MEGA had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis. Agree

with the manuscript’s results and conclusions: MEGA BJC EB JG GKR VB. Designed the experiments/the study: MEGA BJC EB JG GKR VB. Analysed the data: MEGA BJC. Collected data/did experiments for the study: VB EB. Enrolled patients: VB EB. Wrote the first draft of the paper: MEGA. Contributed to the writing of the paper: BJC EB JG GKR VB. Co-principal investigators of the Million Women Study: VB JG GKR. The authors have no competing interests Enzalutamide cost to declare. We thank the women who participated in the Million Women Study, the NHS Breast Screening Centre collaborators, and the steering committee of the Million Women Study (see below). We also thank the Information and Statistics Division in Scotland and the Information Centre for Health and Social Care and Northgate Solutions in England for the hospital admission data.

Funding: The Million Women Study is funded by LDK378 cost Cancer Research UK, the Medical Research Council, and the NHS Breast Screening Programme. The researchers act independently of the funders. Steering Committee: Joan Austoker, Emily Banks, Valerie Beral, Judith Church, Ruth English, Jane Baricitinib Green, Julietta Patnick, Richard Peto, Gillian Reeves, Martin Vessey, and Matthew Wallis. NHS Breast Screening Centres collaborating in the Million Women Study (in alphabetical order): Avon, Aylesbury, Barnsley, Basingstoke, Bedfordshire & Hertfordshire, Cambridge & Huntingdon, Chelmsford & Colchester, Chester, Cornwall, Crewe, Cumbria, Doncaster, Dorset, East Berkshire, East Cheshire, East Devon, East of Scotland, East Suffolk, East Sussex, Gateshead, Gloucestershire,

Great Yarmouth, Hereford & Worcester, Kent (Canterbury, Rochester, Maidstone), Kings Lynn, Leicestershire, Liverpool, Manchester, Milton Keynes, Newcastle, North Birmingham, North East Scotland, North Lancashire, North Middlesex, North Nottingham, North of Scotland, North Tees, North Yorkshire, Nottingham, Oxford, Portsmouth, Rotherham, Sheffield, Shropshire, Somerset, South Birmingham, South East Scotland, South East Staffordshire, South Derbyshire, South Essex, South Lancashire, South West Scotland, Surrey, Warrington Halton St Helens & Knowsley, Warwickshire Solihull & Coventry, West Berkshire, West Devon, West London, West Suffolk, West Sussex, Wiltshire, Winchester, Wirral and Wycombe.

4%) was the most frequently isolated species amongst the controls ( Table 2). No significant Selleckchem ZD1839 differences in staphylococcus counts were observed amongst the subgroups for CD4T cells; however, counts were significantly lower in the subgroup with a viral load of less than 400 copies/mm3 (Table 3). The HIV-positive group showed a higher percentage of individuals positive

for Enterobacteriaceae and Pseudomonadaceae (77.7%) than the control (44.4%) (p = 0.001). Also, the counts of these microorganisms were significantly higher amongst HIV-positive patients than in the control group (p = 0.0001) ( Table 1). Enterobacter cloacae was the most frequently isolated species in both groups (18.8% in the HIV-positive group and 16.32% in the control group). Amongst Pseudomonadaceae species, Chryseomonas luteola was the most 17-AAG mouse common in both studied groups (7.3% in the HIV-positive group and 6.1% in the control group). Other species identified are shown in Table 4. Counts of Enterobacteriaceae and Pseudomonadaceae were significantly lower in the subgroup with <200 CD4 cells/mm3. With respect to viral load, significantly lower counts of staphylococci in the subgroup with <400 copies/mm3 were observed ( Table 3). One of the most challenging problems involving staphylococci has been their increasing resistance to methicillin, vancomycin and other antibiotics.23, 24 and 25 Oral reservoirs of these microorganisms may be

