A wealth of quality genomic and mRNA/EST sequences in recent years has provided the data required for large-scale genome-wide analysis of alternative splicing. We have capitalized on this by constructing a database that contains alternative splicing information organized as splicing graphs, where all transcripts arising from a single gene are collected, organized and classified. The splicing graph then serves as the basis for the classification of the various types of alternative splicing events. is a database of Drosophila melanogaster exons obtained from FlyBase arranged in a splicing graph form that permits the creation of simple rules allowing for the classification of alternative splicing events. Pfam domains were also mapped onto the protein sequences allowing users to access the impact of alternative splicing events on domain organization.DEDB DEDB's catalogue of splicing graphs facilitates genome-wide classification of alternative splicing events for genome analysis. The splicing graph viewer brings together genome, transcript, protein and domain information to facilitate biologists in understanding the implications of alternative splicing. Drosophila melanogaster genome in March 2000 [Drosophila melanogaster Exon Database) [The completion of the draft sequence of the rch 2000 ,2 and thrch 2000  presentsrch 2000 . We thenrch 2000  domains rch 2000 . All theatabase) . To aid Drosophila melanogaster genome annotations (release 3.2) were obtained from FlyBase [ FlyBase  as Game The transcripts contained in the database were retrieved and clustered on the basis that they occupy overlapping genomic positions. Splicing graphs are then constructed using these clusters of transcripts. In each cluster, exons and introns having identical start and end positions are merged into nodes and connections respectively. The nodes are then linked via connections to form the complete splicing graph that is loaded into the database. In cases where the transcripts are located on the negative strand, the entire splicing graph is reverse complemented so that all the splicing graphs contained in the database have sense strand nucleotide sequences. These steps are graphically depicted in Figure Rules are then derived to detect specific alternative splicing events as shown in Figure . Users can query the database using FlyBase gene names, FlyBase Gene Symbols, Pfam Accession Numbers or Pfam Identifiers via the query page. Users can also query the database using BLAST [Drosophila melanogaster homology together with alternative splicing information of a particular gene. Lists of splicing graphs for the various types of alternative splicing events are also provided on the website for users who are interested in a certain type of alternative splicing. For users who wish to use large subsets of the data, they can download the XML files available from the same site. To aid parsing and validation of the XML file, a XML schema is available. DEDB can also be accessed via links on Flybase gene records, under the external database links section. Correspondingly, the DEDB Splicing Graph Viewer provides links back to FlyBase Gene and Annotation records, where experimental evidence for the gene structure has also been provided. Basic statistical analysis of the database can be found at the DEDB website .The database together with the splicing graph viewer is freely available at ng BLAST  searchesThe splicing graph viewer consists of HTML pages created using a series of Python CGI (common gateway interface) scripts served by the Apache web server. The splicing graph viewer Figure  is a thrDrosophila moe gene, splicing graph 916, on the DEDB methodology link) resulting in the user having to correlate large amounts of data to comprehend all the alternative splicing events taking place. The DEDB schematic representation of the splicing graph is different from the one proposed by Heber et al. [Drosophila melanogaster homology of their gene of interest, which is made possible through a BLAST search on the DEDB query page. The splicing graph of the Drosophila melanogaster homology may provide insights into the possible splice variants in the gene of interest. It could also provide information on the level of conservation of alternative splicing between orthologs.By condensing all the various splicing variants into a single graph, where each splicing variant is a path through the graph, users can quickly establish the types and effects of various alternative splicing events present in the gene. Users can quickly pick up bifurcations which denote alternative splicing events far quicker than in the case of the traditional approach of presenting separate schematic representations of each splice variant, where the user has to correlate the splicing patterns from the transcript diagrams, to determine the impact and type of alternative splicing. The classical approach is particularly tedious in cases where the number of splice variants are numerous  are alternatively splicing. This is significantly less than the amount of alternative splicing found in higher eukaryotes like humans , but sufDrosophila melanogaster are also thought to result in greater intron definition. The data observed could be due to the splicing machinery adopting a definition model dependent on the length of the intron or exon in question [Failure of intron definition is more likely to result in intron retention as opposed to exon definition in which case, failure leads to cassette exons. Initial analysis of the DEDB data indicates a bias towards cassette exons (1,228) over intron retention (983) events, so that exon definition is less stringent than intron definition. The short introns in question . This isInformation content analysis indicates that alternative donor and acceptor sites  possess less information than constitutive sites . This observation is consistent with the general notion that alternatively spliced exons exhibit splicing motifs deviating more from the consensus motifs . CassettThe addition of Pfam domain information allows users to assess the impact of alternative splicing events on the proteins generated, enabling correlations not possible with the genome annotations alone.Future work would focus on integrating other relevant information onto the splicing graphs, such as three-dimensional structural information as well as DEDB analysis results. Expansion of the splicing graph representation available in DEDB to other organisms is also underway.The data housed in DEDB is organized as splicing graphs, which allows for ease of alternative splicing classifications. This has allowed DEDB to provide clean sets of data containing specific types of alternative splicing events. These specific sets of data could prove useful in understanding the biological basis of alternative splicing because different forms of alternative splicing have different biological basis. The splicing graph viewer provided allows biologists to quickly and intuitively understand the effects of alternative splicing on a gene of interest, thus aiding their research. suitable for most graphical web browser. XML files of the data contained in the database are also available together with an XML schema to aid parsing.The database is available at BTKL carried out the construction of the database as well as the splicing graph viewer. TTW and SR are responsible for the database concept and participated in its design and construction. All authors have read and approved the final manuscript.


Normalization is a critical step in analysis of gene expression profiles. For dual-labeled arrays, global normalization assumes that the majority of the genes on the array are non-differentially expressed between the two channels and that the number of over-expressed genes approximately equals the number of under-expressed genes. These assumptions can be inappropriate for custom arrays or arrays in which the reference RNA is very different from the experimental samples.We propose a mixture model based normalization method that adaptively identifies non-differentially expressed genes and thereby substantially improves normalization for dual-labeled arrays in settings where the assumptions of global normalization are problematic. The new method is evaluated using both simulated and real data.The new normalization method is effective for general microarray platforms when samples with very different expression profile are co-hybridized and for custom arrays where the majority of genes are likely to be differentially expressed. Microarray technology provides simultaneous measurements of expression levels for thousands of genes. Each step from sample preparation to data analysis, however, contains potential sources of bias and variability. Proper normalization adjusts for differences which interfere with the comparison of intensities of different labels at a given probe and with the comparison of intensities of corresponding probes on different arrays. Proper data normalization should allow for the comparison of expression levels across different arrays. Subsequent data analysis results are heavily dependent on effective normalization.Normalization issues differ for dual-labeled platforms compared to single labeled platforms such as the Affymetrix GeneChip arrays. In this paper we address normalization for dual-labeled arrays with either cDNA or oligonucleotide probes. The objective of normalization for dual-labeled arrays is to correct for differences in intensities for the two labels on the same array. These differences arise from factors such as differences in sample concentrations, differences in photomultiplier tube setting, and differences in the affinity of the two labels for DNA.Median or mean based global normalization methods use a single normalization factor applied to all genes on the array to adjust for labeling bias ,2. Such There are some methods which attempt to adaptively identify the subset of 'housekeeping' genes -8. Theseet al. proposed a Gamma-Gamma-Bernoulli model for identifying differentially expressed genes in dual labeled arrays [Newton d arrays . We haveAs described in the Methods section, we have applied our adaptive method to both the simulated data and real data. We have also compared our method with the global method and the intensity-based lowess method.2 scale) when the number of under-, null, and over-expressed genes were 200, 100, and 100, to more than a three fold difference  when the number of under-, null, and over-expressed genes were 200, 50, and 50. The RMSEs for the adaptive method ranged from 0.078 to 0.159 in log2 scale.Results of the simulation studies are shown as bar plots in Figure We compared the histogram of observed intensities to the fitted marginal density from the adaptive method as a simple check to see whether the proposed model and the estimation procedure are in line with available data. Figure 2 scale  Figure . Better Results comparing RMSEs for the adaptive method, the global method and the lowess method with real data are shown in Table In this paper, we propose a new method for normalization of dual-labeled arrays in cases where the number of differentially expressed genes is substantial and not necessarily symmetric in direction. The method performed effectively with both simulated and real data.c into Newton's Gamma-Gamma-Bernoulli model [We started our model building initially by introducing an unknown constant li model . The mixcRk) ~ Gamma(a, sk)log(Gk) ~ Gamma(a, sk)     (1A)log(sk ~ Gamma(a0, γ)cRk) ~ Gamma(a, )log(Gk) ~ Gamma(a, )     (1B)log( ~ Gamma(a0, γ) ~ Gamma(a0, γ)We found that when the differential expression was symmetric between the two samples, the model worked well. However, the error increased significantly when the ratio of the numbers of under- to over-expressed genes shifted from 1.