Breast tumors consist of several different tissue components. Despite the heterogeneity, most gene expression analyses have traditionally been performed without prior microdissection of the tissue sample. Thus, the gene expression profiles obtained reflect the mRNA contribution from the various tissue components. We utilized histopathological estimations of area fractions of tumor and stromal tissue components in 198 fresh-frozen breast tumor tissue samples for a cell type-associated gene expression analysis associated with distant metastasis. Sets of differentially expressed gene-probes were identified in tumors from patients who developed distant metastasis compared with those who did not, by weighing the contribution from each tumor with the relative content of stromal and tumor epithelial cells in their individual tumor specimen. The analyses were performed under various assumptions of mRNA transcription level from tumor epithelial cells compared with stromal cells. A set of 30 differentially expressed gene-probes was ascribed solely to carcinoma cells. Furthermore, two sets of 38 and five differentially expressed gene-probes were mostly associated to tumor epithelial and stromal cells, respectively. Finally, a set of 26 differentially expressed gene-probes was identified independently of cell type focus. The differentially expressed genes were validated in independent gene expression data from a set of laser capture microdissected invasive ductal carcinomas. We present a method for identifying and ascribing differentially expressed genes to tumor epithelial and/or stromal cells, by utilizing pathologic information and weighted t-statistics. Although a transcriptional contribution from the stromal cell fraction is detectable in microarray experiments performed on bulk tumor, the gene expression differences between the distant metastasis and no distant metastasis group were mostly ascribed to the tumor epithelial cells of the primary breast tumors. However, the gene PIP5K2A was found significantly elevated in stroma cells in distant metastasis group, compared to stroma in no distant metastasis group. These findings were confirmed in gene expression data from the representative compartments from microdissected breast tissue. The method described was also found to be robust to different histopathological procedures. HER2/neu) are considered routinely for treatment decisions Female breast cancer counts for over 465 000 deaths annually world wide (approximately 1.300.000 new cases), and is the most common cancer type among women Whole-genome DNA microarrays for gene expression have made it possible to unravel biological mechanisms underlying the disease at the transcriptomic level Breast carcinomas are heterogeneous “organs” with a microenvironment of various cell types both within the tumor area and in the surroundings. Lymphocytic infiltrations, fibrosis, angio- and lymphanogenesis, and stromal cells , leukocytes, and myoepithelial cells may influence the development and further progression of the carcinoma cells in silico, the relative content of tumor cells, benign hyperplastic epithelium, stroma, and dilated cystic glands in 88 prostate specimens to gene expression levels determined by microarray analysis. They were able to identify gene expression differences between non-malignant and malignant epithelial cells without being confounded by the gene expression of stromal cells. Since the content of tumor cells varies between patient samples, a threshold for minimum content of tumor cells within a sample is often applied. Such a threshold is quite arbitrary and would exclude potentially important cell type interactions.Cleator and colleagues in silico method to ascribe differentially expressed gene-probes (DEGs) in primary breast tumors between patients experiencing DM and patients experiencing NoDM to either tumor epithelial and/or stroma cells.In this study we utilized the histopathological information of tumor epithelial cell and stromal cell area-fractions to perform cell type-associated gene expression analyses of primary breast tumors. We compared a group of breast cancer patients who experienced distant metastasis (DM) with a group who experienced no distant metastasis (NoDM) during a median follow up period of >15 years. In this approach, using tumor epithelial cell-based weights, a sample where these cells cover a small percentage of the area of the tissue sample, will contribute less than a sample where tumor cells are more abundant when investigating gene expression differences originating in tumor tissue. We provide here an UWA-list . Similar results were obtained when using variations of the t-statistics, as presented by Tusher and colleagues Initially a standard t-test was performed, referred to as the “un-weighted analysis”, between the DM and NoDM tumor groups in a set of primary breast tumor samples from 198 patients. Clinical characteristics are given in For every tumor sample, histopathologic information regarding the content of different tissue components  was available see .The distIn order to test if the pathological numbers of the area fraction of tumor epithelial and stromal cells made an impact on the expression data we collected a list of genes reported to be stromal or tumor cell related in breast cancer by literature search The histopathological numbers utilized in the weighted analyses were crude estimates of area-fractions of tumor epithelial, stroma and adipocytic cells. In order to assess the robustness of the weighting approach in relation to the histopathological procedure, stereological point counting was performed additionally on a representative selection of the tissue sections. The numbers of area fraction from the stereological point counting were then utilized to calculate new weights for the sub-selection of samples. The new weights were compared to the initial weights used in the analyses. If the weights from the two methods were significantly different, then the output from the analyses would depend on the histopathological method applied. The first step of the validation was to test if the 109 samples selected for stereological point counting were representative for the whole set of 198 samples. None of the three statistical tests which compared the mean area-fraction for tumor epithelial, stroma and adipocytes respectively in the two sets of samples were found to be significant. Therefore we concluded that the 109 samples were representative for the whole data set. The second step was to compare the area-fraction numbers of tumor-epithelial, stroma and adipocytes in the 109 samples common for the two histopathological procedures. The differences in area fractions between the two procedures were highly significant for all three area fractions estimated. The stereological point counting tended to give a lower scoring of tumor epithelium and adipocytes and a higher scoring of stroma area-fraction, compared to the initial histopathological estimates. Hence, these tests revealed significant differences in the exact area fraction values measured with the two methods which are in accordance with previous publications determining that semiquantitative measurements tend to produce an overestimation as compared to stereology c): Firstly, we assumed that total adipocyte transcription level was equal to stroma cells and tumor epithelial cells (c = 1); secondly, we assumed no transcription in adipocytes (c = 0). The weighted analyses were divided into tumor-focused analyses (TFA) and stroma-focused analyses (SFA). In TFA, each sample was weighed proportionally to the area fraction tumor epithelium of that tumor sample, while in SFA, by the stromal cell percentage of that tumor sample. Thus, higher impact in the statistical analysis was given to samples containing more tumor epithelium or stromal cells, respectively. The TFA and the SFA respectively returned 169 and 77 DEGs, under the assumption of no transcription in adipocytes (c = 0). When the adipocyte transcription was weighed equally as for tumor epithelium and stroma (c = 1), the numbers of DEGs found between DM and NoDM tumor groups were 169 in the TFA and 85 in SFA, respectively  and were among the most differentially expressed gene-probes between the two outcome groups. In addition, most of the 182 probes in UWA (representing 181 different genes) were also detected in the weighted analyses (141 for c = 0 and 138 for c = 1). The remaining probes  were not identified in the weighted analysis at 5% FDR but appeared as significant at 10 or 20% FDR. It is interesting that both the TFA and SFA identified probes that appeared as statistically non-significant in the standard un-weighted analysis. These were 48 and 55 tumor-associated probes  and 12 and 16 stroma-associated probes   when of c = 1 . The intctively) .c = 0 to 1 . In summary, in the TFA there were 105 DEGs which appeared for all the selected adipocyte transcription levels  (SACat) (Lists of DEGs identified through weighing by tumor epithelium and stroma content were then generated separately for other various assumptions of adipocyte transcription levels from  TACat ) , while t (SACat) . Based od, in five intervals from 0.2 to 1 , and identified five lists of DEGs in the tumor-weighted analysis  was identified regardless of the assumption on the level of stroma cell transcription d  was always identified regardless of the assumption on the level of transcription of stromal cells  from the analyses based on all 5 values of d (TACsev and SACsev), since these are most likely to withhold the true d   . We inteTACsev (94 DEG-list) that were not identified in SACsev (31 DEG-list). However, 38 of the 68 DEGs were identified by one or more of the stroma weighted analyses under different assumptions of stromal transcription level and could therefore not be ascribed exclusively to tumor epithelial cells , cAMP responsive element binding protein-like 2 (CREBL2), and RAB31, member RAS oncogene family (RAB31).We identified 68 DEGs in al cells . These 3SACsev list we could not ascribe any DEGs purely to stroma cells since there were no DEGs between DM and NoDM that were not identified in at least one of the tumor-focused analyses (i.e. TACsev). In fact, four out of five SACsev gene probes not found in TACsev were still identified by four out of five tumor focused analyses when the contribution of stroma expression was varied (PIP5K2A) was only identified by one of the five TACsev-analyses. These DEGs are therefore referred to as “stroma cells associated”. However, it is interesting to note that these genes were identified by the tumor focused analyses for the lower values of d, hence, differences in gene expression from these five genes, and PIP5K2A in particular, might mainly be associated to stromal cells. PIP5K2A is part of the v-akt murine thymoma viral oncogene homolog 1 (AKT1) signaling pathway PIP5K2A has been reported to be involved in differentiation and motility; hence it might influence the metastatic process. The remaining four DEGs of the SACsev list that were not in TACsev were flavin containing monooxygenase 5 (FMO5), isovaleryl Coenzyme A dehydrogenase (IVD), leucine rich repeat containing 46 (LRRC46), and double C2-like domains, alpha (DOC2A), all found to be lower expressed in the DM group. Longer relapse-free survival has previously been reported for patients with higher expression of FMO5FMO5 in our study. Isovaleryl-CoA dehydrogenase (IVD) is a mitochondrial matrix enzyme that catalyzes the third step in leucine catabolism, and was found to be lower expressed in rats injected with estradiol valerate IVD-expression was lower in the DM-group, in which tumors also showed lower expression of estrogen receptor 1 (ESR1) when compared to the NoDM group. It is on the other hand, questionable how comparable these two studies are. Regarding LRRC46, a recent study reports that this gene was expressed higher in estrogen receptor positive breast cancer tumors which are comparable to what was observed in our study DOC2A was reported to be highly expressed in brain cells where the protein was found to interact with Ca2+ and phospolipid in neurotransmission DOC2A-probes present on the microarray used in this study, and only one of these showed significant difference in expression between the DM and NoDM groups. These probes might represent different splice variants, and need to be further validated.In the s varied . The fifTACsev, and SACsev) (NAT1) MYB), both reported earlier as a potential prognostic markers in estrogen receptor positive breast tumors NAT1 and MYB were found to be lower expressed in the DM group in our data. Another gene of interest  is the zinc finger, MYND-type containing 10 (ZMYND10) gene, which has been suggested to act as a tumor suppressor gene in nasopharyngeal carcinomas TACsev and SACsev lists were also found by the UWA. This