potential sources for infection in immunosuppressed

patients.26 In this study, staphylococci were isolated from 86.6% of the control group and 84.4% of HIV-positive patients. Previous studies reported a variable presence of staphylococcus in systemically diseased patients. These values varied from 28% amongst patients with malignant neoplasias3 to 96% in patients with rheumatoid arthritis.27 High percentages of patients positive for CYTH4 staphylococci in the oral cavity have been reported in the literature, with values from 94%27 to 95.6%28 amongst adults. Jackson et al.29 also observed a higher frequency of isolation in the oral cavities of healthy children (92%). The results obtained in this study confirm the conclusion of Smith et al.10 that staphylococcus species can often be isolated from the oral cavities of healthy or diseased children and adults. Although staphylococci have been considered part of the normal oral microbiota,27 and 29 their presence in the oral cavity may be associated with local and systemic infections, especially in immunosuppressed patients.10 With respect to the species identified in this study, S. epidermidis and S. aureus were the most prevalent coagulase-negative and coagulase-positive species, respectively, in both groups. The isolation of these species in the oral cavity and periodontal sites has been reported in the literature. 27, 28, 29, 30 and 31 The HIV-positive group showed a greater diversity of coagulase-negative species; the presence of S. warneri, S. capitis, S.

Serra-Majem G.

Sesti M. Shah J. Shen L. Shi K. Shoghi S. Shrestha M. Siegrist H. Sies J. Sievenpiper R. Smith C.E. Smith J. Snell-Bergeon F. Sofi A. Solini Y. Song M. Songini G. Sorice F. Soriguer E. Spinedi R.A. Stein E. Stener-Victorin V. Stocchi B. Strasser G.E. Striker I. Strychar A. Sukumar W. Sulowicz G. Sun H. Taegtmeyer K. Taku P.S. Tappia L. Tappy G. Targher A. Tavani A. Tchernof D. Teegarden E. Teijeira-Fernandez L. Temme P. Tessari S. Tessier A. Thanopoulou H. Thibault J. Thomas D. Toniolo M.K. Townsend M.G. Traber G. Tripepi V. Trischitta H. Tsuneki J.A. Tur E.E. Turcotte M.E. Tushuizen J. Ukropec J. Uribarri O. Vaccaro P. Valensi G. Valerio S. Valtuena E. Van Belle E. Van Craenenbroeck R.M. van Dam C.E. van den Brom J. van der Pols G. Van Wye D. Vanuzzo T. Vasankari A. Vatrella this website M. Velussi E.T. Vestergaard P. Vestergaard J. Viikari N. Vilarrasa D.T. Villareal H.K. Vincent F. Visioli S.L. Volpe A. von Eckardstein M.B. Vos T. Vrijkotte K. Walker L. Wang X. Wang E. Warensjo J. Warnberg J. Watson K.T. Weber

M. Weickert K. Weinger E.P. Weiss F.K. Welty S.L. White R.A. Whitmer I. Wilcox A.L. Willig E. Windler K.K. Witte T.M.S. Wolever T. Yamaguchi H. Yan A.I. Younis F. Zaccardi DNA Damage inhibitor A. Zambon S. Zambon M. Zamboni M. Zeyda A. Zittermann G. Zoppini G. Zuliani “
“The Isoconazole Editors are grateful to all the members of the editorial board and to the following colleagues for their extremely valuable help in the editorial process in 2011: N. Abate T.C. Adam G.F. Adami L.A. Afman C. Agnoli C. Agostoni P. Agostoni M. Aikawa R. Ajjan E. Alasaarela C. M. Albert

