The input data of the algorithm include the number and depths of

layers, the IOPs of each layer, the absorption coefficient of the bottom, light conditions (zenith and azimuth solar angles, ratio of light coming from a diffuse sky) as well as the wind conditions (speed and direction) to calculate wave roughness (see Cox & Munk 1954). For each calculation a diffuse light ratio of 0.3 was used, and the atmospheric phase function was approximated by Rayleigh theory. The depth of 2000 m was chosen as being large enough to avoid any bottom-related effects; the wind speed was set at 5 m s− 1. The phase functions used as input data for our modelling where chosen to fit the PD-1/PD-L1 inhibitor drugs same value of the backscattering ratio.

They are the average Petzold phase function (Mobley 1994), the Henyey-Greenstein phase function with average cosine g = 0.9185, and four Fournier-Forand phase functions. All have the same value of the backscattering ratio Smad inhibitor bb/b = 0.0183. Freda & Piskozub (2007) showed that the refractive index parameter n of Fournier-Forand phase functions, best fitted to measurements, can vary from less than 1.01 to about 1.25. Consequently, values of n equal to 1.01, 1.05, 1.1 and 1.2 were chosen to obtain various shapes of FF phase functions, calculated using ( Forand & Fournier 1999): equation(2) β˜cum=11−δδv1−δv+1−12sinθ/21−δv+1++1−δ180v16πδ180−1δ180v[cosθ−cos3(θ)], where v=3−μ2,u=2sinθ2,δ=u23n−12, and δ180 is δ determined for a scattering angle θ = 180 deg. Values of the second FF parameters μ, for given bb/b, were obtained from equation(3) μ=2log2bb/bδ90−1+1logδ90 where δ90 is δ determined for a scattering angle

θ = 90 deg. The input phase functions were prepared in cumulative form. But they are shown (see Figure 1) as phase functions (non-cumulative) so as to depict more details for backward angles (90–180 degrees). Because for an infinitely deep ocean, the IOP parameter controlling the light field as a function of optical depth is the single scattering albedo ω0 = b/c, we present our results as its function (unlike Figures 6 and 7 of CMLK06, which used bb/a). Thalidomide This choice of presentation was arbitrary because we limited ourselves to one backscattering ratio (one of the average Petzold functions) and therefore the only free parameter we had was the absorption coefficient a. We simply decided that b/c was a more ‘natural’ way of showing this variability than bb/a. The results are presented in Figure 2 as the ratio of the Monte Carlo calculated RSR for a given phase function to the value calculated for the average Petzold phase function. The results show that in most of the single scattering albedo domain the choice of FF functions of identical bb/b may result in a difference of up to 5% in calculated RSR values. This variability is independent of the variability between FF-modelled and measured phase functions observed in CMLK06.

The components with condition scores at or below the 25th percentile in the Worst10% (the ‘worst of the worst’) included 11 habitats, 16 species or species groups, 3 ecological processes and 1 physical process (Table 6). Nineteen components (10 habitats, 6 species groups, 2 ecological processes, and 1 physical/chemical LY2109761 ic50 process) occur in both

the ‘best of the best’ and ‘worst of the worst’ categories, indicating a high level of spatial variability in their condition at the national scale. The condition and trends in the biodiversity and ecosystem health data of the N and SE regions demonstrate contrasting patterns. The regions have contrasting patterns of condition between the Best10% and Worst10% areas, and although both regions had high levels of stability overall, in both regions the Worst10% areas were considered to have GDC-0449 mw low levels of region-specific stability and high levels of region-specific deterioration (Fig. 5, Fig. 6). This region-specific pattern of condition, stability and deterioration mainly results from the scores and grades

assigned to habitats, although this was a weaker pattern in the N where the condition scores were overall higher than in the SE region. The overall condition of Australia’s marine environment was judged to be consistent with the ‘Good’ grade as defined in the scoring gradient (Table 2). This was determined using the median of all scores assigned to condition for all components in all regions. However, as Reverse transcriptase expected, substantive variability between regions was identified. The condition overall of biodiversity and ecosystem health in the N region is considered to be better than that of the two southern regions (SW and SE), and there is considerable large-scale

variability within each region. These patterns are related to the greater impacts of the human development and sea-based pressures. Within a single region, the range between the best and worst conditions is greatest in the SE, E and SW regions, adjacent to the landmass where the majority of Australia’s population reside, and where the history of pressures is greatest (eg Hewitt et al., 2004). Nonetheless, all regions have areas where there are high levels of past and current pressure that have substantively affected the condition of biodiversity and ecosystem health. While the national marine environment as a whole and that of each individual region was graded as either Good or Very Good, and the greatest number of biodiversity and ecosystem health components are considered to be Stable, the number of components that are considered to be Deteriorating substantially exceed the number of components that are Improving in condition. System trajectory overall therefore appears to be trending downwards. Also, there are examples of components where decline in condition is considered to have been halted, but there are few examples where components have recovered to Good or Very Good condition.

