Istruzioni per scrivere la tesi sperimentale per studenti di CTF, Farmacia e CQPS

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Efficient self-supervised metric information retrieval: A bibliography based method applied to covid literature

The literature on coronaviruses counts more than 300,000 publications. Finding relevant papers concerning arbitrary queries is essential to discovery helpful knowledge. Current best information retrieval (IR) use deep learning approaches and need supervised train sets with labeled data, namely to know a priori the queries and their corresponding relevant papers. Creating such labeled datasets is time-expensive and requires prominent experts’ efforts, resources insufficiently available under a pandemic time pressure. We present a new self-supervised solution, called SUBLIMER, that does not require labels to learn to search on corpora of scientific papers for most relevant against arbitrary queries. SUBLIMER is a novel efficient IR engine trained on the unsupervised COVID-19 Open Research Dataset (CORD19), using deep metric learning. The core point of our self-supervised approach is that it uses no labels, but exploits the bibliography citations from papers to create a latent space where their spatial proximity is a metric of semantic similarity; for this reason, it can also be applied to other domains of papers corpora. SUBLIMER, despite is self-supervised, outperforms the Precision@5 (P@5) and Bpref of the state-of-the-art competitors on CORD19, which, differently from our approach, require both labeled datasets and a number of trainable parameters that is an order of magnitude higher than our

Novel directions for the management of dual antiplatelet therapy in patients with coronary artery disease

Dual antiplatelet therapy has been extensively studied in the last two decades, giving opportunities for thorough evidence-based recommendations. The ESC/EACTS guidelines on DAPT in CAD provide guidance for DAPT type and duration, maintaining a patient-centred focus. Patient-specific and dynamic evaluation of the ischaemic and bleeding risks is key for optimal treatment decisions

More, More, More: Reducing Thrombosis in Acute Coronary Syndromes Beyond Dual Antiplatelet Therapy-Current Data and Future Directions.

© 2018 The Authors. Published on behalf of the American Heart Association, Inc., by Wiley.Common to the pathogenesis of acute coronary syndromes (ACS) is the formation of arterial thrombus, which results from platelet activation and triggering of the coagulation cascade.1 To attenuate the risk of future thrombotic events, patients with ACS are treated with dual antiplatelet therapy (DAPT), namely, the combination of aspirin with a P2Y12 inhibitor, such as clopidogrel, ticagrelor, or prasugrel. Despite DAPT, some ≈10% of ACS patients experience recurrent major adverse cardiovascular events over the subsequent 30 days,2 driving the quest for more effective inhibition of thrombotic pathways. In this review, we provide an overview of studies to date and those ongoing that aim to deliver more effective combinations of antithrombotic agents to patients with recent ACS. We have chosen to confine the review to ACS patients without atrial fibrillation because those with atrial fibrillation have a clear indication for combination therapy that includes oral anticoagulation and should, we feel, be treated as a separate cohort. In this article, we discuss the limitations of the currently available clinical trial data and future directions, with suggestions for how practice might change to reduce the risk of coronary thrombosis in those at greatest risk, with minimal impact on bleeding.Peer reviewedFinal Published versio