Consider a meta-analysis where a 'head-to-head' comparison of diagnostic tests for a disease of interest is intended. Assume there are two or more tests available for the disease, where each test has been studied in one or more papers. Some of the papers may have studied more than one test, hence the results are not independent. Also the collection of tests studied may change from one paper to the other, hence incomplete matched groups.pOR, a baseline function capturing the way OR changes across papers. The POR model does not assume homogeneity of ORs, but merely specifies a relationship between the ORs of the two tests.We propose a model, the proportional odds ratio (POR) model, which makes no assumptions about the shape of One may expand the domain of the POR model to cover dependent studies, multiple outcomes, multiple thresholds, multi-category or continuous tests, and individual-level data.In the paper we demonstrate how to formulate the model for a few real examples, and how to use widely available or popular statistical software (like SAS, R or S-Plus, and Stata) to fit the models, and estimate the discrimination accuracy of tests. Furthermore, we provide code for converting ORs into other measures of test performance like predictive values, post-test probabilities, and likelihood ratios, under mild conditions. Also we provide code to convert numerical results into graphical ones, like forest plots, heterogeneous ROC curves, and post test probability difference graphs.The flexibility of POR model, coupled with ease with which it can be estimated in familiar software, suits the daily practice of meta-analysis and improves clinical decision-making. A diagnostic test, in its simple form, tries to detect presence of a particular condition (disease) in a sample. Usually there are several studies where performance of the diagnostic test is measured by some statistic. One may want to combine such studies to get a good picture of performance of the test, a meta-analysis. Also, for a particular disease there may be several diagnostic tests invented, where each of the tests is subject of one or more studies. One may also want to combine all such studies to see how the competing tests are performing with respect to each other, and choose the best for clinical practice.To pool several studies and estimate a summary statistic some assumptions are made. One such assumption is that differences seen between individual study results are due to chance (sampling variation). Equivalently, this means all study results are reflecting the same "true" effect . HoweverFor example, sensitivity and specificity are a pair of statistics that together measure the performance of a diagnostic test. One may want to compute an average sensitivity and an average specificity for the test across the studies, hence pooling the studies together. Instead, one may choose to extract odds ratio (OR) from each paper (as test performance measure), and then estimate the average OR across the studies. The advantage is that widely different sensitivities (and specificities) can point to the same OR. This means one is relaxing the assumption that all the studies are pointing to the same sensitivity and specificity, and accepts that different studies are reporting "truly different" sensitivity and specificity, and that the between-study variation of them is not due to random noise alone, but because of difference in choice of decision threshold . Therefore the major advantage of OR, and its corresponding receiver-operating-characteristic (ROC) curve, is that it provides measures of diagnostic accuracy unconfounded by decision criteria . An addiThe above process may be used once more to relax the assumption that every study is pointing to the same OR, thus relaxing the "OR-homogeneity" assumption. In other words, in some cases, the remaining variation between studies, after utilizing OR as the summary performance measure, is still too much to be attributed to random noise. This suggests OR may vary from study to study. Therefore one explicitly assumes different studies are measuring different ORs, and that they are not pointing to the same OR. This difference in test performance across studies may be due to differences in study design, patient population, case difficulty, type of equipment, abilities of raters, and dependence of OR on threshold chosen . Nelson There are a few implementations of the heterogeneous ROC. One may classify them into two groups. The first group is exemplified by Tosteson and Begg . They shWhen comparing two (or more) diagnostic tests, where each study reports results on more than one test, the performance statistics (in the study results) are correlated. Then standard errors computed by SROC are invalid. Toledano and Gatsonis  use the We propose a regression model that accommodates more general heterogeneous ROC curves than SROC. The model accommodates complex missing patterns, and accounts for correlated results . FurtherBesides accounting better for between-study variation, we show how to use the POR model to "explain why" such variation exists. This potentially gives valuable insights and may have direct clinical applications. It may help define as to when, where, how, and on what patient population to use which test, to optimize performance.We show how to use "deviation" contrast, in parameterization of categorical variables, to relax the restriction that a summary measure may be reported only if the respective interaction terms in the model are insignificant. This is similar to using grand mean in a "factor effects" ANOVA model (compared to "cell means" ANOVA model).We show how to use nonparametric smoothers, instead of parametric functions of true positive rate (TPR) and/or false positive rate (FPR), to generate heterogeneous ROC for a single diagnostic test across several studies.Our proposed POR model assumes the shape of the heterogeneous ROC curve is the same from one test to the other, but they differ in their locations in the ROC space. This assumption facilitates the comparison of the tests. However, one may want to relax the POR assumption, where each test is allowed to have a heterogeneous ROC curve with a different shape. One may implement such generalized comparison of the competing diagnostic tests by a mixed effects model. This may improve generalizability of meta-analysis results to all (unobserved) studies. Also, a mixed effects model may take care of remaining between-study variation better.i,j,p = PERFi,p - PERFj,p, where the difference Δ depends on paper index p, where PERFi,p is observed performance of test i in paper p. To simplify notation, assume that a single number measures performance of each test in each paper. We relax this assumption later, allowing for the distinction between the two types of mistakes . We decompose the differencesTo compare two diagnostic tests i and j, we want to estimate the difference in their performance. However, in reality such difference may vary from one paper (study) to the other. Therefore Δ(1) Δi,j,p = i,p PERF- j,pPERF= i,j δ+ i,j,pδ,δi,j is the 'average' difference between the two tests, and δi,j,p is deviation of the observed difference within paper p from the average δi,j. The δi,j is an estimator for the difference between performance of the two tests. Note by using deviation parameterization (similar to an ANOVA model) [[i,j,p and then the mean parameter for the distribution is estimated. In other words, one does not need to assume 'homogeneous' difference of the two tests across all the papers, and then estimate the 'common' difference [where  model) [, pp.51 &fference .The observed test performance, PERF, may be measured in several different scales, such as paired measures sensitivity and specificity, positive and negative predictive values, likelihood ratios, post test odds, and post test probabilities for normal and abnormal test results; as well as single measures such as accuracy, risk or rate ratio or difference, Youden's index, area under ROC curve, and odds ratio (OR). When using OR as the performance measure, the marginal logistic regression model(2) pt) logit is fitted to the (repeated measures) grouped binary data, where the 2-by-2 tables of gold-standard versus test results are extracted from each published paper. In the model (2) Result is an integer-valued variable for positive test result ; Disease is an indicator for actual presence of disease, ascertained by the gold standard; PaperID is a categorical variable for papers included in the meta-analysis; and TestID is a categorical variable for tests included. Regression coefficients the data . Note moFrom model (2) one can show thatpt LOR= 1 β+ 3β*pt PaperID+ 5β* pt TestID+ 7β*ptTestID*ptPaperIDand therefore the difference between performance of two tests i and j, measured by LOR, ispi LOR- pj LOR= 5β* pi TestID- 5β* pj TestID+ 7β*piTestID*pi PaperID- 7β*pjTestID*pjPaperIDδi,j of the decomposition model (1) with the 5β* pi TestID- 5β*pjTestID, and identify δi,j,p with 7β*piTestID*pi PaperID- 7β*pjTestID*pjPaperID.where we identify 5β*pt TestIDis the average deviation of the LOR of test t from the overall LOR (the 1β), where the overall LOR is the average over all tests and all papers. Therefore 5β*pt TestIDof model (2) will be equivalent to the δi,j of the decomposition model (1), and 7β*ptTestID*pt PaperIDequivalent to δi,j,p.If