402 research outputs found
Studi Kebutuhan Angkutan Umum Penumpang Perkotaan di Kota Palu (Studi Kasus: Trayek Mamboro - Manonda)
One of cause of public transport services is not optimal is the incompatibility between the number ofpublic transport with passenger numbers to be served. Incompatibility of the number of publictransport with the number of passengers served would also affect the income of public transportoperators. The purpose of this study is to estimate the needs of the urban public transport routesMamboro – Manonda, Palu City based on the number of passengers along the route. Primary datacollection survey was conducted in January of 2010 for 3 days at each peak morning, afternoon andevening. Survey of primary data through questionnaires and enumeration of the number ofpassengers. The primary data include the number of passengers up and down along the route, roundtrip time of the public transport, time headway of each the public transport. Secondary data includepath of Mamboro – Manonda route, the number of public transportation of Mamboro – Manondaroute. Methods and standards used in estimating the number of public transportation refer to theTechnical Guidelines for the delivery of the Urban Passenger Transport, 2002, the Ministry ofTransportation, Republic of Indonesia.The results have been found several conclusions as follows: there are differences in the needs ofpublic transport in every peak hour morning, afternoon and evening, number of public transport thatoperates not according to the number of passengers served, increasing the number of publictransport is not required for the next few years
Detecting spatio-temporal mortality clusters of European countries by sex and ag
[EN] Background: Mortality decreased in European Union (EU) countries during the last century. Despite these similar
trends, there are still considerable differences in the levels of mortality between Eastern and Western European
countries. Sub-group analysis of mortality in Europe for different age and sex groups is common, however to our
knowledge a spatio-temporal methodology as in this study has not been applied to detect significant spatial
dependence and interaction with time. Thus, the objective of this paper is to quantify the dynamics of mortality in
Europe and detect significant clusters of mortality between European countries, applying spatio-temporal
methodology. In addition, the joint evolution between the mortality of European countries and their neighbours over
time was studied.
Methods: The spatio-temporal methodology used in this study takes into account two factors: time and the
geographical location of countries and, consequently, the neighbourhood relationships between them. This
methodology was applied to 26 European countries for the period 1990-2012.
Results: Principally, for people older than 64 years two significant clusters were obtained: one of high mortality
formed by Eastern European countries and the other of low mortality composed of Western countries. In contrast, for
ages below or equal to 64 years only the significant cluster of high mortality formed by Eastern European countries
was observed. In addition, the joint evolution between the 26 European countries and their neighbours during the
period 1990-2012 was confirmed. For this reason, it can be said that mortality in EU not only depends on differences in
the health systems, which are a subject to national discretion, but also on supra-national developments.
Conclusions: This paper proposes statistical tools which provide a clear framework for the successful implementation
of development public policies to help the UE meet the challenge of rethinking its social model (Social Security and
