Fully-automated μMRI morphometric phenotyping of the Tc1 mouse model of Down Syndrome

We describe a fully automated pipeline for the morphometric phenotyping of mouse brains from μMRI data, and show its application to the Tc1 mouse model of Down syndrome, to identify new morphological phenotypes in the brain of this first transchromosomic animal carrying human chromosome 21. We incorporate an accessible approach for simultaneously scanning multiple ex vivo brains, requiring only a 3D-printed brain holder, and novel image processing steps for their separation and orientation. We employ clinically established multi-atlas techniques-superior to single-atlas methods-together with publicly-available atlas databases for automatic skull-stripping and tissue segmentation, providing high-quality, subject-specific tissue maps. We follow these steps with group-wise registration, structural parcellation and both Voxel- and Tensor-Based Morphometry-advantageous for their ability to highlight morphological differences without the laborious delineation of regions of interest. We show the application of freely available open-source software developed for clinical MRI analysis to mouse brain data: NiftySeg for segmentation and NiftyReg for registration, and discuss atlases and parameters suitable for the preclinical paradigm. We used this pipeline to compare 29 Tc1 brains with 26 wild-type littermate controls, imaged ex vivo at 9.4T. We show an unexpected increase in Tc1 total intracranial volume and, controlling for this, local volume and grey matter density reductions in the Tc1 brain compared to the wild-types, most prominently in the cerebellum, in agreement with human DS and previous histological findings

MSH3 polymorphisms and protein levels affect CAG repeat instability in huntington's disease mice

Expansions of trinucleotide CAG/CTG repeats in somatic tissues are thought to contribute to ongoing disease progression through an affected individual's life with Huntington's disease or myotonic dystrophy. Broad ranges of repeat instability arise between individuals with expanded repeats, suggesting the existence of modifiers of repeat instability. Mice with expanded CAG/CTG repeats show variable levels of instability depending upon mouse strain. However, to date the genetic modifiers underlying these differences have not been identified. We show that in liver and striatum the R6/1 Huntington's disease (HD) (CAG)~100 transgene, when present in a congenic C57BL/6J (B6) background, incurred expansion-biased repeat mutations, whereas the repeat was stable in a congenic BALB/cByJ (CBy) background. Reciprocal congenic mice revealed the Msh3 gene as the determinant for the differences in repeat instability. Expansion bias was observed in congenic mice homozygous for the B6 Msh3 gene on a CBy background, while the CAG tract was stabilized in congenics homozygous for the CBy Msh3 gene on a B6 background. The CAG stabilization was as dramatic as genetic deficiency of Msh2. The B6 and CBy Msh3 genes had identical promoters but differed in coding regions and showed strikingly different protein levels. B6 MSH3 variant protein is highly expressed and associated with CAG expansions, while the CBy MSH3 variant protein is expressed at barely detectable levels, associating with CAG stability. The DHFR protein, which is divergently transcribed from a promoter shared by the Msh3 gene, did not show varied levels between mouse strains. Thus, naturally occurring MSH3 protein polymorphisms are modifiers of CAG repeat instability, likely through variable MSH3 protein stability. Since evidence supports that somatic CAG instability is a modifier and predictor of disease, our data are consistent with the hypothesis that variable levels of CAG instability associated with polymorphisms of DNA repair genes may have prognostic implications for various repeat-associated diseases

Identification of Cellular Infiltrates during Early Stages of Brain Inflammation with Magnetic Resonance Microscopy

A comprehensive view of brain inflammation during the pathogenesis of autoimmune encephalomyelitis can be achieved with the aid of high resolution non-invasive imaging techniques such as microscopic magnetic resonance imaging (μMRI). In this study we demonstrate the benefits of cryogenically-cooled RF coils to produce μMRI in vivo, with sufficient detail to reveal brain pathology in the experimental autoimmune encephalomyelitis (EAE) model. We could visualize inflammatory infiltrates in detail within various regions of the brain, already at an early phase of EAE. Importantly, this pathology could be seen clearly even without the use of contrast agents, and showed excellent correspondence with conventional histology. The cryogenically-cooled coil enabled the acquisition of high resolution images within short scan times: an important practical consideration in conducting animal experiments. The detail of the cellular infiltrates visualized by in vivo μMRI allows the opportunity to follow neuroinflammatory processes even during the early stages of disease progression. Thus μMRI will not only complement conventional histological examination but will also enable longitudinal studies on the kinetics and dynamics of immune cell infiltration