there is an obvious and generally accepted diagnostic test that can serve as a reference category (RefCat) to which other tests can be compared, then a "simple" parameterization for tests is sufficient, However, usually it is not the case. When there is no perceived referent test to which the other tests are to be compared, a "deviation from means" coding is preferred for the tests. Using the deviation parameterization for both TestID and PaperID in the model (2), one can show that 6β*ptDisease*ptTestID*ptPaperID. Then for the reduced modelModel (2) expands each study to its original sample size, and uses patients as primary analysis units. Compared to a random-effects model where papers are the primary analysis units, it has more degrees of freedom. However, in a real case, not every test is studied in every paper. Rather majority of tests are not studied in each paper. Therefore the data structure of tests-by-papers is incomplete with many unmeasured cells. The three-way interaction model (2) may become over-parameterized. One may want to drop the term (3) pt) logit model, where the ratio of odds ratios of two tests is assumed to be constant across papers, while odds ratio of each test is allowed to vary across the papers. Note the difference with the proportional odds model where ratio of odds is assumed to be constant [we have constant . In the (4) pt OR= p OR*  , t = 1, 2, ..., k, p = 1, 2, ..., mk diagnostic tests, and p is an index representing the m papers included in the analysis. p ORis a function capturing the way OR changes across papers. Then to compare two diagnostic tests i and jwhere t is an index for the pi OR/ pj OR= p ORacross the papers. Thus the model makes no assumptions about the shape of p OR(and in particular homogeneity of ORs) but merely specifies a relationship between the ORs of the two tests.where the ratio of the two ORs depends only on the difference between the effect estimates of the two tests, and is independent of the underlying One may want to replace the PaperID variable with a smooth function of FPR or TPR, such as natural restricted cubic splines. There are two potential advantages. This may preserve some degrees of freedom, where one can spend by adding covariates to the model to measure their potential effects on the performance of the diagnostic tests. Thus one would be able to explain why performance of the same test varies across papers. Also, this allows plotting a ROC curve where the OR is not constant across the curve, a flexible ROC (HetROC) curve.(5) pt) logit(Result= 0 β+ 1β*pt Disease+ 2β*pt) S(FPR+ 3β*ptDisease*pt) S(FPR+ 4β*pt TestID+ 5β*ptDisease*pt TestID+ 6β*pt X+ 5β*ptDisease*ptXTo test the POR assumption one may use model (2) where the three-way interaction of Disease and TestID with PaperID is included. However, in majority of real datasets this would mean an over-parameterized model. Graphics can be used for a qualitative checking of the POR assumption. For instance, the y-axis can be LOR, while the x-axis is paper number. To produce such plot, it may be better to have the papers ordered in some sense. One choice is to compute an unweighted average of (observed) ORs of all the tests the paper studied, and use it as the OR of that paper. Then sort the papers based on such ORs. The OR of a test may vary from one paper to the other (with no restriction), but the POR assumption is that the ratio of ORs of two tests remains the same from one paper to another. If one shows ORs of a test across papers by a smooth curve, then one expects that the two curves of the two tests are proportional to each other. In the log-OR scale, this means the vertical distance of the two curves remains the same across the x-axis. To compute the observed LOR for a test in a paper one may need to add some value (like 1/2) to the cell counts, if some cell counts are zero. However, this could introduce some bias to the estimates.Among the approaches for modeling repeated-measures data, we use generalized estimating equations to estimate the marginal logistic regression . SoftwarOne may use a non-linear mixed effects modeling approach on the cell-count data for estimation of parameters of the POR model. The Paper effect is declared as random, and interaction of the random effect with Disease is included in the model, as indicated in model (2). However, such mixed effects non-linear models are hard to converge, especially for datasets where there are many papers studying only one or a small number of the included tests (such as the dataset presented as example in this paper). If the convergence is good, it may be possible to fit a mixed model with the interaction of Disease, Test, and the Paper random effect. Such model relaxes the POR assumption, besides relaxing the assumption of OR-homogeneity. In other words, one can use the model to quantitatively test the POR assumption. One should understand that the interpretation of LOR estimate from a marginal model is of a population-average, while that of a mixed model is a conditional-average. Therefore there is a slight difference in their meaning.One may use the frameworks of the generalized linear models (GLM) and the generalized estimating equations (GEE) to extend the POR model and apply it to different scenarios. By using suitable GLM link function and random component [, p.72], One may avoid dichotomizing results of the diagnostic test by using the 'likelihood ratio' as the performance measure, and fitting a PP model to such continuous outcome. For a scenario where performance of a single test has been measured multiple times within the same study, for example with different diagnostic calibrations (multiple thresholds), the POR estimated by the GEE incorporates data dependencies. When there is a multi-layer and/or nested clustering of repeated measures, software to fit a mixed-effects POR model may be more available than an equivalent GEE POR.When POR is implemented by a logistic regression on 2-by-2 tables, it uses a grouped binary data structure. It takes a minimal effort to fit the same logistic model to the "ungrouped" binary data, the so-called "individual level" data.Methods of meta-analysis that allow for different outcomes (and different numbers of outcomes) to be measured per study, such as that of Gleser and Olkin , or DuMoTo demonstrate how to fit the POR model, we use a recent meta-analysis of diagnostic tests for deep vein thrombosis (DVT) by Heim et al. . In this1 βis the "overall mean" for the log-OR. This is similar to an ANOVA analysis where the overall mean is estimated by the model. Therefore the average OR is equal to exp(2.489) = 12.049. Components of 5 βestimate deviation of LOR of each test from the overall LOR. Software gives estimates of SEs, plus confidence intervals and p-values, so inference is straightforward.Since we have used deviation contrast for the variables, estimate of A forest plot may be used to present the results of the modeling in a graphical way. This may connect better with clinically oriented audience. In Figure The horizontal axis is log-OR, representing test performance. The dashed vertical line shows overall mean LOR. For each diagnostic test the solid square shows the LOR, while the horizontal line shows the corresponding 95% CI. If the horizontal line does not intersect the vertical line, the test is significantly different from the overall mean LOR.Note that the CIs in the plot are computed by adding the overall LOR to the CI for the deviation effect of each particular test. This ignores the variability of the overall LOR estimate. One can estimate the LOR of a test and its CI more accurately by some extra computations, or by fitting a slightly modified model. A method is illustrated and implemented see . HoweverOne can translate LOR to other measures of test performance. There are numerous types of these measures. We provide code to convert the LOR estimated by the POR model to such measures. Note that majority of them, unlike LOR, are in pairs. This means in order to compare two tests, one needs to use two numbers to represent each single test. For example, sensitivity-specificity is a pair. If a test has a higher sensitivity than the other test, while having a lower specificity, it is not immediately clear which test is better. Also, note that some performance measures are independent of disease prevalence, while others depend on prevalence. This means the same test would perform differently for populations with different disease prevalence.Note in order to compute some of the performance measures, one needs to assume a prevalence and sensitivity or specificity. We assumed a disease prevalence of 40%, and a specificity of 90%, for Table We suggest graphs to compare tests when using such "prevalence-dependent paired performance measures" . In FiguThe way one may read the graph is that, given a particular population with a known prevalence of disease like 40%, we perform the diagnostic test on a person picked randomly from the population. If the test turns normal, the probability the person has disease decreases from the average 40% to about 4% . If the test turns abnormal, the probability the person is diseased increases from 40% to about 57%. The dotted green diagonal line represents a test no better than flipping a coin, an uninformative test. The farther the two curves from the diagonal line, the more informative