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Objectives-The purpose of this study was to determine the dependence of breast tissue elasticity on the menstrual cycle of healthy volunteers by means of real-time sonoelastography.
Methods-Twenty-two healthy volunteers (aged 18-33 years) were examined once weekly during two consecutive menstrual cycles using sonoelastography. Group 1 (n = 10) was not taking hormonal medication; group 2 (n = 12) was taking oral contraceptives.
Results-The breast parenchyma appeared softer than the dermis and harder than the adipose tissue, and elasticity varied over the menstrual cycle and between groups. Group 1 (no hormone intake) showed continuously increasing elasticity with relatively soft breast parenchyma in the menstrual and follicular phases and harder parenchyma in the luteal phase (P = .012). Group 2 (oral contraceptives) showed no statistically significant changes in breast parenchymal elasticity according to sonoelastography. The parenchyma was generally softer in group 1 compared with group 2 throughout the menstrual cycle (P = .033). The dermis, the subcutaneous adipose tissue, and the pectoralis major muscle showed no changes in elasticity. Comparison of measurements made during the first and the second menstrual cycles showed similar patterns of elasticity in both groups.
Conclusions-Sonoelastography is a reproducible method that can be used to determine the dependence of breast parenchyma elasticity on the menstrual cycle and on the intake of hormonal contraceptives.
Polygenic risk scores (PRS) aggregating results from genome-wide association studies are the state of the art in the prediction of susceptibility to complex traits or diseases, yet their predictive performance is limited for various reasons, not least of which is their failure to incorporate the effects of gene-gene interactions. Novel machine learning algorithms that use large amounts of data promise to find gene-gene interactions in order to build models with better predictive performance than PRS. Here, we present a data preprocessing step by using data-mining of contextual information to reduce the number of features, enabling machine learning algorithms to identify gene-gene interactions. We applied our approach to the Parkinson's Progression Markers Initiative (PPMI) dataset, an observational clinical study of 471 genotyped subjects (368 cases and 152 controls). With an AUC of 0.85 (95% CI = [0.72; 0.96]), the interaction-based prediction model outperforms the PRS (AUC of 0.58 (95% CI = [0.42; 0.81])). Furthermore, feature importance analysis of the model provided insights into the mechanism of Parkinson's disease. For instance, the model revealed an interaction of previously described drug target candidate genes TMEM175 and GAPDHP25. These results demonstrate that interaction-based machine learning models can improve genetic prediction models and might provide an answer to the missing heritability problem.
Objective: Demographic changes are increasing the pressure to improve therapeutic strategies against cognitive decline in Alzheimer disease (AD) and mild cognitive impairment (MCI). Besides drug treatment, physical activity seems to be a promising intervention target as epidemiological and clinical studies suggest beneficial effects of exercise training on cognition. Using comparable inclusion and exclusion criteria, we analyzed the efficacy of drug therapy (cholinesterase inhibitors, memantine, and Ginkgo biloba) and exercise interventions for improving cognition in AD and MCI populations. Methods: We searched The Cochrane Library, EBSCO, OVID, Web of Science, and U.S Food and Drug Administration data from inception through October 30, 2013. Randomized controlled trials in which at least one treatment arm consisted of an exercise or a pharmacological intervention for AD or MCI patients, and which had either a non-exposed control condition or a control condition that received another intervention. Treatment discontinuation rates and Standardized Mean Change score using Raw score standardization (SMCR) of cognitive performance were calculated. Results: Discontinuation rates varied substantially and ranged between 0% and 49% with a median of 18%. Significantly increased discontinuation rates were found for galantamine and rivastigmine as compared to placebo in AD studies. Drug treatments resulted in a small pooled effect on cognition (SMCR: 0.23, 95% CI: 0.20 to 0.25) in AD studies (N = 45, 18,434 patients) and no effect in any of the MCI studies (N = 5, 3,693 patients; SMCR: 0.03, 95% CI: 0.00 to 0.005). Exercise interventions had a moderate to strong pooled effect size (SMCR: 0.83, 95% CI: 0.59 to 1.07) in AD studies (N = 4, 119 patients), and a small effect size (SMCR: 0.20, 95% CI: 0.11 to 0.28) in MCI (N = 6, 443 patients). Conclusions: Drug treatments have a small but significant impact on cognitive functioning in AD and exercise has the potential to improve cognition in AD and MCI. Head-to-head trials with sufficient statistical power are necessary to directly compare efficacy, safety, and acceptability. Combining these two approaches might further increase the efficacy of each individual intervention. Identifier: PROSPERO (2013:CRD42013003910).