health care) and make it sustainable in the medium term.The authors are grateful for the financial support provided by the Ministry of Economy and Competitiveness, project MTM2013-45381-P. Adina Iftimi gratefully acknowledges financial support from the MECyD (Ministerio de Educacion, Cultura y Deporte, Spain) Grant FPU12/04531. Francisco Montes is grateful for the financial support provided by the Spanish Ministry of Economy and Competitiveness, project MTM2016-78917-R. The research by Patricia Carracedo and Ana Debon has been supported by a grant from the Mapfre Foundation.Carracedo-Garnateo, P.; Debón Aucejo, AM.; Iftimi, A.; Montes-Suay, F. (2018). Detecting spatio-temporal mortality clusters of European countries by sex and ag. International Journal for Equity in Health. 17:1-19. https://doi.org/10.1186/s12939-018-0750-zS11917Anderson TW, Goodman LA. Statistical Inference about Markov Chains. Ann Math Stat. 1957; 28(1):89–110.Anselin L. Local Indicators of Spatial Association–LISA. Geographical Anal. 1995; 27(2):93–115.Bilbao-Ubillos J. Is there still such a thing as the ‘European social model’?. Int J Soc Welf. 2016; 25:110–25.Bivand R. spdep: Spatial Dependence:Weighting Schemes, Statistics and Models. 2012. R package version 0.5-53. http://CRAN.R-project.org/package=spdep .Bivand R, Hauke J, Kossowski T. Computing the Jacobian in Gaussian Spatial Autoregressive Models: An Illustrated Comparison of Available Methods. Geographical Anal. 2013; 45(2):150–79.Bivand R, Keitt T, Rowlingson B. rgdal: Bindings for the Geospatial Data Abstraction Library. 2016. R package version 1.1-10. https://CRAN.R-project.org/package=rgdal .Bivand R, Lewin-Koh N. maptools: Tools for Reading and Handling Spatial Objects. 2016. R package version 0.8-39 https://CRAN.R-project.org/package=maptools .Bonneux L, Huisman C. de Beer J. Mortality in 272 European regions, 2002-2004: an update. Eur J Epidemiol. 2010; 25(1):77–85. Reporting year: 2010.Charpentier A. Computational Actuarial Science with R. Chapman y Hall/CRC. 2014.Cliff AD, Ord JK. Spatial autocorrelation. London: Pion; 1973.Cutler D, Deaton A, Lleras-Muney A. The Determinants of Mortality. J Econ Perspect. 2006; 20(3):97–120.Debón A, Chaves L, Haberman S, Villa F. Characterization of between-group inequality of longevity in European Union countries. Insur Math Econ. 2017; 75:151–65.Fleiss J, Levin B, Paik M. Statistical Methods for Rates and Proportions: Wiley; 2013.Gordon M. Gmisc: Descriptive Statistics, Transition Plots, and More. 2016. R package version 1.3.1. https://CRAN.R-project.org/package=Gmisc .Hinde A. Demographic methods. Routledge: Routledge; 1998.Hyndman RJ, Booth H, Tickle L, Maindonald J. demography: Forecasting mortality, fertility, migration and population data. 2014. package version 1.18. https://CRAN.R-project.org/package=demography .Human Mortality Database. University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany). 2016. Available at www.mortality.org or www.humanmortality.de (data downloaded on 12th July 2016).Hatzopoulos P, Haberman S. Common mortality modeling and coherent forecasts. An empirical analysis of worldwide mortality data. Insurance Math Econ. 2013; 52(2):320–37.Iftimi A, Montes F, Santiyán AM, MartÃnez-Ruiz F. Space–time airborne disease mapping applied to detect specific behaviour of varicella in Valencia, Spain Spatial Spatio-Temporal Epidemiol. 2015; 14:33–44.Julious S, Nicholl J, George S. Why do we continue to use standardized mortality ratios for small area comparisons?. J Public Health. 2001; 23(1):40–6.Laurent T, Ruiz-Gazen A, Thomas-Agnan C. GeoXp: An R package for exploratory spatial data analysis. J Stat Softw. 2012; 47(2):1–23.Leon DA. Trends in European life expectancy: a salutary view. Int J Epidemiol. 2011; 40:271–7.Li H, Li L, Wu B, Xiong Y. The End of Cheap Chinese Labor. J Econ Perspect. 2013; 26(4):57–74.Mackenbach JP, Karanikolos M, McKee M. The unequal health of Europeans: successes and failures of policies. The Lancet. 2013; 381(9872):1125–34.Meslé F. Mortality in Central and Eastern Europe: Long-term trends and recent upturns. Demographic Res. 2004; 2:45–70.Meslé F, Vallin J. Mortality in Europe: The divergence between East and West. Population (English Edition). 2002; 57(1):157–97.Moran PAP. Notes on continuous stochastic phenomena. Biometrika. 1950; 37(1-2):17–23.Moran PAP. A Test for the Serial Independence of Residuals. Biometrika. 1950; 37(1/2):178–81.Neuwirth E. RColorBrewer: ColorBrewer Palettes. R package version. 