Characterization of carotid plaque composition, more specifically the amount of lipid core, fibrous tissue, and calcified tissue, is an important task for the identification of plaques that are prone to rupture, and thus for early risk estimation of cardiovascular and cerebrovascular events. Due to its low costs and wide availability, carotid ultrasound has the potential to become the modality of choice for plaque characterization in clinical practice. However, its significant image noise, coupled with the small size of the plaques and their complex appearance, makes it difficult for automated techniques to discriminate between the different plaque constituents. In this paper, we propose to address this challenging problem by exploiting the unique capabilities of the emerging deep learning framework. More specifically, and unlike existing works which require a priori definition of specific imaging features or thresholding values, we propose to build a convolutional neural network that will automatically extract from the images the information that is optimal for the identification of the different plaque constituents. We used approximately 90,000 patches extracted from a database of images and corresponding expert plaque characterizations to train and to validate the proposed convolutional neural network (CNN). The results of cross-validation experiments show a correlation of about 0.90 with the clinical assessment for the estimation of lipid core, fibrous cap, and calcified tissue areas, indicating the potential of deep learning for the challenging task of automatic characterization of plaque composition in carotid ultrasound

Colorectal cancer (CRC) is one of the most frequent neoplasms and an important cause of mortality in the developed world. This cancer is caused by both genetic and environmental factors although 35% of the variation in CRC susceptibility involves inherited genetic differences. Mendelian syndromes account for about 5% of the total burden of CRC, with Lynch syndrome and familial adenomatous polyposis the most common forms. Excluding hereditary forms, there is an important fraction of CRC cases that present familial aggregation for the disease with an unknown germline genetic cause. CRC can be also considered as a complex disease taking into account the common disease-commom variant hypothesis with a polygenic model of inheritance where the genetic components of common complex diseases correspond mostly to variants of low/moderate effect. So far, 30 common, low-penetrance susceptibility variants have been identified for CRC. Recently, new sequencing technologies including exome- and whole-genome sequencing have permitted to add a new approach to facilitate the identification of new genes responsible for human disease predisposition. By using whole-genome sequencing, germline mutations in the POLE and POLD1 genes have been found to be responsible for a new form of CRC genetic predisposition called polymerase proofreading-associated polyposis.

Background and Objective: The measurement of Carotid Intima Media Thickness (CIMT) in ultrasound images can be used to detect the presence of atherosclerotic plaques. Usually, the CIMT estimation strategy is semi-automatic, since it requires: 1) a manual examination of the ultrasound image for the localization of a Region Of Interest (ROI), a fast and useful operation when only a small number of images need to be measured; and 2) an automatic delineation of the CIM region within the ROI. The existing efforts for automating the process have replicated the same two-step structure, resulting in two consecutive independent approaches. In this work, we propose a fully automatic single-step approach based on semantic segmentation that allows us to segment the plaque and to estimate the CIMT in a fast and useful manner for large data sets of images.

Preimplantation genetic diagnosis represents a valid reproductive option for couples affected of propionic acidemia, in order to avoid transmission to offspring.

Background The optimal distribution between physical activity (PA) levels and sedentary behaviour (SB) for the greatest benefits for body composition among older adults with overweight/obesity and chronic health conditions remains unclear. We aimed to determine the prospective association between changes in PA and in SB with concurrent changes in body composition and to examine whether reallocating inactive time into different physical activity levels was associated with 12-month change to body composition in older adults. Methods Longitudinal assessment nested in the PREDIMED-Plus trial. A subsample (n = 1564) of men and women (age 55–75 years) with overweight/obesity and metabolic syndrome from both arms of the PREDIMED-Plus trial was included in the present analysis. Participants were followed up at 6 and 12 months. Physical activity and SB were assessed using validated questionnaires. Out of 1564 participants, 388 wore an accelerometer to objectively measure inactive time and PA over a 7-day period. At each time point, participants’ body composition was measured using dual-energy X-ray absorptiometry (DXA). Standard covariate-adjusted and isotemporal substitution modelling were applied to linear mixed-effects models. Results Increasing 30 min of total PA and moderate-to-vigorous physical activity (MVPA) was associated with significant reductions in body fat (β − 0.07% and − 0.08%) and visceral adipose tissue (VAT) (− 13.9 g, and − 15.6 g) at 12 months (all p values < 0.001). Reallocating 30 min of inactive time to MVPA was associated with reductions in body fat and VAT and with an increase in muscle mass and muscle-to-fat mass ratio (all p values < 0.001). Conclusions At 12 months, increasing total PA and MVPA and reducing total SB and TV-viewing SB were associated with improved body composition in participants with overweight or obesity, and metabolic syndrome. This was also observed when substituting 30 min of inactive time with total PA, LPA and MVPA, with the greatest benefits observed with MVPA. Trial registration International Standard Randomized Controlled Trial (ISRCTN), 89898870. Retrospectively registered on 24 July 2014

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To explore the role of chronic inflammation inherent to autoimmune diseases in the development of subclinical atherosclerosis and arterial stiffness, this study recruited two population-based samples of individuals with and without autoimmune disease (ratio 1:5) matched by age, sex, and education level and with a longstanding (≥6 years) diagnosis of autoimmune disease. Common carotid intima media thickness (IMT) and arterial distensibility and compliance were assessed with carotid ultrasound.Multivariable linear and logistic regression models were adjusted for 10-year cardiovascular risk. In total, 546 individuals with and without autoimmune diseases (91 and 455, respectively) were included. Mean age was 66 years (standard deviation 12), and 240 (43.9%) were women. Arterial stiffness did not differ according to presence of autoimmune diseases. In men, the diagnosis of autoimmune diseases significantly increased common carotid IMT [beta-coefficient (95% confidence interval): 0.058 (0.009; 0.108); p-value=0.022] and the percentage having IMT ≥ percentile 75 [1.012 (0.145; 1.880); p-value=0.022]. Women without autoimmune disease were more likely to have IMT ≥ percentile 75 [-2.181 (-4.214; -0.149); p-value=0.035] but analysis of IMT as a continuous variable did not yield significant results. In conclusion, subclinical carotid atherosclerosis, but not arterial stiffness, was higher in men with autoimmune diseases. Women did not show significant differences in any of these carotid features.Sex was an effect modifier in the association between common carotid IMT values and the diagnosis of autoimmune diseases.