Ann Thorac Surg 1995, 60:1348–1352.PubMedCrossRef 28. Ong LC, Jin Y, Song IC, Yu S, Zhang K, Chow PK: 2-[18F]-2-deoxy-D-glucose (FDG) uptake in human tumor cells is related to the expression of GLUT-1 and hexokinase II. Acta Radiol 2008, 49:1145–1153.PubMedCrossRef Atezolizumab supplier 29. Dang CV, Semenza GL: Oncogenic alterations of metabolism. Trends Biochem Sci 1999, 24:68–72.PubMedCrossRef 30. Semenza GL: Targeting HIF-1 for cancer therapy. Nat Rev Cancer 2003, 3:721–732.PubMedCrossRef 31. Berger KL, Nicholson SA, Dehdashti F, Siegel BA: FDG PET evaluation

of mucinous neoplasms: correlation of FDG uptake with histopathologic features. AJR Am J Roentgenol 2000, 174:1005–1008.PubMedCrossRef 32. Hirayama A, Kami K, Sugimoto www.selleckchem.com/products/BI6727-Volasertib.html M, Sugawara M, Toki N, Onozuka H, Kinoshita T, Saito N, Ochiai A, Tomita M, Esumi H, Soga T: Quantitative metabolome profiling of colon and stomach cancer microenvironment by capillary electrophoresis time-of-flight mass spectrometry. Cancer Res 2009, 69:4918–4925.PubMedCrossRef 33. Rajagopalan KN, DeBerardinis RJ: Role of glutamine

in cancer: therapeutic and imaging implications. J Nucl Med 2011, 52:1005–1008.PubMedCrossRef Competing interests The authors declare that they have no competing interests. Authors’ contributions RT: Analyzing data, experimental work, and drafting article. KI: Conception, design, experimental work, and acquiring data. YY: Acquiring and analyzing data of FDG-PET. RK: Acquiring and analyzing data of FDG-PET. HM: Acquiring clinical data. TM: Revising the manuscript, and statistical analysis. YS: Enhancing its intellectual content. All authors read and approved the final manuscript.”
“Background Surgery accompanied with radiotherapy and chemotherapy is the most successful treatment strategy for Buspirone HCl breast cancer. However, 40% of patients die of advanced breast cancer recurrence and metastasis [1]. TA2 mouse strains were bred by the Animal Center of Tianjin Medical University twenty years ago. TA2

mice have a high incidence of spontaneous breast cancer without chemical stimulus. The morbidity of breast cancer in multiparous TA2 mice reaches 84.1% and the average time it takes for tumor initiation and development is 280 days [2]. TA2 spontaneous breast cancer tumor cells show high metastatic ability and the rate of lung metastasis reaches more than 80% [2]. When injecting TA2 breast cancer tumor cells into normal TA2 mice, 1 × 105 cells for each mouse can form a palpable tumor 9 days after injection. Matrix metalloproteinase (MMPs) are very important in the processes of tumor invasion and metastasis through their degradation of the extracellular matrix (ECM) [3, 4]. There are many members of the MMP family. MMPs play an important role in the tissue remodeling associated with various physiological and pathological processes such as morphogenesis, angiogenesis, tissue repair and metastasis.

c) 4-Amino-6-methyl-N 1 -phenyl-1H-pyrazolo[3,4-d]pyrimidine 4c Yield 70 %; mp 160 °C; IR (cm−1); ν NH2 3090, 3320; ν C=N 1597, 1638, 1663; RMN 1H (δ ppm,