γR and γG for the red channel and green channel intensities. For this modified two-component mixture, the error still remained large. We then extended the model into a three-component mixture model listed as Equations 8A-8C in the additional material [see In order to make the model more flexible, we modified the model by assigning different scale factors rial see . The mod2(ratio) became larger . The mole 3 see  and Figule 3 see .We compared our adaptive method with the global method and the intensity-based lowess method. The lowess method assumes that in each intensity interval either the majority of genes are non-differentially expressed or the numbers of up- and down-regulated genes are equal. The global median normalization makes these assumptions only over the array as a whole. It is not surprising that our method performed much better that the above two methods, because the global median method only works well when the assumptions are valid while the intensity-based lowess method is only effective when there are intensity-dependent biases.et al. proposed using the lowess normalization separately within each grid on the array [Correlation structure is complicated for the thousands of genes on a microarray. In our model, the intensity of each channel is conditionally independent given the scale parameter, but not marginally independent.Therefore, we are not assuming the intensities in two channels are independent. Although we did not generate correlated genes in our simulated data sets, correlations of genes do exist in the real data sets we tested. Spatial correlations are also possible but our method is not designed for that purpose. Yang he array . Our algLimited simulations were performed in this study. We also tried to use real data to test our method. Since an appropriate data set with known normalization factor was not available, we synthesized such data sets by sub-setting large arrays in which the true normalization factor could be accurately estimated. In the process of synthesizing such small arrays we had to choose an empirical threshold to stratify the differentially expressed genes and non-differentially expressed genes. Although we do not believe that the superiority shown for our algorithm depends critically on the threshold chosen nor on details of the synthesis, it would be preferable to evaluate the algorithm on real data sets with know normalization factors.Although our method is designed for dual-labeled cDNA array, it can be extended to single channel Affymetrix chip data. The most popular normalization method for the Affymetrix chip compares each array to a single base line array for probe set summaries. The assumptions behind the normalization method are that the majority of the genes are non-differentially expressed and the numbers of over- and under-expressed genes are roughly equal; the same assumptions as those for dual-labeled cDNA arrays. We could treat the base line array as the 'reference channel' and the other array as the 'test' channel and apply our algorithm to probe set summaries. For Affymetrix chip data, there are multiple base pairs in a probe set and each probe has an intensity measurement. Several alternative normalization methods of Affymetrix arrays utilize the probe level information. For example, method based on an 'invariant set' proposed by Li and Wong assumes that a probe of a non-differentially expressed genes in two arrays to have similar ranks and uses an iterative procedure to identify the invariant set which presumably consists of points from non-differentially expressed genes .Our new normalization method does not require that the majority of genes be non-differentially expressed, and doesn't require multiple array replicates, dye swaps, spiked controls, or housekeeping genes. It appears much more effective than standard methods when the numbers of over- and under-expressed genes are unequal, and the majority of the genes are differentially expressed. It can be very useful for general microarray platforms when samples with very different expression profile are co-hybridized and for custom arrays where the majority of genes are likely to be differentially expressed. In both of these settings, standard normalization methods are problematic.k in two channels as Rk (red) and Gk (green). Let c be a positive constant which is related to the normalization constant. The observed intensities for gene k in two channels are cRk and Gk. We assume the logarithm of intensity in each channel has a Gamma distribution. The genes on the array belong to three different groups: 1) non-differentially expressed; 2) under-expressed; and 3) over-expressed. The overall data will be fitted into a mixture model listed below.We define true intensities for a specific gene k,For a non-differentially expressed gene cRk) ~ Gamma(a, sk)log(Gk) ~ Gamma(a, sk)     (2A)log(sk ~ Gamma(a0, γ).k,For an under-expressed gene cRk) ~ Gamma(a, )log(Gk) ~ Gamma(a, )     (2B)log( ~ Gamma(a0, γ1) ~ Gamma(a0, γ2).k,For an over-expressed gene cRk) ~ Gamma(a, )log(Gk) ~ Gamma(a, )     (2C)log( ~ Gamma(a0, γ2) ~ Gamma(a0, γ1).Gamma distributions, the parameters a and a0 are shape factors, and the parameters sk, γ, γ1,γ2, ,  are scale factors. The parameters a, a0, γ, γ1, γ2 will be estimated from the data.In the above pu(Rk, Gk), po(Rk, Gk) and pn(Rk, Gk) be the densities of (Rk, Gk) for under-expressed, over-expressed and non-differentially expressed genes, respectively. The joint distributions of (Rk, Gk) in three groups can be derived as follows [details see Let θ denote the unknown parameter vector (a, a0, γ, γ1, γ2, c), which can be estimated by maximizing the likelihood function of observed data. We used the EM algorithm [p1 be the proportion of under-expressed genes and p2 be the proportion of over-expressed genes. We define indicator binary variable zk1 to be 1 if the kth gene is under expressed, 0 otherwise; and zk2 to be 1 if the kth gene is over expressed, 0 otherwise. The complete-data loglikelihood for all spots can be derived as follows,Let lgorithm  for thisp1 and p2. This yieldsIn the M-step, we first take derivative on Equation (4) with respect to K is the total number of genes on the array.where θ.To maximize Equation (4), we only need to maximize Equation (6) because the left out terms do not depend on the parameter zk1 and zk2 given the other parameters from the M-step.In the E-step, we compute the conditional expectations of c was obtained, the normalization constant for log intensity ratio data can be calculated as log(1/c).Once the constant c = 1.5, a = 118, a0 = 410, γ = 31, γ1 = 23, and γ2 = 29. Six scenarios were included using different proportions of non-differentially expressed genes and different ratios of under- to over- expressed genes, as listed in Table Simulation studies were performed by generating two channel intensities from the mixture model with 2(1/) and the true log2(1/c) was calculated. The global method takes the median log2(ratio) of all genes in each data set as the normalization factor. The lowess method performs robust locally linear fits of M-A plot and corrects the biases that are dependent on spot intensity [2(ratio) using the lowess method and the normalized log2(ratio) using the global method with the true normalization factor (log2(1/c)) for all genes was also calculated for the same 100 data sets.One hundred data sets were generated for each scenario and the RMSE between the estimated logntensity . The RMS2 scale to reflect the experimental noise in inbred strains of mice or cell line data and 0.5 in log2 scale to reflect a larger experimental noise in human tissue data [Gaussian white noise was also added when generating the simulated data. We used standard deviation of 0.25 in logsue data .In-silico studies were performed on real data. We tested the method on ten arrays from publicly available breast cancer data [2(ratio) of all genes was considered as the true normalization factor c. The genes were then divided into three groups: over-expressed genes (log2(ratio)>1), non-differentially expressed genes(-1.5<log2(ratio)<1.5), and under-expressed genes (log2(ratio)<-1). We randomly sampled a specified number of genes from each group  and then combined them into an in-silico array. We constructed 100 datasets for each of the 10 arrays in this way and the RMSE between the estimated log2(1/ ) and the true log2(1/c) was calculated.cer data . Each arc = 1.5, a = 90, a0 = 120, γ = 8, γ1 = 6, and γ2 = 10 at three different noise levels (A) SD = 0; (B) SD = 0.25; and (C) SD = 0.50.Figure 4: Bar plots show comparison of RMSE by using the adaptive method (black bar) and global method (grey bar) with simulated data generated from a mixture model with Click here for filec = 1.5, a = 90, a0 = 120, γ = 8, γ1 = 6, and γ2 = 10. The superimposed curve on each plot is generated from the fitted model.Figure 5: Histograms and the estimated densities of log(ratio) and log(intensity) for a simulated data of a mixture model with Click here for fileEquations 8A-8C: a three-component mixture model.Click here for fileFigure 6: Histograms and the estimated densities of log(ratio) and log(intensity) for a set of real data generated from array svcc109. The superimposed curve on each plot is generated from the fitted model. The procedure to generate the data was described in the paper and the sampling rate was shown in Table 3 see .Click here for fileTable 3: Different number of genes sampled in each interval.Click here for fileTable 4: Comparison of RMSE by using the adaptive method and global method with real data by a different sampling method. The procedure to generate the data was described in the paper and the sampling rate was shown in Table 3 see .Click here for filepn, pu, and po Derivation of joint distributions of (R, G) for Click here for file


Breast cancer cells frequently metastasize to the skeleton and induce extensive bone destruction. Cancer cells produce proteinases, including matrix metalloproteinases (MMPs) and the plasminogen activator system (PAS) which promote invasion of extracellular matrices, but whether these proteinases degrade bone matrix is unclear. To characterize the role that breast cancer cell proteinases play in bone degradation we compared the effects of three human breast cancer cell lines, MDA-MB-231, ZR-75-1 and MCF-7 with those of a normal breast epithelial cell line, HME. The cell lines were cultured atop radiolabelled matrices of either mineralized or non-mineralized bone or type I collagen, the principal organic constituent of bone.2 antiplasmin, prevented collagen dissolution. During ECM degradation breast cancer cell lines expressed urokinase-type plasminogen activator (u-PA) and uPA receptor, and MMPs-1, -3, -9,-13, and -14. The normal breast epithelial cell line expressed low levels of MMPs-1, and -3, uPA and uPA receptor. Inhibitors of both the PAS (aprotinin and PA inhibitor-1) and MMPs  blocked collagen degradation, demonstrating the requirement of both plasminogen activation and MMP activity for degradation. The activation of MMP-13 in human breast cancer cells was prevented by plasminogen activator inhibitor-1 but not by tissue inhibitor of metalloproteinase-1, suggesting that plasmin activates MMP-13 directly.The 3 breast cancer cell lines all produced significant degradation of the 3 collagenous extracellular matrices (ECMs) whilst the normal breast cell line was without effect. Breast cancer cells displayed an absolute requirement for serum to dissolve collagen. Degradation of collagen was abolished in plasminogen-depleted serum and could be restored by the addition of exogenous plasminogen. Localization of plasmin activity to the cell surface was critical for the degradation process as aprotinin, but not αThese data demonstrate that breast cancer cells dissolve type I collagen and that there is an absolute requirement for plasminogen activation and MMP activity in the degradation process. Breast cancer is the most frequent cancer in the female population of industrialized countries. Metastasis of breast cancer cells to the skeleton occur in >70% of patients with progressive disease, resulting in debilitating symptoms such as severe bone pain, fractures, hypercalcaemia and spinal cord or nerve compressions due to extensive bone loss and tumour cell growth and expansion. Such bone loss occurs as a result of increased bone matrix resorption but the mechanisms by which cancer cells mediate this increased degradation have not been fully elucidated. Obviously, tumour expansion in bone requires the removal of the extracellular matrix (ECM) that is particularly abundant in bone. Cancer cells express matrix metalloproteinases (MMPs) and the plasminogen activator system (PAS) -3 and thThe matrix metalloproteinases (MMPs) constitute a large family of structurally related matrix degrading proteases that have pivotal roles in development, tissue remodelling, and cancer -6. The gThe PAS comprises: tissue-type plasminogen activator (t-PA) and urokinase-type plasminogen activator (u-PA), their inhibitors, and receptors. T-PA is thought to be more important in fibrinolysis, due to its fibrin binding capacity, whilst u-PA, especially when it is bound to its specific cell surface receptor (u-PAR), is thought to be involved in tissue remodelling and cell migration processes . WhereasTo date, emphasis has focused on the ability of breast cancer cells to stimulate the formation and activity of osteoclasts, the cell primarily responsible for bone resorption under physiological conditions. The ability of osteoclasts to degrade bone lies in their ability to secrete protons and specialized collagenolytic proteinases, the cysteine proteinases in the acidic microenvironment that underlies osteoclasts during bone resorption .in vivo support the idea that they play an important role in metastasis of tumour cells [in vivo breast cancer metastasis to bone show promise when given as a preventive treatment to mice [Experimental studies showing that increased expression of MMPs and the PAS is associated with increased cellular invasion ur cells ,11. Obvi to mice ,13. Howein vitro using matrix degradation assays and compared their effects with those of a normal breast epithelial cell line, HME. We correlated the degradation activity of the breast cancer cells with their expression of MMPs and the PAS and we assessed the ability of group-selective proteinase inhibitors to prevent degradation of the organic aspect of bone by breast cancer cells.We have therefore assessed the ability of three human breast cancer cell lines, MDA-MB-231 (MDA-231), ZR-75-1 and MCF-7 to degrade bone collagen -10M) and cultured in the presence of 10% FCS the cancer cells induced significant degradation of type I collagen (range 70–80%), whereas minimal degradation was observed in the absence of serum . When the breast cancer cells (MDA-231, ZR-75-1 or MCF-7) were stimulated with TGFβ(10The ability of the cancer cells to degrade the type I collagen is consistent with the expression of proteolytic activity. However, the midpoint melting temperature of reconstituted firbrillar type I collagen 47°C) is lower than that of authentic type I collagen in tissues (55°C to 60°C) [7°C is loThe described sequential gene expression of differentiating osteoblasts  was veriWhen TGFβ-stimulated breast cancer cell lines were cultured as a monolayer on either non-mineralized Fig.  or minerTo characterize the profile of MMPs expressed constitutively and upon stimulation with TGFβ, breast cancer cells were cultured on type I collagen. Total RNA was isolated from 24-h cultures and screened by reverse transcription-PCR for MMP-1 through MMP-17. Under these conditions, four secreted MMPs were identified: MMP-1, MMP-3, MMP-9, and MMP-13 and the membrane-anchored MMP, MT1-MMP in all 3 breast cancer cell lines Fig All 3 breast cancer cell lines expressed u-PA and u-PAR Fig. .To analyze the functional activities of MMPs and PAs expressed in breast cancer cells, collagenase activity in BCCM was measured by the degradation of FITC labelled type I collagen in the presence and absence of plasminogen. TGFβ markedly stimulated collagenase activity only in the presence of plasminogen Fig. . uPA pro2-antiplasmin, which is a poor inhibitor of cell surface bound plasmin but an excellent inhibitor of plasmin in solution, did not prevent collagen degradation  inhibit the activities of all MMPs, except the membrane-type MMPs in the case of TIMP-1, we cannot identify the contribution of individual MMP members to bone matrix degradation. However, our demonstration of a direct requirement of plasmin in the activation of proMMP-13 suggests that this fibrillar collagenase may play a prominent role in the degradative activity of breast cancer cells. However, the possibility of other known MMPs also being involved in plasminogen-dependent collagenolysis by breast cancer cells cannot be excluded. The participation of the membrane-type MMPs such as MMP-14, -15 and -16 is improbable due to the ability of TIMP-1 to effectively block collagen dissolution by breast cancer cells. MMP-2 and – 8 were not expressed in detectable amounts by breast cancer cells, as assessed by RT-PCR. The existence of very small, but functionally important, amounts of MMP-2 or – 8 however cannot be excluded unequivocally by the expression studies. MMP-1 is expressed by the human breast cancer cells in this study and may contribute to plasmin-dependent fibrillar collagen dissolution.This investigation has demonstrated a significant difference in the ability of breast cancer cells and normal breast epithelial cells to degrade the organic aspect of bone matrix Currently, little is known with regard to the physiological mechanisms by which MMPs undergo activation in intact cell systems . HoweverThe increased bone resorption that accompanies breast cancer cell infiltration of bone may arise as a result of (1) breast cancer cells stimulating osteoblast and osteoclast activity and (2) the production by breast cancer cells of proteinases that participate in degrading the organic aspect of bone matrix which would also facilitate the access of osteoclasts to the underlying mineralized matrix.Even if tumour-derived MMPs do not directly digest mineralized matrix, they may participate in osteolysis via a mechanism that is analogous to a known osteoblast function. It is believed that all endosteal surfaces are covered with a layer of nonmineralized matrix. In areas of bone formation, this layer is called osteoid and it is easily observed in stained sections . EvidencIn conclusion, we have shown that breast cancer cells can degrade the organic aspect of bone matrix in contrast to non-tumourogenic breast epithelial cells. There is an absolute requirement for both the PA system and MMPs in the degradation process.The MMP inhibitor CT-1166 was a generous gift from Dr A Docherty, Celltech (Slough, UK). The PAS inhibitor aprotinin, plasminogen and TGFβ were purchased from Sigma. The human mammary epithelial cell (HMEC) line, Mammary Epithelial Basal Medium (MEBM), insulin, hydrocortisone, gentamicin/amphotericin-B and Bovine Pituitary Extract (BPE) were purchased from Clonetics . Human breast tumour cell lines MDA-231, ZR-75-1 and MCF-7 were purchased from ATCC (American Type Culture Collection, Manassas, VA, USA). Gelatinase-A, TIMP-1 and polyclonal sheep antibodies to human MMP-1, -2,- 3, -9 and -13 were gifts from Dr J Reynolds, Strangeways Research Laboratory, Cambridge, UK. Neutralizing mAbs against human uPA or human uPAR were from American Diagnostica, Greenwich, CT, USA.2 plastic tissue culture flasks (Costar Corning, Cambridge, MA, USA) as follows: ZR-75-1, MDA-231, MCF-7 and MC-3T3-E1 in α-MEM and HME cells in MEBM medium. All media were supplemented with 10% heat inactivated fetal bovine serum (FBS), 2 mM L-glutamine, 100 U/ml penicillin and 100 ug/ml streptomycin . Cultures were maintained at 37°C in a humidified atmosphere of 95% air and 5% carbon dioxide and subcultured every third day.Cell lines were cultured in 75-cm14C-labelled collagen films were prepared as described previously [5 /well. After 4 h incubation at 37°C in MEM supplemented with 10% FCS, the cells were washed twice with phosphate buffered saline (PBS) to remove residual FCS, and incubated for 24 h with 1 ml/well of serum-free MEM with or without the indicated concentrations of plasminogen, TGFβ and the proteinase inhibitors to be tested. At the end of the culture period the media were centrifuged (15 min, 1200 × g) to remove any collagen fibrils, and radioactivity released during collagen degradation quantified by liquid scintillation counting. Residual collagen was digested with bacterial collagenase (50 μg/ml) and assayed for radioactivity. Collagenolysis was expressed as radioactivity released from the films as a percentage of the total ± SEM.eviously . Breast in vivo bone formation [4 cells/well on collagen-coated 24-well plates (Becton Dickinson, MA, USA). After 4–5 days, when the cultures had reached confluence and the formation of an ECM had started, fresh medium was added containing 1 μCi/ml 3H amino acid mixture for 7 days  to create a non-mineralized radiolabelled ECM. In order to create a mineralized bone matrix, the medium was supplemented with 10 mM β-glycerol phosphate and the cells cultured for 14 days and the radiolabelled medium changed every 3 days. Unincorporated 3H-radiolabelled amino acids were washed from the remaining ECM using 2 water washes and 75% (v/v) ethanol. The matrices were dried and stored at -20°C until use.The murine calvarial-derived MC3T3-E1 is a well characterized osteoblast culture system providing a suitable model of osteogenesis analogous to ormation . The culMineralization of matrices was determined by von Kossa staining. The matrices were rinsed with cold PBS and fixed in 10% neutral buffered formalin for 15 min, rinsed with distilled water and left in distilled water for 15 min. Matrices were stained with 2.5 % silver nitrate solution for 30 min at room temperature. The silver nitrate was removed and matrices were rinsed with distilled water before the addition of sodium carbonate formaldehyde for 5 min. After rinsing, the matrices were counterstained with toluidine blue, rinsed with tap water and air dried.5 /16 mm culture well) were seeded onto the bone matrix as for the type I collagen degradation assay. After 4 h incubation at 37°C in MEM supplemented with 10% FCS, the cells were washed twice with phosphate buffered saline (PBS) to remove residual FCS, and incubated for 24 h with 1 ml/well of serum-free MEM with or without the indicated concentrations of plasminogen, TGFβ and the proteinase inhibitors that were tested. After 24 h incubation, the media were removed and the extent of degraded 3H-radiolabelled matrix released into the medium was determined by liquid scintillation counting. The remaining matrix was degraded as for the type I collagen assay and matrix degradation expressed as above.Breast cancer cells (10-10 M) in serum-free medium for 24 h. For PCR analysis, oligonucleotide primers were synthesized based on the published sequences for 14 MMPs (i.e., MMP-1, -2, -3, -7,-8,-9,-10, -11, -12, -13,-14,-15, -16, and -17) [Confluent breast tumour cells were stimulated with TGFβ (10Enzymes and buffers for the reverse transcriptase and PCR reactions were obtained from Perkin Elmer (CA, USA). Reverse transcriptase reactions were done according to the manufacturer's protocol using 1 μg of total RNA collected from untreated or TGF-β treated breast cells. PCR reactions were performed in an automated DNA thermal cycler (Perkin Elmer) for 25 cycles of denaturation (95°C, 1 min), annealing (variable time and temperature) and polymerisation (60°C, variable time). Amplification of the house-keeping gene, G3PDH mRNA (35 cycles), provided an internal control for the efficiency of the RT-PCR process. RT-PCR products were analyzed against molecular weight standards (pBR322 HaeIII digest) on a 2.5% agarose gel stained with ethidium bromide, electrophoresed in 0.5× TBE buffer at 100 V for 90 minutes. Gels were examined under ultraviolet light and photographed. The authenticity of the PCR products was verified by sequencing .To identify MMP proteins and uPA present in breast cell conditioned medium, Western blot analysis was performed using their specific antibodies as previously described . Briefly-10 M)-stimulated breast cells were cultured in the presence/absence of 2 ug/ml of human plasminogen for 24 h. Collagenase activity was measured by the degradation of fluorescein isothiocyanate (FITC)-labelled type I collagen using type I activity assay kits. One unit of these activities degrades 1 μg of collagen per min at 37°C.To measure collagenase activity, TGFβ (10Cells were plated in 6-well trays and grown to near confluence, the medium was removed, and the cells were washed and incubated with 2 ml of serum-free medium for 24 hr. The medium was collected, centrifuged, and frozen until assayed. The cells were lysed in 300 ul of 0.1 M Tris (pH 8.1) and 0.1% Triton X-100. The u-PA (10 ul of conditioned medium and 10 μg of cell protein) was assayed as previously described [41] using plasminogen and S2251, the chromogenic substrate for plasmin.Differences between control and treatment groups were determined by the Mann Whitney U-test.


