Increased extracellular Ca2+ concentrations are potent chemical signals for cell migration and directed growth [59] and [60], as well as homing signals that bring together Autophagy Compound Library cell line the different cell types required for the initiation of

bone remodeling [61]. Pi is also a regulator of osteoblast proliferation and differentiation [62], and the high concentration of Pi in the microenvironment induced osteoblast apoptosis in vitro [63] and the in vivo mineralization of the bone matrix [64]. Moreover, specific Ca2+ and Pi concentrations have been suggested to induce the higher proliferation and osteogenic differentiation of MSC [65]. CaP bioceramics appear to be candidates for scaffold-aimed bone tissue engineering because they can release inorganic ions during dissolution. As discussed above, many factors are associated with mimicking of the cell niche/microenvironment to enforce regeneration by the application of tissue engineering. Not only scaffold properties

affect stem cell functions; cells can also induce the deformation and degradation of scaffolds during the process of regeneration, leading to the altered mechanical properties of scaffolds (Fig. 3). Thus, the cell–scaffold interplay is bidirectional and involves a feedback loop of cells and scaffolds. Forces are generated in the context of cell adhesion to ECM/scaffolds, and mechanotransduction occurs to transduce them click here into intracellular signals that drive functional modulations in cells: proliferation, differentiation, migration,

and apoptosis [55]. The actin cytoskeleton plays the most prominent role in these events [66]. Human MSC also express specific transcription factors of mechanotransduction and undergo tissue-specific cell fate switches when cultured on ECMs with mechanical stiffness mirroring the physical properties of respective specific tissue [67]. To ADP ribosylation factor instruct stem cells and modify their fates, scaffold biomaterial should provide informative microenvironments mimicking a physiological niche of the target tissue. Biomaterials can have a suitable design to transmit specific signals to cells that can be decoded into biochemical signals depending on its composition and processing methods. Both biophysical and biochemical cues have been involved in cell–ECM interactions in nature. Hence, topography, chemistry and physical properties are involved and are critical for determining cell fate [68]. To accomplish the desirable interplay between cells, biomaterial designing should include parameters within (a) surface, (b) mechanical, (c) morphological, and (d) electrical properties (Fig. 3). Surface topography and chemical composition were able to drive cell adhesion, proliferation, migration, and differentiation [55], [69] and [70].

Therefore, even if Candida does not proliferate to high levels, oral candidiasis can develop as a result of the alterations in the human host defenses, such as hyposalivation, which increases the risk of Candida colonization. There is an inverse relationship between salivary flow and oral Candida colonization [43], [44] and [45]. www.selleckchem.com/products/cobimetinib-gdc-0973-rg7420.html A similar relationship between salivary flow and candidiasis was observed in the Dry Mouth Clinic at Tsurumi

University Dental Hospital. Out of 2678 patients (male:female = 446:2232, mean age = 66.8), 1548 (57.8%) had oral colonization by Candida. To examine the relationship between salivary flow and Candida colonization at our institution, study subjects were divided into 5 groups according to their level of salivation ( Table 4). We measured the WRS and WSS, and hyposalivation was defined as a WRS less than 1.5 ml/15 min and/or WSS less than 10 ml/10 min. The highest CFU for Candida was observed in patients with Sjögren’s

syndrome (Group I), followed by non-Sjögren’s syndrome patients with decreased WRS and WSS (Group II), patients with only decreased WRS (Group III), patients with only decreased WSS (Group IV), and a group with WRS and WSS within the normal limits (Group V). These results suggest that hyposalivation is INK1197 in vitro a risk factor for an increase of Candida CFU, especially in Sjögren’s syndrome patients. Although the reason why Sjögren’s syndrome patients showed the highest CFU has not yet been elucidated, alterations in the salivary composition might be related to the observed Candida colonization [46] and [47]. The results also suggest that unstimulated saliva flow contributed more to an increase of Candida CFU than the stimulated saliva flow, because there were more Candida colonies in Group III than Group IV. An increase of Candida CFU due to hyposalivation may possibly lead to the onset of oral candidiasis. Oral candidiasis is classified into three major variants based on the clinical manifestation; pseudomembranous, erythematous (atrophic) and hyperplastic [48]. A diagnosis of candidiasis is based on the clinical findings and is supported by the identification of blastospores and pseudohyphae

in staining smears from a lesion, identification of colonies by culture on Sabouraud’s medium, or by histological 6-phosphogluconolactonase examination [49]. Although an interpretation of the results of a microbial examination is often complicated because Candida is present as a commensal organism in the oral cavities of up to 40% of individuals [48], fungal examination is helpful to distinguish candidiasis from non-specific stomatitis due to the hyposalivation. The occurrence of signs of erythematous candidiasis, such as atrophy of lingual papilla and redness of the oral mucosa was significantly higher in patients who had Candida detected in their mouths than in those that did not [50]. Such a relationship has been recognized previously in the literature [51]. A fungal examination is then needed to support the diagnosis.