2014; 1:1–2. https://CRAN.R-project.org/package=RColorBrewer .Oleckno WA. Epidemiology: concepts and methods: Waveland Press, Inc.; 2008.Quah D. Galton’s Fallacy and Tests of the Convergence Hypothesis. Scand J Econ. 1993; 95(4):427–43.R Core Team. R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. 2015. https://www.R-project.org/ .Rey S. In: Fischer MM, Nijkamp P, (eds).Spatial Dynamics and Space-Time Data Analysis. Berlin, Heidelberg: Springer: Handbook of Regional Science; 2014, pp. 1365–83.Rey SJ. Spatial Empirics for Economic Growth and Convergence. Geogr Anal. 2001; 33(3):195–214.Riffe T. Reading Human Fertility Database and Human Mortality Database data into R. Technical Report TR-2015-004, MPIDR. 2015.Schofield R, Reher D, Bideau A. The Decline of Mortality in Europe. International studies in demography. Oxford: Clarendon Press; 1991.Shaw M, Orford S, Brimblecombe N, Dorling D. Widening inequality in mortality between 160 regions of 15 European countries in the early 1990s. Soc Sci Med. 2000; 50(7-8):1047–58.Spinakis A, Anastasiou G, Panousis V, Spiliopoulos K, Palaiologou S, Yfantopoulos J. Expert Review and Proposals for Measurement of Health Inequalities in the European Union. European Commission. Technical report,Luxembourg: European Commission Directorate General for Health and Consumers; 2011. http://ec.europa.eu/health/social_determinants/docs/full_quantos_en.pdf .Staehr K. Economic transition in Estonia. Background, reforms and results In: Rindzeviciute E, editor. Contemporary Change in Estonia. Baltic and East European Studies. Sodertorns hogskola: Baltic and East European Studies: 2004. p. 437–67.Trnka L, Dankova D, Zitova J, Cimprichova L, Migliori GB, Clancy L, Zellweger J. Survey of BCG vaccination policy in Europe: 1994-96. Bull World Health Organ. 1998; 76(1):85–91.United Nations Inter–agency Group for Child Mortality Estimation. Levels & Trends in Child Mortality: Report 2013. New York: Technical report, United Nations Children’s Fund; 2013. Avaliable at www.who.int/maternal_child_adolescent/documents/levels_trends_child_mortality_2013.pdf Accessed 27 Oct 2016.VÃ¥gerö D. The east–west health divide in Europe: Growing and shifting eastwards. Eur Rev. 2010; 18(01):23–34.Vaupel JW, Zhang Z, van Raalte AA, Vaupel JW, Zhang Z, van Raalte AA. Life expectancy and disparity: an international comparison of life table data. BMJ Open. 2011; 1:e000128.Wickham H, Chang W. devtools: Tools to Make Developing R Packages Easier. R package version 1.11.1. 2016. https://CRAN.R-project.org/package=devtools .Wilcox R. Introduction to robust estimation and hypothesis testing, 3rd Edition.San Diego: Academic Press; 2012
Association of genetic variants in the promoter region of genes encoding p22phox (CYBA) and glutamate cysteine ligase catalytic subunit (GCLC) and renal disease in patients with type 1 diabetes mellitus
<p>Abstract</p> <p>Background</p> <p>Oxidative stress is recognized as a major pathogenic factor of cellular damage caused by hyperglycemia. NOX/NADPH oxidases generate reactive oxygen species and NOX1, NOX2 and NOX4 isoforms are expressed in kidney and require association with subunit p22phox (encoded by the <it>CYBA </it>gene). Increased expression of p22phox was described in animal models of diabetic nephropathy. In the opposite direction, glutathione is one of the main endogenous antioxidants whose plasmatic concentrations were reported to be reduced in diabetes patients. The aim of the present investigation was to test whether functional single nucleotide polymorphisms (SNPs) in genes involved in the generation of NADPH-dependent O<sub>2</sub><sup>•- </sup>(-675 T → A in <it>CYBA</it>, unregistered) and in glutathione metabolism (-129 C → T in <it>GCLC </it>[rs17883901] and -65 T → C in <it>GPX3 </it>[rs8177412]) confer susceptibility to renal disease in type 1 diabetes patients.</p> <p>Methods</p> <p>401 patients were sorted into two groups according to the presence (n = 104) or absence (n = 196) of overt diabetic nephropathy or according to glomerular filtration rate (GFR) estimated by Modification of Diet in Renal Disease (MDRD) equation: ≥ 60 mL (n = 265) or < 60 mL/min/1.73 m<sup>2 </sup>(n = 136) and were genotyped.