The analysis was repeated for a subset of 29 couples with strong genetic support for transmission. The couple effect was the major determinant of viral load set-point  and the size of the effect was 27% (37% adjusted). Individuals within epidemiologically linked couples with genetic support for transmission had similar viral load set-points. The most parsimonious explanation is that this is due to shared characteristics of the transmitted virus, a finding which sheds light on both the role of viral factors in HIV-1 pathogenesis and on the evolution of the virus.It has been hypothesized that HIV-1 viral load set-point is a surrogate measure of HIV-1 viral virulence, and that it may be subject to natural selection in the human host population. A key test of this hypothesis is whether viral load set-points are correlated between transmitting individuals and those acquiring infection. We retrospectively identified 112 heterosexual HIV-discordant couples enrolled in a cohort in Rakai, Uganda, in which HIV transmission was suspected and viral load set-point was established. In addition, sequence data was available to establish transmission by genetic linkage for 57 of these couples. Sex, age, viral subtype, index partner, and self-reported genital ulcer disease status (GUD) were known. Using ANOVA, we estimated the proportion of variance in viral load set-points which was explained by the similarity within couples (the ‘couple effect’). Individuals with suspected intra-couple transmission (97 couples) had similar viral load set-points ( During the long period of asymptomatic infection with HIV-1 there is considerable variability in viral load set-point between infected individuals. Higher viral load set-points increase infectivity and decrease survival. Previous work has shown that the most commonly observed viral load set-points are those intermediate viral load set-points which lead to the largest number of opportunities to transmit HIV-1 in an infectious person's lifetime, balancing survival and infectiousness. This coincidence between the most common viral load set-points and the optimum for lifetime transmission could be the result of population-level selection acting on HIV-1. However, this could only have happened if viral load set-point is a heritable characteristic of the virus, i.e. if viral load set-points are similar between both partners of transmitting couples. By studying viral load set-points amongst heterosexual couples, we show that viral load set-points are similar in these couples. When we study only those couples with strong genetic support for transmission, their viral loads are even more similar than when we study the whole group. These results suggest that there are viral factors which are passed from one infected individual to the next which play a role in determining viral load set-point and that population-level selection could act upon these viral factors. The severity of HIV-1 infection is thought to result from an interplay of factors in the host, the virus, and the environment (for instance the presence of co-infections). Much work has focused on resolving host genetic factors which contribute to virulence The existence of heritable viral factors influencing disease progression, and their contribution relative to other factors, is of interest for at least two reasons. Firstly, such factors, if they exist, have implications for how the virus influences the course of infection within an infected person. Secondly, if viral factors exist which affect virulence and can be preserved from one infection to the next, then these factors will be subject to natural selection at the population level At present, limited evidence suggests that viral load set-point is regulated by viral factors, although to an extent this reflects a paucity of research on the topic. The existence of a specific recombinant form associated with high viral loads strongly suggests that viral factors can play a role in at least some circumstances p = 0.03) Finally, and most convincingly, a study of 115 HIV transmitting heterosexual couples in Zambia showed that 19% of variance in viral loads could be explained by shared homologous virus between couples . Antiretroviral therapy (ART) was not available in Rakai at the time of the study, but participants were offered free general health care and treatment for opportunistic infections.Serum samples from venous blood provided at survey visits were tested for HIV-1 RNA levels quantified by a reverse-transcriptase polymerase chain reaction (RT-PCR) assay  with a lower detection limit of 400 copies/mL  were also excluded. Following these exclusions 112 couples were identified for whom suitable viral load measurements were available. For those individuals with more than one viral load measurement, the set-point was defined as the mean logacd) on serum samples HIV-1 subtype was determined for 171 individuals using a Multi-region Hybridization Assay (MHAp24) and gp41 regions were available for comparison in both partners to help identify transmitting couples. To decrease the risk of spurious linkages between sequences, p24 and gp41 sequences for the couples were analysed together with sequences from 511 other infected individuals in the cohort p24 and 614 gp41 sequences were analysed (See gp41 sequences by the Akaike Information Criterion (AIC). It was the third most appropriate model for p24 (with ΔAICc = 12.2) but was also used for this locus for comparability between loci.For a subset of the couples viral sequence data from the gag . For 14 couples, sequence data were only available for both individuals at Couples were considered to be strongly linked if their sequences were monophyletic and the clade had bootstrap support of greater than or equal to 80%. This condition was imposed on both single locus and concatenated sequences to account for the possible effects of recombination in distorting the phylogenetic signal. Our approach to determining linkage within couples is thus conservative. Couples were considered to have no support for linkage if their sequences were polyphyletic.acd. The second subgroup included couples with strong genetic support for linkage (monophyletic with greater than 80% bootstrap support).To analyse the data on viral load set-points within transmitting couples, we performed our analysis on two groups. The first group included all couples, with and without genetic data, but excluding both those with sequence data who had no genetic support for linkage and those with discordant subtypes determined by MHASymptoms of genital ulcer disease (GUD) over the interval prior to sample collection were ascertained via interview, and by physical examination for ulcers reported to be present at the time of a study visit. GUD has previously been found to be a significant predictor of viral load in this cohort Since the data used in this study were not collected with the analysis presented here in mind, there are incomplete data on sequencing and epidemiological data. This may lead to unidentified biases in the data.We used analysis of variance (ANOVA) to test whether there was greater similarity in viral load set-points of individuals within transmitting couples than between all individuals. In other words, we decomposed the variance in viral load set-point into the sum of within-couple variance and between-couple variance. To perform ANOVA, a general linear model was formulated with a regression coefficient for each couple see . In a fiR2, the proportion of variance explained by the model. Since one parameter is introduced for each couple, a proportion of variance is explained spuriously due to decreased residual degrees of freedom. The adjusted R2, denoted The strength of the effect is measured by To adjust for possible confounders, the general linear model was extended to include the effects of gender, age and GUD status, which have all been previously shown to affect viral load set-point within this study population 2R are equivalent concepts.Since the biological origin of any similarity in viral loads is hypothesized to be due to similarity in viral genotypes between transmitting individuals, the measured association may be interpreted as an estimate of the effect of viral genotype on viral load set-point. In this context, the study design is analogous to pedigree studies in classical genetics which are used to study the association between genotype and phenotype We thus estimate heritability, as  by  see .p-values obtained from the unadjusted analysis were equal to the proportion of permutation tests  which gave the same or larger p-values obtained by comparing the distribution of differences in viral loads within and between couples, thus confirming the validity of ANOVA to analyse these data .The validity of our statistical approach is supported by the observation that the A related question of interest is the extent to which the viral load set-point of one individual can be used to predict the set-point of the person they infect. The strength of association in a unidirectional analysis . There were indications that 8 couples (14%) did not transmit to each other and the remaining 21 couples were indeterminate (37%). The couples with strong support for transmission have distinctly closer tree distances than the rest of the sample .Following the phylogenetic and subtype analysis, the statistical analyses were performed on 97 couples with moderate support for transmission  and a su10 RNA viral load set-point was 4.39 log10 cps/mL with values in the range 2.60 log10 cps/mL (the limit of detection) to 7.14 log10 cps/mL (p = 0.14).The average log0 cps/mL . The ave0 cps/mL . AmongstViral load set-points of transmitting couples are presented in p = 0.054) by single factor ANOVA. The estimated size of this couple effect was 16%.When analysing the 97 couples with moderate support for transmission, the couple effect . The adjusted estimate for the couple effect was 23% , the couple effect was a significant predictor of viral load set-point (adjusted  was 23% . To test was 23% .p = 0.067), but significant when adjusting for confounders (p = 0.036). The size of the couple effect was 27%. The couple effect was a key determinant of viral load for most of the multivariate analyses which were performed (When looking at the subgroup of couples showing strong support for transmission (29 couples), the couple effect was of borderline significance in the unadjusted analysis  might be considered as a small control group for our study. Unfortunately, this group is too small to form a definitive control group. Nonetheless, for completeness, we estimated the couple effect. It was not found to be significant (p = 0.32) and the effect size was smaller (12%).p = 0.054 in single factor model, p = 0.0057 adjusting for confounders) and that this effect explained 16% (23% adjusting for confounders) of the variability in viral load set-points. When the analysis was repeated for the subgroup of couples for whom there was strong genetic support for viral linkage, couples infected with similar viruses also had similar viral load set-points . The size of the couple effect was estimated to be larger, 27% in single factor model (37% adjusting for confounders), suggesting that the transmitted virus plays a role in determining viral load set-point.Our analysis showed that, in this study population, individuals within transmitting couples had similar viral load set-points Click here for additional data file.Text S2Statistical model and partitioning variance(0.39 MB PDF)Click here for additional data file.Figure S1Clustering of sequences from the couples for whom sequence data was available at both loci, based on concatenated sequences. As (0.75 MB PDF)Click here for additional data file.Figure S2gp41. As Clustering of sequences from the couples for whom sequence data was available at (0.76 MB PDF)Click here for additional data file.Figure S3p24. As Clustering of sequences from the couples for whom sequence data was available at (0.76 MB PDF)Click here for additional data file.Figure S4Flow diagram for inclusion in the study groups of 97 couples with moderate support for transmission, which includes those with epidemiological linkage together and where available, weak to strong support for transmission (blue); the sub-group of 29 couples with strong support for transmission (green); the 15 couples which genetic evidence suggested did not transmit to each other (red).(0.58 MB PDF)Click here for additional data file.Figure S5Distribution of tree distances. The distribution of tree distances between couples is given for couples for whom there was strong support for transmission (green) and weak genetic support for transmission (blue). In addition, the distribution of tree distances for all other pairwise comparisons between individuals in the trees  is inclu(0.01 MB PDF)Click here for additional data file.Figure S6Distribution of differences in viral load set-points. The distribution of absolute differences in viral load setpoints for 29 couples with strong support for transmission (green), the remaining 68 couples from the 97 with moderate support for transmission (blue) and all other male to female pairwise comparisons (black).(0.08 MB PDF)Click here for additional data file.Table S1Size of couple effect for different model structures for 97 couples in main analysis. Black circles indicate factors included in the model. The Type III p-value for the couple effect and the adjusted R-squared for the model are given.(0.13 MB PDF)Click here for additional data file.Table S2Size of couple effect for different model structures for subgroup of 29 couples. Black circles indicate factors included in the model. The Type III p-value for the couple effect and the adjusted R-squared for the model are given.(0.13 MB PDF)Click here for additional data file.


