Meanwhile, we can make indirect comparisons [15] using studies that compare CCR5 inhibitors and other new drugs with placebo. We performed a systematic review

and meta-analysis of RCTs that compared new antiretroviral drugs with placebo among treatment-experienced patients on optimized background therapy (OBT). We evaluated the overall virological and immunological efficacy of new antiretroviral drugs compared with placebo, as well as the factors associated with efficacy. We also performed an indirect comparison between CCR5 inhibitors and other new drugs using immunological efficacy data at week 48 (W48). We included RCTs that were published or presented at conferences between January 2003 and March 2010. Eligible studies were those that enrolled treatment-experienced HIV-infected patients with a plasma HIV-1 RNA level of at least 1000 copies/mL at the selleck chemicals screening visit on stable antiretroviral therapy. Studies compared, at W48, the immunological Fostamatinib ic50 and virological responses in patients on OBT plus new antiretroviral drugs with responses in patients on OBT plus placebo. New drugs included maraviroc and vicriviroc (CCR5 inhibitors), enfuvirtide (a fusion inhibitor), raltegravir (an integrase inhibitor), etravirine [a nonnucleoside reverse transcriptase inhibitor (NNRTI)], tipranavir and darunavir [protease inhibitors (PIs)]. When

studies evaluated multiple doses of a new drug, we only included the group assigned to the recommended dose. Although vicriviroc was not licensed at the time of data collection, it was in advanced clinical development. We included studies that administered a 30 mg/day dose, in accordance with Phase III clinical trials. Patients were defined as treatment-experienced based on their treatment histories and/or

current genotypic sensitivity score (GSS) or phenotypic sensitivity score (PSS). Although definitions differed among studies, all patients had previously taken at least one NRTI, one NNRTI, and one PI for at least 3–6 months or they had documented genotypic or phenotypic resistance to drugs in at least two or three of these classes. We included studies in any language in which HIV-1-infected patients aged ≥16 years were enrolled and that reported CD4 cell counts and HIV RNA levels at W48. Orotidine 5′-phosphate decarboxylase In accordance with the Cochrane collaboration guidelines [16], we conducted our search in the Medline database, the Cochrane controlled clinical register, clinicaltrials.gov, and the websites of major international conferences. We used the following keywords: HIV, adult, treatment-experienced, maraviroc, vicriviroc, enfuvirtide, raltegravir, etravirine, tipranavir, and darunavir. Two reviewers (M.P. and L.C.) independently screened titles and abstracts and obtained the full text of potentially eligible articles. Two reviewers (M.P. and L.C.) used a structured questionnaire, in accordance with the PRISMA method [17], to independently extract data. A third reviewer (Y.Y.) resolved any discrepancies.

LDH was lower in HIV-positive patients, but the other laboratory parameters, namely CPK, creatinine, AST and Quick prothrombin time, did not differ significantly between the groups. Roughly similar proportions of HIV-positive (7%) and HIV-negative (8%) patients had bacteria detected in valid respiratory samples and/or blood cultures and/or urine antigens at admission (Table 2b). Streptococcus pneumoniae was the most common bacterium, accounting

for 12 (71%) of the 17 bacteria detected. As expected, a substantial proportion of HIV-infected patients (95%; n=53) were treated with oseltamivir. This proportion was higher than that in HIV-negative patients (71%; n=119) (P=0.0003) (Table 3). However, roughly similar proportions of HIV-positive (52%; n=20) and HIV-negative (49%; n=82) patients received antibacterial therapy (P=0.6997). There was a trend towards LY294002 a Buparlisib in vitro shorter duration of hospital stay (mean±standard deviation) in HIV-positive patients (1.1±2.3) than in HIV-negative patients (2.0±3.4) (P=0.0812), and fewer HIV-positive patients (n=15; 27%) were admitted for 1 day or longer compared with HIV-negative patients (n=70; 42%) (P=0.0564). Concordantly, a higher proportion of HIV-positive patients (77%; n=43) than HIV-negative patients (56%; n=94) showed clinical recovery in less than 1 week (P=0.0068). None (0%) of the HIV-positive patients died, but three (2%) of the HIV-negative

