57 and 2 54 pg WHO 2005 TEQ/kg body weight (b w) , and identified

57 and 2.54 pg WHO 2005 TEQ/kg body weight (b.w)., and identified seafood, dairy products and meat products as the main sources (EFSA, 2012b). The data presented in this paper can be used in risk calculations where contributions from other sources are known. As an example: 660 g salmon per week would

contribute to 50% of the TWI based on our data from 2011. However, predicting the contribution from other food sources on a global scale is beyond the scope of this paper. Therefore the maximum tolerable intake limits proposed here consider only salmon as the exposure source. The EFSA, the Joint FAO/WHO Expert Committee on Food Additives (JECFA), SCF and WHO have derived TWIs for several of the contaminants which have been evaluated in this paper. TWIs have been established for AZD6244 purchase some of the pesticides, some metals, and the sum of dioxins and dl-PCBs. For all compounds except Hg and the sum of dioxins

and dl-PCBs, the measured amounts were negligible compared to the current TWIs, therefore calculations were limited to Hg and the sum of dioxins and dl-PCBs. There is a general agreement that 70–100% of the Hg in fish and seafood is present, in its most toxic chemical form, as MeHg+ (Amlund et al., selleck chemicals llc 2007, EFSA, 2012a and EFSA, 2012b). Accordingly, the TWI for MeHg+ was used in the risk calculations of the Norwegian farmed Atlantic salmon fillet. TWIs derived in Europe were chosen for the exposure calculation, SCF TWI for dioxins and dl-PCBs (SCF, 2001), and the EFSA TWI for MeHg+ (EFSA, 2012a and EFSA, 2012b). Based on Lowest Observed Adverse Effect Level (LOAEL) observed in the most sensitive rodent studies, the SCF issued a PTWI of 14 pg WHO 1998 TEQ/kg b.w. for dioxins and dl-PCBs (SCF, 2001). This PTWI included an uncertainty

factor of 3.2 based on intraspecies toxicokinetic and toxicodynamic differences. Furthermore, the use of the LOAEL instead of the No Observed Adverse Effect Level (NOAEL), added an uncertainty factor of 3, resulting in a total uncertainty factor of 9.6. The interspecies differences were already calculated based on examined data, and were therefore not added Lumacaftor concentration again as an uncertainty factor (SCF, 2001). By comparison the Environmental Protection Agency of the United States (US-EPA) issued a PTWI for dioxins and dl-PCBs of 4.9 pg/kg b.w. (EPA, 2012). In 2012 EFSA issued a PTWI for MeHg+ of 1.3 μg/kg b.w (EFSA, 2012a and EFSA, 2012b). This TWI was based on results from epidemiological studies performed in the Faroe Islands and the Seychelles, and the confounding effects of nutrients from fish were also taken into account. Based on the these studies, the US-EPA issued a Reference Dose (RfD) of 0.1 μg/kg b.w. per day (EFSA, 2012a and EFSA, 2012b). The guidelines used in Europe and the USA appear to diverge substantially. Previous food safety assessments of farmed Atlantic salmon have shown varying results.

48- and 1 65-fold, respectively, for PgSS; 1 53- and 1 62-fold, r

48- and 1.65-fold, respectively, for PgSS; 1.53- and 1.62-fold, respectively, for PgSE). The transcript levels of PgDDS under conditions of intermediate stage and opened AZD2014 chemical structure stage were 4.2- and 4.6-fold higher, respectively, than that of the closed leaf stage. In this study, we used 3-yr-old hydroponic-cultured ginseng for ginsenoside analysis. Ginseng grown with this method has a different ginsenoside composition compared with that of soil-cultivated ginseng, as shown in a study of 1-yr-old ginseng by Kim et al [20]. First, the leaves and roots of hydroponic ginseng contain the ginsenoside Rh1, which is not detected in soil-cultivated ginseng roots [19].