2c). Analysis of the compression testing of vertebrae based on two-way ANOVA (Table 4) showed no significant interaction between factor age and treatment. Stiffness and maximum force to failure were affected by both age and treatment, energy to failure was affected only by treatment. The predicted tissue modulus (based on finite element analysis) was dependent on age but not treatment. Unpaired t-test comparisons showed significant increases in stiffness within each group as a function of time (age) ( Fig. 3a). Significant increases in

maximum force to failure were observed both as a function of age within each group, as well as in treated groups at both time points ( Fig. 3b). Energy to failure was significantly lower SCH-900776 in the treated for 4 weeks Lumacaftor solubility dmso animals compared to respective controls ( Fig. 3c). Interestingly, FE analysis based on the μ-CT data predicted significant differences only for the tissue modulus in the treated animal groups as a function of age ( Fig. 3d). The qBEI image taken before the nanoindentation experiment showed the typical region selected for testing in one β-APN treated rat (Fig. 4a) and the image observed by environmental scanning electron microscopy (ESEM) after indentation shows the line of indents marked by red circles (Fig. 4b). The

ESEM image was overlaid on to the qBEI image and small square grids were placed over the indents and the quantitative mineral content at these points was extracted from the relevant pixels on the qBEI image taken before indentation (Fig. 4c). The mapping of calcium content from the qBEI measurements and the mapping of mechanical properties such as the indentation modulus, Er, and the hardness are shown in Figs. 4 (d–f). The calcium content was found to be lower in newly formed region near the outer sides of the trabeculae and, accordingly,

lower stiffness and hardness values were observed in these newly formed bone regions. The relation between the indentation moduli and the local calcium content is represented in Fig. 4g. Phospholipase D1 The values of the indentation modulus and of the hardness in the newly formed bone of the β-APN treated tissues are decreased by 35% (p < 0.001) and 40% (p < 0.003), respectively, compared to control samples in areas with 19 wt.% calcium or less, which typically correspond to newly formed bone. For older mature bone, with calcium content typically higher than 19 wt.%, there were no significant changes in the indentation modulus or in the hardness (Figs. 4h and i). Spectroscopic analysis of L5 vertebrae revealed no significant differences between control and treated animals in mineral to matrix ratio as a function of either animal age or treatment (based on two-way ANOVA analysis; data not shown) in any of the surfaces analyzed. Additionally, there were no significant differences in mineral maturity/crystallinity at any of the examined surfaces between normal and treated groups at either time point (data not shown).

1 and 1.3 m−1, and chlorophyll a concentrations 1.3 < Ca < 33 mg m−3 – both values similar to those recorded in the Baltic – see Figure 5, Darecki et al. 2008, Kowalczuk et al. 2010), displays a TSA HDAC cost broad peak on the reflectance spectrum at 560–580 nm and resembles the shape of the remote sensing reflectance spectra usually

observed in the Baltic Sea (see e.g. Darecki et al. 2003). The second type has a very high CDOM absorption coefficient (usually aCDOM(440 nm) > 10 m−1, up to 17.4 m−1) in Lake Pyszne; they have a relatively low reflectance (Rrs < 0.001 sr−1) over the entire spectral range, and two visible reflectance spectra peaks at ca 650 and 690–710 nm. The third type represents waters with a lower CDOM absorption coefficient, (usually aCDOM(440 nm)< 5 m−1) and a high chlorophyll a concentration (usually Ca > 4 mg m−3, up to 336 mg m−3 in Lake Gardno). The third type of remote

