02 and AqAnalisys, Lynx Tecnologia Eletronica

Ltda, São Paulo, Brazil). The tests were repeated 3 times for each load. Between trials the strain gauges were allowed PS-341 nmr to recover. Gauges that did not recover to zero strain after 3 min were recalibrated (zeroed) in the software prior to the next experiment. All plastic mandibles (n = 10) were tested sequentially for seven conditions. The groups are identified as: Cont, B1, B1/SpCR, B1/SpW, B1/SpWCR, B1/SpFgExt, and B1/SpFgInt. (1) The Cont group, with no bone loss and no splinting, represented the control group (Fig. 3A and B). Fig. 3.  A plastic mandible for the seven experimental dental support conditions. (A) Buccal view in Cont group (no bone loss). (B) Lingual view in Cont group. (C) Bl group (bone loss). (D) Bl/SpCR group (bone loss, composite resin splint). (E) Bl/SpW group (bone loss, wire splint). (F) Bl/SpWCR group (bone loss, combination of wires and composite resin splint). (G) Bl/SpFgExt group (bone loss, extracoronal fibre-reinforced composite

and composite resin splint). (H) Bl/SpFgInt group (bone loss, intracoronal fibre-reinforced composite and composite resin splint). The collected strain data was subjected to a 3-way analysis of variance (ANOVA) to examine the effect of support tissue condition (with or without bone loss), tooth region, and mandible surface, as well as the interaction between these 3 parameters on the strain under 50, 100, and 150 N loading. Ponatinib chemical structure The Scheffe’s test was performed to determine click here differences between factor levels. All tests were performed at a significance level of α = .05. Statistical software (SPSS/PC, Version 10.0, SPSS, Chicago, IL) was used for statistical data analysis. The results of the 3-way ANOVA for the support tissue conditions, tooth regions, and mandible surfaces are presented in Table 1 for 50 N loading, in Table 2 for 100 N and in Table 3 for 150 N. The 3-way ANOVA indicated significant differences between the three factors (support tissue conditions, tooth regions, and mandibular surfaces; P < .05), irrespective

of load level. Of the 2-factor interactions, only the interaction between tooth region and mandible surface at the 50 N load level was significant (P = .03). The results of Scheffe’s multiple comparison test are shown in Table 4 for each of the three different load levels. At each load level same letters indicate mean strain values that were not significantly different (P > .05). Irrespective of the load levels, the mean strain values measured on the buccal surfaces were significantly higher than on the lingual surfaces, indicated by the different number indices (P < .001). The mean strain values obtained at the central incisor region were significantly higher than for the lateral incisor region, irrespective of load level or mandible surface (P < .001).

We first examined the whole cell conductance of the cells transfected using the SV40 and the CMV promoters (Fig. 2). Expectedly, 24 h after transfection, the whole cell conductance of SV40 plasmid cells was significantly lower than that of CMV promoter. Interestingly, Selleck FK228 48 h after transfection, the whole cell conductance was comparable between high and low expression cells. If the abilities

of these promoters did not change over time, this result suggests that the half-lives of Kir2.1 were different depending on the expression level. We next attempted to measure the half-life. We pulse-labeled the SNAP-Kir2.1 with a membrane-permeable fluorescent substrate for the SNAP tag, SNAP-cell-TMR-Star, 24 h after the transfection. SNAP-cell-TMR-Star covalently binds to the SNAP tag domain (Fig. 1A). After the washing-out of unbound dye for 2 h, we examined it microscopically and found that the SNAP-Kir2.1 fusion protein was successfully labeled in both cells transfected using the SV40 and the CMV promoters (Fig. 3A). The fluorescence of the cells transfected with the CMV promoter plasmid was significantly higher than that of the cells transfected with the SV40 promoter plasmid as we observed in whole cell current. Reportedly, HEK293 cells

endogenously express the O6-alkylguanine-DNA-alkyltransferase JNJ-26481585 nmr (Keppler et al., 2004), but the background fluorescence was negligible compared with SNAP-Kir2.1 (data not shown). This is probably due to the high level expression of SNAP-Kir2.1 and the 20-fold higher activity of the mutant SNAP-tag, which we used here. Initially, the fluorescence was mostly located at the plasma membrane of 293T cells in both cases, but some intracellular, punctuated fluorescence was observed in the CMV promoter-transfected cells (Fig. 3A). The intensity of the fluorescence decreased over