is as expected since the criteria of the 26 gene list is that the DEGs were identified by all values of d in both TFA and SFA, also including the TFA with d = 0,2, a test being close to the UWA. Thus, the expression differences between the DM and NoDM groups for these probes seem to be sufficient to overshadow the weighing by either cell type. Conceptually, the cell type ascription of these DEGs could be explained by different scenarios; a) ascribed to tumor epithelial cells assuming the expression of tumor epithelial cells overshadows any effect of stroma; b), ascribed to stromal cells by assuming the expression differences in stroma were adequately profound to be detectable in a stroma focused analysis at low values of d  or c) these genes are differentially expressed between DM and NoDM in both cell type compartments. Analysis of expression differences for these genes in the validation data set indicates that a) is the most likely scenario of the three. However, we did validate one gene (ABAT) that was significantly differentially expressed between DM and NoDM in both tumor epithelial and tumor stroma.There were 26 DEGs in common between the two consensus lists of DEGs ( SACsev) . We consSACsev list (PIP5K2A), and 4 probes from the TACsev list . MAPRE2  was found highly expressed in the DM group, and has been suggested (due to homology) to be involved in tumorigenesis of colorectal cancers and proliferation control in normal cells TACsev nor SACsev. However, when we considered all 312 DEGs identified in the weighted analyses for all values of d between 0,2 - 1, there were 137 genes that were not present in the 182 UWA-list, and conversely, only 6 probes (6 genes) in the UWA list that were not present in any list from the weighted analyses  gene expression differences between the DM and NoDM groups, the expression of the genes in the consensus gene list  was testTo assess the differences in expression of each gene between the DM and NoDM group a simple t-test was utilized. Out of the 30 tumor ascribed DEGs in  21 had aOut of the 38 tumor associated DEGs 29 had available gene expression in the tumor-epithelial LCM validation set. Eight out of the 29 genes were found significantly differentially expressed (p<0.05) between DM and NoDM patients in the validation set with coherent up and downregulation in the respective clinical groups compared to the initial analyses. Fourteen additional genes showed up and downregulation in the same direction as what was found in the initial analyses. However, these genes did not reach significant p-values in the validation set (0.05<p<0.5). The remaining 6 genes showed no difference between DM and NoDM .Out of the 26 overlapping DEGs 19 had available gene expression in the tumor-epithelial LCM and the tumor-stroma validation sets. Eight out of the 19 genes were found significantly differentially expressed (p<0.05) between DM and NoDM patients in the tumor-epithelial validation set. The eight genes had coherent up and downregulation in the respective clinical groups compared to the initial analyses. Six additional genes showed up and downregulation in the same direction as what was found in the initial analyses. However, these genes did not reach significant p-values in the validation set (0.05<p<0.5). The remaining 5 genes showed no difference between DM and NoDM . In the tumor-stroma validation set, one gene (ABAT) was found significantly differentially expressed (p<0.05), and was among the eight significant genes in the tumor-epithelial set. Eight genes showed up and downregulation in the same direction as what was found in the initial analyses. However, these genes did not reach significant p-values in the validation set (0.05<p<0.5). The remaining 10 genes showd no difference between DM and NoDM .Out of the five stroma ascribed genes, one gene (PIP5K2A) was found significantly differentially expressed in the tumor-stroma validation set. This gene was not significantly differentially expressed in the tumor-epithelial validation set. Thus, the PIP5K2A seem to be validated as a gene being significant differentially expressed in the tumor-stroma cells but not in tumor-epithelial cells of DM vs NoDM patients. This differentially expressed gene was also the most stroma related as it was only identified for one value of d in the tumor focused analyses . One genThe unweighted approach returned a list of DEGs in which there were varying numbers of false positives at any chosen FDR. When the statistics was weighed by cell type content under various assumptions of transcription levels in adipocytes and stromal cells, we identified a set of re-occurring genes that were differentially expressed between the DM and NoDM group. DEGs identified under different statistical scenarios appear more robust and are more likely true positives. However, the effect of weighing samples differently in the analyses is similar to decreasing the sample size of the dataset, since each sample count less than 1. Hence, some of the differences among the lists of DEGs could be due to a sample size effect and not the weighing it-self. The different assumptions on transcription levels in adipocytes showed a miniscule impact on the numbers of identified DEGs, and were thus set to zero, although we acknowledge that there are studies emphasizing the importance of adipocytes in breast cancer differences (between DM and NoDM) of a gene could be ascribed or associated.It should be noted that we, on the basis of weighing, can not state whether or not a gene is expressed to a higher extent in tumor epithelial cells compared to stromal cells. We merely propose to which cell type the expression in silico method for utilizing histopathological information on tissue content of tumor biopsies in the statistical calculations of differentially expression of genes in microarray data. This approach was used to identify genes significantly differentially expressed between tumors from patients who experienced distant metastasis and tumors from those who did not. The weighted analyses were utilized to ascribe and associate gene expression differences between the two patient groups to tumor epithelial and stromal cells. Although the stromal transcription contribution was measurable in data obtained from grossly dissected tumor tissue, the differences in gene expression between DM and NoDM seem mainly to be related to tumor epithelial cells, and the majority of these genes can also be identified by the unweighted bulk tumor expression. The analyses also aided in identifying a set of core genes which seems to be profoundly different in their expression patterns between the two groups, independent of the cellular constitution. Thus, genes reappearing in the weighted analyses under different assumptions of cellular contribution to the overall transcription level have a higher probability of being true findings.We present an The study was approved by the national ethics committee  and by “Datatilsynet” (The Data Inspectorate - an independent administrative body under the The Ministry of Government Administration and Reform). Oral informed consent was mandatory, and a procedure approved by the ethical committee at that time (1982–1987).The 198 breast tumor samples with high quality gene expression data utilized in this study were part of 267 fresh frozen tumor samples (stored at −80°C in Denmark) available after total mastectomy surgery. The tumor samples were originally stored for the purpose of estrogen receptor staining in the Danish Breast Cancer Group 82 b and c cohort (DBCG82b&c). DBCG82 b and c studies comprise a collection of tumor tissues from 3,083 high-risk Danish breast cancer patients diagnosed in the period 1982–1990. High-risk was defined as positive lymph nodes and/or tumor size larger than 5 cm and/or invasion of tumor to surrounding skin or pectoral fascia. Total mastectomy with partial axillary dissection was performed on all women, and a median of seven lymph nodes was removed from the axilla.Premenopausal women (DBCG b protocol) were randomized to receive radiation therapy and CMF  or only CMF chemotherapy (9 cycles) Postmenopausal women (DBCG c protocol) were randomized to receive radiation therapy+tamoxifen (30 mg daily for 1 year) or tamoxifen alonePatients were followed routinely at regular intervals the first 10 years or until the first recurrence, death or new primary cancer. Recorded endpoints were locoregional recurrence (LRR), distant metastases (DM) and contralateral breast cancer (CBC) 3) from a subset of 267 out of the 3,083 patients in the DBCG82 b and c protocols were available for this study. All tumor samples were cut in three and the centre section was used for total RNA extraction whereas the two flanking pieces were used for histopathological analyses using Haematoxylin & Eosin (HE) staining (Fresh frozen tumor samples (approximately 5–10 mmstaining . ApproxiHaematoxylin & Eosin (HE)-stained sections were prepared from the two flanking tissue pieces facing the centre section used for RNA extraction , and HE Most sections were entirely comprised of invasive tumor, and in these the amount of normal tissue stroma was negligible.The microarray system used in the study was the Applied Biosystem Human Genome Survey Microarray version 2.0. These are whole genome arrays spotted with 32878 probes covering 29098 genes. The platform utilizes chemiluminescence labeling in a single channel system. The probes are 60-mers, mostly mapped within 1500 bases of the 3′ end of the source transcript, and were designed to match common regions of multiple alternative transcripts for a particular gene. For some genes there are multiple probes, although 87% of the genes are targeted by a single probe. In addition to the human gene probes, the microarray contains numerous internal control probes for monitoring the experimental steps in the amplification, labeling and hybridization procedure. A second set of control probes is present for intra array normalization purposes . These probes are 20-mers printed alongside the gene-probes in every spot. The shorter control-probes are complementary to a fluorescently labeled control sequence that is added to the hybridization solution. The fluorescent signal emitted should be identical for every spot; the gene-spot signal is normalized parallel to the difference in the fluorescent signal in the same spot compared to all the other spots First, flagged intensities and probes with signal to noise ratio below 3 were removed from the raw data. Next, the number of probes was further reduced to 17910, by removing those that had missing values in more that 80% of the samples. The quantile normalization procedure was applied using ABarray library in R and the missing values were imputed using the k-nearest neighbors algorithm of the smida library in R. After re-scaling the expression on each array so that they sum to 1 a log2 transformation was applied. Data is accessible at NCBI GEO database accession GSE24117.Across the whole dataset, the percentages of stroma cell area varied from 5% to 85%, for tumor cells; from 5% to 85%, and for adipocytes; from 0% to 50%. A two-sample Kolmogorov-Smirnov test was used to compare the distribution of the different cell types in the DM and NoDM groups iw and jw is the mean percentage (average of the two sections per sample) of area covered by tumor (or stroma) in the sample. The difference of the means was taken and standardized giving the statistics To incorporate the information on cell type abundance into the testing procedure, the tumor-weighted or stroma-weighted mean expression of the gene was computed instead of the arithmetic mean, for both groups of patients (DM and NoDM) as (c) of transcription of adipocytes compared to tumor cells. For different values of c ranging from 0 to 1, the adipocytes percentage area (a) was scaled to ac and the percentages of area covered by tumor and stroma were re-scaled so that they summed up to 100. The re-scaled tumor (t′) and stroma (s′) weights were computed as t′ = t/(t+s+ac) and s′ = s/(t+s+ac), where t, s and a are respectively, tumor, stromal and adipocyte cell area proportions in the original data and c the relative level of transcription of adipocytes compared to the other two tissue components.The tumor-focused and the stroma-focused analyses were carried out under various assumptions. In the first series of analyses we assumed that the total gene transcription of the three tissue components  occurred at an equal level. However, due to uncertainty around the transcription level of adipose tissue, the weighted analysis was repeated for various relative efficiencies c gave different sets of genes, differentially expressed between patients who developed distance metastasis and those who did not. In both series of analyses , some genes seemed to be consistently differentially expressed regardless of the assumption on the level of transcription of adipocytes. Next, the level of transcription of the adipocytes was set to zero and the cell type-weighted analysis was performed under different assumptions on the level of transcription d of stroma cells compared to tumor cells. Analogous to the first series of analyses, for each level of d the percentages of area covered by tumor and stroma were re-scaled to sum up to 1. These new weights were computed ad t″ = t′/(t′+s′d) and s″ = (t′+s′)−t″.The analyses carried out for the other selected values of Gene expression data and associated clinical information on metastatic disease were available from microdissected tumor stroma and tumor epithelium from patients with invasive ductal carcinoma Figure S1RNA extraction. Fresh-frozen tumor piece cut in three, a, b and c. Sections cut from pieces a and c for pathological estimation of area percentages of adipocytes, stromal cells and carcinoma cells. Centre piece b was used for total RNA extraction and microarray analyses.