Studien zur Altägyptischen Kultur Nr. 41 (2012) - Abstracts

Abstracts der Artikel zu den "SAK" 41 (2012). Die Autoren sind: H. Altenmüller, R. Assem, L. Baqué-Manzano, M. Bommas, A. Brawanski/H.-W. Fischer-Elfert, F. Breyer, G.P.F. Broekman, G. Gabra, B. Haring, A. Jiménez-Serrano, J. Kahl, J. Kahl/M. El-Khadragy/U. Verhoeven/M. Abdelrahiem/M. van Elsbergen/H. Fahid/A. Kilian/C. Kitagawa/T. Rzeuska/M. Zöller-Engelhardt, M. Lehmann, J. Moje, M. Panov, H. Satzinger/D. Stefanović, A.J. Spalinger, M. Tarasenko, V. Vasiljević und M. Verner

Automated DNA Sequence-Based Early Warning System for the Detection of Methicillin-Resistant Staphylococcus aureus Outbreaks

BACKGROUND: The detection of methicillin-resistant Staphylococcus aureus (MRSA) usually requires the implementation of often rigorous infection-control measures. Prompt identification of an MRSA epidemic is crucial for the control of an outbreak. In this study we evaluated various early warning algorithms for the detection of an MRSA cluster. METHODS AND FINDINGS: Between 1998 and 2003, 557 non-replicate MRSA strains were collected from staff and patients admitted to a German tertiary-care university hospital. The repeat region of the S. aureus protein A (spa) gene in each of these strains was sequenced. Using epidemiological and typing information for the period 1998–2002 as reference data, clusters in 2003 were determined by temporal-scan test statistics. Various early warning algorithms (frequency, clonal, and infection control professionals [ICP] alerts) were tested in a prospective analysis for the year 2003. In addition, a newly implemented automated clonal alert system of the Ridom StaphType software was evaluated. A total of 549 of 557 MRSA were typeable using spa sequencing. When analyzed using scan test statistics, 42 out of 175 MRSA in 2003 formed 13 significant clusters (p < 0.05). These clusters were used as the “gold standard” to evaluate the various algorithms. Clonal alerts (spa typing and epidemiological data) were 100% sensitive and 95.2% specific. Frequency (epidemiological data only) and ICP alerts were 100% and 62.1% sensitive and 47.2% and 97.3% specific, respectively. The difference in specificity between clonal and ICP alerts was not significant. Both methods exhibited a positive predictive value above 80%. CONCLUSIONS: Rapid MRSA outbreak detection, based on epidemiological and spa typing data, is a suitable alternative for classical approaches and can assist in the identification of potential sources of infection

An open toolkit for tracking open science partnership implementation and impact.

Serious concerns about the way research is organized collectively are increasingly being raised. They include the escalating costs of research and lower research productivity, low public trust in researchers to report the truth, lack of diversity, poor community engagement, ethical concerns over research practices, and irreproducibility. Open science (OS) collaborations comprise of a set of practices including open access publication, open data sharing and the absence of restrictive intellectual property rights with which institutions, firms, governments and communities are experimenting in order to overcome these concerns. We gathered two groups of international representatives from a large variety of stakeholders to construct a toolkit to guide and facilitate data collection about OS and non-OS collaborations. Ultimately, the toolkit will be used to assess and study the impact of OS collaborations on research and innovation. The toolkit contains the following four elements: 1) an annual report form of quantitative data to be completed by OS partnership administrators; 2) a series of semi-structured interview guides of stakeholders; 3) a survey form of participants in OS collaborations; and 4) a set of other quantitative measures best collected by other organizations, such as research foundations and governmental or intergovernmental agencies. We opened our toolkit to community comment and input. We present the resulting toolkit for use by government and philanthropic grantors, institutions, researchers and community organizations with the aim of measuring the implementation and impact of OS partnership across these organizations. We invite these and other stakeholders to not only measure, but to share the resulting data so that social scientists and policy makers can analyse the data across projects