Following stimulation and processing, 5 μl of appropriately Cell Cycle inhibitor diluted IFN-γ Alexa488 (BD), CD3 PerCP·Cy5.5 (BD), CD28 PE-Cy7 (BD), TNF-α V450 (BD), IL-2 Alexa488 (BD), CD45 V500 (BD) and PE-conjugated monoclonal antibodies to CD40L, CD152,

CD137, CD134 or isotype control were added for 15 min in the dark at room temperature. Cells were washed and events acquired and analysed as described above. Aliquots of whole blood were incubated with 10−6 M methylprednisolone for 18 h then stimulated for cytokine production and analysed as reported previously [8]. Statistical analysis was performed using the Kruskal–Wallis test and post-hoc analysis using Mann–Whitney and Spearman’s rho correlation tests using spss software and differences between groups of P < 0·05 were considered significant. Corrections for multiple comparisons were not performed. There was no significant difference

in the absolute lymphocyte counts for controls and transplant patients [1·5 (1·4–1·9), 1·6 (1·3–2·1), 1·6 (1·3–2·2) × 109/l, check details median and range for controls, stable patients and patients with BOS, respectively, P > 0·05]. There was no change in the percentage of CD4 or CD8 T cells between controls or transplant groups (61 ± 11·7, 62 ± 12·8, 60 ± 11·9 CD4 and 39 ± 6·7, 38 ± 6·8, 39 ± 8·1 CD8 T cells for controls, stable transplant and BOS patients, respectively). The percentage of CD28null/CD4+ T cells in stable transplant patients was decreased significantly compared to control subjects (Fig. 1). In BOS, there were significant increases in the percentage GBA3 of both CD28null/CD4+

and CD28null/CD8+ T cells compared with both controls and stable transplant patients (Fig. 1). CD28null/CD8+ T cells were increased significantly when compared to CD28null/CD4+ in patients with BOS (Fig. 1). There was a significant increase in the percentage of both CD28null/CD4+ and CD28null/CD8+ T cells expressing perforin in stable transplant patients and in patients with BOS compared with controls (Fig. 2a). A similar increase was noted in the CD28+ subgroup (0·2%, 1·0% and 1·1%; and 0·3%, 2·3% and 2·5% CD28+/perforin+/CD4+ and CD28+/perforin+/CD8+ for controls, stable patients and patients with BOS, respectively) (all P < 0·05). There was an increase in the percentage of both CD28null/CD4+ and CD28null/CD8+ T cells expressing granzyme B (GB) in patients with BOS compared with controls (Fig. 2b). For CD4+ T cells expressing GB, the increase was significantly greater in BOS patients compared with stable transplant patients and controls, and in stable transplant patients compared with controls (Fig. 2b). The percentage of CD28null/GB+/CD8+ T cells was higher in all groups compared to the CD4+ subset (Fig. 2b).

The study was conducted in the Parasitology–Mycology laboratory of Farhat Hached hospital (Sousse, Tunisia). The investigated patients were addressed to the laboratory selleck chemicals llc by the service of Dermatology of the same hospital and to a lesser extent by private practitioners. Two sample collections were investigated in this study: Two hundred and eighty-one nail specimens were

investigated. They include the following: (i) 201 samples collected from patients with suspected onychomycosis and (ii) 80 nail specimens obtained from healthy individuals with no clinical nor mycological evidence of onychomycosis and considered as negative controls. All collected samples were divided into three portions. The first portion was examined microscopically in 30% KOH for the presence Pritelivir cell line of fungal elements. The second was cultured on Sabouraud dextrose agar supplemented with chloramphenicol and/or cyclohexemide at 27 °C for up to 4 weeks. The third portion of the nail specimen was used for PCR analysis. Clinical isolates were identified at the species level on the basis of macroscopic and microscopic characteristics of the colonies. To optimise the PCR protocol and the specificity of the primers, 70 strains mycologically identified as dermatophytes including 23 T. rubrum (TR), 35 T. mentagrophytes (TM) and five other species obtained from nail scrapings, skin and hair fragments from patients with dermatophytosis were

