1,143 research outputs found

    Cost effectiveness of daclatasvir/asunaprevir versus peginterferon/ribavirin and protease inhibitors for the treatment of hepatitis c genotype 1b Naïve patients in Chile

    Full text link
    © 2015 Vargas et al. Introduction: Daclatasvir and Asunaprevir (DCV/ASV) have recently been approved for the treatment of chronic hepatitis C virus infection. In association, they are more effective and safer than previous available treatments, but more expensive. It is unclear if paying for the additional costs is an efficient strategy considering limited resources. Methods: A Markov model was built to estimate the expected costs in Chilean pesos (CL)andconvertedtoUSdollars(US) and converted to US dollars (US) and benefits in quality adjusted life years (QALYs) in a hypothetic cohort of naive patients receiving DCV/ASV compared to protease inhibitors (PIs) and Peginterferon plus Ribavirin (PR). Efficacy was obtained from a mixed-treatment comparison study and costs were estimated from local sources. Utilities were obtained applying the EQ-5D survey to local patients and then valued with the Chilean tariff. A time horizon of 46 years and a discount rate of 3% for costs and outcomes was considered. The ICERs were estimated for a range of DCV/ASV prices. Deterministic and probabilistic sensitivity analyses were performed. Results: PIs were extendedly dominated by DCV/ASV. The ICER of DCV/ASV compared to PR was US16,635/QALYatatotaltreatmentpriceofUS 16,635/QALY at a total treatment price of US 77,419; US11,581/QALYatapriceofUS11,581 /QALY at a price of US 58,065; US6,375/QALYatapriceofUS 6,375/QALY at a price of US 38,710; and US1,364/QALYatapriceofUS 1,364 /QALY at a price of US 19,355. The probability of cost-effectiveness at a price of US38,710was91.6 38,710 was 91.6%while there is a 21.43% probability that DCV/ASV dominates PR if the total treatment price was US 19,355. Although the results are sensitive to certain parameters, the ICER did not increase above the suggested threshold of 1 GDP per capita. Conclusions: DCV/ASV can be considered cost-effective at any price of the range studied. These results provide decision makers useful information about the value of incorporating these drugs into the public Chilean healthcare system

    Mitochondrial fission facilitates the selective mitophagy of protein aggregates

    Get PDF
    Within the mitochondrial matrix, protein aggregation activates the mitochondrial unfolded protein response and PINK1–Parkin-mediated mitophagy to mitigate proteotoxicity. We explore how autophagy eliminates protein aggregates from within mitochondria and the role of mitochondrial fission in mitophagy. We show that PINK1 recruits Parkin onto mitochondrial subdomains after actinonin-induced mitochondrial proteotoxicity and that PINK1 recruits Parkin proximal to focal misfolded aggregates of the mitochondrial-localized mutant ornithine transcarbamylase (ΔOTC). Parkin colocalizes on polarized mitochondria harboring misfolded proteins in foci with ubiquitin, optineurin, and LC3. Although inhibiting Drp1-mediated mitochondrial fission suppresses the segregation of mitochondrial subdomains containing ΔOTC, it does not decrease the rate of ΔOTC clearance. Instead, loss of Drp1 enhances the recruitment of Parkin to fused mitochondrial networks and the rate of mitophagy as well as decreases the selectivity for ΔOTC during mitophagy. These results are consistent with a new model that, instead of promoting mitophagy, fission protects healthy mitochondrial domains from elimination by unchecked PINK1–Parkin activity

    Macrosystems ecology: Understanding ecological patterns and processes at continental scales

    Get PDF
    Macrosystems ecology is the study of diverse ecological phenomena at the scale of regions to continents and their interactions with phenomena at other scales. This emerging subdiscipline addresses ecological questions and environmental problems at these broad scales. Here, we describe this new field, show how it relates to modern ecological study, and highlight opportunities that stem from taking a macrosystems perspective. We present a hierarchical framework for investigating macrosystems at any level of ecological organization and in relation to broader and finer scales. Building on well-established theory and concepts from other subdisciplines of ecology, we identify feedbacks, linkages among distant regions, and interactions that cross scales of space and time as the most likely sources of unexpected and novel behaviors in macrosystems. We present three examples that highlight the importance of this multiscaled systems perspective for understanding the ecology of regions to continents

