Identification and staining of distinct populations of secretory organelles in astrocytes.

Increasing evidence indicates that astrocytes, the most abundant glial cell type in the brain, respond to an elevation in cytoplasmic calcium concentration ([Ca(2+)]i) by releasing chemical transmitters (also called gliotransmitters) via regulated exocytosis of heterogeneous classes of organelles. By this process, astrocytes exert modulatory influences on neighboring cells and are thought to participate in the control of synaptic circuits and cerebral blood flow. Studying the properties of exocytosis in astrocytes is a challenge, because the cell biological basis of this process is incompletely defined. Astrocytic exocytosis involves multiple populations of secretory vesicles, including synaptic-like microvesicles (SLMVs), dense-core granules (DCGs), and lysosomes. Here we summarize the available information for identifying individual populations of secretory organelles in astrocytes, including DCGs, SLMVs, and lysosomes, and present experimental procedures for specifically staining such populations.

Validation of tissue modelization and classification techniques in T1-weighted MR brain images

We propose a deep study on tissue modelization andclassification Techniques on T1-weighted MR images. Threeapproaches have been taken into account to perform thisvalidation study. Two of them are based on FiniteGaussian Mixture (FGM) model. The first one consists onlyin pure gaussian distributions (FGM-EM). The second oneuses a different model for partial volume (PV) (FGM-GA).The third one is based on a Hidden Markov Random Field(HMRF) model. All methods have been tested on a DigitalBrain Phantom image considered as the ground truth. Noiseand intensity non-uniformities have been added tosimulate real image conditions. Also the effect of ananisotropic filter is considered. Results demonstratethat methods relying in both intensity and spatialinformation are in general more robust to noise andinhomogeneities. However, in some cases there is nosignificant differences between all presented methods.

Genome-wide association study of virologic response with efavirenz-containing or abacavir-containing regimens in AIDS clinical trials group protocols.

BACKGROUND: Efavirenz and abacavir are components of recommended first-line regimens for HIV-1 infection. We used genome-wide genotyping and clinical data to explore genetic associations with virologic failure among patients randomized to efavirenz-containing or abacavir-containing regimens in AIDS Clinical Trials Group (ACTG) protocols. PARTICIPANTS AND METHODS: Virologic response and genome-wide genotype data were available from treatment-naive patients randomized to efavirenz-containing (n=1596) or abacavir-containing (n=786) regimens in ACTG protocols 384, A5142, A5095, and A5202. RESULTS: Meta-analysis of association results across race/ethnic groups showed no genome-wide significant associations (P<5×10) with virologic response for either efavirenz or abacavir. Our sample size provided 80% power to detect a genotype relative risk of 1.8 for efavirenz and 2.4 for abacavir. Analyses focused on CYP2B genotypes that define the lowest plasma efavirenz exposure stratum did not show associations nor did analysis limited to gene sets predicted to be relevant to efavirenz and abacavir disposition. CONCLUSION: No single polymorphism is associated strongly with virologic failure with efavirenz-containing or abacavir-containing regimens. Analyses to better consider context, and that minimize confounding by nongenetic factors, may show associations not apparent here.

Docetaxel and gemcitabine combination in 133 advanced soft-tissue sarcomas: a retrospective analysis.

Advanced soft-tissue sarcomas are usually resistant to cytotoxic agents such as doxorubicin and ifosfamide. Antitumor activity has been observed for gemcitabine and docetaxel combination. We conducted a retrospective study on 133 patients (58 males/75 females) with unresectable or metastatic soft-tissue sarcoma. The median age at diagnosis was 51.7 (18-82), with 76 patients with leiomoyosarcoma and 57 patients with other histological subtypes. The initial localizations were limb (44), uterine (32), retroperitoneal (23) and organs or bone (34). Patients received 900 mg/m2 of gemcitabine (days 1 and 8) over 90 min plus 100 mg/m2 of docetaxel (day 8), intravenously every 21 days. Gemcitabine/docetaxel combination was well tolerated with an overall response of 18.4% and with no clear statistical difference between leiomyosarcomas and other histological subtypes (24.2% versus 10.4% (p=0.06)). No difference was found between uterine soft-tissue sarcomas versus others. The median overall survival was 12.1 months (1-28). Better overall survival was correlated with leiomyosarcoma (p=0.01) and with the quality of the response, even for patients with stable disease (p<10(-4)). No statistical difference was found for the initial localization. Response to treatment and overall survival were better for patients in World Health Organization (WHO) performance status classification (PS) 0 at baseline versus patients in WHO PS-1, 2 or 3 (p=0.023 and p<10(-4), respectively). Gemcitabine/docetaxel combination was tolerable and demonstrated better response and survival for leiomyosarcoma, especially for patients in WHO PS-0 at baseline. For the other histological subtypes, the response was not encouraging, but the survival for patients in response or stable suggests further investigation.

Classification of very high spatial resolution imagery using mathematical morphology and support vector machines

We investigate the relevance of morphological operators for the classification of land use in urban scenes using submetric panchromatic imagery. A support vector machine is used for the classification. Six types of filters have been employed: opening and closing, opening and closing by reconstruction, and opening and closing top hat. The type and scale of the filters are discussed, and a feature selection algorithm called recursive feature elimination is applied to decrease the dimensionality of the input data. The analysis performed on two QuickBird panchromatic images showed that simple opening and closing operators are the most relevant for classification at such a high spatial resolution. Moreover, mixed sets combining simple and reconstruction filters provided the best performance. Tests performed on both images, having areas characterized by different architectural styles, yielded similar results for both feature selection and classification accuracy, suggesting the generalization of the feature sets highlighted.

Prognosis in Duke's B colorectal carcinoma: the Jass classification revisited.