time. In the high-expression cells transfected with the CMV promoter plasmid, most SNAP-Kir2.1 proteins were internalized from the plasma membrane and the fluorescence was punctuated 24 h after labeling. In the low-expression cells Nintedanib (BIBF 1120) transfected with the SV40 promoter plasmid, most SNAP-Kir2.1 proteins were still located at the plasma membrane 24 h after the labeling, and some even after 48 h. We measured the fluorescence in the whole area of each cell and estimated the half-lives of the SNAP-Kir2.1 protein expressed by the two promoters (Fig. 3B). The fluorescence decreased faster in the high-expression cells than low-expression cells. The half-life was significantly shorter in the high-expression cells (18.2±1.9 h) than in the low-expression cells (35.1±2.3 h, n=5, p<0.0005, Student′s t-test) ( Fig. 3C). This result supports a hypothesis that a high level of Kir2.1 accelerates its own degradation. Microscopic measurement of fluorescence intensity can be affected by cell division, i.e., the density of labeled SNAP-Kir2.

The role of clusterin in brain cell death is contradictory, as both gene-deficiency and overexpression of clusterin inhibic brain damage in mice [29]. Although biomarkers of sepsis are not widely used in research or clinical practice, it is possible to evaluate

the utility of approaches that are currently available. this website The optimal use of biomarkers as surrogates in informing the design of definitive clinical trials presupposes a valid and extensive understanding of the natural history of the biomarker in the population of interest, and how its levels are modified by therapeutic intervention [8]. These data can then be integrated using meta-analytic techniques to evaluate the capacity of a biomarker to predict a clinically important outcome [30]. A methodology for evaluating the level of evidence that a given

check details biomarker might serve as a reliable surrogate outcome measure has recently been proposed, but its utility in the assessment of biomarkers for diseases such as sepsis where mortality is considerable is unknown [31]. In conclusion, we here provide the first clusterin serum analysis of pediatric patients with sepsis and septic shock. We have shown a significant relationship between the levels of clusterin and pediatric patients with septic state. Further studies are required to elucidate the clinical impact of the observed organ-protective properties of clusterin and next studies are needed to examine his potential roles as predictive outcome markers, as well as his precise functional roles in sepsis, or possible therapeutic potential. JZ – study design, data collection and interpretation, literature search, MF – acceptance of final manuscript version. None declared. None declared.

The work described in this article have been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for experiments involving humans; EU Directive 2010/63/EU for animal experiments; Uniform Requirements for manuscripts submitted to Biomedical journals. The own research were conducted according to the Carbachol Good Clinical Practice guidelines and accepted by local Bioethics Committee, all patients agreed in writing to participation and these researches. “
“Jednym z najczęstszych niepożądanych skutków antybiotykoterapii u dzieci jest biegunka. Definiuje się ją jako oddawanie przez dziecko stolców częściej niż dotychczas i/lub stolców o luźniejszej konsystencji, których pojawianie się ma związek z antybiotykoterapią, a nie można ich wytłumaczyć inną przyczyną. Na związek wystąpienia biegunki z antybiotykoterapią wskazuje okres wystąpienia objawów nie dłuższy niż 6 tygodni od rozpoczęcia stosowania antybiotyku [1]. Najcięższą postacią kliniczną biegunki związanej z antybiotykotetrapią jest rzekomobłoniaste zapalenie jelita grubego wywołane zakażeniem beztlenową bakterią Clostridium difficile.

, 2003 and Parris et al., 1999), as well as causing

the dysfunction of pre-synaptic muscarinic (M2) receptors, which enhances the release of acetylcholine (Nie et al., 2009). Chronic exposure to TNF has also been associated with the desensitisation of G-protein coupled receptors (Guo et al., 2005, Kang et al., 2006 and Osawa et al., 2007). The latter two mechanisms have been implicated in asthma pathogenesis. It is noteworthy that other mechanisms may also be involved, as the elevated secretion of TNF causes airway smooth cell contraction by activating different intracellular pathways, depending on pre- and post-transcriptional activity (Tirumurugaan et al., 2007 and Jude et al., 2011). In selleck products addition, we herein show that the TNF action is not dependent on the enhanced protein expression of TNFR1 or TNFR2, which suggests that the elevated concentration of this cytokine in response to in vivo HQ exposure may alter the ability of TNFRs to activate muscarinic receptors, the sensitivity or expression of muscarinic receptors, or subsequent signalling pathways. Mast cell degranulation is a hallmark of airway hyperresponsiveness. Selleckchem RO4929097 Existing data on the mechanism by which TNF promotes mast cell degranulation and consequently, the release of a wide range of smooth muscle cell active mediators, including histamine, cytokines and leukotrienes, is controversial (Brzezińska-Blaszczyk et al., 2000,