F. Albuquerque N.M. Al-daghri L.H. Allen G.L. Ambrosini G.Ø. Andersen G. H. Anderson S. Anderson F. Angelico K. Anil A. Arnaiz F. Arturi J. F. Ascaso V.G. Athyros A. Atkin D. Aune A. Avignon A. Avogaro A. Aziz M. Azizi S. Aznar G.H. Bahrami P. Balagopal D. Baldassarre B. Balkau K.D. Ballard J. Ballesteros N.M. Bandarra N. Barengo J. Barnard M.G. Baroni T. Barringer M.T. Barrio López E. Bartoli S. Basili J.A. Bauer J. Bauersachs K.B. Baumgartner A. Baylin C. Beauloye G. Bedogni D. D. Belke S. Bellentani A. Bellia A.P. Beltrami J. Beltrand A. Benetos K. Berger P. Bergman F. Bernini S.E. Berry S. Bertolini G. Biagini G. Biolo F. Biscetti H. Bjermo L. Blais S.N. Bleich G.J. Boersmaa S. Bokor F. Bolaños-Jiménez P.Jr. Bolin G. Bolli N. Boon S. Booth D.A. Booth G. Bos L. Bozzetto A. Branchi J.C. Brand-Miller S.J. Brener F. Brites M. Brochu K.G. Brodovicz C.M. Brown I. Brown C. Brufani N.S. Bryan M. Bucci M. Buckingham B. Buijsse S. Bunnapradist R. Burcelin B.M. Burton-Freeman L. Butler N.F. Butte N.M. Byrne P. Calabrò K.L. Campbell U. Campia H. Campos J.H. Capdevila N. Caporaso J.A. Carbayo C. Cardillo J.J. Carlson S.

Etoposide (1 μg/mL) was used as a positive control. The number of cells in both the control and treated cell samples were estimated based on their total nucleic acid content, as described by Cingi et al. (1991). Cells were seeded at 5 × 104 cells/well in 96-well tissue culture plates and exposed to different concentrations of ConA or ConBr lectins (1–200 μg/ml) dissolved Inhibitor Library solubility dmso in the RPMI medium (with 1% FBS).

After 72 h of incubation, cells were fixed (5% trichloroacetic acid), washed twice with ice-cold PBS, and a soluble nucleotide pool was extracted with cold ethanol. The cell pellet was dissolved in 0.5 M NaOH at 37 °C overnight. Following this, the absorbance at 260 nm of the NaOH fraction was used as an index of the cell number (Bianchi and Fortunati, 1990). The results are expressed as mean percentages of absorbance at 260 nm in treated cells compared to the controls. Etoposide (1 μg/ml) was used as a positive control. In MTT and NAC assays the concentration AG-014699 price that inhibits 50% of cell proliferation (IC50) was determined from plots of cell viability. Proliferating cells can be identified using DNA labeling with nucleotide analogs such as bromodeoxyuridine (BrdU). Leukemic cells were plated in 24-well tissue culture plates (0.3 × 106 cells/mL) and treated with lectins at different concentrations dissolved in RPMI medium (with 1% FBS). After 21 h of exposure, 20 μl of BrdU (10 mM) was added

to each well and incubated for 3 h at 37 °C. To determine the amount of BrdU incorporated into DNA (Pera et al., 1977), cells were harvested and then transferred to cytospin slides and allowed to dry for 2 h at room temperature. Cells that had incorporated BrdU were Amrubicin labeled by direct peroxidase immunocytochemistry using the chromogen diaminobenzidine (DAB). Slides were counterstained with hematoxylin, mounted, and coverslipped. Determination of BrdU positivity was performed by light microscopy (Olympus, Tokyo, Japan). Two hundred cells were counted per sample to determine the percentage of BrdU-positive

cells. Etoposide (1 μg/ml) was used as a positive control. The comet assay, which is used to detect DNA strand breaks, was conducted under alkaline conditions as described by Singh et al. (1988) with minor modifications (Klaude et al., 1996) following the recommendations of the International Workshop on Genotoxicity Test Procedures (Tice et al., 2000). HL-60 and MOLT-4 (0.3 × 106 cells/ml) cells were incubated for 24 h with lectins at 5, 25, and 50 μg/ml. After this, the cells were centrifugated and resuspended in the medium. Subsequently, 20 μl of the cells in suspension (∼106 cells/ml) were dissolved in 0.75% low melting point agarose and immediately spread onto a glass microscope slide precoated with a layer of 1% normal melting point agarose. The agarose was allowed to set at 4 °C for 5 min. The slides were incubated in an ice-cold lysis solution (2.