the test is. In other words, the test performs better.One can summarize the two curves of a test in a single curve, by computing the vertical distance between the two. The solid black curve in the figure is such "difference" curve. It seems this particular test is performing the best in populations with disease prevalence of around 75%.One can use the difference curve to compare several tests, and study effect of prevalence on the way the tests compare to each other. In Figure The black solid curve is comparing the two tests. For populations with low disease prevalence (around 17%), the D-Dimer is performing better than VIDAS. However, when the prevalence is higher (around 90%), VIDAS is preferred. Simultaneous confidence bands around the comparison curve would make formal inference possible.A nonlinear mixed effects POR model fitted to cell counts of the DVT dataset does not converge satisfactorily. We fitted the mixed model to a subset of the data where only two tests and seven papers are included, Table Five of the seven papers have studied both the tests. Result of SAS Proc NLMixed still is sensitive to initial values of parameters. The three-way interaction term of disease, test, and paper in the mixed model (where POR is not assumed) is insignificant, Table The estimate of overall LOR from both the POR-mixed model and POR-marginal model are significantly different from zero. However, the mixed model estimate of LOR is much smaller than the marginal one. For non-linear models, the marginal model describes the population parameter, while the mixed model describes an individual's [, p.135].Sometimes one may be interested in constructing the ROC curve for the diagnostic test. A homogeneous ROC curve assumes the performance of the test (as measured by LOR) is the same across the whole range of specificity. However, this assumption may be relaxed in a HetROC. We fitted a simplified version of model (5) for test SimpliRED,pt) logit(Result= 0 β+ 1β*pt Disease+ 2β*pt) S(FPR+ 3β*ptDisease*pt)S may be used to reject significance of the three-way interaction term. However, the dataset gathered for the DVT meta-analysis is such that no single paper covers all the tests. Moreover, out of 21, there are 7 tests that have been studied in only one paper. For Figure We sorted the papers, the x-axis, based on average LOR within that paper. We fitted Lowess smooth lines to the observed LORs of each test separately. Figure Also, to verify how good the model fits the data, one may use an observed-versus-fitted plot. Plots or lists of standardized residuals may be helpful finding papers or tests that are not fitted well. This may provide a starting point for further investigation.A comparison of the relative accuracy of several diagnostic tests should ideally be based on applying all the tests to each of the patients or randomly assigning tests to patients in each primary study. Obtaining diagnostic accuracy information for different tests from different primary studies is a weak design . CompariIn meta-analysis of two (or more) diagnostic tests, where attention is mainly on the difference between performances of two tests, having a homogeneous estimate of performance of each single test is of secondary importance, and it may be treated as nuisance. The POR model assumes differences between LORs of two tests are the same across all papers, but does not assume the OR of a test is the same in every paper. Hence no need for homogeneity of OR of a test across papers that reported it, but shifting the assumption one level higher to POR.3 βin the model logit(Result) = 0 β+ 1β*Disease + 2β*PaperID + 3β*Disease*PaperID, to interpret 1β, the overall LOR. This means the POR model explicitly accepts that performance of the diagnostic test varies across the papers, but at the same time estimates its mean value. McClish explains if a test for OR homogeneity shows heterogeneity, there may be no 'common' measure to report, but still there is an 'average' measure one can report. [The POR model uses "deviation from means" parameterization. Then one does not need to drop the interactions coefficient  report. We demonstrated how to fit the POR model to the cell counts, rather than to the OR values. This, we believe, has several advantages. 1. One does not need assuming normality of some summary measure. This results in binomial distributional assumption that is more realistic. 2. Also, different study sample sizes are incorporated into the POR model without faulty bias-introducing weighting schemes, as shown by Mosteller & Chalmers . And extlogit(Result) = 0 β+ 1β*Disease + 2β*PaperID . Then we have log = 0 β+ 1 β+ 2β*PaperID. Substituting a value for the covariate (here PaperID) such as a modal or average value, and using the model estimates for the betas, one gets the log-odds. Then one exponentiates it to get the TP/FN, call it Q. Now it is easy to verify that sensitivity = Q/(1+Q). Likewise we have log = 0 β+ 2β*PaperID, that we call = log(W). Then specificity = 1/(1+W). Also, one can apply separate weights to the log and log, to balance the true positive and false positive rates for decision making in a particular clinical practice.The way the random-effects model is usually implemented is by extracting OR from each paper, and assuming LOR being normally distributed. Then the distinction between the two types of mistakes  is lost, since one enters the LOR as datapoints into the model. The bivariate model by Houwelingen et al  tries toWhen collecting papers from biomedical literature for meta-analysis of a few diagnostic tests, it is hard to come up with a complete square dataset, where every paper has included all the tests of interest. Usually the dataset contains missing values, and a case-wise deletion of papers with missing tests means a lot of data is thrown away. A method of analysis that can utilize incomplete matched groups may be helpful. The POR model allows complex missing patterns in data structure. Convergence of marginal POR model seems much better than non-linear mixed model, when fitted to cell counts of incomplete matched groups. This is an advantage for using GEE to estimate POR.The fact that one can use popular free or commercial software to fit the proposed models, facilitates incorporation of the POR modeling in the practice of meta-analysis.The POR model utilizes the variation in the observed performance of a test across papers. Explaining when and how the performance of the test changes, and finding the influential factors, is an important step in advancing science. In other words, rather than calling it 'heterogeneity', treated as 'unwanted' and unfortunate, one calls it 'variability' and utilizes the observed variability to estimate and explain when and how to use the agent or the test in order to optimize their effects.Victor  emphasizThe POR model incorporates risk of events in the control group via a predictor, such as observed prevalence, hence a 'control rate regression'. Although implementing the HetROC means that one accepts the diagnostic test performs differently in different FPRs along the ROC curve, in some implementations of HetROC, such as method of summary ROC, one compares tests by a single point of their respective ROCs. This is not optimal. (The Q test of the SROC method is a single point test, where that point on the ROC may not be the point for a specific cost-benefit case.) In such method although one produces a complete SROC, but one does not use it in comparing the diagnostic tests. In the POR model, one uses LOR as the measure for diagnostic discrimination accuracy, and builds statistical test based on the LOR-ratio, hence the test corresponds to whole ROCs .The ROC graph was designed in the context of the theory of signal detectability ,28. ROC In building HetROC curve, the POR model accommodates more general heterogeneous ROCs than SROC, because it uses nonparametric smoother instead of arbitrary parametric functions used in SROC method. When in the POR model the smoother covariate is replaced by log{TPR*FPR/ [(1-TPR)*(1-FPR)]}, a HetROC similar to SROC of Moses et al is produced.When one uses a smooth function of FPR in the POR model, it is equivalent to using a function of outcome as predictor. This resembles a 'transition model'. Ogilvie and Creelman  claim thWhen there is only one diagnostic test, and one is mainly interested in pooling several studies of the same test, the POR model estimates effect sizes that are more generalizable. By using the smoother (instead of PaperID), one fits a sub-saturated model that allows inclusion of other covariates, hence it is possible to estimate effect of study level factors on performance and explain the heterogeneity. Also it does not assume any a priori shape of the ROC, including monotonicity. Plus, it enables graphing of the HetROC. It does not need omission of interaction terms to estimate the overall performance, and it does not need assumption of OR homogeneity. If several performance measurements of the same test is done in a single study, like evaluating the same test with different diagnostic calibrations, the POR model provides more accurate estimates, by incorporating the dependence structure of the data.When there is heterogeneity between a few studies for the same diagnostic test, one solution to absorb the extra between-study variation is to use a random/mixed effects