46 research outputs found

    Incidence and prognostic value of tumour cells detected by RT–PCR in peripheral blood stem cell collections from patients with Ewing tumour

    Get PDF
    To retrospectively evaluate the incidence of tumour cell contamination of peripheral blood stem cell (PBSC) collections and to correlate these data with the clinical outcome after high-dose chemotherapy (HDCT) with stem cell rescue in patients with a high-risk Ewing tumour. Peripheral blood stem cell collections obtained from 171 patients were analysed. Tumour contamination was assessed by reverse transcriptase–polymerase chain reaction (RT–PCR). The files of 88 patients who underwent HDCT followed by PBSC reinfusion were reviewed in detail, and their outcome compared to the PBSC RT–PCR results. Seven of 88 PBSC collections (8%) contained tumour cells as detected by RT–PCR. Peripheral blood stem cells were collected after a median of five cycles of chemotherapy. No clinical factor predictive of tumour cell contamination of PBSC harvest could be identified. Event-free survival (EFS) and overall survival (OS) of the whole study population were 45.3 % and 51.8 % at 3 years from the date of the graft, respectively. Forty-five patients relapsed with a median time of 15 months after graft, only four of whom had tumour cell contamination of the PBSC harvest. Tumour cell contamination of PBSC collection is rare and does not seem to be associated with a significantly poorer EFS or OS in this high-risk population

    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

    Increasing frailty is associated with higher prevalence and reduced recognition of delirium in older hospitalised inpatients: results of a multi-centre study

    Get PDF
    Purpose Delirium is a neuropsychiatric disorder delineated by an acute change in cognition, attention, and consciousness. It is common, particularly in older adults, but poorly recognised. Frailty is the accumulation of deficits conferring an increased risk of adverse outcomes. We set out to determine how severity of frailty, as measured using the CFS, affected delirium rates, and recognition in hospitalised older people in the United Kingdom. Methods Adults over 65 years were included in an observational multi-centre audit across UK hospitals, two prospective rounds, and one retrospective note review. Clinical Frailty Scale (CFS), delirium status, and 30-day outcomes were recorded. Results The overall prevalence of delirium was 16.3% (483). Patients with delirium were more frail than patients without delirium (median CFS 6 vs 4). The risk of delirium was greater with increasing frailty [OR 2.9 (1.8–4.6) in CFS 4 vs 1–3; OR 12.4 (6.2–24.5) in CFS 8 vs 1–3]. Higher CFS was associated with reduced recognition of delirium (OR of 0.7 (0.3–1.9) in CFS 4 compared to 0.2 (0.1–0.7) in CFS 8). These risks were both independent of age and dementia. Conclusion We have demonstrated an incremental increase in risk of delirium with increasing frailty. This has important clinical implications, suggesting that frailty may provide a more nuanced measure of vulnerability to delirium and poor outcomes. However, the most frail patients are least likely to have their delirium diagnosed and there is a significant lack of research into the underlying pathophysiology of both of these common geriatric syndromes

    Chemical mechanism development : laboratory studies and model application

    Get PDF
    Within the German Tropospheric Research Programme (TFS) numerous kinetic and mechanistic studies on the tropospheric reaction/degradation of the following reactants were carried out:oxygenated VOC,aromatic VOC,biogenic VOC,short-lived intermediates, such as alkoxy and alkylperoxy radicals.At the conception of the projects these selected groups were classes of VOC or intermediates for which the atmospheric oxidation mechanisms were either poorly characterised or totally unknown. The motivation for these studies was the attainment of significant improvements in our understanding of the atmospheric chemical oxidation processes of these compounds, particularly with respect to their involvement in photooxidant formation in the troposphere.In the present paper the types of experimental investigations performed and the results obtained within the various projects are briefly summarised. The major achievements are highlighted and discussed in terms of their contribution to improving our understanding of the chemical processes controlling photosmog formation in the troposphere
    corecore