Observation of D^{+}→K_{S}^{0}a_{0}(980)^{+} in the Amplitude Analysis of D^{+}→K_{S}^{0}π^{+}η

We perform for the first time an amplitude analysis of the decay D^{+}→K_{S}^{0}π^{+}η and report the observation of the decay D^{+}→K_{S}^{0}a_{0}(980)^{+} using 2.93  fb^{-1} of e^{+}e^{-} collision data taken at a center-of-mass energy of 3.773 GeV with the BESIII detector. As the only W-annihilation-free decay among D to a_{0}(980) pseudoscalar, D^{+}→K_{S}^{0}a_{0}(980)^{+} is the ideal decay in extracting the contributions of the W-emission amplitudes involving a_{0}(980) and to study the final-state interactions. The absolute branching fraction of D^{+}→K_{S}^{0}π^{+}η is measured to be (1.27±0.04_{stat}±0.03_{syst})%. The branching fractions of intermediate processes D^{+}→K_{S}^{0}a_{0}(980)^{+} with a_{0}(980)^{+}→π^{+}η and D^{+}→π^{+}K[over ¯]_{0}^{*}(1430)^{0} with K[over ¯]_{0}^{*}(1430)^{0}→K_{S}^{0}η are measured to be (1.33±0.05_{stat}±0.04_{syst})% and (0.14±0.03_{stat}±0.01_{syst})%, respectively.

Observation of Significant Flavor-SU(3) Breaking in the Kaon Wave Function at 12<Q^{2}<25 GeV^{2} and Discovery of the Charmless Decay ψ(3770)→K_{S}^{0}K_{L}^{0}

We present cross sections for the reaction e^{+}e^{-}→K_{S}^{0}K_{L}^{0} at center-of-mass energies ranging from 3.51 to 4.95 GeV using data samples collected in the BESIII experiment, corresponding to a total integrated luminosity of 26.5  fb^{-1}. The ratio of neutral-to-charged kaon form factors at large momentum transfers (12 Collapse

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Affiliation(s)

M Ablikim Institute of High Energy Physics, Beijing 100049, People's Republic of China
M N Achasov Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
P Adlarson Uppsala University, Box 516, SE-75120 Uppsala, Sweden
X C Ai Zhengzhou University, Zhengzhou 450001, People's Republic of China
R Aliberti Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
A Amoroso University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
M R An Liaoning Normal University, Dalian 116029, People's Republic of China
Q An State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Y Bai Southeast University, Nanjing 211100, People's Republic of China
O Bakina Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
I Balossino INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
Y Ban Peking University, Beijing 100871, People's Republic of China
H-R Bao University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
V Batozskaya Institute of High Energy Physics, Beijing 100049, People's Republic of China National Centre for Nuclear Research, Warsaw 02-093, Poland
K Begzsuren Institute of Physics and Technology, Peace Avenue 54B, Ulaanbaatar 13330, Mongolia
N Berger Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
M Berlowski National Centre for Nuclear Research, Warsaw 02-093, Poland
M Bertani INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
D Bettoni INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
F Bianchi University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
E Bianco University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
A Bortone University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
I Boyko Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
R A Briere Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
A Brueggemann University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
H Cai Wuhan University, Wuhan 430072, People's Republic of China
X Cai Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
A Calcaterra INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
G F Cao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
N Cao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S A Cetin Turkish Accelerator Center Particle Factory Group, Istinye University, 34010 Istanbul, Turkey
J F Chang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
T T Chang Xinyang Normal University, Xinyang 464000, People's Republic of China
W L Chang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
G R Che Nankai University, Tianjin 300071, People's Republic of China
G Chelkov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
C Chen Nankai University, Tianjin 300071, People's Republic of China
Chao Chen Soochow University, Suzhou 215006, People's Republic of China
G Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China
H S Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
M L Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S J Chen Nanjing University, Nanjing 210093, People's Republic of China
S L Chen North China Electric Power University, Beijing 102206, People's Republic of China
S M Chen Tsinghua University, Beijing 100084, People's Republic of China
T Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X R Chen Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X T Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y B Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y Q Chen Jilin University, Changchun 130012, People's Republic of China
Z J Chen Hunan University, Changsha 410082, People's Republic of China
S K Choi Chung-Ang University, Seoul, 06974, Republic of Korea
X Chu Nankai University, Tianjin 300071, People's Republic of China
G Cibinetto INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
S C Coen Bochum Ruhr-University, D-44780 Bochum, Germany
F Cossio INFN, I-10125, Turin, Italy
J J Cui Shandong University, Jinan 250100, People's Republic of China
H L Dai Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
J P Dai Yunnan University, Kunming 650500, People's Republic of China
A Dbeyssi Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
R E de Boer Bochum Ruhr-University, D-44780 Bochum, Germany
D Dedovich Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
