• 2018-07
  • 2018-10
  • 2018-11
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • The Czech Republic is a country


    The Czech Republic is a country with increasing CDI incidence (1.1 cases per 10,000 patient bed-days in 2008–4.4 cases in 2011–2012 and 6.2 cases per 10,000 patient bed-days in 2012–2013) (Bauer et al., 2011, Davies et al., 2014) and relatively high rates of antibiotic resistant C. difficile strains (Freeman et al., 2015). Implementation of CDI surveillance based on the recently released CDI surveillance protocol Control (ECDC, 2015) in the Czech Republic would fill the gap in Czech CDI epidemiology with national CDI incidence data, including clinical case information and C. difficile isolate antibiotic susceptibility results.
    Conclusion The molecular characterisation of 2201 Czech clinical C. difficile isolates revealed 53 different CE-ribotyping profiles and 40 multi-locus sequence types. Of 2201C. difficile isolates, 2024 were toxigenic (tcdA and tcdB), and of these, 677 isolates carried genes for binary toxin production (cdtA, cdtB). The results of molecular characterisation showed a high read full post of C. difficile strains circulating in the Czech Republic with prevailing representation of RTs 001 and 176 (027-like). CE-ribotyping applied on a Czech C. difficile isolate collection demonstrates its high discrimination capability and the results highlight the need to use a standardised protocol as well as a standardised CE-ribotyping profile library to gain inter-laboratory comparable data on clinically and/or epidemiologically significant C. difficile isolates.
    Ethical statement
    Conflicts of interest
    Funding Supported by the MH CZ – DRO, University Hospital Motol, Prague, Czech Republic 00064203.
    Acknowledgements We thank the ESCMID Study Group for Clostridium difficile (ESGCD) for their professional support.
    Introduction Energy sustainability is one of major components of United Nations Millennium Development Goals (MDG) [1]. Renewable energy sources (RES) are the major enablers to achieve these goals and ensuring sustainability. Wind energy is one of the most promising RES and is growing at a very fast rate. For example, in India, the share of wind energy is more than half of the total installed capacity (IC) of RES [2]. Framing policy framework for enabling wind energy projects is under active consideration [3]. However, the focus on the social, economic and environmental benefits of wind energy have somehow sidelined the consideration of impact of wind integration on power systems. Due to uncertain nature of wind resource, the traditional requirement of reliability and security must be met by the conventional sources [4]. In national power systems with large share of thermal generation (e.g. 197 GW of 345 GW in India) [5], traditional security aspects of power systems must rest on the existing thermal generators (TG). Further, in view of the large investments made in thermal power plants (TPPs) in emerging economies such as India and China, RES such as wind farms are unlikely to replace the existing TGs, rather, these are expected to be included as additional generation sources. This in turn reduces the load on the TGs thus resulting in reduction in capacity factor of the generators. Reduced capacity factor signifies uneconomic part-load operation of the TGs. Therefore, the cost reduction resulting from addition of wind power in power systems may be overwhelmed by the increase in cost due to reduced capacity factor of the existing conventional generators. This negative impact of wind integration must be combined with the positive impact of cost savings and emission reduction.
    Problem formulation The exact problem setup to evaluate the impact on operational parameters is to first compute the terms defined in (17a) to (17d) for the selected test systems. Then CS, ER, PLV and CFVI are combined to obtain the unified index using (16). The capacity factor of a generator is evaluated using Equation (18).where is the average generation obtained from optimal schedules and is the installed capacity of the thermal generator which is taken to be the maximum generation limit ().