(0.27 MB PDF)Click here for additional data file.Table S1Genes associated with tumor epithelial and stromal expression. List of genes (Gene symbol and Celera Gene ID) with reported cell type association in literature, were compared to the fold change  from a SAM analysis between the 15 samples with the highest tumor cell percentage and the 15 samples with the highest stroma cell percentage. The Pearson correlations  for each gene to the tumor and stroma cell percentage across all 198 samples are also included.(0.04 MB XLS)Click here for additional data file.Table S2UWA geneprobe list. List of DEGs from the unweighted analysis, 182 gene-probes differentially expressed in tumor samples from patients who experienced distant metastasis compared with those who did not.(0.04 MB XLS)Click here for additional data file.Table S3DEGs under various assumptions of d. Lists of all DEGs found in the various weigthed analyses under different assumptions of the contribution of mRNA transcription from stroma.(0.10 MB XLS)Click here for additional data file.Table S4Validated DEGs in LCM stroma and tumor epithelial expression data. List of 74 genes that were matched to the stroma and tumor epithelial expression validation set. Left columns in the split table show the original regulation of genes in DM vs. NoDM found in the stroma and tumor weighted analyses, respectively. The next column shows the regulation seen in the validation sets . The significant differences in the validation sets are highlighted in red.(0.03 MB XLS)Click here for additional data file.Table S5LCM Stroma. List of all gene probes with gene expression data for the matching 74 genes in the LCM stroma. Left column annotates no distant metastasis = 0 and metastasis = 1.(0.15 MB XLS)Click here for additional data file.Table S6LCM Epi. List of all gene probes with gene expression data for the matching 74 genes in the LCM tumor epithelial. Left column annotates no distant metastasis = 0 and metastasis = 1.(0.18 MB XLS)Click here for additional data file.


Gary Maartens and colleagues describe the direct heath care costs and identify the drivers of cost over time in an HIV managed care program in Southern Africa. There is a paucity of data on the health care costs of antiretroviral therapy (ART) programmes in Africa. Our objectives were to describe the direct heath care costs and establish the cost drivers over time in an HIV managed care programme in Southern Africa.10 copies/ml respectively. There was a peak in costs in the period around ART initiation (from 4 mo before until 4 mo after starting ART) driven largely by hospitalisation, following which costs plateaued for 5 years. The variables associated with changes in mean total costs varied with time. Key early associations with higher costs were low baseline CD4+ cell count, high baseline HIV viral load, and shorter duration in HIV care prior to starting ART; whilst later associations with higher costs were lower ART adherence, switching to protease inhibitor-based ART, and starting ART at an older age.We analysed the direct costs of treating HIV-infected adults enrolled in the managed care programme from 3 years before starting non-nucleoside reverse transcriptase inhibitor-based ART up to 5 years afterwards. The CD4 cell count criterion for starting ART was <350 cells/µl. We explored associations between variables and mean total costs over time using a generalised linear model with a log-link function and a gamma distribution. Our cohort consisted of 10,735 patients (59.4% women) with 594,497 mo of follow up data (50.9% of months on ART). Median baseline CD4+ cell count and viral load were 125 cells/µl and 5.16 logDrivers of mean total costs changed considerably over time. Starting ART at higher CD4 counts or longer pre-ART care should reduce early costs. Monitoring ART adherence and interventions to improve it should reduce later costs. Cost models of ART should take into account these time-dependent cost drivers, and include costs before starting ART.Please see later in the article for the Editors' Summary About 30 million people  are infected with the human immunodeficiency virus (HIV), the cause of acquired immunodeficiency syndrome (AIDS). HIV destroys immune system cells , leaving infected individuals susceptible to other infections. Early in the AIDS epidemic, on average HIV-positive people died within 10 years of infection. Then, in 1996, highly active antiretroviral therapy  was developed. For people living in affluent, developed countries HIV/AIDS became a chronic, treatable condition, but for the millions of HIV-infected people living in low- and middle-income countries, effective treatment was unavailable and HIV/AIDS remained a fatal illness. In 2003, this situation was declared a global health emergency and governments, international agencies, and funding bodies began to implement plans to increase ART coverage in developing countries. By the end of 2008, of the 9.5 million people in need of ART in low- and middle-income countries, more than 4 million people were receiving treatment.Good progress is being made towards achieving universal access to ART, partly because the cost of antiretroviral drugs has plummeted in developing countries. But the provision of antiretroviral drugs is not the only direct cost associated with ART. General practitioner, specialist, and maternity-related care for patients receiving ART, hospital accommodation when necessary, and the investigations that are needed to monitor the progress of HIV infection such as CD4 cell counts and viral load measurements all incur considerable costs. To use their limited resources effectively, public-health officials in developing countries need to know as much as possible about the direct costs of HIV health care but few studies have investigated these costs, particularly those incurred before an individual starts taking ART. In this study, the researchers explore health care costs in a South African private-sector HIV/AIDS program and examine the variables that drive the costs of HIV health care around the time of ART initiation and during later phases of ART.The researchers analyzed the direct costs of treating more than 100,000 HIV-infected adults enrolled in a private HIV care program in South Africa from 3 years before they started ART until up to 5 years after ART initiation; within this program, individuals began to receive ART when their CD4 cell count fell below 350 cells/µl of blood. The researchers found a peak in direct health costs from 4 months before to 4 months after starting ART (the “peri-ART” period), which was driven mainly by hospital costs. After the peri-ART period, costs dropped  and stabilized at an intermediate level for the next 5 years. Detailed statistical analyses suggest that the key variables associated with higher costs in the peri-ART period were a low baseline CD4 cell count, a high baseline HIV viral load, and a shorter time in HIV care before ART initiation. The key variable associated with higher costs later in ART was lower adherence to the drug therapy. That is, costs were higher among patients who did not take their antiretroviral drugs regularly.This study involved patients enrolled in a private health care program in which the criteria for initiating ART differed somewhat from those recommended by the World Health Organization for ART initiation in resource-limited settings. Thus, the absolute mean total costs calculated by the researchers are unlikely to be generalizable to public HIV care systems in South Africa and in other resource-poor settings. However, the finding that the drivers of mean total costs change considerably over time may be generalizable and provides some useful information for public-health planners that can now be tested in other, more resource-limited patient populations. In particular, the findings of this study suggest that the high early costs of ART programs could be reduced by starting ART at higher CD4 cell counts or by providing longer pre-ART care. In addition, the findings suggest that monitoring ART adherence and introducing interventions to improve ART adherence could reduce the later direct costs of ART programs.http://dx.doi.org/10.1371/journal.pmed.1000189.Please access these Web sites via the online version of this summary at HIV infection and AIDSInformation is available from the US National Institute of Allergy and infectious diseases on HIV InSite has comprehensive information on all aspects of HIV/AIDSAvert, an international AIDS charity on many aspects of HIV/AIDS, including information on the HIV and AIDS in Africa, and on universal access to AIDS treatment (in English and Spanish)Information is available from universal access to AIDS treatment, including the September 2009 progress report (in English and French)The World Health Organization provides information about global efforts to deal with the HIV/AIDS epidemicThe US Centers for Disease Control and Prevention also provides information on  Access to combination antiretroviral therapy (ART) is rapidly expanding in resource-limited settings. Data on the costs of providing HIV health care and how these change over time are important for guiding resource allocation. However, there are few good quality studies of the direct health care costs of HIV infection, as illustrated by a recent systematic review that found only nine studies from the ART era that fulfilled inclusion criteria Delays in establishing public ART programmes in South Africa until 2003 The objective of this study was to explore health care costs in a South African private sector HIV/AIDS programme, with a special focus on the determinants of costs around the period of ART initiation, as well as the determinants of costs during the later phases of ART.The study was approved by the Research Ethics Committee, University of Cape Town and by the Board of Directors of Aid for AIDS (AfA). Informed consent was not required as the data were analyzed anonymously, but all patients signed consent for their information to be entered into the AfA database.Data for this study were extracted from a database of patients enrolled with AfA, a group that manages HIV-related care for a number of medical insurance funds and companies in the private sector in Southern Africa Two of the medical insurance funds contracted to AfA were selected on the grounds that they had large numbers of patients, similar treatment benefits, and required no co-payment for ART. This selection allowed us to describe costs and drivers of costs without relating to the patient's ability to pay, which has been reported to influence outcomes on ART Direct health care costs were analysed from the provider's perspective The prices of antiretroviral drugs have fallen dramatically over the period of our study. To account for this decrease we deflated ART prices to the April 2007 level. All other health care costs have increased; these were inflated to the April 2007 level using the consumer price index net of mortgage payments (CPIX) The mean total cost and its components were explored from 36 mo before starting ART to 60 mo on ART. Costs were broken down into the following components: ART, other medication, hospitalisation, investigations, CD4+ cell count and HIV viral load monitoring, general practitioner consultations, specialist consultations, maternity, and auxiliary care. This exploratory analysis revealed a marked peak in cost from 4 mo before starting ART until 4 mo on ART. This 8-mo interval is denoted the “peri-ART” period in this study.Even though health care costs are often right-skewed, with a minority of patients incurring very high costs, the health economics literature argues that health care policy decisions are best guided by analyses of arithmetic mean costs, as the mean provides information on the costs of treating the entire