Combining estimates of interest in prognostic modelling studies after multiple imputation: current practice and guidelines

Background: Multiple imputation (MI) provides an effective approach to handle missing covariate data within prognostic modelling studies, as it can properly account for the missing data uncertainty. The multiply imputed datasets are each analysed using standard prognostic modelling techniques to obtain the estimates of interest. The estimates from each imputed dataset are then combined into one overall estimate and variance, incorporating both the within and between imputation variability. Rubin's rules for combining these multiply imputed estimates are based on asymptotic theory. The resulting combined estimates may be more accurate if the posterior distribution of the population parameter of interest is better approximated by the normal distribution. However, the normality assumption may not be appropriate for all the parameters of interest when analysing prognostic modelling studies, such as predicted survival probabilities and model performance measures. Methods: Guidelines for combining the estimates of interest when analysing prognostic modelling studies are provided. A literature review is performed to identify current practice for combining such estimates in prognostic modelling studies. Results: Methods for combining all reported estimates after MI were not well reported in the current literature. Rubin's rules without applying any transformations were the standard approach used, when any method was stated. Conclusion: The proposed simple guidelines for combining estimates after MI may lead to a wider and more appropriate use of MI in future prognostic modelling studies

Comparison of techniques for handling missing covariate data within prognostic modelling studies: a simulation study

Background: There is no consensus on the most appropriate approach to handle missing covariate data within prognostic modelling studies. Therefore a simulation study was performed to assess the effects of different missing data techniques on the performance of a prognostic model. Methods: Datasets were generated to resemble the skewed distributions seen in a motivating breast cancer example. Multivariate missing data were imposed on four covariates using four different mechanisms; missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR) and a combination of all three mechanisms. Five amounts of incomplete cases from 5% to 75% were considered. Complete case analysis (CC), single imputation (SI) and five multiple imputation (MI) techniques available within the R statistical software were investigated: a) data augmentation (DA) approach assuming a multivariate normal distribution, b) DA assuming a general location model, c) regression switching imputation, d) regression switching with predictive mean matching (MICE-PMM) and e) flexible additive imputation models. A Cox proportional hazards model was fitted and appropriate estimates for the regression coefficients and model performance measures were obtained. Results: Performing a CC analysis produced unbiased regression estimates, but inflated standard errors, which affected the significance of the covariates in the model with 25% or more missingness. Using SI, underestimated the variability; resulting in poor coverage even with 10% missingness. Of the MI approaches, applying MICE-PMM produced, in general, the least biased estimates and better coverage for the incomplete covariates and better model performance for all mechanisms. However, this MI approach still produced biased regression coefficient estimates for the incomplete skewed continuous covariates when 50% or more cases had missing data imposed with a MCAR, MAR or combined mechanism. When the missingness depended on the incomplete covariates, i.e. MNAR, estimates were biased with more than 10% incomplete cases for all MI approaches. Conclusion: The results from this simulation study suggest that performing MICE-PMM may be the preferred MI approach provided that less than 50% of the cases have missing data and the missing data are not MNAR

Developing an algorithm for pulse oximetry derived respiratory rate (RRoxi): a healthy volunteer study

Objective The presence of respiratory information within the pulse oximeter signal (PPG) is a well-documented phenomenon. However, extracting this information for the purpose of continuously monitoring respiratory rate requires: (1) the recognition of the multi-faceted manifestations of respiratory modulation components within the PPG and the complex interactions among them; (2) the implementation of appropriate advanced signal processing techniques to take full advantage of this information; and (3) the post-processing infrastructure to deliver a clinically useful reported respiratory rate to the end user. A holistic algorithmic approach to the problem is therefore required. We have developed the RROXI algorithm based on this principle and its performance on healthy subject trial data is described herein