</p> <p>Results</p> <p>No differences were found in the frequency of genotypes between diabetic and non-diabetic subjects. The frequency of GFR < 60 mL/min was significantly lower in the group of patients carrying <it>CYBA </it>genotypes T/A+A/A (18.7%) than in the group carrying the T/T genotype (35.3%) (P = 0.0143) and the frequency of GFR < 60 mL/min was significantly higher in the group of patients carrying <it>GCLC </it>genotypes C/T+T/T (47.1%) than in the group carrying the C/C genotype (31.1%) (<it>p </it>= 0.0082). Logistic regression analysis identified the presence of at least one A allele of the <it>CYBA </it>SNP as an independent protection factor against decreased GFR (OR = 0.38, CI95% 0.14-0.88, <it>p </it>= 0.0354) and the presence of at least one T allele of the <it>GCLC </it>rs17883901 SNP as an independent risk factor for decreased GFR (OR = 2.40, CI95% 1.27-4.56, <it>p </it>= 0.0068).</p> <p>Conclusions</p> <p>The functional SNPs <it>CYBA </it>-675 T → A and <it>GCLC </it>rs17883901, probably associated with cellular redox imbalances, modulate the risk for renal disease in the studied population of type 1 diabetes patients and require validation in additional cohorts.</p
Identification of a biomarker panel for improvement of prostate cancer diagnosis by volatile metabolic profiling of urine
Background: The lack of sensitive and specific biomarkers for the early detection of prostate cancer (PCa) is a major hurdle to improve patient management. Methods: A metabolomics approach based on GC-MS was used to investigate the performance of volatile organic compounds (VOCs) in general and, more specifically, volatile carbonyl compounds (VCCs) present in urine as potential markers for PCa detection. Results: Results showed that PCa patients (n = 40) can be differentiated from cancer-free subjects (n = 42) based on their urinary volatile profile in both VOCs and VCCs models, unveiling significant differences in the levels of several metabolites. The models constructed were further validated using an external validation set (n = 18 PCa and n = 18 controls) to evaluate sensitivity, specificity and accuracy of the urinary volatile profile to discriminate PCa from controls. The VOCs model disclosed 78% sensitivity, 94% specificity and 86% accuracy, whereas the VCCs model achieved the same sensitivity, a specificity of 100% and an accuracy of 89%. Our findings unveil a panel of 6 volatile compounds significantly altered in PCa patients' urine samples that was able to identify PCa, with a sensitivity of 89%, specificity of 83%, and accuracy of 86%. Conclusions: It is disclosed a biomarker panel with potential to be used as a non-invasive diagnostic tool for PCa.info:eu-repo/semantics/publishedVersio
Correlation Network Analysis Applied to Complex Biofilm Communities
The complexity of the human microbiome makes it difficult to reveal organizational principles of the community and even more challenging to generate testable hypotheses. It has been suggested that in the gut microbiome species such as Bacteroides thetaiotaomicron are keystone in maintaining the stability and functional adaptability of the microbial community. In this study, we investigate the interspecies associations in a complex microbial biofilm applying systems biology principles. Using correlation network analysis we identified bacterial modules that represent important microbial associations within the oral community. We used dental plaque as a model community because of its high diversity and the well known species-species interactions that are common in the oral biofilm. We analyzed samples from healthy individuals as well as from patients with periodontitis, a polymicrobial disease. Using results obtained by checkerboard hybridization on cultivable bacteria we identified modules that correlated well with microbial complexes previously described. Furthermore, we extended our analysis using the Human Oral Microbe Identification Microarray (HOMIM), which includes a large number of bacterial species, among them uncultivated organisms present in the mouth. Two distinct microbial communities appeared in healthy individuals while there was one