A prediction model for assessing residential radon concentration in Switzerland

Indoor radon is regularly measured in Switzerland. However, a nationwide model to predict residential radon levels has not been developed. The aim of this study was to develop a prediction model to assess indoor radon concentrations in Switzerland. The model was based on 44,631 measurements from the nationwide Swiss radon database collected between 1994 and 2004. Of these, 80% randomly selected measurements were used for model development and the remaining 20% for an independent model validation. A multivariable log-linear regression model was fitted and relevant predictors selected according to evidence from the literature, the adjusted R², the Akaike's information criterion (AIC), and the Bayesian information criterion (BIC). The prediction model was evaluated by calculating Spearman rank correlation between measured and predicted values. Additionally, the predicted values were categorised into three categories (50th, 50th-90th and 90th percentile) and compared with measured categories using a weighted Kappa statistic. The most relevant predictors for indoor radon levels were tectonic units and year of construction of the building, followed by soil texture, degree of urbanisation, floor of the building where the measurement was taken and housing type (P-values <0.001 for all). Mean predicted radon values (geometric mean) were 66 Bq/m³ (interquartile range 40-111 Bq/m³) in the lowest exposure category, 126 Bq/m³ (69-215 Bq/m³) in the medium category, and 219 Bq/m³ (108-427 Bq/m³) in the highest category. Spearman correlation between predictions and measurements was 0.45 (95%-CI: 0.44; 0.46) for the development dataset and 0.44 (95%-CI: 0.42; 0.46) for the validation dataset. Kappa coefficients were 0.31 for the development and 0.30 for the validation dataset, respectively. The model explained 20% overall variability (adjusted R²). In conclusion, this residential radon prediction model, based on a large number of measurements, was demonstrated to be robust through validation with an independent dataset. The model is appropriate for predicting radon level exposure of the Swiss population in epidemiological research. Nevertheless, some exposure misclassification and regression to the mean is unavoidable and should be taken into account in future applications of the model.

Alpha- versus beta-particle radiopeptide therapy in a human prostate cancer model (213Bi-DOTA-PESIN and 213Bi-AMBA versus 177Lu-DOTA-PESIN)

Recurrent prostate cancer presents a challenge to conventional treatment, particularly so to address micrometastatic and small-volume disease. Use of α-radionuclide therapy is considered as a highly effective treatment in such applications due to the shorter range and exquisite cytotoxicity of α-particles as compared with β-particles. (213)Bi is considered an α-emitter with high clinical potential, due to its short half-life (45.6 minutes) being well matched for use in peptide-receptor radionuclide α-therapy; however, there is limited knowledge available within this context of use. In this study, two novel (213)Bi-labeled peptides, DOTA-PEG(4)-bombesin (DOTA-PESIN) and DO3A-CH(2)CO-8-aminooctanoyl-Q-W-A-V-G-H-L-M-NH(2) (AMBA), were compared with (177)Lu (β-emitter)-labeled DOTA-PESIN in a human androgen-independent prostate carcinoma xenograft model (PC-3 tumor). Animals were injected with (177)Lu-DOTA-PESIN, (213)Bi-DOTA-PESIN, or (213)Bi-AMBA to determine the maximum tolerated dose (MTD), biodistribution, and dosimetry of each agent; controls were left untreated or were given nonradioactive (175)Lu-DOTA-PESIN. The MTD of (213)Bi-DOTA-PESIN and (213)Bi-AMBA was 25 MBq (0.68 mCi) whereas (177)Lu-DOTA-PESIN showed an MTD of 112 MBq (3 mCi). At these dose levels, (213)Bi-DOTA-PESIN and (213)Bi-AMBA were significantly more effective than (177)Lu-DOTA-PESIN. At the same time, (177)Lu-DOTA-PESIN showed minimal, (213)Bi-DOTA-PESIN slight, and (213)Bi-AMBA marked kidney damage 20 to 30 weeks posttreatment. These preclinical data indicate that α-therapy with (213)Bi-DOTA-PESIN or (213)Bi-AMBA is more efficacious than β-therapy. Furthermore, (213)Bi-DOTA-PESIN has a better safety profile than (213)Bi-AMBA, and represents a possible new approach for use in peptide-receptor radionuclide α-therapy treating recurrent prostate cancer.