Stratification is by far the most common adjustment method used in benchmark reports. The National Healthcare Safety Network (NHSN) and the International Nosocomial Infection Control Consortium (INICC) previously reported type-specific rates of device-associated HAI stratified by critical care unit types for adults and paediatric patients and

by weight groups for neonatal patients [2] and [14]. Additionally, dialysis access-related infections were stratified according to the type of vascular access [15], and procedure-specific surgical site infection (SSI) rates (actual proportions) were stratified according to the NHSN risk index category, which is based on the American Society of Anesthesiologists’ scores, TGF-beta inhibitor procedure duration, and wound classification [16]. Although stratification is a straightforward and powerful method of adjustment, the question remains whether studies use the correct levels of stratification. For example, it was shown that procedure-specific stepwise logistic regression models for SSI data yielded new procedure-specific

risk factors that were more predictive than the current risk index category [17]. Another potential problem with stratification PI3K Inhibitor Library in vitro is that as the rate of HAI decreases, small units (such as coronary care units) may have too few outcomes to allow statistically meaningful comparisons over a specified time (usually one month). Multivariate regression adjustment and indirect standardization are increasingly used in reporting HAI surveillance metrics. A number of studies have adjusted HAI ifoxetine prevalence and antimicrobial use for the case-mix (i.e., heterogeneity regarding the patient’s risk) using multivariate logistic regression models and an

indirect standardization method to allow for fair inter-hospital comparisons [11], [18] and [19]. Approximately two decades ago, the National Nosocomial Infections Surveillance (NNIS) system introduced the standardized infection ratio (SIR) to indirectly standardize SSI rates using a standard population to enable fair comparisons of SSI rates between a healthcare facility and a benchmark with a different risk index category [20]. Recently, the NHSN promoted the expansion of SIR use to report a single SIR for a specified device-associated HAI from multiple hospital locations (such as specialty care areas) to adjust for differences in HAI incidence between these locations [21].

[16] and [17] The association between cold hemagglutination and hemolysis was first reported in 1937.18 CA can be determined semi-quantitatively by the titer, based on their ability to agglutinate erythrocytes at 4 °C.4 Screening for CA

have shown that a high proportion of the adult population has CA in serum without any evidence of hemolysis or other disease.[5] and [15] These normally occurring CA are polyclonal and are found in low titers, usually below 64 and rarely exceeding 256.5 On the contrary, in 172 consecutive individuals with monoclonal IgM in serum, significant CA activity was found in 8.5% with titers between 512 and 65,500, and all individuals Talazoparib clinical trial with detectable CA had hemolysis.19 Thus, monoclonal CA are generally far more pathogenic than polyclonal CA. The thermal amplitude is defined as the highest temperature at which the CA will react with the antigen.[4] and [20] In general, the pathogenicity of CA is more dependent on the thermal amplitude than on the titer.[20] and [21] The normally occurring CA have low thermal amplitudes. If the thermal amplitude exceeds 28–30 °C, erythrocytes will agglutinate in

the circulation in acral parts of the body even at mild ambient temperatures and, often, complement fixation and complement-mediated hemolysis will ensue. CA should not be confused with cryoglobulins. Occasionally, Selleck Epigenetics Compound Library however, patients have been reported in whom the cryoprotein had both CA and cryoglobulin properties.[8], [22] and [23] CA are most often directed against

the Ii blood group system.[4] and [24] About 90% of CA are anti-I specific while most of the remaining ones show specificity for i.[3] and [5] The I and i antigens are carbohydrate macromolecules and the densities of these antigens on the erythrocyte surface are inversely proportional. Neonatal red blood cells almost exclusively express the i antigen, while the I antigen predominates in individuals of 18 months of age and older.25 Hence, CA with anti-I specificity are generally more pathogenic in children and adults than those specific for the i antigen.[5], [25] and [26] Occasionally, CA show specificity against the erythrocyte surface protein antigen designated Pr and such CA can be highly pathogenic.[26] and [27] 5-FU Several other specificities have been reported but are probably very rare. Cooling of blood during passage through acral parts of the circulation allows CA to bind to erythrocytes and cause agglutination (Fig. 1). Antigen-bound IgM-CA is more prone than IgG to bind complement protein C1 and thereby initiate the classical complement pathway.[28], [29], [30] and [31] C1 esterase activates C4 and C2, generating C3 convertase which leads to the formation of C3b. Upon returning to central parts of the body with a temperature of 37 °C, IgM-CA detaches from the cell surface, allowing agglutinated erythrocytes to separate from each other, while C3b remains bound.