frutescens. The cytotoxicity assay was performed using OVCAR-8 (ovarian adenocarcinoma), NCI-H358M (bronchoalveolar lung carcinoma) and PC-3M (metastatic prostate carcinoma) human tumour cell lines, all obtained selleck chemical from the National Cancer Institute, Bethesda, MD. Cells were grown in RPMI-1640 medium supplemented with 10% foetal bovine serum, 2 mM glutamine, 100 μg/ml streptomycin and 100 U/ml penicillin. Cells were maintained at 37 °C in a 5% CO2 atmosphere. Sarcoma 180 tumour cells, which had been maintained in the peritoneal cavity of Swiss mice, were obtained from the Laboratory of Experimental Oncology at the Federal University of Ceará, Brazil. A total of 40 Swiss

mice (males, 25–30 g), obtained from the central animal house at the Federal University of Sergipe, Brazil, were used. Animals were housed in cages with free access

to food and water. All animals were kept under a 12:12 h light–dark cycle (lights on at 6:00 a.m.). Animals were treated according to the ethical principles for animal Afatinib price experimentation of SBCAL (Brazilian Association of Laboratory Animal Science), Brazil. The Animal Studies Committee at the Federal University of Sergipe approved the experimental protocol (number 08/2012). X. frutescens leaves were collected in July 2011 at “Mata do Junco” in the Municipality of Capela, Sergipe State, Brazil, coordinates: S 10° 57′ 52″ W 37° 04′ 65″. The species was identified by Dr. Ana Paula do Nascimento Prata. Voucher specimen number 22178 was deposited at the Herbarium of the Federal University of Sergipe, Brazil. The leaves were obtained from plants that were flowering and in

fructification. X. frutescens leaves (200 g) were dried in an oven with circulating air at 40 °C for 24 h and submitted to hydrodistillation for 4 h using a Clevenger-type apparatus (Amitel, São Paulo, Brazil). The essential oil was dried over anhydrous sodium sulfate and the percentage content (v/w) was calculated on the basis of the dry weight of plant material. The essential oils were stored in a freezer until analysis. Hydrodistillation Mannose-binding protein-associated serine protease was performed in triplicate. GC analyses were carried out using a Shimadzu GC-17A fitted with a flame ionisation detector (FID) and an electronic integrator (Shimadzu, Kyoto, Japan). Separation of the compounds was achieved using a ZB-5MS fused capillary column (30 m × 0.25 mm × 0.25 μm film thickness; Phenomenex, Torrance, CA). Helium was the carrier gas at 1.0 ml/min flow rate. The column temperature program was: 40 °C for 4 min, a rate of 4 °C/min to 240 °C, then a rate of 10 °C/min to 280 °C, and then 280 °C for 2 min. The injector and detector temperatures were 250 °C and 280 °C, respectively. Samples (10 mg/ml in CH2Cl2) were injected with a 1:50 split ratio. The injection volume was 0.5 μl. Retention indices were generated with a standard solution of n-alkanes (C8–C20). Peak areas and retention times were measured by an electronic integrator.

The two plates were incubated for 1 h at 37 °C then 160 μl of

the second plate was added to the first plate to initiate the reaction. To calculate the percentage of lipase inhibition, the reagent blanks were subtracted from the corresponding controls or samples and the following formula was applied: Percentage of Lipase Inhibition=1-((Polymer Sample-Inhibition Control)/(Lipase Control-Inhibition Control))×100Percentage of Lipase Inhibition=1-((Polymer Sample-Inhibition Control)/(Lipase Control-Inhibition Control))×100 The olive oil assay system uses a modified version of the method of Vogel and Zieve (1963). The turbidimetric method measures Alisertib concentration the reduction in turbidity that occurs following the breakdown of TAGs to free fatty acids by lipase. Olive oil, with a specific viscosity of 72.5 (±10), (specific viscosity used here is unitless as it is derived from a ratio of the oil to MG-132 that of water) was used throughout the series of experiments. The olive oil was passed through aluminium oxide (80 × 15 mm deep in a glass chromatography column) to remove free fatty acids. 10.0 g of the olive oil free from fatty acids was made up to 100 ml with acetone giving a 10% solution. This is turn was diluted 1 in 10 with acetone to achieve a 1% olive oil stock solution. The stock solution was stored at 4 °C for up to four weeks and was used over the entire series of experiments. For use in the assay, an olive oil