included. In (-)-p-Bromotetramisole Oxalate addition, six reference strains, 30 non-dermatophyte fungal strains (moulds and yeast) and 2 human DNA specimens were tested (Table 1). Reference strains were purchased from the Central Bureau voor Schimmelcultures (CBS, Utrecht, the Netherlands). DNA was extracted from nail material by using the QiaAmp DNA extraction Kit (Qiagen, Venlo (Pays Bas), Germany). Prior to the extraction, whole nails or relatively large nail fragments weighing between 3 and 5 mg, were cut into small pieces with a surgical blade. Subsequently, nucleic acid extraction was performed according to the manufacturer’s instructions. At the end of the procedure, the DNA pellet was dissolved in 50–70 μl

hydration solution, depending on the amount of the nail material used at the beginning. A quantity of 2 µl of DNA was added to the PCR mixture. To extract DNA from fungal cultures, we used the rapid mini preparation method described by Liu et al. [24] In brief, a small lump of mycelia (1 cm2 of diameter) was added to 500 μl of lysis buffer (Tris–HCl, EDTA, NaCl, SDS) and 150 μl of potassium acetate. The tube was vortexed and an equal volume of isopropyl alcohol was added to the supernatant. Then, the mixture was centrifuged and the resultant DNA pellet was washed in 70% ethanol and dissolved in 50 μl Tris–EDTA. Extracted DNA was kept at −20 °C until use. A quantity of 2 µl of DNA was added in PCR mixture. The nucleotide sequences of the different dermatophytes were selected from the NCBI nucleotide database.

The average waiting time for a transplant is about 4 years, but waits of up to 7 years are not uncommon. On average one Australian dies each week while waiting for a transplant.[10] There are also paradoxical factors impacting on the outcome of dialysis patients such as that of high body mass index being PI3K Inhibitor Library associated with improved survival.[11] A similar reverse epidemiology of obesity has been described in geriatric populations.[12] The ‘reverse epidemiology’ of obesity or dialysis-risk-paradoxes’ need to be considered in the decision-making equation. Efforts

to obtain a better understanding of the existence, aetiology and components of the reverse epidemiology and their role in maintenance dialysis patients remain of paramount importance for future study. Newly

emerging predictors of mortality in the non-dialysis population include a high comorbidity score,[4, 5, 13] functional impairment[3] and acute kidney injury secondary to a sentinel event or events on a background of chronic kidney disease (CKD). A predictive model that comprehensively incorporates variables relevant to the prognostic outcome of the non-dialysis population has yet to be developed. The evaluation of the needs in the Australian population in context to these click here scores must also be considered in the decision-making process and remains and unanswered area requiring investigation. The majority of the models below were specifically designed for the dialysis pathway population. The JAMA Kidney Failure Risk Equation (KFRE) is a predictive model, which uses demographic information and routine laboratory markers of

CKD to predict which patients selleck kinase inhibitor with CKD stages 3 to 5 will progress to the need for dialysis.[1] Risk is given as a 5-year percentage risk of progression to ESKD. Population validated for: CKD stages 3 to 5 (c-statistic, 0.917 (95% confidence interval, 0.901–0.933)) Advantages: Uses routine demographic and laboratory markers of CKD (Table 1)   The first predictive model to accurately predict CKD progression to ESKD Disadvantages: Awaiting validation in the Australian CKD population   Requires a risk calculator available as:   ● an Office Excel spreadsheet (http://jama.ama-assn.org.wwwproxy0.library.unsw.edu.au/content/305/15/1553.full.pdf+html)   ● smartphone app (http://www.qxmd.com/Kidney-Failure-Risk-Equation) The MCS[5] was adapted from the original Charlson Comorbidity Index[8] to identify the subpopulation of sicker dialysis patients with a 50% 1-year mortality rate. It is a simple scoring system that adds scores for comorbidities to scores for age (Tables 2, 3).[9] Population validated for: Dialysis patients (c-statistic = 0.

Three of the five ‘classical’ HIES patients

had known STAT3 mutations (R382W twice and V463del) [5] (Table 1). Two of the patients with ‘classical’ HIES had no STAT3 mutation. To investigate the immunological functional properties RG7204 concentration with respect to Th17 responses in HIES patients with different mutations, PBMC from healthy volunteers, ‘classical’ HIES patients and three members from a HIES family with ‘variant’ HIES were assessed for the capacity to mount IL-17 responses. We have developed a new methodology of Th17 generation using human PBMC stimulated with whole microbial stimuli relevant for HIES: S. aureus and C. albicans[18]. HIES patients had a defective response to C. albicans, although IL-17 was measurable in all patients (Fig. 2a). Interestingly, IL-17 production was completely absent in PBMC stimulated with S. aureus in all ‘classical’ HIES patients (Fig. 2b). In contrast, PBMC isolated from the variant HIES patients, bearing the STAT3 mutations in the linker domain, were able to produce IL-17 in response to S. aureus, albeit at lower concentrations when compared to healthy volunteers (Fig. 2b and c). IFN-γ production was distorted in HIES patients when compared to healthy controls, while IL-10 was found to be elevated in HIES patients when stimulated with