The mass transfer resistances were analyzed by estimating the Biot number (Eq. (11)), which is a dimensionless number used in transient mass transfer

and consisting of the ratio www.selleckchem.com/products/PLX-4032.html between mass transfer resistances inside and at the surface of a particle. This parameter is used to estimate whether or not the mass inside a particle will vary significantly in space, from a mass gradient applied to its surface. Other parameter often used is the apparent Thiele modulus (Eq. (12)) that is the ratio between intrinsic chemical reaction rate in the absence of mass transfer limitation and the rate of diffusion through the particle. equation(11) Bi=ksRDef equation(12) ϕap=R29vobsDefC0where vobs=ΔCΔt The PSO version used in this study was based on the work of Schwaab, Biscaia, Monteiro, & Pinto (2008) which presents a detailed description of the algorithm. The PSO technique GW572016 was originally proposed by Kennedy & Eberhart (1995) based on the social behavior of collection of animals. Each individual of the swarm, called particle, remembers the best solution found by itself and by the whole swarm along the search trajectory. The particles

move along the search space and exchange information with others particles, in accordance with the following equations: equation(13) vp,dk+1=w·vp,dk+c1·r1(xp,dind−xp,dk)+c2·r2(xdglo−xp,dk) equation(14) xp,dk+1=xp,dk+vp,dk+1 In the Eqs. (13) and (14), p denotes the particle, d is the search direction, k represents the interaction number,

v is the velocity (or pseudo-velocity) of the particle and x is the position of particle, xind and xglob represent the regions of the search space where the objective function attains low (optimum) values, where xind is the best position found by the particle itself, while xglob is the best position found by whole swarm. In addition, r1 and r2 are two random numbers with uniform distribution in the range comprehended between 0 and 1. The parameters before w, c1 and c2 are search parameters, which there are called of inertial weight, the cognition and social parameters, respectively. The PSO was configured according previous works ( Burkert et al., 2011 and Moraes et al., 2009), using forty particles, and the inertial weight, cognition and social parameters were set at 0.7, 1.0, 1.0, respectively. Fig. 1 presents the kinetic of single component adsorption of glucose, fructose and sucrose on various cationic forms of X zeolite. It is observed that the equilibrium was reached within 60 min in all cases, which corroborates with Heper et al. (2007), which reported that the glucose adsorption reaches the steady state within 30 min. From the Fig. 1, it is seen that the NaX zeolite made it possible to adsorb about 200 g/L of the initial concentration of glucose and fructose after 60 min.

In another application of this line, BRAF expression was associated with a distinct gene signature that resembled expression profiles of embryonic neural crest stem/progenitor cells, thereby motivating White

et al. [ 30••] to screen for suppressors of this embryonic phenotype. A class of compounds, called inhibitors of dihydroorotate dehydrogenase (DHODH), was found to selectively abrogate neural crest development in zebrafish as well as melanoma growth in mouse xenografts and human cell lines. Currently being followed in Phase I/II clinical trials, the DHODH inhibitor leflunomide is a pivotal demonstration of how an embryonic phenotype can be translated to findings about RO4929097 in vitro the human disease and lead molecules from zebrafish research into clinical investigation. Detailed live imaging of melanocytes in a temperature sensitive mitfa (mitfavc7) mutant has provided novel insights into the direct consequences of mitfa activity on tumorigenesis. Reduced mitfa activity caused a dramatic increase in melanocyte Compound Library supplier cell division [ 31] and was found to directly affect tumor morphology and formation in the BRAF model [ 32•]. As these findings could be reversed with the restoration of mitfa’s