Surfactant protein D modulates allergen particle uptake and inflammatory response in a human epithelial airway model

Background Allergen-containing subpollen particles (SPP) are released from whole plant pollen upon contact with water or even high humidity. Because of their size SPP can preferentially reach the lower airways where they come into contact with surfactant protein (SP)-D. The aim of the present study was to investigate the influence of SP-D in a complex three-dimensional human epithelial airway model, which simulates the most important barrier functions of the epithelial airway. The uptake of SPP as well as the secretion of pro-inflammatory cytokines was investigated. Methods SPP were isolated from timothy grass and subsequently fluorescently labeled. A human epithelial airway model was built by using human Type II-pneumocyte like cells (A549 cells), human monocyte derived macrophages as well as human monocyte derived dendritic cells. The epithelial cell model was incubated with SPP in the presence and absence of surfactant protein D. Particle uptake was evaluated by confocal microscopy and advanced computer-controlled analysis. Finally, human primary CD4+ T-Cells were added to the epithelial airway model and soluble mediators were measured by enzyme linked immunosorbent assay or bead array. Results SPP were taken up by epithelial cells, macrophages, and dendritic cells. This uptake coincided with secretion of pro-inflammatory cytokines and chemokines. SP-D modulated the uptake of SPP in a cell type specific way (e.g. increased number of macrophages and epithelial cells, which participated in allergen particle uptake) and led to a decreased secretion of pro-inflammatory cytokines. Conclusion These results display a possible mechanism of how SP-D can modulate the inflammatory response to inhaled allergen.

An optimized in vitro model of the respiratory tract wall to study particle cell interactions

As a part of the respiratory tissue barrier, lung epithelial cells play an important role against the penetration of the body by inhaled particulate foreign materials. In most cell culture models, which are designed to study particle-cell interactions, the cells are immersed in medium. This does not reflect the physiological condition of lung epithelial cells which are exposed to air, separated from it only by a very thin liquid lining layer with a surfactant film at the air-liquid interface. In this study, A549 epithelial cells were grown on microporous membranes in a two chamber system. After the formation of a confluent monolayer the cells were exposed to air. The morphology of the cells and the expression of tight junction proteins were studied with confocal laser scanning and transmission electron microscopy. Air-exposed cells maintained monolayer structure for 2 days, expressed tight junctions and developed transepithelial electrical resistance. Surfactant was produced and released at the apical side of the air-exposed epithelial cells. In order to study particle-cell interactions fluorescent 1 microm polystyrene particles were sprayed over the epithelial surface. After 4 h, 8.8% of particles were found inside the epithelium. This fraction increased to 38% after 24 h. During all observations, particles were always found in the cells but never between them. In this study, we present an in vitro model of the respiratory tract wall consisting of air-exposed lung epithelial cells covered by a liquid lining layer with a surfactant film to study particle-cell interactions.

New Constraints on Dark Matter Effective Theories from Standard Model Loops

We consider an effective field theory for a gauge singlet Dirac dark matter particle interacting with the standard model fields via effective operators suppressed by the scale Λ≳1  TeV. We perform a systematic analysis of the leading loop contributions to spin-independent Dirac dark matter–nucleon scattering using renormalization group evolution between Λ and the low-energy scale probed by direct detection experiments. We find that electroweak interactions induce operator mixings such that operators that are naively velocity suppressed and spin dependent can actually contribute to spin-independent scattering. This allows us to put novel constraints on Wilson coefficients that were so far poorly bounded by direct detection. Constraints from current searches are already significantly stronger than LHC bounds, and will improve in the near future. Interestingly, the loop contribution we find is isospin violating even if the underlying theory is isospin conserving.

Untangling a Holocene pollen record with forest model simulations and independent climate data

Adaptation potential of forests to rapid climatic changes can be assessed from vegetation dynamics during past climatic changes as preserved in fossil pollen data. However, pollen data reflect the integrated effects of climate and biotic processes, such as establishment, survival, competition, and migration. To disentangle these processes, we compared an annually laminated late Würm and Holocene pollen record from the Central Swiss Plateau with simulations of a dynamic forest patch model. All input data used in the simulations were largely independent from pollen data; i.e. the presented analysis is non-circular. Temperature and precipitation scenarios were based on reconstructions from pollen-independent sources. The earliest arrival times of the species at the study site after the last glacial were inferred from pollen maps. We ran a series of simulations under different combinations of climate and immigration scenarios. In addition, the sensitivity of the simulated presence/absence of four major species to changes in the climate scenario was examined. The pattern of the pollen record could partly be explained by the used climate scenario, mostly by temperature. However, some features, in particular the absence of most species during the late Würm could only be simulated if the winter temperature anomalies of the used scenario were decreased considerably. Consequently, we had to assume in the simulations, that most species immigrated during or after the Younger Dryas (12 000 years BP), Abies and Fagus even later. Given the timing of tree species immigration, the vegetation was in equilibrium with climate during long periods, but responded with lags at the time-scale of centuries to millennia caused by a secondary succession after rapid climatic changes such as at the end of Younger Dryas, or immigration of dominant taxa. Climate influenced the tree taxa both directly and indirectly by changing inter-specific competition. We concluded, that also during the present fast climatic change, species migration might be an important process, particularly if geographic barriers, such as the Alps are in the migrational path.