both S. aureus and C. albicans. The in vitro stimulations described above suggest that HIES patients have a significant defect in the generation of Th17 cells. This was selleck kinase inhibitor indeed the case for the patients with ‘classical’ HIES, either bearing STAT3 mutations or not (Fig. 3). Surprisingly, when the familial variant HIES patients were challenged with disease-related microorganisms, they showed a clear induction

of single IL-17-positive and IL-17/IFN-γ-positive CD4+ cells compared to normal controls (Fig. 3). IL-6 augmented IL-17 production induced by Progesterone C. albicans and S. aureus in cells isolated from healthy controls (Fig. 4a). No effect was apparent in the HIES patients, independently of the type of STA3 mutation. In contrast to IL-6, IL-10 reduced the amount of IL-17, and this effect was observed both in healthy controls and HIES patients (Fig. 4b). Mutations in the SH2 and DNA-binding domain of STAT3 have been reported to be the cause of disease in a large proportion of HIES patients [4]. These mutations function as dominant-negative mutations [4] and result in a defective Th17 response in these patients [9,10], explaining many of the clinical features of HIES. In the present study we confirm, on one hand, the relationship between HIES and defective Th17 responses; on the other hand, we also refine this notion to include the relationships between the type of STAT3 mutation, immunological response to relevant microbial stimulation and clinical phenotype of the patients.

In this regard, fibrocytes resemble fibroblasts. Fibrocytes were first described by Bucalla www.selleckchem.com/products/DAPT-GSI-IX.html et al. in 1994 as possessing

a CD34+vimentin+collagen+ phenotype [10], They were found capable of circulating as members of a population of peripheral blood mononuclear cells and were shown to enter wound chambers implanted in subcutaneous tissue. They were identified in connective tissue scars. Once fibrocytes have infiltrated injured target tissues undergoing remodelling, they assume a fibroblast-like morphology. Moreover, they appear to lose their surface expression of CD34 as they develop into fibroblasts [13], suggesting that this protein behaves as a progenitor marker. Fibrocytes are believed to interact with other mononuclear cells that have also been recruited from the circulation. They can also cross-talk with residential fibroblasts. Currently it is uncertain exactly what roles fibrocytes play in tissue regeneration or how they might participate in the formation of fibrosis. Moreover, the mechanisms and signalling pathways through which they exchange molecular information with other cells are only partially identified. A major hurdle BAY 80-6946 datasheet in characterizing fibrocytes and distinguishing them from fibroblasts continues to result from the absence of specific surface markers. Identification of fibrocytes

as a distinct cell type has resulted from a rigorous set of characterization studies which should now allow greater PRKACG precision in classifying their biological functions and attributing them to specific subpopulations of cells. Initial studies examining the phenotype of fibrocytes involved observations made following their initiation and propagation in cell culture. Subsequently, their activities have been described in vivo. Much of what we now know about their behaviour has been generated in animal models. In mice, fibrocytes appear to develop from CD115+CD11b+Gr1+ monocytes. When mouse splenocytes were cultured for 14 days, Niedermeier et al. [14] found an outgrowth of spindle-shaped cells. When analysed by flow cytometry, they appear as collagen I-expressing

cells which also display a CD45+CD11b+CD16/32+ phenotype but lack CXCR4, CD34 or CD115 expression. When depleted of certain leucocyte subsets such as CD11b+, CD115+, CD16/32+ or Gr1+, considerably fewer fibrocytes are generated. A number of factors extrinsic to fibrocytes have been implicated in their regulation. Of particular interest, the study by Niedermeier et al. demonstrated that CD4+ lymphocytes support fibrocyte differentiation [14]. The presence of non-activated CD4+ cells substantially enhances fibrocyte in vitro. Conversely, the absence of these lymphocytes reduces differentiation, both in vitro and in vivo. When activated, CD4+ T cells release TNF-α, interleukin (IL)-4, interferon (IFN)-γ, and IL-2. The fibrosis induced by unilateral ureteral obstruction can be reduced substantially by IL-2 and TNF-α, as can the appearance of fibrocytes.

