Aims  The aim of this study was to determine the contemporary use of reperfusion therapy in the European Society of Cardiology (ESC) member and affiliated countries and adherence to ESC clinical practice guidelines in patients with ST-elevation myocardial infarction (STEMI). Methods and results  Prospective cohort (EURObservational Research Programme STEMI Registry) of hospitalized STEMI patients with symptom onset <24 h in 196 centres across 29 countries. A total of 11 462 patients were enrolled, for whom primary percutaneous coronary intervention (PCI) (total cohort frequency: 72.2%, country frequency range 0–100%), fibrinolysis (18.8%; 0–100%), and no reperfusion therapy (9.0%; 0–75%) were performed. Corresponding in-hospital mortality rates from any cause were 3.1%, 4.4%, and 14.1% and overall mortality was 4.4% (country range 2.5–5.9%). Achievement of quality indicators for reperfusion was reported for 92.7% (region range 84.8–97.5%) for the performance of reperfusion therapy of all patients with STEMI <12 h and 54.4% (region range 37.1–70.1%) for timely reperfusion. Conclusions  The use of reperfusion therapy for STEMI in the ESC member and affiliated countries was high. Primary PCI was the most frequently used treatment and associated total in-hospital mortality was below 5%. However, there was geographic variation in the use of primary PCI, which was associated with differences in in-hospital mortality.

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A new proposal for taxonomic species description is presented to replace the traditional descriptive texts. This is an attempt to enhance the species description rate and to make the description output available to other scientific disciplines, machine learning, lucid identification keys, big data analysis and its applications. The method consists in presenting the description of the overall morphology in a coded matrix, following a character list with detailed observed conditions for each character. The method is supposed to be dynamic and open to amendments and new data addition as they become available. We test the new method describing five new species of Collembola Symphypleona of the genus Pararrhopalites as a generalized model and made the coded output available. We conclude that a coded taxonomic description is an advance to the traditional taxonomic text, with potential to enhance the global descriptions rate. The generated data is a dynamic matrix that can be expanded with any data that becomes available, also it can be easily used in other fields of science, allowing non-experts to access the data for phylogenetic, biogeographic, ecological studies and big data analysis. Furthermore, it is a step forward to a general template to semi-automated taxon recognition and auxiliary tools for species description using machine learning.

Introduction: Genes associated with coronary artery disease (CAD) and traditional cardiovascular risk factors (TCRF) present a limited individual predictive value. It is expected that the inclusion in global scores may increase the predictive ability. In genetic terms, there are no validated risk scores to predict the occurrence of cardiovascular disease or its complications. Hypothesis: Evaluate the ability of a multifactorial genetic risk score (GRS) be able to add predictive power, for the development of CAD, to the model developed only with TCRF. Methods: A case-control study was performed with 1321 consecutive coronary patients (mean age 53.4±8.1 years, 78.8% male) and 1148 controls selected to be similar to cases in terms of gender and age. TCRF were evaluated according to the International criteria. The genetic variants were analyzed with specific primers and the GRS was determined in the entire population, based on 29 genetic polymorphisms previously associated with atherosclerotic disease in general and, in particular, with CAD. A multiplicative model was then used based on risk multiplication (odds ratio - OR) of each genotype of the 29 studied genes. Subsequently, a multivariate analysis was done with the TCRF only or the TCRF with the GRS and a ROC curve was constructed for both situations. Pairwise comparison of the two ROC Curve was done by the DeLong test. Results: After multivariate analysis, the GRS was found to be an independent predictor for CAD (OR=2.1; CI: 1.7-2.5; p<0.0001). The AUC increased from 0.71 to 0.74 after the inclusion of GRS to the TCRF in the multivariate analysis and this increase was highly significant (p<0.0001) (Fig.). Conclusions: In our population, the multiplicative GRS was an independent predictor for CAD. When analyzed together with TCRF, it adds predictive value. Its usefulness, in clinical practice, may be directed to the intermediate risk group, in which a possible risk reclassification can have different therapeutic measures.