population The time-varying associations between mean total cost and the variables was modelled using three methods: a separate model for each 4-mo time interval using categorical variables, and two models with categorical or continuous variables over the entire interval with time included as a variable, which also interacted with the other variables. Effect estimates and their significance at the 95% level were assessed using robust standard errors with clustering at an individual level. Data storage, basic calculations, and data extraction was handled in Microsoft Access 2003 The following variables were considered in our analysis: baseline CD4+ cell count and HIV viral load (baseline was defined as the most recent result within 6 mo before starting ART), ART adherence assessed by monthly pharmacy claims, age, sex, the NNRTI and the NRTI combination used in patients on first line therapy, whether the patient switched to PI-based second line ART, and the duration of CD4+ cell count monitoring (as a proxy for being in HIV care) prior to starting ART. Patients with less than 4 mo of claims data after starting ART were excluded on the grounds that we were unable to assess their ART adherence over shorter time intervals. We split the continuous variables into the following categories: (1) Baseline CD4+ cell count was divided into four groups: 0–49, 50–199, 200–349, and ≥350 cells/µl. (2) HIV viral load was categorised as ≥100,000 copies/ml or <100,000 copies/ml. (3) The mean ART adherence was determined using pharmacy refill data and divided into quartiles. (4) The NNRTI was included as a binary variable (either efavirenz or nevirapine); whereas (5) the NRTI combination in first line was divided into three groups: zidovudine and lamivudine, stavudine and lamivudine, or any other combination. (6) A binary variable was used to reflect whether or not the patient was on second line ART. (7) Age was divided into quartiles. (8) Sex was included as a binary variable. (9) Patient follow-up for HIV prior to starting ART was measured by the length of time between the first CD4+ cell count and the date of starting ART, and was categorised as less than 6 mo and more than 6 mo.2, 52% were ≥18.5 kg/m2 and <25 kg/m2, and 35% were ≥25 kg/m2. The most common first line and second line antiretroviral regimens were zidovudine, lamivudine, and efavirenz and lopinavir/ritonavir, zidovudine, and didanosine, respectively. CD4 and viral load monitoring were done 1.5 times per annum on average. Hospitalisation rates were 441 d per 100 patient years of observation (PYO) in the first 6 mo of ART and 179 d per 100 PYO subsequently. Hospitalisation incidence was highest in patients in the lowest CD4 count stratum.10,735 patients met our eligibility criteria. The characteristics of the cohort are described in 10, p = 0.0016), proportion female sex , and age . Importantly, these baseline differences are not clinically significant and the CD4+ cell count did not differ significantly .The proportion of patients who left the scheme was 31% overall and 24% at 2 y. Patients who left the scheme either changed their employment, switched to a different medical insurance scheme, or voluntarily stopped their contributions to the insurance scheme. Patients who left the scheme differed from those who did not leave in the following baseline characteristics : viral load  described time-dependent association between time and total mean cost and its interaction with the other variables. The results from this multiple regression analysis are found in We found that costs were very high in the peri-ART period. Mean monthly costs were more than 3 times higher in this period and the association between costs and baseline CD4 count and baseline viral load were more marked in the peri-ART than in later time periods. In the above analysis, we excluded patients who died within the first 4 mo on ART because we could only estimate ART adherence over a period of 4 mo or longer. However given that patients who died might incur significant costs, we performed an additional subanalysis including these early deaths. This subset included 8,559 patients. The findings were similar but the associations between variables and total mean costs diminished marginally when we repeated the multiple regression analysis with the ART adherence variable excluded (unpublished data).Finally, we explored continuous models for ART adherence, age at starting ART, baseline viral load, and baseline CD4+ cell count : polynomial functions of the 2nd degree were used for all the variables except ART adherence  and baseline HIV viral load  were used. We felt that the duration of CD4+ cell count monitoring before staring ART was better handled as a categorical variable. Time and its interactions with the other variables displayed nonlinear associations with total mean cost for the first 24 mo; thereafter trend was approximately linear. A restricted cubic spline (a cubic spline with linear tails) with three knots placed −4 to −1 mo, 4–7 mo, and 16–19 mo fitted the observed trends in our data; we experimented with the placement and number of knots using the Akaike Information Criteria and predictive plots to guide the final model selection. An interaction with the spline function for time was used for all variables except ART (first line versus second line) as the trend over time was difficult to quantify. Overall, the model was able to describe the trends in the data well, though in some intervals the trends in the baseline viral load and age variables were not well approximated at the extremes. While the main findings from this analysis using continuous variables did not differ from the previous analysis using categorical variables, some subtleties not previously shown were found in the relationships between costs and baseline CD4+ cell count and ART adherence. The association between baseline CD4+ cell count and mean total costs over time is shown in We analysed the direct health care costs of treating over 10,000 HIV-infected adults enrolled in a Southern African managed care ART programme with almost 600,000 patient months of follow-up, spanning 3 y before ART to 5 y on ART. We found a peak in costs in the period around the time of ART initiation, thereafter total mean costs dropped off to a plateau that persisted for 5 y. An important and novel feature of our study was the presentation of time-dependent associations between total mean costs and relevant variables. We identified lower baseline CD4+ cell count, higher baseline viral load, and shorter duration of CD4+ cell count monitoring before starting ART (as a proxy for HIV care) as being independently associated with higher costs in the early time periods. Lower ART adherence, being on second line ART, and starting ART at an younger age were most strongly associated with lower mean costs in later time periods, and the association with ART adherence became more marked over time.The peak in costs in the peri-ART period we observed was largely driven by the high proportion of patients requiring hospitalisation. High rates of early morbidity, often resulting in hospitalisation or death, are characteristic of antiretroviral programmes in resource-limited settings. Patients on ART in low-income countries have higher early mortality compared with high-income countries, even after correcting for baseline differences in CD4+ cell counts We found that higher ART adherence was associated with lower costs particularly after removing antiretroviral drug costs. The magnitude of this association becomes greater as duration on ART increases. However, the continuous model showed that while highly adherent patients (>92%) were associated with the lowest total mean costs in later time intervals, they were associated with higher costs in the early time intervals. A similar association was found with high baseline CD4+ cell counts (>300 cells/µl) being associated with higher costs initially. These findings could be attributed to increased health-seeking behaviour leading to increased costs initially, but reduced costs over time. Very low ART adherence was associated with low total mean costs in all time intervals as these patients are presumably accessing minimal services. Our group has previously reported that ART adherence assessed by pharmacy refills in this cohort predicted both virological suppression Our analysis of the time-dependent associations with increased costs has several important public health implications. The high early costs of ART programmes could be reduced by starting ART at a CD4+ cell count of <350 cells/µl rather than <200 cells/µl (for patients without major symptomatic HIV disease). Our cohort does not allow for an evaluation of starting ART in patients with baseline CD4+ cell counts ≥350 cells/µl because AfA guidelines only allow these patients to start ART following an AIDS-defining illness or with other serious co-morbidity: costs were actually higher in this group compared with those starting ART with baseline CD4+ cell counts 200–349 cells/µl, presumably reflecting the costs of treating the morbidity that was the criterion for starting ART. The second intervention that could reduce early costs would be the earlier identification of HIV infection, illustrated by our finding that being in HIV care for more than 6 mo prior to starting ART reduced costs in the peri-ART period. The key driver of later costs with public health implications is ART adherence. Higher adherence prolongs time on the cheaper first line regimen, but also reduces non-ART direct costs in our study. The third intervention that might reduce costs would be to encourage ART programmes to invest in systems to monitor ART adherence and implement effective interventions if adherence is suboptimal. ART adherence could be monitored over short time periods of 3 to 4 mo, which identifies patients incurring higher costs and those at risk of virological failure We estimate that annual total direct health care costs are approximately US$2,400 (after the peak in costs in the peri-ART period) for patients accessing ART in the private sector. Lower costs were reported in two other South African studies. Harling reported costs of $2,502 in year one and $1,372 in year two of a donor-funded public sector program Some findings of other ART programme cost studies differed from our analysis. We found that the ART component of costs was relatively small compared with other studies in resource-limited settings 2 in 13% of our cohort compared with 19% in a South African public sector cohort There are a number of limitations to this analysis. First, our cohort consisted of private sector patients when the majority of patients in resource-limited settings are treated in the public sector. However, the baseline characteristics of our cohort  are comparable with cohorts from low-income countries 10 viral load) were small and are of questionable importance. Fifth, we chose to use the tariff amount as opposed to the amount claimed or reimbursed so that similar services would take the same monetary value and have further assumed that these tariffs are a suitable proxy for opportunity costs. While this could be a shortcoming, it is common to assume that market prices are a proxy for opportunity costs in economic evaluation given the difficulties in evaluating the latter Second, the impact of specific AIDS-defining illnesses on outcomes and costs was not included in this analysis because these data were not available. Third, as a provider's perspective was chosen for this analysis, the cost to society is not fully represented because we did not have data on direct non-health care costs and indirect costs. However, a provider's perspective is more appropriate for the aim of this study, which was to unpack the key drivers of health care costs in order to inform appropriate budgeting and planning. Fourth, the characteristics of the patients who left the scheme were different from those who remained, which may have affected our findings. However, there was no significant difference in the key baseline characteristic of CD4+ cell count and many of the other differences Click here for additional data file.Figure S2The proportional change in mean total monthly costs over time associated with baseline HIV viral load. Baseline HIV viral load was compared with the referent group  within each time interval from 4 mo before starting ART to 60 mo on ART with lighter blue indicating higher relative costs.(0.31 MB TIF)Click here for additional data file.Figure S3The proportional change in mean total monthly costs compared over time associated with age at starting ART. Age at ART was compared with the referent group (37 y) within each time interval from 4 mo before starting ART to 60 mo on ART with lighter blue indicating higher relative costs.(0.40 MB TIF)Click here for additional data file.