major type in disease. Bacterial modules in all communities did not overlap, indicating that bacteria were able to effectively re-associate with new partners depending on the environmental conditions. We then identified hubs that could act as keystone species in the bacterial modules. Based on those results we then cultured a not-yet-cultivated microorganism, Tannerella sp. OT286 (clone BU063). After two rounds of enrichment by a selected helper (Prevotella oris OT311) we obtained colonies of Tannerella sp. OT286 growing on blood agar plates. This system-level approach would open the possibility of manipulating microbial communities in a targeted fashion as well as associating certain bacterial modules to clinical traits (e.g.: obesity, Crohn's disease, periodontal disease, etc)
Neglected Tropical Diseases outside the Tropics
Neglected Tropical Diseases (NTDs) have been targeted due to their prevalence and the burden of disease they cause globally, but there has been no significant focus in the literature on the subject of NTDs as a group in immigrants and travelers, and no specific studies on the emerging phenomenon of imported NTDs. We present the experience of a Tropical Medicine Unit in a major European city, over a 19-year period, describing and comparing NTDs diagnosed amongst immigrants, travelers and travelers visiting friends and relatives (VFRs). NTDs were diagnosed outside tropical areas and occurred more frequently in immigrants, followed by VFR travelers and then by other travelers. The main NTDs diagnosed in immigrants were onchocerciasis, Chagas disease and ascariasis; most frequent NTDs in travelers were schistosomiasis, onchocerciasis and ascariasis, and onchocerciasis and schistosomiasis in VFRs. Issues focusing on modes of transmission outside endemic areas and how eradication programs for some NTDs in endemic countries may have an impact in non-tropical Western countries by decreasing disease burden in immigrants, are addressed. Adherence to basic precautions such as safe consumption of food/water and protection against arthropod bites could help prevent many NTDs in travelers
Novel mutations in the TBX5 gene in patients with Holt-Oram Syndrome
The Holt-Oram syndrome (HOS) is an autosomal dominant condition characterized by upper limb and cardiac malformations. Mutations in the TBX5 gene cause HOS and have also been associated with isolated heart and arm defects. Interactions between the TBX5, GATA4 and NKX2.5 proteins have been reported in humans. We screened the TBX5, GATA4, and NKX2.5 genes for mutations, by direct sequencing, in 32 unrelated patients presenting classical (8) or atypical HOS (1), isolated congenital heart defects (16) or isolated upper-limb malformations (7). Pathogenic mutations in the TBX5 gene were found in four HOS patients, including two new mutations (c.374delG; c.678G > T) in typical patients, and the hotspot mutation c.835C > T in two patients, one of them with an atypical HOS phenotype involving lower-limb malformations. Two new mutations in the GATA4 gene were found in association with isolated upper-limb malformations, but their clinical significance remains to be established. A previously described possibly pathogenic mutation in the NKX2.5 gene (c.73C > 7) was detected in a patient with isolated heart malformations and also in his clinically normal father
Sodium lauryl ether sulfate (SLES) degradation by nitrate-reducing bacteria
The online version of this article (doi:10.1007/s00253-017-8212-x) contains supplementary material, which is available to authorized users.The surfactant sodium lauryl ether sulfate (SLES) is widely used in the composition of detergents and frequently ends up in wastewater treatment plants (WWTPs). While aerobic SLES degradation is well studied, little is known about the fate of this compound in anoxic environments, such as denitrification tanks of WWTPs, nor about the bacteria involved in the anoxic biodegradation. Here, we used SLES as sole carbon and energy source, at concentrations ranging from 50 to 1000 mg L1, to enrich and isolate nitrate-reducing bacteria from activated sludge of a WWTP with the