Previous research on educational data has demonstrated that Rasch fit statistics (mean squares and t-statistics) are highly susceptible to sample size variation for dichotomously scored rating data, although little is known about this relationship for polytomous data. These statistics help inform researchers about how well items fit to a unidimensional latent trait, and are an important adjunct to modern psychometrics. Given the increasing use of Rasch models in health research the purpose of this study was therefore to explore the relationship between fit statistics and sample size for polytomous data.Data were collated from a heterogeneous sample of cancer patients (n = 4072) who had completed both the Patient Health Questionnaire – 9 and the Hospital Anxiety and Depression Scale. Ten samples were drawn with replacement for each of eight sample sizes (n = 25 to n = 3200). The Rating and Partial Credit Models were applied and the mean square and t-fit statistics (infit/outfit) derived for each model.The results demonstrated that t-statistics were highly sensitive to sample size, whereas mean square statistics remained relatively stable for polytomous data.It was concluded that mean square statistics were relatively independent of sample size for polytomous data and that misfit to the model could be identified using published recommended ranges. Although Rasch Models  were oriRasch Models are a family of measurement models  which caThere has been and there continues to be a considerable debate around the issue of which is the most appropriate fit statistic to use, what range of fit statistics to be employed when evaluating fit, and how fit statistics should be interpreted ,16.The use of chi-square statistics or infit and outfit mean squares to assess item fit to the model (described in more detail below) has been advocated. The mean squares can be converted through a cube-root transformation (Wilson-Hilferty) to (infit/outfit) t-statistics.The mean square fit statistics are perhaps the most commonly used fit statistics in health research. A series of ranges has been suggested  to be emMore recently, studies  have demIn addition to research on the dichotomous model, recent work on the polytomous  model with simulated data has suggested that the variability of mean squares is dependent on sample size and furthermore that the standard deviations for the t-statistics are generally smaller than their expected value (unity) . These aFinally, Smith & Suh  have conHowever, despite this assertion there are a number of other methodological studies ,20 whichThe evaluation and identification of item misfit is critical to the development of unidimensional instruments, and reliable fit statistics play an important part in this. There is uncertainty in the literature to assist health researchers in determining the most appropriate fit statistic to select for developing or modifying questionnaires. Previous research on simulated datasets has focused on the relationship between sample size and fit statistics at the level of groups of items. However, for test users the emphasis is more on which fit statistics are able to identify misfit consistently for individual items. Identification and removal of misfitting items will not only reduce patient burden, but may also improve person measure assessment .Therefore the aim of this study was to investigate the impact of sample size on four commonly used fit statistics, i.e. infit/outfit mean square and their t-statistics for two polytomous Rasch models using data collected from a cancer patient sample.The study attempted to determine: 1). whether fit statistics  vary with sample size and 2). whether there were any differences in this variation between the different types of fit statistic.Patient data were pooled from a number of studies carried out by Cancer Research – UK Psychological Medicine Group, Western General Hospital, Edinburgh (Scotland) over the past decade. The data have been collated from patients who completed a touchscreen version of both the HADS and the PHQ-9 in outpatient oncology clinics.A total of 4072 patients completed the HADS , and 3556 patients completed the PHQ-9 . The average age of the sample was 60 years. Further clinical and demographic details are available from the published studies .The studies from which these data have been drawn have all received ethical approval from the local research ethics committee.The Hospital Anxiety and Depression Scale (HADS)  was origThe Patient Health Questionnaire – 9 (PHQ-9)  is a nink response categories, there are k-1 thresholds. Each threshold has its own estimate of difficulty (Fk). The Rating Scale Model [see Additional file ni of a person with ability Bn, choosing a given category with a threshold Fk and item difficulty Di. A single set of thresholds is estimated for all items in a scale. The Partial Credit Model [N items there will be N(k – 1) threshold estimates for the Partial Credit Model.Both the Rating Scale and Partial Credit models are members of the family of Rasch Models . The Ratit Model  can be sRasch fit statistics describe the fit of the items to the model. The mean square fit statistics have a chi-square distribution and an expected value of 1, where fit statistics greater than 1 can be interpreted as demonstrating more variation between the model and the observed scores, e.g. a fit statistic of 1.25 for an item would indicate 25% more variation (or "noise") than predicted by the Rasch model , in otheni) [see Additional file There are two commonly used mean square fit statistics, namely the infit mean square  and outfit (or unweighted) mean square. Both mean squares are derived from the squared standardised residuals for each item/person interaction [see Additional file The infit and outfit mean squares can be converted to an approximately normalised t-statistic using the Wilson-Hilferty transformation [see Additional file Winsteps version 3.64 [Winsteps)) for each item. This process was completed for both Rasch models.The relationship between sample size and fit statistics was explored using the two Rasch models, that is, the Rating Scale Model , and Parion 3.64 . Eight sTables Tables However for the PHQ-9 Tables  and 4 coFinally, it can also be seen that the standard errors were uniformly smaller for the mean square statistics than those for the t-statistics, indicating greater levels of stability in the parameter estimates. This was particularly noticeable for the HADS, but also applied to some extent to the PHQ-9.The relationship between sample size and fit statistics is shown in Tables It can be seen that for both the infit and outfit mean squares few items were identified with mean square fit statistics < 0.7 for the HADS subscales Tables  and 7 anThere was a clear link observed between sample size and fit statistic when comparing infit and outfit mean squares above 1.2 with t-statistics > +2. Once again, t-statistics increased in proportion to sample size, whereas the mean square equivalents remained approximately invariant to sample size changes  which had demonstrated fit across all four statistics is shown in Figure  which haFor the Rating Scale Model one HADS-A item (7) and one HADS-D item (6), as well as two PHQ-9 items 2 and 4) were identified as overfitting by the t-statistics but not by the mean squares. The Partial Credit Model demonstrated overfit for HADS-D6, as well as HADS-A3, and PHQ2. Figure  and 4 weHADS-D5 and HADS-D7 were identified as underfitting on the Rating Scale Model (RSM) for both the in- and outfit t-statistics, but not the mean squares, although neither was identified as misfitting on the Partial Credit Model (PCM); HADS-D4 was identified as misfitting (i.e. underfitting) on the Partial Credit Model by the infit t alone. PHQ3 and 5 were identified as misfitting by both infit and outfit t-statistics for the RSM and PCM, but not by the mean squares. Finally, HADS-A item 6 was consistently identified as misfitting (underfitting) by all four statistics, yet when the four statistics are plotted against sample size . instances where mean square statistics fell within the critical (0.7 – 1.3) range (i.e. "fit"), and 2). instances where mean square statistics fell outside this range, in particular exceeding 1.3 (misfit).Items identified as falling within range (0.7 – 1.3) showed consistent mean squares (infit/outfit) as sample size increased; on the other hand, the corresponding t-statistics increased with sample size (i.e. identified misfit where none was identified as such by the corresponding mean square). Items identified as falling outside the critical range (0.7 – 