Distribution and dynamics of Tc-99m-pertechnetate uptake in the thyroid and other organs assessed by single-photon emission computed tomography in living mice

Background: Tc-99m pertechnetate is a well-known anion, used for clinical imaging of thyroid function. This gamma emitter is transported by the sodium iodide symporter but is not incorporated into thyroglobulin. Scintigraphy using Tc-99m pertechnetate or 123 iodide represents a powerful tool for the study of sodium iodide symporter activity in different organs of living animal models. However, in many studies that have been performed in mice, the thyroid could not be distinguished from the salivary glands. In this work, we have evaluated the use of a clinically dedicated single-photon emission computed tomography (SPECT) camera for thyroid imaging and assessed what improvements are necessary for the development of this technique. Methods: SPECT of the mouse neck region, with pinhole collimation and geometric calibration, was used for the individual measurement of Tc-99m pertechnetate uptake in the thyroid and the salivary glands. Uptake in the stomach was studied by planar whole-body imaging. Uptake kinetics and biodistribution studies were performed by sequential imaging. Results: This work has shown that thyroid imaging in living mice can be performed with a SPECT camera originally built for clinical use. Our experiments indicate that Tc-99m pertechnetate uptake is faster in the thyroid than in the salivary glands and the stomach. The decrease in Tc-99m pertechnetate uptake after injection of iodide or perchlorate as competitive inhibitors was also studied. The resulting rate decreases were faster in the thyroid than in the salivary glands or the stomach. Conclusions: We have shown that a clinically dedicated SPECT camera can be used for thyroid imaging. In our experiments, SPECT imaging allowed the analysis of Tc-99m pertechnetate accumulation in individual organs and revealed differences in uptake kinetics

Biological cells classification using bio-inspired descriptor in a boosting k-NN framework

International audienceHigh-content imaging is an emerging technology for the analysis and quantification of biological phenomena. Thus, classifying a huge number of cells or quantifying markers from large sets of images by experts is a very time-consuming and poorly reproducible task. In order to overcome such limitations, we propose a supervised method for automatic cell classification. Our approach consists of two steps: the first one is an indexing stage based on specific bio-inspired features relying on the distribution of contrast information on segmented cells. The second one is a supervised learning stage that selects the prototypical samples best representing the cell categories. These prototypes are used in a leveraged k-NN framework to predict the class of unlabeled cells. In this paper we have tested our new learning algorithm on cellular images acquired for the analysis of pathologies. In order to evaluate the automatic classification performances, we tested our algorithm on the HEp2 Cells dataset of (Foggia et al, CBMS 2010). Results are very promising, showing classification precision larger than 96% on average, thus suggesting our method as a valuable decision-support tool in such cellular imaging applications

Sanitary status of 47 pig manures in Brittany: comparison of the effectiveness of manure treatments on the levels of indicator bacteria and two pathogenic bacteria

The hygienic performance of three manure treatment systems (simple storage, biological treatment or thermal treatment) was evaluated for effluents collected from 47 piggeries across Brittany, France. Microbial analyses were carried out on raw manure, on the sludge stored in a tank after biological treatment and on the liquid phase stored in a lagoon after sludge settling or after thermal treatment. The effect of the treatments on E. coli, enterococci, Salmonella and Listeria monocytogenes was evaluated. The concentrations of indicator bacteria were highly variable regardless of the farm or the manure management

Definition of motionless phases for monitoring gated reconstruction of SPECT images in alive mice

To be filled INInternational audienceThe present method aims at defining motionless phases for monitoring gated reconstruction of SPECT images in the movable area containing lungs and liver among others. It is based on the filtering of gating signals that are generated from an abdominal pressure variation signal. This method is considering gating signals only for cycles for which the period is included in a defined range around periods mean. This correction is essential to improve the quality of SPECT reconstruction

Classification of biological cells using bio-inspired descriptors

International audienceThis paper proposes a novel automated approach for the categorization of cells in fluorescence microscopy images. Our supervised classification method aims at recognizing patterns of unlabeled cells based on an annotated dataset. First, the cell images need to be indexed by encoding them in a feature space. For this purpose, we propose tailored bio-inspired features relying on the distribution of contrast information. Then, a supervised learning algorithm is proposed for classifying the cells. We carried out experiments on cellular images related to the diagnosis of autoimmune diseases, testing our classification method on the HEp-2 Cells dataset of Foggia et al (CBMS 2010). Results show classification precision larger than 96% on average, thus confirming promising application of our approach to the challenging application of cellular image classification for computer-aided diagnosis