Rh1 has been reported to possess antiallergic and anti-inflammatory activities [24]. Second, hydroponic-cultured leaves contain a lower ratio of PPD/PPT (0.19) compared with soil-cultivated ginseng leaves (0.35), as shown by Han et al [25]. In particular, the percentage of the ginsenoside Re in hydroponic-cultured ginseng leaves (about 60%) was about three times higher than in its root (about 20%). Soil-cultivated ginseng

leaves also contain the highest amount of Re compared with the other ginsenosides, see more but this amount is only 40–50% of the total ginsenoside content [21]. Re is well known to be a physiologically active substance with anti-inflammatory effects [26] and antidiabetic activities [27]. The levels of this ginsenoside can reach up to 60% in ginseng berries [23]; the highest amount found in the ginseng plant. Based on these findings, hydroponic culturing of ginseng leaves can be used to produce Re. These data confirm that the composition of individual ginsenosides may differ depending on the cultivation system [20]. The higher content of PPT-type ginsenosides in leaves could be related to the positive

correlation between light and PPT-type ginsenosides, which corresponds with the observation that high light Selleckchem Cobimetinib transmission increased PPT-type ginsenosides in the leaves of ginseng plants [19]. To the best of our knowledge, information about the changes in ginsenoside content in the leaves and roots of ginseng during its different foliation stages has not been reported. During foliation, the production and composition of ginsenosides changes in leaves and roots (summarized in Fig. 5). The total ginsenoside content decreased in the roots (Fig. 3) and increased in the leaves (Fig. 2), with an increased accumulation of genes related with ginsenoside biosynthesis (Fig. 4) observed when the shoots elongated and the leaves opened. After sprouting, the metabolites already stored in the roots from the last season might be transported to parts of the plant above ground. During photosynthesis, the main sugar products are synthesized in the leaves and are transported to the roots for storage.

00) (Ara), and β-D-xylopyranosy (δ 5 43) (Xyl) were identified T

00) (Ara), and β-D-xylopyranosy (δ 5.43) (Xyl) were identified. The above evidence suggested that 3 possesses the structure of 20(S)-protopanaxadiol 3-O-β-D-xylopyranosyl-(1→2)-β-D-glucopyranosyl -(1→2)-β-D-glucopyranoside-20-O-α-L-arabinopyranosyl-(1→6)-β-D-glucopyranoside (notoginsenoside-FZ). The known compounds were identified as notoginsenoside-Fa (4) [15], ginsenoside-Rb1 (5) [15], notoginsenoside-Fc (6) [15], vina-ginsenoside-R7 (7) [18], ginsenoside-Rc (8) [17], ginsenoside-Rd (9) [18], notoginsenoside-Fe (10) [15], gypenoside-IX (11) [15], 20(S)-ginsenoside-Rh1

(12) [19], 20(R)-ginsenoside-Rh1 (13) [19], ginsenoside-F1 (14) [20], 20(R)-protopanaxadiol PLX-4720 (15) [21], 20(S)-protopanaxadiol (16) [21], protopanaxatriol (17) [22], panaxadiol (18) [21], 20(S)-ginsenoside-Rh2 (19) [23], 20(R)-ginsenoside-Rh2 (20) [23], and 20(S)-ginsenoside-Mc (21) [16] by NMR

and mass spectrometric analyses and by comparison of obtained values with literature values of the corresponding compounds. The current data (Table 2) suggest that protopanaxadiol (PPD)-type aglycones are more effective than dammarane triperpenoids having more than three sugars and that the presence of sugar moieties reduces the PTP1B inhibitory activity of the compounds. Compound 15 [20(R)-PPD] was more effective than compound 16 [20(S)-PPD] with inhibitory concentration 50 (IC50) values of 21.27 μM and 57.14 μM, respectively, despite the GW3965 datasheet fact that they differ from each other only by the absolute configuration of chiral carbon of C-20. Compound 20 [20(R)-ginsenoside-Rh2] was also more effective than compound 19 [20(S)-ginsenoside-Rh2]. These results suggest that 20(R)-PPD-type triterpenoids are more effective than Chloroambucil 20(S)-PPD-type triterpenoids. The IC50 values of 12, 13, 14, and 17 showed that protopanaxatriol (PPT)-type triterpenoids exhibit no PTP1B inhibitory activity at all. Ginsenosides possess antidiabetic activity, but their mechanisms are different. For example, ginsenoside Rb1 promotes adipogenesis through the regulation of peroxisome proliferator-activated receptor (PPAR)-γ and microRNA-27b, providing a good illustration to explain