Brzezińska-Blaszczyk et al., 2007 and Brzezińska-Blaszczyk and Pietrzak, 1997). Here we show that in vivo HQ exposure

causes CTMC and MMC degranulation that is dependent Astemizole on TNF release, as the pharmacological inhibition of TNF synthesis reduced mast cell degranulation. Furthermore, TNF-induced mast cell degranulation of the HQ-induced tracheal hyperresponsiveness to MCh was further highlighted by the fact that pre-treatment with mast cell stabilizer partially reversed tracheal hyperresponsiveness. The data shown herein strongly suggest that the release of TNF by tracheal epithelium after low levels of HQ exposure triggers airway hyperresponsiveness in response to cholinergic stimulation. In addition, secreted TNF plays an important role in mast cell degranulation, with the subsequent release of chemical mediators that contribute to the maintenance of HQ-induced tracheal hyperresponsiveness. Together, the activation of these pathways may contribute to the development of airway diseases in subjects chronically exposed to HQ, such as smokers and inhabitants of polluted areas. The authors declare that there are no conflicts of interest. The authors thank Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) for financial support (grants no. 08/55382-7; 09/03964-5). Sandra H. P. Farsky and Wothan Tavares de Lima are fellows of the Conselho Nacional de Pesquisa e Tecnologia (CNPq). Simone M.

In protocol with colchicine, the cytotoxic, as observed by MI decrease, and chromosomal

abnormalities effects were observed in lymphocytes in all cell cycle phases analyzed. On the other hand, only in G1 phase, PHT was active in all concentrations tested. This implies that G1 phase seen to be more sensitive to PHT effects. Interestingly, PHT induced an increase in mitotic index in experimental protocols without colchicine, corroborating its action as an antitubulin agent. The most expressive was found in G2 phase, where the MI of control was 0.2%. In the presence of PHT (0.25, 0.5, 1.0, 2.0 or 4.0 μM), the MI were 1.9%, 3.2%, 3.5%, 3.0%, and 2.5%, respectively. PTH was able to increase the MI LY294002 concentration from 0.2% to 3.5 % (Table 3). The interaction of tubulin inhibitors with microtubules results in alteration of microtubule dynamics, which may lead to damage of the mitotic spindle (Kanthou et al., 2004; Vitale et al., 2007). Herein, the mutagenic potential of a representative of the phenstatin family, tubulin inhibitors, was evaluated for the first time. Ours results suggesting that PHT induces DNA damage and exerts clastogenic effects in human lymphocytes. Ponatinib mw Although this genotoxic effect of PHT could be biologically relevant as an alternative strategy for killing tumor cells, this effect needs to be extensively evaluated to assess the safety of this chemical. The effects of PHT

on DNA integrity were evaluated using the alkaline comet assay in peripheral blood lymphocytes. The comet assay is a genotoxicity test widely applied, both in vivo and in vitro, to different organs and tissues ( Hartmann et Histidine ammonia-lyase al., 2003 and Collins, 2004). PHT treatments increased the levels of DNA damage. Antitubulin agents have been previously tested in the comet assay in vitro and in vivo, and they display a variety of genotoxic results. Some studies showed that paclitaxel ( Lee et al., 2003), colchicine ( Villani et al., 2010), or vincristine ( Recio et al., 2010) do not induce DNA damage (negative results in the comet assay). The lack of detectable DNA damage using

the comet assay is consistent with tubulin, rather than DNA, as a primary cellular target of these agents ( Recio et al., 2010). On the contrary, Branham et al. (2004) showed that the chemotherapeutic drug paclitaxel induces DNA damage (detected by the comet assay) in peripheral blood lymphocytes. This effect seems to be influenced by drug concentration, time of exposure, and the mechanism of DNA repair. However, the exact mechanism by which antitubulin agents induce DNA damage is not clear. CAs and MI were determined in cultured human lymphocytes treated with PHT during different cell cycle times: G1 (Table 1), G1/S (Table 2), and G2 (Table 3). The experimental protocols of CA analysis were performed in the presence or absence of colchicine to evaluate the action of the PHT in the mitotic phase.