Z Y Deng Institute of High Energy Physics, Beijing 100049, People's Republic of China
A Denig Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
I Denysenko Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
M Destefanis University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
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X X Ding Peking University, Beijing 100871, People's Republic of China
Y Ding Liaoning University, Shenyang 110036, People's Republic of China
Y Ding Jilin University, Changchun 130012, People's Republic of China
J Dong Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
L Y Dong Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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X Dong Wuhan University, Wuhan 430072, People's Republic of China
M C Du Institute of High Energy Physics, Beijing 100049, People's Republic of China
S X Du Zhengzhou University, Zhengzhou 450001, People's Republic of China
Z H Duan Nanjing University, Nanjing 210093, People's Republic of China
P Egorov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
Y H Fan North China Electric Power University, Beijing 102206, People's Republic of China
J Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
S S Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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Y Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Q Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
R Farinelli INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
L Fava University of Eastern Piedmont, I-15121 Alessandria, Italy INFN, I-10125, Turin, Italy
F Feldbauer Bochum Ruhr-University, D-44780 Bochum, Germany
G Felici INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
C Q Feng State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
J H Feng Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
K Fischer University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
M Fritsch Bochum Ruhr-University, D-44780 Bochum, Germany
C D Fu Institute of High Energy Physics, Beijing 100049, People's Republic of China
J L Fu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y W Fu Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Gao University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y N Gao Peking University, Beijing 100871, People's Republic of China
Yang Gao State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
S Garbolino INFN, I-10125, Turin, Italy
I Garzia INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy University of Ferrara, I-44122 Ferrara, Italy
P T Ge Wuhan University, Wuhan 430072, People's Republic of China
Z W Ge Nanjing University, Nanjing 210093, People's Republic of China
C Geng Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
E M Gersabeck University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
A Gilman University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
K Goetzen GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
L Gong Liaoning University, Shenyang 110036, People's Republic of China
W X Gong Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
W Gradl Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
S Gramigna INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy University of Ferrara, I-44122 Ferrara, Italy
M Greco University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
M H Gu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y T Gu Guangxi University, Nanning 530004, People's Republic of China
C Y Guan Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z L Guan Henan University of Technology, Zhengzhou 450001, People's Republic of China
A Q Guo Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L B Guo Nanjing Normal University, Nanjing 210023, People's Republic of China
M J Guo Shandong University, Jinan 250100, People's Republic of China
R P Guo Shandong Normal University, Jinan 250014, People's Republic of China
Y P Guo Fudan University, Shanghai 200433, People's Republic of China
A Guskov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
J Gutierrez Indiana University, Bloomington, Indiana 47405, USA
T T Han Institute of High Energy Physics, Beijing 100049, People's Republic of China
W Y Han Liaoning Normal University, Dalian 116029, People's Republic of China
X Q Hao Henan Normal University, Xinxiang 453007, People's Republic of China
F A Harris University of Hawaii, Honolulu, Hawaii 96822, USA
K K He Soochow University, Suzhou 215006, People's Republic of China
K L He Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
F H H Heinsius Bochum Ruhr-University, D-44780 Bochum, Germany
C H Heinz Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
Y K Heng Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C Herold Suranaree University of Technology, University Avenue 111, Nakhon Ratchasima 30000, Thailand
T Holtmann Bochum Ruhr-University, D-44780 Bochum, Germany
P C Hong Fudan University, Shanghai 200433, People's Republic of China
G Y Hou Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X T Hou Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y R Hou University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z L Hou Institute of High Energy Physics, Beijing 100049, People's Republic of China
B Y Hu Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
H M Hu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J F Hu South China Normal University, Guangzhou 510006, People's Republic of China
T Hu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y Hu Institute of High Energy Physics, Beijing 100049, People's Republic of China
G S Huang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
K X Huang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
L Q Huang Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X T Huang Shandong University, Jinan 250100, People's Republic of China