the antidiabetic effect of the ginsenoside [24]. The total saponins and ginsenoside Rb1 of ginseng stimulate the secretion of glucagon-like peptide-1 (GLP1) in vivo and in vitro, demonstrating an antidiabetic effect [25]. Ginsenoside Re reduces insulin resistance by activating the PPAR-γ pathway and inhibiting tumor necrosis factor (TNF)-α production [26]. However, the current study shows that the antidiabetic effects of P. notoginseng may be a result of the inhibitory activity of some ginsenosides against PTP1B. In the present study, we isolated three new dammarane-type triterpenoids, elucidated as notoginsenoside-LX (1), notoginsenoside-LY (2), and notoginsenoside-FZ (3), along with 18 known compounds from P. notoginseng leaves and all compounds were firstly evaluated for the inhibitory activity against PTP1B.

The unit can process up to four samples independently

The unit can process up to four samples independently GPCR Compound Library at the same time. The ParaDNA Sample Collector (Life Technologies®: 4484203) is a disposable plastic device used in a similar manner as a traditional cotton swab (Electronic Supplementary Material Fig. 1b). Collection of cellular material

occurs through adsorption onto the plastic head of the device and can be recovered from both evidential swabs (termed indirect sampling) or directly from an evidence item (termed direct sampling). The device is operated by pushing the collar, forcing four sampling tips into a closed position ready for collection (Electronic Supplementary Material Fig. 1c). After sampling, this process is reversed separating the tips before the sample collector is inserted into the 4-well PCR plate, introducing the DNA template while

simultaneously sealing the PCR wells. The ParaDNA Screening Test (Life Technologies®: 4484202) contains four independent PCR [19] reactions pre-loaded into the custom designed 4-well PCR plate (Electronic Supplementary Material Fig. 1d). The assay uses HyBeacon™ technology [9] and [20] to amplify ERK phosphorylation and detect 2 STRs and the Amelogenin gender marker. The TH01 locus (amplified fragment size 143-187 bp, alleles detected 5-9.3 + ), D16S539 locus (amplified fragment size 131-183 bp, alleles detected alleles 8-15 + ) and the gender marker Amelogenin (amplified fragments size 188-194 bp, alleles detected X, Y) are separated into each of the four wells. The ParaDNA Software controls the instrument, analyzes the data and displays the screening result. The software detects changes in fluorescence (ΔRFU) as

a HyBeacon probe melts away from its amplified allele at a specific melting temperature (TM) between 20 °C and 70 °C (Electronic Supplementary Material Fig. 2a). The temperature at which this fluorescence change occurs varies with the length of the amplified allele. This temperature separation enables the software to attribute a proportion of the overall fluorescence change to each possible allele. System variability causes small fluorescence DCLK1 changes even when an allele has not been amplified. This system noise is determined by considering data from a large number of samples (Electronic Supplementary Material Fig. 2b). Some of these are known to contain the allele of interest and others do not. The noise is then rejected with a simple threshold. The software converts this data into an easily interpretable colour-coded ‘DNA Detection’ result as follows: • Red–No DNA Detected. Fluorescence change consistent with negative control data.

