Plasma concentrations of coffee polyphenols and plasma biomarkers of diabetes risk in healthy Japanese women

Coffee consumption has been reported to reduce the risk of type 2 diabetes in experimental and epidemiological studies. This anti-diabetic effect of coffee may be attributed to its high content in polyphenols especially caffeic acid and chlorogenic acid. However, the association between plasma coffee polyphenols and diabetic risks has never been investigated in the literature. In this study, fasting plasma samples were collected from 57 generally healthy females aged 38-73 (mean 52, s.d. 8) years recruited in Himeji, Japan. The concentrations of plasma coffee polyphenols were determined by liquid chromatography coupled with mass tandem spectrometer. Diabetes biomarkers in the plasma/serum samples were analysed by a commercial diagnostic laboratory. Statistical associations were assessed using Spearman's correlation coefficients. The results showed that plasma chlorogenic acid exhibited negative associations with fasting blood glucose, glycated hemoglobin and C-reactive protein, whereas plasma total coffee polyphenol and plasma caffeic acid were weakly associated with these biomarkers. Our preliminary data support previous findings that coffee polyphenols have anti-diabetic effects but further replications with large samples of both genders are recommended

Recent advances in circulating nucleic acids in oncology

International audienceCirculating cell-free DNA (cfDNA) is one of the fastest growing and most exciting areas in oncology in recent years. Its potential clinical uses cover now each phase of cancer patient management care (predictive information, detection of the minimal residual disease, early detection of resistance, treatment monitoring, recurrence surveillance, and cancer early detection/screening). This review relates the recent advances in the application of circulating DNA or RNA in oncology building on unpublished or initial findings/work presented at the 10th international symposium on circulating nucleic acids in plasma and serum held in Montpellier from the 20th to the 22nd of September 2017. This year, presenters revealed their latest data and crucial observations notably in relation to (i) the circulating cell-free (cfDNA) structure and implications regarding their optimal detection; (ii) their role in the metastatic or immunological processes; (iii) evaluation of miRNA panels for cancer patient follow up; (iv) the detection of the minimal residual disease; (v) the evaluation of a screening tests for cancer using cfDNA analysis; and (vi) elements of preanalytical guidelines. This work reviews the recent progresses in the field brought to light in the meeting, as well as in the most important reports from the literature, past and present. It proposes a broader picture of the basic research and its potential, and of the implementation and current challenges in the use of circulating nucleic acids in oncology

Exploiting Context Information for Image Description

Integrating ontological knowledge is a promising research direction to improve automatic image description. In particular, when probabilistic ontologies are available, the corresponding probabilities could be combined with the probabilities produced by a multi-class classifier applied to different parts in an image. This combination not only provides the relations existing between the different segments, but can also improve the classification accuracy. In fact, the context often gives cues suggesting the correct class of the segment. This paper discusses a possible implementation of this integration, and the first experimental results shows its effectiveness when the classifier accuracy is relatively low. For the assessment of the performance we constructed a simulated classifier which allows the a priori decision of its performance with a sufficient precision

Automatic Images Annotation Extension Using a Probabilistic Graphical Model

International audienceWith the fast development of digital cameras and social media image sharing, automatic image annotation has become a researcharea of great interest. It enables indexing, extracting and searching in large collections of images in an easier and faster way. In this paper, wepropose a model for the annotation extension of images using a probabilistic graphical model. This model is based on a mixture of multinomialdistributions and mixtures of Gaussians. The results of the proposed model are promising on three standard datasets: Corel-5k, ESP-Gameand IAPRTC-12