OBJECTIVE: To assess whether Jass staging enhances prognostic prediction in Dukes' B colorectal carcinoma. DESIGN: A historical cohort observational study. SETTING: A university tertiary care centre, Switzerland. SUBJECTS: 108 consecutive patients. INTERVENTIONS: Curative resection of Dukes' B colorectal carcinoma between January 1985 and December 1988, Patients with familial adenomatous polyposis; hereditary non-polyposis colorectal cancer; Crohns' disease; ulcerative colitis and synchronous and recurrent tumours were excluded. A comparable group of 155 consecutive patients with Dukes' C carcinoma were included for reference purposes. MAIN OUTCOME MEASURES: Disease free and overall survival for Dukes' B and overall survival for Dukes' C tumours. RESULTS: Dukes' B tumours in Jass group III or with an infiltrated margin had a significantly worse disease-free survival (p = 0.001 and 0.0001, respectively) and those with infiltrated margins had a significantly worse overall survival (p = 0.002). Overall survival among those with Dukes' B Jass III and Dukes' B with infiltrated margins was no better than overall survival among all patients with Dukes' C tumours. CONCLUSION: Jass staging and the nature of the margin of invasion allow patients undergoing curative surgery for Dukes' B colorectal carcinoma to be separated into prognostic groups. A group of patients with Dukes' B tumours whose prognosis is inseparable from those with Dukes' C tumours can be identified, the nature of the margin of invasion being used to classify a larger number of patients.

Multi-source composite kernels for urban image classification

This letter presents advanced classification methods for very high resolution images. Efficient multisource information, both spectral and spatial, is exploited through the use of composite kernels in support vector machines. Weighted summations of kernels accounting for separate sources of spectral and spatial information are analyzed and compared to classical approaches such as pure spectral classification or stacked approaches using all the features in a single vector. Model selection problems are addressed, as well as the importance of the different kernels in the weighted summation.

Comparison and validation of tissue modelization and statistical classification methods in T1-weighted MR brain images.

This paper presents a validation study on statistical nonsupervised brain tissue classification techniques in magnetic resonance (MR) images. Several image models assuming different hypotheses regarding the intensity distribution model, the spatial model and the number of classes are assessed. The methods are tested on simulated data for which the classification ground truth is known. Different noise and intensity nonuniformities are added to simulate real imaging conditions. No enhancement of the image quality is considered either before or during the classification process. This way, the accuracy of the methods and their robustness against image artifacts are tested. Classification is also performed on real data where a quantitative validation compares the methods' results with an estimated ground truth from manual segmentations by experts. Validity of the various classification methods in the labeling of the image as well as in the tissue volume is estimated with different local and global measures. Results demonstrate that methods relying on both intensity and spatial information are more robust to noise and field inhomogeneities. We also demonstrate that partial volume is not perfectly modeled, even though methods that account for mixture classes outperform methods that only consider pure Gaussian classes. Finally, we show that simulated data results can also be extended to real data.

Mixed spectral-structural classification of very high resolution images with summation kernels

In this paper, mixed spectral-structural kernel machines are proposed for the classification of very-high resolution images. The simultaneous use of multispectral and structural features (computed using morphological filters) allows a significant increase in classification accuracy of remote sensing images. Subsequently, weighted summation kernel support vector machines are proposed and applied in order to take into account the multiscale nature of the scene considered. Such classifiers use the Mercer property of kernel matrices to compute a new kernel matrix accounting simultaneously for two scale parameters. Tests on a Zurich QuickBird image show the relevance of the proposed method : using the mixed spectral-structural features, the classification accuracy increases of about 5%, achieving a Kappa index of 0.97. The multikernel approach proposed provide an overall accuracy of 98.90% with related Kappa index of 0.985.

Cluster-based active learning for compact image classification

In this paper, we consider active sampling to label pixels grouped with hierarchical clustering. The objective of the method is to match the data relationships discovered by the clustering algorithm with the user's desired class semantics. The first is represented as a complete tree to be pruned and the second is iteratively provided by the user. The active learning algorithm proposed searches the pruning of the tree that best matches the labels of the sampled points. By choosing the part of the tree to sample from according to current pruning's uncertainty, sampling is focused on most uncertain clusters. This way, large clusters for which the class membership is already fixed are no longer queried and sampling is focused on division of clusters showing mixed labels. The model is tested on a VHR image in a multiclass classification setting. The method clearly outperforms random sampling in a transductive setting, but cannot generalize to unseen data, since it aims at optimizing the classification of a given cluster structure.

Current Issues in the Diagnosis and Classification of Peripheral T-Cell Lymphomas

Mature T-cell and T/NK-cell neoplasms are both uncommon and heterogeneous, among the broad category of non-Hodgkin's lymphomas. Due to the lack of specific genetic alterations in the vast majority of cases, most currently defined entities show overlapping morphologic and immunophenotypic features and therefore pose a challenge to the diagnostic pathologist. The goal of the symposium is to address current criteria for the recognition of specific subtypes of T-cell lymphoma, and to highlight new data regarding emerging immunophenotypic or molecular markers. This activity has been designed to meet the needs of practicing pathologists, and residents and fellows enrolled in training programs in anatomic and clinical pathology. It should be a particular benefit to those with an interest in hematopathology. Upon completion of this activity, participants should be better able to: -To be able to state the basis for the classification of mature T-cell malignancies involving nodal and extranodal sites. -To recognize and accurately diagnose the various subtypes of nodal and extranodal peripheral T-cell lymphomas. -To utilize immunohistochemical and molecular tests to characterize atypical T-cell proliferations. -To recognize and accurately diagnose T-cell lymphoproliferative lesions involving the skin and gastrointestinal tract, and be able to provide guidance regarding their clinical aggressiveness and management -To be able to utilize flow cytometric data to identify diverse functional T-cell subsets.