model. However, Greenland  cautionsWe suggest these follow-ups: 1. the POR model has been implemented both by marginal and mixed models. It would be useful to implement a marginalized mixed POR model; 2. in clinical practice, usually a group of diagnostic tests is performed on an individual, for a particular disease. Some of these tests are requested simultaneously and some in sequence. It would be useful, and practically important, to extend the POR model such that it incorporates such sequence of testing and a priori results; 3. the utility of POR model may be extended to meta-analysis of therapeutics.The author(s) declare that they have no competing interests.MSS conceived of the model, and participated in its design and implementation. JS participated in implementation of the model and performing of the example analysis. Both authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:In this file we present sample codes for a few of the models presented in the paper. The estimation mostly has been done in SAS, while the graphing (and some model-fitting) has been done in R.Click here for fileThis zipped file contains 8 data files, in the .csv  and .xls (MS Excel) formats. They are to be used with the SAS and R codes we presented in the Appendix [additional file 1]. Five files are for the SAS codes presented in the Appendix. The file names are "data5.xls", "data5_t12&17.xls", "u125.xls", "data5_t18.xls", "data6.xls". Three files are for the R codes presented in the Appendix. The file names are "obsVSfit.csv", "dataNewExcerpt2.csv", and "data6_lor2.csv".Click here for file


Commercial influences on research results can in turn lead to "hype" in science and medicine news stories There is a growing recognition of the importance of the popular press in the communication of science. The media is often the primary source of science information and, as such, can have a profound impact on how the public views the risks and benefits of scientific advances. Dorothy Nelkin suggests that the “media serve as brokers between science and the public, framing the social reality for their readers and shaping the public consciousness about science related events” Because of this influential role, many commentators have been highly critical of the quality of media reporting, suggesting that reporting is “hyped”, irresponsible, and hurtful to the public's understanding of important scientific issues. In 1999, the United Kingdom House of Commons Science and Technology Committee was concerned enough to recommend that the media be governed by a Code of Practice that “stipulates that scientific stories should be factually accurate” faithful portrayal of commercially influenced research results.But is it fair to point an accusing finger solely at the popular press? There are many examples of science reporting that has been less than perfect, such as the coverage of behavioural genetics and human cloning. And there is no doubt that an entertaining or controversial spin will win out over a muted message. But there is also evidence that the media does a surprisingly good job There is an expanding body of evidence that suggests that the increasingly commercial nature of biomedical research is having an impact on how science stories are portrayed. Studies have shown that papers in peer-reviewed journals are more likely to contain positive findings if the research is funded by industry Commercial influence on public representations of science has the potential to create a skewed picture of biomedical research—a picture that emphasises benefits over risks, and predictions of unrealistic breakthroughs over a tempered explanation of the incremental nature of the advancement of scientific knowledge. In the area of genetics, for example, there is concern that this commercial influence will lead to a simplistic and overly deterministic view of the role of genes in human health and may have an adverse impact on public dialogue Given the increasingly close connection between the media's portrayal of science and the broader agenda of commercialisation, some media representations can be viewed as a subtle form of marketing, albeit often inadvertent. One commentator has gone so far as to suggest that, to a large degree, “medical news is actually unpaid advertising” BlumDKnudsonM1997A field guide to science writing: The official guide of the national association of science writersNew YorkOxford University Press304pBernhardtBAGellerGTamborEMountcastle-ShahEMullJGet al2000Analysis of media reports on the discovery of breast and prostrate cancer susceptibilityAm J Hum GenetSuppl 27662CaulfieldT2000Underwhelmed: Hyperbole, regulatory policy and the genetic revolutionMcGill Law J4543746012688281CohnVCopeL2001News and numbers: A guide to reporting statistical claims and controversies in health and related fields, 2nd edAmes (Iowa)Iowa State University Press211pConditCM1998Determinism and mass media portrayals of geneticsAm J Hum Genet629799849529342ConradP1999Uses of expertise: Sources, quotes, and voice in the reporting of genetics in the newsPublic Underst Sci8285302GarfieldE1979SIPI: Scientists taking scientific information to the publicCurr Contents41290293GellerGBernhardtBHoltzmanN2002The media and public reaction to genetic researchJAMA28777311851549MoynihanRBeroCLRoss-DegnanDHenryDLeeKet al2000Coverage by the news media of the benefits and risks of medicationsN Engl J Med3421645165010833211NelkinDLindeeMS1995The DNA mystique: The gene as a cultural iconNew YorkW. H. Freeman312pHargreavesILewisJSpeersT2003Toward a better map: Science, the public and the mediaSwindon (United Kingdom)Economic and Social Research Councilhttp://www.esrc.ac.uk/esrccontent/DownloadDocs/Mapdocfinal.pdf. Accessed 28 September 2004Available: RansohoffDRansohoffR2001Sensationalism in the media: When scientists and journalists may be complicit collaboratorsEff Clin Pract418518811525108This is not to say that science reporting is part of a coordinated effort to promote a particular product. On the contrary, there is rarely a specific product to promote, and the media is just looking for an interesting and intriguing story that will help sell papers. However, in the long run, a continued, systemic trend toward positive, industry-influenced reporting may operate in much the same way as an explicit promotional campaign. In fact, optimistic media portrayals could be considered more powerful than promotional campaigns. The message is separated from an obvious marketing agenda and often includes a trusted voice, such as a university-based researcher. Paradoxically, this trust is based in part on a belief in the perceived independence of university researchers.There is nothing inherently wrong with commercial involvement in biomedical research. After all, in most countries with an advanced biomedical research infrastructure, commercial entities, rightly or not, are an essential element of the technology-development process. Nevertheless, we need to be sensitive to the influence of market forces on how science is represented to the public. Eventually, the public will catch on. And when they do, public trust in the biomedical research enterprise may be irreparably harmed.In an increasingly knowledge-based economy, there seems to be little doubt that private industry will continue to play a significant role in the funding of science. The research community must adjust to this inevitability by taking steps to ensure that portrayals of science remain as balanced as possible. As thoughtfully noted by Tom Wilkie: “If science is to manage the transition from its older, academic tradition to a new style, while keeping popular assent and the popular image of science as an impartial means of getting at the truth, then the scientific community itself must recognise the importance of maintaining impartial sources of public information.” What can be done? For a start, reporters should always ask for and researchers should always offer information about the nature of the funding and the financial relationship of the researchers to the sponsor. Increasingly, this information is disclosed in peer-reviewed journals. However, it may not be communicated in other popular representations of research results. As motivation, the research community should remember that the media also likes a good conflict-of-interest story The research community should also consider the establishment of various sources of independent science information, including a venue for the publishing of negative results and a list of respected researchers who may be able to provide an alternative view. Not only would this create an outlet for results that do not correspond with commercial interests, it would also serve as a resource for the media. Reporters are often under extremely tight deadlines, and it is not always easy to find an independent second opinion, an indispensable component of balanced reporting.Naturally, commercial pressure isn't the only source of science hype, and it is understandable that researchers may want to promote their latest findings. But commercial influence is emerging as a known source of bias, and it is a phenomenon that could have a profound impact on how the public perceives the entire research enterprise. Developing strategies, starting with the modest ones suggested above, seems to be an essential element of any communication strategy.