Flow and pressure signals were then passed through 8-pole Bessel

Flow and pressure signals were then passed through 8-pole Bessel low-pass filters (902LPF, Frequency Devices, Haverhill, MA, USA) with the corner frequency set at 100 Hz, sampled at 200 Hz with a 12-bit analog-to-digital converter (DT2801A, Data Translation, Marlboro, MA, USA), and stored on a microcomputer. All data were collected using LABDAT Ivacaftor mouse software (RHT-InfoData Inc., Montreal, QC, Canada). Lung resistive (ΔP1) and viscoelastic/inhomogeneous (ΔP2) pressures, static elastance (Est), and viscoelastic component

of elastance (ΔE) were computed by the end-inflation occlusion method ( Bates et al., 1985 and Bates et al., 1988). Briefly, after end-inspiratory occlusion, there is an initial fast drop in transpulmonary

pressure (ΔP1) from the pre-occlusion value down to an Baf-A1 manufacturer inflection point (Pi) followed by a slow pressure decay (ΔP2), until a plateau is reached. This plateau corresponds to the elastic recoil pressure of the lung (Pel). ΔP1 selectively reflects airway resistance in normal animals and humans and ΔP2 reflects stress relaxation, or viscoelastic properties of the lung, together with a small contribution of time constant of alveoli ( Bates et al., 1988 and Saldiva et al., 1992). Lung static and dynamic elastances (Est and Edyn, respectively) were calculated by dividing Pel and Pi by tidal volume, respectively. ΔE was calculated as Est − Edyn, and reflects the viscoelastic component of elastance ( Bates et al., 1985 and Bates et al., 1988). Heparin (1000 IU) was intravenously injected immediately after the determination of pulmonary mechanics. The trachea was clamped at end-expiration and the animals were euthanized by exsanguinations via sectioning of the abdominal aorta and the vena cava. The lungs were removed and weighed. Functional residual capacity (FRC) was determined by volume displacement (Scherle, 1970). Left lungs were then fixed with Millonig formaldehyde (100 ml HCHO, 900 ml H2O, 18.6 g

selleck compound NaH2PO4, 4.2 g NaOH), routinely prepared for histology, embedded in paraffin, and two 3-μm-thick longitudinal slides from the left lung were cut and stained with hematoxylin–eosin. Morphometric analysis was performed with an integrating eyepiece with a coherent system made of a 100-point and 50-line (1250-μm-long each) grid coupled to a conventional light microscope (Axioplan, Zeiss, Oberkochen, Germany). The fraction areas of collapsed and normal alveoli were determined by the point-counting technique at a magnification of ×200 across 10 random non-coincident microscopic fields per animal. Points falling on normal or collapsed alveoli were expressed as percentage of points hitting those alveoli (Weibel, 1990). Polymorphonuclear (PMN) and pulmonary tissue were evaluated at ×1000 magnification across 10 random non-coincident microscopic fields in each animal.

Reliance on water transport of coal and culm bank recovery of coa

Reliance on water transport of coal and culm bank recovery of coal fines from the 1840s through the remainder of the 19th century increased the amount of coal fines or culm relative to earlier times demonstrates that the potential for particulate coal to become a prominent sediment marker in alluvial systems is substantial. Given that Pennsylvania Clean Stream Laws of the first half of the 20th century and more environmentally conscious mining methods have reduced the amount of coal silt entering streams, one would assume that deposition of the coal alluvium directly related to mining activities had ceased after 1960 AD. Therefore, a conservative age range estimate

http://www.selleckchem.com/products/ipi-145-ink1197.html for the MCE is 1840–1960 AD. Uncertainties regarding the potential number of events within the MCE still remain. A synthesis of archeological data suggest that deposits in which coal sands/silts predominate likely date no earlier Everolimus than 1841 AD and could

originate at a variety of times later in the 19th century. Deposits in which coal sands/silts are present but not a visually distinctive component date after 1825 AD and before 1841 AD. Flood histories also provide some clue as to event timing for the MCE. A combination of snow/ice, rapid warming and rain, led to a major flood along the Lehigh River in January, 1841. In addition to ice packs, large amounts of debris that included canal boats loaded with coal, contributed to the flood debris (Shank, 1972). A number of large floods