[ 11••, 12 and 13]). The TP53 somatic mutations were aggregated, their spectrum was reported as specific for the given cancer type, and this spectrum

was then compared to mutations generated experimentally in in vitro or in vivo systems [ 11•• and 13]. It should Selleck MDV3100 be noted that the mutational spectra of other genes, albeit rarely, were also used for such analysis [ 14]. These early studies revealed a significant heterogeneity of the TP53 spectra across different cancer types, which allowed associating some patterns of mutation to known carcinogens. Here, we provide a brief summary of some of the more important findings while details could be found in Refs. [ 11••, 12 and 13]. The TP53 spectrum of skin carcinomas exhibited C > T and CC > TT mutations at dipyrimidines (all substitutions and dinucleotide substitutions are referred to by the pyrimidine(s) of the mutated Watson-Crick base pair). This was consistent with the in vitro described

mutational signature of UV light. The TP53 mutational spectrum derived from lung cancers CSF-1R inhibitor in tobacco smokers was overwhelmed by C > A substitutions, which coincided with the class of mutation produced experimentally as a result of bulky adduct formation by tobacco carcinogens on guanine [ 15]. In other tobacco associated cancers, such as oesophageal and head and neck tumours, C > A mutations (while still ubiquitous) were less common while there was a significant increase of T > C mutations. Interestingly, in both smokers and non-smokers, C > T and C > G mutations at non-CpG sites were elevated when these compared to all other cancer types, with bladder tumours harbouring the most

C > G mutations [ 11••]. Additionally, it was demonstrated that C > A transversions were common in hepatocellular cancers and these mutations were believed to be associated with aflatoxin, a known carcinogen commonly found in food from southern Africa and Asia [ 16]. Lastly, all cancer types harboured at least some C > T mutations at CpG dinucleotides (mutated base underlined), a process attributed to the normal cellular event of deamination of 5-methylcytosine [ 11••]. The analyses of TP53 spectra were the first attempts to bridge the gap between molecular cancer genetics and epidemiology [ 17]. The large number of studies examining TP53 spectra required a computational resource to facilitate and retrieve the already identified somatic mutations. At first these data were managed by the researchers that were generating it but in 1994 the International Agency for Research on Cancer (IARC) started to maintain a database while providing a free access to it [ 17]. The first release of the IARC TP53 database contained ∼3 000 somatic mutations [ 18] while the most recent version (R16) released in November of 2012, which can be found at http://p53.iarc.fr/, contains almost 30 000 somatic mutations in TP53. Though extremely informative, the data gathered from single gene studies have significant limitations.

The states of the variable are described as intervals, which are quite large but can be easily modified if necessary. There is one specific amount for the oil spills, of 30 000 ton, which is the largest oil spill considered by the authorities in Finland, and reflects the preparedness level for Finland, see SYKE (2011). It is an independent variable, which exists in three states: spring (Mar.–May), summer (Jun.–Aug.) and autumn (Sept.–Nov.). Winter

is excluded for several reasons, first as oil-spill combating during ice season is different than during the other seasons. Second, some of the oil-combating vessels are not capable of operating in ice conditions. Third, there is no reliable click here prediction model for the movement of oil in ice conditions in the GOF, (Helle et al., 2011). The prior distribution for the variable Season is presented in Table 2 and informs about the probability that an accident resulting in an oil spill would occur on the Gulf of Finland specifically during this season of the year. The distribution was gained from the compiled accident statistics of HELCOM between the years 1989 and 2005 – ( HELCOM, 2013). It is one of the most important factors affecting the cost of the clean-up operation. It affects the cost in a multitude of ways, starting from the Galunisertib in vivo way that the spilled

oil spreads in water, which affects the time it takes for the spill to reach the shoreline. In addition, heavier oil has the tendency to sink; this in turn affects the possible recovery Evodiamine percentage of the oil-combating vessels.

The oil type also affects the efficiencies of the combating vessels, due to the fact that some oils are less likely to adhere to the brushes used by the combating vessels. In the presented model, this variable exists in three states: light, medium and heavy. The probabilities for each state are given in Table 3. They are based on an estimation made by experts from the Finnish Environment Institute considering the oil tankers traffic in the Gulf of Finland, see for example Juntunen et al. (2005). For the Gulf of Finland, it is estimated that an oil slick would arrive ashore quite quickly. In the case of an accident taking place in the middle of the sea, it could take between one to nine days for the oil to reach the shoreline, see for example Andrejev et al., 2011, Viikmäe and Soomere, 2013 and Soomere et al., 2011. Therefore the variable is set to consist altogether of ten intervals, ranging from zero to ten days. We assume, the prior distribution for this variable follows the Gaussian distribution, with μ = 5 days and σ = 2 days. However, if the spill takes place in Finnish waters of the Gulf of Finland, it is estimated that it would take a maximum of three days before the oil reaches the shore, ( Hietala and Lampela, 2007).