sensing reflectance spectra in lake waters always exhibits three peaks (Rrs > 0.005 sr−1): a broad one at 560–580 nm, a smaller one at ca 650 nm and a well-pronounced one at 690–720 nm. These Rrs(λ) peaks correspond to the relatively low absorption of light by the various OACs of the lake water and the considerable scattering due to the high SPM concentrations there. The remote sensing maximum at λ ≈ 690–720 nm is higher still Tanespimycin in vivo as a result of the natural fluorescence of chlorophyll a ( Mitchell & Kiefer 1988). The position of this maximum in the red region shifts distinctly in the direction of the longer waves with increasing chlorophyll a concentration and are the signals available for the remote sensing detection of chlorophyll a ( Gitelson et al. 2007). This is shown for one of the lakes (L. Gardno) in Figure 6 a, b. The change in position of this maximum was used to construct a correlation formula linking Rrs and Ca. The correlations of the spectral reflectance band ratio with the concentrations of particular OACs enable the approximate

levels of these stiripentol components in the euphotic zones of the lakes investigated to be determined from reflectance spectra measurements. For example, the correlation shown in Figure 7 was obtained for chlorophyll a; it is described by the exponential equation: equation(1) Ca=6.432e4.556X,where X = [max Rrs(695 ≤ λ ≤ 720) – Rrs(λ = 670)]/max Rrs(695 ≤ λ ≤ 720), and the coefficient of determination R2 = 0.95. This approximation does not include the discrepant data from the dystrophic lake (humic lake – with brown water). The usefulness of this correlation is confirmed by its high coefficient of determination. We obtained another good correlation for the concentration CSPM ( Figure 8) and a slightly weaker one for aCDOM(440 nm) ( Figure 9). The use of these correlations may facilitate the monitoring of the state of these lakes with the aid of reflectance measurements. The errors of approximation were also estimated.

Thus the most significant gains in Tm due to protein deuteration are only observed at temperatures around 50 K and below. Unlike the 1/Tm temperature dependence, the spin longitudinal relaxation rate (1/T1) does not show any major difference between the non-deuterated and all-deuterated samples, which indicates that within this temperature range, the nuclear spins

do not play a significant role in the spin–lattice relaxation mechanism. For both samples 1/T1 shows slight temperature dependence, and during the observed temperature range selleck inhibitor it does not approach the value of 1/Tm suggesting that T1 processes do not have a significant effect on the electron spin echo dephasing [3]. We have shown the strong effect of protein deuteration on Tm. However as Tm is extended it becomes more sensitive to other effects like instantaneous diffusion and electron spin–spin diffusion [17]. The electron spin echo dephasing observations, in which the histone octamer was increasingly segmentally deuterated, showed, in addition to strong ESEEM modulations, an oscillation resulting from the electron dipole–dipole interaction between the two spin labels present on the protein (see Fig. 2). Such distance dependent dipolar Androgen Receptor Antagonist interactions were only observed in the case of H3-D/H4-D and All-D samples due to their long Tm. In Fig. 2 we can see that the longer the Tm, the more pronounced the electron dipole–dipole interaction.

The observation that the 2 pulse ESE experiment is capable of detecting electron spin–spin interactions in biradicals has been made previously [16]. In a two-pulse echo experiment, when the second pulse is applied and flips two dipole-coupled spins simultaneously, the dipole interaction does not refocus and leads mafosfamide to a dephasing of spin pairs, this effect is known as instantaneous diffusion. Two cases

can be envisaged. One situation is where the spin pairs are randomly distributed and so there will be a wide distribution of dipolar interactions, D, and therefore the echo oscillations, occurring at a range of frequencies, average out, leaving only an exponential-like echo decay contribution. In the second case, when the spin pairs have a defined dipole interaction, D, the echo decay will be modulated by the dipole–dipole frequency, y(τ) ∼ cos(Dτ) [16]. The H3-D/H4-D and All-D histone octamer constructs clearly fulfil the requirements for a dipolar interaction to be observed in a 2 pulses ESE experiment since they are double spin labeled, with a defined dipole–dipole interaction, and have long Tm. The Fourier transform of the ESE decay yielded a dipolar coupling which is in good agreement with the PELDOR data (see supplementary material Fig. S4a and b). In order to get more insight into the effect of deuteration, we have also studied the concentration dependence of Tm ( Fig. 5) for the fully deuterated sample at 50 K.

