0000000000354726

AUTHOR

Yuanyuan Liu

Ferroptosis and Its Potential Role in Human Diseases

Ferroptosis is a novel regulated cell death pattern discovered when studying the mechanism of erastin-killing RAS mutant tumor cells in 2012. It is an iron-dependent programmed cell death pathway mainly caused by an increased redox imbalance but with distinct biological and morphology characteristics when compared to other known cell death patterns. Ferroptosis is associated with various diseases including acute kidney injury, cancer, and cardiovascular, neurodegenerative, and hepatic diseases. Moreover, activation or inhibition of ferroptosis using a variety of ferroptosis initiators and inhibitors can modulate disease progression in animal models. In this review, we provide a comprehensiv…

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Study of BESIII trigger efficiencies with the 2018 J/psi data

Using a dedicated data sample taken in 2018 on the $J/\psi$ peak, we perform a detailed study of the trigger efficiencies of the BESIII detector. The efficiencies are determined from three representative physics processes, namely Bhabha-scattering, dimuon production and generic hadronic events with charged particles. The combined efficiency of all active triggers approaches $100\%$ in most cases with uncertainties small enough as not to affect most physics analyses.

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Observation of the doubly radiative decay η′→γγπ0

Based on a sample of 1.31 billion J/psi events collected with the BESIII detector, we report the study of the doubly radiative decay eta' -> gamma gamma pi(0) for the first time, where the eta' meson is produced via the J/psi -> gamma eta' decay. The branching fraction of eta' -> gamma gamma pi(0) inclusive decay is measured to be B(eta' -> gamma gamma pi(0))(Incl) = (3.20 +/- 0.07(stat) +/- 0.23(sys)) x 10(-3), while the branching fractions of the dominant process eta' -> gamma omega and the non-resonant component are determined to be B(eta' -> gamma omega) x B(omega -> gamma pi(0)) = (23.7 +/- 1.4(stat) +/- 1.8(sys)) x 10(-4) and B(eta' -> gamma gamma pi(0))(NR) = (6.16 +/- 0.64(stat) +/-…

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