substrate solution was prepared by adding 4 ml of 1% stock olive oil solution to a heated solution (70 °C) of 100 ml 0.05 M Tris buffer at pH 8.3 containing 0.35% sodium deoxycholate. This solution was maintained at 70 °C and homogenised for 10 min. Once the froth had settled and the solution had returned to room temperature, the substrate solution could be used in the assay for up to 6 h. The enzyme solution contained

1.29 mg/ml lipase and 18 μg/ml colipase in deionised water. Orlistat was added (0.025 mg/ml) to the enzyme solution as an inhibition control. Biopolymers were added to the freshly prepared substrate solution containing the olive oil to give 3.6, 0.9 and 0.23 mg/ml. The samples were incubated at 37 °C for 15 min. After the incubation the substrate solution was added to the solution containing the Etofibrate enzyme solution or deionised water. The assay was maintained at 37 °C and read every 5 min at 405 nm for 35 min. To calculate the percentage of lipase inhibition, the blanks were subtracted from their respective controls and the following equation was applied Percentage of lipase inhibition=1-((Inhibition Control-Polymer Sample)/(Inhibition Control-Lipase Control))×100Percentage of lipase inhibition=1-((Inhibition Control-Polymer Sample)/(Inhibition Control-Lipase Control))×100 All data were analysed using GraphPad Prism 4 statistical software. The comparison of inhibition levels with seaweed species was made by using a two way ANOVA.

ESI(−) is a soft ionisation technique and mass spectra mainly detected components as their intact deprotonated molecules. ESI(−)–MS provides therefore fingerprinting characterisation of propolis extracts via characteristic

profiles of their chemical composition in terms of the most polar and acidic or basic http://www.selleckchem.com/products/Erlotinib-Hydrochloride.html components (Sawaya et al., 2004 and Sawaya et al., 2007). Fig. 1 shows the ESI(−)–MS fingerprints of ODEP fractions and indicates great differences in the chemical composition between the fractions. OLSx1 and OLSx2 were complex fractions with several high to medium abundant ions, such as m/z 387, 311, 341 and 275 (OLSx1). For OLSx2, the highest abundant ions were those of m/z 315, 339, with medium abundant ions of m/z 265, 325, 293 and 377. Fractions OLSx 3–6 were simpler and showed mostly a single major ion. OLSx3 showed mostly the ions of m/z 247, 231, 301 and 393 whereas OLSx4 showed those of m/z 301, 245, 393 and 287 as most abundant. Fractions OLSx5 and OLSx6 showed check details a major common ion of m/z 299 and other less abundant ions of m/z 329 and 387 (OLSx5) and m/z 249, 285, 311 and 387 (OLSx6). To characterise the fractions constituents, LC–MS and LC–MS/MS were performed. Via comparisons with the fragmentation pattern of previously identified compounds

(Marcucci, Sawaya, Custodio, Paulino, & Eberlin, 2008), several components have been identified: 3,4-dihydroxi-5-prenyl-cinnamic acid (m/z 247, OLSx3); dihydrokaempferide (m/z 301, OLSx3 and OLSx4); 3-prenyl-4-hydroxicinnamic acid (m/z 231, OLSx3); (E)-3–4-hydroxy-3-[(E)-4-(2,3-dihydrocinnamoyl oxy)-3-methyl-2-butenyl]-5-prenylphenyl-2-propenoic

GBA3 acid (m/z 447, OLSx2). Isosakuranetin (m/z 285, OLSx4 and OLSx6) was identified by comparison of its MS/MS fragmentation with a standard kindly provided by Vassya S. Bankova, (Bulgarian Academy of Science). We have identified some of those compounds previously in the oil extracts of propolis ( Buriol et al., 2009). In addition, LC–MS identified two compounds with the same m/z 299 but different retention time. Compound in OLSx2 with [M–H]− of m/z 299 and tR 24 min showed in its ESI(−)–MS/MS a fragmentation to the ion of m/z 255 via initial loss of 45 Da and of m/z 244, 200 and 145. It was identified as 3,5-diprenyl-4-hydroxicinnamic acid also known by artepellin C. ESI(−)–MS/MS of the other compound with [M–H]− of m/z 299 but tR 14 min in OLSx5 and OLSx6 underwent fragmentation to the ion of m/z 284 via initial loss of a methyl radical and was identified as kaempferide. Other fragment ions of m/z 164, 151, and 107 are typical of the cleavage of the flavonoids central C-ring ( Cuyckens & Claeys, 2004) ( Table 1).