DMSO): 2.65 (3H, s, CH3), 4.28 (2H, s, NH2), 7.28 (1H, t, J = 7.3 Hz, ArH4), 7.56 (2H, t, J = 7.3 Hz, ArH3 and ArH5), 8.19 (2H, d, J = 7.3 Hz, ArH2 and ArH6), 8.29 (1H, s, H6); RMN13C (δ ppm, DMSO): 14.44 (CH3), 100.24 (C-3a), Carom 120.24 (C-2′ and C-6′), 124.67 (C-4′), 129.16 (C-3′ and C-5′), 138.8 (C-3), 142.79 HDAC inhibitors cancer (C-1′); C3 154.14 (C-7a), 156.51 (C-4),158.58 (C-6); HRMS Calcd. for C12H11N5 : https://www.selleckchem.com/products/DAPT-GSI-IX.html 225.1014, found: 225.1016.   7-Imino-N 1-phenyl-1,7-dihydropyrazolo[3′,4′:4,5]pyrimido[1,6-a]pyrimidine 5a–e A mixture of compound 4 (1.0 mmol), ketene ethoxymethylene compounds 1 or

ethyl-2-cyano-3-ethoxyalkyl-2-enoate (1.0 mmol) and a catalytic amount of acetic acid was refluxed for 2 h in 10 ml ethanol. The formed precipitate was filtered, washed by diethyl ether, dried and recrystallized from ethanol to give compound 5 in good yield. a) 6-Cyano-7-imino-3-methyl-N 1 -phenyl-1,7-dihydropyrazolo[3′,4′:4,5]pyrimido[1,6-a]pyrimidine 5a Yield 68 %; mp 290 °C; IR (cm−1); ν NH 3356; ν C≡N 2212;

ν C=N 1534, Reverse transcriptase 1554, 1587; RMN 1H (δ ppm, DMSO): 2.51 (3H, s, CH3); 7.38 (1H, t, J = 7.3 Hz, ArH4); 7.53 (2H, t, J = 7.3 Hz, ArH3 and ArH5); 7.71 (2H, d, J = 7.3 Hz, ArH2 and ArH6); 8.02 (1H, s, H5); 8.38 (1H, s, H9); 8.66 (1H, s, NH); RMN13C (δ ppm, DMSO): 14.64 (CH3); 91.81 (C-6); 105.88 (C-3a); 116.24 (CN); Carom 120.46 (C-2′ and C-6′), 124.17 (C-4′), 129.27 (C-3′ and C-5′), 137.89 (C-1′),143.42 (C-10a), 149.71 (C-3),159.61 (C-5),161.88 (C-9), 162.15 (C-4a); 163.43 (C-7); HRMS Calcd. for C16H11N7 :301.1076, found: 301.1051.   b) 6-Cyano-7-imino-3,5-dimethyl-N 1 -phenyl-1, 7-dihydropyrazolo[3′, 4′:4, 5]pyrimido[1, 6-a]pyrimidine 5b Yield 54 %; mp 182 °C; IR (cm−1): ν NH 3324; ν C≡N 2230; ν C=N 1509, 1562, 1586; RMN 1H (δ ppm, DMSO): 2.50 (3H, s, CH3), 2.64 (3H, s, CH3); 7.26 (1H, t, J = 7.3 Hz, ArH4); 7.51 (2H, t, J = 7.3 Hz, ArH3 and ArH5); 7.54 (2H, d, J = 7.3 Hz, ArH2 and ArH6); 8.19 (1H, s, H9); 8.27 (1H, s, NH); RMN13C (δ ppm, DMSO): 14.42 (CH3); 21.00 (CH3); 87.23 (C-6); 100.25 (C-3a); 109.00 (CN); 120.22 (C-2′ and C-6′), 125.51 (C-4′), 128.98 (C-3′ and C-5′), 138.89 (C-1′); 142.79 (C-10a); 154.17 (C-3), 156.49 (C-5), 164.59 (C-9), 165.71 (C-4a), 167.94 (C-7); HRMS Calcd. for C17H13N7 : 315.1232, found: 315.1214.

