Mapping the disease-specific LupusQoL to the SF-6D

Purpose To derive a mapping algorithm to predict SF-6D utility scores from the non-preference-based LupusQoL and test the performance of the developed algorithm on a separate independent validation data set. Method LupusQoL and SF-6D data were collected from 320 patients with systemic lupus erythematosus (SLE) attending routine rheumatology outpatient appointments at seven centres in the UK. Ordinary least squares (OLS) regression was used to estimate models of increasing complexity in order to predict individuals’ SF-6D utility scores from their responses to the LupusQoL questionnaire. Model performance was judged on predictive ability through the size and pattern of prediction errors generated. The performance of the selected model was externally validated on an independent data set containing 113 female SLE patients who had again completed both the LupusQoL and SF-36 questionnaires. Results Four of the eight LupusQoL domains (physical health, pain, emotional health, and fatigue) were selected as dependent variables in the final model. Overall model fit was good, with R2 0.7219, MAE 0.0557, and RMSE 0.0706 when applied to the estimation data set, and R2 0.7431, MAE 0.0528, and RMSE 0.0663 when applied to the validation sample. Conclusion This study provides a method by which health state utility values can be estimated from patient responses to the non-preference-based LupusQoL, generalisable beyond the data set upon which it was estimated. Despite concerns over the use of OLS to develop mapping algorithms, we find this method to be suitable in this case due to the normality of the SF-6D data

A simulation study on the effects of neuronal ensemble properties on decoding algorithms for intracortical brain-machine interfaces

Background: Intracortical brain-machine interfaces (BMIs) harness movement information by sensing neuronal activities using chronic microelectrode implants to restore lost functions to patients with paralysis. However, neuronal signals often vary over time, even within a day, forcing one to rebuild a BMI every time they operate it. The term "rebuild" means overall procedures for operating a BMI, such as decoder selection, decoder training, and decoder testing. It gives rise to a practical issue of what decoder should be built for a given neuronal ensemble. This study aims to address it by exploring how decoders' performance varies with the neuronal properties. To extensively explore a range of neuronal properties, we conduct a simulation study. Methods: Focusing on movement direction, we examine several basic neuronal properties, including the signal-to-noise ratio of neurons, the proportion of well-tuned neurons, the uniformity of their preferred directions (PDs), and the non-stationarity of PDs. We investigate the performance of three popular BMI decoders: Kalman filter, optimal linear estimator, and population vector algorithm. Results: Our simulation results showed that decoding performance of all the decoders was affected more by the proportion of well-tuned neurons that their uniformity. Conclusions: Our study suggests a simulated scenario of how to choose a decoder for intracortical BMIs in various neuronal conditions

Upregulation of the cell-cycle regulator RGC-32 in Epstein-Barr virus-immortalized cells

Epstein-Barr virus (EBV) is implicated in the pathogenesis of multiple human tumours of lymphoid and epithelial origin. The virus infects and immortalizes B cells establishing a persistent latent infection characterized by varying patterns of EBV latent gene expression (latency 0, I, II and III). The CDK1 activator, Response Gene to Complement-32 (RGC-32, C13ORF15), is overexpressed in colon, breast and ovarian cancer tissues and we have detected selective high-level RGC-32 protein expression in EBV-immortalized latency III cells. Significantly, we show that overexpression of RGC-32 in B cells is sufficient to disrupt G2 cell-cycle arrest consistent with activation of CDK1, implicating RGC-32 in the EBV transformation process. Surprisingly, RGC-32 mRNA is expressed at high levels in latency I Burkitt's lymphoma (BL) cells and in some EBV-negative BL cell-lines, although RGC-32 protein expression is not detectable. We show that RGC-32 mRNA expression is elevated in latency I cells due to transcriptional activation by high levels of the differentially expressed RUNX1c transcription factor. We found that proteosomal degradation or blocked cytoplasmic export of the RGC-32 message were not responsible for the lack of RGC-32 protein expression in latency I cells. Significantly, analysis of the ribosomal association of the RGC-32 mRNA in latency I and latency III cells revealed that RGC-32 transcripts were associated with multiple ribosomes in both cell-types implicating post-initiation translational repression mechanisms in the block to RGC-32 protein production in latency I cells. In summary, our results are the first to demonstrate RGC-32 protein upregulation in cells transformed by a human tumour virus and to identify post-initiation translational mechanisms as an expression control point for this key cell-cycle regulator