The reducing power was highly correlated with the total polyphenol contents (r = 0.998, p < 0.001; Table 2). Significant correlations have been observed between the polyphenol contents of HGR and the ABTS, DPPH, and reducing power (r = 0.998, −0.646, 0.999, respectively; p < 0.01, p < 0.001). Significant correlations also exist between the polyphenol contents of HGL and the ABTS, DPPH, and reducing power (r = 0.998, −0.646, 0.999, respectively; p < 0.01). In conclusion, the total ginsenoside contents of HGR and HGL following heat treatment were significantly higher than those of the raw material. Furthermore, the ginsenoside

contents of HGL were higher than those of HGR. The antioxidant activities of HGR and HGL can be enhanced by heat treatment, and the antioxidant activity of HGL was higher than that of HGR. These results DZNeP solubility dmso may aid in improving the biological activity and quality of ginseng subjected to heat treatments, and in general applying to the additional food and natural products for antioxidant capability. All authors declare no conflicts of interest. “
“Ginseng (Panax ginseng Meyer)

is a multifunctional therapeutic herb that is commonly used find more throughout the world. Primarily in East Asia, ginseng has been used as traditional medicine to enhance the immune system, control blood pressure, and strengthen the cardiovascular system [1]. The ginseng herb is processed using various methods. For example, peeled ginseng root turns white when dried in the sun, which has led to it being called

white ginseng, whereas red ginseng is produced by steaming and drying. A wide variety of pharmacological properties have been reported for ginseng, such as anti-oxidant, anti-stress, neuroprotective, hypoglycemic, and anti-tumor effects [2], [3], [4] and [5]. The ginseng herb and ginseng-derived Edoxaban products include multiple secondary metabolites, such as protopanaxadiol (PPD)-type (e.g., ginsenoside Rb1, Rb2, Rc, Rd, and Rg3), protopanaxatriol (PPT)-type (e.g., ginsenoside Rg1, Re, Rf, and Rg2), and oleanane (OCO)-type ginsenosides (e.g., ginsenoside Ro) [6]. Different ginsenoside ratios have been reported for different species, geographical origins, and processing methods, and such ratios are considered to be responsible for the different bioactivities [7] and [8]. Metabolomics primarily focuses on comprehensive and quantitative profiling for small-molecule metabolites in a biological system. It has been applied to a variety of areas, such as plant toxicology, nutrition, and systems biology [9], [10] and [11]. Multiple analytical methods, including nuclear magnetic resonance, gas chromatography-mass spectrometry, and liquid chromatography-mass spectrometry, have been applied in metabolic profiling in order to differentiate Panax species [12], [13] and [14].

About 70% of the Swedish productive forest land is certified according to either FSC or PEFC, or both systems. The average proportion retained area per clearcut is 3% (Swedish Forest Agency, 2012). In January 2005 a storm, “Gudrun”, hit southern Sweden and 70 million m3 trees fell, equivalent INCB018424 clinical trial to twice the amount of the normal annual cut in the storm area (Swedish Forest Agency, 2006),

and also strongly affecting retention amounts and patterns. Based on data from the long-term Swedish National Forest Inventory (NFI), we here assess what can be achieved by the retention approach. The aim is to quantify the development over time of retained living trees (solitary and small tree groups) and dead trees after final harvest, with a focus on young forests (0–10 years old). We want to describe such changes in relation to regions, stand age classes, ownership categories, tree diameter, tree species (living trees), tree position (dead trees; standing or lying) and decay class (dead trees). Since Sweden was so early in application of the retention approach, results PLX3397 ic50 can demonstrate more general trends and help assess and predict development in countries and regions in which the retention approach has been introduced more recently. Forests cover about 55%

of Sweden’s land area of 41 million ha (Swedish Forest Agency, 2012) and more than 90% of the productive forest land is managed more or less intensely with the clearcutting method, introduced large-scale in the 1950s. Practices have since then been largely similar for small private forest owners, large forestry companies and other forest owners. After clearcutting and soil preparation, regeneration is secured through planting (or sometimes with natural regeneration) of the conifers Picea abies (L.) Karst. and Pinus sylvestris L., later followed by pre-commercial thinning and thinning. Also Dichloromethane dehalogenase birch, Betula pendula Roth.,Betulapubescens Ehrh. is favoured to some extent.