Estimates of the proportion of soil carbon emitted in the event of deforestation range from 25 % (Guo and Gifford 2002; Busch et al. 2009) to 40 % (Kindermann et al. 2008). We did not account for any carbon removals or additions associated with subsequent agricultural cover. It has been estimated that approximately 12 million ha have been deforested per year in the period 1990–2005, mostly in developing countries (Food and Agriculture buy Y-27632 Organisation 2006). Therefore, deforestation of 12 million ha was adopted in this study as a “business as usual” (BAU) scenario for annual deforestation through 2050. These estimates do not include

land-cover change outside forests, or reforestation and afforestation. To reflect the uncertainties involved, and given that our analysis covers conversion of any natural MI-503 manufacturer landscape, not just forested land, we also ran two alternative BAU scenarios, with 50 % more (i.e. 18 million

ha per year—“high BAU”) and 50 % less (6 million ha per year—“low BAU”) annual deforestation. Our scenarios assume deforestation would occur in Latin America (including the Caribbean), sub-Saharan Africa and South, East and South East Asia (including countries from Oceania). The geographic distribution of agricultural expansion was estimated using our likelihood of conversion map (Fig. 2), on the assumption that those areas characterised by the highest likelihood of conversion are being converted first. Once a grid cell was selected to be converted, the fraction of

the grid cell converted within the BAU scenario corresponded to the predicted conversion (fraction of grid cell) for the year 2050. In the High BAU scenario, the amount converted per grid cell was increased by 50 % in relation to the BAU scenario. Fig. 2 Likelihood Baricitinib of land-cover change until 2050. Likelihood that a cell will experience at least 10 % of further conversion by the year 2050. Different colour scales are applied for forests and non-forest areas. Deserts and Annex-I countries (not developing countries) are shaded grey Lastly, we ran two further scenarios that incorporate the implementation of the REDD element of a REDD + scheme. The first scenario assumed that REDD is 100 % effective (no further conversion in forested grid cells), the second that REDD is 50 % effective (conversion in forested grid cells is 50 % of that grid cell’s BAU conversion). Using these scenarios, we investigated land-cover change-associated emissions in non-forest lands, if no other measures to decrease land demand are implemented. Results Selection of explanatory variables During the selection of explanatory variables by the model describing land cover, GDP per capita as a proxy for consumption patterns was found to have a worse fit than calorific intake per capita (selected by the model). PA status was also found not to be significant (P > 0.05).

According to the thermionic emission model [3], the direct reflection of the SBH is the reverse current density, and therefore, by controlling the Schottky barrier height, we can modulate the current density and acquire the needed contact type without modifying the fabrication process. In a previous study, Connelly et al. [4] have raised a method to reduce the SBH of the metal/Si contact by using

a thin Si3N4 through the creation of a dielectric dipole [5]. Similar researches have been dedicated to the study of the SBH modulation on Ge [6–9], GaAs [10], InGaAs [10, 11], GaSb [12], ZnO [13], and organic material [14] by inserting different dielectrics or bilayer dielectrics. According to the bond polarization theory [15], an electronic dielectric dipole is formed between the inserted insulator and semiconductor native oxide which results in a shift of the SBH, as

Figure 1 depicts. The origin of GSK2126458 in vivo the dipole formation at the dielectric/SiO2 interface is described in Kita’s model [16], and in this model, the areal density difference of oxygen atoms at the dielectric/SiO2 interface is the driving force to form the dipole. Since the areal density of oxygen atoms (σ) of Al2O3 is larger than that of SiO2, the σ difference at the interface will be compensated by oxygen transfer from the higher-σ to the lower-σ oxide which creates oxygen vacancies in the higher-σ oxide (Al2O3) and negatively charged centers in the lower-σ oxide Rapamycin (SiO2), and the corresponding direction of the dipole moment is from SiO2 to Al2O3. selleck chemicals llc As a result, this dipole is a positive dipole which can reduce the SBH and therefore increases the current density. As the thickness of the inserted insulator increases, it becomes

