Gene expression profiling of noninvasive primary urothelial tumours using microarrays

At present, the mechanism leading to bladder cancer is still poorly understood, and our knowledge about early events in tumorigenesis is limited. This study describes the changes in gene expression occurring during the neoplastic transition from normal bladder urothelium to primary Ta tumours. Using DNA microarrays, we identified novel differentially expressed genes in Ta tumours compared to normal bladder, and genes that were altered in high-grade tumours. Among the mostly changed genes between normal bladder and Ta tumours, we found genes related to the cytoskeleton (keratin 7 and syndecan 1), and transcription (high mobility group AT-hook 1). Altered genes in high-grade tumours were related to cell cycle (cyclin-dependent kinase 4) and transcription (jun d proto-oncogene). Furthermore, we showed the presence of high keratin 7 transcript expression in bladder cancer, and Western blotting analysis revealed three major molecular isoforms of keratin 7 in the tissues. These could be detected in urine sediments from bladder tumour patients

Automated Discovery of Food Webs from Ecological Data Using Logic-Based Machine Learning

Networks of trophic links (food webs) are used to describe and understand mechanistic routes for translocation of energy (biomass) between species. However, a relatively low proportion of ecosystems have been studied using food web approaches due to difficulties in making observations on large numbers of species. In this paper we demonstrate that Machine Learning of food webs, using a logic-based approach called A/ILP, can generate plausible and testable food webs from field sample data. Our example data come from a national-scale Vortis suction sampling of invertebrates from arable fields in Great Britain. We found that 45 invertebrate species or taxa, representing approximately 25% of the sample and about 74% of the invertebrate individuals included in the learning, were hypothesized to be linked. As might be expected, detritivore Collembola were consistently the most important prey. Generalist and omnivorous carabid beetles were hypothesized to be the dominant predators of the system. We were, however, surprised by the importance of carabid larvae suggested by the machine learning as predators of a wide variety of prey. High probability links were hypothesized for widespread, potentially destabilizing, intra-guild predation; predictions that could be experimentally tested. Many of the high probability links in the model have already been observed or suggested for this system, supporting our contention that A/ILP learning can produce plausible food webs from sample data, independent of our preconceptions about “who eats whom.” Well-characterised links in the literature correspond with links ascribed with high probability through A/ILP. We believe that this very general Machine Learning approach has great power and could be used to extend and test our current theories of agricultural ecosystem dynamics and function. In particular, we believe it could be used to support the development of a wider theory of ecosystem responses to environmental change

Imaging the Impact of Prenatal Alcohol Exposure on the Structure of the Developing Human Brain

Prenatal alcohol exposure has numerous effects on the developing brain, including damage to selective brain structure. We review structural magnetic resonance imaging (MRI) studies of brain abnormalities in subjects prenatally exposed to alcohol. The most common findings include reduced brain volume and malformations of the corpus callosum. Advanced methods have been able to detect shape, thickness and displacement changes throughout multiple brain regions. The teratogenic effects of alcohol appear to be widespread, affecting almost the entire brain. The only region that appears to be relatively spared is the occipital lobe. More recent studies have linked cognition to the underlying brain structure in alcohol-exposed subjects, and several report patterns in the severity of brain damage as it relates to facial dysmorphology or to extent of alcohol exposure. Future studies exploring relationships between brain structure, cognitive measures, dysmorphology, age, and other variables will be valuable for further comprehending the vast effects of prenatal alcohol exposure and for evaluating possible interventions

Mechanisms of progression of chronic kidney disease

Chronic kidney disease (CKD) occurs in all age groups, including children. Regardless of the underlying cause, CKD is characterized by progressive scarring that ultimately affects all structures of the kidney. The relentless progression of CKD is postulated to result from a self-perpetuating vicious cycle of fibrosis activated after initial injury. We will review possible mechanisms of progressive renal damage, including systemic and glomerular hypertension, various cytokines and growth factors, with special emphasis on the renin–angiotensin–aldosterone system (RAAS), podocyte loss, dyslipidemia and proteinuria. We will also discuss possible specific mechanisms of tubulointerstitial fibrosis that are not dependent on glomerulosclerosis, and possible underlying predispositions for CKD, such as genetic factors and low nephron number