Domácnosti v ČR: příjmy, spotřeba, úspory a dluhy v letech 1993 až 2012

Household final consumption expenditure and changes of the dynamics of individual components. Savings and debt. International comparison. Unconsumer attributes of the life

Baseline characteristics of the patients with UA, categorized according to the development of RA after a median follow-up of 9 months (range – months).

<p>Baseline characteristics of the patients with UA, categorized according to the development of RA after a median follow-up of 9 months (range – months).</p

Comparison of gene expression differences between samples from patients with OA, RA and SA, using high-density arrays versus qPCR.

<p>Independent sets of samples were hybridized on high-density (HGU133 Plus 2.0 GeneChip) and analyzed by qPCR (Taqman low density array). (A) Differences in mean (log2-transformed) gene expression values between OA and (RA+SA) samples are displayed for the samples analyzed using high-density arrays (x axis) versus qPCR (y axis). (B) The same data from OA and (RA+SA) samples are displayed after normalization of each mean (log2-transformed) gene expression value by its standard deviation.</p

Baseline characteristics of the patients with a known diagnosis at the time of the needle-arthroscopy procedure.

<p>Baseline characteristics of the patients with a known diagnosis at the time of the needle-arthroscopy procedure.</p

Effect of disease activity on gene expression in RA samples.

<p>Mean centered log2-transformed expression levels of selected T cell activation-associated transcripts were extracted from HGU133 Plus2.0 GeneChip array data sets of 32 patients with RA. DAS28-CRP scores were retrieved from the medical files of the patients, and the samples are sorted by ascending DAS28-CRP. Correlation coefficients (Pearson <i>r</i>) between gene expression and DAS28-CRP are displayed for each transcript.</p

Impact of qPCR data on the determination of the nearest neighbors in UA samples.

<p>Both matrices show the nearest neighbors of each biopsy sample from the cohort of patients with UA, for whom qPCR data are available (n = 31). Each sample is represented by a column, and its nearest neighbors are greyed out in that column. The cell on the diagonal is red if the sample is misclassified and black otherwise. Samples from patients with the same diagnosis are surrounded by a dashed square. (A) Nearest neighbors are determined using only clinical data (<i>ρ</i> = 1). More than 5 nearest neighbors are displayed for each sample due to the presence of ties. (B) Nearest neighbors are determined using a combination of clinical and qPCR data (<i>ρ</i> = 0.2), demonstrating the tie-breaking effect of the qPCR data.</p

Balanced classification rate (BCR) of a nearest neighbor classifier as a function of the signature size (number of genes) used for prediction.

<p>Lists of genes of progressively decreasing sizes were determined based on high-density transcriptomic data, and used in order to predict diagnosis in 25 patients with RA, SLE, OA, SA and MIC. BCR is plotted in function of the signature size. Lists of genes containing between 20 and 100 probe sets provide performances that range between 83% and 85%.</p

Comparison of gene expression differences between samples from patients with OA, RA and SA, using high-density versus low-density microarrays.

<p>Independent sets of samples were hybridized on high-density (HGU133 Plus 2.0 GeneChip) and low-density (DualChip) microarrays. (A) Differences in mean (log2-transformed) gene expression values between OA and (RA+SA) samples are displayed for the samples hybridized on high-density (x axis) versus low-density (y axis) arrays. (B) The same data from OA and (RA+SA) samples are displayed after normalization of each mean (log2-transformed) gene expression value by its standard deviation. (C) Normalized TCR gamma alternate reading frame protein (TARP), lymphocyte-specific protein tyrosine kinase (LCK) and Interleukin-7 Receptor (IL7R) gene expression data in OA versus RA and SA samples observed using low-density arrays. (D) Normalized Placental Growth Factor (PGF) gene expression data in OA versus RA and SA samples observed using low-density arrays. Mean values are represented by a horizontal bar. <i>p</i> values are calculated using Student’s t tests.</p

High-density gene expression data used for the design of the low-density platform.

<p>Analyses performed on high density transcriptomic data resulted in the selection of 100 probe sets differentiating patients with RA, SLE, OA, MIC and SA. The probes and gene symbols are also listed in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0122104#pone.0122104.s004" target="_blank">S3 Table</a>. (A) Hierarchical clustering algorithms using the high density gene expression values of these genes (and based on the Pearson correlation distance) distribute the samples into “inflammatory” (RA and SLE) and “high extra-cellular matrix turn-over” (OA, SA and MIC) clusters. They also identify diagnostic subdivisions. (B) The high density gene expression values of these 100 genes are displayed according to the clinical diagnosis of the samples.</p