Behavioral studies have shown that infants can form associations between environmental events and produce anticipatory actions for the predictable event, but the neural mechanisms for the learning and anticipation of events in infants are not known. Recent neuroimaging studies revealed that the association cortices of infants show activation related to auditory-stimulus discrimination and novelty detection during sleep. In the present study, we expected that when an auditory cue (beeps) predicted an auditory event , specific regions of the infant cortex would show anticipatory activation before the event onset even while sleeping.We examined the cortical activation of 3-month-old infants during delays between the cue and the event by using multi-channel near-infrared spectroscopy. To investigate spatiotemporal changes in cortical activation over the experimental session, we divided the session into two phases (early and late phase) and analyzed each phase separately. In the early phase, the frontal regions showed activation in response to the cue that was followed by the event compared with another cue that was not followed by any event. In the late phase, the temporoparietal region, in addition to the frontal region, showed prominent activation in response to the cue followed by the event. In contrast, when the cue was followed by an event and no-event in equal proportions, cortical activation in response to the cue was not observed in any phase.Sleeping 3-month-old infants showed anticipatory cortical activation in the temporoparietal and frontal regions only in response to the cue predicting the event, suggesting that infants can implicitly form associations between temporally separated events and generate the anticipatory activation before the predictable event. Furthermore, the different time evolution of activation in the temporoparietal and frontal regions suggests that these regions may be involved in different aspects of learning and predicting future events. Our ability to learn regularities and associations in the external world is critical for adaptation, and this mechanism is potentially available early in life to guide perceptual development Previous neuroimaging studies with adults reported that the sensory and the association regions, including prefrontal cortex, showed cortical activation before stimulus onset when a preceding cue predicts the coming of the stimulus In the present study, 3-month-old infants were exposed to a series of auditory stimuli during sleep to examine their ability to associate temporally separated events. We prepared two different frequency beeps as auditory cues in the experimental group; one of them was followed by an attractive event of a female voice reading a book with a time delay, and the other was not followed by any event. For comparison to this group, we presented a single frequency beep as a cue that was followed by an event or no-event in equal proportions to the control group. We assumed that auditory information processing remained operative even when they were sleeping. This assumption is based on previous studies that showed that neural responses to speech sounds were observed during sleep in both infants To examine the cortical activation of the infants noninvasively and safely, we used a multi-channel NIRS system n = 32; girls, n = 24; mean age, 110.6 days; range 99–123 days; SD 5.6) participated in the present study. Twenty-eight infants were allocated to the experimental group , and the other twenty-eight infants were allocated to the control group . An additional seventeen infants were tested but excluded from the analysis because of awakening during the experiment (n = 11), head movements producing large motion artifacts in the signals (n = 5), or failure in probe placement because of obstruction by hair (n = 1). The parents of the infants gave written informed consent before the initiation of experiments. These experiments were approved by the ethical committee of the Graduate School of Education (University of Tokyo).Fifty-six full-term, healthy Japanese infants at 3 months of age  with a 250 ms inter-stimulus-interval . A female voice reading the book The experimental session consisted of two conditions: a cue was followed by an event with a delay (CE), and a cue was not followed by any event (CNE) as shown in Infants were held by an experimenter who sat on a chair in front of the speaker in a sound-attenuated room. We tested during the daytime while infants were sleeping soundly and were almost motionless. The infants' sleeping state corresponded to quiet sleep (QS). QS is the dominant and stable state in daytime sleep of infants around 3 months old We used an NIRS instrument . This instrument measured the time course of relative changes in the concentrations of oxyhemoglobin (oxy-Hb) and deoxyhemoglobin (deoxy-Hb) at multiple channels with 0.1 s time resolution by using two wavelengths of near infrared light (785 nm and 830 nm) and applying data analyses based on the modified Lambert–Beer law We examined relative changes in oxy-Hb and deoxy-Hb signals, which estimate changes in regional cerebral blood oxygenation during brain activation. In each individual data set, baseline drifts were corrected by high-pass filtering, implemented using the fifth-order Butterworth filter with cut-off frequencies under 1/60 Hz. Then, we extracted trial data from the time-course data. Each trial data ranged from 1.0 s prior to the cue onset to 25.0 s after the cue onset. We adjusted the baseline by fitting the mean signal of the first 10 time points (1.0 s) to zero. By detecting rapid changes in the oxy-Hb signals, trial data with a low signal-to-noise ratio because of obstruction by hair and those that included movement artifacts were eliminated. We used infant data that contained a minimum of six good trial data for each condition. Following the screening process, we obtained data on 56 infants for further analysis. The mean number of trials was 7.4 in all conditions. By averaging the signal changes over trial data for each infant under each condition, we obtained the hemodynamic responses at each measurement channel. Then, we determined averaged time course among all measurement channels under each condition. In further analysis, we focused on oxy-Hb signals because their magnitude of relative signal changes was greater than those of deoxy-Hb signal changes To examine spatiotemporal changes in cortical activation during the progress of the trials, we divided the experimental session into early and late phases . Given tt tests for each channel. First, a t test of the signal changes under each condition was performed against zero baselines (one tail). Then, we compared mean signal differences between conditions on a channel-by-channel basis in each group . To identify the cortical regions that were activated in both early and late phases, we performed conjunction analysis of these t tests between phases t test, one tail) and also compared the mean signal differences between the conditions in the early phase with those in the later phase . We furthermore compared the mean signal differences between the conditions in the experimental group with those in the control group on a channel-by-channel basis . We also examined cortical activation in response to the auditory events. We analyzed the maximum time points of the averaged time course in Exp-CE and Cont-CE  and calculated mean signal changes in the time range from 9.5 s to 11.5 s in each phase for each infant. Then, the t tests for each channel were performed by considering the individual data as random effects (one-sample t test). To take into account multiple comparisons among 64 channels, we applied the False Discovery Rate correction with α = 0.05 in all analyses To identify the cortical regions that showed an increase in oxy-Hb signals during the delay period in the early and late phases, we calculated mean signal changes in the time range from 4.0 s to 5.0 s after the cue onset under each condition. We considered the individual data as random effects and performed t test). In the early phase, the bilateral frontal and the right temporal and temporoparietal regions showed significant activations against zero baseline in response to the cue followed by the auditory events  = 3.7, p<0.0005], Ch61 in the right temporal region , Ch56 in the right frontal region , and Ch9 in the left frontal region . In contrast, there was no measurement channel in which activation in response to the cue followed by no event (Exp-CNE) surpassed the statistical threshold. In the late phase, the bilateral temporal, temporoparietal, and frontal regions exhibited significant activations in response to the cue of Exp-CE (t(24) = 3.8, p<0.0004], Ch13 in the left temporoparietal region , Ch34 in the right temporoparietal region , and Ch56 in the right frontal region . There was no measurement channel in which activation in response to the cue of Exp-CNE surpassed statistical threshold in the late phase or in the early phase.We investigated the cortical regions showing anticipatory activation in the early and late phases of the experimental group by conducting statistical tests on the mean signal changes during the delay period (4.0 s–5.0 s after the cue onset) for each measurement channel  = 3.1, p<0.003] showed the most significant difference. In contrast, the same comparisons in the late phase revealed highly significant differences in the bilateral temporoparietal regions . In addition, the bilateral frontal regions also showed significant differences . The conjunction analysis of these comparison tests revealed that the bilateral frontal regions showed significant differences in both early and late phases  during either phase, and there was no significant difference between conditions.t test). While no significant group difference was observed in the early phase, the bilateral temporoparietal regions showed significantly greater signal differences between Exp-CE and Exp-CNE compared with the differences between Cont-CE and Cont-CNE in the late phase (t(53) = 3.1, p<0.002; right Ch41, t(50) = 3.1, p<0.002). To examine the difference in the cue effect between the groups, the mean signal differences between conditions in the experimental group were directly compared with those in the control group for each channel Click here for additional data file.