Two human coronaviruses are known since the 1960s: HCoV-229E and HCoV-OC43. SARS-CoV was discovered in the early spring of 2003, followed by the identification of HCoV-NL63, the fourth member of the coronaviridae family that infects humans. In this study, we describe the genome structure and the transcription strategy of HCoV-NL63 by experimental analysis of the viral subgenomic mRNAs.The genome of HCoV-NL63 has the following gene order: 1a-1b-S-ORF3-E-M-N. The GC content of the HCoV-NL63 genome is extremely low (34%) compared to other coronaviruses, and we therefore performed additional analysis of the nucleotide composition. Overall, the RNA genome is very low in C and high in U, and this is also reflected in the codon usage. Inspection of the nucleotide composition along the genome indicates that the C-count increases significantly in the last one-third of the genome at the expense of U and G. We document the production of subgenomic (sg) mRNAs coding for the S, ORF3, E, M and N proteins. We did not detect any additional sg mRNA. Furthermore, we sequenced the 5' end of all sg mRNAs, confirming the presence of an identical leader sequence in each sg mRNA. Northern blot analysis indicated that the expression level among the sg mRNAs differs significantly, with the sg mRNA encoding nucleocapsid (N) being the most abundant.The presented data give insight into the viral evolution and mutational patterns in coronaviral genome. Furthermore our data show that HCoV-NL63 employs the discontinuous replication strategy with generation of subgenomic mRNAs during the (-) strand synthesis. Because HCoV-NL63 has a low pathogenicity and is able to grow easily in cell culture, this virus can be a powerful tool to study SARS coronavirus pathogenesis. Until recently only two human coronaviruses were known – human coronavirus (HCoV) 229E and HCoV-OC43, representatives of the group 1 and 2 coronaviruses, respectively. Both were identified in 1960s and are generally considered as common cold viruses. An outbreak of severe acute respiratory syndrome (SARS) in the spring of 2003 led to the rapid identification of SARS-CoV . We idenHCoV-NL63 is a member of the coronaviridae family that clusters together with arteri-, toro- and roniviruses in the order of the nidovirales. Coronaviruses are enveloped viruses with a positive, single stranded RNA genome of approximately 27 to 32 kb. The 5' two-third of a coronavirus genome encodes a polyprotein that contains all enzymes necessary for RNA replication. The expression of the complete polyprotein requires a -1 ribosomal frameshift during translation that is triggered by a pseudoknot RNA structure ,9. The pCoronavirus replication is a complex, not yet fully understood mechanism ,14. The In this study, we analyzed the genome structure of HCoV-NL63. First, we focus on the unusual nucleotide composition of the RNA genome. We describe in detail the bias in the nucleotide composition and its influence on the codon usage of this virus. We provide a possible mechanistic explanation for a shift in nucleotide bias at two-third of the HCoV-NL63 genome that is based on the RNA replication mechanism. Second, we describe in detail the different sg mRNAs generated during HCoV-NL63 replication and their relative abundance.We described previously that the newly identified HCoV-NL63 virus has a typical coronavirus genome structure and gene order [To investigate if all coding regions of HCoV-NL63 display a similarly strong preference for U and against C, we also plotted the nucleotide count for the individual genes and 5' and 3' non-coding regions Figure . The typWe plotted the nucleotide distribution along the genome Figure  to deterRecently, Grigoriev reported an interesting feature within coronaviral genomes that is visible when the cumulative GC-skew is plotted ,21. CumuThe bias in the nucleotide count led us to compare the codon usage of HCoV-NL63 with that of human mRNA Table . The codThe 5' end of HCoV-NL63 genome RNA contains the L sequence of 72 nucleotides that ends with the L TRS element. This TRS has a high similarity to short sequences that are located in front of each open reading frame (S-ORF3-E-M-N) . We prevUA  RNA, which was found to exhibit a typical coronavirus pattern with an abundance of U (39 %) and shortage of G (20%) and C (14%). In fact, HCoV-NL63 has the most pronounced nucleotide bias among the coronaviridae.There is a significant fluctuation in the nucleotide count among the HCoV-NL63 genes. For instance, ORF3 and M appear as extreme U-rich and A-poor islands. It is possible that the unique nucleotide composition of some structural genes reflects their evolutionary origin, perhaps suggesting that some of these functions were acquired recently from another viral or cellular origin by gene transfer. These properties mimic the pathogenicity islands of prokaryotic genomes . ConsistInspection of the nucleotide composition along the genome indicates a bi-phasic pattern. The 5' two-third of the genome encoding the 1ab polyprotein has a stable nucleotide count with the typical U>A>G>C order, but rather striking differences are observed in the 3' one-third of the genome that encodes the structural proteins Figure . Most noWe show that U-counts reach the highest values and C-counts the lowest values at the third position of the HCoV-NL63 codons Figure . AnalysiInspection of the viral genome sequence led us to predict that the 1ab polyprotein is expressed from the genomic RNA and the 3' structural proteins and ORF3 from 5 distinct sg mRNAs. This was confirmed experimentally. We observed that sg mRNAs are more abundant when the corresponding TRS is located closer to the 3' end of the genome. The exception is formed by the E sg mRNA, which is relatively underexpressed. This may correlate with the low expression level of this protein. The general trend of increased gene expression along the genome has been reported previously for other coronaviruses . A possiUA. The presence of a sub-optimal core sequence may in fact explain the lower than expected expression level of this sg mRNA  window and 10-nt step). Codon usage was assessed using DNA 2.0 software. Data was processed in Microsoft Excel datasheet and all statistical analysis was performed with SPSS 11.5.0 software. The level of significance of the nucleotide bias was established for 300-nt non-overlapping windows with the non-parametric Mann-Whitney U test for two independent samples. Cumulative GC-skew graphs were generated as described previously  with the7) after 6 days of culture (virus passage 7). Mouse Hepatitis Virus (MHV) RNA was obtained by infecting 2 × 107 LR7 cells with MHV strain A59. The medium was removed and the cells were dissolved in 15 ml TRIzol® and RNA was isolated according to the standard TRIzol® procedure. RNA was subsequently precipitated with 0.8 volume of isopropanol, dried and dissolved in 50 μl H2O. Integrity of the RNA was analyzed by electrophoresis on a non-denaturating 0.8% agarose gel. RNA was stored at -150°C.HCoV-NL63 RNA was obtained from virus-infected LLC-MK2 cells (2 × 102 and 50 μM of each dNTPs at 37°C for 1 hour. The single stranded cDNA product was made into double-stranded DNA in a standard PCR reaction with 1.25 U of Taq polymerase (Perkin-Elmer) per reaction with appropriate primers (see below).The cDNA used for sequencing and probe construction was made by MMLV-RT on viral RNA with 1 μg of random hexamer DNA primers in 10 mM Tris pH 8.3, 50 mM KCl, 0.1% Triton-X100, 6 mM of MgCl32P]dCTP. Prehybridization and hybridization was done in ULTRAhyb buffer (Ambion) at 50°C for 1 and 12 hours, respectively. The membrane was then washed at room temperature with low-stringency buffer (2×SSC, 0.2% SDS) and at 50°C in high stringency buffer (0.1×SSC, 0.2% SDS). Images were obtained using the STORM 860 phosphorimager (Amersham Biosciences) and data analysis was performed with the ImageQuant software package. The size of sg mRNA fragments of HCoV-NL63 were estimated from their migration on the Northern blot using the sg mRNA of MHV as size marker.Gel electrophoresis of viral RNA was performed on a 1% agarose gel with 7% of formaldehyde at 100 Volt in 1×MOPS buffer (40 mM MOPS, 10 mM sodium acetate, pH 7.0). Transfer onto a positively charged nylon membrane (Boehringer Mannheim) was done overnight by means of capillary force. RNA was linked to the membrane in a UV crosslinker (Stratagene). For generation of the HCoV-NL63 probe, the RT-PCR product was further amplified with 5' primer N5PCR1 (CTG TTA CTT TGG CTT TAA AGA ACT TAG G) and 3' primer N3PCR1 (CTC ACT ATC AAA GAA TAA CGC AGC CTG). Similarly, the MHV probe was amplified with 5' primer MHV_UTR-B5' (GAT GAA GTA GAT AAT GTA AGC GT) and 3' primer MHV_UTR-B3' (TGC CAC AAC CTT CTC TAT CTG TTA T). Labeling of the probes was done in a standard PCR reaction with specific 3' primers (N3PCR1 and MHV_UTR-B3') in presence of [α-The L/body TRS junctions were PCR-amplified from an HCoV-NL63 cDNA bank. We performed 35 cycle PCR with the 5' L primer (L5 – TAA AGA ATT TTT CTA TCT ATA GAT AG) and gene specific 3' primers . The PCR products were analyzed by electrophoresis on a 0.8% agarose gel and products of discrete size were used for sequencing using the BigDye terminator kit (ABI) and ABI Prism 377 sequencer (Perkin Elmer). Sequence analysis was performed by Sequence Navigator and AutoAssembler 2.1 software.The complete genome sequence of HCoV-NL63 [The authors declare that they have no competing interests.KP carried out the viral RNA isolation, RT-PCR, sequencing of sg mRNAs, Northern blot evaluation and all computer analysis done in this study; MFJ carried out the full genome sequencing; all authors participated in writing the manuscript. Lv/dH and BB are the principal investigators


How much is the aggression we observe in nonhuman primates the result of culture, and will the answer provide insights into our own violent behaviour? Dances with Wolves (the 1990 movie with Kevin Costner) is violent, people look at you as if you are crazy. They see an idyllic, sentimental movie, with beautiful landscapes, showing a rare white man who respects American Indians. The bloody scenes barely register.Upon arrival from Europe, now more than two decades ago, I was taken aback by the level of violence in the American media. I do not just mean the daily news, even though it is hard getting used to multiple murders per day in any large city. No, I mean sitcoms, comedies, drama series, and movies. Staying away from Schwarzenegger and Stallone does not do it; almost any American movie features violence. Inevitably, desensitization sets in. If you say, for example, that Saturday Night Live for its inside commentary on peculiarly American phenomena, such as cheerleaders, televangelists, and celebrity lawyers. But SNL is incomplete without at least one sketch in which someone's car explodes or head gets blown off. Characters such as Hans and Franz (“We're going to pump you up!”) appeal to me for their names alone (and yes, I do have a brother named Hans), but when their free weights are so heavy that their arms get torn off, I am baffled. The spouting blood gets a big laugh from the audience, but I fail to see the humor.Comedy is no different. I love, for example, Did I grow up in a land of sissies? Perhaps, but I am not mentioning this to decide whether violence in the media and our ability to grow immune to it—as I also have over the years—is desirable, or not. I simply wish to draw attention to the cultural fissures in how violence is portrayed, how we teach conflict resolution, and whether harmony is valued over competitiveness. This is the problem with the human species. Somewhere in all of this resides a human nature, but it is molded and stretched into so many different directions that it is hard to say if we are naturally competitive or naturally community-builders. In fact, we are both, but each society reaches its own balance between the two. In America, the squeaky wheel gets the grease. In Japan, the nail that stands out gets pounded into the ground.Pan troglodytes) being far more violent than that equally close relative of ours, the bonobo (P. paniscus)  . But alsThere was a time when no review of human nature would be complete without assertions about our inborn aggressiveness. The first scientist to bring up this issue, not coincidentally after World War II, was Seville Statement on Violence  and the rich array of tool use by wild chimpanzees, orangutans (Pongo pymaeus), and capuchin monkeys (Cebus spp.)  . HoweverSocial culture is obviously harder to document than tool use. In human culture, for instance, it is easy to tell if people eat with knife and fork or with chopsticks, but to notice if a culture is egalitarian or hierarchical, warm or distant, collectivistic or individualistic takes time and is difficult to capture in behavioral measures. A well-documented monkey example of social culture is the inheritance of rank positions in macaque and baboon societies. The future position in the hierarchy of a newborn female can be predicted with almost one hundred percent certainty on the basis of her mother's rank. Females with relatives in high places are born with a silver spoon in their mouth, so to speak, whereas those of lowly origin will spend their life at the bottom. Despite its stability, the system depends on learning. Early in life, the young monkey finds out against which opponents it can expect help from her mother and sisters. When sparring with peer A she may utter screams that recruit massive support to defeat A. But against peer B she can scream her lungs out and nothing happens. Consequently, she will come to dominate A but not B. Experiments manipulating the presence of family members have found that when support dwindles dominant females are unable to maintain their positions . In otheMacaca mulatta), known as quarrelsome and violent, were housed with the more tolerant and easy-going stumptail monkeys (M. arctoides)  . Stumptactoides) . PrimatePLoS Biology, we now have the first field evidence that primates can go the flower power route (Papio anubis) on the plains of the Masai Mara, in Kenya, wage wars of nerves, compromising their rivals' immune systems and pushing up the level of their blood cortisol (Not unlike rhesus monkeys, baboons have a reputation for fierce competition and nasty fights. With the study by Robert Sapolsky and Lisa Share published in this issue of er route . Wild bacortisol . An acciIt is somewhat ironic that at a time when researchers on human aggression are increasingly attracted, albeit with a far more sophisticated approach, to the Lorenzian idea of a biological basis of aggression , studentLet us hope this applies to humanity as well.


Understanding the factors that affect patients' utilisation of health services is important for health service provision and effective patient management. This study aimed to investigate the specific morbidity and demographic factors related to the frequency with which general practice patients visit a general practitioner/family physician (GP) in Australia.A sub-study was undertaken as part of an ongoing national study of general practice activity in Australia. A cluster sample of 10,755 general practice patients were surveyed through a random sample of 379 general practitioners. The patient reported the number of times he/she had visited a general practitioner in the previous twelve months. The GP recorded all the patient's major health problems, including those managed at the current consultation.Patients reported an average of 8.8 visits to a general practitioner per year. After adjusting for other patient demographics and number of health problems, concession health care card holders made on average 2.6 more visits per year to a general practitioner than did non-card holders (p < .001). After adjustment, patients from remote/very remote locations made 2.3 fewer visits per year than patients from locations where services were highly accessible (p < .001). After adjustment for patient demographics, patients with diagnosed anxiety made on average 2.7 more visits per year (p = 0.003), those with diagnosed depression 2.2 more visits than average (p < .0001), and those with back problems 2.4 more visits (p = 0.009) than patients without the respective disorders.Anxiety, back pain and depression are associated with greater patient demand for general practice services than other health problems. The effect of sociodemographic factors on patient utilisation of general practice services is complex. Equity of access to general practice services remains an issue for patients from remote areas, while concession health care card holders are attending general practice more frequently than other patients relative to their number of health problems. The frequency of patient visits to general/family practice is affected by a range of factors including patient characteristics, physician/practice factors and broader issues such as access to services. A major contributor to the variance in visit rates to general/family practitioners is the complexity of health problems experienced by the patient. Those patients who attend general practice frequently report poorer health than those who attend less frequently ,2. Not aNumerous studies have found that for a subset of patients, the presence of psychological health problems is associated with frequent attendance at medical services -6. HigheThe general practitioner (GP) also influences patients' attendance rates through doctor-initiated visits and some practitioners attract a larger proportion of frequent attenders than others . RemunerPositive aspects to increasing general practice attendance includes recalling patients for chronic conditions as part of a program of structured care ,12. PatiThe available evidence on visit frequency to general/family practice comes from a range of settings in several countries. There are differences in general practice between countries that may affect patterns of patient visits and some studies are based on a limited number of practices, which may affect the generalisability of the findings ,15. AlthThe study reported here is part of the Bettering the Evaluation and Care of Health (BEACH) program, a national study of general practice activity in Australia. The BEACH method has been described in detail elsewhere . In summThis paper is based on a sub-sample of GPs who participated in BEACH between August 2001 and March 2002. For a subset of 30 out of the 100 encounters, the GP recorded, in addition to the problems managed at the encounter, any other major health problems of the patient that had not been managed at the current encounter. GPs were instructed to include chronic illnesses requiring ongoing care, past problems that affect present and future care and social problems that influence health. As many as 12 extra problems could be recorded per patient and when added to the maximum of four problems managed at the encounter this allowed up to 16 health problems to be recorded per patient. The GP recorded problems in free text, which were secondarily classified according to the International Classification of Primary Care (ICPC-2) using an extended vocabulary of terms ,18. ICPCMental health (particularly depression), asthma, diabetes, osteoarthritis and cardiovascular disease are five National Health Priority Areas that are major contributors to mortality, morbidity and health service costs in Australia . They arThe GP asked each patient in the sub-sample to recall how many times he/she had visited a GP in the previous 12 months.Commonwealth concession health care cards are available to people on limited incomes and entitle the holder to health services at greatly reduced cost. In 1998, around one third of Australians aged 15 years and over held a concession health care card . ConcessThe Remote Index of Australia (ARIA) was used to classify patients according to geographical remoteness and accessibility of services . PatientThe patient sample was a single stage cluster design with the GP as the primary sampling unit. Observations recorded by the same GP were therefore not independent and the statistical analysis adjusted for the correlation between patients within each cluster. We used procedures in SAS software V8.2 that adjust the standard error for the intra-cluster correlation  and all Number of annual visits to a GP was the main outcome of interest. After checking the linear relationship between number of visits and other ordinal variables , multiple regression was performed in two stages to identify predictors of visit frequency.1) Demographic predictorsWe performed multiple regression, with self-reported annual GP visits as an ordinal outcome to identify the independent demographic predictors of the frequency of visits to a GP after controlling for morbidity. We fitted the morbidity covariate as the total number of major health problems recorded for the patient.2) Morbidity predictorsWe fitted separate regression models to estimate the effect of specific morbidity on visit frequency, after adjusting for patient age, sex, other significant demographic predictors and the number of other health problems of the patient. The morbidities of interest were chronic problems most commonly managed in general practice, specifically depression, anxiety, back problems, diabetes, hypertension, asthma, ischaemic heart disease, and osteoarthritis . We inteThree hundred and seventy nine GPs participated in the sub-study. Six hundred and eighteen patients (5.4%) did not answer the question on visit frequency, giving a final sample of 10,755 respondents. Patient respondents and non-respondents were not significantly different in mean age, sex distribution or mean number of health problems recorded.While all states of Australia were well represented in the GP sample, the sample had a smaller proportion of GPs aged less than 35 years and was somewhat more urban than the population of Australian GPs Table .Patients aged less than 25 years were under-represented in the sample relative to all patients who had visited a GP in Australia at least once in 2001, while patients aged 65 years and older were over-represented (Table The majority of patients in the sample (83.3%) were from locations where services were "highly accessible", and 8.3% spoke a language other than English at home.The mean patient self-reported visit rate for the sample was 8.8 (95% CI: 8.3–9.2) visits to a GP per year. On average the GPs recorded 2.9 morbidities per patient, and half (51.1%) of the recorded problems were being managed by the GP at the current consultation (results not tabled).There was no difference in self-reported visit frequency between the sexes (p = 0.11) (results not tabled). The mean visit rate for children aged less than 5 was 5.8 visits per year, which fell to 4.3 for children 10–14 years of age, then increased linearly after age 15 years (p < .0001). There was a simple linear increase in frequency of visits with increasing number of recorded health problems (p < .0001). Therefore age was fitted as a categorical variable and number of problems as a numeric variable in the multiple regression models.After adjusting for age and sex, there was no difference in visit frequency according to language spoken at home Table . After aThe mean annual visit rates for patients with common chronic disorders were well above average Table . After aAs would be expected the number of health problems recorded for the patient was a major predictor of frequency of attendance at general practice. However this study demonstrated that specific chronic disorders had differential effects on visit frequency. Anxiety, back complaints, and depression had the greatest effects on increasing patient annual visits to a GP.The relationship between psychological health problems and increased medical visits has been demonstrated across many settings -3,5. TheOther research has reported a link between unexplained back pain and frequent attendance at medical services . In the Other research indicates that patients with diabetes make more general practice visits than average  and struAs expected, patients from remote areas reported visiting general practice less frequently relative to their health needs, confirming that geographic access to services remains a significant barrier to meeting the health care needs of patients from remote locations ,14.Concession health care card holders have more chronic and psychological health problems managed in general practice relative to the rest of the Australian general practice population . In thisThis study had the advantage of randomly sampling clusters of patients across a wide range of practices in all states and regions of Australia. A further advantage was having a qualified medical practitioner to record patient morbidity and the use of the medical record to