Important prognostic factors for the long-term survival of lung cancer subjects in Taiwan

<p>Abstract</p> <p>Background</p> <p>This study used a large-scale cancer database in determination of prognostic factors for the survival of lung cancer subjects in Taiwan.</p> <p>Methods</p> <p>Total of 24,910 subjects diagnosed with lung cancer was analysed. Survival estimates by Kaplan-Meier methods. Cox proportional-hazards model estimated the death risk (hazard ratio (HR)) for various prognostic factors.</p> <p>Results</p> <p>The prognostic indicators associated with a higher risk of lung cancer deaths are male gender (males versus females; HR = 1.07, 95% confidence intervals (CI): 1.03–1.11), males diagnosed in later periods (shown in 1991–1994 versus 1987–1990; HR = 1.13), older age at diagnosis, large cell carcinoma (LCC)/small cell carcinoma (SCC), and supportive care therapy over chemotherapy. The overall 5-year survival rate for lung cancer death was significantly poorer for males (21.3%) than females (23.6%). Subjects with squamous cell carcinoma (SQCC) and treatment by surgical resection alone had better prognosis. We find surgical resections to markedly increase 5-year survival rate from LCC, decreased risk of death from LCC, and no improved survival from SCC.</p> <p>Conclusion</p> <p>Gender and clinical characteristics (i.e. diagnostic period, diagnostic age, histological type and treatment modality) play important roles in determining lung cancer survival.</p

Life experiences throughout the ifespan: What do people say (or not) about them?

Life experiences have been a topic of interest for researchers and clinicians for decades. Current knowledge is rooted in two distinct approaches, i.e., personality psychology and psychosomatics. Whereas the first is interested in ordinary life stories of nonclinical individuals, based on a more qualitative, in-depth, and person-driven approach, psychosomatics stresses negative events, mainly in clinical samples, and presents a more quantitative, general, and construct-driven approach. Consequently, available evidence is dispersed and unrelated and many basic questions remain unanswered. This study aimed to explore occurrence, developmental stage, valence, and impact of life experiences and to analyze critical answering patterns (i.e., “I don’t remember,” missingness). Through a cross-sectional retrospective design, 394 adults from the community answered the Lifetime Experiences Scale, which covers 75 life experiences organized in eight domains (i.e., school, job, health, leisure, living conditions, adverse experiences, achievements, and people and relationships). Occurrence of life experiences varied greatly, and the mean number of experiences reported was approximately 30. Regarding developmental stage, most experiences were reported in just one stage—mainly adulthood—however, some could be considered chronic. Globally, life experiences tended to be clearly rated as positive or as negative; additionally, assessed experiences were mainly appraised as positive. Moreover, participants presented their experiences as significant, rating them as high impact. Overall, critical answering patterns were not very expressive: “I don’t remember” and missing answers were below 2 and 5%, respectively, in the majority of experiences. These findings offer several important new insights, suggesting that life experiences are mainly an idiosyncratic topic.This manuscript is part of a doctoral dissertation, which had the support of the Portuguese Foundation for Science and Technology (FCT), through the PhD grant with the reference SFRH/ BD/76022/2011, funded by POPH-QREN-Typology 4.1-Advanced Training, reimbursed by the European Social Fund and national funds from State Budget. This study was conducted at Psychology Research Centre (UID/PSI/01662/2013), University of Minho, and supported by the Portuguese Foundation for Science and Technology and the Portuguese Ministry of Education and Science through national funds and co-financed by FEDER through COMPETE2020 under the PT2020 Partnership Agreement (POCI-01-0145-FEDER-007653).info:eu-repo/semantics/publishedVersio