1.3) were consistently identified as misfitting by mean squares, but only identified as such by the corresponding t-statistics when sample size exceeded 200. Beyond these limits t-statistics increased in proportion with sample size. In other words, the t-statistic only identified items as misfitting for larger sample sizes.The aim of this study was to explore the relationship between sample size and four commonly used fit statistics for two polytomous Rasch Models. The results of this study demonstrated that Type I error rates – defined strictly in this study as falsely rejecting an item as not fitting the Rasch model – for the t-statistic were at least twice those of the corresponding fit statistic for both infit and outfit for both Rasch Models. In addition, the results of the analysis of sample size and fit statistic suggested that whereas the mean square fit statistics broadly remained constant in the number of items whether identified as fitting or misfitting (under and over), the instances of misfit identified by the t-statistics increased proportionally with sample size. Further analysis of the individual item fit and sample size suggested that although in the majority of cases there was agreement between mean square and t-statistics in terms of identifying fit and misfit (>50% for both models and instruments), there were discrepancies in Type I error rate as defined in this study and a lack of sample size invariance for the t-statistics.The results of the study suggest that t-statistics are highly dependent on sample size which has the effect of inflating putative Type I error rates. Specifically, for cases where mean square statistics fell within the range 0.7 – 1.3, the t-statistics increased in magnitude as sample size increased, therefore for the t-statistic the Type I error rate was inflated and the probability of identifying misfit where none was identified by the mean square statistics increased with sample size. Similarly, where mean square statistics identified misfit outside the 0.7 – 1.3 range, t-statistics only identified misfit as the sample size increased to beyond 200.In terms of Type I error rates, for Rating Scale Model the outfit mean square statistics provided the most stable rates, whereas the infit mean squares were more stable for the Partial Credit Model, although there was little difference in identifying misfit between the 1.2 and 1.3 criteria for mean squares.Taken together these results suggest that both infit and outfit mean square statistics are relatively insensitive to sample size variation for polytomous data, and that t-statistics may vary considerably with sample size. The latter has confirmed previously reported findings using simulated data sets .The potential cause for this sample size dependence for the t-statistics may lie with the standard deviations. The results of previous research have demonstrated that the variability of the mean squares decreases significantly  by samplAlthough the results for the t-statistics confirm results from previous studies (e.g. "Knox Cube Test")  they difThis gives rise to a potential discrepancy between the dichotomous and polytomous Rasch Models and Type I error rates, suggesting a dependence between sample size and fit for the dichotomous model for both mean square and t-statistics, in contrast to sample size independence for mean square fit statistics for the polytomous model as demonstrated in this study, and further research will be required to elucidate this issue.There are a number of limitations to this study: 1). The primary limitation is that "real" data directly derived from patients were used rather than simulated data. Previous work on the HADS in particular had demonstrated the presence of misfitting items in the scale . The aimThe presence of underfitting items in instruments may have a potentially significant impact by severely degrading the measures, whereas overfitting items will tend to overestimate differences in raw scores . The forIn summary, the study suggests that for polytomous Rasch Models when evaluating against accepted threshold criteria the t-statistics are sample size dependent. In contrast to this sample size invariance appears to exist for the mean square fit statistics. It may therefore be recommended that t-statistics should be adjusted or interpreted with caution when judging item fit or attempting to identify misfit in data, particularly for large samples and polytomous data.The authors declare that they have no competing interests.LJF, GV and MS contributed the data for this study. The data analysis was performed by ABS and RR. The manuscript was drafted by ABS with critical comments provided by RR, LJF, GV and MS. All authors read and approved the final manuscript.The pre-publication history for this paper can be accessed here:The file format is in MS Word, and is entitled "Appendix 1". It contains five formulae with descriptions.Click here for file


The majority of microsatellite instable (MSI) colorectal cancers are sporadic, but a subset belongs to the syndrome hereditary nonpolyposis colorectal cancer (HNPCC). Microsatellite instability is caused by dysfunction of the mismatch repair (MMR) system that leads to a mutator phenotype, and MSI is correlated to prognosis and response to chemotherapy. Gene expression signatures as predictive markers are being developed for many cancers, and the identification of a signature for MMR deficiency would be of interest both clinically and biologically. To address this issue, we profiled the gene expression of 101 stage II and III colorectal cancers ) using high-density oligonucleotide microarrays. From these data, we constructed a nine-gene signature capable of separating the mismatch repair proficient and deficient tumours. Subsequently, we demonstrated the robustness of the signature by transferring it to a real-time RT-PCR platform. Using this platform, the signature was validated on an independent test set consisting of 47 tumours , of which 45 were correctly classified. In a second step, we constructed a signature capable of separating MMR-deficient tumours into sporadic MSI and HNPCC cases, and validated this by a mathematical cross-validation approach. The demonstration that this two-step classification approach can identify MSI as well as HNPCC cases merits further gene expression studies to identify prognostic signatures. Colorectal cancer is a major public health problem as it accounts for about 13% of all cancers and is the second most common cause of cancer death in the western world . Tumour MLH1 or MSH2 genes, leading to failures in the mismatch repair (MMR) system. In sporadic MSI cases, the main cause of MMR failure was later found to be silencing of the MLH 1 promoter by methylation of CpG islands  families and found to be caused by mutations in the A new diagnostic approach is molecular classification of tumours based on DNA microarrays, that has shown the ability to produce gene expression signatures with predictive power for a variety of tumour types  and 17 normal mucosa samples were used for gene expression profiling. To enable the construction of a general MMR gene expression signature, caution was paid to avoid over-representation of a particular subtype of MSI tumour. Thus, MSI tumours of both sporadic and HNPCC origin were selected. The histology subtypes of the MSI tumours were selected to cover both ordinary and mucinous adenocarcinomas. Special attention was also paid to select cancers of the right and left colons as well as the rectum. Similarly, the normal samples were both from MSI and MSS patients  and represented both the right and left colons. A brief summary of the sample set used for gene expression profiling can be found in An independent sample set consisting of 47 stage II cancers (10 MSI and 37 MSS) was used for real-time RT-PCR , and sern=16) or MSH2 (n=2) mutations identified by sequencing.The analysis of MSI origin (sporadic or HNPCC) was performed on a sample set consisting of 37 MSI cancers . All HNPCC cases included in this study carry MLH1  or GenElute Kits (Sigma) according to the manufacturers' instructions. RNA integrity was evaluated on a 2100 Bioanalyzer using the RNA 6000 Nano LabChip kit (Agilent). Only samples with intact RNA were used for gene expression analysis.