activity, this work substantiates the notion that mitfa is a modifier of BRAF-driven melanoma and provides a functional link between low MITF expression in patients with their poor melanoma prognosis. Recent studies using a KRASG12D-driven model of embryonal rhabdomyosarcoma (ERMS) [ 11] have highlighted the importance of the cell of origin as a determinant of ERMS. For example, Ignatius et al. [ 33] used dynamic cellular imaging of a mosaic transgenic rag2-KRASG12D model to track the movement and evolution of ERMS cell subpopulations in embryonic and adult zebrafish. Their findings revealed new roles for differentiated ERMS cells in tumor growth and suggest that mechanisms governing their homeostatic maintenance in regulating growth could be relevant considerations

in developing Thymidine kinase potential therapeutic treatment. In a similar approach, using promoters representing various stages of muscle development (cdh15, rag2, mylz2), Storer et al. [ 34] drove expression of KRASG12D and observed that tumors that originated from the more progenitor like cells were more invasive and undifferentiated. These tumors were found to closely recapitulate subgroups of human ERMS based on differentiation status and harbor unique signaling pathways in each subgroup. Confirmation of these pathways as therapeutic targets awaits further study but demonstrates how cross-species oncogenomics can be used to guide therapeutic targeting strategies. Important insights have also been described in other zebrafish models that cannot be described here [35, 36, 37, 38 and 39] (reviewed in [40••, 41••, 42 and 43]). It is apparent though that some tumor types are better modeled in zebrafish than others.

9 °C, with a high standard deviation. Even at the highest experimental temperature of 42.4 °C the wasps showed “rest” according to our definition at least for some minutes ( Fig. 2D, data point (D) in Fig. 3). Some wasps like the individual in Fig. 2E (Ta = 38.5 °C) showed an unusually cool spot at the head which was caused by wetting of the mouthparts

with regurgitated liquid droplets. This behavior cools the head and to some extent also the thorax at high temperatures. However, those wasps were usually active, cooling individuals at rest were an exception. Negative values Androgen Receptor Antagonist of the thoracic temperature excess (i.e. the thorax was cooler than the abdomen) may have been caused by the aforementioned evaporative cooling of head and thorax in some individuals, but may also have occurred due to slight vertical temperature gradients inside the measurement chamber and the orientation of the wasp body in this gradient ( Fig. 3, e.g. individual at Ta = 12 °C). Respiration data from clearly identified V. vulgaris   and V. germanica   ( Bellmann, 1995 and Clapperton et al., 1989) did not differ significantly (ANOVA: P   = 0.4857, F   = 0.49), so results of all individuals were pooled (V. vulgaris  : n   = 26, V.

germanica  : n   = 12). With increasing experimental ambient temperature (T  a), CO2 production rate increased exponentially, from 5.658 μl g−1 min−1 at 8.3 °C to 18.504 μl g−1 min−1 at 20.2 °C, 58.686 μl g−1 min−1 at 35.3 °C, and approaching 102.84 μl g−1 min−1 at 40 °C ( Fig. 4). The following exponential function fitted the data best: VCO2=A1∗expTa/t1+A2∗expTa/t2+A3∗expTa/t3+y0VCO2=A1∗expTa/t1+A2∗expTa/t2+A3∗expTa/t3+y0where

Everolimus clinical trial VCO2VCO2 is carbon dioxide production rate [μl g−1 min−1] and Ta   is the ambient temperature [°C] in the measurement chamber (R  2 = 0.96275, n   = 846, 38 individuals; the range of validity is 7.7–42.4 °C). Parameters: A1 = 9.7023*10−5, Protein tyrosine phosphatase t  1 = 3.11195, A2 = 4.63097, t  2 = 14.6382, A3 = 56769.01521, t  3 = 3.81259*1084, y0 = −56770.80269. The mean Q10 was 2.27 (SD = 0.30, n   = 23). However, with this function the Q10 was not constant. It decreased from 2.98 at a mean T  a of 13 °C (±5 °C) to 1.97 at a T  a of 23 °C and increased to 2.84 at a T  a of 35 °C. This function fitted the data better than a conventional exponential equation (VCO2=a∗bTaVCO2=a∗bTa; R2 = 0.9404; a = 1.37152, b = 1.11652) particularly in the range of Ta = 20 to 35 °C. At high Ta above 35 °C ( Fig. 4, dashed line) CO2 production increased steeply until the wasp’s upper respiratory critical thermal maximum (resp CTmax). Individual wasps differed in their thermal tolerance. Our experiments were not conducted to determine the lethal temperature, nevertheless some wasps died due to continuous exposure to high experimental temperatures. Below 35 °C all wasps survived at least for 6 h (which was the minimal duration of an experiment). At higher temperatures some wasps died already at a Ta below the mean CTmax.