help validate patient recall of GP visits.The methodology of this study was limited by using patient self-report of visit frequency and by sampling patients at the point of consultation. The average number of GP visits among Australians who visit a general practitioner at least once in a 12 month period is known to be six visits per year, increasing with increasing age , while the self-reported visit rate in the current study was 8.8 visits per year. Because the patients were sampled at the consultation, older patients who attend general practice more frequently had a greater chance of being sampled and this was reflected in the older age distribution of the patient sample. Even allowing for this selection bias, the mean annual self-reported visits was somewhat higher than expected given the sample's age structure, indicating some overestimation by patients in their recall of the number of GP visits. One Australian study that validated patient self-report against actual Medicare benefit claims found that patients who overestimate recent use of medical services are "telescoping" real but less recent events into the nominated time period and are in fact higher users of health services over the long term . In the This study has demonstrated that number of visits to a GP by patients in Australia is largely explained by the number of health problems the patient is experiencing. However psychological health problems and sociodemographic factors have differential effects on visit rates. GPs need to be alert to the particular psychological and social needs of patients who are attending frequently with multiple health problems. Equity of access, in terms of equal utilisation of services, continues to be an issue for patients living in remote locations.None declared.SK designed and performed the statistical analysis, researched and wrote the main draft. HB directed the study, participated in the design and assisted in writing and reviewing the final article.The pre-publication history for this paper can be accessed here:


Statistics in Medicine 2003; 22; 1837–1848) developed a formula to calculate the age-conditional probability of developing a disease for the first time (ACPDvD) for a hypothetical cohort. The novelty of the formula of Fay et al (2003) is that one need not know the rates of first incidence of disease per person-years alive and disease-free, but may input the rates of first incidence per person-years alive only. Similarly the formula uses rates of death from disease and death from other causes per person-years alive. The rates per person-years alive are much easier to estimate than per person-years alive and disease-free. Fay et al (2003) used simple piecewise constant models for all three rate functions which have constant rates within each age group. In this paper, we detail a method for estimating rate functions which does not have jumps at the beginning of age groupings, and need not be constant within age groupings. We call this method the mid-age group joinpoint (MAJ) model for the rates. The drawback of the MAJ model is that numerical integration must be used to estimate the resulting ACPDvD. To increase computational speed, we offer a piecewise approximation to the MAJ model, which we call the piecewise mid-age group joinpoint (PMAJ) model. The PMAJ model for the rates input into the formula for ACPDvD described in Fay et al (2003) is the current method used in the freely available DevCan software made available by the National Cancer Institute.Fay, Pfeiffer, Cronin, Le, and Feuer ( Fay, Pfeiffer, Cronin, Le, and Feuer  showed hHere we detail two more complicated models for the rates. The first model is a segmented regression model or joinpoint model for the rates, where the rate function is a series of linear functions that join at the mid-points of the age groups, and the rate function is constant before the first mid-point and after the last "mid-point" . We will call this model the MAJ (mid-age group joinpoint) model for the rates. In Figure Notice that the MAJ model gives a more smoothly changing and probably a better modeled rate. The only place where the MAJ model may not perform better than the piecewise constant model is at peaks or valleys, where there may be some bias. In Figure A problem with the mid-age group joinpoint model is that it requires numeric integration for its calculation. The second model uses a series of piecewise constant values to approximate the mid-age group joinpoint model. We call this second model the PMAJ (piecewise mid-age group joinpoint) model. The PMAJ does not require numeric integration, so it is much faster than the MAJ model. The PMAJ model is a piecewise constant model that only differs from the piecewise constant model of Fay et al  in that The outline of this paper is as follows. The review and overview section reviews the issues in estimating the age conditional probability of developing disease from surveillance data. This section includes a motivation for using this type of statistic to describe population data. The review and overview section additionally gives graphical descriptions of the MAJ and PMAJ methods. The paper is structured so that readers not interested in the details may skip the next two sections and the appendix, which give precise and notationally involved definitions of the MAJ estimators. The examples and discussion section gives examples of the estimator of ACPDvD using three different methods for estimating the rates, the simple piecewise constant method proposed in Fay et al , the MAJConsider a surveillance program like the SEER program of the U.S. National Cancer Institute. This program attempts to count every incidence of cancer within the catchment area of the program. Because cancer is a disease in which the rates of the disease are highly dependent on age, in order to give interpretability to the counts within the SEER registries, we must somehow account for the age distribution in the popoulation.One simple and popular statistic is the age adjusted rate or directly standardized rate (DSR). In the SEER Cancer Statistics Review  DSRs areThe ACPDvD uses cross-sectional incidence and mortality rates to estimate the age-conditional probabilities of developing disease in a hypothetical cohort in which we assume the age specific rates do not change over time. This gives a personal interpretation to the cross-sectional data, allowing statements like the following: if the incidence and mortality rates remain at their present values (as observed in SEER 12, 1998–2000), then a female born today would have a 13.5% chance of developing breast cancer over her lifetime  within each age group and divide by the total number of person-years estimated by the population. For counts of a single year, the person-years are estimated by the mid-year population of the catchment area . Note that the incident cases may include individuals who have previously been diagnosed with the cancer and have developed a new primary cancer.For the ACPDvD for any specific disease we would like the rate of first incidence per person-years alive and disease-free. Thus, there are two difficulties, (1) the usual age specific incidence rates include persons with multiple primary cancers, and (2) the denominators include persons who have previously been diagnosed. Merrill and Feuer  discuss In calculating the ACPDvD we use only first incident of the disease of interest as in , but we x and y, given alive and cancer-free at age x, may be written as the fraction,In the following let the disease of interest be "cancer". The ACPDvD between ages To calculate the numerator, we integrate over the probability that the first cancer occurred at exactly age a. In math notation this probability isfc(a) is a probability function representing the probability that the first cancer occurred at exactly age a. One key result described in Fay et al [fc(a) can be written as the product of two functions,where ay et al  is that λc(a) = the probability that the first cancer occurred at exactly age a, given the individual is alive just before age a, andSa(a-) = the probability that the individual is alive just before age a.λc(a) is known as a cause-specific hazard function, and it is estimated by some function of the age-specific rates, such as the piecewise constant model of Fay et al [Sa(a-) asThe function ay et al  or the Mλa(u)  is the usual hazard function. We estimate λa(u) using some function of the age-specific rates. Thus, the numerator can be written aswhere If we use the MAJ for both hazard functions, then there is no closed form solution. To see why this is so, note that within the exponential, the integral of a piecewise linear function is the sum of a series of quadratic functions, and the overall integral has no closed form solution. This problem motivates the piecewise mid-age joinpoint (PMAJ) model, where we use a series of piecewise constant functions to approximate the MAJ model. Figure x, denoted . For reference, in Table a to represent all causes of events instead of a blank subscript. For example, we let S*(u) = . Other notation in this paper is defined as it is introduced. Fay et al [x and y given alive and cancer-free just before age x isNow consider the denominator of the ACPDvD, the probability of being alive and cancer-free at age ay et al  assumed The details of the MAJ and the PMAJ models are given in the next two sections.Readers only interested in the practical ramifications of the choice in models may skip to the examples and discussion section.In Fay et al , the rata0 <a1 < ··· <ak <ak+1 = ∞. Here we use a joinpoint model with joins at the midpoints ,We introduce new notation for breaking up the ages. Fay et al  used 0 =Let, match up with the count notation of [j (for j = c, d, or o) at ti (for i = 0,1,..., k) is. We defti, ti+1) for i = 1,..., k, we define  as the point on the line defined by connecting the points (ti, ) and (ti+1, ). In other words,Then for t ∈ [Whereandαj,-1 =  and βj,-1 = 0, and similarly by taking limits as tk+1 → ∞ then αj,k =  and βj,k = 0.Thus,  for u ∈ [ti, ti+1) isNow k)Note that (for ℓ = 0,1,..., i = 0,1,...,k,so that for u < ∞)Also notice that (when u ∈ [ti, ti+1),Therefore when (x, y) be the estimator of A(x, y) using the MAJ model. The two integrals we need to estimate for (x, y) are of the type,Let (x, y) we need  (i.e., j = c and h = a in equation 7), and in the denominator of (x, y) we need . Suppose, without loss of generality, that t ∈ [ti,ti+1), thenwhere in the numerator of Rj,h(tℓ, v) (for ℓ = - 1,0,1,2,..., i and v ≤ tℓ+1) is defined implicitly (see the Appendix). Then,where k + 2 intervals. Here we define those intervals in both the ti notation and the ai notation.In the MAJ model we divided up the age line into ith interval (i = 1,...,k) for the jth rate type (j = a, c, d, o) is a line defined by connecting the points (ti-1, ) and (ti, ) (see equations 2 and 3 for definition of ). In the PMAJ model we divide the ith interval into mi equal sized intervals, and use a piecewise constant estimate on each of those mi intervals. One way to define mi is to chose mi so that each equal sized interval is 1/2 year long. In other words, mi = 2(ti - ti-1). This is the definition of mi that we use for the DevCan software  of the mi intervals within interval i (for i = 1,...,k) for rate type j (for j = a, c, d, o). This interval isHere are the details. Consider the For convenience we introduce new notation for the ends of this interval, letti-1,0 = ti-1 and  = ti. At the beginning of this interval the value of the rate isso that αj,i-1 and βj,i-1). Similarly at the end of this interval the rate is(see equations 4 and 6 for definitions of t ∈ [ti-1,h-1,ti-1,h) we estimate the rate withFor the PMAJ model we simply assume a constant rate equal to the average of the beginning and the end values of the rate over this interval. In other words, under the PMAJ model for any Since the PMAJ model is a piecewise model, we can use Appendix A of  to expreFor convenience write these interval ends with only a single index as and m0 = 1. In other words, t-1 = τ0 and for i = 0,1,..., k, then ti= τg(i) and ti,h = τg(i)+h, where .where A(x,y) under the PMAJ model be denoted (x,y). Let τi ≤ x <τi+1 and τj <y ≤ τj+1 for x <y,i ≤ j, and j ≤ M + 2. For convenience we regroup the ages after inserting group delimiters at x and y. Let the new delimiters be 0 = b0 ≤ b1 ≤ b2 ≤ ··· ≤ bM+3 = ∞ where b0 = τ0,..., bi = τ i, bi+1 = x, bi+2 = τ i+1,..., bj+1= τ j, bj+2 = y, bj+3 = τj+1,..., bM+3 = τM+1 = ∞. We letNow we can follow very similar notation to Appendix A of . We now  and . In this notation, the probability of developing cancer by age y given survival until age x is A(x, y) = A(bi+1, bj+2), and under the PMAJ model we estimate it withand similarly  or  may equal zero and bℓ+1 may equal infinity, we let . These integrals areBecause λ = 0 and bℓ+1 = ∞ is one of the "impossible" hypothetical cohorts . The mid-year population estimates  come from the sum U.S. Census estimates of mid-year populations from 1998, 1999, and 2000 for the SEER 12 catchment areas for the appropriate sex group .For all of the examples we use data from 1998–2000 . The incIn Table In conclusion, we have described several methods for estimating rates for input into a formula to calculate ACPDvD, and we have shown that the PMAJ method provides fast and reasonable estimators for the rates.Rj,h(tℓ, v) represents an integral with 4 parameters. We can write it asRecall that tℓ = u and αjℓ = αj,βjℓ = bj,αhℓ = ah, and βhℓ = bh.To simplify notation substitute let Thus,v → ∞ then bj = 0 and bh = 0, so this is an important special case.For our application, whenever bj = 0 and bh = 0 and ah = 0 and we obtainWhen v → ∞.which goes to ∞ when bj = 0 and bh = 0 and ah ≠ 0 and we obtainWhen aj/ah when v → ∞.which goes to R(u, v, aj, bj, ah, bh) for finite v, we can use an adaptive use of Romberg's algorithm for numeric integration /n, and letDivide the interval [n→∞ Tn = R(u, v, aj, bj, ah, bh).Then limA more accurate approximation uses Romberg's algorithm, be our estimate of R. The algorithm we use to calculate  is as follows:Let n.1. Choose Tn.2. Calculate Tn2.3. Calculate Imax do:4. For i = 1 to  then let  and stop.