μg of total RNA and hybridised to the Human Genome U133A GeneChip array (Affymetrix). This array contains 22 289 probesets representing approximately 15 000 genes. The readings from the quantitative scanning were analysed by the Affymetrix Software MAS 5.0 and normalized using the quantile normalization procedure implemented in robust multiarray analysis (RMA)  was used to design intron-spanning primer pairs and to select appropriate hybridisation probes from the Human Probe Library (Exiqon™). The hybridisation probes of the Probe Library uses a unique nucleotide chemistry called locked nucleic acid (LNA). In practice the LNA probes function as classical TaqMan probes, but because of the LNA properties they are much shorter, only 8–9 bases. A description of primers and probes can be found in The web-based assay design software from (Exiqon™)  and 17 normal biopsies using high-density oligonucleotide arrays representing ≈15 000 genes. Redundant probesets and probesets with a variation across all samples smaller than 0.5 were removed, resulting in 1239 genes for further analysis. The clustering algorithm essentially separated the samples into three tumour clusters and a cluster with normal biopsies . Two of The MSI cluster contained three morphologically normal tissue specimens and six tumours were found in the cluster dominated by normal biopsies. This may be because of an atypical tissue composition in those samples, as described previously by Another observation was that the two MSS clusters were either dominated by Danish samples (19 out of 25) or by Finnish samples (26 out of 37), indicating a systematic difference between the two countries. Based on these observations, we performed a series of statistical tests that showed that the observed separation of tumours into MSS and MSI groups, as well as into Danish and Finnish groups, was highly significant even at highly strict criteria .To define a signature for microsatellite status, we used state-of-the-art supervised classification methods. We built a maximum-likelihood classifier using the 101 tumour samples and evaluated the classifier using a ‘leave-one-out’ cross-validation scheme. For classification, we selected those predictive genes that performed best in cross-validation and showed the largest possible separation of the two groups. Each tumour was classified according to its proximity to the mean of the three groups. We tested the classifier's performance using 1–100 genes in cross-validation loops, and obtained the best correlation to microsatellite status by using a 15-gene cross-validation scheme. For the final signature, we selected nine genes that were used in at least 70% of the cross-validations, and that represented both up- and downregulated genes . With thBy including samples from both Finland and Denmark, we identified genes that could discriminate between MSS and MSI, independent of the geographical origin of the two groups. The genes we used for the classifier were a subset of the highest scoring genes for MSS/MSI difference for both the Finnish and the Danish samples, and they have no separating power between Finnish and Danish samples.SET and ATP9a did not work well in the PCR reaction, we used only seven of the nine classifier genes  in our final RT-PCR-based classifier. We quantified the transcripts from these seven genes by real-time RT-PCR in a new independent test set consisting of 47 tumours, 35 MSS and 12 MSI tumours. Using this approach, the classification of 45 of 47 tumours was consistent with MS analysis. The two misclassified tumours were almost equally spaced between the groups of MSI and MSS tumours   in a clinical setting, we correlated our classification to the overall survival of the patients. We used the MSI classification data we generated on the training set with the nine-gene classifier, and Kaplan–Meier plots were constructed for stage II and III tumours separately . The ove=0.0014) . Thus, iP=0.55)  .t-statistics for difference between the two groups. As the number of MSI tumours available was limited, we could not analyse an independent test set. We therefore made an extensive mathematical testing. To evaluate the significance of the two genes, we randomly permuted the group labels MSI and HNPCC 500 times and for each permutation calculated the 22.215 t-values for difference between the two pseudo groups, and recorded the maximum of the absolute values. The largest absolute value among these 500 maxima was 4.37, showing that the t-value -4.89 for PIWIL1 was highly significant. Only two of the following maxima were larger than 4.2, which showed that the MLH 1 t-test value of 4.21 had a P-value of less than 1%. Thus, the confidence was better than 99% in case of MLH1, and even much higher in case of PIWIL1. The mismatch repair gene MLH1 showed a general downregulation in sporadic disease, whereas PIWIL1 was lower expressed in hereditary cases (The group of patients with MSI tumours includes both sporadic and inherited (HNPCC) cancers. As the inherited cases need an extensive clinical genetic examination of the family, a signature for this group of patients would be clinically relevant. We therefore sought for genes whose expression would identify such inherited cases. We subjected the 18 sporadic MSI samples and 16 HNPCC samples plus additional three samples (one MSI (stage I) and two HNPCC (stage I and IV)) to supervised classification as described above. The smallest number of errors in cross-validation was one error obtained when using two genes only . In 36 ory cases .The main objective of this study was to build a robust gene expression classifier for MSI reflecting MMR deficiency in colorectal cancer. Our first step was to perform an unsupervised cluster analysis of tumour samples and normal biopsies. Normal samples readily separated from tumour samples, and the tumour samples spontaneously separated into MSS and MSI. This demonstrates that MSI has a profound effect on the gene expression pattern of colon cancer.Surprisingly, the geographic origin of the patients also had an influence on the transcriptional pattern in the tumours. This was reflected in two MSS clusters, each dominated by either Danish or Finnish tumours. As the samples were labelled in mixed batches, we could exclude that the country difference was due to a labelling effect. The gene expression data in this study were derived from macroscopically removed pieces of resected tumours. The tissue used for RNA isolation was subsequently inspected by a trained pathologist and was estimated to contain from 50% tumour cells and up to more than 95%. A difference in the composition of cell types in the Danish and Finnish samples seemed unlikely, as we found no difference in muscle index  were classified in consistence with subsequent microsatellite analysis; one of the misclassified tumours was clearly inconsistent, whereas the other was classified with low confidence. Tumours that are problematic to allocate to either MSS or MSI may display intratumour heterogeneity (In the present study, only one case of the hereditary MSI tumours was caused by a MSH2 mutation. However, this tumour was classified correctly as microsatellite unstable. It is not known how many sporadic MSI tumours are that caused by MLH1 hypermethylation, but based on the expression levels of MLH1 and reports from the literature a realistic estimate would be 80–90% (MLH1 and PIWIL1. The classification resulted in only one misclassification out of 37. Analysis of this case for mutations in MLH1 and MSH2 and for the Finnish founder deletion in exon 16 of MLH 1 was negative, but were have not explored for deletions in MSH2, which is an important cause for HNPCC (MSH2 or MSH6 genes a correct classification would be expected because MLH1 is normally transcribed in these tumours. The second classifier gene PIWIL1 is a member of the human Argonaute family that contain a conserved RNA-binding PAZ domain and may be involved in the development and maintenance of stem cells through the RNA-mediated gene-quelling mechanisms associated with DICER (We were able to classify MSI tumours into sporadic and hereditary cases based on the expression of only two genes, The approach described here, to detect MSI in a first step and to identify HNPCC in a second step, if confirmed in other studies, is an improvement compared to previous strategies. In the near future, molecular classification of CRC into MSI and MSS subtypes may become a routine procedure because of the emerging different treatment strategies (The present gene expression signatures may, supplemented with future signatures, form the basis for developing a universal colon cancer diagnostic microarray capable of classifying tumour origin, type, stage and propensity to metastasise, as well as predicting their response to chemotherapy and likelihood of recurrence. We have demonstrated that such microarray-based assays may also be transferred to alternative platforms such as real-time RT-PCR.