Y P Huang Institute of High Energy Physics, Beijing 100049, People's Republic of China
T Hussain University of the Punjab, Lahore-54590, Pakistan
N Hüsken Indiana University, Bloomington, Indiana 47405, USA Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
N In der Wiesche University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
M Irshad State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
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S Jaeger Bochum Ruhr-University, D-44780 Bochum, Germany
S Janchiv Institute of Physics and Technology, Peace Avenue 54B, Ulaanbaatar 13330, Mongolia
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X L Ji Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
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M Kavatsyuk University of Groningen, NL-9747 AA Groningen, The Netherlands
B C Ke Zhengzhou University, Zhengzhou 450001, People's Republic of China
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C Li Qufu Normal University, Qufu 273165, People's Republic of China
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D M Li Zhengzhou University, Zhengzhou 450001, People's Republic of China
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H Li State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
H B Li Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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J R Li Tsinghua University, Beijing 100084, People's Republic of China
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Q X Li Shandong University, Jinan 250100, People's Republic of China
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T Li Shandong University, Jinan 250100, People's Republic of China
W D Li Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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X H Li State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X L Li Shandong University, Jinan 250100, People's Republic of China
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Y G Li Peking University, Beijing 100871, People's Republic of China
Z J Li Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
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C Liang Nanjing University, Nanjing 210093, People's Republic of China
H Liang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H Liang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
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Y T Liang Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
G R Liao Guangxi Normal University, Guilin 541004, People's Republic of China
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Y P Liao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J Libby Indian Institute of Technology Madras, Chennai 600036, India
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D X Lin Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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K Y Liu Liaoning University, Shenyang 110036, People's Republic of China
Ke Liu Henan University of Technology, Zhengzhou 450001, People's Republic of China
L Liu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
L C Liu Nankai University, Tianjin 300071, People's Republic of China
Lu Liu Nankai University, Tianjin 300071, People's Republic of China
M H Liu Fudan University, Shanghai 200433, People's Republic of China
P L Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
Q Liu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S B Liu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
T Liu Fudan University, Shanghai 200433, People's Republic of China
W K Liu Nankai University, Tianjin 300071, People's Republic of China
W M Liu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X Liu Lanzhou University, Lanzhou 730000, People's Republic of China
Y Liu Lanzhou University, Lanzhou 730000, People's Republic of China
Y Liu Zhengzhou University, Zhengzhou 450001, People's Republic of China
Y B Liu Nankai University, Tianjin 300071, People's Republic of China
Z A Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z Q Liu Shandong University, Jinan 250100, People's Republic of China
X C Lou Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
F X Lu Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
H J Lu Huangshan College, Huangshan 245000, People's Republic of China
J G Lu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
X L Lu Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Lu Central South University, Changsha 410083, People's Republic of China
Y P Lu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Z H Lu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C L Luo Nanjing Normal University, Nanjing 210023, People's Republic of China
M X Luo Zhejiang University, Hangzhou 310027, People's Republic of China
T Luo Fudan University, Shanghai 200433, People's Republic of China
X L Luo Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
X R Lyu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y F Lyu Nankai University, Tianjin 300071, People's Republic of China
F C Ma Liaoning University, Shenyang 110036, People's Republic of China
H Ma Yunnan University, Kunming 650500, People's Republic of China
H L Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China
J L Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L L Ma Shandong University, Jinan 250100, People's Republic of China
M M Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Q M Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China
R Q Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Y Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y Ma Peking University, Beijing 100871, People's Republic of China
Y M Ma Institute of Modern Physics, Lanzhou 730000, People's Republic of China
F E Maas Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
M Maggiora University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