Rotation times vary between 60 and 100 years. NFI started 1923 and performs annual inventories of all land in Sweden, providing data at national and regional levels, with focus on forest and other wooded land. The present design was introduced in 1983 (Ranneby et al., 1987). Data on trees, forests and management history are recorded by field teams in a stratified random systematic cluster design with partial replacement, and in plots with radius of 7 m, 10 m or 20 m, depending on variable. Permanent plots are surveyed every 5–10 years, and at least 5 years of data are usually needed for reliable estimates (Axelsson et al., 2010). The list of recorded variables in the NFI is extensive, covering both forestry and environmental aspects. Living and dead tree volumes and numbers can be compiled for regions, ownership categories and age classes.

Currently 29 operational indicators are reported under the 12 headline indicators, covering various aspects of 17 of the 20 Aichi Targets (BIP, 2013 and Chenery et al., 2013). These 29 indicators typically relate (but are not identical) to one of the 97 AHTEG indicators in a further operational form. Although termed operational, most cases of the 97 AHTEG indicators will need to be transformed into specific verifiable “sub-topic” indicators that can actually be measured (cf.

Table 2). It is important to note that the AHTEG framework http://www.selleckchem.com/products/crenolanib-cp-868596.html is flexible enough to allow the transformation and addition of indicators as needed. Types of indicators and indicators relevant for genetic diversity are described further in Appendix B. The indicator sequence used by the UNEP/CBD/AHTEG, 2011a and UNEP/CBD/AHTEG, 2011b system is S–P–B–R, as it is considered to be the logical sequence of the four basic questions listed in Table 1. This is in contrast to the R–S–P–B sequence recommended by Sparks et al. (2011), who emphasize that response (rather than pressure) is the indicator that will be used to guide policy and practice. The sequence can be discussed and Sparks et al. (2011) therefore present the framework as a “feedback loop” subject to iterative modifications. From the 97 operational indicators

proposed by UNEP/CBD/AHTEG, 2011a and UNEP/CBD/AHTEG, 2011b, we have selected those that selleck chemicals we consider to have potential relevance for monitoring tree genetic diversity. They are all listed in Table 2, using the S–P–B–R sequence of UNEP/CBD/AHTEG, 2011a and UNEP/CBD/AHTEG, 2011b.

In constructing Table 2, we followed the suggestions for headline indicators and operational indicators considered relevant (“most relevant” or “other relevant”) by UNEP/CBD/AHTEG, 2011a and UNEP/CBD/AHTEG, 2011b under the two Aichi Targets directly addressing genetic diversity (Targets 13 and 16), providing 14 operational indicators. These comprise only state and pentoxifylline response indicators. We have added those operational indicators that address tree species distribution, population trends and extinction risks, thus targeting intra-specific variation (cf. e.g., Rogers and Ledig, 1996 and Bariteau, 2003), but not mentioned as such by UNEP/CBD/AHTEG, 2011a and UNEP/CBD/AHTEG, 2011b. This provides an additional set of nine operational indicators, of which two are classified as state indicators, five as pressure indicators, and one each as a benefit and response indicator. In addition we have included three operational indicators that reflect benefit, value and condition of ecosystem services for adequate coverage of the benefits of genetic diversity. We have added one operational response indicator covering capacity building, knowledge transfer and uptake into policy, areas which are of obvious importance for the conservation, management and use of genetic diversity.

