The Transcriptional Landscape of Marek’s Disease Virus in Primary Chicken B Cells Reveals Novel Splice Variants and Genes

Marek’s disease virus (MDV) is an oncogenic alphaherpesvirus that infects chickens and poses a serious threat to poultry health. In infected animals, MDV efficiently replicates in B cells in various lymphoid organs. Despite many years of research, the viral transcriptome in primary target cells of MDV remained unknown. In this study, we uncovered the transcriptional landscape of the very virulent RB1B strain and the attenuated CVI988/Rispens vaccine strain in primary chicken B cells using high-throughput RNA-sequencing. Our data confirmed the expression of known genes, but also identified a novel spliced MDV gene in the unique short region of the genome. Furthermore, de novo transcriptome assembly revealed extensive splicing of viral genes resulting in coding and non-coding RNA transcripts. A novel splicing isoform of MDV UL15 could also be confirmed by mass spectrometry and RT-PCR. In addition, we could demonstrate that the associated transcriptional motifs are highly conserved and closely resembled those of the host transcriptional machinery. Taken together, our data allow a comprehensive re-annotation of the MDV genome with novel genes and splice variants that could be targeted in further research on MDV replication and tumorigenesis

Modeling the Impact of Antiretroviral Use in Developing Countries

Bertozzi and Bautista-Arredondo discuss the implications of a new PLoS Medicine study that models the impact of antiretroviral drugs upon HIV transmission in developing countries

Motivation to Control Prejudice Predicts Categorization of Multiracials

Multiracial individuals often do not easily fit into existing racial categories. Perceivers may adopt a novel racial category to categorize multiracial targets, but their willingness to do so may depend on their motivations. We investigated whether perceivers' levels of internal motivation to control prejudice (IMS) and external motivation to control prejudice (EMS) predicted their likelihood of categorizing Black-White multiracial faces as Multiracial. Across four studies, IMS positively predicted perceivers' categorizations of multiracial faces as Multiracial. The association between IMS and Multiracial categorizations was strongest when faces were most racially ambiguous. Explicit prejudice, implicit prejudice, and interracial contact were ruled out as explanations for the relationship between IMS and Multiracial categorizations. EMS may be negatively associated with the use of the Multiracial category. Therefore, perceivers' motivations to control prejudice have important implications for racial categorization processes

RTVP-1 regulates glioma cell migration and invasion via interaction with N-WASP and hnRNPK

Glioblastoma (GBM) are characterized by increased invasion into the surrounding normal brain tissue. RTVP-1 is highly expressed in GBM and regulates the migration and invasion of glioma cells. To further study RTVP-1 effects we performed a pull-down assay using His-tagged RTVP-1 followed by mass spectrometry and found that RTVP-1 was associated with the actin polymerization regulator, N-WASP. This association was further validated by co-immunoprecipitation and FRET analysis. We found that RTVP-1 increased cell spreading, migration and invasion and these effects were at least partly mediated by N-WASP. Another protein which was found by the pull-down assay to interact with RTVP-1 is hnRNPK. This protein has been recently reported to associate with and to inhibit the effect of N-WASP on cell spreading. hnRNPK decreased cell migration, spreading and invasion in glioma cells. Using co-immunoprecipitation we validated the interactions of hnRNPK with N-WASP and RTVP-1 in glioma cells. In addition, we found that overexpression of RTVP-1 decreased the association of N-WASP and hnRNPK. In summary, we report that RTVP-1 regulates glioma cell spreading, migration and invasion and that these effects are mediated via interaction with N-WASP and by interfering with the inhibitory effect of hnRNPK on the function of this protein

Chapter 7: Grading a Body of Evidence on Diagnostic Tests

10.1007/s11606-012-2021-9Journal of General Internal Medicine27SUPPL.1S47-S55JGIM

GPs' reasons for referral of patients with chest pain: a qualitative study

<p>Abstract</p> <p>Background</p> <p>Prompt diagnosis of an acute coronary syndrome is very important and urgent referral to a hospital is imperative because fast treatment can be life-saving and increase the patient's life expectancy and quality of life. The aim of our study was to identify GPs' reasons for referring or not referring patients presenting with chest pain.</p> <p>Methods</p> <p>In a semi-structured interview, 21 GPs were asked to describe why they do or do not refer a patient presenting with chest pain. Interviews were taped, transcribed and qualitatively analysed.</p> <p>Results</p> <p>Histories of 21 patients were studied. Six were not referred, seven were referred to a cardiologist and eight to the emergency department. GPs' reasons for referral were background knowledge about the patient, patient's age and cost-benefit estimation, the perception of a negative attitude from the medical rescue team, recent patient contact with a cardiologist without detection of a coronary disease and the actual presentation of signs and symptoms, gut feeling, clinical examination and ECG results.</p> <p>Conclusion</p> <p>This study suggests that GPs believe they do not exclusively use the 'classical' signs and symptoms in their decision-making process for patients presenting with chest pain. Background knowledge about the patient, GPs' personal ideas and gut feeling are also important.</p

A new method for determining physician decision thresholds using empiric, uncertain recommendations

