Expression of CD44 and Oct4 identified large populations of benign and malignant cells in the prostate, which did not fit the definition of stem cells as a small fraction of the total cell population. Our results suggest that combined expression of embryonic stem cell markers EZH2 and SOX2 might identify potential cancer stem cells as a minor (<10%) subgroup in CD44+ prostatic adenocarcinoma.

The present study examined the relationship of prenatal cocaine exposure to infant information processing in the first year of life.In a prospective, longitudinal study of 177 cocaine-exposed and 175 non-exposed infants, the Fagan Test of Infant Intelligence (FTII) was used to measure attention, visual recognition memory and information processing speed at 6.5 and 12 months of age. Groups were compared over time using mixed linear model analyses.Prenatal cocaine exposure predicted poorer visual recognition memory at 12 months, with exposed infants obtaining lower mean scores and a higher percentage of scores in the risk range. Across exposure groups, information processing speed increased with age, demonstrating a developmental effect. Tobacco and marijuana exposures were related to faster looking times, which did not relate to visual recognition memory.Cognitive deficits and attentional problems noted in prior studies of cocaine-exposed children at later ages may be detectable in infancy.

Background:Identification of individual prostatic glandular structures is an important prerequisite to quantitative histological analysis of prostate cancer with the aid of a computer. We have developed a computer method to segment individual glandular units and to extract quantitative image features, for computer identification of prostatic adenocarcinoma.Methods:Two sets of digital histology images were used: database I (n = 57) for developing and testing the computer technique, and database II (n = 116) for independent validation. The segmentation technique was based on a k-means clustering and a region-growing method. Computer segmentation results were evaluated subjectively and also compared quantitatively against manual gland outlines, using the Jaccard similarity measure. Quantitative features that were extracted from the computer segmentation results include average gland size, spatial gland density, and average gland circularity. Linear discriminant analysis (LDA) was used to combine quantitative image features. Classification performance was evaluated with receiver operating characteristic (ROC) analysis and the area under the ROC curve (AUC).Results:Jaccard similarity coefficients between computer segmentation and manual outlines of individual glands were between 0.63 and 0.72 for non-cancer and between 0.48 and 0.54 for malignant glands, respectively, similar to an interobserver agreement of 0.79 for non-cancer and 0.75 for malignant glands, respectively. The AUC value for the features of average gland size and gland density combined via LDA was 0.91 for database I and 0.96 for database II.Conclusions:Using a computer, we are able to delineate individual prostatic glands automatically and identify prostatic adenocarcinoma accurately, based on the quantitative image features extracted from computer-segmented glandular structures.