However, this locus exhibited selleckchem a D value of 0.43 with an allele number of seven and thus significantly contributed to the genotyping of the O26 isolates. As such, three loci (EH111-8, EH111-11, and EH111-14) were specifically present in O111 but were of a certain level of usefulness for this serogroup because they exhibited moderate D values (0.21, 0.24, and 0.17, respectively). Our results indicate that these four loci can be used for genotyping the O26 and O111 isolates. Figure 1b shows the results of our evaluation of the 18 loci for the isolates belonging to all the three serogroups together. The allele numbers ranged from 3 to 45, and the D values ranged from 0.34 to 0.92. In this analysis, six loci (EH157-12, O157-34, O157-37,

O157-9, EHC-1, and EHC-2) exhibited higher D values than did the other loci. The overall D values were 0.991 (95% CI = 0.989–0.993), 0.988 (95% CI = 0.986–0.990), and 0.986 (95% CI = 0.979–0.993) for the O26,

O111, and O157 isolates, respectively. These values indicate that our system is useful for genotyping EHEC isolates of not only the O157, but also the O26 and O111 serogroups. As the results mentioned above indicated BGJ398 nmr that our expanded MLVA system was useful for genotyping the O26 and O111 isolates, we next carried out cluster analyses of the O26 and O111 isolates by using the new MLVA system. In this analysis, we included the isolates collected during nine O26 outbreaks and three O111 outbreaks, as Methocarbamol well as assessing the applicability of our system for detecting outbreak-related strains in these two serogroups. As shown in Figure 3, the isolates

collected during each of the 12 outbreaks formed unique clusters. Isolates from three outbreaks (26OB5, 26OB6, and 111OB3 outbreaks) did not exhibit any repeat copy number variations for all 18 loci. With regard to the other nine outbreaks, variations were observed for some loci in a few isolates obtained during the same outbreak (Table 2). However, in eight of the nine outbreaks, variations were mainly found in the O157-37 and/or EHC-6 loci, both of which are located in large plasmids, such as pO157, suggesting that entire plasmids may have been lost or parts of these plasmids may have been deleted in some strains during the outbreaks or after strain isolation. These results indicate that the MLVA system can be useful for detecting outbreaks of the EHEC strains belonging to the O26 and O111 serogroups. The O26 and O111 isolates were also subjected to cluster analyses based on PFGE profiles (Fig. 4). Each of the outbreaks formed a unique cluster, as shown in Figure 3. The relative positions of the PFGE-based clusters, however, did not always match those of the MLVA-based clusters. For example, the positions of the clusters of 26OB3 and 26OB7 in the PFGE analysis were closely matched; however, their positions were completely different in the MLVA. Moreover, the subtypes within a cluster defined in each method did not completely match.

The BabA-MBS was significantly higher in the cancer than the non-cancer group (P= 0.019), but there was no significant difference for SabA-MBS. A weak correlation EX 527 between BabA-MBS and SabA-MBS (r= 0.418) was observed, the positive correlation being higher in the cancer than the non-cancer group (r= 0.598 and 0.288, respectively). The isolates were classified into two groups: a BabA-high-binding and a BabA-low-binding group (in comparison to the average for BabA-MBS). The average SabA-MBS in the BabA-high-binding group was significantly higher than in the BabA-low-binding

group (P < 0.0001). Analysis of babA2 middle region diversity (AD1–5) revealed that AD2-type was predominant in isolates irrespective of BabA-MBS. H. pylori BabA-MBS might have an effect on SabA-MBS and relate to the severity of gastric disorders, including gastric cancer. Evaluation of MBS of the combined two adhesins would be helpful for predicting damage in the H. pylori infected stomach. H. pylori is a Gram-negative, spiral and microaerophilic bacterium that colonizes the human stomach. H. pylori infection occurs mostly in early childhood (1) and causes chronic gastritis, peptic ulcer, gastric cancer (2) and gastric mucosa-associated lymphoid tissue lymphoma (3). H. pylori begins its colonization by binding to certain adhesive molecules

on the epithelial cells via H. pylori outer membrane proteins such as BabA, SabA, AlpA, AlpB and HopZ, leading to persistent infection and tissue damage (4–7). Two glycoconjugates, Panobinostat nmr fucosylated Lewis b blood group (Leb) and the sialic acid antigens (sLex and sLea), have been identified as cognate substrate molecules of the H. pylori adhesins, BabA and SabA, respectively (4, 5). BabA and SabA are ID-8 encoded by the babA2 and sabA genes, respectively, which mediate the attachment of H. pylori to human gastric epithelial cells (4, 5, 8). The relationship between the detection of these genes, babA2 and sabA, with PCR and clinical manifestations has been investigated (9–14).