    Phenoloxidase activity acts as a mosquito innate immune response against infection with semliki forest virus

    Get PDF
    Several components of the mosquito immune system including the RNA interference (RNAi), JAK/STAT, Toll and IMD pathways have previously been implicated in controlling arbovirus infections. In contrast, the role of the phenoloxidase (PO) cascade in mosquito antiviral immunity is unknown. Here we show that conditioned medium from the Aedes albopictus-derived U4.4 cell line contains a functional PO cascade, which is activated by the bacterium Escherichia coli and the arbovirus Semliki Forest virus (SFV) (Togaviridae; Alphavirus). Production of recombinant SFV expressing the PO cascade inhibitor Egf1.0 blocked PO activity in U4.4 cell- conditioned medium, which resulted in enhanced spread of SFV. Infection of adult female Aedes aegypti by feeding mosquitoes a bloodmeal containing Egf1.0-expressing SFV increased virus replication and mosquito mortality. Collectively, these results suggest the PO cascade of mosquitoes plays an important role in immune defence against arboviruses

    Fungi in the Marine Environment: Open Questions and Unsolved Problems.

    Get PDF
    Terrestrial fungi play critical roles in nutrient cycling and food webs and can shape macroorganism communities as parasites and mutualists. Although estimates for the number of fungal species on the planet range from 1.5 to over 5 million, likely fewer than 10% of fungi have been identified so far. To date, a relatively small percentage of described species are associated with marine environments, with ∼1,100 species retrieved exclusively from the marine environment. Nevertheless, fungi have been found in nearly every marine habitat explored, from the surface of the ocean to kilometers below ocean sediments. Fungi are hypothesized to contribute to phytoplankton population cycles and the biological carbon pump and are active in the chemistry of marine sediments. Many fungi have been identified as commensals or pathogens of marine animals (e.g., corals and sponges), plants, and algae. Despite their varied roles, remarkably little is known about the diversity of this major branch of eukaryotic life in marine ecosystems or their ecological functions. This perspective emerges from a Marine Fungi Workshop held in May 2018 at the Marine Biological Laboratory in Woods Hole, MA. We present the state of knowledge as well as the multitude of open questions regarding the diversity and function of fungi in the marine biosphere and geochemical cycles

    Sensitivity and specificity of NT-proBNP to detect heart failure at post mortem examination

    Get PDF
    NT-proBNP, a marker of cardiac failure, has been shown to be stable in post mortem samples. The aim of this study was to assess the accuracy of NT-proBNP to detect heart failure in the forensic setting. One hundred sixty-eight consecutive autopsies were included in the study. NT-proBNP blood concentrations were measured using a chemiluminescent immunoassay kit. Cardiac failure was assessed by three independent forensic experts using macro- and microscopic findings complemented by information about the circumstances of body discovery and the known medical story. Area under the receiving operator curve was of 65.4% (CI 95%, from 57.1 to 73.7). Using a standard cut-off value of >220 pg/mL for NT-proBNP blood concentration, heart failure was detected with a sensitivity of 50.7% and a specificity of 72.6%. NT-proBNP vitreous humor values were well correlated to the ones measured in blood (r2 = 0.658). Our results showed that NT-proBNP can corroborate the pathological findings in cases of natural death related to heart failure, thus, keeping its diagnostic properties passing from the ante mortem to the post mortem setting. Therefore, biologically inactive polypeptides like NT-proBNP seem to be stable enough to be used in forensic medicine as markers of cardiac failure, taking into account the sensitivity and specificity of the test