patients died. Causes of death in each patient were multifactorial. Table 3 shows a list of specific complications in HIV-positive and HIV-negative patients identified after admission. Similar proportions of HIV-positive (13%; n=7) and HIV-negative (11%; n=18) patients developed intrahospital complications (P=0.8066). Interestingly, there were three patients (two HIV-positive and one HIV-negative) who developed myocarditis and/or ischaemic cardiovascular episodes, one of whom had no previous history of cardiovascular disease. There were also three patients with acute hepatitis (one HIV-positive

and two HIV-negative); in two of these patients this was attributed to oseltamivir. There were more HIV-positive (48 of 56; 86%) than HIV-negative Tolmetin (65 of 168; 39%) patients without comorbidities. When the two groups were compared, therapy with oseltamivir was found to be significantly more common, and there was a trend towards more frequent antibacterial therapy, in HIV-positive patients than in HIV-negative patients (Table 4). There were no significant differences between the groups in the proportion of patients with a delayed influenza A H1N1 diagnosis, pneumonia or respiratory failure. There were no differences either in the duration of hospital stay, clinical recovery, intrahospital complications and evolution to death. Nevertheless, all three patients who died belonged to the HIV-negative group without comorbidities.

As all groups comprise neurotypical adults, we hypothesized equal variance between populations in order to control for differences in group size (Penny & Holmes, 2003). Common brain response irrespective of expertise was investigated using a minimum statistic conjunction (Nichols et al., 2005) between the three groups. Brain response specific of each group was assessed by masking exclusively the effect of this group by a global null conjunction (P < 0.05 uncorrected) of the other two groups; for instance, the contrast between Acheulean and Oldowan in Naïve is exclusively masked by a conjunction of the same contrast in Trained and Expert subjects. Our procedure used exclusive masking instead of interactions, which were

not significant at the threshold used, to favour the effects within the group of interest over GSI-IX solubility dmso the reversed effects in the Metformin other groups, which are included in the statistics of interactions (Culham, 2006). All contrasts were thresholded at P < 0.05 FDR-corrected with an extent threshold of 20 voxels. Anatomical localization was performed using a brain atlas (Duvernoy, 1999) and, in particular for inferior frontal and parietal clusters, functional localization made use of distribution analysis of the activated voxels on the basis of probabilistic

cytoarchitectonic maps (Eickhoff et al., 2007) implemented in SPM (Eickhoff et al., 2005). For the sake of consistency, only anatomical labels are used in the tables. Thus, clusters attributed to Brodmann area (BA) 44 were labelled ‘pars opercularis’ (Amunts et al., 1999), those attributed to BA45 were labelled ‘pars triangularis’ (Amunts et al., 1999), and those attributed to BA6 were labelled ‘precentral gyrus’ (Geyer, 2003). In the parietal cortex, clusters attributed to areas PF and PG (Caspers et al., 2006) were labelled ‘inferior parietal lobule’, and those attributed to hIP1 and hIP2 (Choi et al., 2006) were labelled ‘anterior intraparietal sulcus’. While recognizing that functional localization and anatomical landmarks may not strictly overlap in individuals, these conventions were adopted in the interest of coherence in the presentation

of results. Statistical maps were rendered Immune system on FreeSurfer’s fsaverage pial surface with 50 inflation steps (http://surfer.nmr.mgh.harvard.edu). In order to assess the effect of Group, local activity in clusters of interest was further characterized using the SPM extension toolbox MarsBar (http://marsbar.sourceforge.net/) to extract percentage signal change in 5-mm radius volumes centred on the maximum of each cluster, then analysed with spss. Across all subject groups, the contrast of Toolmaking conditions with Control yielded activations is a series of cortical regions, including a large cluster extending from the primary visual and lateral occipital cortices to the inferior temporal cortices, intraparietal sulci, inferior parietal cortices and postcentral gyrii bilaterally.

