinal blood flow, and the increases in retinal ACE and p22phox in the diabetic rats, were all significantly attenuated (p < 0.05) by the administration of candesartan in drinking water within one week. Neither STZ nor candesartan induced any changes in tissue levels of superoxide dismutase (SOD-1), 4-hydroxynonenal (4-HNE), or nitrotyrosine. We conclude that one additional benefit of candesartan (and other angiotensin II antagonists) may be to normalize retinal blood flow, which may have clinical benefits in diabetic retinopathy.     

Female adolescents of alcohol misusers: Disordered eating features

Studies show that children of alcohol-abusing parents constitute an at-risk population. This study attempted to understand the impact of parental alcohol misuse on the disordered eating behaviors of their female adolescent children, based on a sample of 532 teenagers from alcohol-misusing parents in Minnesota. These female adolescents had significantly higher prevalence rates of all the seven eating disordered symptoms that were studied. The study also identified a few protective factors: Those who did not develop any of the disordered eating symptoms were more satisfied with their present weight. Further, they worried less about abuse from parents and perceived that their school personnel cared about them. © 1995 by John Wiley & Sons, Inc.     

Reliability of a functional magnetic resonance imaging task of emotional conflict in healthy participants

Task‐based functional neuroimaging methods are increasingly being used to identify biomarkers of treatment response in psychiatric disorders. To facilitate meaningful interpretation of neural correlates of tasks and their potential changes with treatment over time, understanding the reliability of the blood‐oxygen‐level dependent (BOLD) signal of such tasks is essential. We assessed test–retest reliability of an emotional conflict task in healthy participants collected as part of the Canadian Biomarker Integration Network in Depression. Data for 36 participants, scanned at three time points (weeks 0, 2, and 8) were analyzed, and intra‐class correlation coefficients (ICC) were used to quantify reliability. We observed moderate reliability (median ICC values between 0.5 and 0.6), within occipital, parietal, and temporal regions, specifically for conditions of lower cognitive complexity, that is, face, congruent or incongruent trials. For these conditions, activation was also observed within frontal and sub‐cortical regions, however, their reliability was poor (median ICC < 0.2). Clinically relevant prognostic markers based on task‐based fMRI require high predictive accuracy at an individual level. For this to be achieved, reliability of BOLD responses needs to be high. We have shown that reliability of the BOLD response to an emotional conflict task in healthy individuals is moderate. Implications of these findings to further inform studies of treatment effects and biomarker discovery are discussed.     

Automated classification of depression from structural brain measures across two independent community‐based cohorts

Major depressive disorder (MDD) has been the subject of many neuroimaging case–control classification studies. Although some studies report accuracies ≥80%, most have investigated relatively small samples of clinically‐ascertained, currently symptomatic cases, and did not attempt replication in larger samples. We here first aimed to replicate previously reported classification accuracies in a small, well‐phenotyped community‐based group of current MDD cases with clinical interview‐based diagnoses (from STratifying Resilience and Depression Longitudinally cohort, ‘STRADL’). We performed a set of exploratory predictive classification analyses with measures related to brain morphometry and white matter integrity. We applied three classifier types—SVM, penalised logistic regression or decision tree—either with or without optimisation, and with or without feature selection. We then determined whether similar accuracies could be replicated in a larger independent population‐based sample with self‐reported current depression (UK Biobank cohort). Additional analyses extended to lifetime MDD diagnoses—remitted MDD in STRADL, and lifetime‐experienced MDD in UK Biobank. The highest cross‐validation accuracy (75%) was achieved in the initial current MDD sample with a decision tree classifier and cortical surface area features. The most frequently selected decision tree split variables included surface areas of bilateral caudal anterior cingulate, left lingual gyrus, left superior frontal, right precentral and paracentral regions. High accuracy was not achieved in the larger samples with self‐reported current depression (53.73%), with remitted MDD (57.48%), or with lifetime‐experienced MDD (52.68–60.29%). Our results indicate that high predictive classification accuracies may not immediately translate to larger samples with broader criteria for depression, and may not be robust across different classification approaches.