Some patients with chronic obstructive pulmonary disease (COPD) may benefit from oral steroid therapy. These steroid-responsive patients are diagnosed based on laboratory spirometry. We hypothesize that daily, home-based spirometry is a better tool.Thirty patients with COPD underwent a single-blinded study, with a crossover design. They received 2 weeks of placebo followed by 2 weeks of prednisone therapy (40 mg/day). Laboratory spirometry was done at the beginning and end of the study and daily home-based spirometry was done twice a day.1) was 72.5 mL (25th percentile of 40 mL and 75th percentile of 130 mL). The daily FEV1 variation was 70 mL (25th percentile of 50 mL and 75th percentile of 100 mL). The overall laboratory FEV1 variability was larger after the steroid course (P < 0.001), but not clinically significant. The variability was not significant postplacebo treatment compared with the baseline values. For home-based spirometry, steroid treatment was not significantly different. The majority (97%) completed more than 80% of the measurements. Ninety percent of the performed tests were considered acceptable. Only 53% of the tests were considered accurate. Overall both laboratory and home-based measurements did not show significant association between airway responsiveness and dyspnea or exercise capacity.Analysis of variance model was used. The variability of the median day-to-day forced expiratory volume in 1 s (FEV1 might be better than the conventional approach to identify steroid responsive COPD patients. However, this finding was only statistically but not clinically significant. Therefore, we would not recommend this approach to identify COPD patients with steroid responsiveness.Twice-daily home measurements of FEV A steroid responsiveness is characterized by an increase in the FEV1 by a certain level. This positive response suggests continuing the use of steroids, in the form of inhaled corticosteroids (ICS) or less likely oral steroids.Chronic obstructive pulmonary disease (COPD) based on the definition by the American Thoracic Society (ATS) is a pre1, in a small group of patients, rather than a moderate improvement in the group overall.[Recent research has cast doubt on the strategy of using an oral steroid trial to guide the management of individual patients with COPD. There have been more than 30 trials examining the effect of oral steroids in COPD. Methodologic concerns were noted in some of these trials. For example, some did not include a control group,–4 and ot3 overall.–10 In th1 in approximately 10% of patients with stable COPD. Although such a finding might encourage physicians to look for steroid responsiveness in their patients with COPD, the results of the meta-analysis also suggest that the findings should be extrapolated to individual patients cautiously. There was a marked variability among the trials in the degree of steroid-responsiveness. To some degree, this may be accounted for by the differences in the patients recruited, disease definition, steroid dosing, and the study methodology.In a meta-analysis by Calahan and colleagues, 10 studies of oral steroids in stable COPD were examined. There wa1 improvement following a 1- or 2-week course of systemic corticosteroids. Reference is sometimes made to COPD patients as being either “steroid-responsive” or “steroid-nonresponsive,” and the existence of these subtypes is implied in published guidelines for outpatient treatment of COPD. However, the ISOLDE study[Some small trials suggest that a few patients with stable COPD experience an unusually large FEVLDE study failed tRandom fluctuations in breathing measurements could be mistaken for a positive response to therapy. Evidence of previous research indicates that steroid overuse may result from such random fluctuation being overinterpreted by physicians. The accuracy of the steroid trial might be improved by the use of repeated measurements throughout the period of steroid administration. In the past, it has been impractical and expensive to make twice or even daily measurement of lung function in the pulmonary function testing (PFT) laboratories. However, new handheld portable electronic devices can allow patients to perform this test themselves, at home, as frequently as needed to gauge responsiveness accurately.1 of at least 20% and 200 mL will be regarded as steroid responsiveness. The ATS recommends in COPD patients with suboptimal control of symptoms that the physician should consider an oral steroid trial, for example, prednisone, up to 40 mg/day for 10–14 days.[11 ≥ 10% of the predicted value and/or >200 mL. The trial should be done in an exacerbation-free period, using a dose of 0.4-0.6 mg/kg for a duration of 2–4 weeks.The Canadian guidelines for the assessment and management of COPD recommen1 before and after, pre and post, respectively, treatment. We hypothesize that twice-daily home measurements of FEV1 will be better than conventional clinic-based measurement of FEV1 to distinguish steroid responsive from steroid nonresponsive COPD patients.The present study is designed to test the usefulness of repeated home-based measurements to detect clinically important steroid responses among patients with stable COPD. The study also compares this method of treatment assessment with the current clinical method of measuring FEV1 measurements than with laboratory’s pre- and post-BD FEV1 measurements alone. Assessing the association of variability with subjective and objective measurements of improvements is considered as well. In addition, we would like to explore the rate of adherence and technical inadequacy at home in using the home spirometer by the included patients.In this study, we are interested to determine the spirometric variability of patients with clinically stable COPD and to obtain the measurements using a hand-held electronic spirometer. We are also testing the hypothesis that stable COPD patients will be deemed steroid nonresponsive more often using daily FEVFor this study, participants were drawn from outpatients attending the respiratory clinics at the Toronto Western Hospital  between 2003 and 2004. Patients were included in the study if they had an established diagnosis of COPD according to the ATS criteria. Each patExclusion criteria included patients with major nonrespiratory diseases, such as congestive heart failure; chronic renal failure; diabetes mellitus or glucose intolerance; history of tuberculosis; use of steroid medication, oral or inhaled, 1 month prior to enrolment; or any known contraindication to the use of oral steroids. All the participants signed a written consent prior to their enrolment in the study.After obtaining the research ethics board approval, the participants were identified and recruited for the study. The study protocol and rationale were explained to all of them, both verbally and in writing.The study has a single-blinded prospective placebo-control design of 30 stable COPD patients who received 2 weeks of placebo medication followed by 2 weeks of prednisone at a dose of 40 mg/day. To maintain blinding, the order of the placebo medication first and the oral steroid second was not outlined for the cardiopulmonary technologist performing the spirometry.Spirometries pre- and post-BD (standard procedure) were performed in a laboratory setting before and after each treatment period. The participants were asked to abstain from inhaled BDs for 6 h and from sustained release theophylline for 48 h before the baseline spirometry. For spirometry repeated at the end of the placebo and prednisone treatment periods, only short-acting inhaled BDs were to be withheld before testing, and long-acting preparations were continued if they were part of the patients’ usual maintenance regimen. Post-BD testing was done 30 min after the administration of 4 puffs of inhaled salbutamol/ipratropium combination  administered via adult aerochamber.® . This is a portable spirometer, which allows the patients to perform spirometry at home or anywhere outside the hospital. The device’s control unit has memory for several hundred spirometric recordings, including graphical storage of flow–volume curves (FVCs). The participants were instructed about spirometry for about 1 h, and they received written information on how to perform spirometry at home. After a learning period of 1 week, all the patients were able to produce technically acceptable flow–volume loops.The participants were instructed by a research physician about thst visit for recruitment), beginning of week 2 (2nd visit to start placebo), beginning of week 4 (3rd visit to start prednisone), and at the end of the study after week 5.All the 30 patients were asked to use the Diary Cards 4 times a day at home during the 2 treatment periods of the trial. The expected number of spirometric measurements for a patient was 112, as 4 spirograms were done per day for a total duration of 28 days. The participants had to complete 4 follow-up visits. They were seen in the clinic at the beginning of week 1 . To be considered technically acceptable, the test had to be performed correctly by inspection of the curve, and the chosen values should not exceed the next highest by more than 5%. Each participant used the same Diary Card for both periods of treatment. To obtain a true measure of the baseline pulmonary function, only the pre-BD home-based measurements of FEV1 during the placebo treatment period were used. The day-to-day variability was obtained by calculating the difference in home-based FEV1 (only AM measurements) compared with the first day value. The daily FEV1 variation was obtained by comparing the differences of FEV1 AM–PM measurements with the one presented on the first day of the placebo treatment. The participants were re-assessed to measure their transitional dyspnea index (TDI)[In the 2org Scale to repororg Scale and 6-miorg Scale took pladex (TDI) at the eThe subjects’ adherence with the use of the Diary Card was calculated using the formula:1 readings obtained from the Dairy Card measurements were analyzed for quality control according to 2 criteria: the appearance of the graphic record of the FVC and the arithmetic reproducibility criteria. A spirogram was considered acceptable if it was free from artifacts (see Data handling section), and had a good start and a satisfactory exhalation. The percentage of acceptable spirometric recordings was calculated using the formula:The FEVadequate if it was considered technically acceptable and the chosen values did not exceed the next highest by more than 5%. The percentage of adequate spirometric recordings was calculated using the formula:A spirogram was considered Patient-generated data (results of home spirometry) were analyzed for quality control before statistical analysis. Quality control of the Diary Card took place in the first 4 visits. All the flow–volume loops recorded were assessed. Using customary criteria, the loops were excluded if the graphic record suggested inadequate effort or improper technique.1 using this design. The latter percentage was considered the minimal clinically significant difference in the FEV1 for a responder to steroid treatment.