have occurred in the past ∼250 years and any one Oxalosuccinic acid could serve as a means to transport and deposit coal silt along floodplains and terraces in southeastern Pennsylvania. Dating any alluvial deposit may, of course, hinge on data unique to a specific locality. A cultural resource-mandated geomorphology study of Mill Creek, a tributary of the Schuylkill River, uncovered a coal sand deposit that ranged in thickness from 5 to 60 cm (Wagner, 2001). This deposit is unique in that it overlies a late 19th–early 20th century bottle dump. Growing on top of the coal sand deposit were trees estimated to be 50–60 years of age. These data suggest the MCE at the Mill Creek locality falls within the currently accepted age range of 1840–1960 AD and could possibly further refine the age of the MCE to less than a century in duration, e.g., 1900–1950 AD. Further refinement and potential subdivision of the MCE requires continued integration of stratigraphic data from archeological sites, flood histories, and continued research that evaluates the historical trends in the mining, processing, and transport of coal. One concern is the potential reworking of the alluvial coal event resulting in remobilization and deposition of MCE deposits (i.e., post-MCE).

4–5) Other terms to denote humans as an agent of global change w

4–5). Other terms to denote humans as an agent of global change were proposed in the early 20th century. From the 1920s to 1940s, for example, some European scientists referred to the Earth as entering an anthropogenic era known as the “noösphere” ( Teilhard de Chardin, 1966 and Vernadsky,

Decitabine 1945), signaling a growing human domination of the global biosphere (see Crutzen, 2002a and Zalasiewicz et al., 2008, p. 2228). Stoppani, Teilhard de Chardin, and Vernadsky defined no starting date for such human domination and their anthropozoic and noösphere labels were not widely adopted. Nonetheless, they were among the first to explicitly recognize a widespread human domination of Earth’s systems. More recently, the concept of an Anthropocene found traction when scientists, the media, and the public grappled with the growing recognition that anthropogenic influences are now on scale with some of the major geologic

events of the past (Zalasiewicz et al., 2008, p. 2228). Increased concentrations of atmospheric greenhouse gases and the discovery of the ozone hole over Antarctica, for example, CCI-779 price led to increased recognition that human activity could adversely affect the functioning of Earth’s systems, including atmospheric processes long thought to be wholly natural phenomena (Steffen et al., 2011, pp. 842–843). Journalist Andrew Revkin (1992) referenced the Anthrocene in his book on global climate change and atmospheric warming and Vitousek et al.’s (1997)Science paper summarized human domination of earth’s ecosystems. It was not until Crutzen and Stoermer (2000; also see Crutzen, 2002a and Crutzen,

2002b) explicitly proposed that the Anthropocene began with increased atmospheric carbon levels caused by the industrial revolution in the late 18th century (including invention of the steam Inositol oxygenase engine in AD 1784), that the concept began to gain momentum among scientists and the public. Geological epochs are defined using a number of observations ranging from sediment layers, ice cores, and the appearance or disappearance of distinctive forms of life. To justify the creation of an Anthropocene epoch as a formal unit of geologic time, scientists must demonstrate that the earth has undergone significant enough changes due to human actions to distinguish it from the Holocene, Pleistocene, or other geological epochs. As justification for the Anthropocene concept, Crutzen (2002a) pointed to growing concentrations of carbon dioxide and methane in polar ice, rapid human population growth, and significant modification of the world’s atmosphere, oceans, fresh water, forests, soils, flora, fauna, and more, all the result of human action (see also Crutzen and Steffen, 2003 and Steffen et al., 2011). The Anthropocene concept has been increasingly embraced by scholars and the public, but with no consensus as to when it began.

This finding is in line with previous research done on trends in

This finding is in line with previous research done on trends in N selleck compound and P in Estonian rivers (Iital et al., 2005). Iital et al. (2005) found a downward trend in the amount of N in 91% of the studied sites while a downward