There is no apparent relationship between the prevalence of sabA and gastric disease types (9). However, the sabA-negative genotype may be attributable to false negative PCR due to subtle mutations in the primer regions. On the other hand, the presence of babA2 has been shown to be associated with chronic gastritis (10), intestinal metaplasia (13) and duodenal ulcer (11), whereas several reports have shown no significant association between babA2 status and clinical manifestations in some countries, including Japan (12, 15, 16). In particular, the babA gene possesses high homologous sequences with minor diversity between babA1, babA2 and babB genes within a microorganism and among individual strains. These suggest that use of several primer pairs in PCR based-detection somewhat mitigates that risk and provides reliable findings.

Predisposing factors that lead to obstructive sleep apnoea in DS include the characteristic mid-face hypoplasia, tongue enlargement and

mandibular hypoplasia. This small upper airway, combined with relatively large tonsils and adenoids, contributes to airway obstruction and increases susceptibility to infections. Upper airway obstruction due to adenoids and tonsillar hypertrophy was reported in 30 (6%) of 518 DS children seen consecutively [72]. Those with severe Protein Tyrosine Kinase inhibitor obstructive symptoms, e.g. snoring, were found to be more likely to have tracheobronchomalacia, laryngomalacia, macroglossia and congenital tracheal stenosis. Five patients required tracheostomy because of persistent obstruction. Gastro-oesophageal reflux may result in aspiration of gastric contents into airway causing lung inflammation or a reflex mechanism of the lower oesophagus triggering bronchospasm [73]. It is recommended to rule out gastro-oesophageal reflux in children presenting with recurrent lung disease without other explanation. Recurrent aspiration of thin fluids is well known to be

associated with increased incidence of lower respiratory tract infections [74,75]. The hypotonia associated with DS includes poor pharyngeal muscle tone that increases the risk for aspiration [76]. Subclinical aspiration may account for up to 12% of cases of chronic respiratory complaints in non-DS children, and Acalabrutinib up to 42% in DS children [77,78]. Zarate and collaborators [79] studied oesophagograms of 58 DS subjects and 38 healthy controls, finding 15 of the DS participants with higher tracer retention than the upper limit of the controls’ retention. Five were reported definitely abnormal, with achalasia

documented in two subjects. Eight had frequent vomiting/regurgitation. DS children would benefit from evaluation of swallowing function [80]. Up to 40–50% of DS newborns may have external ear canal stenosis [81,82] and the Eustachian tube may also be of small width, contributing to the collection SPTBN5 of middle ear fluid and chronic otitis media [83]. Otitis media may explain the high incidence of hearing loss and the delayed development of language reported in DS [84]. Early health supervision and advances in medical care have lengthened the life expectancy of children with DS. Frequent respiratory tract infections is considered a significant component of the morbidity of DS children; however, few studies help to define the current epidemiology of infections in the DS population. It appears that the incidence of respiratory infections has declined in the last decade, due most probably to the progress in the management of infections and the awareness of the medical problems that are common to DS patients.

IgG derived

from a SS patient positive for antibodies to the www.selleckchem.com/products/obeticholic-acid.html third extracellular loop had no effect on (Ca2+)I, as well as IgG derived from an anti-M3R antibody-negative SS patient (Figs 3e and 4). Recently, anti-M3R antibodies have been the focus of interest in rheumatology because of their potential pathogenic role, use as diagnostic markers and being therapeutic targets in patients with SS [1]. Several methods have been used to detect anti-M3R antibodies in SS patients [1]. In functional assays using smooth muscles, IgG fractions from patients with SS (SS-IgG) inhibited carbachol-evoked or nerve-evoked bladder or colon contractions [8,9]. In salivary gland cells, SS-IgG inhibited the rise in (Ca2+)i induced by carbachol, and also inhibited pilocarpine-induced AQP5 trafficking to the apical membrane from the cytoplasm [2]. The inhibitory actions of SS-IgG on