A total of 86 obese adolescents (39 boys and

47 girls) who entered the Interdisciplinary Obesity Program of the Federal University of São Paulo – Paulista Medical School were Selleckchem Androgen Receptor Antagonist assigned to two sub-groups: hyperleptinemic (H) or non-hyperleptinemic (n-H). Those who were considered hyperleptinemic presented baseline values above 20 ng/ml for boys and 24 ng/ml for girls, as based on reference values cited by Gutin et al. [12] and Whatmore et al. [44]. These patients were submitted to weight loss therapy. The evaluations were performed at baseline, after 6 months and after 1 year of an interdisciplinary approach. The ages of the participants ranged from 15 to 19 years (16.6 ± 1.67 years). BMI was 37.03 ± 3.78 kg/m2. All participants were confirmed as meeting the inclusion criteria of post-pubertal Stage V [40] (based on the Tanner stages of obesity (BMI >95th percentile of the CDC reference growth charts)) [6]. Exclusion criteria were identified genetic, metabolic or endocrine disease and previous drug utilization. Informed consent was obtained from all subjects and/or their parents, including agreement of the adolescents and their families to participate as volunteers. This study was performed in accordance with the principles of the Declaration of Helsinki and MK-1775 price was formally approved by the Institutional Ethical Committee (#0135/04). The subjects were medically screened; their pubertal stages and their anthropometric

measures were assessed (height, weight, BMI and body composition). The endocrinologist completed a clinical interview, including GNA12 questions to determine eligibility based on inclusion and exclusion criteria. A blood sample was collected and analyzed, and ultrasound (US) was performed

to measure visceral and subcutaneous fat. All subjects underwent an ergometric test. Indeed, the procedures were scheduled for the same time of day to remove any influence of diurnal variations. Subjects were weighed wearing light clothing and no shoes on a Filizola scale to the nearest 0.1 kg. Height was measured to the nearest 0.5 cm by using a wall-mounted stadiometer (Sanny, model ES 2030). BMI was calculated as body weight divided by height squared. Body composition was estimated by plethysmography in the BOD POD body composition system (version 1.69, Life Measurement Instruments, Concord, CA) [10]. Blood samples were collected in the outpatient clinic around 8 h after an overnight fast. Insulin resistance was assessed by the homeostasis model assessment-insulinesistance (HOMA-IR) index and the quantitative insulin sensitivity check index (QUICKI). HOMA-IR was calculated using the fasting blood glucose (FBG) and immunoreactive insulin (I): [FBG (mg/dL) × I (mU/L)]/405; QUICKI was calculated as 1/(log I + log FBG). Total cholesterol, TG, HDL, LDL and VLDL were analyzed using a commercial kit (CELM, Barueri, Brazil). The HOMA-IR data were analyzed according to reference values reported by Schwimmer et al. [35].

The differentially expressed genes indentified by microarray were used to find pathways up- and down-regulated in the different tissues. The total RNA samples used for the

microarray analysis were also used for validation of the microarray results by quantitative RT-PCR (qPCR). cDNA synthesis for qPCR was performed using QScript (Quanta Biosciences) using 100 ng total RNA in 10 μl final MAPK Inhibitor Library reaction volume according to the manufacturer’s instructions. A tissue specific RNA sample was made for each of the five tissues by mixing 1 μl total RNA from all samples within a tissue. From each of the tissue specific RNA samples a negative RT control was made by excluding the RT enzyme in the cDNA synthesis. An experiment wide RNA pool was made by mixing 2 μl from each of the tissue specific RNA samples together. The experiment wide RNA pool was used to make a dilution series with

250, 125, 62, and 31 ng RNA in the cDNA synthesis which was used to evaluate assay efficiency and linearity. After cDNA synthesis all cDNA samples were diluted 1:10 in water and stored at − 20 °C until analyzed. qPCR analysis was carried out in 384 well plates on Applied Biosystems 7900HT real time instrument. Cell Cycle inhibitor A semi-fast cycling protocol was used, consisting of 3 min denaturation at 95 °C followed by 40 cycles of 5 s at 95 °C and 15 s at 60 °C. All amplifications were run in 5 μl volume with 1.5 μl cDNA, 900 nM of each primer and 200 nM probe and 2 × Briliant III Ultra-Fast QPCR Master Mix (Agilent Technologies). ROX was added to a final concentration of 300 nM as a passive reference dye. Eight different assays were run on each plate, always including the assay for elongation factor 1α

Protirelin (EF1α). In addition “No Template Control” (NTC; water), − RT from all tissues and the dilution series were included for all assays. Data were analyzed using SDS 2.4 and RQ Manager 1.2.1, with baseline and threshold for Cq values set manually for each gene and kept identical for all plates. Data were further analyzed in R (http://www.R-project.org). Relative quantification of gene expression was carried out according to the ΔΔCt method (Livak and Schmittgen, 2001), using normalized RNA template amounts (thus omitting an endogenous standard gene) and ovary as calibrator tissue. To validate the microarray data, a gene specific qPCR analysis was performed on 12 genes with probes on the microarray. All genes were analyzed for each of the five tissues, and the relative transcription compared to the corresponding probe intensities from the microarray analysis. All assays demonstrated a high level of correlation between the qPCR and microarray result in all five tissues, confirming the microarray results (Fig. 2 and Supplementary Fig. 1).

