3 resultados para Bayesian animal model

em Aston University Research Archive


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Prostate cancer (CaP) patients with disseminated disease often suffer from severe cachexia, which contributes to mortality in advanced cancer. Human cachexia-associated protein (HCAP) was recently identified from a breast cancer library based on the available 20-amino acid sequence of proteolysis-inducing factor (PIF), which is a highly active cachectic factor isolated from mouse colon adenocarcinoma MAC16. Herein, we investigated the expression of HCAP in CaP and its potential involvement in CaP-associated cachexia. HCAP mRNA was detected in CaP cell lines, in primary CaP tissues and in its osseous metastases. In situ hybridization showed HCAP mRNA to be localized only in the epithelial cells in CaP tissues, in the metastatic foci in bone, liver and lymph node, but not in the stromal cells or in normal prostate tissues. HCAP protein was detected in 9 of 14 CaP metastases but not in normal prostate tissues from cadaveric donors or patients with organ-confined tumors. Our Western blot analysis revealed that HCAP was present in 9 of 19 urine specimens from cachectic CaP patients but not in 19 urine samples of noncachectic patients. HCAP mRNA and protein were also detected in LuCaP 35 and PC-3M xenografts from our cachectic animal models. Our results demonstrated that human CaP cells express HCAP and the expression of HCAP is associated with the progression of CaP and the development of CaP cachexia. © 2003 Wiley-Liss, Inc.

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Synthetic calcium phosphates, despite their bioactivity, are brittle. Calcium phosphate-mullite composites have been suggested as potential dental and bone replacement materials which exhibit increased toughness. Aluminium, present in mullite, has however been linked to bone demineralisation and neurotoxicity: it is therefore important to characterise the materials fully in order to understand their in vivo behaviour. The present work reports the compositional mapping of the interfacial region of a calcium phosphate-20 wt% mullite biocomposite/soft tissue interface, obtained from the samples implanted into the long bones of healthy rabbits according to standard protocols (ISO-10993) for up to 12 weeks. X-ray micro-fluorescence was used to map simultaneously the distribution of Al, P, Si and Ca across the ceramic-soft tissue interface. A well defined and sharp interface region was present between the ceramic and the surrounding soft tissue for each time period examined. The concentration of Al in the surrounding tissue was found to fall by two orders of magnitude, to the background level, within similar to 35 mu m of the implanted ceramic.

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In this paper, the problem of semantic place categorization in mobile robotics is addressed by considering a time-based probabilistic approach called dynamic Bayesian mixture model (DBMM), which is an improved variation of the dynamic Bayesian network. More specifically, multi-class semantic classification is performed by a DBMM composed of a mixture of heterogeneous base classifiers, using geometrical features computed from 2D laserscanner data, where the sensor is mounted on-board a moving robot operating indoors. Besides its capability to combine different probabilistic classifiers, the DBMM approach also incorporates time-based (dynamic) inferences in the form of previous class-conditional probabilities and priors. Extensive experiments were carried out on publicly available benchmark datasets, highlighting the influence of the number of time-slices and the effect of additive smoothing on the classification performance of the proposed approach. Reported results, under different scenarios and conditions, show the effectiveness and competitive performance of the DBMM.