S Malde University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
Q A Malik University of the Punjab, Lahore-54590, Pakistan
A Mangoni INFN Sezione di Perugia, I-06100 Perugia, Italy
Y J Mao Peking University, Beijing 100871, People's Republic of China
Z P Mao Institute of High Energy Physics, Beijing 100049, People's Republic of China
S Marcello University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
Z X Meng University of Jinan, Jinan 250022, People's Republic of China
J G Messchendorp GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany University of Groningen, NL-9747 AA Groningen, The Netherlands
G Mezzadri INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
H Miao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
T J Min Nanjing University, Nanjing 210093, People's Republic of China
R E Mitchell Indiana University, Bloomington, Indiana 47405, USA
X H Mo Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B Moses Indiana University, Bloomington, Indiana 47405, USA
N Yu Muchnoi Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
J Muskalla Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
Y Nefedov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
F Nerling Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
I B Nikolaev Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
Z Ning Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
S Nisar COMSATS University Islamabad, Lahore Campus, Defence Road, Off Raiwind Road, 54000 Lahore, Pakistan
Q L Niu Lanzhou University, Lanzhou 730000, People's Republic of China
W D Niu Soochow University, Suzhou 215006, People's Republic of China
Y Niu Shandong University, Jinan 250100, People's Republic of China
S L Olsen University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Q Ouyang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S Pacetti INFN Sezione di Perugia, I-06100 Perugia, Italy University of Perugia, I-06100 Perugia, Italy
X Pan Soochow University, Suzhou 215006, People's Republic of China
Y Pan Southeast University, Nanjing 211100, People's Republic of China
A Pathak Jilin University, Changchun 130012, People's Republic of China
P Patteri INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
Y P Pei State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
M Pelizaeus Bochum Ruhr-University, D-44780 Bochum, Germany
H P Peng State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Y Y Peng Lanzhou University, Lanzhou 730000, People's Republic of China
K Peters GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
J L Ping Nanjing Normal University, Nanjing 210023, People's Republic of China
R G Ping Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S Plura Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
V Prasad Instituto de Alta Investigación, Universidad de Tarapacá, Casilla 7D, Arica 1000000, Chile
F Z Qi Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Qi State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
H R Qi Tsinghua University, Beijing 100084, People's Republic of China
M Qi Nanjing University, Nanjing 210093, People's Republic of China
T Y Qi Fudan University, Shanghai 200433, People's Republic of China
S Qian Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
W B Qian University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C F Qiao University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J J Qin University of South China, Hengyang 421001, People's Republic of China
L Q Qin Guangxi Normal University, Guilin 541004, People's Republic of China
X S Qin Shandong University, Jinan 250100, People's Republic of China
Z H Qin Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
J F Qiu Institute of High Energy Physics, Beijing 100049, People's Republic of China
S Q Qu Tsinghua University, Beijing 100084, People's Republic of China
C F Redmer Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
K J Ren Liaoning Normal University, Dalian 116029, People's Republic of China
A Rivetti INFN, I-10125, Turin, Italy
M Rolo INFN, I-10125, Turin, Italy
G Rong Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Ch Rosner Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
S N Ruan Nankai University, Tianjin 300071, People's Republic of China
N Salone National Centre for Nuclear Research, Warsaw 02-093, Poland
A Sarantsev Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
Y Schelhaas Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
K Schoenning Uppsala University, Box 516, SE-75120 Uppsala, Sweden
M Scodeggio INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy University of Ferrara, I-44122 Ferrara, Italy
K Y Shan Fudan University, Shanghai 200433, People's Republic of China
W Shan Hunan Normal University, Changsha 410081, People's Republic of China
X Y Shan State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
J F Shangguan Soochow University, Suzhou 215006, People's Republic of China
L G Shao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
M Shao State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
C P Shen Fudan University, Shanghai 200433, People's Republic of China
H F Shen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
W H Shen University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Y Shen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B A Shi University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H C Shi State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
J L Shi Fudan University, Shanghai 200433, People's Republic of China
J Y Shi Institute of High Energy Physics, Beijing 100049, People's Republic of China
Q Q Shi Soochow University, Suzhou 215006, People's Republic of China
R S Shi Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Shi Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
J J Song Henan Normal University, Xinxiang 453007, People's Republic of China