Similarly, Socially Responsible Investing (SRI) is fast becoming a growing part of the investment market place, accounting for about

12.1% of the total financial investments in the United States (about $3.07 trillion) [21]. While it is still unclear whether SRI funds always outperform their non-SRI counterparts, it is important to note that these funds perform as well as their non-SRI counterparts when compared to other market benchmark funds [22]. The FIRME provides an approach that could derive momentum from a growing consensus on solutions for our oceans e.g., [23] and the need for political commitments on the ‘green economy’ theme (e.g., United Nations Conference on Sustainable Development UNCSD/Rio +20). In 2010 at Nagoya, the Conference of the Parties (COP10) of the UN Convention on Biological Diversity (CBD) identified the selleck chemicals llc AZD5363 need for innovative financing to underpin sustainability investments in nature. Meeting that challenge with conservation financing schemes, such as the FIRME, would be an explicit validation

that society is aiming to properly value and secure the natural capital base upon which we all depend. The authors acknowledge the contributions of many individuals who helped shape our ideas during numerous consultations hosted by WWF. In addition, we are greatly indebted to staff at the Prince’s Charities’ International Sustainability

Unit (Charlotte Cawthorne, Jack Gibbs, John Goodlad) and to the participants of ISU hosted workshops. Thanks also to: Tim Bostock (The World Bank); Ian Glew (Memorial University of Newfoundland); Jeffrey Hutchings (Dalhousie University); Astrid Scholz (Ecotrust); Rashid Sumaila (University of British Columbia). And to our many WWF colleagues, most notably: Daniela Diz, Mark Eckstein, Michael Harte, Vivian Okonkwo, David Schorr, Alfred Schumm, and Jessie Sitnick. “
“How to link fisheries science with competent and fair governance processes? In EU fisheries management, aminophylline mathematical and statistical modelling has long been the central analytical method used for producing scientific advice informing the European decision makers. Strong tensions have grown in some fisheries between scientists and industry, in particular around questions of credibility and legitimacy of scientific advice based on the use of such models [1] and [2]. This credibility crisis has been identified as an important issue hampering the Common Fisheries Policy (CFP) to provide biological and economic sustainability (e.g., [3], [4], [5], [6], [7], [8], [9], [10] and [11]. Uncertainties challenge the ‘good’ governance in fisheries. Adequate handling and communication of uncertainty in fisheries science is still poorly addressed.

4% and 27.6% of the GEI SS, respectively. Unlike for early FSRY, the % treatment SS attributed to GEI was higher than that to environments for CBSD-RN and CMD-S. For FSRY, CBSD-RN and CMD-S, the % GEI SS attributed to IPCA1 was more than twice that attributed to IPCA2. Since the IPCA2 for all four traits was non-significant, the AMMI1 model was adopted and for each trait, the genotype and location IPCA1 scores were plotted against the mean performances of the genotypes

and locations. A genotype or location with high IPCA1 scores (negative or positive) indicated high interaction and was considered to be unstable PLX4032 across the respective locations or genotypes, while a genotype or location with low IPCA1 scores near zero indicated low click here interaction and was considered to be stable. Even though the GEI and associated IPCA1 were non-significant for early FSRY, the apparent performance and interaction patterns were presented in an