When testing for prostate cancer, as many as 75% of men with a raised prostate-specific antigen (PSA) have a benign biopsy result. Little is known about the psychological effect of this result for these men.−1 and a negative biopsy result. Distress and negative mood were measured at four time-points: two during diagnostic testing and two after a negative biopsy result.In all, 330 men participating in the prostate testing for cancer and treatment (ProtecT) study were studied; aged 50–69 years with a PSA level of ⩾3 ng mlP<0.001). These levels remained elevated immediately after the negative biopsy result ; P<0.001) and 12 weeks later ; P=0.009). Psychological mood at the time of PSA testing predicted high levels of distress and anxiety at subsequent time-points.The majority of men were not greatly affected by testing or a negative biopsy result. The impact on psychological health was highest at the time of the biopsy, with around 20% reporting high distress (33 out of 171) and tense/anxious moods (35 out of 180). Longitudinal analysis on 195 men showed a significant increase in distress at the time of the biopsy compared with levels at the PSA test  score: 9.47; 95% confidence interval (CI) ; Most men coped well with the testing process, although a minority experienced elevated distress at the time of biopsy and after a negative result. Men should be informed of the risk of distress relating to diagnostic uncertainty before they consent to PSA testing. Prostate cancer is a serious public health problem, motivating research to determine whether population screening is effective. The recent publication of two randomised controlled trials still leaves the benefits of screening uncertain and controversial (−1) are used to recommend referral for biopsy. PSA levels are affected by measurement error and conditions other than prostate cancer, so it is not uncommon for PSA levels to rise and fall  may be left to cope with uncertain results. Increases in negative mood have been found in studies of women with abnormal but benign results for breast (−1) were offered a transrectal ultrasound-guided biopsy carried out by an urologist to a standard 10-core protocol at the local hospital. Men diagnosed with clinically localised cancer were eligible for randomisation to one of the three treatments. Between June 2007 and September 2008, men receiving a negative biopsy result (with no immediate requirement for a re-biopsy) were identified for this substudy, by local research nurses from eight of the UK sites.Participants in this study were men enrolled in the Prostate testing for cancer and Treatment (ProtecT) study, a randomised trial of treatment for localised prostate cancer  when attending for the first PSA test (before the result was known); (2) when attending for biopsy (in clinic before the procedure); (3) within a few days of receiving the negative biopsy result  and (4) ∼12 weeks later  (see Current states of mood were assessed by the Profile of Mood States – short form (POMS-SF) , a 37-adMultiple responses to an individual item on the POMS questionnaire were considered as errors and treated as missing values. Single missing values within a subscale were replaced with the individual's mean response to items within the rest of that subscale . In cases of two or more missing values, no score was calculated for that POMS subscale. Missing items on the IES resulted in no subscale score or total score. This substudy was embedded in the ProtecT trial and as the POMS and IES were added in an amendment to the original protocol, some men identified with a negative biopsy result had completed time-points 1 and 2 before the measures were introduced, resulting in lower numbers of men completing the measures at earlier time-points see .n=195); this included men who had already undergone PSA testing and biopsy when this substudy started and so had their earlier questionnaire responses missing completely at random. T-tests compared the scores at PSA test for this cohort of men to those who completed questionnaires at PSA test but not all of the subsequent assessments. For longitudinal analyses, linear regression models with questionnaire subscale scores as outcome measures were fitted using Stata 10. Models compared questionnaire scores at the subsequent time-points to responses at PSA test (time-point 1). P-values were calculated using parametric bootstrap estimates of the s.e.  and high anxiety (POMS tension-anxiety score) at attendance for biopsy and after having received a negative biopsy result, and explored potential predictors: demographic , clinical  and psychological . Univariable logistic regression models were fitted to examine each of these 13 baseline measures in turn as a predictor of high distress and anxiety at later time-points in the testing process.P=0.016, P=0.006, respectively). After accounting for non-responders and missing responses on questionnaire items, data were available from 294 out of 330 and 229 out of 287 men, respectively . POMS and IES data were available for 133 out of 330 of the study sample at PSA assessment (time-point 1), and for 180 out of 330 at biopsy assessment (time-point 2) – smaller numbers due to introducing the measures part-way through the ProtecT study. In total, 330 men were contacted after a negative biopsy result. Overall, rates of psychological distress (IES scores) and negative mood (POMS scores) were relatively low at all time-points, with around 80–95% of individuals reporting levels below the clinical threshold at each stage . HoweverP<0.001), and men experienced significantly more distress ; P<0.001). After receiving a negative biopsy result, scores for tense and anxious moods returned to levels similar to those recorded at the PSA test. However, reports of distress were significantly higher than at the time of the PSA test ; P<0.001). Approximately 12 weeks after having received a negative result (time-point 4), distress relating to prostate cancer testing remained more prevalent than distress at the time of the PSA test ; P=0.009), with 8.9% of men (16 out of 179) reporting scores of clinical concern. For these IES results, logistic regression of dichotomised scores supported the same conclusions, although this sensitivity analysis suggested that the elevation at the 12-week follow-up assessment could have arisen by chance (odds ratio (OR): 1.42; 95% CI ; P=0.23). Feelings of confusion-bewilderment were at a significantly lower level at the 12-week follow-up than at PSA testing ; P=0.004). Additional analysis of the cohort of men with complete data available for all four of the assessment time-points (n=66) was consistent with the results presented here and the same conclusions are supported.Within the study sample, 195 men responded to all questionnaires they were sent, with POMS and IES scores missing for one or both of the first two assessments only if the man joined the study before these measures were introduced. These earlier assessments at time-points 1 and 2 were assumed to be missing at random, enabling changes in mood over the course of the testing process to be observed without confounding by determinants of non-response . This coP=0.001), although psychological factors did ; tors did .post hoc analysis to explore other possible reasons why men may experience higher distress levels 12 weeks after biopsy, we retrieved information on any further biopsies the study men had undergone. Twenty-three men were found to have had a second biopsy before being sent their 12-week follow-up questionnaires, of which 17 men returned data for this assessment (73.9%). The mean IES total scores  for these 17 men were 1.29  at time-point 1; 5.00  at time-point 2; 9.12  at time-point 3 and 8.53  at time-point 4. There was no significant difference between IES scores at the 12-week follow-up assessment for those who had a repeat biopsy and those who did not (P=0.096). Of the total number of men from the complete cohort who had high distress at the 12-week follow-up assessment, 18% (4 out of 22) had undergone a further biopsy.For This study shows that most men seem to cope well with a raised PSA result followed by a negative biopsy. Mean scores for all the questionnaire scales were relatively low with regard to the thresholds for clinical significance and the maximum possible ranges. However, nearly 20% experienced high levels of tension-anxiety and psychological distress at the time of the biopsy, as well as 8.9% with high tension-anxiety and 16.9% with psychological distress immediately after the receipt of a negative biopsy result. These findings were substantiated in the longitudinal cohort. Men exhibited significant increases in distress and tense and anxious moods from baseline to the time of the biopsy, which then declined somewhat after receipt of a negative biopsy result, before returning to much lower levels 12 weeks later. However, distress was significantly more prevalent at all of the subsequent time-points compared with distress at the time of the PSA test, and almost 10% reported distress levels of clinical concern at the 12-week follow-up. Baseline levels of mood were found to predict levels of distress and anxiety at the time of biopsy, after the negative biopsy result and 12 weeks later.The present investigation was conducted within the ProtecT study, enabling large numbers of men based in the community to participate. This detailed quantitative study of the emotional consequences of prostate cancer testing is the first study of its kind in the United Kingdom, using more detailed psychological measures and using a longer follow-up time than one other study conducted in the Netherlands  referred to the complete testing process for example ‘I had dreams about being tested for prostate cancer’. In contrast, the POMS consisted of an adjective list of emotions with no direct reference to particular events in time. The pattern of mood levels over time was similar whether measured by the IES or POMS, although POMS scores seemed to return to pre-PSA levels sooner. At every time-point, men were asked to respond with regard to how they had felt during the preceding week. However, at the 12-week follow-up, the prompt to think about the complete testing process may have triggered memories of earlier mood states. This could have influenced men's responses, rather than just reporting on recent intrusive thoughts and avoidant behaviours that had troubled them during the previous week. It is possible that distress in some men 12 weeks after the biopsy could be due to the knowledge that a negative biopsy result does not necessarily indicate the ‘all clear’. Further research, preferably qualitative, is required to explore this issue and other explanations for distress after a negative biopsy result.It has previously been shown that older age, a positive family history, and a higher PSA level did not predict anxiety in men during testing for prostate cancer (The findings of this study have clear practical relevance. Despite its controversy, PSA testing is widespread. These results, particularly that high levels of distress may be encountered by some men, should be included in information presented to men by GPs before they consent to receiving a test. For men undergoing testing, whether as part of screening, or in primary or secondary care, the concomitant collection of POMS and IES data could be used to identify men who are experiencing tension-anxiety and early distress symptoms at the time of the PSA test, and thus identify those who might benefit from additional support and information to prevent this developing into distress later on (see also The intention of this study was to capture men's psychological experiences during prostate cancer testing. Focusing on those who received a negative biopsy result, it revealed that the majority of men who undergo testing are not significantly adversely affected, although some men do find the process distressing. Men should be informed of the risk of distress before agreeing to a PSA test.