• If , and continue.• Otherwise calculate n = 100 and δ = 10-5 and Imax = 100.For example, one could use Comparing the method of Wun, Merrill, and Feuer (1998) to the PMAJ method. We calculate lifetime risks of developing certain cancers for different race and sex combinations. For each lifetime risk we give the old method of Wun, Merrill, and Feuer [4], the PMAJ method, and the percent difference. In general, the two methods agree to within about 2 percent.Click here for file


Investigating individual, as opposed to predetermined, quality of life domains may yield important information about quality of life. This study investigated the individual quality of life domains nominated by youth with type 1 diabetes.Eighty young people attending a diabetes summer camp completed the Schedule for the Evaluation of Individual Quality of Life-Direct Weighting interview, which allows respondents to nominate and evaluate their own quality of life domains.The most frequently nominated life domains were 'family', 'friends', 'diabetes', 'school', and 'health' respectively; ranked in terms of importance, domains were 'religion', 'family', 'diabetes', 'health', and 'the golden rule'; ranked in order of satisfaction, domains were 'camp', 'religion', 'pets', and 'family' and 'a special person' were tied for fifth. Respondent age was significantly positively associated with the importance of 'friends', and a significantly negatively associated with the importance of 'family'. Nearly all respondents nominated a quality of life domain relating to physical status, however, the specific physical status domain and the rationale for its nomination varied. Some respondents nominated 'diabetes' as a domain and emphasized diabetes 'self-care behaviors' in order to avoid negative health consequences such as hospitalization. Other respondents nominated 'health' and focused more generally on 'living well with diabetes'. In an ANOVA with physical status domain as the independent variable and age as the dependent variable, participants who nominated 'diabetes' were younger (M = 12.9 years) than those who nominated 'health' (M = 15.9 years). In a second ANOVA, with rationale for nomination the physical status domain as the independent variable, and age as the dependent variable, those who emphasized 'self care behaviors' were younger (M = 11.8 years) than those who emphasized 'living well with diabetes' (M = 14.6 years). These differences are discussed in terms of cognitive development and in relation to the decline in self-care and glycemic control often observed during adolescence.Respondents nominated many non-diabetes life domains, underscoring that QOL is multidimensional. Subtle changes in conceptualization of diabetes and health with increasing age may reflect cognitive development or disease adjustment, and speak to the need for special attention to adolescents. Understanding individual quality of life domains can help clinicians motivate their young patients with diabetes for self-care. Future research should employ a larger, more diverse sample, and use longitudinal designs. Quality of life (QOL) is now recognized as an important outcome for people with diabetes. In general, diabetes has been shown to negatively impact QOL . TighterThe measurement of QOL is evolving, and a state of the art review identified numerous different QOL measures . Most QOBy pre-selecting life domains, and/or limiting items to those that are diabetes relevant, we limit our breadth and depth of understanding of youth with diabetes. While diabetes impacts nearly all aspects of a young person's life, it may not be the central, organizing construct under which all other domains fall. If a child with diabetes was asked "What is important to you?" and "How is that important thing going for you right now?" diabetes may or may not be mentioned. For example, in a sample of adolescents with type 1 diabetes, fewer than 1/3 of participants ranked diabetes as the most important life domain, the remainder rated it much lower . And wheThese questions are not of solely theoretical interest. On an individual patient basis, such information may be clinically useful. Understanding the QOL domains that are important to an individual patient gives a provider information about how to motivate that individual for improved self-care. This is true for diabetes-related life domains and those domains that are not directly diabetes-related. For example, a provider treating a child who values athletics could use this information to motivate the child to improve diabetes self-care by demonstrating to him that improved glycemia could enhance his athletic performance. Using a patient's own value system to promote healthy behavior has been advocated by others  and is cThe Schedule for the Evaluation of Individual Quality of Life-Direct Weight (SEIQoL-DW) is a theory based  structurParticipants were campers, and young counselors who had previously been campers, at an overnight summer camp for children with diabetes. The camp serves children with diabetes in northern New England. One hundred and twenty campers from 8–15 years old attend a 2-week session. The majority of the staff has diabetes.For campers, parents completed a survey of demographic and disease variables for themselves, their family, and their child with diabetes. Counselors who participated in the study completed the survey of demographic and disease variables themselves. Demographic variables included family structure, socioeconomic status, school performance, and race/ethnicity. Disease variables included disease duration, treatment regimen, HbA1c (gold standard measure of glycemic control), frequency of complications, and emergent use of health care services.As described by Browne et al , there aIn the second stage, the respondents rate each domain on a 0–100 mm vertical visual analogue scale anchored at the two extremes by the terms 'best possible' and 'worst possible'. These anchors are designed to allow individuals to use their own criteria when assessing their status within each domain.The third stage involves a weighting procedure wherein the respondent judges the relative importance of each domain. In the original version of the SEIQoL a technique known as judgment analysis was used. Because of practical limitations of judgment analysis, a direct weighting procedure has been developed . The dirStudies have shown the SEIQoL to have good internal consistency, ranging from .6 to .9 -26. It hThis project was carried out in accordance with the American Psychological Association guidelines for ethical conduct of research. It was approved by the University of Connecticut Health Center's institutional review board. One week prior to the two-week camp session, a letter was sent to the parents of campers, describing the study. Parents were sent a consent form for themselves, an assent form for their child, and a survey of disease and demographic data. Upon their arrival at camp, the materials were collected from parents and reviewed for completeness. Sixty one percent (n = 73) of campers and parents handed in completed questionnaires on the first day of camp. The most common reason given for not participating was lack of interest on the part of the child. During the 2-week camp session children were pulled one at a time from regular camp activities and administered the SEIQOL-DW. Participants were allowed to choose a sugar free treat (soda, gum, or mints) for their participation.During the one-week of pre-camp , counselors with diabetes who had previously been campers were approached and invited to participate in the study. Informed consent was obtained, and staff members were administered the SEIQoL-DW individually at their convenience. All eligible staff chose to participate (n = 17).Two interviewers completed all 90 interviews. They were the first author who is a clinical psychologist, and the third author who is a medical student with an M.P.H. in health behavior. Both interviewers studied the SEIQoL-DW manual, role-played giving the SEIQoL-DW, and conferred about validity, boredom, and fatigue scoring.Consenters were compared to the total camp population for systematic differences. Results of a chi square test show no significant differences in gender. Results of an ANOVA show no significant differences in age, HbA1c, or duration of diabetes. Likewise, there were no significant differences between campers and counselors for quality of life total scores, gender, type of diabetes, age of diagnosis, number of daily injections, hypoglycemic episodes in the previous month, as well as number of sick days, hospitalizations, ketoacidosis episodes, and emergency room visits in the previous year.On average, participants were 13 years old, came from 2-adult homes (74%) with 1 or 2 siblings. All but one participant were European American. Most did "well" or "very well" in school (69%), and had parents with at least 2 years education beyond high school. Participants had diabetes for an average of about 6 years and had been attending diabetes camp for an average of 4 years. All were on multiple injection regimens or insulin pumps (n = 29), and their average glycemic control was fair (mean HbA1c = 8.02). Participants had missed an average of 3 days of school in the last year due to their diabetes, and had experienced about 6 episodes of hypoglycemia in the previous month.Data from ten children were deemed invalid due to interviewers' judgment that the participant was unable to understand the SEIQoL-DW task. For example, one child did not understand the concept of 'importance', and instead only rated domains in terms of his happiness with them. All children whose data were deemed invalid were under 12 years old, with a mean age of 9.25. The data from these ten children (1/3 of those children under 12) are deleted from the following results. Table Of the 400 total domains nominated by the 80 participants with valid data, only 21 domains (5%) were nominated with the assistance of the standard list. These 21 were for the third, fourth, and fifth domains. Thus, every respondent could nominate at least 2 domains without suggestion, and only a handful needed help with the additional 3 domains. Table The most frequently nominated domain was 'family'. Family was nominated by 76/80 respondents. Answers were coded 'family' if the participant used the terms 'family' or 'parents'. Reasons given tended to involve either instrumental support (e.g., they provide me with clothes and a place to live) or emotional support (they love me and I can go to them with problems). Many respondents stated that their families were important because they helped with