Insecticide-treated nets (ITNs) are an integral component of malaria control programmes in Africa. How much pyrethroid resistance in malaria vectors will impact on the efficacy of ITNs is controversial. The purpose of this study was to evaluate knockdown and killing effects of ITNs on a metabolic-based resistant or tolerant malaria vector strain.2 permethrin EC treated bednets was assessed on the OCEAC laboratory (OC-Lab) strain of Anopheles gambiae s.s.. This strain is resistant to DDT and tolerant to pyrethroids, with elevated mixed function oxidases. The Kisumu reference susceptible strain of A. gambiae s.s. was used as control. Nets were impregnated in February 1998 and used by households of the Ebogo village. Then they were collected monthly over six months for Bio-assays (WHO cone test). Knockdown and mortality rates were compared between the OC-Lab and the Kisumu strains, by means of the Mantel-Haenszel chi-square test.Bio-efficacy of 500 mg/mDuring the whole trial, permethrin EC knockdown rates were impressive (mostly higher than 97%). No significant difference was observed between the two strains. However, the mortality rates were significantly decreased in the OC-Lab strain (40–80%) compared with that of the Kisumu strain (75–100%). The decrease of killing effect on the OC-Lab strain was attributed to permethrin EC tolerance, due to the high oxidase metabolic activity.These data suggested an impact of pyrethroid tolerance on the residual activity of ITNs. More attention should be given to early detection of resistance using biochemical or molecular assays for better resistance management. Malaria is the most important vector-borne disease in Africa. It is estimated that 80 to 90% of the 300 million annual cases and one million deaths occur on this continent . The shaInsecticide-treated nets (ITNs) are one of the main vector control tools against malaria. They are as effective as indoor residual spraying (IRS)  and stroAnopheles gambiae complex and the Anopheles funestus group, the most important malaria vectors in Africa, is a threat to the effectiveness of ITNs [kdr mutation have been reported in A. gambiae s.s. from West and East Africa [450 oxidases) are involved in insect detoxification. Elevation of their activity usually results in resistance to insecticides such as pyrethroids. In Cameroon, elevated esterase, oxidase or glutathione S-transferase activities were reported as the main resistance mechanisms in many populations of the A. gambiae complex [However, the emergence of pyrethroid resistance in the  of ITNs -11. Thist Africa ,13. In at Africa ,15. Thre complex .A. gambiae s.s with a likely oxidase-based pyrethroid tolerance.Malaria vector resistance to pyrethroids has been clearly demonstrated in Africa. However, its operational implication in terms of reducing efficacy of ITNs, especially in the case of metabolic-based resistance is not well documented. The aim of this study was to assess the knockdown and killing effects of ITNs on a metabolic-based pyrethroid resistance or tolerance strain of malaria vector. This study reports on the decrease of ITN's killing effect against a laboratory strain of The study was undertaken in the entomology laboratory of the Organisation de Coordination pour la lutte contre les Endémies en Afrique Centrale (OCEAC), in Yaoundé (Cameroon).2) manufactured by SiamDutch Mosquito Netting Co. Ltd. (Bangkok, Thailand). Two sizes of bednets were used : X-family (16.3 m2) and Family (13.13 m2). Both were strengthened on the lower part by a 20 cm sheeting border (made of more polyester filaments) to prevent tearing while being tucked in. They were impregnated with the target dosage of 500 mg/m2 permethrin EC and hung in households of the Ebogo village, for use during the period of March-September, 1998.Bednets were made of white multifilament polyester fabric  is about 65 km far from Yaoundé , in the equatorial forest. man/year . This viA total of 50 permethrin EC impregnated bednets were distributed in the village, in addition to about 30 old nets that were retreated by the study team. All the new nets were identified by a code number. Immediately after impregnation, two nets were randomly chosen and brought to the laboratory. Then, two others were collected each month from the Ebogo households and systematically replaced by unused ones. Replacement nets and old ones were properly identified so that they could not later be collected from the field and used for bio-assays. People were asked not to wash their bednets during the trial.In the laboratory, netting portions were isolated from the lower part of bednets collected from the field and wrapped in aluminum sheets. Each sample was identified by a code number and kept at 4°C until Bio-assays were performed (less than one month).A. gambiae s.s., originated from Yaoundé and laboratory-reared for about 15 years without insecticide selection. The Kisumu susceptible reference strain of A. gambiae s.s., originated from Kenya and provided by LIN/IRD Montpellier, was used as a control.The bio-efficacy of treated nets was assessed on the OC-Lab strain of The OC-Lab. strain is known to be strongly resistant to DDT and tolerant to pyrethroids, response to WHO susceptibility test  performep < 0.001). Esterase and glutathione S-transferase levels were lower in the OC-Lab strain than in the Kisumu strain. For esterases, two substrates were used, α-naphtyl acetate and paranitrophenyl acetate. With α-naphtyl acetate, the activities of the OC-Lab and Kisumu strains were 0.057 ± 0.011 and 0.117 ± 0.083 μmol α-naphtol produced/min/mg protein .Biochemical analysis of esterase, mixed function oxidase and glatathione S-transferase enzyme systems using microtitre plates and spectrophotometer as described by Penilla et al.  and Brogp-nitrophenol produced/min/mg protein . For glutathione S-transferases, the activities of the OC-Lab and Kisumu strains were 0.013 ± 0.024 and 0.087 ± 0.104 μmol GSH conjugated/min/mg protein .With paranitrophenyl acetate, the activities of the OC-Lab and Kisumu strains were 0.002 ± 0.008 and 0.053 ± 0.132 μmol A. gambiae s.s. and those of the Kisumu strain to the S molecular form. Individuals of the OC-Lab. strain which survived to WHO susceptibility test were screened by PCR [PCR analysis  showed td by PCR , all of After treatment, ten batches of five unfed females (2–5 days old) of the OC.Lab strain and those of the Kisumu strain were exposed under WHO's plastic cones to netting from newly treated nets for three minutes. Ten other batches of each mosquitoe strain were exposed to netting from untreated nets as control. Mosquitoes were then transferred in white cups and the knockdown rates were recorded at 60 minutes post-exposure. They were then supplied with a 15% glucose solution and held under laboratory conditions, at 80 per cent relative humidity and 27°C (± 2°C) temperature. The mortality rates were recorded after 24 hours.A. gambiae s. s. from the OC-Lab strain and 100 specimens from the Kisumu strain were tested each month (from M0 to M6). For the control, 50 specimens from the OC-Lab and 50 others from the Kisumu strain were exposed to untreated netting.Bio-assays were also performed on used nets. Between March and September 1998, 100 females of Each month knockdown and mortality rates of the OC-Lab strain and the Kisumu strain were then compared by means of the Mantel-Haenszel chi-square test.Table 0), more than 95 per cent of mosquitoes from both strains were knocked down 60 minutes post-exposure. The difference between the two strains was not significant at the five per cent level (p = 0.08, df = 1).No knockdown effect was observed in mosquitoes exposed to netting from untreated nets (control), either in the Kisumu strain or in the OC-Lab strain. With netting from permethrin EC freshly treated nets (Mp < 0.001, df = 1).With netting from untreated nets, mortality rate in each strain did not exceed 2 per cent. Using netting from permethrin EC freshly treated nets, the mortality rate in the OC-Lab strain did not exceed 70 per cent, while about 90 per cent mosquitoes of the Kisumu strain were killed, difference between the two strains was highly significant (p > 0.05, df = 1). For both strains, the knockdown rate was mostly higher than 90 per cent, except in the fifth month during which about 70 per cent were registered.During the whole trial, no knockdown effect was observed in mosquitoes exposed to netting from untreated nets, either in the Kisumu strain or in the OC-Lab strain, while most of the mosquitoes exposed to netting from treated nets were knocked down during the 60 minutes post-exposure. The profile of knockdown rate variations during the six month evaluation is given in Figure p < 0.001, df = 1). From the fourth to the sixth month, there was no longer a significant difference between the two strains (0.13 <p < 0.57, df = 1).The mortality rates in the control netting were constantly lower than five per cent for both strains. Conversely, numerous mosquitoes exposed to netting from treated nets were killed during the 24 hours post-exposure. The graph of the mortality rate variations during the six month evaluation is given in Figure A. gambiae s.s. which originated from Yaoundé city was not selected in the laboratory. The selective pressure was performed in the field several years prior to the collection of the strain. DDT was used in Yaoundé for residual indoor spraying during the 1950's [et al. [kdr Leu-Phe mutation was not involved in this case of DDT resistance or pyrethroid tolerance. Bio-assays using WHO cone test with a cyfluthrin EW 50 mg active ingredient per m2 of netting resulted in 35 per cent mortality rate versus 95 per cent rate for the Kisumu strain. The difference in knockdown rates was not significant (95–100 per cent for both strains). It was seen that the strain was not suitable for that trial. In fact, the cyfluthrin bio-efficacy was assessed with the Kisumu susceptible reference strain [A. gambiae s.l. from cotton and rice fields in northern Cameroon [A. gambiae s. s., such as the Kisumu strain. The study was not carried out with field mosquitoes because the cotton fields are actually located 1,000 km from