more difficult for the current to tunnel through the insulator, and the tunneling barrier is the dominant factor of the total barrier height, which decreases the current density in the end. Figure 1 A schematic band diagram of a shift in the metal/semiconductor’s high barrier height. This is done by forming an electronic dielectric dipole between the insulator and the oxide of semiconductor in accordance with the bond polarization theory. In this work, we demonstrate the modulation of the current density in the metal/n-SiC contact by inserting a thin Al2O3 layer into a metal-insulator-semiconductor (MIS) structure. Al2O3 is chosen as the interfacial insulator for its large areal oxygen density (σ) which means that the formation of dipole is much stronger and shifts the SBH more effectively than that induced by other insulators based on the bond polarization theory [15] and Kita’s model [16]. As for the choice of metal, aluminum (Al) is suitable due to its low work function (4.06 to 4.26 eV) for the investigations of the Fermi level shift toward the conduction band of SiC (electron affinity = 3.3 eV).

Nature Materials 2008, 7:442–453.CrossRef 9. Pillai S, Catchpole KR, Trupke T, Green MA: Surface plasmon enhanced silicon solar cells. Journal of Applied Physics 2007,101(9):093105/1–093105/8.CrossRef 10. Tan H, Santbergen R, Smets AH, Zeman M: Plasmonic light trapping in thin-film

silicon solar cells with improved self-assembled silver nanoparticles. Nano Letters 2012,12(8):4070–4076.CrossRef 11. Matheu P, Lim SH, Derkacs check details D, McPheeters C, Yu ET: Metal and dielectric nanoparticle scattering for improved optical absorption in photovoltaic devices. Applied Physics Letters 2008,93(11):113108/1–113108/3.CrossRef 12. Grandidier J, Weitekamp RA, Deceglie MG, Callahan DM, Battaglia C, Bukowsky CR, Ballif C, Grubbs RH, Atwater HA: Solar cell efficiency enhancement via light trapping in printable resonant dielectric nanosphere arrays. Physica Status Solidi (a) 2013,210(2):255–260.CrossRef 13. Nakayama K, Tanabe K, Atwater HA: Plasmonic nanoparticle enhanced light absorption in GaAs solar cells. Applied Physics Letters 2008, 12:121904/1–121904/3. DZNeP 14. Westphalen M, Kreibig U,

Rostalski J, Lüth H, Meissner D: Metal cluster enhanced organic solar cells. Solar Energy Materials & Solar Cells 2000, 61:97–105.CrossRef 15. Ihara M, Kanno M, Inoue S: Photoabsorption-enhanced dye-sensitized solar cell by using localized surface plasmon of silver nanoparticles modified with polymer. Physica E: Low-dimensional Systems and Nanostructures 2010,42(10):2867–2871.CrossRef 16. Atwater HA, Polman A: Plasmonics for improved photovoltaic devices. Nature Materials 2010, 9:205–213.CrossRef 17. Catchpole KR, Polman A: Design principles for particle plasmon enhanced solar cells. Applied Physics Letters 2008, 19:191113/1–191113/3.

18. Grandidier J, Callahan Galeterone DM, Munday JN, Atwater HA: Light absorption enhancement in thin-film solar cells using whispering gallery modes in dielectric nanospheres. Advanced Materials 2011,23(10):1272–1276.CrossRef 19. Spinelli P, Verschuuren MA, Polman A: Broadband omnidirectional antireflection coating based on subwavelength surface Mie resonators. Nature Communications 2012, 3:692–696.CrossRef 20. Garcia Etxarri A, Gómez-Medina R, Froufe-Pérez LS, López C, Chantada L, Scheffold F, Aizpurua J, Nieto-Vesperinas M, Sáenz JJ: Strong magnetic response of submicron silicon particles in the infrared. Optics Express 2011,19(6):4815–4826.CrossRef 21. Bohren CF, Huffman DR: Absorption and scattering of light by small particles. New York: Wiley; 1983. 22. Hoffmann J, Hafner C, Leidenberger P, Hesselbarth J, Burger S: Comparison of electromagnetic field solvers for the 3D analysis of plasmonic nano antennas. Proceedings of the Society of Photo-Optical Instrumentation 2009, 7390:73900J/1–73900J/11. 23. Palik ED: Handbook of optical constants of solids. Boston: Academic; 1985. 24. Jellison GE, Modine FA: Parameterization of the optical functions of amorphous materials in the interband region. Applied Physics Letters 1996,69(3):371–373.