Resorbiertes MeHg bindet an SH-Gruppen von Proteinen in Blut und Geweben, in geringerem Ausmaß dagegen an SH-Gruppen z. B. von Cystein und GSH. Durch die Zellmembran wird es hauptsächlich an Cystein gebunden transportiert, und zwar vom Large Neutral Amino Acid Transporter („Transporter für große neutrale Aminosäuren”) [58]. Darüber hinaus sind MAPK Inhibitor Library high throughput noch weitere Mechanismen an der Aufnahme in Zellen beteiligt, darunter auch passive Diffusion [59]. Die Verteilung aus dem Blut in die Gewebe verläuft langsam und das Gleichgewicht stellt sich erst 4 Tage nach einer Exposition ein. Etwa 10% der Körperlast wird im Kopfbereich gefunden. Die Aufnahme ins Gehirn erfolgt langsamer als die in andere Organe. Das

Gehirn weist jedoch eine höhere Affinität für MeHg auf, und es wurde gezeigt, dass die Konzentration im Gehirn 3- bis 6-mal höher ist als im Blut. Etwa 20% des MeHg im Gehirn ist wasserlöslich und liegt hauptsächlich als MeHg-GSH-Komplex vor. Im übrigen Körper

ist MeHg mehr oder weniger gleichmäßig verteilt, obwohl in der Leber und der Niere einige konzentrationsabhängige Effekte auftreten. MeHg wird durch die Plazenta transportiert und im Fetus abgelagert. Im Gleichgewicht kann das Gehirn des Fetus MeHg in derselben Konzentration enthalten wie das Gehirn der Mutter. Jedoch ist beim Menschen die Konzentration im fetalen u. U. höher als im mütterlichen Blut. Möglicherweise liegt dies an Unterschieden Sirolimus ic50 beim Hämoglobin, da dies das wichtigste Bindungsprotein für MeHg in Erythrozyten ist und sich der Hämoglobingehalt zwischen Mutter und Fetus unterscheidet. Es wurde gezeigt, dass bei langfristiger Verabreichung von MeHg an Affen die Hg2+-Menge nur langsam ansteigt [60]. Das anorganische Quecksilber reichert sich Bacterial neuraminidase vor allem in Astrozyten und der Mikroglia an. Die Bedeutung dieses Prozesses im Rahmen der Neurotoxizität von MeHg wird später diskutiert. Die Exkretion von MeHg erfolgt

hauptsächlich über die Galle und die Nieren. Die tägliche Netto-Exkretionsrate von 1% der Körperlast resultiert in einer Halbwertszeit von etwa 70 Tagen. Diese Schätzung passt sehr gut zu den Daten in der umfangreichen Datenbank, die während der Vergiftungsepidemie im Irak [61] erstellt wurde. Die enterohepatische Rezirkulation von MeHg ist ein wichtiger Faktor im Zusammenhang mit der Exkretion von MeHg über die Faeces. Clarkson et al. entwickelten ein SH-Harz zur oralen Einnahme, um den enterohepatischen Kreislauf zu unterbrechen und so die Exkretionsrate von MeHg zu erhöhen [62]. Demethylierung im Darm kann signifikant zu einer erhöhten fäkalen Exkretion beitragen, da Hg2+ über den enterohepatischen Kreislauf nicht im demselben Ausmaß reabsorbiert wird wie MeHg. MeHg hat eine hohe Affinität zu SH-Gruppen; der logK liegt im Bereich von 15 bis 23 [63]. Trotz der hohen Affinität findet ein äußerst rascher Austausch des MeHg zwischen SH-Gruppen statt, der zu einer schnellen Umverteilung des MeHg führt, wenn neue SH-Gruppen verfügbar werden [64].