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The ALICE experiment has measured low-mass dimuon production in pp collisions at √s=7 TeV in the dimuon rapidity region 2.5<y<4. The observed dimuon mass spectrum is described as a superposition of resonance decays (η,ρ,ω,η′,ϕ) into muons and semi-leptonic decays of charmed mesons. The measured production cross sections for ω and ϕ are σω(1<pt<5 GeV/c,2.5<y<4)=5.28±0.54(stat)±0.49(syst) mb and σϕ(1<pt<5 GeV/c,2.5<y<4)=0.940±0.084(stat)±0.076(syst) mb. The differential cross sections d2σ/dydpt are extracted as a function of pt for ω and ϕ. The ratio between the ρ and ω cross section is obtained. Results for the ϕ are compared with other measurements at the same energy and with predictions by models.
Heavy flavour decay muon production at forward rapidity in proton–proton collisions at √s=7 TeV
(2012)
The production of muons from heavy flavour decays is measured at forward rapidity in proton–proton collisions at √s=7 TeV collected with the ALICE experiment at the LHC. The analysis is carried out on a data sample corresponding to an integrated luminosity Lint=16.5 nb−1. The transverse momentum and rapidity differential production cross sections of muons from heavy flavour decays are measured in the rapidity range 2.5<y<4, over the transverse momentum range 2<pt<12 GeV/c. The results are compared to predictions based on perturbative QCD calculations.
Harmonic decomposition of two particle angular correlations in Pb–Pb collisions at √sNN=2.76 TeV
(2012)
Angular correlations between unidentified charged trigger (t) and associated (a) particles are measured by the ALICE experiment in Pb–Pb collisions at √sNN=2.76 TeV for transverse momenta 0.25<pTt,a<15 GeV/c, where pTt>pTa. The shapes of the pair correlation distributions are studied in a variety of collision centrality classes between 0 and 50% of the total hadronic cross section for particles in the pseudorapidity interval |η|<1.0. Distributions in relative azimuth Δϕ≡ϕt−ϕa are analyzed for |Δη|≡|ηt−ηa|>0.8, and are referred to as “long-range correlations”. Fourier components VnΔ≡〈cos(nΔϕ)〉 are extracted from the long-range azimuthal correlation functions. If particle pairs are correlated to one another through their individual correlation to a common symmetry plane, then the pair anisotropy VnΔ(pTt,pTa) is fully described in terms of single-particle anisotropies vn(pT) as VnΔ(pTt,pTa)=vn(pTt)vn(pTa). This expectation is tested for 1⩽n⩽5 by applying a global fit of all VnΔ(pTt,pTa) to obtain the best values vn{GF}(pT). It is found that for 2⩽n⩽5, the fit agrees well with data up to pTa∼3–4 GeV/c, with a trend of increasing deviation as pTt and pTa are increased or as collisions become more peripheral. This suggests that no pair correlation harmonic can be described over the full 0.25<pT<15 GeV/c range using a single vn(pT) curve; such a description is however approximately possible for 2⩽n⩽5 when pTa<4 GeV/c. For the n=1 harmonic, however, a single v1(pT) curve is not obtained even within the reduced range pTa<4 GeV/c.
The ALICE Collaboration reports the measurement of the relative J/ψ yield as a function of charged particle pseudorapidity density dNch/dη in pp collisions at √s=7 TeV at the LHC. J/ψ particles are detected for pt>0, in the rapidity interval |y|<0.9 via decay into e+e−, and in the interval 2.5<y<4.0 via decay into μ+μ− pairs. An approximately linear increase of the J/ψ yields normalized to their event average (dNJ/ψ/dy)/〈dNJ/ψ/dy〉 with (dNch/dη)/〈dNch/dη〉 is observed in both rapidity ranges, where dNch/dη is measured within |η|<1 and pt>0. In the highest multiplicity interval with 〈dNch/dη(bin)〉=24.1, corresponding to four times the minimum bias multiplicity density, an enhancement relative to the minimum bias J/ψ yield by a factor of about 5 at 2.5<y<4 (8 at |y|<0.9) is observed.
Highlights:
• Assessment of body composition parameters in a large cohort of patients with HCC undergoing TACE.
• Fully automated artificial intelligence-based quantitative 3D volumetry of abdominal cavity tissue composition.
• Skeletal muscle volume and related parameters were independent prognostic factors in patients with HCC undergoing TACE.
Background & Aims: Body composition assessment (BCA) parameters have recently been identified as relevant prognostic factors for patients with hepatocellular carcinoma (HCC). Herein, we aimed to investigate the role of BCA parameters for prognosis prediction in patients with HCC undergoing transarterial chemoembolization (TACE).
Methods: This retrospective multicenter study included a total of 754 treatment-naïve patients with HCC who underwent TACE at six tertiary care centers between 2010–2020. Fully automated artificial intelligence-based quantitative 3D volumetry of abdominal cavity tissue composition was performed to assess skeletal muscle volume (SM), total adipose tissue (TAT), intra- and intermuscular adipose tissue, visceral adipose tissue, and subcutaneous adipose tissue (SAT) on pre-intervention computed tomography scans. BCA parameters were normalized to the slice number of the abdominal cavity. We assessed the influence of BCA parameters on median overall survival and performed multivariate analysis including established estimates of survival.
Results: Univariate survival analysis revealed that impaired median overall survival was predicted by low SM (p <0.001), high TAT volume (p = 0.013), and high SAT volume (p = 0.006). In multivariate survival analysis, SM remained an independent prognostic factor (p = 0.039), while TAT and SAT volumes no longer showed predictive ability. This predictive role of SM was confirmed in a subgroup analysis of patients with BCLC stage B.
Conclusions: SM is an independent prognostic factor for survival prediction. Thus, the integration of SM into novel scoring systems could potentially improve survival prediction and clinical decision-making. Fully automated approaches are needed to foster the implementation of this imaging biomarker into daily routine.
Impact and implications: Body composition assessment parameters, especially skeletal muscle volume, have been identified as relevant prognostic factors for many diseases and treatments. In this study, skeletal muscle volume has been identified as an independent prognostic factor for patients with hepatocellular carcinoma undergoing transarterial chemoembolization. Therefore, skeletal muscle volume as a metaparameter could play a role as an opportunistic biomarker in holistic patient assessment and be integrated into decision support systems. Workflow integration with artificial intelligence is essential for automated, quantitative body composition assessment, enabling broad availability in multidisciplinary case discussions.