Appl Environ Microbiol 2001, 67: 561–568.PubMedCrossRef 69. Acheson DWK, Linciome LL, Jacewicz MS, Keusch GT: Shiga toxin interaction with intestinal epithelial cells. In Escherichia coli 0157: H7 and other shiga-toxin producing E. coli strains. Edited by: Kaper JB, O’Brien AD. Washington DC, ASM Press; 1998:140–147. 70. Mater DDG, Langella P, Corthier G, Flores MJ: Evidence of vancomycin resistance gene transfer between enterococci of human origin in the gut of mice harbouring Alvelestat human microbiota. J Antimicrob Chemother 2005,

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YitA and YipA protein increased with an increase in yitR copy number (Figure 2, lanes 5–6). The sizes of the YitA and YipB protein produced by all the strains under environmental conditions were similar (Figure 2, lanes 2, 5, 6). No detectable YitA or YipA protein was produced by the KIM6+ΔyitR deletion mutant (data not shown). In vitro production of YitA and YipA by Y. pestis is dependent on growth temperature but not on culture Small molecule library cell line medium Y. pestis KIM6+, KIM6+ (pWKS130::yitR), and KIM6+ (pCR-XL-TOPO::yitR) were grown in BHI at 10°C, 22°C, 28°C, or 37°C overnight to determine YitA and YipA synthesis at

different growth temperatures. YitA production in parental KIM6+ was detected after growth at 10°C (Figure 3A, lane 2). Full-size YipA was not detected in KIM6+ at any temperature (Figure 3A, lanes 2, 5, 8, and 11). When plasmid pWKS130::yitR was present, YitA was seen at all temperatures, with buy PR-171 the maximum level at 10°C; the level decreased when the growth temperature was 37°C (Figure 3A, lanes 3, 6, 9, and 12). When plasmid pWKS130::yitR was present, YipA production was also greatest after growth

at 10°C (Figure 3A, lane 3) and decreased when the growth temperature was 37°C (Figure 3A, lanes 6, 9, and 12); however, very little was seen at 37°C and the larger molecular weight band was no longer present (Figure 3A, lane 12). Y. pestis KIM6+ with the high-copy number pCR-XL-TOPO::yitR had the greatest production of YitA and YipA, which also decreased when the growth temperature was 37°C (Figure 3A, lanes 4, 7, 10, and 13). For each of the strains tested, levels of YitA and YipA were comparable after growth at 22°C or 28°C (Figure 3A, lanes 5, 6, 7, 8, 9 and 10). Figure 3 Maximal synthesis of YitA and YipA during growth at low temperatures. A) KIM6+ (lanes 2, 5, 8, and 11), KIM6+ (pWKS130::yitR) (lanes 3, 6, 9, and 12) and PtdIns(3,4)P2 KIM6+ (pCR-XL-TOPO::yitR) (lanes 4, 7, 10, and 13) grown overnight

at 10°C, 22°C, 28°C or 37°C in BHI broth. YitA and YipA purified from E. coli (lane 15). B) KIM6+ (lanes 2, 5, 8, and 11), KIM6+ (pWKS130::yitR) (lanes 3, 6, 9, and 12) and KIM6+ (pCR-XL-TOPO::yitR) (lanes 4, 7, 10, and 13) grown overnight at 22°C or 37°C in either RPMI 1640 (RPMI) or whole sheep blood (Blood). YitA and YipA purified from E. coli (lanes 15 and 16). Panels show Western blots probed with anti-YitA, anti-YipA, or anti-Ail (sample loading control) antiserum. YitA and YipA production following growth in both blood and RPMI 1640 was equivalent to production following growth in BHI. YitA and YipA were produced to the greatest extent after growth at 22°C in RPMI 1640 and blood (Figure 3B, lanes 2–7) and levels dramatically decreased following growth at 37°C (Figure 3B, lanes 8–12). As with growth in BHI, Y.