    Arbovirus-Derived piRNAs Exhibit a Ping-Pong Signature in Mosquito Cells

    Get PDF
    The siRNA pathway is an essential antiviral mechanism in insects. Whether other RNA interference pathways are involved in antiviral defense remains unclear. Here, we report in cells derived from the two main vectors for arboviruses, Aedes albopictus and Aedes aegypti, the production of viral small RNAs that exhibit the hallmarks of ping-pong derived piwi-associated RNAs (piRNAs) after infection with positive or negative sense RNA viruses. Furthermore, these cells produce endogenous piRNAs that mapped to transposable elements. Our results show that these mosquito cells can initiate de novo piRNA production and recapitulate the ping-pong dependent piRNA pathway upon viral infection. The mechanism of viral-piRNA production is discussed

    Effect of exploitation and exploration on the innovative as outcomes in entrepreneurial firms

    Full text link
    [EN] The main aim of this study is to establish the effect of the Exploitation and Exploration; and the influence of these learning flows on the Innovative Outcome (IO). The Innovative Outcome refers to new products, services, processes (or improvements) that the organization has obtained as a result of an innovative process. For this purpose, a relationship model is defined, which is empirically contrasted, and can explains and predicts the cyclical dynamization of learning flows on innovative outcome in knowledge intensive firms. The quantitative test for this model use the data from entrepreneurial firms biotechnology sector. The statistical analysis applies a method based on variance using Partial Least Squares (PLS). Research results confirm the hypotheses, that is, they show a positive dynamic effect between the Exploration and the Innovative as outcomes. In the same vein, they results confirm the presence of the cyclic movement of innovative outcome with the Exploitation.In addition, this research is part of the Project ECO2015-71380-R funded by the Spanish Ministry of Economy, Industry and Competitiveness and the State Research Agency. Co-financed by the European Regional Development Fund (ERDF).Vargas-Mendoza, NY.; Lloria, MB.; Salazar Afanador, A.; Vergara Domínguez, L. (2018). Effect of exploitation and exploration on the innovative as outcomes in entrepreneurial firms. International Entrepreneurship and Management Journal. 14(4):1053-1069. https://doi.org/10.1007/s11365-018-0496-5S10531069144Alegre, J., & Chiva, R. (2008). Assessing the impact of organizational learning capability on product innovation performance: an empirical test. Technovation, 28, 315–326.Amara, N., Landry, R., Becheikh, N., & Ouimet, M. (2008). Learning and novelty of innovation in established manufacturing SMEs. Technovation, 28, 450–463.Aragón-Mendoza, J., Pardo del Val, M., & Roig, S. (2016). The influence of institutions development in venture creation decision: a cognitive view. Journal of Business Research, 69(11), 4941–4946.Ardichvili, A. (2008). Learning and knowledge sharing in virtual communities of practice: motivators, barriers, and enablers. Advances in Developing Human Resources, 10(4), 541–554.Argyris, C., & Schön, D. (1978). Organizational learning: a theory of action perspective. Reading: Addison Wesley.Bagozzi, R. P., Yi, Y., & Singh, S. (1991). On the use of structural equation models in experimental designs: two extensions international. Journal of Research in Marketing, 8, 125–140.Belda, J., Vergara L., Salazar, A., & Safont G. (2018). Estimating the Laplacian matrix of Gaussian mixtures for signal processing on graphs, accepted for publication in Signal Processing.Boland, R. J. J., & Tenkasi, R. V. (1995). Perspective making and perspective taking in communities of knowing. Organization Science, 6(4), 350–372.Bontis, N., (1998). Intellectual capital: an exploratory study that develops measures models. Management Decision, 36, 63–76.Bontis, N. (1999). Managing an organizational learning system by aligning stocks and flows of knowledge: an empirical examination of intellectual capital, knowledge management, and business performance. 