<p>Abstract</p> <p>Background</p> <p>The concept of risk thresholds has been studied in medical decision making for over 30 years. During that time, physicians have been shown to be poor at estimating the probabilities required to use this method. To better assess physician risk thresholds and to more closely model medical decision making, we set out to design and test a method that derives thresholds from actual physician treatment recommendations. Such an approach would avoid the need to ask physicians for estimates of patient risk when trying to determine individual thresholds for treatment. Assessments of physician decision making are increasingly relevant as new data are generated from clinical research. For example, recommendations made in the setting of ocular hypertension are of interest as a large clinical trial has identified new risk factors that should be considered by physicians. Precisely how physicians use this new information when making treatment recommendations has not yet been determined.</p> <p>Results</p> <p>We derived a new method for estimating treatment thresholds using ordinal logistic regression and tested it by asking ophthalmologists to review cases of ocular hypertension before expressing how likely they would be to recommend treatment. Fifty-eight physicians were recruited from the American Glaucoma Society. Demographic information was collected from the participating physicians and the treatment threshold for each physician was estimated. The method was validated by showing that while treatment thresholds varied over a wide range, the most common values were consistent with the 10-15% 5-year risk of glaucoma suggested by expert opinion and decision analysis.</p> <p>Conclusions</p> <p>This method has advantages over prior means of assessing treatment thresholds. It does not require physicians to explicitly estimate patient risk and it allows for uncertainty in the recommendations. These advantages will make it possible to use this method when assessing interventions intended to alter clinical decision making.</p

Common decisions made and actions taken during small-animal consultations at eight first-opinion practices in the United Kingdom

In order for veterinary surgeons to undertake an evidence-based approach to making decisions about theirpatients, it is important that new evidence is generated to support the clinical decision-making process.Many of the decisions are likely to be around the actions taken to treat or manage health problemsdiscussed during the consultation, and little is currently known about the factors which affect the typeof action taken. The aim of this study was to determine the decisions made and actions taken for healthproblems discussed during first-opinion small-animal consultations, as well as identifying factors whichmay affect the decision-making process.Data were gathered during direct observation of small-animal consultations conducted by 62 veterinarysurgeons in eight first-opinion practices in the United Kingdom. For each patient presented, data weregathered on all health problems discussed during the consultation. The decision made (whether an actionwas taken or not) and the action taken where applicable (e.g. therapeutic treatment with antibiotics) wasalso recorded. A three-level multivariable logistic-regression model was developed, with problem (Level1) nested within patient (Level 2) nested within consulting veterinary surgeon (Level 3), and a binaryoutcome variable of action versus no action.At least one action was taken for 69% (n = 2203/3192) of all problems discussed. Therapeutic treatmentwas the most common action taken (n = 1286/3192 problems; 40.3%), followed by management advice(n = 1040/3192; 32.6%) and diagnostic work-up (n = 323/3192; 10.1%). The most common therapeutictreatment was antibiotics (n = 386/1286; 30%), while the most common management advice given wasdietary advice (n = 509/1040; 48.9%). The three explanatory variables remaining in the final model werewhether the problem was a presenting or non-presenting problem, the type of diagnosis made, andthe body system affected. Explanatory variables which did not remain in the final model were patientsignalment, problem history, consultation type, clinical examination type, and who raised the problem(veterinary surgeon or owner).For over two-thirds of problems discussed, an action was taken which suggests these problems maybe seen as important by the veterinary surgeon and/or pet owner. No action was taken for almost a thirdof cases which could represent ‘watchful waiting’, which has been highlighted as important in humanhealthcare. Future research should focus on the common actions taken, further exploring the complexdecision-making process, and examining the effect of the decisions made on long-term patient outcomes

Extensions to decision curve analysis, a novel method for evaluating diagnostic tests, prediction models and molecular markers

<p>Abstract</p> <p>Background</p> <p>Decision curve analysis is a novel method for evaluating diagnostic tests, prediction models and molecular markers. It combines the mathematical simplicity of accuracy measures, such as sensitivity and specificity, with the clinical applicability of decision analytic approaches. Most critically, decision curve analysis can be applied directly to a data set, and does not require the sort of external data on costs, benefits and preferences typically required by traditional decision analytic techniques.</p> <p>Methods</p> <p>In this paper we present several extensions to decision curve analysis including correction for overfit, confidence intervals, application to censored data (including competing risk) and calculation of decision curves directly from predicted probabilities. All of these extensions are based on straightforward methods that have previously been described in the literature for application to analogous statistical techniques.</p> <p>Results</p> <p>Simulation studies showed that repeated 10-fold crossvalidation provided the best method for correcting a decision curve for overfit. The method for applying decision curves to censored data had little bias and coverage was excellent; for competing risk, decision curves were appropriately affected by the incidence of the competing risk and the association between the competing risk and the predictor of interest. Calculation of decision curves directly from predicted probabilities led to a smoothing of the decision curve.</p> <p>Conclusion</p> <p>Decision curve analysis can be easily extended to many of the applications common to performance measures for prediction models. Software to implement decision curve analysis is provided.</p