In addition, it is well established that p53 mutation is the most common genetic alteration in 60.6% of ESCC [9]. By contrast, gene methylation is an alternative mechanism of gene inactivation that occurs early tumor progression and thus alters gene expression without changing the DNA sequence [10–12]. Similar to genetic mutations, transcriptional silencing by CpG methylation is stably inherited to the next cell generation and may therefore allow the clonal expansion of a cell population with a selective advantage during tumor

progression. Various tumor-suppressor genes that regulate apoptosis, the cell cycle, and cell signaling are Adriamycin aberrantly methylated in ESCC [12–14].Given these observations, uncovering the molecular

pathogenesis of Kazakh ESCC, especially the detection of aberrant CpG methylation, is therefore likely to provide new approaches to the prevention, diagnosis and treatment of ESCC. MicroRNA (miRNA), a class of small regulatory RNA molecules, acts as tumor suppressors and oncogenes by negatively regulating their mRNA targets in a sequence-specific manner through post-transcriptional repression and influencing the proliferation and cell cycle progression, apoptosis, invasion and metastasis of cancer [10]. Widespread miRNA is dysregulated in various human malignancies by changes in DNA copy number and epigenetic inactivation, although their exact functions during carcinogenesis are still being examined [15–17]. In esophageal cancer, the reduced expression of MK-2206 cost miR-143 or the overexpression of miR-7 is reportedly correlated with the depth of invasion and lymph node metastasis of ESCC [18]. Among the types of miRNAs, the miR-34a gene, which resides in chromosome 1q36.22 and belongs to the miR-34

family, reportedly is directly regulated by the p53 transcription factor [19, 20]. The miR-34a downregulates numerous important regulatory proteins of cell cycle progression and apoptosis, such as E2F3, c-MYC, Bcl2, c-MET, Rucaparib and CDK4/6, suggesting that miR-34a itself may mediate tumor suppression [21]. The reduced or absent expression of miR-34a was reported in 110 cancer cells lines, such as breast, lung, colon, kidney, melanoma, bladder, pancreatic carcinoma, lymphoma, and myeloma and cell lines, and two different types of primary cancers (melanoma and primary neuroblastoma samples) because of the aberrant CpG methylation of its promoter [22–24]. However, only one study have reported that the miR-34a was silenced in ESCC cell lines and re-expression miR-34a can inhibit the ESCC proliferation by reducing the C-met and Cyclin D1 expression [24], yet the correlation between downregulation/loss of miR-34a expression and promoter methylation in ESCC was not clean, especially in the Kazakh population.

J Bacteriol 2002, 184:1430–1437.CrossRefPubMed 7. Nakano M, Kawano Y, Kawagishi M, Hasegawa T, Iinuma Y, Ohta M: Two-dimensional analysis of exoproteins of methicillin-resistant Staphylococcus aureus

(MRSA) for possible epidemiological application. Micro Immunol 2002, 46:11–22. 8. Blevins JS, Gillaspy AF, Rechtin TM, Hurlburt BK, Smeltzer MS: The staphylococcal accessory regulator ( sar ) represses transcription of the Staphylococcus aureus collagen adhesin gene ( cna ) in an agr -independent manner. Mol Microbiol 1999, 33:317–326.CrossRefPubMed 9. Chan PF, Foster J: Role of SarA in virulence determinant production and environmental signal transduction in Staphylococcus aureus. J Bacteriol 1998, 180:6232–6241.PubMed 10. Bayer MG, Heinrichs JH, Cheung AL: The learn more molecular architecture of the sar locus in Staphylococcus aureus. J Bacteriol 1996, 178:4563–4570.PubMed 11. Becker K, Friedrich AW, Lubritz G, Weilert M, Peters G, Christo von Eiff : Prevalence of genes encoding pyrogenic toxin superantigens and exfoliative toxins among strains of Staphylococcus aureus isolated from blood and nasal specimens. J Clin Microbiol 2003, 41:1434–1439.CrossRefPubMed