the rise in (Ca2+)i was acutely reversible [10]. Anti-M3R antibodies from SS patients can be detected by immunofluorescent analysis using rat lacrimal glands [11], and by flow cytometry using the M3R-transfected Chinese hamster ovary (CHO) cell line [12]. Moreover, anti-M3R antibodies in sera of SS patients were detected by ELISA using synthetic peptides or recombinant proteins of the second extracellular loop of M3R [13]. We have reported previously the presence of anti-M3R antibodies in a group of patients with SS, which recognized the second extracellular loop by ELISA using synthetic check details peptides [4,5]. In the present study, we established a standard method to detect anti-M3R antibodies that can be used for screening large patient populations. Functional assays and flow cytometry are too laborious for routine use. Although ELISA is easy, the results from some ELISA systems used for screening anti-M3R antibodies differ Acyl CoA dehydrogenase widely with regard to the prevalence of anti-M3R antibodies (from 11 to 90%) [4,14]. Furthermore, Cavill et al.[15]

reported failure to detect anti-M3R antibodies by ELISA using synthetic peptides. In the present study, we reported higher frequencies and titres of anti-M3R antibodies against all extracellular domains in SS patients than the control. The prevalence of anti-M3R antibodies against the second extracellular loop in SS (55%) determined in the present study was much higher than that reported in our previous study (11%) [4]. The reason for this difference is probably related to the change in the methodology, such as increased sensitivity resulting from purity of the synthetic peptides, modification of the washing procedure or other factors introduced in the modified ELISA system. In the present study, we also determined the precise B cell epitopes of M3R molecules.

We observed a significant increase in the production of anti-MSP-119 IgG antibody

in normal and heterozygous children during the 12 months of follow-up, but see more not in homozygous mutants. Normal children had a significantly lower malaria incidence rate compared to other genotypes (χ2 = 115.59; P < 0.01). We conclude that the presence of the c.1264 T>G mutation that leads to CD36 deficiency is closely associated with reduced IgG production and higher malaria incidence. It is most likely that deficiency of CD36 which is known to modulate dendritic cell function suppresses the production of protective IgG antibodies directed to Plasmodium falciparum MSP-119 antigen, which predisposes to the acquisition of clinical malaria in children. Adhesion molecules are proteins located on the cell surface, involved in binding

with other cells or with the extracellular matrix in a process called cytoadherence, and which occurs as a result of the interaction between parasite ligands and host receptors. FDA-approved Drug Library high throughput Cytoadherence of infected erythrocytes containing late developmental stages of the malaria parasites (trophozoites and schizonts) to the endothelium of capillaries and venules is characteristic of Plasmodium falciparum infections [1]. Cytoadherence is an important mechanism in the pathogenesis of malaria. Polymorphisms in more than 30 human genes determining the immune response have been associated with susceptibility to malaria [2], particularly in genes

involved in cell adhesion and immunity. Cell adhesion molecules (CAM) have been identified as receptors for infected red blood cell and are associated with susceptibility to malaria. The adhesion molecules include CD36, intercellular adhesion molecule 1 (ICAM-1, CD54), platelet/endothelial cell adhesion molecule1 (PECAM-1, CD31), vascular cell adhesion molecule1 (VCAM-1), thrombospondin, E-selectin, P-selectin and chondrotin sulphate A [3]. Most P. falciparum antigens bind to CD36 molecules, which Gefitinib concentration are thus considered to be the major endothelial receptors for sequestration [4]. Mutations in the CD36 gene may, therefore, influence the outcome of malaria. CD36 is an 88-kDa member of the class B scavenger receptor family of cell surface glycoproteins. CD36 is broadly expressed on monocytes, macrophages, dendritic cells, fat cells, muscle cells, capillary endothelial cells and platelets [5]. CD36 acts as a facilitator of fatty acid uptake and is a receptor for a wide range of ligands including collagen, thrombospondin, anionic phospholipids, oxidized low density lipoprotein and erythrocytes parasitized with P. falciparum [6–8], and participate in macrophage fusion induced by IL-4 cytokines [9]. In vitro studies carried out by Urban and colleagues revealed that P.