T Z Song Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
W M Song Institute of High Energy Physics, Beijing 100049, People's Republic of China Jilin University, Changchun 130012, People's Republic of China
Y J Song Fudan University, Shanghai 200433, People's Republic of China
Y X Song Peking University, Beijing 100871, People's Republic of China
S Sosio University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
S Spataro University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
F Stieler Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
Y J Su University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
G B Sun Wuhan University, Wuhan 430072, People's Republic of China
G X Sun Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Sun University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H K Sun Institute of High Energy Physics, Beijing 100049, People's Republic of China
J F Sun Henan Normal University, Xinxiang 453007, People's Republic of China
K Sun Tsinghua University, Beijing 100084, People's Republic of China
L Sun Wuhan University, Wuhan 430072, People's Republic of China
S S Sun Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
T Sun Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
W Y Sun Jilin University, Changchun 130012, People's Republic of China
Y Sun China University of Geosciences, Wuhan 430074, People's Republic of China
Y J Sun State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Y Z Sun Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z T Sun Shandong University, Jinan 250100, People's Republic of China
Y X Tan State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
C J Tang Sichuan University, Chengdu 610064, People's Republic of China
G Y Tang Institute of High Energy Physics, Beijing 100049, People's Republic of China
J Tang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
Y A Tang Wuhan University, Wuhan 430072, People's Republic of China
L Y Tao University of South China, Hengyang 421001, People's Republic of China
Q T Tao Hunan University, Changsha 410082, People's Republic of China
M Tat University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
J X Teng State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
V Thoren Uppsala University, Box 516, SE-75120 Uppsala, Sweden
W H Tian Shanxi Normal University, Linfen 041004, People's Republic of China
W H Tian Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
Y Tian Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z F Tian Wuhan University, Wuhan 430072, People's Republic of China
I Uman Near East University, Nicosia, North Cyprus, 99138, Mersin 10, Turkey
Y Wan Soochow University, Suzhou 215006, People's Republic of China
S J Wang Shandong University, Jinan 250100, People's Republic of China
B Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China
B L Wang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Bo Wang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
C W Wang Nanjing University, Nanjing 210093, People's Republic of China
D Y Wang Peking University, Beijing 100871, People's Republic of China
F Wang University of South China, Hengyang 421001, People's Republic of China
H J Wang Lanzhou University, Lanzhou 730000, People's Republic of China
J P Wang Shandong University, Jinan 250100, People's Republic of China
K Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
L L Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China
M Wang Shandong University, Jinan 250100, People's Republic of China
Meng Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
N Y Wang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S Wang Fudan University, Shanghai 200433, People's Republic of China
S Wang Lanzhou University, Lanzhou 730000, People's Republic of China
T Wang Fudan University, Shanghai 200433, People's Republic of China
T J Wang Nankai University, Tianjin 300071, People's Republic of China
W Wang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
W Wang University of South China, Hengyang 421001, People's Republic of China
W P Wang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X Wang Peking University, Beijing 100871, People's Republic of China
X F Wang Lanzhou University, Lanzhou 730000, People's Republic of China
X J Wang Liaoning Normal University, Dalian 116029, People's Republic of China
X L Wang Fudan University, Shanghai 200433, People's Republic of China
Y Wang Tsinghua University, Beijing 100084, People's Republic of China
Y D Wang North China Electric Power University, Beijing 102206, People's Republic of China
Y F Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y L Wang Henan Normal University, Xinxiang 453007, People's Republic of China
Y N Wang North China Electric Power University, Beijing 102206, People's Republic of China
Y Q Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Yaqian Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China Hebei University, Baoding 071002, People's Republic of China
Yi Wang Tsinghua University, Beijing 100084, People's Republic of China
Z Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Z L Wang University of South China, Hengyang 421001, People's Republic of China
Z Y Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Ziyi Wang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
D Wei University of Science and Technology Liaoning, Anshan 114051, People's Republic of China
D H Wei Guangxi Normal University, Guilin 541004, People's Republic of China
F Weidner University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
S P Wen Institute of High Energy Physics, Beijing 100049, People's Republic of China
C W Wenzel Bochum Ruhr-University, D-44780 Bochum, Germany
U Wiedner Bochum Ruhr-University, D-44780 Bochum, Germany
G Wilkinson University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
