Assessment of a fragment of e-cadherin as a serum biomarker with predictive value for prostate cancer

In prostate cancer, biomarkers may provide additional value above standard clinical and pathology parameters to predict outcome after specific therapy. The purpose of this study is to evaluate an 80 kDa fragment of the cell adhesion molecule e-cadherin as a serum biomarker. A broad spectrum of prostate cancer serum samples, representing different stages of prostate cancer disease, including benign prostatic hyperplasia (BPH), localised (Loc PCA) and metastatic prostate cancer (Met PCA), was examined for the cleaved product. There is a significant difference in the expression level of the 80 kDa fragment in the serum of healthy individuals vs patients with BPH and between BPH vs Loc PCA and Met PCA (P<0.001). Highest expression levels are observed in advanced metastatic disease. In the cohort of Loc PCA cases, there was no association between the 80 kDa serum concentration and clinical parameters. Interestingly, patients with an 80 kDa level of >7.9 μg l−1 at the time of diagnosis have a 55-fold higher risk of biochemical failure after surgery compared to those with lower levels. This is the first report of the application of an 80 kDa fragment of e-cadherin as a serum biomarker in a broad spectrum of prostate cancer cases. At an optimised cutoff, high expression at the time of diagnosis is associated with a significantly increased risk of biochemical failure, potentially supporting its use for a tailored follow-up protocol for those patients

Foraging patterns of acorn woodpeckers (Melanerpes formicivorus) on valley oak (Quercus lobata Née) in two California oak savanna-woodlands

Landscape characteristics and social behavior can affect the foraging patterns of seed-dependent animals. We examine the movement of acorns from valley oak (Quercus lobata) trees to granaries maintained by acorn woodpeckers (Melanerpes formicivorus) in two California oak savanna-woodlands differing in the distribution of Q. lobata within each site. In 2004, we sampled Q. lobata acorns from 16 granaries at Sedgwick Reserve in Santa Barbara County and 18 granaries at Hastings Reserve in Monterey County. Sedgwick has lower site-wide density of Q. lobata than Hastings as well as different frequencies of other Quercus species common to both sites. We found acorn woodpeckers foraged from fewer Q. lobata seed source trees (Kg = 4.1 ± 0.5) at Sedgwick than at Hastings (Kg = 7.6 ± 0.6) and from fewer effective seed sources (Nem* = 2.00 and 5.78, respectively). The differences between sites are due to a greater number of incidental seed sources used per granary at Hastings than at Sedgwick. We also found very low levels of seed source sharing between adjacent granaries, indicating that territoriality is strong at both sites and that each social group forages on its own subset of trees. We discovered an interesting spatial pattern in the location of granaries. At Sedgwick, acorn woodpeckers situated their granaries within areas of higher-than-average tree density, while at Hastings, they placed them within areas of lower-than-average tree density, with the outcome that granaries at the two sites were located in areas of similar valley oak density. Our results illustrate that landscape characteristics might influence the number of trees visited by acorn woodpeckers and the locations of territories, while woodpecker social behavior, such as territoriality, shapes which trees are visited and whether they are shared with other social groups

The Urban Environment and Childhood Asthma (URECA) birth cohort study: design, methods, and study population

<p>Abstract</p> <p>Background</p> <p>The incidence and morbidity of wheezing illnesses and childhood asthma is especially high in poor urban areas. This paper describes the study design, methods, and population of the Urban Environment and Childhood Asthma (URECA) study, which was established to investigate the immunologic causes of asthma among inner-city children.</p> <p>Methods and Results</p> <p>URECA is an observational prospective study that enrolled pregnant women in central urban areas of Baltimore, Boston, New York City, and St. Louis and is following their offspring from birth through age 7 years. The birth cohort consists of 560 inner-city children who have at least one parent with an allergic disease or asthma, and all families live in areas in which at least 20% of the population has incomes below the poverty line. In addition, 49 inner-city children with no parental history of allergies or asthma were enrolled. The primary hypothesis is that specific urban exposures in early life promote a unique pattern of immune development (impaired antiviral and increased Th2 responses) that increases the risk of recurrent wheezing and allergic sensitization in early childhood, and of asthma by age 7 years. To track immune development, cytokine responses of blood mononuclear cells stimulated <it>ex vivo </it>are measured at birth and then annually. Environmental assessments include allergen and endotoxin levels in house dust, pre- and postnatal maternal stress, and indoor air nicotine and nitrogen dioxide. Nasal mucous samples are collected from the children during respiratory illnesses and analyzed for respiratory viruses. The complex interactions between environmental exposures and immune development will be assessed with respect to recurrent wheeze at age 3 years and asthma at age 7 years.</p> <p>Conclusion</p> <p>The overall goal of the URECA study is to develop a better understanding of how specific urban exposures affect immune development to promote wheezing illnesses and asthma.</p

Groundwater resources in the Jabal Al Hass region, northwest Syria: an assessment of past use and future potential

In many cases, the development of groundwater resources to boost agricultural production in dry areas has led to a continuous decline in groundwater levels; this has called into question the sustainability of such exploitation. In developing countries, limited budgets and scarce hydrological data often do not allow groundwater resources to be assessed through groundwater modeling. A case study is presented of a low-cost water-balance approach to groundwater resource assessments in a 1,550 k

A genetic programming approach to development of clinical prediction models: A case study in symptomatic cardiovascular disease

BACKGROUND:Genetic programming (GP) is an evolutionary computing methodology capable of identifying complex, non-linear patterns in large data sets. Despite the potential advantages of GP over more typical, frequentist statistical approach methods, its applications to survival analyses are rare, at best. The aim of this study was to determine the utility of GP for the automatic development of clinical prediction models. METHODS:We compared GP against the commonly used Cox regression technique in terms of the development and performance of a cardiovascular risk score using data from the SMART study, a prospective cohort study of patients with symptomatic cardiovascular disease. The composite endpoint was cardiovascular death, non-fatal stroke, and myocardial infarction. A total of 3,873 patients aged 19-82 years were enrolled in the study 1996-2006. The cohort was split 70:30 into derivation and validation sets. The derivation set was used for development of both GP and Cox regression models. These models were then used to predict the discrete hazards at t = 1, 3, and 5 years. The predictive ability of both models was evaluated in terms of their risk discrimination and calibration using the validation set. RESULTS:The discrimination of both models was comparable. At time points t = 1, 3, and 5 years the C-index was 0.59, 0.69, 0.64 and 0.66, 0.70, 0.70 for the GP and Cox regression models respectively. At the same time points, the calibration of both models, which was assessed using calibration plots and the generalization of the Hosmer-Lemeshow test statistic, was also comparable, but with the Cox model being better calibrated to the validation data. CONCLUSION:Using empirical data, we demonstrated that a prediction model developed automatically by GP has predictive ability comparable to that of manually tuned Cox regression. The GP model was more complex, but it was developed in a fully automated way and comprised fewer covariates. Furthermore, it did not require the expertise normally needed for its derivation, thereby alleviating the knowledge elicitation bottleneck. Overall, GP demonstrated considerable potential as a method for the automated development of clinical prediction models for diagnostic and prognostic purposes