A trigonometric polynomial is used to assign values at any model time and for all of the grid points. Initial phytoplankton values Palbociclib datasheet for January and December are very limited, so a constant value of 0.1 mgC m−3 is defined; but the model is not sensitive to the initial conditions of phytoplankton concentration (in January). Also, the data for the detritus content at the bottom are not available, so the instantaneous sinking of detritus is a more arbitrary model assumption. The initial amount of detritus at the bottom is prescribed as 200 mgC m−2 for the whole Baltic Sea.

The initial values for total inorganic nitrogen are taken from SCOBI 3D-model for January. The initial vertical distributions of nutrient, phytoplankton, zooplankton and detritus pool are known: Phyt(x, y, z, 0)=Phyt0(x, y, z)0≤z≤H,Nutr(x, y, z, 0)=Nutr0(x, y, z)0≤z≤H,Detr(x, y, z, 0)=Detr0(x, y, H)z=H.The

vertical gradients of the phytoplankton and nutrient concentration fluxes are zero at the sea surface (z = 0): FPhyt(x, y, 0, t)≡Kz∂Phyt(x, y, z, t)∂z|z=0−wzPhyt(x, y, 0, t)=0,FNutr(x, y, 0, t)≡Kz∂Nutr(z, t)∂z|z=0=0. The bottom flux condition for phytoplankton and nutrient is given by FPhyt(x, y, H, t)≡−wzPhyt(x, y, H, t),FNutr(x, y, H, t)≡Kz∂Nutr(x, y,z, t)∂z|z=H=gNREMD.This flux Fphyt(H) enters the benthic detritus equation as a source term. The boundary condition provides click here the mechanism by which the water column is replenished by nutrients derived from benthic remineralization. In order to assess the accuracy of the CEMBSv1 model for determining the parameters of the Baltic ecosystem, we compared the temperatures and chlorophyll a concentrations obtained from the model with those measured in situ and in water samples for five years Adenosine (2000–2004). For these comparisons

the relevant errors of these simulations were calculated in accordance with the principles of arithmetic and logarithmic statistics: 1. Arithmetic statistics: 2. Logarithmic statistics: a) Relative mean error:〈ε〉〈ε〉 [%] (systematic) 〈ε〉=1N∑iεiwhere εi=xi, mod−xi, exp/xi, expεi=xi, mod−xi, exp/xi, exp e) Mean logarithmic error: g〈ε〉〈ε〉g [%] (systematic) g〈ε〉=10〈L〉−1〈ε〉g=10〈L〉−1where L=log(xi, mod/xi, exp)L=log(xi, mod/xi, exp) b) Standard deviation of ε: σε [%] σε=1N(∑i(εi−〈ε〉)2) f) Standard error factor: χ χ=10σLχ=10σLwhere σL is standard deviation of L c) Absolute mean error: 〈ε′〉〈ε′〉 [%] 〈ε′〉=1N∑iεi′where εi′=xi, mod−xi, exp g) Statistical logarithmic errors: σ–, σ+ [%] σ−=1/χ−1σ+=χ−1 d) Standard deviation of ε′: σε′ [%] σε′=1N(∑i(εi′−〈ε′〉)2) Full-size table Table options View in workspace Download as CSVwhere xi, mod – calculated values, xi, exp – measured values. The following aspects were taken into account in the assessment of the modelled ecosystem parameters: 1.

Hence the conversion of reducing sugars into ethanol during fermentation was initiated Selleckchem SCH727965 with high inoculums loads of yeast cells. The gross energy value of ethanol produced from the different steam pretreated biomasses at laboratory scale

were 2.21, 1.75, 1.16, 1.69, 1.46 MJ/kg respectively for the A. mangium leaves, A. mangium pods, Ficus leaves, paddy straw and sorghum stubbles. The ethanol-equivalent energy consumption from pretreatment of biomass to ethanol production was equivalent to 0.81 MJ/kg (based on the operating parameters of high-pressure steam vessel and fermentor). The pseudo-net energy value of ethanol produced from the steam pretreated A. mangium leaves, A. mangium pods, Ficus leaves, paddy straw and sorghum stubbles were 1.39, 0.94, 0.34, 0.88, 0.65 MJ/kg of the biomass respectively. The leaves of Acacia showed high net-pseudo energy value and Ficus with less net energy value of ethanol yield. It also suggests though a significant level of energy is consumed for the lignocellulosic ethanol production