International targets for access to antiretroviral therapy (ART) have over-estimated the capacity of health systems in low-income countries in Sub-Saharan Africa. The WHO target for number on treatment by end 2005 for Tanzania was 10 times higher than actually achieved. The target of the national Care and Treatment Plan (CTP) was also not reached. We aimed at estimating the capacity for ART provision and created five scenarios for ART production given existing resource limitations.limiting factor for ART scale-up, assuming that sufficient drugs and supplies are provided by donors. We created a simple formula to estimate the number of patients on ART based on availability and productivity of clinical staff, time needed to initiate vs maintain a patient on ART and patient losses using five different scenarios with varying levels of these parameters.A situation analysis including scrutiny of staff factors, such as available data on staff and patient factors including access to ART and patient losses, made us conclude that the lack of clinical staff is the main initiated on ART up to 2008, but is considerably below the national CTP target of 90% coverage for 2009, corresponding to 420,000 on ART and 710,000 life-years saved (LY's). Our analysis suggests that a coverage of 40% or 175,000 on treatment and 350,000 LY's saved is more achievable.Our scenario assuming medium productivity (40% higher than that observed in 2002) and medium loss of patients  coincides with the actual reported number of patients A comparison of our scenario estimations and actual output 2006–2008 indicates that a simple user-friendly dynamic model can estimate the capacity for ART scale-up in resource-poor settings based on identification of a limiting staff factor and information on availability of this staff and patient losses. Thus, it is possible to set more achievable targets. Despite considerable international funding for antiretroviral treatment (ART) in heavily HIV-affected low-income countries in sub-Saharan Africa At the time the model was created Tanzania had an estimated adult HIV prevalence of 6.5%, corresponding to 1.4 (uncertainty range 1.2–1.5) million infected individuals maximally could access ART under each scenario.To show that there are limitations to achieve these targets in weak health systems, and to demonstrate that it is possible for local planners in low income countries, to set more achievable targets, we worked out a simple method to estimate the maximum possible number of patients that could access ART in Tanzania. Estimates were based on the number of available clinical staff taking into account their productivity and consultation time needed to initiate vs maintain a patient on ART as well as the expected patient losses. We outline five different scenarios with varying levels of staff productivity and patient loss using observational data from human resource monitoring and available data on consultation time and patient losses derived from on-going ART programs. For a simplified model, we assumed that drug procurement and management issues would not be limiting factors during the implementation period . The resWe first reviewed the national Care and Treatment plan (CTP) for 2004–2009 including the ART output targets and the number of new Care and Treatment Centres (CTCs) that are to be accredited for scaling-up of access to ART.We then made a situation analysis of the major bottlenecks for rapid scale-up of access to ART in Tanzania and other low-income countries including a review of the existing literature, unpublished documents and verbal information from key informants. These findings were summarized in a conceptual framework displaying the main determinants of ART output .We proceeded to create a formula containing the main factors for ART output i.e. the number on treatment by the end of each year, including the number of new and existing patients, the availability and productivity of clinical staff as well as patient losses.initiated on ART at the end of February 2008 as well as with the output planned in the CTP  in mainland Tanzania (excluding Zanzibar) must increase from 20 to 240 centres in less than 4 years .prescribing  and evaluating clinicians . The average consultation time required per first-year patient on ART, is estimated to 4 hours (2 visits à 30 minutes and 9 visits à 20 minutes) and after the first year, 1.3 hours (4 visits à 20 minutes) of clinician care per year and patient. Thus, three times more times is assumed to be required for first year than for post-first year patients As staff is estimated to work 800 hours with patients per year this translates into 200 first-year patients (newly initiated on ART) and 600 post-first-year patients annually for every clinician According to the plan, CTCs should be located at hospitals and run by treatment teams of 18 staff members including 7 counsellors. The staff requirements for the CTP were derived from estimating the need for consultation time per patient for the first and subsequent years on ART for each staff category, including clinicians, counsellors, pharmacists and laboratory personnel . In all The annual number of HIV-infected Tanzanians in immediate need of ART was estimated to 160,000 new individuals assuming stable incidence rates (as those currently observed) the main limiting staff factor for ART scale-up. HIV patients do not need to see a medical doctor, but they need to meet clinical staff trained to assess history, signs and symptoms and who can prescribe and evaluate the effect of ARV drugs. The IMF-induced hiring freeze of government health staff in 1993 has resulted in an old workforce where more than 15% are 50 years or older. The total number of health workers, of which 65% work in government services, 22% in private-not-for-profit and 14% in private-for-profit institutions, has declined from 67,000 in 1995 to 54,000 in 2002 and has been estimated to fall even further to 49,000 by 2015 In our model we assumed that the availability of staff would be the main limiting factor of ART scale-up and that clinicians would be According to the national plan, mainly hospitals should be accredited CTCs for ART provision, but since there are only about 190 hospitals suitable for ART provision, most of them in urban areas, an expansion to primary health care level in rural areas with even fewer staff per facility will be necessary to reach high ART coverage. Thus, in contrast to the CTP, we took into consideration that less staff per facility would be available at the primary health care level after CTCs have been established at all available hospitals.Also, since half of the 1.4 million HIV infected Tanzanians live in rural areas Considering the shortage of medical officers we assumed that both prescription and evaluation will have to be done by any clinical staff available and categorized both prescribing and evaluating clinicians as “clinicians”. Using the 2001/2 Ministry of Health figures of existing clinicians, we calculated the average number of clinicians per type of health facility and further assumed, in line with previous estimates Few clinicians spend all their HIV working time on ART. Other activities include voluntary counselling and testing (VCT), condom distribution, education in schools, prevention of mother-child transmission (PMTCT), treatment of STIs, palliative care, monitoring of immune status and treatment of opportunistic infections in patients not yet qualifying for ART.In Rufiji district, with an adult HIV prevalence in 2003 of 7%, similar to the country average, sexually transmitted infections (STIs) including HIV/AIDS constituted 22% of the burden of disease (BoD) Thus, we assumed that that a maximum of 20% of clinicians' patient time would be devoted to ART. A higher proportion would affect both other HIV activites and other priority health interventions as assessed by WHO's Commission on Macroeconomics and Health st-year patients per year The CTP expects prescribing and evaluating clinicians to work 800 hours/year and to treat 200 first-year patients (newly initiated on ART) and 600 post- 1Thus, we used 640 hours/year as our low-productivity estimate, while a 40% productivity increase corresponding to 900 working hours per year per clinician was considered a medium productivity scenario and a doubling of the by Kurowski et al observed productivity would be considered high productivity, in our scenario analysesHence, we assumed that patients newly initiated on ART would have an average first-year mortality of 15% in addition to a basic age-specific mortality of 1%The 1% mortality rate assumed by the CTP is most likely an underestimation since data from seven ART programmmes providing ARVs free of charge showed an average mortality of 11% after 6 months In this study, we assumed both a low loss scenario of 10%, a medium-loss scenario of 20% and a high loss-scenario of 40%.Hospital-based fee-for-service programmes in Uganda and Malawi show one-year patient losses of 56% and 59%, respectively, mainly due to lack of money among patients and irregular drug supply at health centres Our situation analysis resulted in a conceptual framework with four major determinants of ART output in Tanzania :staff availability and productivity,patient-related factors including adherence, stigma, education level, socio-economic situation and family support,management and logistics and,financial resources, drugs and laboratory supplies.initial five-year period  and that it will be possible to use existing staff for counselling and supportive functions given the relatively small number of patients currently enrolled. We also judged that stigma and low awareness would not affect the demand for services during the implementation period 2004–2009 since attendance of VCT has increased rapidly during the last years and since many HIV positive women are diagnosed at antenatal care clinics and make up over 60% of all patients on ART in Tanzania (pc NACP).) We also assumed that patient related factors listed above will be largely captured in our patient loss estimates.and with two main factors limiting service provision during the current five-year planning period 2004–2009: 1) the availability and productivity of clinicians and 2) retention of patients in the treatment programmes. We assumed an average duration of 9 years from infection to AIDS first year mortality, age-specific mortality, other patient losses (loss to follow-up), average number of clinicians per facility type, time devoted to HAART, new patients/year, old (post-first-year) patients/year(clinician productivity). The input values of each variable can easily be changed by entering new data into a spread