AMMI1 biplot, given that early FSRY was the focus of this research. Genotypes Akena, CT2, CT4 and NASE14 had low IPCA1 scores for early FSRY and were accordingly the most stable genotypes for this trait (Fig. 1). NASE4, NASE3 and CT1 were the least stable, in view of their large IPCA1 scores. Grouping of genotypes according to their mean early FSRY indicated that CT2 was the highest early FSRY performer, followed by Akena, NASE4, and CT3 while Nyaraboke, followed by NASE3, NASE14 and Bukalasa 11 were the lowest early FSRY performers. Ranking of genotypes based MycoClean Mycoplasma Removal Kit on GSI, which incorporates both the IPCA1 and mean performance rankings, identified Akena and CT2 as the best genotypes combining high early FSRY and stability (Table 3). Considering IPCA1 scores alone, 67% of the genotypes had IPCA1 scores less than unity, implying that a majority

of the genotypes were stable for early FSRY. Namulonge had no interaction effects for this trait with genotypes, indicated by negligible IPCA1 scores. Nakasongola and Jinja had high contrasting interaction effects for early FSRY with genotypes, indicated by high contrasting IPCA1 scores. Nakasongola, though unstable, was the best location for early FSRY, followed by Jinja. For SRN, CT5, Akena, Nyaraboke and CT4 had low IPCA1 scores and were the most stable genotypes, whereas Bukalasa 11, TME14, NASE4 and CT3 were the least stable considering their large IPCA1 scores (Fig. 2). NASE4 had the highest SRN, followed by CT2, CT1 and TME14. Nyaraboke, followed by NASE3, Bukalasa 11 and Akena had the lowest SRN. With the lowest GSI ranking, CT5 was the overall best genotype combining high SRN and stability, followed by CT4, CT1 and CT2 (Table 4). Jinja showed effectively no interaction with genotype, as indicated by its negligible IPCA1 score, and was considered the most stable location across the genotypes for the trait. As evidenced by their high IPCA1 scores of opposite sign, Namulonge and Jinja showed high and contrasting interactions with genotype.

Ao contrário, os PL após 3 meses de tratamento com a AZA mostraram relação com resposta sustentada ao fármaco. Portanto, quando a AZA foi eficaz a longo prazo, verificou‐se descida dos valores dos leucócitos (r = –0,295, p = 0,013), da

PCR (r = –0,332, p = 0,005) e das plaquetas (r = –0,360, p = 0,03) e houve aumento da hemoglobina (r = 0,307, p = 0,010) e do VGM (r = 0,255, p = 0,047), de forma estatisticamente significativa. A tabela 3 mostra a evolução analítica dos PL (antes e após o tratamento), de acordo com a eficácia do tratamento. No grupo de doentes em que o tratamento não foi eficaz verificou‐se também descida do valor dos leucócitos e aumento do VGM com significância estatística, contudo, em menor grandeza relativamente ao grupo de doentes em que o tratamento foi eficaz a longo prazo. Com base em análise multivariada, através INK1197 da regressão linear (método enter) confirmou‐se Selleck NSC 683864 que os PL aos 3 meses predizem o sucesso terapêutico (R = 0,517, p = 0,005), ao contrário dos PL antes do tratamento (r = 0,287;

p = 0,444). Através da regressão linear (método stepwise), verificou‐se que os PL aos 3 meses que predizem mais o sucesso terapêutico a longo prazo da AZA são a PCR e os leucócitos (r = 0,501, p = 0,000). Verificou‐se ainda que a duração do tratamento com 5‐ASA se correlacionou com a eficácia a longo prazo da AZA, sendo que quanto maior a duração do 5‐ASA maior a eficácia Urease da AZA (r = 0,258, p = 0,029). A suspensão dos corticoides também se correlacionou com a eficácia a longo prazo da AZA (r = 0,265, p = 0,041). A AZA mostrou ser efetiva na terapêutica de manutenção da DC e da CU3, 4, 5, 6, 7, 8 and 19. Contudo, os fatores preditivos de resposta sustentada são pouco conhecidos, existindo escassos estudos que avaliam como end‐point primário

esta problemática. Na nossa série avaliámos a eficácia e os fatores de resposta sustentada à AZA numa população de doentes com DII seguidos em consulta no Hospital de Faro. Nesta população, 23,6% dos doentes iniciaram imunossupressão com AZA, tendo sido o fármaco eficaz em 66,7% dos doentes. Discriminado, de acordo com o tipo de doença, a AZA foi eficaz em 70,6% dos doentes com CU e em 60% dos doentes com DC. Noutros estudos a eficácia da AZA foi avaliada entre 40‐81% dos casos 4, 8, 11, 12, 13, 20, 21 and 22, sendo, contudo, usadas diferentes definições de resposta ao fármaco. De facto, uma das desvantagens do uso das tiopurinas é a dificuldade em avaliar a resposta clínica. No nosso estudo, a avaliação foi retrospetiva e os critérios utilizados foram critérios clínicos/endoscópicos com a subjetividade inerente.