The Mind and the Machine. On the Conceptual and Moral Implications of Brain-Machine Interaction

Brain-machine interfaces are a growing field of research and application. The increasing possibilities to connect the human brain to electronic devices and computer software can be put to use in medicine, the military, and entertainment. Concrete technologies include cochlear implants, Deep Brain Stimulation, neurofeedback and neuroprosthesis. The expectations for the near and further future are high, though it is difficult to separate hope from hype. The focus in this paper is on the effects that these new technologies may have on our ‘symbolic order’—on the ways in which popular categories and concepts may change or be reinterpreted. First, the blurring distinction between man and machine and the idea of the cyborg are discussed. It is argued that the morally relevant difference is that between persons and non-persons, which does not necessarily coincide with the distinction between man and machine. The concept of the person remains useful. It may, however, become more difficult to assess the limits of the human body. Next, the distinction between body and mind is discussed. The mind is increasingly seen as a function of the brain, and thus understood in bodily and mechanical terms. This raises questions concerning concepts of free will and moral responsibility that may have far reaching consequences in the field of law, where some have argued for a revision of our criminal justice system, from retributivist to consequentialist. Even without such a (unlikely and unwarranted) revision occurring, brain-machine interactions raise many interesting questions regarding distribution and attribution of responsibility

Relative efficacy of different types of exercise for treatment of knee and hip osteoarthritis: Protocol for network meta-analysis of randomised controlled trials

Background: “Exercise” is universally recommended as a core treatment for knee and hip osteoarthritis (OA). However, there are very few head-to-head comparative trials to determine the relative efficacy between different types of exercise. The aim of this study is to benchmark different types of exercises against each other through the use of a common comparator in a network meta-analysis of randomised controlled trials (RCTs). Methods: This study will include only RCTs published in peer-reviewed journals. A systematic search will be conducted in several electronic databases and other relevant online resources. No limitations are imposed on language or publication date. Participants must be explicitly identified by authors as having OA. Interventions that involved exercise or comparators in any form will be included. Pain is the primary outcome of interest; secondary outcomes will include function and quality of life measures. Quality assessment of studies will be based on the modified Cochrane’s risk of bias assessment tool. At least two investigators will be involved throughout all stages of screening and data acquisition. Conflicts will be resolved through discussion. Conventional meta-analysis will be performed based on random effects model and network meta-analysis on a Bayesian model. Subgroup analysis will also be conducted based on study, patient and disease characteristics. Discussion: This study will provide for the first time comprehensive research evidence for the relative efficacy of different exercise regimens for treatment of OA. We will use network meta-analysis of existing RCT data to answer this question

Identification of Achaete-scute complex-like 1 (ASCL1) target genes and evaluation of DKK1 and TPH1 expression in pancreatic endocrine tumours

<p>Abstract</p> <p>Background</p> <p><it>ASCL1 </it>role in pancreatic endocrine tumourigenesis has not been established. Recently it was suggested that ASCL1 negatively controls expression of the Wnt signalling antagonist <it>DKK1</it>. Notch signalling regulates expression of TPH1, the rate limiting enzyme in the biosyntesis of serotonin. Understanding the development and proliferation of pancreatic endocrine tumours (PETs) is essential for the development of new therapies.</p> <p>Methods</p> <p><it>ASCL1 </it>target genes in the pancreatic endocrine tumour cell line BON1 were identified by RNA interference and microarray expression analysis. Protein expressions of selected target genes in PETs were evaluated by immunohistochemistry.</p> <p>Results</p> <p>158 annotated <it>ASCL1 </it>target genes were identified in BON1 cells, among them DKK1 and TPH1 that were negatively regulated by ASCL1. An inverse relation of ASCL1 to DKK1 protein expression was observed for 15 out of 22 tumours (68%). Nine tumours displayed low ASCL1/high DKK1 and six tumours high ASCL1/low DKK1 expression. Remaining PETs showed high ASCL1/high DKK1 (n = 4) or low ASCL1/low DKK1 (n = 3) expression. Nine of twelve analysed PETs (75%) showed TPH1 expression with no relation to ASCL1.</p> <p>Conclusion</p> <p>A number of genes with potential importance for PET tumourigenesis have been identified. <it>ASCL1 </it>negatively regulated the Wnt signalling antagonist <it>DKK1</it>, and <it>TPH1 </it>expression in BON1 cells. In concordance with these findings DKK1 showed an inverse relation to ASCL1 expression in a subset of PETs, which may affect growth control by the Wnt signalling pathway.</p