sheet. For our scenarios we only varied patient losses, new patients/year and old patients/year( clinician productivity), but kept the other variables constant.We constructed a simple dynamic model to estimate the number of new and post-first-year-patients on ART at the end of each year based on annual availability and productivity of clinicians, and, on the proportion of patients lost to follow-up or death,. The variables in this formula  include:y-1)(N and the loss of patients until the end of the year old)(L. Clinician-time available to care for old patients y-1×(1-Lold/2)/Pold)(N is determined by the number of these post-first-year patients retained in the ART program at the beginning of each year y-1) old/2)(F (ave)(C(ART) , multipl) ((Cave) multipli)((TART) .new)(1-L.The number of new patients that can be recruited depends on 1) the productivity of clinicians 2) the consultation time clinicians need for “old” (i.e. post-first-year) and how much time is left over for the new patients after the “old” patients have been treated 3) how much time is needed to initiate a new patient on ART (i.e. 4 hours/year according to the CTP). The number of new patients retained at the end of each calendar year is thus determined by: 1) the amount of clinician-time available for recruitment of new patients multiplied with 2) the number of new patients per clinician year We calculated the number of patients per clinician per year, both for the first year of treatment and the following years. Total ART output was defined as the sum of outputs from all accredited ART facilities that year. ART coverage was defined as the annual number of new patients initiated on ART divided by the estimated number of 160,000 eligible new HIV-infected individuals that annually need ART.staff productivity and patient loss, to demonstrate the effect on the number patients that could access ART (ART output) in Tanzania.Based on our situation analysis, the resulting assumptions and the above-defined formula, we created five scenarios with varying levels of low productivity scenario of 160 (640/4) first-year patients (Pnew) or 480 (640/1.3) post-first-year patients (Pold) per working year per clinician. If productivity is raised by 40%, defined as medium productivity i.e. to 900 working hours per year per clinician, the input per year per clinician is 225 first-year patients or 675 post- first-year patients. If productivity is doubled, defined as high productivity, 320 first-year patients or 960 post-first-year patients would be treated per clinician per year.Using results from Kurowski et al Low loss is defined by age-specific mortality 1%+first year mortality 15% and patient losses due to other causes of 10%. Medium loss is defined by age-specific mortality 1%+first year mortality 15% and patient losses due to other causes of 20%. High loss is defined by age-specific mortality 1%+first year mortality 15% and patient losses due to other causes of 40% . The number of LYs saved would be a little over 350,000.If we use the productivity figures of the CTP of 200 and 600 patients per year for first and post-first year patients respectively and assume medium losses, the results are similar with a coverage of 38% and a number of LYs saved of 320.000If the fourth “medium productivity-high loss” scenario would materialise, which assumes a 40% patient loss due to other causes than death, ART coverage would increase to 41% in line with the discussion above. The number of LYs saved would decrease to 274,000 .The fifth scenario “high productivity-high loss” assumes that clinicians become twice as productive, i.e. that they either double their working time or double the number of patients seen without increasing the number of hours. This scenario would result in 251,000 of those in need having access to ART corresponding to a 58% ART coverage in 2009, 500,000 LYs would have been saved and an equal number of people dying without having had access to ART .Assuming figures on first year mortality of 18.5% and patient losses of 10% observed in Malawi Thus, If the medium productivity-medium loss or the medium productivity–high loss scenarios were implemented, the number of AIDS deaths would be reduced by 40% from 160.000 to around 95.000. Thus, most infected people would still die without ever having had access to ART.patient losses (losses due to other causes such as loss to follow-up), first year mortality and the average number of clinicians per facility type was performed. It shows that a 1% change in the average number of clinicians directly translated into a corresponding 1% change in ART output irrespective of scenario. A 1% change in patient losses resulted in 0.1% change in ART output in the low productivity-low loss-scenario, a 0.2% change in medium loss scenarios and a 0.5% change in the high loss scenarios. A 1% change in first year mortality resulted in a very small change of service output, less than or equal to 0.1% in all five scenarios.A sensitivity analysis for the parameters We aim to estimate the capacity for ART provision in Tanzania. Using a simple user-friendly model we are able to demonstrate that the current plans of scaled-up access to ART in Tanzania were never feasible. Reality also shows that the implementation of ART is progressing at a slower pace than foreseen in the national Care and Treatment Plan (CTP). This papers shows both that the major reasons why targets have not been reached, are that the current plans assume a constant availability of sufficient staff and that the loss of patients on ART has not been taken into account and that it is possible to estimate achievable targets if a limiting staff factor can be identified.initiated on ART. This number largely coincides with what our medium productivity-medium-loss scenario predicted two years earlier. This indicates that estimating more achievable targets based on our assumptions is feasible.Our estimations suggest that around 175,000 patients will be on ART in Tanzania by mid 2009, which is 40% of the target in the CTP . The repIn our model we see the ART provision as a result of staff productivity and patient losses. We also assumed that possible weaknesses in management would not be limiting during the implementation period. The actual production may have been even smaller if for example poor management capacity limits scale-up. In the model we did not include that, but estimated what maximally could be achieved by the existing system under three main assumptions :Clinicians (number and productivity) is the main limiting staff factor. We estimated they would spend 20% of their working time on ARTPatient losses (deaths and losses to follow-up) constitute a measure that also captures the sum of all other patient factors including stigma, transport etc.The other factors, logistics, management and funding are not limiting during the implementation periodIf all these assumptions are in agreement with the actual findings in a country our estimations should be valid. Variations in the number of clinicians and staff productivity are captured by the model as are variations in patient factors through losses. The time devoted to ART is part of the function.Setting achievable targets is important for planning including estimations of resource needs. Even if the CTP is implemented according to plan with 420,000 on ART in 2009 , this imOur analysis indicates that the main limitation for ART scale-up is the low number and low productivity of clinical staff. The rapid increase in ART initiations after March 2007 might indicate that losses are high, making it possible to initiate more new patients on treatment. However, patient loss does not affect output as much since losses of previously enrolled patients on ART allows for recruitment of new patients , but theThe shortage of staff limits both recruitment of new patients for ART and further reduction of the number of AIDS deaths. The scarcity of trained clinical staff will be further accentuated during the last two years of the planning period by the expansion of ART services from hospitals to lower level health centers with much fewer clinical staff. An increase in the number of clinical staff available and/or an increase in productivity of existing clinical staff would be needed to counter the tendency of increase in the number of avoidable deaths due to AIDS shown in the scenario curves after 2008 . Some reSome of the assumptions may also not be correct. Accurate estimates for planning of ART require evidence on staff productivity and patient losses in scaled-up program in highly affected low-income countries. Such evidence is so far only available for some parameters from one large national programmes in sub-Saharan Africa The CTP targets are more achievable than those set by the WHO, but our study shows that also CTP underestimated existing constraints for ART in the Tanzanian health system. A possible increase in the number of qualified health staff for ART will also compete with other important health care needs including other priority HIV interventions, such as male circumcision The monitoring of patients not yet eligible for ART (CD4 counts >200) would, according to the CTP, require almost as many staff as is required to treat patients in acute need of ART and 30% more clinicians in 2009 . Thus, ilimiting factor for ART scale-up may be either overly optimistic or overly pessimistic given the lack of existing evidence of clinician-time requirements for ART patients, but it does not restrict the use of the model since it can be applied to any type of limiting staff category.Our assumption that clinicians rather than nurses, pharmacists and counselors, will be the main Our model estimates what maximally could be achieved by the existing system under the assumptions outlined in the Although our simple model does not explicitly capture the function of the system and the effects of scaling up, it has the advantages that it could be applied to any limiting staff category, to any country, is based on data that is routinely collected or that could be collected relatively easily through surveys, and contributes to the understanding of what factors are critical for ART provision.In conclusion, the agreement in magnitude between scenario and reality two years later suggests that it is feasible to outline achievable scale-up plans also in low resource settings like Tanzania. The model demands identification of limitimg factors and gives the magnitude of what can be produced under each scenario. As improved input data become available the range of scenarios can be narrowed and the estimations improved. This would make it possible to further improve planning and resource allocation.