In fact, the thermocycler model affected the fingerprints due to the difference in the thermal ramp. DNA preparation of each strain

and enzyme lots affected the fingerprints considerably. Especially, amplification of the 2.8-kb band appeared in strains of L. paraplantarum Selleckchem Small molecule library depended on the activity of the polymerase. Therefore, we used a single thermocycler model with a single program and the bands that appeared at least three or more times among the five experiments were considered. Cluster analysis of the band profiles divided the strains into three clusters: the main cluster, AE, consisting exclusively of the L. paraplantarum strains; cluster BE, consisting of Lactobacillus curvatus and Lactobacillus sakei; and cluster CE, consisting of phenotypically hard-to-distinguish Lactobacillus pentosus and L. plantarum (Fig. 1b). The phylogenetic tree showed a similarity

coefficient of 57.0% among the L. paraplantarum strains (Fig. 1b, cluster AE), but only 8.1% between these and BE. In order to confirm the discriminatory effectiveness of the ERIC-PCR-based techniques, we performed ERIC analysis of 141 strains of LAB including 74 identified and 67 unidentified strains in our collection. The phylogenetic tree Selleckchem Pirfenidone based on ERIC-PCR showed a cluster consisting of L. paraplantarum strains, in which five unidentified strains were included. After sequencing analysis and multiplex PCR (Torriani et al., 2001a, b), these strains were identified to the species L. paraplantarum (Table 1). This result showed that ERIC analysis is useful for the preliminary discrimination of L. paraplantarum from other Lactobacillus species. Together with nine additional strains of L. paraplantarum, we performed ERIC analysis of 43 strains of Lactobacillus (Supporting Information, Fig. S1). The phylogenetic tree based on ERIC-PCR showed three clusters: a cluster Niclosamide consisting of L. paraplantarum strains, a cluster consisting of L. plantarum strains, and a cluster consisting of strains of L. pentosus, L. curvatus, and L. sakei. In the third cluster, a subcluster consisting strains of L. pentosus was distinguished from others consisting of strains

of L. curvatus and L. sakei. Although L. paraplantarum, L. plantarum, and L. pentosus are considered to be phenotypically close (Curk et al., 1996), ERIC-PCR produced considerable DNA polymorphisms among these species; five bands of 3, 1.25, 1.05, 0.82, and 0.35 kb were typically observed in strains of the species L. plantarum, whereas the band of 0.82 kb was common to strains of the species L. pentosus. Further, three intensive bands of 1.15, 0.95, and 0.45 kb were common to most strains of the species L. curvatus. These data suggest that the ERIC-1R and ERIC-2 primers are useful for generating discriminatory polymorphisms from different species of Lactobacillus. In RAPD-PCR, none of the four primers yielded a band that was specific to L.

We recommend patients who have repeated high-risk exposures but persistently normal transaminases are screened with anti-HCV and HCV-PCR, or HCV-PCR alone if previously successfully treated for or spontaneously have cleared infection and are HCV antibody positive, at 3–6-monthly intervals. Proportion of patients with acute HCV who had an HCV-PCR assay as the screening test Proportion of patients with repeated high-risk exposure who had HCV tests (antibody and PCR) at least twice a year

Proportion of all adults with HIV infection who had an HCV test within 3 months of HIV diagnosis Studies have shown that in HCV/HIV the first test to become positive is the HCV-PCR, often within 1 month [30–31]. It is difficult to be precise about time of exposure to infection but the HCV-PCR learn more is positive a median of 3 months (range 1–9 months) after the last negative PCR test. Transaminases are abnormal in 78% of patients at the time of first positive PCR, rising to 88% 3 months selleck chemical later. The combined HCV antigen/antibody test is more sensitive than the antibody

test alone in detecting acute infection and is being used in many centres for screening patients with risk factors for infection. It is not as sensitive as the PCR assay and is positive in 52% of patients at the time of the first PCR being positive [31]. HCV antibody tests

are the least sensitive for acute infection, being positive in 20–25% at the time of the first PCR positive test. On average, HCV Ab becomes positive 3–7 months after the first positive PCR test but at 9 months 10% of patients remain HCV Ab negative which reduces to 5% at 1 year. Individuals with HCV infection may thus have a negative antibody test. Individuals with unexplained abnormal transaminases, especially if they are in a risk group for HCV exposure, should have an HCV-PCR assay in order to exclude acute HCV infection. In MSM and IDUs who have cleared HCV infection either spontaneously or through treatment, the rate of HCV reinfection is up to 10-times higher than in previously uninfected patients [32–36]. In the EuroSIDA study of HIV-infected patients, 20% of ZD1839 MSM and IDUs who are cured of HCV will be re-infected subsequently [37–38]. Therefore it is important to monitor previously infected individuals frequently, with HCV-PCR being the only reliable assay [35–38]. In HIV-infected men who have sex with men, there is an appreciable rate of HCV infection (6/1000 patient-years in one study [8]), and given the benefits of HCV being diagnosed early, all HIV-infected patients should be tested annually and more frequently if transaminases are raised without obvious cause [30–31,34].

