University of Ioannina

About: University of Ioannina is a education organization based out in Ioannina, Greece. It is known for research contribution in the topics: Population & Large Hadron Collider. The organization has 7654 authors who have published 20594 publications receiving 671560 citations. The organization is also known as: Panepistimio Ioanninon.

Measurement of the inelastic proton-proton cross section at √s=13 TeV

Abstract: A measurement of the inelastic proton-proton cross section with the CMS detector at a center-of-mass energy of $ \sqrt{s}=13 $ TeV is presented. The analysis is based on events with energy deposits in the forward calorimeters, which cover pseudorapidities of −6.6 4.1 GeV and/or M$_{Y}$ > 13 GeV, where M$_{X}$ and M$_{Y}$ are the masses of the diffractive dissociation systems at negative and positive pseudorapidities, respectively. The results are compared with those from other experiments as well as to predictions from high-energy hadron-hadron interaction models.

Measurement of the differential and double-differential Drell-Yan cross sections in proton-proton collisions at s√ = 7 TeV

Abstract: Measurements of the differential and double-differential Drell-Yan cross sections are presented using an integrated luminosity of 4.5 (4.8) fb−1 in the dimuon (dielectron) channel of proton-proton collision data recorded with the CMS detector at the LHC at s√ = 7 TeV. The measured inclusive cross section in the Z-peak region (60–120 GeV) is σ(ll) = 986.4 ± 0.6 (stat.) ± 5.9 (exp. syst.) ± 21.7 (th. syst.) ± 21.7 (lum.) pb for the combination of the dimuon and dielectron channels. Differential cross sections dσ/dm for the dimuon, dielectron, and combined channels are measured in the mass range 15 to 1500 GeV and corrected to the full phase space. Results are also presented for the measurement of the double-differential cross section d2σ/dm d|y| in the dimuon channel over the mass range 20 to 1500 GeV and absolute dimuon rapidity from 0 to 2.4. These measurements are compared to the predictions of perturbative QCD calculations at next-to-leading and next-to-next-to-leading orders using various sets of parton distribution functions.

Risk of Symptomatic Intracerebral Hemorrhage After Intravenous Thrombolysis in Patients With Acute Ischemic Stroke and High Cerebral Microbleed Burden: A Meta-analysis.

Abstract: IMPORTANCE Cerebral microbleeds (CMBs) have been established as an independent predictor of cerebral bleeding. There are contradictory data regarding the potential association of CMB burden with the risk of symptomatic intracerebral hemorrhage (sICH) in patients with acute ischemic stroke (AIS) treated with intravenous thrombolysis (IVT). OBJECTIVE To investigate the association of high CMB burden (>10 CMBs on a pre-IVT magnetic image resonance [MRI] scan) with the risk of sICH following IVT for AIS. DATA SOURCES Eligible studies were identified by searching Medline and Scopus databases. No language or other restrictions were imposed. The literature search was conducted on October 7, 2015. This meta-analysis has adopted the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and was written according to the Meta-analysis of Observational Studies in Epidemiology (MOOSE) proposal. STUDY SELECTION Eligible prospective study protocols that reported sICH rates in patients with AIS who underwent MRI for CMB screening prior to IVT. DATA EXTRACTION AND SYNTHESIS The reported rates of sICH complicating IVT in patients with AIS with pretreatment MRI were extracted independently for groups of patients with 0 CMBs (CMB absence), 1 or more CMBs (CMB presence), 1 to 10 CMBs (low to moderate CMB burden), and more than 10 CMBs (high CMB burden). An individual-patient data meta-analysis was also performed in the included studies that provided complete patient data sets. MAIN OUTCOMES AND MEASURES Symptomatic intracerebral hemorrhage based on the European Cooperative Acute Stroke Study-II definition (any intracranial bleed with ≥4 points worsening on the National Institutes of Health Stroke Scale score). RESULTS We included 9 studies comprising 2479 patients with AIS. The risk of sICH after IVT was found to be higher in patients with evidence of CMB presence, compared with patients without CMBs (risk ratio [RR], 2.36; 95% CI, 1.21-4.61; P = .01). A higher risk for sICH after IVT was detected in patients with high CMB burden (>10 CMBs) when compared with patients with 0 to 10 CMBs (RR, 12.10; 95% CI, 4.36-33.57; P < .001) or 1 to 10 CMBs (RR, 7.01; 95% CI, 3.20-15.38; P < .001) on pretreatment MRI. In the individual-patient data meta-analysis, high CMB burden was associated with increased likelihood of sICH before (unadjusted odds ratio, 31.06; 95% CI, 7.12-135.44; P < .001) and after (adjusted odds ratio, 18.17; 95% CI, 2.39-138.22; P = .005) adjusting for potential confounders. CONCLUSIONS AND RELEVANCE Presence of CMB and high CMB burdens on pretreatment MRI were independently associated with sICH in patients with AIS treated with IVT. High CMB burden may be included in individual risk stratification scores predicting sICH risk following IVT for AIS.

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques.

Abstract: Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3–5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.