Figure options Download full-size image Download high-quality image (472 K) Download as PowerPoint slide Microhaps have an additional ability: qualitative identification of mixtures with the potential to quantify the components, i.e., to disentangle mixtures in a quantitative RG7420 solubility dmso way. If three or more different sequences are seen at sufficient numbers of reads at a microhap locus, the three alleles constitute evidence that DNA from more than one person

was present in the sample. The relative numbers of reads of the multiple sequences can quantitate the relative amounts of each sequence in the sample assuming sufficient reads for meaningful statistical analysis. With many loci multiplexed and with more loci consisting of three SNPs defining four or more haplotypes, the microhaps become powerful markers to identify and quantify components of mixtures. With allele (haplotype) frequencies defined in multiple populations, computer software should be able to accurately predict the likelihood and levels of mixture based on observing more than two sequence types at a locus and the numbers of Sirolimus mouse occurrences of each type. Ideally, before achieving status as a “final” microhap panel, ready for all routine applications, a microhap panel must consist of sufficient appropriate loci. These 31 loci were NOT selected for ancestry inference

or for individual identification irrespective of ancestry in the way that our previous SNP panels were. The STRUCTURE analyses (Supplemental Fig. S4) show that these 31 multiallelic loci are not as good as our 55 Ancestry Informative SNPs [12] for defining more than 5 groups of individuals. The difference is expected because these microhaps were not selected for high Fst among the populations. The selection was for high average heterozygosity Meloxicam as needed for kinship/lineage

inference. Fig. 4 illustrates two different patterns of variation seen among the 31 loci. The microhap at RXRA (Fig. 4a) has the lowest Fst of the 31 loci and illustrates a locus with extremely low Fst globally. This pattern is analogous to the individual identification panels of SNPs and would give similar levels of lineage information globally while providing little ancestry information. In contrast, the microhap at EDAR (Fig. 4b) has the lowest average heterozygosity and highest Fst with obvious information on population groupings. Because heterozygosity levels are low outside of Africa, the locus provides little individual identification or lineage information outside of Africa. This diversity of heterozygosity and allele frequency patterns among the loci and populations is reflected in the match probabilities illustrated in Fig. 2. They vary considerably among regions of the world in contrast to the greater uniformity in our individual identification panel [1] and [2].

In all animals exposed to alumina dust the presence of alumina crystals in the lung (alveolar spaces and airways) was qualitatively evaluated under polarized light (Axioplan, Zeiss, Oberkochen, Germany) at

1000× magnification. The right lungs were homogenized in 1 mL of PBS with protease inhibitors (1 μg/mL leupeptin and 1 μg/mL pepstatin). Homogenates were centrifuged (Centrifuge 5415R, Hamburg, Germany, 4 °C, 6700 × g, 15 min) and then, the supernatant was collected for transforming growth factor beta (TGF-β) and interleukin-1beta (IL-1β) assays by ELISA (R&D Systems Inc., Minneapolis, MN, USA), according to the manufacturer’s protocol. Total protein concentration in lung homogenates was determined by Bradford’s method ( Bradford, 1976). Concentration of cytokines in lung homogenates selleck chemical was further normalized to protein concentration in the samples and expressed GSK2118436 research buy as picograms per milligram of protein. Optical density was measured at 450 nm by a microplate reader (SpectraMax 190, Molecular Devices, Sunnyvale, CA, USA). The normality of the data and the homogeneity of variances were tested by Kolmogorov–Smirnov test with Lilliefors’ correction and Levene median test, respectively. In all instances both conditions were satisfied and parametric

tests were run. One-way ANOVA was used to compare the values of body weight measured every 7 days, throughout 4 weeks in each group. Weight differences between control and exercise groups at every 7 days were

evaluated by Student’s t-test. Two-way ANOVA was applied to the remaining parameters (factors: exercise and alumina). For all ANOVAs, the Student–Newman–Keuls to was used as a post hoc test. The morphometric data, originally expressed as percent, underwent an arcsine transformation, in order to generate a normal distribution. The statistical analyses were carried out by the SigmaStat 9.0 software (SYSTAT, Point Richmond, CA, USA). In all instances p < 0.05 was considered a statistically significant difference. Metal composition of alumina dust is presented in Table 1. A high concentration of the element Al, followed by Fe and Hg was found. Scanning electron micrographs of particles are shown in Fig. 1, demonstrating the frequency distribution of diameters of particle sample. 90% of particles diameter are under 150 μm, being 50% below 100 μm and 10% smaller than 57 μm. A progressive increase in body weight was observed along time in animals not submitted to physical exercise. In exercising group, a decrease in body weight occurred during the first week of aquatic training, but thereafter the values did not differ from those in control mice (Fig. 2). All mechanical parameters (ΔP2, ΔE and Est) but ΔP1 were higher after alumina dust exposure in animals not submitted to physical exercise. Additionally, exercise training before particle exposure caused no changes in resistive and viscoelastic components, but Est increased in this group ( Fig. 3).