[1 was examined for this difference.We assumed a variance of 0.3, that is, a standard deviation (SD) of 0.55, based on the findings of several clinical trials.6718 Thir67th and 75th percentile points. Comparisons were made with a significance level of 0.05.The data were tested for normality and equal variance. For normally distributed data, paired t test was performed, reporting the mean and SD. For nonnormally distributed data, nonparametric Wilcoxon signed rank test was used, reporting the median and the 251 measurements. General linear regression model was built to assess the association between the variability of FEV subjectively  and objectively (using 6MWT).The analysis of variance was used to assess the reliability of the FEVAll data were analyzed with a database and statistical package .1 in the male group was 1.15 L , and it was 1.15 L  in the female group.Thirty participants were recruited for the study, 14 males and 16 females. The mean age of the men and women was 70.0 and 68.3 years, respectively. The mean initial FEV1 was 72.5 mL . The 25th and 75th percentiles were 40 and 130 mL, respectively. The daily FEV1 variation was 70 mL  with a 25th percentile of 50 mL and 75th percentile of 100 mL [The variability of the median day-to-day FEVf 100 mL .P < 0.001). Furthermore, there was a statistically significant difference between the FEV1 values poststeroid treatment compared with both placebo treatment and baseline. A nonstatistically significant difference was found for the FEV1 values postplacebo treatment compared with the baseline values.The overall variability in spirometric measurements was found to be larger than expected from random variability in the treatment, and therefore the treatments had different effects than would be expected by chance . The BDI/TDI ratios for responders and nonresponders were 1.12 and 0.7, respectively (P = 0.09). However, 6MWT distance was associated significantly with airway responsiveness in participants using the Diary Card measurements with a mean percentage of increase in the walk distance, after oral steroid or placebo trial, of 13.3% among responders and 5.74% among nonresponders (P < 0.01) [Both conventional and Diary Card measurements did not show significant association between airway responsiveness to steroids and symptoms of dyspnea assessed by Borg Scale, BDI, and dyspnea index measurements. The Borg Scale percentage of decrease in the mean of the baseline rating, after oral steroid or placebo trial, for responders was 22.51%, whereas it was 18.02% for nonresponders ( < 0.01) .The percentage of performed tests was calculated for each participant. The majority  completed more than 80% of the measurements. Ninety percent of performed tests were considered acceptable. However, only 16 participants (53%) met the criteria for adequacy according to their test performance.1 would be better than conventional clinic-based measurements of FEV1 to distinguish steroid responsive COPD patients from steroid nonresponsive COPD patients. The design of the study and the criteria used to assess the responsiveness to steroids are according to the existing ATS guidelines at the time when the study began.In this study, a new method of assessing the response in stable COPD patients to a short trial of oral steroid was assessed. Our hypothesis was that twice-daily home measurements of FEV1 was less than 80% of the predicted value. Therefore, we specifically designed the study so no asthmatic patients would be included as possible. In addition, the inclusion criteria were chosen to eliminate potential biases related to smoking status and degree. By excluding recent exacerbations, we intended to prevent a potential bias as such patients would be more susceptible to respond to steroids than patients with a stable disease.The inclusion criteria limited the possibility of confounders. All the patients had a smoking history of at least 20 pack-years, were exsmokers for at least 1 year, had a stable disease, and their FEVA major advantage of crossover studies is that they usually require smaller sample size than parallel group trials, as patients form their own controls. The reason for not randomizing the order of treatment was the uncertainty regarding the “wash-out” duration and the risk of steroids’ carried-over effect. A 2-week treatment period was used as it is felt that maximum improvement in airway obstruction, treated with steroids is achieved in approximately 8 days.1 is increased in patients with obstructive lung disease. Since this study is the first one to use repeated measurements of FEV1 in COPD patients, one of the objectives was to determine the spirometric variability in these participants while they have a stable disease. In our study participants, the day-to-day and daily variability of FEV1 was much lower than the values mentioned in other studies conducted on COPD patients.It was known from other studies that the variability in the FEV1 represents a lower value than the clinically significant, since the difference found was less than 200 mL. This could be explained by the differences in the variability of FEV1 calculated using laboratory or home (Diary Card) measurements. To detect a smaller change in the FEV1 than planned, a bigger sample size would be needed given the assumed variance in the sample size formula. It is probable, however, that the real variance is smaller than the one chosen for the sample size. If the variance used for the sample size calculation was the same as the one found in our data, there would be no statistically significant difference. However, one of the objectives was to prove that FEV1 would be less variable using the Diary Card measurements. Higher variability of FEV1 measurements in the laboratory could appear to have a statistically significant difference after the steroid treatment. If the Diary Card measurements were more consistent, there would be less variation of FEV1, and so less frequent false-positive responses to steroids could be detected.We showed that laboratory measurements have a statistically significant response to steroids, whereas the daily home-based measurements did not. However, the size of improvement in the FEV1 is better than the conventional clinic-based measurement of FEV1 to distinguish steroid responsive from steroid nonresponsive COPD patients was validated only from the statistical point of view (P < 0.005). The differences in the FEV1 were not clinically significant as the absolute difference was less than what was considered a priori a significant threshold for an acceptable steroid responsiveness, that is, 200 mL.The hypothesis that twice-daily home measurements of FEV1 home measurements according to the aspect of the graphic record criteria. This proves that the Diary Card machine could be used for monitoring PFTs for COPD patients as well. A much smaller number of corrected recordings according to both criteria  could be explained by the absence of the technicians and their coaching ability during the home spirometry performance. With more training and may be frequent check-up of the quality of the recordings, the percentage of correct recordings could be improved. This might be achieved by increasing the frequency of clinic visits to once a week for each treatment period instead of only at the beginning and the end of treatment.Eighty-three percent of the enrolled participants performed more than 91% of the required 112 home spirometry measurements. This means that they were adherent to the study intervention. Previous data concludeWe recognize some limitations in our study. The study has a “fixed” crossover design and therefore was not randomized. This may limit the internal validity of the trial. However, we attempted to improve this limitation by blinding both participants and outcome assessors. Another limitation, also related to the study design, is the small sample size. In order to improve inferences and the external validity of the study to be more generalizable, a larger sample size might be required.1 using Diary Card at home could be used in a future study. This could be done to assess the response to inhaled steroids.Many existing COPD guidelines13 still 1 above baseline as a criterion for steroid response instead of 20% used in our study, a bigger sample size would be needed for a future study. The results of our study allow calculating an estimate of the variance of the variability of single measurements on these participants. This estimate may be used to determine the required sample size in the subsequent study.Since the GOLD guidelines use 15% increase in the FEVOur method to assess steroid responsiveness that may lead to a reduction in prescribing excessive steroids in COPD could have significant impact on provincial and national health care costs. This also needs to be explored in further research.Finally, since inhaled steroids have fewer contraindications than oral steroids, the inclusion criteria for a following study should not be strict as the ones used in this study. This means that more eligible participants for the study could be allocated in a limited timeframe. In this situation, a crossover design with a randomization of 2 treatments  could be more feasible than in the present study.1 are better than the conventional clinic-based measurement of FEV1 to distinguish steroid responsive from steroid nonresponsive COPD patients. However, this finding was statistically but not clinically significant. Therefore, we would not recommend this approach to identify COPD patients with steroid responsiveness. Further research is needed in this area to explore further aspects of this interesting clinical problem.This is the first study demonstrating that twice-daily home measurements of FEV


Standard methods of mutation detection are time consuming in Hemophilia A (HA) rendering their application unavailable in some analysis such as prenatal diagnosis.To evaluate the feasibility of combinatorial sequencing-by-hybridization (cSBH) as an alternative and reliable tool for mutation detection in FVIII gene.We have applied a new method of cSBH that uses two different colors for detection of multiple point mutations in the FVIII gene. The 26 exons encompassing the HA gene were analyzed in 7 newly diagnosed Italian patients and in 19 previously characterized individuals with FVIII deficiency.Data show that, when solution-phase TAMRA and QUASAR labeled 5-mer oligonucleotide sets mixed with unlabeled target PCR templates are co-hybridized in the presence of DNA ligase to universal 6-mer oligonucleotide probe-based arrays, a number of mutations can be successfully detected. The technique was reliable also in identifying a mutant FVIII allele in an obligate heterozygote. A novel missense mutation (Leu1843Thr) in exon 16 and three novel neutral polymorphisms are presented with an updated protocol for 2-color cSBH.cSBH is a reliable tool for mutation detection in FVIII gene and may represent a complementary method for the genetic screening of HA patients. Hemophilia A (HA) is a common inherited recessive X-linked disorder of blood clotting caused by deficiency of factor VIII in the coagulation cascade and affects approximately 1 in 5,000 males world-wide.1 The FVII351012We increased the quality of results with a new cSBH method that use two different colors (TAMRA and QUASAR). The platform is an indirect method which uses standard chemistry of base-specific hybridization of complementary nucleic acids to indirectly assemble the order of bases in a target DNA. Short oligonucleotide probes are arrayed in the form of high-density arrays of universal sequence and hybridized to sample DNA molecules. The resulting hybridization pattern is used to generate the target sequence using computer algorithms.We report development of a strategy to implement 2-color cSBH to screen a range of mutations within the FVIII gene.http://www.aiceonline.itURL: ), an organization which was founded with the aim to organize clinical and experimental national activities in inherited bleeding disorders. Patients were considered to have HA according to the international consensus of the 2001 International Society on Thrombosis and Haemostasis (ISTH) Factor VIII and Factor IX Subcommittee. image). Robust pattern recognition statistics provide the correct location of spots in imperfect arrays made of shifted subarrays and tolerates a number of missing marker spots. Automated image analysis allows integration of chip scanning with final data analysis. The base-calling software uses an iterative approach to finding the most likely sample sequence, starting from the input reference sequence. For different analyses, it is assumed that the input reference matches the sample sequence more than 90 to 97%. In addition, other limitations  are specified. Under these conditions, the algorithm starts by determining the high confidence reference bases, and then testing for various possible mutations to the reference. The program automatically combines hybridization data from two strands or repeated experiments. The following outline lists the main steps of the base-calling process:The image analysis and base-calling programs are written in CNormalize/correct raw spot scores for local variation in array preparation and chip loading.Create table of normalized scores for all 4,000,000 11-mers by combining all 128 labelled 5-mer probes from each pool with 4,096 6-mer sequences. Groups of 128 11-mers have the same score.Calculate a base score for each reference position by applying robust statistics on 11 (22 for two strands) overlapping 11-mer intensity scores reading a given base.Use reference sequence segments that have a strong base score (very likely have no mutation) to remove all full match and mismatch signals expected to be contributed by these sequences to the normalized spot scores.P-values.Generate all single-base changes to the reference (including insertions and deletions) and calculate mutation base intensity scores (the same statistics used as in step 3) and corresponding P-values. Two sequence variants with a high P-value would indicate a heterozygote site. For each candidate mutation, modify the reference and calculate the new P-values for the region surrounding the potential mutation. Apply a confidence threshold to the new P-values and record the accepted mutation.Identify candidate mutations with significant P-value for the reference and all single-base variants, multiple mutations (currently including up to 20 base deletions and up to three base insertions), can be tested following the same general scheme (steps 5 and 6). To detect compound heterozygotes, multiple mutation variants have to be searched across the entire sequence length by a special function.For sequence sites that have low The implemented base-calling algorithm efficiently uses available data for fast determination  of most likely sequences present in the analyzed sample using common computers . The speed and accuracy is achieved by combining: 1) iterative sequence assembly starting by first finding non-mutated segments; 2) robust base-calling statistics that use carefully normalized hybridization scores of 11–22 overlapping probes; and, 3) a spot signal deconvolution function based on a database of expected hybridization/ligation efficiency factors for each full-match and mismatch 11-mer. An accurate database of probe behavior is established by statistical analysis of sequencing data obtained on 64 kb of specially prepared DNA. This recently introduced signal deconvolution function is critical for accurate analysis of complex multikilobase samples contributing several single mismatch results per each of 32,768 dot scores representing 128 11-mers each.The new 2-color cSBH is an indirect sequencing method in which overlapping probes of known sequence are hybridized to DNA target molecules. In cSBH, which is represented in a schematic overview in Background bases, which are not called by the computer software, are present at the bottom of all graphs. Use of informative pools of labelled probes allows combinatorial ligation of all labelled and bound probes on one glass slide with only eight universal replica arrays of bound probes. This is the most efficient way to obtain hybridization data for all 4,194,304 11-mers using 32,768  probe spots, an array that can fit only one half of all possible 8-mers. Data of pooled 11-mers are much more informative than data for individual 8-mers because each base is read with 11 instead of eight overlapping probes.A total of 16 clinically diagnosed Italian haemophilic A patients and one obligate carrier previously genotyped and found to have disease-causing mutations in the FVIII gene were included in this study. Patient samples included a range of DNA mutations mapping throughout the FVIII gene. cSBH results are shown in The correct genotyping of a patient (sample 95) and an obligate carrier (sample 73) with 2-bp deletions starting at codons 2275 and 49, respectively, was accomplished using a more advanced version of the software.We also analyzed samples of 7 haemophilic patients with unknown mutations, who were excluded to carry one of the common HA mutations, inversion of the intron 22 or intron 1. In this setting, we have found a previously undetected missense mutation Leu1843Thr in exon 16 in the sample P25. The mutation was then confirmed by direct gene sequencing. In sample R32 we found another polymorphic change in Ex 23 (Ser 2182 His). In addition, we found a polymorphism in the 3’UTR region ) in sample P21, Cz1,Cz13 and Cz34 and Cz36 and another polymorphism in the 3’UTR region at nucleotide 8519 A→G (samples Cz 36 and Cz 1).We demonstrated that cSBH, and the related HyChip product, is capable of identifying a range of point mutations located within the FVIII gene making this platform an attractive alternative to standard sequencing methods.We confirmed known FVIII gene mutations and found that large regions of genomic DNA can be sequenced with excellent readability using only one HyChip array. Because the maximal length of a single PCR product that could be sequenced by the chip is 1.2 kb in size, long exons such as exons 14 and 26 had to be divided in three and two fragments, respectively. The other exons are shorter and could be sequenced in entirety. cSBH sequence data showed 100% accuracy with only 0.2% of bases not called.[The reliability of this technique has been further provided by analyzing a series of HA patients in whom no gene mutation was detected by the first FVIII gene screening, using common strategies . This approach was able to identify a new point mutation (Leu1843Thr), which was subsequently confirmed by direct gene sequencing. In the remaining patients, no mutation was found also when the direct gene sequencing approach was applied. Finally, how well this technique does work was provided by the identification of the mutant allele in a heterozygous carrier with one normal factor VIII gene.Recently, a different approach using a microarray technology has been applied for the detection of FVIII gene mutations. As compacSBH is an efficient, reliable, and easy to use re-sequencing technique that offers an alternative approach for screening of large genes. cSBH allows sequencing of large segments of DNA on only one chip, thereby drastically reducing the time it takes to analyze a specific sequence. Actually the mean time to screen the coding regions of FVIII gene was three days. This period was reduced as compared to the time used in standard methods and comparable to that needed for sequencing analysis. The only equipment required, aside from basic tools , is access to a standard array scanner, which makes it a very affordable technique to implement. The required data analysis software, which runs on standard PC computers, will be provided as part of the universal HyChip system and is expected to be economically priced. cSBH is a non-radioactive method and generates sequencing results that can be obtained from genomic DNA within a day. Another major advantage of cSBH are the truly universal HyChip arrays which can be applied to screen any DNA sequence without further probe or oligonucleotide changes, which can be very costly. cSBH offers an efficient and accurate screen for most DNA sequences. The cSBH method is highly reproducible because each base is currently read with 22 overlapped probes (11 per strand). In addition, sample preparation and ligation kits, aliquoted for individual assays, are prepared with all components including enzymes to yield very reproducible biochemical reactions. Furthermore, the base-calling software includes several normalization functions to minimize the influence of experimental variations.P-values. Actually, the presence of short palindromes or repeats may contribute to the number of bases with lower “scores”, as seen in sample 2 [The combinatorial ligation of universal probe sets coupled with informative probe pools provides a large number of long , overlapping probes of predetermined behavior that interrogate each base. Robust statistics take advantage of this redundant dataset to provide accurate base calling in spite of array imperfections , some impaired probe-target hybridizations due to target–target hybridization, significant experimental noise, and the always-present statistical noise related to probe-pooling and sequence repeats. The main software improvement in the future should address target DNA behavior  to aid in a more accurate interpretation of base scores and associated sample 2 . FurtherUniversal arrays use probes shorter than gene specific arrays. As a consequence, there is a higher chance to have repeated sequences longer than the length of probes (currently 11-mers in cSBH) occurring in a DNA sample. Such sequences may lead to uncertainty in the sequence assembly. For example, a stretch of 12 As can be perceived as 11, 12, 13, or more As. Also, a mutation in the middle of a repeated 20-mer or any longer segment may not be assigned to the actual copy of the repeat. This situation can be avoided by amplicon design that separates repeats into two sequencing reactions. Short identical repeats several bases in length or longer imperfect repeats, both found in exons or immediate intronic sequences that flank each exon, usually do not confuse the advanced base-calling software.There are limitations with cSBH analysis: the current software and probe set must be improved to detect DNA alterations within simple repeat regions, which is a problem common to other sequence technologies as well.Another potential complication, which is common to other high resolution methods, including standard Sanger sequencing, is that all sequence changes, polymorphisms and missense changes that do not result in an obvious pathogenic mutation such as a stop codon and are of unknown clinical significance will be detected.In summary, mutation detection for the FVIII gene requires a multimodal approach that can detect the full range of mutations present in this gene. First-stage mutation screening requires a fast, efficient and possibly economical approach to scan the entire gene. cSBH is a technique that is ideally suited. In this study, we showed that cSBH is capable of detecting a broad range of mutations, currently has the capacity to analyze long continuous read lengths of up to 2 kb per chip, and is able to analyze pooled PCR fragments (such as exons 1–25 in this study) on a single universal chip.