anaerobic-anoxic-oxic (A2/O) concept. In the 50 mg L1 enrichment, Comamonas (50%), Pseudomonas (24%), and Alicycliphilus (12%) were present at higher relative abundance, while Pseudomonas (53%) became dominant in the 1000 mg L1 enrichment. Aeromonas hydrophila strain S7, Pseudomonas stutzeri strain S8, and Pseudomonas nitroreducens strain S11 were isolated from the enriched cultures. Under denitrifying conditions, strains S8 and S11 degraded 500 mg L1 SLES in less than 1 day, while strain S7 required more than 6 days. Strains S8 and S11 also showed a remarkable resistance to SLES, being able to grow and reduce nitrate with SLES concentrations up to 40 g L1. Strain S11 turned out to be the best anoxic SLES degrader, degrading up to 41% of 500 mg L1. The comparison between SLES anoxic and oxic degradation by strain S11 revealed differences in SLES cleavage, degradation, and sulfate accumulation; both ester and ether cleavage were probably employed in SLES anoxic degradation by strain S11.This research was supported by the Spanish Ministry of Education and Science (contract project CTQ2007-64324 and 447 CONSOLIDER-CSD 2007-00055). The Regional Government of Castilla y Leon (Ref. GR76) is also gratefully acknowledged. MRD is supported by the WIMEK graduate school (project BAdaptive capacity and functionality of multi-trophic aquatic ecosystems^). AJMS is supported by the Gravitation grant (project 024.002.002) of the Netherlands Ministry of Education, Culture and Science and the Netherlands Science Foundation (NWO). AJMS and AJC are supported by an European ResearchCouncil (ERC) Grant (Project 323009).Thisstudywassupported by the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684) and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020 - Programa Operacional Regional do Norte. This study was alsosupportedbythePortugueseFoundationforScienceandTechnology (FCT) under the scope of the Project RECI/BBB-EBI/0179/2012 (FCOMP-01-0124-FEDER-027462). Joana Alves from University of Minho (Portugal) is acknowledged for support with the molecular techniques.info:eu-repo/semantics/publishedVersio
Assessing Tuberculosis Case Fatality Ratio: A Meta-Analysis
Background: Recently, the tuberculosis (TB) Task Force Impact Measurement acknowledged the need to review the assumptions underlying the TB mortality estimates published annually by the World Health Organization (WHO). TB mortality is indirectly measured by multiplying estimated TB incidence with estimated case fatality ratio (CFR). We conducted a meta-analysis to estimate the TB case fatality ratio in TB patients having initiated TB treatment. Methods: We searched for eligible studies in the PubMed and Embase databases through March 4(th) 2011 and by reference listing of relevant review articles. Main analyses included the estimation of the pooled percentages of: a) TB patients dying due to TB after having initiated TB treatment and b) TB patients dying during TB treatment. Pooled percentages were estimated using random effects regression models on the combined patient population from all studies. Main Results: We identified 69 relevant studies of which 22 provided data on mortality due to TB and 59 provided data on mortality during TB treatment. Among HIV infected persons the pooled percentage of TB patients dying due to TB was 9.2% (95% Confidence Interval (CI): 3.7%-14.7%) and among HIV uninfected persons 3.0% (95% CI: 21.2%-7.4%) based on the results of eight and three studies respectively providing data for this analyses. The pooled percentage of TB patients dying during TB treatment was 18.8% (95% CI: 14.8%-22.8%) among HIV infected patients and 3.5% (95% CI: 2.0%-4.92%) among HIV uninfected patients based on the results of 27 and 19 studies respectively. Conclusion: The results of the literature review are useful in generating prior distributions of CFR in countries with vital registration systems and have contributed towards revised estimates of TB mortality This literature review did not provide us with all data needed for a valid estimation of TB CFR in TB patients initiating TB treatmen
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