the laboratory, it would be difficult to collect sufficient field samples for bio-assays.DDT resistance in the OC-Lab. strain of e 1950's . Further [et al.  reportede strain . SubsequCameroon , which iFrom current data, the insecticide activity of treated nets on the Kisumu reference strain was clearly demonstrated, despite some breakdowns observed after the third month. The decrease of knockdown and mortality rates at this period may be related to bad conditions of net utilization. Previous reports have underlined the impact of external factors such as dirt and fume on the bio-efficacy of treated nets -28. HoweThe decrease of knockdown rate prior to that of mortality rate is known as one of the major modifications of pyrethroid effects associated with kdr mutation ,31. The A. gambiae complex [A. funestus populations from northern Kwazulu/Natal [A. albimanus in southern Mexico [A. gambiae s.s. with kdr gene frequency higher than 70% [kdr gene. Considering the genetic diversity of pyrethoid resistance mechanisms, the efficacy of ITNs against knockdown resistant populations could not be extrapolated to vector populations with elevated monoxygenases activity.In Cameroon, ITNs were found effective in reducing malaria transmission and morbidity during the early 1990s ,33 and,  complex  is of a lu/Natal . By the than 70% . NeverthFrom the current study, it has been seen that the knockdown rates were not decreased in the permethrin-tolerant strain; likewise pyrethroid properties (excito-repellent and irritancy) may not be impeded against metabolic-based resistance mosquitoes. There is a converging suggestion that the impact of insecticide resistance on the efficacy of ITNs used as personal protection tools might not be limited when resistance is due to high metabolic detoxification. Conversely, the decrease of mortality rates puts forward a potential limited impact of ITNs when used at community level as vector control intervention aiming at mass reduction of vector density.Current data call attention to early detection of resistance as one of the key guidelines for insecticide resistance management. Susceptibility tests are the entry point for insecticide resistance studies. However, whether resistance is detected or not, it is necessary to go through biochemical or molecular assays for detection of resistance genes which may not have a great impact on the level of resistance when they stand at very low frequency. In order to preserve efficacy of relevant tools such as ITNs against malaria vectors, it would be easy to control insecticide resistance when the occurrence of the involved gene is at low rather than at high frequency.Moreover, the impact of insecticide resistance on vector control interventions is a complex phenomenon that depends not only on the resistance itself (mechanisms, gene frequency, etc...), but also includes vector behaviour, environment and insecticide properties such as the excito-repellent effect. Drawing a general conclusion on the efficacy of ITNs in areas with metabolic-based resistant vector populations needs further investigation in experimental huts to study their behaviour and at community level to assess epidemiological impact.JE participated in the conception of the study, carried out field and laboratory procedures, analysed and interpreted data, drafted and revised the manuscript.FC carried out biochemical assays, participated in data analysis and interpretation. PG participated in the design of the study and revising it critically. LM conceived the study, participated in its design, coordinated, revised and gave final approval of the version to be published.


Family physicians (FPs) play an important role in cancer control. While FPs' attitudes towards, and use of guidelines in general have been explored, no study has looked at the needs of FPs with respect to guidelines for the continuum of cancer control. The objective of this study was to understand which guideline topics FPs consider important.Five group interviews were conducted by telephone with FPs from across Ontario, Canada. Transcripts were analyzed inductively. Content analysis identified emergent themes. Themes are illustrated by representative quotes taken from the transcripts.The main areas where FPs felt guidelines were needed most included screening – a traditional area of responsibility for FPs – and treatment and follow-up – areas where they felt they lacked the knowledge to best support patients. Confusion over best practice when faced with conflicting guidelines varied according to disease site. FPs defined good guideline formats; the most often cited forms of presentation were tear-off sheets to use interactively with patients, or a binder. Computer-based dissemination was acknowledged as the best way of widely distributing material that needs frequent updates. However, until computer use is a common aspect of practice, mail was considered the most viable method of dissemination. Guidelines designed for use by patients were supported by FPs.Preferred guideline topics, format, dissemination methods and role of patient guidelines identified by FPs in this study reflect the nature of their practice situations. Guideline developers and those supporting use of evidence-based guidelines (e.g., Canadian Strategy for Cancer Control) have a responsibility to ensure that FPs are provided with the resources they identify as important, and to provide them in a format that will best support their use. Family physicians (FPs) play an important role in cancer control. Their traditional involvement has been primarily focused on opposite ends of the cancer control continuum: prevention, screening and diagnosis at the beginning of the continuum, and provision of palliative care at the other end. Treatment and follow-up have typically been the responsibility of secondary or tertiary care physicians.There are indications that FPs would like their traditional roles to include involvement in treatment and follow-up ,2, and tClinical practice guidelines (CPGs) can provide FPs with information and guidance on evidence-based best practices. While FP attitudes on the use of CPGs have been shown on the whole to be positive -11, FPs A broad partnership of key stakeholders known as the Canadian Strategy for Cancer Control (CSCC) is currently working towards a national cancer control strategy. The CSCC initiative builds upon previous work done in Canada, and has the goal of developing, adopting and implementing a national strategy. A focus on "Guidelines and Standards" is one of the CSCC's top priorities with an aim to establish mechanisms and improve capacity for collaborative guideline and standards development . In ordeIn order to learn FPs' views on guidelines on cancer control, a qualitative methodology was considered the most appropriate. To facilitate FPs' involvement (e.g., eliminate travel and time barriers) and ensure representation of FPs practicing in remote areas, the most feasible option was to hold interviews by teleconference . Thus, wWith respect to recruitment, FPs from various regions in Ontario identified through the Canadian Medical Directory were recruited via an information letter and follow-up telephone call. In addition, FP colleagues identified potential participants or individuals who might suggest potential participants. Sampling was purposeful . We feltTo allow for in-depth discussion, three cancer disease sites were selected: lung, colorectal and cervix. The first two were included because of high incidence; the latter because conflicting screening guidelines  currentlFive group interviews were held. There was a minimum of 2 and maximum of 4 FP participants in each session. Each of the five interview sessions lasted between 45 and 60 minutes. In order to accommodate FPs' schedules, three of the five interviews were held in the early evening, and two in the morning. FPs were asked to select either a lottery ticket or telephone card as a small acknowledgement of their participation. Each session was led by the same individual (LZ), an experienced qualitative researcher. Interviews were audio-taped.Audio tapes were transcribed immediately after each interview, and underwent a preliminary analysis allowing for emergent issues or ideas to be explored in future sessions. Transcripts were read by one of the researchers (LZ); latent content analysis (coding and classification into themes) was done manually . As eachA second researcher (EG) was given two transcripts to read and code independently using the list of codes and categories. Coding was compared. Where necessary, definitions of code words were refined and categories expanded upon .Disagreements and differences were resolved through discussion and data was re-examined where necessary . RepreseEthical approval for this study was obtained from the Ottawa Hospital Research Ethics Board.Of the 13 physicians participating in the study, seven were male and six were female; five practiced in a rural setting, the remainder in an urban setting. The majority (9/13) were in group practice. Approximately 5% of FPs contacted agreed to participate in the study. Overall, topics raised by urban and rural FPs were similar except with respect to guidelines on cancer treatment.Using lung, colorectal and cervical cancers as exemplars, FPs were asked for which topics along the cancer control continuum  they most wanted guidelines. Screening was the topic most frequently mentioned, although reasons behind requests for screening guidelines differed for each disease site. For lung cancer, there are currently no evidence-based screening tools or maneuvers, and no screening guidelines. FPs were uncertain whether to routinely screen for lung cancer in their practices. For colorectal cancer, on the other hand, there are a number of screening tools and a number of recommendations made by different organizations. Conflicting guidelines resulted in FPs being uncertain about what to do in practice.6)I'm certainly much less certain of the area of screening for colorectal cancer. I mean there's a lot of different guidelines out there, it depends on who you read and I regard that as an area very much in flux...I still remain a bit