Toll-like receptor 4 mediates lipopolysaccharide-induced muscle catabolism via coordinate activation of ubiquitin-proteasome and autophagy-lysosome pathways.

Cachectic muscle wasting is a frequent complication of many inflammatory conditions, due primarily to excessive muscle catabolism. However, the pathogenesis and intervention strategies against it remain to be established. Here, we tested the hypothesis that Toll-like receptor 4 (TLR4) is a master regulator of inflammatory muscle catabolism. We demonstrate that TLR4 activation by lipopolysaccharide (LPS) induces C2C12 myotube atrophy via up-regulating autophagosome formation and the expression of ubiquitin ligase atrogin-1/MAFbx and MuRF1. TLR4-mediated activation of p38 MAPK is necessary and sufficient for the up-regulation of atrogin1/MAFbx and autophagosomes, resulting in myotube atrophy. Similarly, LPS up-regulates muscle autophagosome formation and ubiquitin ligase expression in mice. Importantly, autophagy inhibitor 3-methyladenine completely abolishes LPS-induced muscle proteolysis, while proteasome inhibitor lactacystin partially blocks it. Furthermore, TLR4 knockout or p38 MAPK inhibition abolishes LPS-induced muscle proteolysis. Thus, TLR4 mediates LPS-induced muscle catabolism via coordinate activation of the ubiquitin-proteasome and the autophagy-lysosomal pathways.

Mathematical morphologic segmentation dedicated to quantitative immunohistochemistry.

OBJECTIVE: To develop automatic segmentation sequences for fully automated quantitative immunohistochemistry of cancer cell nuclei by image analysis. STUDY DESIGN: The study focused on the automated delineation of cancer cell lobules and nuclei, taking breast carcinoma as an example. A hierarchic segmentation was developed, employing mainly the chaining of mathematical morphology operators. The proposed sequence was tested on 22 images of various situations, collected from 18 different cases of breast carcinoma. A quality control procedure was applied, comparing the automated method with manual outlining of cancer cell foci and with manual pricking of cancer cell nuclei. RESULTS: Good concordance was found between automated and manual segmentation procedures (90% for cancer cell clumps, 97% for cancer cell nuclei on average), but the rate of false positive nuclei (small regions labeled as nuclei by the segmentation procedure) could be relatively high (11% on average, with a maximum of 35%) and can result in underestimation of the immunostaining ratio. CONCLUSION: This study examined a preliminary approach to automated immunoquantification, limited to automated segmentation without any color characterization. The automated hierarchic segmentation presented here leads to good discrimination of cancer cell nuclei at the chosen magnification.

Segmentation of color images from serous cytology for automated cell classification.

OBJECTIVE: To design an automated system for the classification of cells based on analysis of serous cytology, with the aim of segmenting both cytoplasm and nucleus using color information from the images as the main characteristic of the cells. STUDY DESIGN: The segmentation strategy uses color information coupled with mathematical morphology tools, such as watersheds. Cytoplasm and nuclei of all diagnostic cells are retained; erythrocytes and debris are eliminated. Special techniques are used for the separation of clustered cells. RESULTS: A large set of cells was assessed by experts to score the segmentation success rate. All cells were segmented whatever their spatial configurations. The average success rate was 92.5% for nuclei and 91.1% for cytoplasm. CONCLUSION: This color information-based segmentation of images of serous cells is accurate and provides a useful tool. This segmentation strategy will improve the automated classification of cells.

Automatic morphological sieving: comparison between different methods, application to DNA ploidy measurements.

The aim of the present study is to propose alternative automatic methods to time consuming interactive sorting of elements for DNA ploidy measurements. One archival brain tumour and two archival breast carcinoma were studied, corresponding to 7120 elements (3764 nuclei, 3356 debris and aggregates). Three automatic classification methods were tested to eliminate debris and aggregates from DNA ploidy measurements (mathematical morphology (MM), multiparametric analysis (MA) and neural network (NN)). Performances were evaluated by reference to interactive sorting. The results obtained for the three methods concerning the percentage of debris and aggregates automatically removed reach 63, 75 and 85% for MM, MA and NN methods, respectively, with false positive rates of 6, 21 and 25%. Information about DNA ploidy abnormalities were globally preserved after automatic elimination of debris and aggregates by MM and MA methods as opposed to NN method, showing that automatic classification methods can offer alternatives to tedious interactive elimination of debris and aggregates, for DNA ploidy measurements of archival tumours.

Generalized region growing operator with optimal scanning: application to segmentation of breast cancer images.

Segmentation of medical images is a complex problem owing to the large variety of their characteristics. In the automated analysis of breast cancers, two image classes may be distinguished according to whether one considers the quantification of DNA (grey level images of isolated nuclei) or the detection of immunohistochemical staining (colour images of histological sections). The study of these image classes generally involves the use of largely different image processing techniques. We therefore propose a new algorithm derived from the watershed transformation enabling us to solve these two segmentation problems with the same general approach. We then present visual and quantitative results to validate our method.

[Neighborhood graphs and image processing. Contribution to images of immunohistochemical staining]. / Graphes de voisinage et traitement d'images. Application à des images de marquage immunohistochimique.

Devising an image analyzer dedicated to the automatic quantification of immunohistochemical staining for clinical oncology implies developing a method for the delimitation of tumoral cell nests, setting aside tumoral stroma, while accounting for the topology of the staining. The representation of images by neighborhood graphs can bring an answer to both requirements. In this paper, a methodological approach is presented. It consists in a preliminary study dealing with nuclear immunostaining images of breast cancer. Segmentation of the graph structure allows to separate clusters of cancer cells and the analysis of this structure can account for the focal or diffuse aspect of the staining within the tumor.