From extraction of physiological features with dynamic µ-SPECT imaging to modelling of iodide biodistribution in stomach

This study investigates the potential retention of iodide in the stomach, for a better understanding of the iodide biodistribution in the body and more precisely of its potential antiseptic role. To that end, we will study the uptake of the 99m Tc-pertechnetate (an iodide ana-log) within the murine stomach observed thanks to a SPECT camera. The temporal evolution of the uptake is analysed thanks to a dedicated multi-compartment model. The addressed challenges consist in 1) estimating the time-activity curves for the different compartments, and 2) identifying the model parameters. Real experiments on different subjects demonstrate a quite good coherence of the computed parameters, and the computed parameter values suggested that there is some iodide retention in the stomach wall

A Bio-inspired Learning and Classification Method for Subcellular Localization of a Plasma Membrane Protein

International audienceHigh-content cellular imaging is an emerging technology for studying many biological phenomena. statistical analyses on large populations (more than thousands) of cells are required. Hence classifying cells by experts is a very time-consuming task and poorly reproducible. In order to overcome such limitations, we propose an automatic supervised classification method. Our new cell classification method consists of two steps: The first one is an indexing process based on specific bio-inspired features using contrast information distributions on cell sub-regions. The second is a supervised learning process to select prototypical samples (that best represent the cells categories) which are used in a leveraged k-NN framework to predict the class of unlabeled cells. In this paper we have tested our new learning algorithm on cellular images acquired for the analysis of changes in the subcellular localization of a membrane protein (the sodium iodide symporter). In order to evaluate the automatic classification performances, we tested our algorithm on a significantly large database of cellular images annotated by experts of our group. Results in term of Mean Avarage Precision (MAP) are very promising, providing precision upper than 87% on average, thus suggesting our method as a valuable decision-support tool in such cellular imaging applications. Such supervised classification method has many other applications in cell imaging in the areas of research in basic biology and medicine but also in clinical histology

Analyse statistique de données radiomiques et métabolomiques : prédiction des lésions mammaires triple-négatives

International audienceLa caractérisation de l’hétérogénéité tumorale à partir des images médicales (appeléeaussi radiomique) et de l’extraction de données omiques est un enjeu majeur en cancérologie,notamment dans la mise en place de la médecine de précision. Or actuellement, le lien entre lesvariables radiomiques (VR) et les caractéristiques biologiques des lésions est encore mal connu.L’objectif de ce travail est d’étudier la corrélation entre les VR et les variables métabolomiques (VM)dans le cancer du sein, et d’analyser leur capacité à prédire le sous-type immunohistochimique deslésions

Adipose Tissue Is a Neglected Viral Reservoir and an Inflammatory Site during Chronic HIV and SIV Infection

International audienceTwo of the crucial aspects of human immunodeficiency virus (HIV) infection are (i) viral persistence in reservoirs (precluding viral eradication) and (ii) chronic inflammation (directly associated with all-cause morbidities in antiretroviral therapy (ART)-controlled HIV-infected patients). The objective of the present study was to assess the potential involvement of adipose tissue in these two aspects. Adipose tissue is composed of adipocytes and the stromal vascular fraction (SVF); the latter comprises immune cells such as CD4+ T cells and macrophages (both of which are important target cells for HIV). The inflammatory potential of adipose tissue has been extensively described in the context of obesity. During HIV infection, the inflammatory profile of adipose tissue has been revealed by the occurrence of lipodystrophies (primarily related to ART). Data on the impact of HIV on the SVF (especially in individuals not receiving ART) are scarce. We first analyzed the impact of simian immunodeficiency virus (SIV) infection on abdominal subcutaneous and visceral adipose tissues in SIVmac251 infected macaques and found that both adipocytes and adipose tissue immune cells were affected. The adipocyte density was elevated, and adipose tissue immune cells presented enhanced immune activation and/or inflammatory profiles. We detected cell-associated SIV DNA and RNA in the SVF and in sorted CD4+ T cells and macrophages from adipose tissue. We demonstrated that SVF cells (including CD4+ T cells) are infected in ART-controlled HIV-infected patients. Importantly, the production of HIV RNA was detected by in situ hybridization, and after the in vitro reactivation of sorted CD4+ T cells from adipose tissue. We thus identified adipose tissue as a crucial cofactor in both viral persistence and chronic immune activation/inflammation during HIV infection. These observations open up new therapeutic strategies for limiting the size of the viral reservoir and decreasing low-grade chronic inflammation via the modulation of adipose tissue-related pathway