Theoretical and technological building blocks for an innovation accelerator

The scientific system that we use today was devised centuries ago and is inadequate for our current ICT-based society: the peer review system encourages conservatism, journal publications are monolithic and slow, data is often not available to other scientists, and the independent validation of results is limited. Building on the Innovation Accelerator paper by Helbing and Balietti (2011) this paper takes the initial global vision and reviews the theoretical and technological building blocks that can be used for implementing an innovation (in first place: science) accelerator platform driven by re-imagining the science system. The envisioned platform would rest on four pillars: (i) Redesign the incentive scheme to reduce behavior such as conservatism, herding and hyping; (ii) Advance scientific publications by breaking up the monolithic paper unit and introducing other building blocks such as data, tools, experiment workflows, resources; (iii) Use machine readable semantics for publications, debate structures, provenance etc. in order to include the computer as a partner in the scientific process, and (iv) Build an online platform for collaboration, including a network of trust and reputation among the different types of stakeholders in the scientific system: scientists, educators, funding agencies, policy makers, students and industrial innovators among others. Any such improvements to the scientific system must support the entire scientific process (unlike current tools that chop up the scientific process into disconnected pieces), must facilitate and encourage collaboration and interdisciplinarity (again unlike current tools), must facilitate the inclusion of intelligent computing in the scientific process, must facilitate not only the core scientific process, but also accommodate other stakeholders such science policy makers, industrial innovators, and the general public

Geographical and temporal distribution of SARS-CoV-2 clades in the WHO European Region, January to June 2020

We show the distribution of SARS-CoV-2 genetic clades over time and between countries and outline potential genomic surveillance objectives. We applied three available genomic nomenclature systems for SARS-CoV-2 to all sequence data from the WHO European Region available during the COVID-19 pandemic until 10 July 2020. We highlight the importance of real-time sequencing and data dissemination in a pandemic situation. We provide a comparison of the nomenclatures and lay a foundation for future European genomic surveillance of SARS-CoV-2.Peer reviewe

Predictors and impact of in-hospital recurrent myocardial infarction in acute coronary syndrome patients: Findings from Gulf RACE-2

IntroductionLittle in the literature is known about the predictors and the adverse impact of recurrent ischemia and infarction in patients with acute coronary syndrome (ACS). Accordingly; our objectives were to determine the risk factors, and long term outcome of patients with recurrent ischemia.MethodsWe evaluated ACS patients who were enrolled in the second Gulf Registry of Acute Coronary Events (Gulf RACE-2).ResultsOut of 7930 ACS patients, 172 (2.2%) developed recurrent myocardial infarction (Re-MI) during their hospitalization. Patients with Re-MI were more likely to be older (mean age 59.12±13.5 vs. 56.8±12.4; P=0.016), had higher rates of hyperlipidemia (41.3% vs. 32.6%; P=0.027) and previous angina (47.7% vs. 37.9%; P=0.006), presented more with STEMI (72.1% vs. 43.9%; P<0.001), and had more Killip class 4 upon admission (8.1% vs. 3.2%; P<0.001) than patients without Re-MI. Management-wise, Re-MI patients received less aspirin (94.8% vs. 98.5%; P=0.002), beta-blockers (59.3% vs. 74.7%; P<0.001), and statin (87.2% vs. 94.9%; P<0.001), and were less frequently assessed by coronary angiogram (30.8% vs. 32.5%; P=0.036). These patients had more in-hospital complications including congestive heart failure (44.2% vs. 12.4%) and cardiogenic shock (25.6% vs. 5.3%) as well as higher mortality rates; both during hospitalization (23.8% vs. 4.1%) and after a discharge period of 30days (27.3% vs. 6.87%) and 1year (29.1% vs. 9.3%). P<0.001 for all comparisons.ConclusionPatients with recurrent infarction have a bad prognosis in terms of in-hospital complications and high mortality rates. High risk patients need to be monitored and managed differently to prevent secondary attacks