Inference of Functional Relations in Predicted Protein Networks with a Machine Learning Approach

Background: Molecular biology is currently facing the challenging task of functionally characterizing the proteome. The large number of possible protein-protein interactions and complexes, the variety of environmental conditions and cellular states in which these interactions can be reorganized, and the multiple ways in which a protein can influence the function of others, requires the development of experimental and computational approaches to analyze and predict functional associations between proteins as part of their activity in the interactome. Methodology/Principal Findings: We have studied the possibility of constructing a classifier in order to combine the output of the several protein interaction prediction methods. The AODE (Averaged One-Dependence Estimators) machine learning algorithm is a suitable choice in this case and it provides better results than the individual prediction methods, and it has better performances than other tested alternative methods in this experimental set up. To illustrate the potential use of this new AODE-based Predictor of Protein InterActions (APPIA), when analyzing high-throughput experimental data, we show how it helps to filter the results of published High-Throughput proteomic studies, ranking in a significant way functionally related pairs. Availability: All the predictions of the individual methods and of the combined APPIA predictor, together with the used datasets of functional associations are available at http://ecid.bioinfo.cnio.es/. Conclusions: We propose a strategy that integrates the main current computational techniques used to predict functional associations into a unified classifier system, specifically focusing on the evaluation of poorly characterized protein pairs. We selected the AODE classifier as the appropriate tool to perform this task. AODE is particularly useful to extract valuable information from large unbalanced and heterogeneous data sets. The combination of the information provided by five prediction interaction prediction methods with some simple sequence features in APPIA is useful in establishing reliability values and helpful to prioritize functional interactions that can be further experimentally characterized.This work was funded by the BioSapiens (grant number LSHG-CT-2003-503265) and the Experimental Network for Functional Integration (ENFIN) Networks of Excellence (contract number LSHG-CT-2005-518254), by Consolider BSC (grant number CSD2007-00050) and by the project “Functions for gene sets” from the Spanish Ministry of Education and Science (BIO2007-66855). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

The side effects of service changes: exploring the longitudinal impact of participation in a randomised controlled trial (DOORWAYS) on staff perceptions of barriers to change

Background: Staff and service users have expressed concerns that service improvements in British mental health wards have been slow or transient. It is possible that certain changes are positive for some (e.g. service users), but negative for others (e.g. staff), which may affect implementation success. In this study, we explore whether a programme of change to improve the therapeutic milieu on mental health wards influenced staff perceptions of barriers to change, 12 months after implementation. Method: A cluster randomised controlled trial called DOORWAYS was conducted on eight British, inner-city acute mental health wards. Randomisation was achieved using a list randomly generated by a computer. A psychologist trained ward staff (mainly nurses) to deliver evidence-based groups and supported their initial implementation. The impact of these changes was measured over 12 months (when 4 wards were randomised), according to nurses’ perceptions of barriers to change (VOCALISE), using unstructured multivariate linear regression models. This innovative analysis method allows maximum use of data in randomised controlled trials with reduced sample sizes due to substantial drop out rates. The contextual influences of occupational status (staff) and of workplace setting (ward) were also considered. Results: Staff who participated in the intervention had significantly worse perceptions of barriers to change at follow up. The perceptions of staff in the control group did not change over time. In both groups (N = 120), direct care staff had more negative perceptions of barriers to change, and perceptions varied according to ward. Across time, direct care staff in the intervention group became more negative than those in the control group. Conclusion: Participation in this program of change, worsened staff perceptions of barriers to change. In addition, occupational status (being from the direct care group) had a negative effect on perceptions of barriers to change, an effect that continued across time and was worse in the intervention group. Those providing direct care should be offered extra support when changes are introduced and through the implementation process. More effort should be placed around reducing the perceived burden of innovation for staff in mental health wards