12. Imura S: Changes in drug susceptibility and toxin genes in Staphylococcus aureus isolated from blood cultures at a university hospital. J Infect HER2 inhibitor Chemother 2004, 10:8–10.CrossRef 13. Hamilton SM, Bryant AE, Carrol KC, Lockary V, Ma Y, Mcindoo E, Miller LG, Perdreau-Remington F, Pullman J, Risi GF, Salmi DB, Stevens DL: In vitro production of Panton-Valentine Leukocidin among strains of methicillin-resistant Staphylococcus aureus causing diverse infections. Clin Infect Dis 2007, 45:1550–1558.CrossRefPubMed 14. Strommenger B, Cuny C, Werner G, Witte W: Obvious lack of association between dynamics of epidemic methicillin-resistant Staphylococcus aureus in central Europe and Depsipeptide agr specificitygroups. Eur J Clin Microbio

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Nine persons were lost to follow up, as they were not registered buy U0126 by the communal personal administration any more. The total number of person-years of observation time was 21,702. The 226 total observed deaths were significantly lower than the expected number of 327.3, resulting in a SMR of 69.0 (95% CI: 60.3–78.7). Table 2 Cause-specific mortality in 570 workers exposed to dieldrin and aldrin stratified into three dose groups Cause of death Total group Low intake Moderate intake High intake Obs SMR (95% CI) Obs SMR (95% CI) Obs SMR (95% CH5424802 solubility dmso CI) filipin Obs SMR (95% CI)

All causes 226 69.0* 60.3–78.7 59 75.1* 57.2–96.9 78 72.1* 57.0–90.0 89 67.0* 53.8–82.4 Neoplasms 82 76.4* 60.8–94.9 27 100.3 66.1–145.9 27 75.1 49.5–109.3 28 66.2* 44.0–95.6 Cardiovascular disease 80 59.9* 47.5–74.6 17 54.1* 31.5–86.6 30 67.6* 45.6–96.6 33 59.4* 40.9–83.4 Respiratory disease 20 74.3 45.4–114.7 5 87.3 28.5–204.9 5 56.0 18.2–130.7 10 84.4 40.5–155.3 Others causes 35 61.1* 42.6–85.0 7 50.2 20.2–103.4 14 76.7 42.0–128.8 14 63.0 34.4–105.7 Unknown 9     3     2     4     Neoplasms, cause specific 82     27     27     28      Oesophagus 4 159.3 43.4–407.9 2 286.5 34.7–1,035.1 1 116.6 3.0–649.4 1 107.5 2.7–599.1  Stomach and small intestine 8 96.0 41.5–189.2 5 249.3 80.9–581.7 2 75.5 9.0–269.2 1 30.0 0.8–167.1  Large intestine 7 96.7 38.9–199.2 1 54.6 1.4–304.0 2 81.9 9.9–296.0 4 139.5 38.0–357.1  Rectum 6 214.8 78.8–467.6 3 441.8 91.1–1,291.2 1 109.7 2.8–610.9 2 175.6 21.3–634.3  Liver and biliary passages 4 216.1 58.9–553.9 2 426.4 51.6–1540.5 2 322.6 39.1–1,165.3 0 0 0–414.4  Pancreas 3 66.5 13.7–194.3 1 86.4 2.2–481.6 0 0 0–197.1 2 113.0 13.7–408.2  Trachea and lung cancer 26 63.0* 41.1–92.3 7 66.7 26.8–137.1 12 85.9 44.4–150.0 7 43.3* 17.4–89.2  Skin 3 302.4 62.4–883.8 1 357.1 9.0–1,989.9 2 611.6 74.1–2,209.4 0 0 0–843.9  Kidney 2 69.8 8.5–252.2 0 0 0–392.1 0 0 0–307.9 2 184.7 22.4–667.1  Prostate cancer 5 55.3 18.0–129.2 2 102.9 12.5–371.6 1 32.8 0.8–182.