The Knee Clinical Assessment Study – CAS(K). A prospective study of knee pain and knee osteoarthritis in the general population: baseline recruitment and retention at 18 months

BACKGROUND: Selective non-participation at baseline (due to non-response and non-consent) and loss to follow-up are important concerns for longitudinal observational research. We investigated these matters in the context of baseline recruitment and retention at 18 months of participants for a prospective observational cohort study of knee pain and knee osteoarthritis in the general population. METHODS: Participants were recruited to the Knee Clinical Assessment Study – CAS(K) – by a multi-stage process involving response to two postal questionnaires, consent to further contact and medical record review (optional), and attendance at a research clinic. Follow-up at 18-months was by postal questionnaire. The characteristics of responders/consenters were described for each stage in the recruitment process to identify patterns of selective non-participation and loss to follow-up. The external validity of findings from the clinic attenders was tested by comparing the distribution of WOMAC scores and the association between physical function and obesity with the same parameters measured directly in the target population as whole. RESULTS: 3106 adults aged 50 years and over reporting knee pain in the previous 12 months were identified from the first baseline questionnaire. Of these, 819 consented to further contact, responded to the second questionnaire, and attended the research clinics. 776 were successfully followed up at 18 months. There was evidence of selective non-participation during recruitment (aged 80 years and over, lower socioeconomic group, currently in employment, experiencing anxiety or depression, brief episode of knee pain within the previous year). This did not cause significant bias in either the distribution of WOMAC scores or the association between physical function and obesity. CONCLUSION: Despite recruiting a minority of the target population to the research clinics and some evidence of selective non-participation, this appears not to have resulted in significant bias of cross-sectional estimates. The main effect of non-participation in the current cohort is likely to be a loss of precision in stratum-specific estimates e.g. in those aged 80 years and over. The subgroup of individuals who attended the research clinics and who make up the CAS(K) cohort can be used to accurately estimate parameters in the reference population as a whole. The potential for selection bias, however, remains an important consideration in each subsequent analysis

Detecting multivariate differentially expressed genes

<p>Abstract</p> <p>Background</p> <p>Gene expression is governed by complex networks, and differences in expression patterns between distinct biological conditions may therefore be complex and multivariate in nature. Yet, current statistical methods for detecting differential expression merely consider the univariate difference in expression level of each gene in isolation, thus potentially neglecting many genes of biological importance.</p> <p>Results</p> <p>We have developed a novel algorithm for detecting multivariate expression patterns, named Recursive Independence Test (RIT). This algorithm generalizes differential expression testing to more complex expression patterns, while still including genes found by the univariate approach. We prove that RIT is consistent and controls error rates for small sample sizes. Simulation studies confirm that RIT offers more power than univariate differential expression analysis when multivariate effects are present. We apply RIT to gene expression data sets from diabetes and cancer studies, revealing several putative disease genes that were not detected by univariate differential expression analysis.</p> <p>Conclusion</p> <p>The proposed RIT algorithm increases the power of gene expression analysis by considering multivariate effects while retaining error rate control, and may be useful when conventional differential expression tests yield few findings.</p

Proteome and Membrane Fatty Acid Analyses on Oligotropha carboxidovorans OM5 Grown under Chemolithoautotrophic and Heterotrophic Conditions

Oligotropha carboxidovorans OM5 T. (DSM 1227, ATCC 49405) is a chemolithoautotrophic bacterium able to utilize CO and H2 to derive energy for fixation of CO2. Thus, it is capable of growth using syngas, which is a mixture of varying amounts of CO and H2 generated by organic waste gasification. O. carboxidovorans is capable also of heterotrophic growth in standard bacteriologic media. Here we characterize how the O. carboxidovorans proteome adapts to different lifestyles of chemolithoautotrophy and heterotrophy. Fatty acid methyl ester (FAME) analysis of O. carboxidovorans grown with acetate or with syngas showed that the bacterium changes membrane fatty acid composition. Quantitative shotgun proteomic analysis of O. carboxidovorans grown in the presence of acetate and syngas showed production of proteins encoded on the megaplasmid for assimilating CO and H2 as well as proteins encoded on the chromosome that might have contributed to fatty acid and acetate metabolism. We found that adaptation to chemolithoautotrophic growth involved adaptations in cell envelope, oxidative homeostasis, and metabolic pathways such as glyoxylate shunt and amino acid/cofactor biosynthetic enzymes