These four trials were all expected with cue and target appearing at the same location, two to the left and two to the right. Disregarding filler and catch trials, the weighting between

expected and unexpected trials was 80 vs. 20%. In the endogenous counter-predictive task there were the same number and ratio of trials as the endogenous predictive task. However, in this task the cue predicted the target to appear at the opposite hand to the cue in 80% of the trials, and in 20% of the trials cue and target appeared at the same hand. In the exogenous task there were the same number of trials as the endogenous tasks (112), Atezolizumab in vivo although in this task cued (cue and target appeared at the same location) and uncued trials (cue and target appeared at opposite location) were equally weighted, 50 cued and 50 uncued trials in each block. As

in the other two tasks there were eight catch trials and four ‘fast filler trials’ (Table 1). The stimuli presentation procedure for each trial was the same for all three tasks (Fig. 1). Each trial started with a 50-ms cue. This was followed by a 750-ms inter-stimulus interval before a 50-ms target. The participant was instructed to respond as quickly as possible by saying ‘pa’ into a microphone as soon as the target appeared. Following their response there was a random inter-trial-interval (ITI) of 1000–2000 ms. If no response was selleckchem made within 1500 ms, the trial ID-8 terminated and the next trial began after the ITI. In the endogenous tasks the participant was instructed about the probabilities of the target appearing at expected compared with unexpected locations, and to use this information to speed up RTs. In the exogenous task the participant was informed that the cue would not predict the target location and therefore to ignore the cue completely.

Behavioural data (mean RTs) were submitted to a 2 × 3 repeated-measures anova with the factors Task (endogenous predictive, exogenous, endogenous counter-predictive) and Cue (cued, uncued). A Task × Cue interaction was followed up by separate analysis for each task. To detangle facilitation and inhibition on a behavioural level in the different tasks, the three conditions expected to be fastest were subjected to an anova with factor Cue [endogenous predictive cued (expected), exogenous uncued, endogenous counter-predictive uncued (expected); Table 1]. Similarly, the predicted three slowest conditions were subjected to a repeated-measures anova with factor Cue [endogenous predictive uncued (unexpected), exogenous cued, endogenous counter-predictive cued (unexpected)]. These predictions of fastest and slowest conditions were based upon well-established behavioural research showing facilitation for endogenously attended over unattended targets and IOR in an exogenous task (Lloyd et al., 1999). Wherever the anova assumption of sphericity was violated, Greenhouse–Geisser-adjusted probability levels were reported.

In the context of several education seminars for travel medicine, we asked the physicians in the audience whether they are interested in taking part in a questionnaire study about TT. These colleagues were listed and contacted within a few weeks after the particular education seminar. All participating physicians received a description of the study, three standardized questionnaires

(Q1–3), and the classification of travelers’ TR according to the Vienna consensus meeting in 2001 (Table 1).24 The three questionnaires are available from the corresponding author Lenvatinib concentration on request. Randomly incoming adult travelers seeking medical travel medicine advice prior to

a LHT were asked to participate in the study. If written informed consent was given, Q1 and Q3 were handed out to them together with an envelope for free return consignment for Q3. Q1 asked for age, gender, travel habits, and their individual assessment of the association between travel and TR. These questions had to be answered during the current consultation. Q3 focused on the actually performed TP measures during the particular prophylaxis, experienced side effects or symptoms suspicious for VTE, the means of transport used predominantly during travel, and the period of time seated during the journey. Q3 had to be answered within 4 weeks after the return from the particular journey for which the traveler sought medical advice. The consulted physician had

to answer Q2 asking for assessment of the TR of the traveler, this website the predominantly used means of transport during the planned journey, the duration of planned LHT, and the kind of recommendation given to the individual traveler for the particular journey to prevent TT. The study was approved by the Institutional Ethics Committee of the University Erlangen-Nuremberg and supported by the “runners-up award” of the International Society of Travel Medicine (5,000 USD). The participating travelers and physicians received an allowance for a completely answered questionnaire Q1 to Q3 of 5, 10, and 10 Euros, respectively. All questionnaires had to be sent to the study center at the university hospital of from Erlangen, Germany. Data were analyzed with statistical software SPSS for Windows, release 15 (SPSS Inc., Chicago, IL, USA), and the statistical software package SAS (version 9.2, SAS Institute, Cary, NC, USA). A descriptive analysis of the important variables was carried out. Associations between the demographic variables age or gender with answers given by the travelers in Q1 were shown in contingency tables and analyzed for significant differences by using the χ2-test or Fisher’s exact test, depending on the cell frequencies.