diabetes, e.g., purchased diabetes supplies, drove them to doctor's appointments, cooked nutritious food, and helped with treatment decisions. Results of a bivariate correlation indicate a significant negative correlation between age of respondent and the importance rating given to the 'family' domain, r = -.34, *p < .01. There was no relationship between respondent age and 'family' satisfaction.The second most frequently nominated domain was 'friends'. Respondents tended to refer to a group of friends, rather than an individual friend. Friends were generally valued for their emotional support, their companionship, and the participant's ability to relax and have fun with them. The ability to "be myself" and still be accepted by friends was a common theme. Results of a bivariate correlation indicate a significant positive correlation between age of respondent and the importance rating given to the 'friends' domain, r = .35, *p < .01. There was no relationship between respondent age and 'friends' satisfaction.School was nominated by many respondents as an important life domain. School was deemed important for a variety of reasons. Some respondents stated that school was important because learning is important in its own right. Some stated that school performance was important to assure acceptance to a good college and have a good job, or because their school performance was important to their parents. Age was not related to 'school' importance or satisfaction.The nomination of 'diabetes' as an important life domain was common. However, respondents' explanations for its nomination were varied. One type of response referred to taking proper care of diabetes in order to avoid negative consequences. These responses included things like eating well, self-monitoring blood glucose, keeping active, and taking injections on time to avoid medical complications, hospitalization, or death. Respondents who gave this sort of answer were fairly concrete and said things such as "I have to take my shots or I will end up in the hospital". Thirty four responses fell into this diabetes 'self-care behaviors' category. Another type of diabetes response referred to living well with diabetes. These responses included things like doing enjoyable activities despite diabetes, successfully negotiating diabetes treatment with parents, feeling proud of self when diabetes is controlled, receiving emotional support for diabetes, and keeping self, friends and family from worrying about diabetes. Respondents who gave this sort of answer provided more abstract explanations, and said things such as "I can't let diabetes stop me" and "It's important that people around me understand what it's like for me to have diabetes". Eleven responses were of this type. Results of an ANOVA with rationale for diabetes nomination (self care vs. living well with diabetes) as the independent variable, and age as the dependent variable revealed that those who provided 'self care behaviors' as a rationale were younger (M = 11.8 years) were than those who provided living well with diabetes as a rationale (M = 14.6 years) F (3, 40) = 2.88, p < .05. There were no group differences for age of diagnosis, number of daily injections, hypoglycemic episodes in the previous month, as well as number of sick days, hospitalizations, ketoacidosis episodes, and emergency room visits in the previous year. Among those who nominated 'diabetes' as a domain, age was not related to 'diabetes' satisfaction.Respondents who nominated 'diabetes' as a domain were asked whether or not they would do so if they were in a non-diabetic environment such as school, as opposed to a diabetes summer camp. All respondents stated yes, they would nominate diabetes. However, some went on to say that they might not use the word 'diabetes' per se, and instead might use the word 'health'. Twenty four respondents did in fact nominate 'health' as a domain (but not diabetes). Explanations for 'health' focused on what might be considered more general wellness. Individuals who nominated 'health' said things like "I have to be healthy in order to do the things I enjoy", "I like staying fit", "you can't be happy without good health", and "when I don't feel well I'm in a bad mood". Six other respondents nominated both 'diabetes' and 'health' as separate domains. Results of a one-way ANOVA reveal that respondents who nominated 'diabetes' only were significantly younger (M = 12.9 years) than respondents who nominated 'health' only (M = 15.9 years) F (3, 75) = 4.53, p < .01. There were no group differences for age of diagnosis, number of daily injections, hypoglycemic episodes in the previous month, as well as number of sick days, hospitalizations, ketoacidosis episodes, and emergency room visits in the previous year. Among those who nominated 'health' as a domain, age was not related to 'health' satisfaction.Many domains were nominated by this sample that are not included in the standard list. They include 'diabetes', 'school', and 'camp', all of which clearly reflect that this was a sample of school aged young people attending a camp for children with diabetes. Some respondents nominated a 'special person' in their lives, such as a teacher or a coach. Other novel domains were nominated the explanations for which are less obvious. One domain involved 'mental attitude' and referred to taking the right approach to life and maintaining a positive outlook. Another involved 'the golden rule' and referred to treating others as one wants to be treated, with fairness and respect.As stated above, the most frequently nominated domains were 'family', 'friends', 'diabetes', 'school', and 'health'. Domains ranked by importance were 'religion', 'family', 'diabetes', 'health', and 'the golden rule'. Domains ranked by satisfaction were 'camp', 'religion', 'pets', 'health', and 'family' and 'a special person' tied for fifth.Total SEIQoL-DW scores ranged from 34.9 – 97.7, M = 78.1, SD = 11.2. Normative data for children are not available. However, these values are similar to those of a small clinic sample of adolescents with type 1 diabetes that reported SEIQoL satisfaction data only [The purpose of this study was to explore the individual QOL domains of youth with diabetes. Consistent with previous findings  results The young people in this sample nominated nearly all the life domains that are offered on the SEIQoL-DW standard list (with the exception of finances). They also went on to nominate additional domains, notably what we have termed 'mental attitude' and 'the golden rule'. These domains are some of the more fundamental aspects of quality of life but are not typically nominated in adult samples. Their nomination raises interesting questions. It is possible that youth are more concerned with these life domains than adults. This may be particularly true for children at overnight summer camp where community living and group cooperation is fundamental and continually reinforced. It is also possible that people with diabetes are more concerned with these life domains than their non-diabetic counterparts. Perhaps these respondents, who have experienced a major negative life event with their diabetes diagnosis and may also have suffered teasing or rejection due to this diagnosis, are more sensitive to issues of positive thinking and treating others with respect. Because this was an uncontrolled study, these are, of course, hypotheses that can only be tested with further investigation of controls.Nearly all respondents nominated a domain that reflected physical status. Younger respondents (who were on average 12 years old) were more likely to focus specifically on diabetes, and emphasize the importance of diabetes self-care behaviors. These respondents stressed adherence to the diabetes treatment regimen in order to avoid medical complications. Older respondents (who were on average 15 years old) were more likely to focus on general health and emphasize the need to live well despite the difficulties of diabetes. These age related data need to be interpreted cautiously due to small sample size, homogeneity of the respondents, and the cross sectional design of the study. The literature would benefit from further use of individual QOL measures with children and with chronic illness populations such as diabetes, in longitudinal designs. Nonetheless, these data do suggest a change in how diabetes is conceptualized during adolescence.There are several possible explanations for these age differences. First, Piaget proposed that formal cognitive operations begin in adolescence. With formal operations, adolescents gain the ability to think about their own thinking, to imagine many possibilities, and to mentally generate possible outcomes and thus rely less on real objects and events. Abstract thought becomes possible. The observed shift in focus from the concrete to the abstract – from 'diabetes' to 'health' and from 'self-care behaviors' to 'living well' – may be a reflection of the cognitive development that occurs with formal operations. Studies have demonstrated a systematic progression of children's understanding of illness that corresponds to Piaget's framework ,31. OtheSecond, as they go through adolescence, youth with diabetes may view their physical well being differently due to more extensive and broadened life experience. Older youth may realize that strict adherence to the medical regimen comes at a cost of an inflexible lifestyle, and may value quality of life over strict medical management. They may become more willing to make compromises in self-care in order to participate more fully in normal activities. They may also, for the first time, be in a position to make these compromises as parental control of day-to-day diabetes management wanes in adolescence. Third, this shift may reflect adolescents desire to fit in with peers, normalize their disease experience, and assimilate their illness. Viewing physical status in terms of health, rather than diabetes, and diabetes in terms of living well, rather than disease management behaviors, serves these functions.This shift in diabetes conceptualization could serve to be adaptive, or conversely it may herald the poor self-care and decreased glycemic control that is often noted in adolescence and young adulthood. Wysocki, Hough, Ward and Green  found thIn addition to differences in domain nomination, we also observed age effects for the importance assigned to domains. As would be expected, age was significantly positively associated with the reported importance of friends, and a significantly negatively associated with the reported importance of family. As young people grow older, the value they place on family and friends changes, and peers become increasingly important. These data are consistent with previous findings that children with diabetes find friends more helpful in diabetes management than many adults . InterveThese data should be interpreted cautiously for several reasons. First, they reflect a select sample of white, higher socioeconomic status, high academic achieving children, many of whom have had several years of diabetes camp experience. This group may nominate different life domains, and endorse a higher QOL, than children who come from more impoverished environments without disease specific psychosocial experiences. These findings should be viewed tentatively until findings are replicated with larger and more diverse samples, in longitudinal designs that employ controls.This article reports age-related differences in health related quality of life domains in youth with type 1 diabetes. It was found that younger respondents nominated 'diabetes' as a domain and focused on 'self-care behaviors', whereas older respondents nominated 'health' and focused more on 'living well with diabetes'. Although limited by sample size and homogeneity, these findings point to age related differences that may help explain deteriorating glycemic control during adolescence. What is clear is that development into maturity is a difficult task made more difficult by diabetes, and that special attention must be given to this population. The Society for Adolescent Medicine  and the JW conceived of the study, and was responsible for design, analysis, and manuscript preparation. GA assisted with data analysis, and manuscript preparation and revision. SL carried out many of the interviews, assisted with data recording, data entry, and study coordination.