Pulmonary Kaposi's sarcoma after heart transplantation: a case report

ABSTRACT: INTRODUCTION: Kaposi's sarcomas have been associated with different conditions of immunosuppression and are also known to be a typical complication of solid organ transplantations. CASE PRESENTATION: We report of a 65 year old man of Turkish origin with a history of heart transplantation 10 months ago who presented for clarification of his dyspnoea. The patient had a known history of chronic obstructive pulmonary disease and a smoking history of 40 pack years. Radiologically, three progressively growing intrapulmonary nodules were detected. The histology was diagnostic for a Kaposi's sarcoma. Visceral and especially primary intrapulmonary Kaposi's sarcomas are very rare and have been described to have a rather unfavourable prognosis. CONCLUSION: Even with a history suggestive for conventional lung cancer, Kaposi's sarcomas should be considered in patients after transplantation of solid organs. It should be noticed that in a minority of cases this tumour exists in the absence of the typical cutaneous lesions

Nitric Oxide Facilitates Delivery and Mediates Improved Outcome of Autologous Bone Marrow Mononuclear Cells in a Rodent Stroke Model

Bone marrow mononuclear cells (MNC) represent an investigational treatment for stroke. The objective of this study was to determine the relevance of vasoactive mediators, generated in response to MNC injection, as factors regulating cerebral perfusion (CP), the biodistribution of MNC, and outcome in stroke.Long Evans rats underwent transient middle cerebral artery occlusion. MNC were extracted from the bone marrow at 22 hrs and injected via the internal carotid artery or the femoral vein 2 hours later. CP was measured with MRI or continuous laser Doppler flowmetry. Serum samples were collected to measure vasoactive mediators. Animals were treated with the Nitric Oxide (NO) inhibitor, L-NAME, to establish the relevance of NO-signaling to the effect of MNC. Lesion size, MNC biodistribution, and neurological deficits were assessed.CP transiently increased in the peri-infarct region within 30 min after injecting MNC compared to saline or fibroblast control. This CP increase corresponded temporarily to serum NO elevation and was abolished by L-NAME. Pre-treatment with L-NAME reduced brain penetration of MNC and prevented MNC from reducing infarct lesion size and neurological deficits.NO generation in response to MNC may represent a mechanism underlying how MNC enter the brain, reduce lesion size, and improve outcome in ischemic stroke

Reversing Melanoma Cross-Resistance to BRAF and MEK Inhibitors by Co-Targeting the AKT/mTOR Pathway

The sustained clinical activity of the BRAF inhibitor vemurafenib (PLX4032/RG7204) in patients with BRAF(V600) mutant melanoma is limited primarily by the development of acquired resistance leading to tumor progression. Clinical trials are in progress using MEK inhibitors following disease progression in patients receiving BRAF inhibitors. However, the PI3K/AKT pathway can also induce resistance to the inhibitors of MAPK pathway.The sensitivity to vemurafenib or the MEK inhibitor AZD6244 was tested in sensitive and resistant human melanoma cell lines exploring differences in activation-associated phosphorylation levels of major signaling molecules, leading to the testing of co-inhibition of the AKT/mTOR pathway genetically and pharmacologically. There was a high degree of cross-resistance to vemurafenib and AZD6244, except in two vemurafenib-resistant cell lines that acquired a secondary mutation in NRAS. In other cell lines, acquired resistance to both drugs was associated with persistence or increase in activity of AKT pathway. siRNA-mediated gene silencing and combination therapy with an AKT inhibitor or rapamycin partially or completely reversed the resistance.Primary and acquired resistance to vemurafenib in these in vitro models results in frequent cross resistance to MEK inhibitors, except when the resistance is the result of a secondary NRAS mutation. Resistance to BRAF or MEK inhibitors is associated with the induction or persistence of activity within the AKT pathway in the presence of these drugs. This resistance can be potentially reversed by the combination of a RAF or MEK inhibitor with an AKT or mTOR inhibitor. These combinations should be available for clinical testing in patients progressing on BRAF inhibitors

Induced pseudoscalar coupling of the proton weak interaction

The induced pseudoscalar coupling $g_p$ is the least well known of the weak coupling constants of the proton's charged--current interaction. Its size is dictated by chiral symmetry arguments, and its measurement represents an important test of quantum chromodynamics at low energies. During the past decade a large body of new data relevant to the coupling $g_p$ has been accumulated. This data includes measurements of radiative and non radiative muon capture on targets ranging from hydrogen and few--nucleon systems to complex nuclei. Herein the authors review the theoretical underpinnings of $g_p$, the experimental studies of $g_p$, and the procedures and uncertainties in extracting the coupling from data. Current puzzles are highlighted and future opportunities are discussed.Comment: 58 pages, Latex, Revtex4, prepared for Reviews of Modern Physic