1). Of the most abundant mRNA species, the P. putida cell reduced its pool for transcripts that are translated into chaperonins, elongation factors EF-Tu

and EF-Ts, ATP synthase and enzymes of the core metabolism. The cells shut down the transcription of operons that encode the biosynthesis of purines, pyrimidines, coenzymes and branched amino acids and those that encode transporters for amino acids, siderophores, polyamines and sulfur compounds. The most strongly downregulated genes encode heat shock proteins and enzymes of the citric acid cycle and of the pathway for the synthesis of valine and leucine. In summary, the cells constrained its mRNA repertoire for biosynthesis, nutrient uptake, core and energy metabolism. Of the top 100 downregulated genes, the encoded function has been experimentally demonstrated for 83 genes in P. putida or in another FK866 purchase Dabrafenib cell line proteobacterium (Hoshino & Kose, 1990a, b; Auerbach et al., 1993; Best & Knauf, 1993; Holtmann et al., 2004; Carruthers & Minion, 2009; Kazakov et al., 2009; Molina-Henares

et al., 2010). In contrast, 67 of the >10-fold upregulated 169 genes at 10 °C were found to be conserved hypotheticals. Other over-represented categories were genes encoding transporters (20), transcriptional regulators (15) or phage proteins, integrases and transposons (11). During cold adaptation, P. putida activated a transcriptional program whose most key players have not been characterized so far in any organism. The most striking upregulation was seen for the two hypotheticals PP1690 and PP1691 that were expressed at a low level at 30 °C, but belonged to the 10 most abundant transcripts at 10 °C. Among the strongly upregulated genes of known encoded function, the majority of genes are orthologs of the alginate biosynthesis regulon in Pseudomonas aeruginosa and the affiliated catabolism of glycerol and glucose through the Entner–Douderoff pathway. Furthermore, the PhoPQ two-component system TCL and the multienzyme complex for the degradation of valine, leucine and isoleucine

were activated. Another interpretable key feature of the cold adaptation was the strong upregulation of the rbfA–nusA–infB operon. The orthologs in E. coli coordinate transcription and translation during cold stress (Bae et al., 2000; Bylund et al., 2001): the cold shock protein RbfA converts nonadapted translationally inactive into cold-adapted translationally competent ribosomes. InfB is necessary for efficient and accurate de novo initiation and re-initiation of translation. NusA is an essential, multidomain protein that functions in both termination and antitermination of transcription. The rbfA–infB–nusA operon is highly conserved, and hence, we assume that its upregulation fulfills similar roles during cold stress for E. coli and P. putida cells.

In contrast, little is known about C-terminal processing of proteins in prokaryotes (Menon et al., 1993; Rossmann et al., 1994; Aceto et al., 1999; Hatchikian et al., 1999; Keiler & Sauer, 2004). The CTP are classified in the MEROPS peptidase database as family S41 (http://merops.sanger.ac.uk) (Rawlings et al., 2008). CTPs can be found in a broad range of different organisms, for example in prokaryotes such as Eubacteria and Archaea, as well as eukaryotes, for example algae, plants and animals (Inagaki & Satoh, 2004; Keiler & Sauer, 2004; Tamura & Baumeister, 2004). In plants, algae and cyanobacteria CTPs have a very specific function in activating the pre-D1

protein by cleaving a small C-terminal peptide (Trost et al., 1997; Fabbri et al., 2005). The mature D1 protein is an important constituent of the photosystem II reaction centre and its processing is essential for photosynthesis and thus for the viability