We modeled temporal trends in the 1- and 5-year survival of 32 499 patients with adenocarcinoma and squamous cell carcinoma of the lung in the Swedish Cancer Register between 1961 and 2000. The 1-year relative survival for adenocarcinoma improved from 37% for patients diagnosed 1961–1965 to 45% for those diagnosed 1996–2000 and from 39 to 45% for squamous cell carcinoma. The adjusted excess mortality ratios for the period 1996–2000 compared with 1961–1965 were 0.80 for adenocarcinoma and 0.81 for squamous cell carcinoma. Thus, a previous report in a Dutch study of a relatively worsening prognosis for adenocarcinoma over time could not be confirmed. Over the past three decades in many Western countries, there has been a relative increase in the incidence of adenocarcinoma of the lung compared with other histological types ; 2003. TData from a regional population-based cancer register in the Netherlands during the period 1975–1994 indicated not only doubling of the relative incidence of adenocarcinoma but also a decline in its relative survival, even as relative survival ratios (RSRs) remained unchanged for other forms of non-small cell lung cancer. It was hypothesised that the decreased survival was also related to increased use of lower-yield cigarettes, and suggested that among adenocarcinomas, those caused by smoking may be more aggressive than those unrelated to smoking , which was established in 1958. Swedish law mandates the report of all newly diagnosed malignant tumours, as well as benign tumours from selected sites. The completeness of reporting to the SCR has been very high throughout its existence; in 1975, it was estimated that almost 100% of all malignant tumours were reported . The SCRCriteria for inclusion in this analysis included age 20–80, diagnosis of a malignant lung tumour (ICD-7: 162.1) between 1961 and 2000 and no prior diagnosis of cancer. Because large cell carcinoma and small cell lung cancer were combined as one category until 1985, we limited the analyses to individuals with the two main types of non-small cell lung cancer: adenocarcinoma (PAD: 96) and squamous cell carcinoma (PAD: 146). From the 39 714 cases originally eligible, we excluded 7 215 individuals who were diagnosed at autopsy or survived less than 1 month after diagnosis. The final dataset consisted of 32 499 patients.Follow-up for mortality is conducted routinely by the SCR through linkage with the Cause of Death Register. We included follow-up for death through the end of 2002 and calculated 1- and 5-year RSRs by sex and histologic type for individuals diagnosed during successive 5-year calendar periods . The RSR represents the ratio of observed survival among individuals with lung cancer to expected survival based on the sex, age (5-year categories) and period-specific mortality of the general population  using Poisson regression. The excess mortality ratio represents the relative excess mortality within each 5-year calendar period compared with the reference period of 1961–1965 . Filter cigarettes first became available in Sweden during the late 1950s, whereas reduced tar and nicotine cigarettes were introduced in the early 1970s and now dominate the market. The substantial increase in adenocarcinoma as a proportion of all cases among men over the past 40 years is consistent with early use of non-filter cigarettes followed by a transition to filter and low-tar cigarettes. Because of their later uptake of smoking, women were always more likely to smoke filter and low-tar cigarettes. Adenocarcinoma has always occurred more frequently than squamous cell carcinoma among women. However, cases in the earlier period were more likely to be non-smoking-related, whereas more recent cases are more likely to be caused by smoking.In 1946, 50% of men and 9% of women in Sweden were daily smokers (Strengths of our study included the nationwide scope, the large number of cases over a 4-decade period and the high case ascertainment. Throughout the study period, less than 3% of all cases lacked histological classification. However, we lacked information on stage at diagnosis and were unable to evaluate the trend in the stage distribution by histologic type. In conclusion, we did not find a worsening trend for survival for adenocarcinoma of the lung in Sweden over a 40-year period. Nevertheless, a variety of studies suggests that adenocarcinoma due to smoking differs from other adenocarcinomas (


As presented in the introduction paper, three sets of differentially regulated genes were found after the analysis of the chicken infection data set from EADGENE. Different methods were used to interpret these results.GOTM, Pathway Studio and Ingenuity softwares were used to investigate the three lists of genes. The three softwares allowed the analysis of the data and highlighted different networks. However, only one set of genes, showing a differential expression between primary and secondary response gave significant biological interpretation.Combining these databases that were developed independently on different annotation sources supplies a useful tool for a global biological interpretation of microarray data, even if they may contain some imperfections (e.g. gene not or not well annotated). Microarray data usually provide lists of genes, and often of hundreds of genes. The challenge is next to give sense to these lists and help to interpret the results. We used the three lists of differentially expressed genes presented in the introductive paper  as exampThe biological interpretation of gene lists was performed by three approaches: study of GO enrichment terms with GOTM (Gene Ontology Tree Machine) and two pathway software with a demonstration version of Pathway Studio (Ariadne Genomics) and Ingenuity Pathway Analysis . These approaches used various gene annotation databases: 1-Gene Ontology (GO) describes the most important ontologies in molecular biology, 2-Pathway Studio database is curated electronically using automated text-mining engines that are also available to investigators and, 3-Ingenuity's knowledge base is the first large database developed on all types of gene-gene and metabolite-gene interrelationships extracted manually from publications.in silico approaches in order to give sense to gene lists from microarray data.The purpose of our study is to evaluate three different Gene ontology (GO)  constituWe run the "Subnetwork Enrichment Analysis" (SNEA) option of the software, with default parameters, which builds small networks consisting of a single "regulator" gene and its targets . The sigThis system, a web-based interface , provideAmong the 11538 oligonucleotides of the microarray with a proposed HUGO name, 10451 HUGO gene names were validated according to HGNC . The HUGFor the 3 condition contrasts MM8-MA8, MM8-MM24 and MM8-PM8, the gene numbers with validated HUGO among the differentially expressed genes were 38 among 85, 354 among 800 and 931 among 1736 respectively. Because GOTM does not use more than 500 genes at once, we had to split the MM8-PM8 list into 2 sets: up and down regulated genes . The combined pathway is represented in Figure Among the 13639 annotated genes from the microarray using the different identifiers, 12395 are recognized by the software and 7219 used to generate networks. For the 3 condition contrasts MM8-MA8, MM8-MM24 and MM8-PM8, the gene numbers mapped with Pathway Studio were 43 among 85, 396 among 800 and 1062 among 1736 respectively  In the PM8-MM8 comparison, 14 networks are involved in significant biological functions compared to the microarray background. Biological functions as immune and inflammatory response, metabolism and proliferation are enriched. Particularly, the merged immunity network shows the down expression of a majority of genes during the secondary response. In accordance with previous results , canonicb) In the MM8-MM24 comparison, 3 networks (out of 7) and the canonical pathways results show mostly a down expression of the genes involved in the actin polymerisation.Eimeria acervulina species compared to Eimeria maxima.c) In the MM8-MA8 comparison, only 1 significant network (score 22) was identified with down expressed genes involved in inflammatory diseases in In this study, we used 3 lists of genes with 3 softwares. It is noticeable that slight differences (+/-10%) exist between the 3 softwares concerning the number of mapped genes. We identified two limits, using these programs: first, GOTM did not allow us to study all the genes at once; second, whatever program we used, the number of genes with an accepted name never reached 100% of the annotated list. The capability of the software to identify the gene names is a limiting step of the analysis.Only primary vs. secondary response gene list (MM8-PM8) gave significant biological interpretation with the 3 softwares: immune function and inflammatory response were much higher in primary response (immune-system related processes).Because these databases were developed on different annotation sources, the results can be considered as complementary. Combining these databases supplies a useful tool for a global biological interpretation of microarray data, even if they may contain some imperfections (gene not or not well annotated).In another way, GOTM offers more accurate information for a list of genes but did not suggest any links between these genes. GOTM is more appropriate for description of the data with a statistical validation.Pathway Studio, in the way it was used in this work, presents two main characteristics that differentiate it from the other softwares. First, it tries to find links only between the genes in the proposed list and not in a more global context like Ingenuity. Secondly, it offers the possibility to identify major regulators for differentially regulated genes and this could be interesting in order to develop further studies. The proposed networks look like stars with the possible regulator in the node.Ingenuity suggests more possible links between the differentially regulated genes and with some other genes in highly relevant networks. The networks are more complex than with Pathway Studio with more nodes, more genes without information about their expression. Ingenuity explores list of genes by very different ways, for example as very high confident information with canonical pathways and more exploratory ways as gene network.These softwares give tools to functionally annotate the gene lists and help the biologists to give sense to huge data. Finally, these softwares offer the possibility to identify more interesting genes among a list in order to undertake further experiments.The authors declare that they have no competing interests.AB carried out the Ingenuity study, SL carried out the GOTM study. GTK carried out the Pathway Studio study and drafted the manuscript. All authors participated to a working group, the redaction work, read and approved the final manuscript.IPA networks. For each of the 3 lists the networks are selected if their score is higher than 21 . The table contains columns with the network number, the name of the comparison list, the names of the genes involved in the network, the score value and the top functions.Click here for fileData were filtered with 2 criterions: IPA threshold p.value (<0.05) and the corresponding microarray p.value. a – Biofunctions: we did not find significant functions for the MM8-MA8 gene list. b – Canonical Pathway. For each list, the pathways are ranked by the score ) using the same criterions. The table includes also the ratio .Click here for file