Article

Chronic bacterial infection and inflammation incite reactive hyperplasia in a mouse model of chronic prostatitis

Department of Surgery, Division of Urology, University of Wisconsin School of Medicine and Public Health, 600, Highland Ave, Madison, WI 53792, USA.
The Prostate (Impact Factor: 3.57). 01/2007; 67(1):14-21. DOI: 10.1002/pros.20445
Source: PubMed

ABSTRACT Chronic inflammation is postulated to contribute to prostate carcinogenesis. We developed a mouse model of chronic prostatitis to test whether infection-induced chronic inflammation would incite reactive changes in prostatic epithelium.
Prostate tissues harvested from either phosphate-buffered saline (PBS) or E. coli-infected mice were evaluated for histological changes and immunostained for markers of oxidative stress and epithelial cell proliferation.
As compared to PBS-treated controls, mice infected with E. coli bacteria for 5 days showed foci of uniformly acute inflammation in the glandular lumen and a persistent inflammation at 12 weeks post-inoculation in the stroma. Prostatic glands showing varying degrees of atypical hyperplasia and dysplasia had stronger staining for oxidative DNA damage and increased epithelial cell proliferation than normal prostatic glands.
These data demonstrate that chronic inflammation induces reactive hyperplasia associated with oxidative stress injury and support the proposed linkage among inflammation, oxidative DNA damage, and prostate carcinogenesis.

0 Followers
 · 
90 Views
  • Source
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this paper we present a neural network for detection of fish, from light detection and ranging (LIDAR) data and have described a classification method for distinguishing between water-layer, bottom and fish. Four multi-layer perceptrons (MLP) were developed for the classification purpose, where classes include fish, bottom and water-layer. The LIDAR data gives a sequence of intensity of laser backscatters obtained from laser shots at various heights above the Earth surface. The data is preprocessed to remove the high frequency noise and then a window of the sample is selected for further processing to extract features for classification purposes. We have used linear predictive coding (LPC) analysis for the feature detection purpose. The results show that the detection technique is effective and can do the required classification with a high degree of accuracy. We have tried our approach with four different MLPs and are presenting the data obtained from each of them.
    Neural Networks, 2003. Proceedings of the International Joint Conference on; 08/2003