confused as to who should have what. After three normal [screens], every two years and discontinue at the age of 70, that's what it says. And then this other one says start at age 18, and after three normals then do it every three years except high risk patients should have annual smears...the American College of OB/GYN recommends its smears always continue annually. The American Cancer Society and the Canadian Task Force recommends screening until age 65 and 69 respectively. So, it's a dog's breakfast. I would say I have people who I am willing to see every 3 years because I feel quite confident that they'll be back; they're good at keeping up and the ones that I'm more uncertain about in terms of their follow up, I'll make sure I do it more frequently just in case...For me it varies very much between 1 and 3 years and it is very much a decision of my own. A lot of the patients I have who go out to a cancer clinic come back and make sure I agree with what they're choosing and the problem is I don't have the information to be able to even aid them in making their decision....So if we had a little bit more information than those flow charts that are 'yes/no' to say [this] is the most recent information for survival rates...that kind of thing would be helpful. Areas where we really need specific evidence-based guidelines are in treatment and follow-up. I mean, although the patient may disappear to the cancer clinic...they certainly do show up in emergency, and sometimes the husband or wife calls us as well and says, "Well you know they're getting this drug or they're getting this radiation, they're really sick and what do you think about this?" And if we don't even know what they're getting or what the potential side-effects are, it's really hard to be helpful. So, we need specific guidelines. ...we are going to be doing more and more of our own follow-up, that's the trend, that's the next century, so we need good guidelines.  were other suggestions.3)I like [the] idea of the tear off sheet to use in discussion with patients. I think guidelines are only useful to the extent that we can go over them again and again and actually be familiar with them ourselves, rather than just having them tucked in a big binder with many other guidelines. So, something that can be used with the patient.  to the consultation. On the whole, FPs felt that the more information patients had, the better. Three FPs felt that guidelines would encourage patients to take responsibility for their own care; patients could remind their FP if they were due for screening.8)So I think the biggest effort is to establish the proper guidelines that are accepted by a group of authorities in Canada and then that would make it easier for me to say, "Well, look, this is the actual guideline that is the result of a great deal of research and in fact you really don't need that mammogram at the age of 40"...I think there has to be an effort to make sure that patients are not given conflicting guidelines. , FPs wanted guidelines that addressed what they should do for routine screening. In the case of colorectal cancer, FPs received conflicting messages about screening, and sought guidance as to which recommendation to use.against routine screening. Currently, differences in recommendations relate to the type of screening maneuver [One barrier to guideline use is if guidelines are considered controversial ,31,32. Fmaneuver ,27.Decisions regarding screening practice also depended on patient factors such as a patient's motivation to adhere to a screening routine. Physician factors  and patient factors have been identified as two of the three determinants of a decision making model for cancer screening in the case of unclear or controversial guidelines. The third determinant was the perceived quality of the physician-patient relationship and the clarity of the recommendation being discussed .As with Feightner et al., FPs in While the FPs in this study play a role in treatment and follow-up, they do not feel they have the information they need to help their patients. Practice location appears to influence the type of treatment information FPs want, although generalizations can not be made on the basis of the qualitative methods used in this study. In urban settings, FPs' roles along the continuum of cancer care principally involve prevention, screening, diagnosis, and palliation. In addition to the above roles, rural physicians are involved in treatment and follow-up, and this involvement is perceived as increasing in the future. Consequently, they state that guidelines on treatment and follow-up would be helpful. Rural physicians participating in a Canada-wide focus group and interview study on FP-oncologist communication also indicated a need for treatment and follow-up information .While FPs expressed a need for guidelines on cancer follow-up, several guidelines have been published. In the case of colorectal cancer for example, a guideline has been published in Ontario under the auspices of Cancer Care Ontario . The chaFPs in this study preferred guidelines in a paper format disseminated by mail rather than electronically. The 2001 Janus survey conducted by the College of Family Physicians of Canada found that only approximately one quarter of FPs across Canada have access to and use computerized CPGs in their office . The FPsThe findings of this study are limited by several factors. The purpose of the study was to gather information on FPs' perspectives on guidelines along the cancer control continuum. As with all qualitative research, the results of the study are not generalizable beyond the sample. However, the intent of sampling in qualitative research is to identify key informants who will illuminate particular aspects of the research topic . FPs agrFurther research should focus on identifying the guideline needs of a larger, nation-wide sample of Canadian FPs to ensure that efforts by initiatives such as the CSCC result in CPGs that will be both viewed positively and adopted by FPs. However, perception of the value of a guideline is not enough to ensure adoption. While much effort has gone into guideline development, the focus on dissemination has been through traditional dissemination strategies . Knowledge transfer is known to be more complex  requirinGuideline topics, format, dissemination, and patient guidelines discussed by FPs in this study reflect their particular practice situations. FPs' strongest preferences were for guidelines on cancer screening, followed by guidelines on treatment that would help them support and provide care for patients. The conflicting messages of some guidelines did not necessarily make decision making problematic for FPs. Rather, it was the reason behind the conflict that created difficulties. FPs saw patient guidelines as an educational tool for both themselves and their patient. Guidelines on treatment and follow-up are available, although they are generally geared towards specialists not FPs. This suggests a need for FP versions to be created. Challenges for Canadian guideline developers/implementers include not only ensuring that the evolving needs of FPs are met, but also that they address the needs of FPs with respect to how that information is formatted, delivered to FPs and how FPs are supported in its use.Dr. Graham is a CIHR New Investigator.LZ participated in the design of the study, conducted the interviews and analysis, and drafted the manuscript. EG participated in the design of the study, the analysis, and contributed to the manuscript. IG participated in the design of the study, and assisted with the pilot test of interview questions. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:


An overview of the molecules and associated cell biology underlying neuron death in Parkinson's Disease The consequence of Parkinson's disease (PD) is well described: a progressive movement disorder that, whilst responding to symptomatic therapy, chronically disables its sufferers and adds an enormous economic burden in an aging society. We have some clues to the process underlying the disease from the snapshot provided by postmortem studies of diseased brains. Groups of neurons in specific brain regions are lost, notably those that produce dopamine in a part of the midbrain called the substantia nigra. Those neurons that do survive to the end of the disease course contain accumulations of proteins and lipids within their cytoplasm. Named after their discoverer, these “Lewy bodies” are one piece of evidence that protein aggregation is related to the ongoing disease process.In contrast, the causes of PD are poorly defined except in those rare variant forms that are clearly genetic. Several families have been described where PD-like syndromes are inherited in either a dominant or recessive fashion, and four of the underlying genes have been identified. The precise relationships between these different syndromes are complex and are the subject of some controversy. For example, it is not clear whether all the genetic diseases given PARK nomenclature have Lewy bodies and should be considered “true” PD—the term parkinsonism is preferred for these syndromes . For the-synuclein. The synaptic protein encoded by this gene, α-synuclein, is prone to aggregation, and, as is the case for other aggregating proteins, mutations in α-synuclein are associated with dominantly inherited disease. Related to this, α-synuclein is a major protein component of Lewy bodies. The phenotype of patients with α-synuclein mutations varies from PD to a more diffuse Lewy body disease in which pathology is detected in the cerebral cortex and other areas of the brain. Mutations in α-synuclein include three point mutations (A30P, E46K, and A53T) and multiplication of the wild-type  gene. All of these mutations increase the tendency of α-synuclein to aggregate, suggesting that disease is a consequence of protein aggregation. An interesting example is the triplication of the wild-type gene: toxicity and aggregation can both be driven by increased expression and are thus qualitative, not quantitative, effects  behaves differently from the others. Instead of forming fibrils, which are insoluble, high-molecular-weight