Clinical impact of different detection methods for disseminated tumor cells in bone marrow of patients undergoing surgical resection of colorectal liver metastases: a prospective follow-up study

<p>Abstract</p> <p>Background</p> <p>Large number of patients with colorectal liver metastasis show recurrent disease after curative surgical resection. Identification of these high-risk patients may guide therapeutic strategies. The aim of this study was to evaluate whether the presence of disseminated tumor cells in bone marrow from patients undergoing surgical resection of colorectal liver metastases can predict clinical outcome.</p> <p>Methods</p> <p>Sixty patients with colorectal liver metastases were planned for a curative resection between 2001 and 2007. All patients underwent bone marrow aspiration before surgery. Detection of tumor cells was performed using immunocytochemical staining for cytokeratin (CK-ICC) combined with automated microscopy or indirectly using reverse transcription-polymerase chain reaction (RT-PCR).</p> <p>Results</p> <p>Disseminated tumor cells were found in 15 of the 46 patients (33%) using CK-ICC and in 9 of 44 of the patients (20%) using RT-PCR. Patients with negative results for RT-PCR had a significant better disease-free survival after resection of their liver metastases (p = 0.02). This group also showed significant better overall survival (p = 0.002). CK-ICC did not predict a worse clinical outcome.</p> <p>Conclusions</p> <p>The presence of disseminated tumor cells in bone marrow detected using RT-PCR did predict a worse clinical outcome. The presence of cells detected with CK-ICC did not correlate with poor prognosis.</p

Reduced Mimicry to Virtual Reality Avatars in Autism Spectrum Disorder

Mimicry involves unconsciously copying the actions of others. Increasing evidence suggests that autistic people can copy the goal of an observed action but show differences in their mimicry. We investigated mimicry in autism spectrum disorder (ASD) within a two-dimensional virtual reality environment. Participants played an imitation game with a socially engaged avatar and socially disengaged avatar. Despite being told only to copy the goal of the observed action, autistic participants and matched neurotypical participants mimicked the kinematics of the avatars’ movements. However, autistic participants mimicked less. Social engagement did not modulate mimicry in either group. The results demonstrate the feasibility of using virtual reality to induce mimicry and suggest mimicry differences in ASD may also occur when interacting with avatars

Testing the Effect of Relative Pollen Productivity on the REVEALS Model : A Validated Reconstruction of Europe-Wide Holocene Vegetation

Reliable quantitative vegetation reconstructions for Europe during the Holocene are crucial to improving our understanding of landscape dynamics, making it possible to assess the past effects of environmental variables and land-use change on ecosystems and biodiversity, and mitigating their effects in the future. We present here the most spatially extensive and temporally continuous pollen-based reconstructions of plant cover in Europe (at a spatial resolution of 1° × 1°) over the Holocene (last 11.7 ka BP) using the 'Regional Estimates of VEgetation Abundance from Large Sites' (REVEALS) model. This study has three main aims. First, to present the most accurate and reliable generation of REVEALS reconstructions across Europe so far. This has been achieved by including a larger number of pollen records compared to former analyses, in particular from the Mediterranean area. Second, to discuss methodological issues in the quantification of past land cover by using alternative datasets of relative pollen productivities (RPPs), one of the key input parameters of REVEALS, to test model sensitivity. Finally, to validate our reconstructions with the global forest change dataset. The results suggest that the RPPs.st1 (31 taxa) dataset is best suited to producing regional vegetation cover estimates for Europe. These reconstructions offer a long-term perspective providing unique possibilities to explore spatial-temporal changes in past land cover and biodiversity

Identification of 12 new susceptibility loci for different histotypes of epithelial ovarian cancer.

To identify common alleles associated with different histotypes of epithelial ovarian cancer (EOC), we pooled data from multiple genome-wide genotyping projects totaling 25,509 EOC cases and 40,941 controls. We identified nine new susceptibility loci for different EOC histotypes: six for serous EOC histotypes (3q28, 4q32.3, 8q21.11, 10q24.33, 18q11.2 and 22q12.1), two for mucinous EOC (3q22.3 and 9q31.1) and one for endometrioid EOC (5q12.3). We then performed meta-analysis on the results for high-grade serous ovarian cancer with the results from analysis of 31,448 BRCA1 and BRCA2 mutation carriers, including 3,887 mutation carriers with EOC. This identified three additional susceptibility loci at 2q13, 8q24.1 and 12q24.31. Integrated analyses of genes and regulatory biofeatures at each locus predicted candidate susceptibility genes, including OBFC1, a new candidate susceptibility gene for low-grade and borderline serous EOC

Testing the Effect of Relative Pollen Productivity on the REVEALS Model: A Validated Reconstruction of Europe-Wide Holocene Vegetation

International audienceReliable quantitative vegetation reconstructions for Europe during the Holocene are crucial to improving our understanding of landscape dynamics, making it possible to assess the past effects of environmental variables and land-use change on ecosystems and biodiversity, and mitigating their effects in the future. We present here the most spatially extensive and temporally continuous pollen-based reconstructions of plant cover in Europe (at a spatial resolution of 1° × 1°) over the Holocene (last 11.7 ka BP) using the ‘Regional Estimates of VEgetation Abundance from Large Sites’ (REVEALS) model. This study has three main aims. First, to present the most accurate and reliable generation of REVEALS reconstructions across Europe so far. This has been achieved by including a larger number of pollen records compared to former analyses, in particular from the Mediterranean area. Second, to discuss methodological issues in the quantification of past land cover by using alternative datasets of relative pollen productivities (RPPs), one of the key input parameters of REVEALS, to test model sensitivity. Finally, to validate our reconstructions with the global forest change dataset. The results suggest that the RPPs.st1 (31 taxa) dataset is best suited to producing regional vegetation cover estimates for Europe. These reconstructions offer a long-term perspective providing unique possibilities to explore spatial-temporal changes in past land cover and biodiversity