trend in the amount of P was observed in only 9% of the studied sites while also some upward trends were observed (9% of the studied sites). Table 2 and Fig. 3 show that there is a lot of variability between the catchments in both east and west. The regional variation in trends can prove interesting for management strategies that aim to reduce nutrient loads into the Baltic Sea. The focus should be more on catchments experiencing an increasing trend or no trend at all. Previous modelling studies projecting future changes of nutrient loads into the Baltic Sea focused on the entire basin-scale (Arheimer et al., 2014, Donnelly et al., 2014, Meier et al., 2012 and Meier et al., ABT-888 order 2014). These modelling studies and their findings are often considered by policy makers to implement management strategies. Since our study demonstrates that large variation exists among the catchments, it can be suggested to look more at catchment-scale interactions when developing management strategies. This might reveal additional information which can lead to more focused and effective approaches to reach targeted reductions. The results

in Table 2 show the potential for nutrient reductions in the BSDB. Upscaling the 0.13 kg km−2 yr−2 reduction in TP observed in 13% of the total BSDB area (east + west) results in a potential reduction of 223 tonnes per year. Considering a similar upscaling for TN, there is a potential reduction of 10,980 tonnes per year. Target reductions Sorafenib cell line set by the Baltic Sea Action Plan (BSAP) correspond to a reduction of 135,000 tonnes TN and 15,250 tonnes TP by 2021 (HELCOM, 2007). If we assume these potential reduction rates for TN and TP, then the target reduction of TP would be reached in 68 years while the target reduction of TN would be reached in 12 years. Although it is unlikely that change rates calculated for the year 1970–2000 will be the same for 2000–2021, these

estimates suggest it is possible to reduce TP in the BSDB but that it will be difficult to reach the target reductions by 2021 without significant shifts in land management. Table 2 and Fig. 3 also show that the focus for management strategies should be more on P reduction rather than on N reduction as more catchments show an increasing trend rather than a negative trend for TP. This suggestion is further reinforced when the N:P ratio is taken into account. The N:P ratio steadily declined in eastern catchments from 30 to 16, which will ultimately affect the N:P ratio for the whole Baltic Sea. The results presented in this study suggest that a declining trend in N:P ratio is largely caused by an increase of TP from eastern catchments.

Multiplex bead arrays with 17 different analytes, including cytok

Multiplex bead arrays with 17 different analytes, including cytokines, chemokines, and a growth factor, were performed using sera and QFT-IT plasma samples using BD FACSVerse™ (BD Biosciences, San Jose, CA, USA). The analytes included IL-1β, IL-2, IL-4, IL-5, IL-6, IL-9, IL-10, IL-12p70, IL-13, IL-17A, IL-22, IFN-γ, TNF-α, IFN-α, sCD40L, CXCL10 (IP-10), and vascular endothelial growth factor A (VEGF-A). The manufacturer’s protocol (eBioscience, San Diego, CA, USA) was followed for the multiplex bead arrays. The concentration of each analyte was calculated using selleck chemical FlowCytomix Pro software

(eBioscience), and values out of standard curve ranges were adjusted by setting minimum and maximum values. Values of 17 analytes in QFT-IT plasma were corrected for background levels by subtracting negative control values (nil tubes). In order to abate false positive responses, responders were defined as those who showed higher values than twice the limits of detection in standard curves: 5.5 pg/mL for IL-9, 27 pg/mL for IL-17A, 34.5 pg/mL for CXCL10, 55 pg/mL for IL-1β, IL-2, IL-4, IL-5, IL-6, IL-10, IL-12p70,

IL-13, IFN-γ, TNF-α, IFN-α, VEGF-A, 110 pg/mL for sCD40L, and 220 pg/mL for IL-22. Concentration differences of the 17 analytes from sera and QFT-IT plasma samples from active TB patients, TB contacts with LTBI, and normal healthy controls were analysed by Kruskal–Wallis tests and Dunn’s multiple comparison tests. Mann Whitney tests were used to analyse concentration differences of 17 analytes between active TB and NTM diseases. Concentrations of the 17 analytes between pre- and post-treatment Natural Product Library datasheet in TB patients were analysed by Wilcoxon signed rank tests. P values were adjusted using Bonferroni correction to account for multiple comparisons. Diagnostic values of 17 analytes in sera and QFT-IT plasma were examined Rebamipide by analysis of the area under the receiver operating characteristic (ROC) curves (AUC). Median concentrations of serum IL-22,