species, A30P forms relatively soluble, partially aggregated species . These iWhether the Lewy body is damaging or neuroprotective, there are clearly several possible targets for toxic α-synuclein within the cell. For example, aggregated α-synuclein can permeabilize cellular membranes and thus might damage organelles . Mitochosynuclein gene is knocked out have no obvious deficits (see Presumably, neurons require α-synuclein for their normal function and thus cannot simply dispense with this protein that has toxic properties, although mice in which the α-parkin, DJ-1, and PINK1. The three protein products of these genes all have different functions, thus implicating several different cellular functions in neuroprotection. Parkin is an E3 ubiquitin–protein ligase, promoting the addition of ubiquitin to target proteins prior to their degradation by the proteasome. The identification of parkin's function was facilitated by the observation that the protein contains a RING finger (Evolution has provided cells with many ways to protect themselves. As we will see, mutations that cause recessive diseases result in the loss of these neuroprotective functions. The genes involved in recessive parkinsonism are, in order of discovery, G finger , a commoG finger . TherefoG finger .Data on PINK1 are limited, but the protein contains two motifs that indicate its likely cellular role. At the amino-terminus of PINK1 is a mitochondrial-targeting sequence, and mitochondrial localization has been confirmed in the one study published to date . Most ofDJ-1 is a member of a large superfamily of genes with several different functions across species (PLoS Biology, that DJ-1 is a molecular chaperone that regulates α-synuclein, among other molecules (The discussion of protein functions gets more complicated in the case of DJ-1. Unlike parkin or PINK1, there are no motifs within DJ-1 that hint strongly at a single function. Instead,  species . These iolecules . It is nDJ-1 knocked out show increased sensitivity to oxidative stress (PLoS Biology shows that dopamine neurons differentiated from embryonic stem cells lacking functional DJ-1 are especially sensitive to oxidative stress (DJ-1 is also known to be responsive to oxidative conditions, under which cysteine residues are oxidized to form cysteine-sulfinic acids. There is some discussion about which cysteine residue is oxidized; the most likely is cysteine 106, which is present in a nucleophile elbow in the protein. We have suggested that modifying this residue precludes DJ-1 oxidation under mild conditions and also blocks the neuroprotective activity of DJ-1 against mitochondrial toxicity . Therefoe stress . Anothere stress .This discussion indicates that the genes responsible for recessive parkinsonism all have different functions but are all, in a broad sense, neuroprotective. A very difficult question to answer is whether this has anything to do with α-synuclein. We have shown that parkin can mitigate the toxicity of mutant α-synuclein . Althougparkin, DJ-1, or PINK1 to protect themselves against the many stresses that they face. However, mutations in these genes do not cause generalized neurodegeneration; in fact, they tend to be more restricted and less progressive than, for example, α-synuclein mutations. This suggests, at least to my mind, that recessive mutations indicate something about the neurons that are damaged in these disorders. Why is this of more than academic importance? Perhaps by identifying the proximal events that are sufficient to cause a specific set of neurons to degenerate, we might begin to design therapies that address the underlying degeneration in PD and not just the consequences.It has also been suggested that DJ-1 can prevent the accumulation of aggregated α-synuclein and that cysteine 53 is critical for this activity . However


Because of the constant threat posed by emerging infectious diseases and the limitations of existing approaches used to identify new pathogens, there is a great demand for new technological methods for viral discovery. We describe herein a DNA microarray-based platform for novel virus identification and characterization. Central to this approach was a DNA microarray designed to detect a wide range of known viruses as well as novel members of existing viral families; this microarray contained the most highly conserved 70mer sequences from every fully sequenced reference viral genome in GenBank. During an outbreak of severe acute respiratory syndrome (SARS) in March 2003, hybridization to this microarray revealed the presence of a previously uncharacterized coronavirus in a viral isolate cultivated from a SARS patient. To further characterize this new virus, approximately 1 kb of the unknown virus genome was cloned by physically recovering viral sequences hybridized to individual array elements. Sequencing of these fragments confirmed that the virus was indeed a new member of the coronavirus family. This combination of array hybridization followed by direct viral sequence recovery should prove to be a general strategy for the rapid identification and characterization of novel viruses and emerging infectious disease. Know your enemy. Description of the ‘virus gene chip’ that helped to classify the SARS virus as a novel coronavirus Over the past two decades, technological advances in molecular biology have fuelled progress in the discovery of new pathogens associated with human diseases. The identification of novel viruses such as hepatitis C virus , sin nomWe have previously described a prototype DNA microarray designed for highly parallel viral detection with the potential to detect novel members of known viral families . This miDuring the initial phase of research into the etiology of SARS, an unknown virus was cultured in Vero cells from a patient suffering from SARS . Total nNSP11 gene (BLAST identity matches of 42/47 and 26/27, respectively). A pair of PCR primers was designed to amplify the intervening sequences between the two conserved regions, and a fragment that possessed 89% identity over 37 amino acids to MHV, a murine coronavirus, was obtained  , we wereA key feature of this approach is that direct recovery of hybridized material from the microarray provides a rapid route for obtaining sequences of novel viruses. By contrast, conventional strategies for subsequent sequence identification would require time-consuming steps such as library screening or additional rounds of PCR primer design and synthesis. In the case of SARS, we were able to ascertain within 24 h that a novel coronavirus was present in the unknown sample, and partial genome sequences of this virus were obtained over the next few days without the need for specific primer design. To our knowledge, this is the first demonstration of the feasibility and utility of directly recovering nucleic acid sequences from a hybridized DNA microarray. In light of the continuous threat of emerging infectious diseases, this overall approach will greatly facilitate the rapid identification and characterization of novel viruses.Total nucleic acid was purified using the automated NucliSens extraction system (BioMerieux, Durham, North Carolina). Following the manufacturer's instructions, 100 μl of each specimen was added to tubes containing 900 μl of prewarmed NucliSens lysis buffer and incubated at 37°C for 30 min with intermittent mixing. Fifty microliters of silica suspension provided in the extraction kit was added to each tube and mixed. The mixtures were then transferred to a nucleic acid extraction cartridge and loaded onto the extractor workstation for processing. Approximately 50 μl of total nucleic acid eluate was recovered.For the culture supernatants, 450 ng of nucleic acid was used as input for the amplification protocol. In parallel, 50 ng of HeLa cell RNA was used as a positive amplification control and water was used for a negative control. Samples were amplified using a random-primer protocol as described by infection-Cy5mock) > 1500 intensity units are listed in DNA microarrays were printed and hybridized essentially as described by PCR primers were designed by aligning the hybridizing oligonucleotides (Oligo IDs 15081544_766 and 12175745_728) to the IBV genome  and selecting stretches of near-identity. Primer-B-amplified material was used as the template for 35 cycles of thermocycling using the following program: 94°C for 30 s, 56°C for 30 s, and 72°C for 60 s.Amplified viral sequences hybridized to individual microarray spots were recovered by scraping a 100 μm area of the microarray using a tungsten wire probe (Omega Engineering, Inc.) mounted on a micromanipulator while visualized by fluorescence microscopy (Nikon TE300). Recovered material was PCR amplified using primer-B, cloned into pCR2.1TOPO (Invitrogen), and sequenced. A detailed protocol is available as Primer-B-amplified nucleic acid (see above) was cloned in pCR2.1TOPO, plated on 2xYT/kan plates, and grown overnight at 37°C. White colonies were picked into 384-well plates containing 2xYT/kan plus 8% glycerol and incubated overnight at 37°C. DNA was purified by magnetic bead isolation. DNA sequencing involved adding 3 μl of water to each bead pellet, followed by 3 μl of Big Dye terminator (v3.1) sequencing cocktail, and incubation for 35 cycles of 95°C for 5 s, 50°C for 5 s, and 60°C for 2 min. Reaction products were ethanol precipitated, resuspended in 25 μl of water, and loaded onto the ABI 3730xl sequencer. The resulting sequence reads were trimmed to remove primer sequences from the RT-PCR step and then assembled by Phrap (P. Green, unpublished data). Resulting contigs were screened by blast to remove any contigs with high human or monkey sequence similarity. The remaining contigs were edited to high quality, making any obvious joins. (Sequences are available as Data S1(91.5 KB DOC)Click here for additional data file.Protocol S1(28 KB DOC)Click here for additional data file.Protocol S2(39.5 KB DOC)Click here for additional data file.Table S1(97 KB DOC)Click here for additional data file.Table S2(2.2 MB XLS)Click here for additional data file.The Gene Expression Omnibus accession number for the array sequence is GSE546.