Practical guidelines for rigor and reproducibility in preclinical and clinical studies on cardioprotection

The potential for ischemic preconditioning to reduce infarct size was first recognized more than 30 years ago. Despite extension of the concept to ischemic postconditioning and remote ischemic conditioning and literally thousands of experimental studies in various species and models which identified a multitude of signaling steps, so far there is only a single and very recent study, which has unequivocally translated cardioprotection to improved clinical outcome as the primary endpoint in patients. Many potential reasons for this disappointing lack of clinical translation of cardioprotection have been proposed, including lack of rigor and reproducibility in preclinical studies, and poor design and conduct of clinical trials. There is, however, universal agreement that robust preclinical data are a mandatory prerequisite to initiate a meaningful clinical trial. In this context, it is disconcerting that the CAESAR consortium (Consortium for preclinicAl assESsment of cARdioprotective therapies) in a highly standardized multi-center approach of preclinical studies identified only ischemic preconditioning, but not nitrite or sildenafil, when given as adjunct to reperfusion, to reduce infarct size. However, ischemic preconditioning—due to its very nature—can only be used in elective interventions, and not in acute myocardial infarction. Therefore, better strategies to identify robust and reproducible strategies of cardioprotection, which can subsequently be tested in clinical trials must be developed. We refer to the recent guidelines for experimental models of myocardial ischemia and infarction, and aim to provide now practical guidelines to ensure rigor and reproducibility in preclinical and clinical studies on cardioprotection. In line with the above guideline, we define rigor as standardized state-of-the-art design, conduct and reporting of a study, which is then a prerequisite for reproducibility, i.e. replication of results by another laboratory when performing exactly the same experiment

Change detection without difference image computation based on multiobjective cost function optimization

10.3906/elk-1006-533Turkish Journal of Electrical Engineering and Computer Sciences196941-95

Generating incident-level artificial data using GIS based crime simulation

2013 10th International Conference on Electronics, Computer and Computation, ICECCO 2013 --7 November 2013 through 8 November 2013 -- Ankara --Most crime analysis tools used to find criminals of a particular incident or, to find the interrelations among the crime incidents, possibly over a GIS (Geographical Information System) map. The development of these tools require access to incident-level crime data. Obtaining real data is very restricted if not possible due to the official regulations. In this paper, a parametric model is proposed to generate the incident-level crime datasets involving crimes, criminals and criminals' suspicious acquaintances where the parameters are used for fine tuned adaptation of the model. The motivation for this approach is that unsupervised approaches for crime analysis do not require fully realistic data set in order to develop decision making algorithms. The model is based on GIS by approximating the characteristics of the population in real-life. Then, results of various GIS related queries are demonstrated on the GIS map to enable the visual analysis of the incidents. © 2013 IEEE