1999. Management of Innovation and New Technology Research Centre, McMaster University.Bontis, N., Keow, W., & Richardson, S. (2000). Intellectual capital and the nature of business in Malaysia. Journal of Intellectual Capital, 1(1), 85–100Bontis, N., Hullan, J., & Crossan, M. (2002). Managing an organizational learning system by aligning stocks and flows. Journal of Management Studies, 39, 438–469.Brachos, D., Kostopulos, K., Sodersquist, K. E., & Prastacos, G. (2007). Knowledge effectiveness, social context and innovation. Journal of Knowledge Management, 11(5), 31–44.Calantone, R. J., Cavusgil, S. T., & Zhao, Y. (2002). Learning orientation, firm innovation capability, and firm performance. Industrial Marketing Management, 31, 515–524.Chang, T. J., Yeh, S. P., & Yeh, I. J. (2007). The effects of joint rewards system in new product development. International Journal of Manpower, 28(3/4), 276–297.Chin, W. (1998). The partial least square approach to structural equation modeling. In G. A. Marcoulides (Ed.) (pp. 294–336). New Jersey: Lawrence Erlbaum Associates.Cho, N., Li, G., & Su, C. (2007). An empirical study on the effect of individual factors on knowledge sharing by knowledge type. Journal of Global Business and Technology, 3(2), 1–15.Cohen, W. M., & Levin, R. C. (1989). Empirical studies of innovation and market structure. In R. Schmalansee & R. D. Willing (Eds.), Handbook of industrial organization II. New York: Elsevier.Cohen, W. M., & Levinthal, D. A. (1990). Absorptive-capacity – a new perspective on learning and innovation. Administrative Science Quarterly, 35, 128–152.Cooper, R. G. (2000). New product performance: what distinguishes the star products. Austrian Journal of Management, 25, 17–45.Crossan, M., & Berdrow, I. (2003). Organizational learning and strategic renewal. Strategic Management Journal, 24, 1087–1105.Crossan, M., & Apaydin, M. (2010). A multi-dimensional framework of organizational innovation: a systematic review of the literature. Journal of Management Studies, 47(6), 1154–1191.Crossan, M., Lane, H. W., & White, R. E. (1999). An organizational learning framework: from intuition to institution. Academy of Management Review, 24, 522–537.Damanpour, F., & Aravind, D. (2012). Managerial innovation: conceptions, processes, and antecedents. Management and Organization Review, 8(2), 423–454.Damanpour, F., & Shanthi, G. (2001). The dynamics of the adoption of products and process innovations in organizations. Journal of Management Studies, 38(1), 21–65.Decarolis, D. M., & Deeds, D. L. (1999). The impact of stock and flows of organizational knowledge on firm performance: An empirical investigation of the biotechnology industry. Strategic Management Journal, 20, 953–968.Demartini, C. (2015). Relationships between social and intellectual capital: empirical Evidence from IC statements. Knowledge and Process Management, 22(2), 99–111.Dupuy, F. (2004). Sharing knowledge: they why and how of organizational change. Hampshire: Palgrave Macmillan.Fornell, C., & Bookstein, F. I. (1982). Two structural equation models: LISREL and PLS applied to consumer exit-voice theory. Journal of Marketing Research, 19, 440–452.Ganter, A., & Hecker, A. (2013). Deciphering antecedents of organizational innovation. Journal of Business Research, 66(5), 575–584.Ganter, A., & Hecker, A. (2014). Configurational paths to organizational innovation: qualitative comparative analyses of antecedents and contingencies. Journal of Business Research, 67, 1285–1292.Gopalakrishnan, S., & Damanpour, F. (1997). A review of innovation research in economics, sociology and technology management. International Journal of Management Science, 25, 15–28.Hedberg, B. (1981). How organizations learn and unlearn. In P. Nystrom & W. Starbuck (Eds.), Handbook of organizational design. New York: Oxford