from the steam pretreated biomass, it is an indispensable source of alternative buy CHIR-99021 fuel energy. The strong crystalline structure of cellulose, complex hemicelluloses and lignin contents of the crop residues and tree leaf litters limits accessibility of plant biomass to hydrolytic enzymes [32]. Bumetanide However the marine bacterial isolate JS-C42 showed the efficient lignocellulolytic ability to release the reducing sugars from steam pretreated biomass due to the increase in the accessible cellulosic surfaces for the enzymatic actions. Thus the synergistic action of cellulolytic enzymes with the steam pretreated substance

helps in the production of cellulosic ethanol by the substantial release of simple reducing sugars. This study enumerated the release of reducing sugars from the lignocellulosic materials by the subsets of lignocellulolytic enzymes secreted by the bacterial isolate JS-C42 without any external input of commercial enzymes. The average diameter size of the bacterial cells grown on tryptic soy broth without cellulose was 0.117 μM. When the cells grown on Sigmacell cellulose, they were colonized on the surface of the cellulose substrate and they appeared plumpier than the cells grown on tryptic soy broth. The average diameter of cells grown on the microcrystalline cell surface was 0.150 μM. Atomic force microscope image analysis of 12 h grown Isoptericola sp. JSC-42 in the present study revealed the mycelial form ( Fig. 4) with embedded cocci shaped cells appearing like beads on a string arranged in an irregular pattern. The diameter of the cells in the mycelium ranged 0.107–0.264 μm.

Os animais foram sorteados por amostragem aleatória simples e designados para o grupo controle

(grupo C) ou para o grupo experimental (grupo E). Estavam acondicionados em gaiolas individuais de polipropileno (49 × 34 × 16 cm, modelo GC‐112, Beiramar), this website com proteção de grade na região superior e maravalha no fundo mantidos em local arejado (Laboratório de Fisiologia do Instituto de Ciências Biológicas da Universidade Federal de Juiz de Fora), com iluminação natural e artificial (12 horas) e escuridão (12 horas) à temperatura ambiente. As gaiolas eram separadas 10 cm uma das outras e receberam numeração de 1C até 20C no grupo controle e de 1E a 20E no grupo experimental de acordo com o sorteio, permanecendo sempre no mesmo local até o final

do experimento. Duas estantes, uma para o grupo controle e outra para o experimental, foram usadas para a disponibilização das gaiolas. As estantes possuíam barras de metal, dispostas de modo horizontal, dividindo o móvel em andares. O andar superior distava 146 cm do chão, enquanto o andar mais inferior estava a apenas 22 cm do solo. Os animais tiveram 7 dias de adaptação ao novo ambiente e receberam água através de garrafas de vidro numeradas (numeração idêntica à da gaiola) e adaptadas a um bico de metal, conectado a uma rolha de borracha, lembrando o aspecto de uma mamadeira. O uso destes materiais procurava evitar o desperdício da água quando a garrafa era colocada de maneira inclinada sobre a grade de proteção da gaiola. A ração foi administrada à vontade durante os 7 dias de período adaptativo. PD-1 assay A maravalha era trocada a cada 5 dias. O experimento teve início no oitavo dia após a chegada dos ratos e seguiu sempre a mesma rotina diária. Os ratos eram pesados e encaminhados para a administração intragástrica por sonda metálica (gavagem) de solução fisiológica (grupo controle) ou tegaserode (grupo experimental). A gavagem foi realizada sempre por 2 pessoas; a primeira introduzindo a sonda Tyrosine-protein kinase BLK metálica

até atingir o estômago e a segunda fixando as patas traseiras do animal com a finalidade de evitar que o mesmo se ferisse ao movimentá‐las (fig. 1). O horário da realização do procedimento girava em torno de 11 horas da manhã. Os ratos do grupo C receberam por 15 dias através de gavagem 1,0 ml de solução fisiológica 0,9% enquanto os ratos do grupo E receberam 1,0 ml de tegaserode na concentração 0,03 mg/ml. A dosagem de 0,03 mg/ml de tegaserode foi obtida pela trituração e maceração do comprimido de 6,0 mg até atingir a forma de pó e diluição em 200 ml de solução salina, para conseguir a concentração desejada. Foram usadas seringas plásticas (para a injeção da solução salina ou tegaserode), da marca Embramac, com 1,0 ml de capacidade, separadas para cada grupo e luvas descartáveis, tamanho médio, marca Embramac para a manipulação dos animais.