Identifier mapping performance for integrating transcriptomics and proteomics experimental results

Background\ud Studies integrating transcriptomic data with proteomic data can illuminate the proteome more clearly than either separately. Integromic studies can deepen understanding of the dynamic complex regulatory relationship between the transcriptome and the proteome. Integrating these data dictates a reliable mapping between the identifier nomenclature resultant from the two high-throughput platforms. However, this kind of analysis is well known to be hampered by lack of standardization of identifier nomenclature among proteins, genes, and microarray probe sets. Therefore data integration may also play a role in critiquing the fallible gene identifications that both platforms emit.\ud \ud Results\ud We compared three freely available internet-based identifier mapping resources for mapping UniProt accessions (ACCs) to Affymetrix probesets identifications (IDs): DAVID, EnVision, and NetAffx. Liquid chromatography-tandem mass spectrometry analyses of 91 endometrial cancer and 7 noncancer samples generated 11,879 distinct ACCs. For each ACC, we compared the retrieval sets of probeset IDs from each mapping resource. We confirmed a high level of discrepancy among the mapping resources. On the same samples, mRNA expression was available. Therefore, to evaluate the quality of each ACC-to-probeset match, we calculated proteome-transcriptome correlations, and compared the resources presuming that better mapping of identifiers should generate a higher proportion of mapped pairs with strong inter-platform correlations. A mixture model for the correlations fitted well and supported regression analysis, providing a window into the performance of the mapping resources. The resources have added and dropped matches over two years, but their overall performance has not changed.\ud \ud Conclusions\ud The methods presented here serve to achieve concrete context-specific insight, to support well-informed decisions in choosing an ID mapping strategy for "omic" data merging

Sharing Detailed Research Data Is Associated with Increased Citation Rate

BACKGROUND: Sharing research data provides benefit to the general scientific community, but the benefit is less obvious for the investigator who makes his or her data available. PRINCIPAL FINDINGS: We examined the citation history of 85 cancer microarray clinical trial publications with respect to the availability of their data. The 48% of trials with publicly available microarray data received 85% of the aggregate citations. Publicly available data was significantly (p = 0.006) associated with a 69% increase in citations, independently of journal impact factor, date of publication, and author country of origin using linear regression. SIGNIFICANCE: This correlation between publicly available data and increased literature impact may further motivate investigators to share their detailed research data

Protein-protein interactions in the RPS4/RRS1 immune receptor complex

Plant NLR (Nucleotide-binding domain and Leucine-rich Repeat) immune receptor proteins are encoded by Resistance (R) genes and confer specific resistance to pathogen races that carry the corresponding recognized effectors. Some NLR proteins function in pairs, forming receptor complexes for the perception of specific effectors. We show here that the Arabidopsis RPS4 and RRS1 NLR proteins are both required to make an authentic immune complex. Over-expression of RPS4 in tobacco or in Arabidopsis results in constitutive defense activation; this phenotype is suppressed in the presence of RRS1. RRS1 protein co-immunoprecipitates (co-IPs) with itself in the presence or absence of RPS4, but in contrast, RPS4 does not associate with itself in the absence of RRS1. In the presence of RRS1, RPS4 associates with defense signaling regulator EDS1 solely in the nucleus, in contrast to the extra-nuclear location found in the absence of RRS1. The AvrRps4 effector does not disrupt RPS4-EDS1 association in the presence of RRS1. In the absence of RRS1, AvrRps4 interacts with EDS1, forming nucleocytoplasmic aggregates, the formation of which is disturbed by the co-expression of PAD4 but not by SAG101. These data indicate that the study of an immune receptor protein complex in the absence of all components can result in misleading inferences, and reveals an NLR complex that dynamically interacts with the immune regulators EDS1/PAD4 or EDS1/SAG101, and with effectors, during the process by which effector recognition is converted to defense activation