University.Hedlund, G., & Nonaka, I. (1993). Models of knowledge management in the west and Japan. In: P. Lorange, B. Chacravrarthy, J. Ross, and J. Van de ven (Eds.) Cambridge: Basil Blackwell.Henseler, J., Ringle, C.M., & Sinkovics, R.R. (2009). The use the partial least squares path modeling. In: R. Sinkovics and N. Pervez (Eds.) 277–319.Hsu, I. (2006). Enhancing employee tendencies to share knowledge-case studies on nine companies in Taiwan. International Journal of Information Management, 26(4), 326–338.Hsu, I. (2008). Knowledge sharing practices as a facilitating factor for improving organizational performance though human capital: a preliminary test. Expert Systems with Application, 35, 316–1326.Huang, Q., Davison, R., & Gu, J. (2008). Impact of personal and cultural factors on knowledge sharing in China. Asia Pacific Journal Management, 25(3), 451–471.Ibarra, H. (1993). Network centrality, power, and innovation involvement – determinants of technical and administrative roles. Academy of Management Journal, 36(3), 471–501.Iebra, I. L., Zegarra, P. S., & Zegarra, A. S. (2011). Learning for sharing: an empirical analysis of organizational learning and knowledge sharin. International Entrepreneurship Management Journal, 7, 509–518.Ipe, M. (2003). Knowledge sharing in organizations: a conceptual framework. Human Resource Development Review, 2(4), 337–359.Jenkin, T. (2013). Extending the 4I organizational learning model: information sources, foraging processes and tools. Administrative Sciences, 3, 96–109.Jiménez-Jiménez, D., & Sanz-Valle, R. (2011). Innovation, organizational learning, and performance. Journal of Business Research, 64, 408–417.Kane, G. C., & Alavi, M. (2007). Information technology and organizational learning: an investigation of exploration and exploitation processes. Organization Science, 18(5), 796–812.Kleinbaum, D. G., Kupper, N. N., Muller, K. E. (1988). Applied regression analysis and other Multivariable’s methods, PWS KENT.Klomp, L., & Van Leeuwen, G. (2001). Linking innovation and firm performance: a new approach. International Journal of the Economics of Business, 8(3), 343–364.Lansisalmi, H., Kivimaki, M., Aalto, P., & Ruoranen, R. (2006). Innovation in healthcare: a systematic review of recent research. Nursing Science Quarterly, 19(1), 66–72.Laperrière, A., & Spence, M. (2015). Enacting international opportunities: the role of organizational learning in knowledge-intensive business services. Journal of International Entrepreneurship, 13(3), 212–241.Levitt, B., & March, J. G. (1988). Organizational learning. Annual Review of Sociology, 14, 319–340.Lin, H. (2007). Knowledge sharing and firm innovation capability: an empirical study. International Journal of Manpower, 28(3/4), 315–332.Lloria, M. B., & Moreno-Luzón, M. D. (2014). Organizational learning: proposal of an integrative scale and research instrument. Journal of Business Research, 67, 692–697.March, J. G. (1991). Exploration and exploitation in organizational learning. Organizational Science, 2, 71–87.Matikainen, M., Terho, H., Parvinen, P., & Juppo, A. (2016). The role and impact of firm’s strategic orientations on launch performance: significance of relationship orientation. Journal of Business & Industrial Marketing, 31(5), 625–639.Mone, M. A., McKinley, W., & Barker, V. L. (1998). Organizational decline and innovation: a contingency framework. Academy of Management Review, 23, 115–132.Moreno-Luzón, M. D., & Lloria, B. (2008). The role of non-structural and informal mechanisms of integration and integration as forces in knowledge creation. British Journal of Management, 19, 250–276.Moskaliuk, J., Bokhorst, F., & Cress, U. (2016). Learning from others' experiences: how patterns foster interpersonal transfer of knowledge-in-use. Computers in Human Behavior, 55, 69–75.Nonaka, I., & Takeuchi, H. (1995). The knowledge-creating company. How Japanese companies create the dynamics of innovation. New York: Oxford University Press.Nonaka, I., & von Krogh, G. (2009). Perspective