find more of these organisms under phototrophic conditions (Satoh & Yamamoto, 2007). Compared with this, the knowledge on bacterial CTPs is extremely limited. The first bacterial CTP that was characterized was the ‘Tail-specific protease’ (Tsp), which was purified from Escherichia coli and showed activity in degrading protein variants with nonpolar C-termini of the λ repressor (Silber et al., 1992). Tsp, more commonly referred to as Prc – is also involved in processing of penicillin-binding protein-3 (PBP-3), by cleaving 11 C-terminal amino acids (Hara et al., 1991) and interacting with lipoprotein NlpI (Tadokoro et al., 2004). Besides that, Prc has been suggested to be part of the SsrA RNA small molecule library screening protein-tagging system for the degradation of incorrectly synthesized proteins. In this system, an SsrA RNA tag is added to mRNAs when ribosomes are stalled due to a lack of termination codons. The resulting C-terminal SsrA peptide tagged periplasmic protein is then recognized by Prc and subsequently degraded (Keiler et al., 1996). CTP-inactivated bacterial mutants show different phenotypes. In E. coli,

inactivation of the prc gene results in leakage of periplasmic proteins, temperature-sensitive very growth under osmotic stress, reduced heat-shock response and increased antibiotic susceptibility (Hara et al., 1991; Seoane et al., 1992). Inactivation of ctpA in Rhizobium leguminosarum led to a decreased desiccation tolerance (Gilbert et al., 2007). Recently, inactivation of CTP was shown to influence the pathogenesis of several Gram-negative bacteria, Brucella suis, Bartonella bacilliformis, Chlamydia trachomatis and Burkholderia mallei (Mitchell & Minnick, 1997; Bandara et al., 2005, 2008; Lad et al., 2007). CTPs seem to influence multiple basal physiological functions in bacteria. The knowledge of their subcellular localization would enable a much better understanding in how these proteases interact and influence other cellular systems.

IL-13 inhibits Th17 cell development in dendritic cells via down-regulation of Th17 stimulatory cytokines (IL-1, IL-6 and IL-23).[46] Despite the inhibitory effect of GATA-3 on Th17 development, it seems that GATA3 probably promotes Th17 development through inhibition of IL-2, STAT1 and suppressors of cytokine signaling 3 (SOCS3).[47] IL-2 is a T cell growth factor that is critical for Treg development. It effectively inhibits Th17 cell development. Two pivotal transcription factors that RG7422 in vivo mediate IL-2 signaling are STAT5a/b. Therefore IL-2 or STAT5 deficiency is associated with

inhibitory effects of Tregs and expansion of Th17 cells.[48-51] The transcription factor Ets-1, which is a positive regulator of Th1 development, is another negative regulator for Th17 development. Ets-1 deficiency leads to increased Th17 differentiation and promotion of IL-22 and IL-23R messenger RNA (mRNA) levels in response to IL-6 and TGF-β1. It seems that the inhibitory effect of Ets-1 on Th17 cells is through enhancing IL-2 production.[52] In a recent report,

it has been shown that microRNA mir-326 can bind to and prevent translocation of Ets-1 mRNA. Thus, microRNAs can promote Th17 development through inhibition of the Th17 inhibitor, Ets-1.[11-58] It should be noted that the transcriptional repressor protein BCL-6 regulates T cell differentiation Enzalutamide mouse by repressing Th2 cells and enhancing follicular Th cells. It is proposed that BCL-6 enhances Th17 differentiation through suppression of Th2 differentiation.[54] Th17 cells are the dominant

pathogenic cellular component in autoimmune inflammatory diseases, including RA.[55] Although the importance of Th17 cells in animal models of arthritis is unquestionable, there are only limited data on the role of Th17 cells and related cytokines in human arthritic diseases. In addition, the characteristics of human Th17 cells have not been fully defined, and there seems to be substantial differences between human and mouse Th17 cells.[56] Functionally, Th17 cells contribute to host defense by having a role in protection against extracellular bacteria. However, their activities are also pivotal in the development of autoimmune diseases under pathologic conditions.[57] The identification of Th17 and IL-17 as a powerful pro-inflammatory cytokine, have Lck focused attention on the role of Th17 cells in RA and other immune-mediated diseases, such as psoriasis, Crohn’s disease and multiple sclerosis.[5, 58] The hyperfunction of Th17 cells is associated with autoimmune diseases, due to the hypersecretion of the pro-inflammatory cytokine IL-17.[59] Studies in rodents, mammalian cell culture systems, as well as clinical settings, support a specific role for IL-17 in promoting RA.[60] Additional supporting evidence came from IL-17 knock-out animals that failed to develop collagen-induced arthritis (CIA).