PD-0462: Towards dosimetric tracking with adaptive VMAT?

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Scanning tunneling microscopy and spectroscopy at low temperatures of the (110) surface of Te doped GaAs single crystals

We have performed voltage dependent imaging and spatially resolved spectroscopy on the (110) surface of Te doped GaAs single crystals with a low temperature scanning tunneling microscope (STM). A large fraction of the observed defects are identified as Te dopant atoms which can be observed down to the fifth subsurface layer. For negative sample voltages, the dopant atoms are surrounded by Friedel charge density oscillations. Spatially resolved spectroscopy above the dopant atoms and above defect free areas of the GaAs (110) surface reveals the presence of conductance peaks inside the semiconductor band gap. The appearance of the peaks can be linked to charges residing on states which are localized within the tunnel junction area. We show that these localized states can be present on the doped GaAs surface as well as at the STM tip apex.Comment: 8 pages, 8 figures, accepted for publication in PR

Increased chromosomal radiosensitivity in asymptomatic carriers of a heterozygous BRCA1 mutation

Background: Breast cancer risk increases drastically in individuals carrying a germline BRCA1 mutation. The exposure to ionizing radiation for diagnostic or therapeutic purposes of BRCA1 mutation carriers is counterintuitive, since BRCA1 is active in the DNA damage response pathway. The aim of this study was to investigate whether healthy BRCA1 mutations carriers demonstrate an increased radiosensitivity compared with healthy individuals. Methods: We defined a novel radiosensitivity indicator (RIND) based on two endpoints measured by the G2 micronucleus assay, reflecting defects in DNA repair and G2 arrest capacity after exposure to doses of 2 or 4 Gy. We investigated if a correlation between the RIND score and nonsense-mediated decay (NMD) could be established. Results: We found significantly increased radiosensitivity in the cohort of healthy BRCA1 mutation carriers compared with healthy controls. In addition, our analysis showed a significantly different distribution over the RIND scores (p = 0.034, Fisher’s exact test) for healthy BRCA1 mutation carriers compared with non-carriers: 72 % of mutation carriers showed a radiosensitive phenotype (RIND score 1–4), whereas 72 % of the healthy volunteers showed no radiosensitivity (RIND score 0). Furthermore, 28 % of BRCA1 mutation carriers had a RIND score of 3 or 4 (not observed in control subjects). The radiosensitive phenotype was similar for relatives within several families, but not for unrelated individuals carrying the same mutation. The median RIND score was higher in patients with a mutation leading to a premature termination codon (PTC) located in the central part of the gene than in patients with a germline mutation in the 5′ end of the gene. Conclusions: We show that BRCA1 mutations are associated with a radiosensitive phenotype related to a compromised DNA repair and G2 arrest capacity after exposure to either 2 or 4 Gy. Our study confirms that haploinsufficiency is the mechanism involved in radiosensitivity in patients with a PTC allele, but it suggests that further research is needed to evaluate alternative mechanisms for mutations not subjected to NMD

Assessment of Microbial Diversity in Biofilms Recovered from Endotracheal Tubes Using Culture Dependent and Independent Approaches

Ventilator-associated pneumonia (VAP) is a common nosocomial infection in mechanically ventilated patients. Biofilm formation is one of the mechanisms through which the endotracheal tube (ET) facilitates bacterial contamination of the lower airways. In the present study, we analyzed the composition of the ET biofilm flora by means of culture dependent and culture independent (16 S rRNA gene clone libraries and pyrosequencing) approaches. Overall, the microbial diversity was high and members of different phylogenetic lineages were detected (Actinobacteria, beta-Proteobacteria, Candida spp., Clostridia, epsilon-Proteobacteria, Firmicutes, Fusobacteria and gamma-Proteobacteria). Culture dependent analysis, based on the use of selective growth media and conventional microbiological tests, resulted in the identification of typical aerobic nosocomial pathogens which are known to play a role in the development of VAP, e.g. Staphylococcus aureus and Pseudomonas aeruginosa. Other opportunistic pathogens were also identified, including Staphylococcus epidermidis and Kocuria varians. In general, there was little correlation between the results obtained by sequencing 16 S rRNA gene clone libraries and by cultivation. Pyrosequencing of PCR amplified 16 S rRNA genes of four selected samples resulted in the identification of a much wider variety of bacteria. The results from the pyrosequencing analysis suggest that these four samples were dominated by members of the normal oral flora such as Prevotella spp., Peptostreptococcus spp. and lactic acid bacteria. A combination of methods is recommended to obtain a complete picture of the microbial diversity of the ET biofilm

Support vector machine versus logistic regression modeling for prediction of hospital mortality in critically ill patients with haematological malignancies

Background: Several models for mortality prediction have been constructed for critically ill patients with haematological malignancies in recent years. These models have proven to be equally or more accurate in predicting hospital mortality in patients with haematological malignancies than ICU severity of illness scores such as the APACHE II or SAPS II [1]. The objective of this study is to compare the accuracy of predicting hospital mortality in patients with haematological malignancies admitted to the ICU between models based on multiple logistic regression (MLR) and support vector machine (SVM) based models. Methods: 352 patients with haematological malignancies admitted to the ICU between 1997 and 2006 for a life-threatening complication were included. 252 patient records were used for training of the models and 100 were used for validation. In a first model 12 input variables were included for comparison between MLR and SVM. In a second more complex model 17 input variables were used. MLR and SVM analysis were performed independently from each other. Discrimination was evaluated using the area under the receiver operating characteristic (ROC) curves (+/- SE). Results: The area under ROC curve for the MLR and SVM in the validation data set were 0.768 (+/- 0.04) vs. 0.802 (+/- 0.04) in the first model (p = 0.19) and 0.781 (+/- 0.05) vs. 0.808 (+/- 0.04) in the second more complex model (p = 0.44). SVM needed only 4 variables to make its prediction in both models, whereas MLR needed 7 and 8 variables in the first and second model respectively. Conclusion: The discriminative power of both the MLR and SVM models was good. No statistically significant differences were found in discriminative power between MLR and SVM for prediction of hospital mortality in critically ill patients with haematological malignancies