tacit knowledge and knowledge conversion: controversy and advancement in organizational knowledge creation theory. Organization Science, 20(3), 635–652.Parida, V., Lahti, T., & Wincent, J. (2016). Exploration and exploitation and firm performance variability: a study of ambidexterity in entrepreneurial firms. International Entrepreneurship Management Journal, 12, 1147–1164.Pew, H., Plowman, D., & Hancock, P. (2008). The involving research on intellectual capital. Journal of Intellectual Capital, 9, 585–608.Potter, R. E., & Balthazard, P. A. (2004). The role of individual memory and attention processes during electronic brainstorming. MIS Quarterly, 28(4), 621–643.Ramadani, V., Hyrije, A. A., Léo-Paul, D., Gadaf, R., & Sadudin, I. (2017). The impact of knowledge spillovers and innovation on firm-performance: findings from the Balkans countries. International Entrepreneurship Management Journal, 13, 299–325.Ren, S., Shu, R., Bao, Y., & Chen, X. (2016). Linking network ties to entrepreneurial opportunity discovery and exploitation: the role of affective and cognitive trust. International Entrepreneurship and Management Journal, 12(2), 465–485.Ringle, C. M., Wende, S., & Will, A. (2005). Smart PLS 2.0 (M3) beta, Hamburg: http://www.smartpls.de .Ringle, C. M., Sarstedt, M., & Straub, D. (2012). A critical look at the use of PLS-SEM. MIS Quarterly, 36(1), iii–xiv.Sanchez, R., & Heene, A. (1997). A competence perspective on strategic learning and knowledge management. En Sanchez, R. and Heene, A. (eds.) Strategic learning and knowledge management. John Wiley and Sons.Seidler-de Alwis, R., & Hartmann, E. (2008). The use of tacit knowledge within innovative companies: knowledge management in innovative enterprises. Journal of Knowledge Management, 12(1), 133–147.Shrivastava, P. (1983). A typology of organizational learning systems. Journal of Management Studies, 20, 7–28.Tansky, J., Ribeiro, D., & Roig, S. (2010). Linking entrepreneurship and human resources in globalization. Human Resource Management, 49(2), 217–223.Teece, D. (2012). Dynamic capabilities: routines versus entrepreneurial action. Journal of Management Studies, 49(8), 1395–1401.Tenenhaus, M., Vinzi, V., Chatelin, Y., & Lauro, C. (2005). PLS path modeling. Computational Statistics and Data Analysis, 49, 159–205.vande Vrande, V., de Jong, J., Vanhaverbeke, W., & Rochemont, M. (2009). Open innovation in SMEs: trends, motives and management challenges. Technovation, 29, 423–437.Vargas, N., & Lloria, M. B. (2014). Dynamizing intellectual capital through enablers and learning flows. Industrial Management and Data Systems, 114(1), 2–20.Vargas, N., & Lloria, M. B. (2017). Performance and intellectual capital: how enablers drive value creation in organisations. Knowledge and Process Management, 24(2), 114–124.Vargas, N., Lloria, M. B., & Roig-Dobón, S. (2016). Main drivers of human capital, learning and performance. The Journal of Technology Transfer, 41(5), 961–978.Vergara, L., Salazar, A., Belda, J., Safont, G., Moral, S., & Iglesias, S. (2017). Signal processing on graphs for improving automatic credit card fraud detection. Proceeding of 2017 I.E. 51st international Carnahan Conference on Security Technology (ICCST 2017), https://doi.org/10.1109/CCST.2017.8167820 , 23–26 Oct, 2017, Madrid, Spain.Wallin, M. W., & Von Krogh, G. (2010). Organizing for open innovation: focus o the integration of knowledge. Organizational Dynamics, 39(2), 145–154.Wang, C. L., & Ahmed, P. K. (2004). Linking innovation and firm performance: a new approach. European International Journal of Technology Management, 27, 674–688.Wold, H. (1980). Model construction and evaluation when theoretical knowledge is scarce. In J. Kmenta & J. B. Ramsey (Eds.), Evaluation of econometric models (pp. 47–74). Cambridge: Academic Press.Wold, H. (1985). Factors influencing the outcome of economic sanctions. In Sixto Ríos Honorary. Trabajos de Estadística and de Investigación Operativa, 36(3), 325–337
    corecore