CXCL10, and VEGF-A were significantly higher in 58 TB patients than in 55 controls (P < 0.05) while only VEGF-A concentration differed between active TB and LTBI groups (P < 0.01) ( Fig. 2A). Analysis of the AUC indicated that serum VEGF-A could be a good biomarker for discriminating active TB from LTBI (AUC = 0.7576, P < 0.001; Supplementary Fig. 1). Concentrations of the 17 analytes in the sera from 38 TB patients (Table 1), before treatment, were compared with those from 42 NTM patients at diagnosis. TB patients had significantly higher concentrations of Th1 and Th2 cytokines, as well as IL-17, than did the NTM patients. Five out of the 17 analytes (IL-2, IL-9, IL-13, IL-17 and TNF-α) were detected at statistically significant higher levels in TB patients than in NTM patients (Fig. 2B). On the other hand, TB patients showed significantly lower concentrations of sCD40L (P < 0.01) than did the NTM patients.

9% (m/v) saline (100 mL) followed by 4% (m/v) formaldehyde at pH

9% (m/v) saline (100 mL) followed by 4% (m/v) formaldehyde at pH 9.5 and 4 °C (800–1000 mL). The brains were removed from the skull, post-fixed for 4 h in the same fixative with the addition

of 20% sucrose and then transferred to 0.02 M potassium phosphate-buffered saline (KPBS) at Luminespib in vitro pH 7.4 with 20% (m/v) sucrose. The brains were sliced in four series of coronal sections (at bregma 2.70 mm, −0.30 mm, −1.80 mm, and −3.14 mm) at a thickness of 30 μm with the use of a freezing microtome and stored at −20 °C in buffered antifreeze solution (Sita et al., 2003). One series of each brain slice was stained by immunohistochemistry as follows: sections were treated in 0.3% (v/v) peroxide in KPBS + 0.3% (v/v)

Triton X-100 for 30 min and incubated in primary antiserum anti-c-Fos (PC38T IgG anti-c-Fos (Ab5) (4-17)) rabbit polyclonal antibody (Calbiochem, La Jolla, CA, USA) at 1:5000 and Roxadustat 3% (v/v) normal goat serum in KPBS + 0.3% (v/v) Triton X-100 for 18 h at room temperature. Sections were rinsed in KPBS and incubated for 1 h in biotinylated secondary antiserum made from goat anti-rabbit antibody (Jackson Labs 1:1000) for one additional hour in avidin–biotin complex (Vector, 1:500). Next, the sections were incubated in diaminobenzidine tetrahydrochloride (DAB; Sigma Chem Co.) and 0.01% (v/v) hydrogen peroxide dissolved in KPBS. The reaction was terminated after 2–3 min with repeated rinses in KPBS. Sections were mounted on slides and intensified with 0.005% (m/v) osmium tetroxide solution. To aid in the identification of brain regions presenting little or no c-Fos-immunoreactive neurons (mainly in the sections of control brain slices), Nissl method of counterstaining with thionin was used (Windle et al., 1943). Photomicrographs were acquired through a Spot RT digital camera (Diagnostics Instruments) adapted to a Leica DMR microscope

and an Apple Macintosh Power PC computer Fossariinae using the software Adobe Photoshop 5.0. Contrast, sharpness, colour balance and brightness were adjusted and images were combined in plates using Corel Draw 11 software. For the intravenous administration of nigriventrine, the rats were anaesthetised with chloral hydrate (7%, 350 mg/kg, ip) and submitted for venous catheterisation. A Silastic catheter containing heparinised saline (10 U/mL of pyrogen-free saline, Sigma, St. Louis, MO) was inserted into the femoral vein and sutured in place. The free end of the catheter was passed under the skin of the back, exteriorised between the scapulae, and plugged with a sterile wire stylet. A week later, nigriventrine (100 ng kg−1) was intravenously applied. For the quantitative analysis of c-Fos-ir and/or NMR1-ir cells, three representative slices of each brain region were chosen for each rat.