M Wolke Uppsala University, Box 516, SE-75120 Uppsala, Sweden
L Wollenberg Bochum Ruhr-University, D-44780 Bochum, Germany
C Wu Liaoning Normal University, Dalian 116029, People's Republic of China
J F Wu Institute of High Energy Physics, Beijing 100049, People's Republic of China China Center of Advanced Science and Technology, Beijing 100190, People's Republic of China
L H Wu Institute of High Energy Physics, Beijing 100049, People's Republic of China
L J Wu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Wu Fudan University, Shanghai 200433, People's Republic of China
X H Wu Jilin University, Changchun 130012, People's Republic of China
Y Wu University of Science and Technology of China, Hefei 230026, People's Republic of China
Y H Wu Soochow University, Suzhou 215006, People's Republic of China
Y J Wu Institute of Modern Physics, Lanzhou 730000, People's Republic of China
Z Wu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
L Xia State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X M Xian Liaoning Normal University, Dalian 116029, People's Republic of China
T Xiang Peking University, Beijing 100871, People's Republic of China
D Xiao Lanzhou University, Lanzhou 730000, People's Republic of China
G Y Xiao Nanjing University, Nanjing 210093, People's Republic of China
S Y Xiao Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y L Xiao Fudan University, Shanghai 200433, People's Republic of China
Z J Xiao Nanjing Normal University, Nanjing 210023, People's Republic of China
C Xie Nanjing University, Nanjing 210093, People's Republic of China
X H Xie Peking University, Beijing 100871, People's Republic of China
Y Xie Shandong University, Jinan 250100, People's Republic of China
Y G Xie Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y H Xie Central China Normal University, Wuhan 430079, People's Republic of China
Z P Xie State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
T Y Xing Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C F Xu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C J Xu Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
G F Xu Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Y Xu University of Jinan, Jinan 250022, People's Republic of China
Q J Xu Hangzhou Normal University, Hangzhou 310036, People's Republic of China
Q N Xu Inner Mongolia University, Hohhot 010021, People's Republic of China
W Xu Institute of High Energy Physics, Beijing 100049, People's Republic of China
W L Xu University of Jinan, Jinan 250022, People's Republic of China
X P Xu Soochow University, Suzhou 215006, People's Republic of China
Y C Xu Yantai University, Yantai 264005, People's Republic of China
Z P Xu Nanjing University, Nanjing 210093, People's Republic of China
Z S Xu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
F Yan Fudan University, Shanghai 200433, People's Republic of China
L Yan Fudan University, Shanghai 200433, People's Republic of China
W B Yan State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
W C Yan Zhengzhou University, Zhengzhou 450001, People's Republic of China
X Q Yan Institute of High Energy Physics, Beijing 100049, People's Republic of China
H J Yang Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
H L Yang Jilin University, Changchun 130012, People's Republic of China
H X Yang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Tao Yang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Yang Fudan University, Shanghai 200433, People's Republic of China
Y F Yang Nankai University, Tianjin 300071, People's Republic of China
Y X Yang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Yifan Yang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z W Yang Lanzhou University, Lanzhou 730000, People's Republic of China
Z P Yao Shandong University, Jinan 250100, People's Republic of China
M Ye Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
M H Ye China Center of Advanced Science and Technology, Beijing 100190, People's Republic of China
J H Yin Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z Y You Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
B X Yu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C X Yu Nankai University, Tianjin 300071, People's Republic of China
G Yu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J S Yu Hunan University, Changsha 410082, People's Republic of China
T Yu University of South China, Hengyang 421001, People's Republic of China
X D Yu Peking University, Beijing 100871, People's Republic of China
C Z Yuan Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L Yuan Beihang University, Beijing 100191, People's Republic of China
S C Yuan Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Yuan Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z Y Yuan Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
C X Yue Liaoning Normal University, Dalian 116029, People's Republic of China
A A Zafar University of the Punjab, Lahore-54590, Pakistan
F R Zeng Shandong University, Jinan 250100, People's Republic of China
S H Zeng University of South China, Hengyang 421001, People's Republic of China
X Zeng Fudan University, Shanghai 200433, People's Republic of China
Y Zeng Hunan University, Changsha 410082, People's Republic of China
Y J Zeng Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Y Zhai Jilin University, Changchun 130012, People's Republic of China
Y C Zhai Shandong University, Jinan 250100, People's Republic of China
Y H Zhan Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
A Q Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B L Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B X Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
D H Zhang Nankai University, Tianjin 300071, People's Republic of China
G Y Zhang Henan Normal University, Xinxiang 453007, People's Republic of China
H Zhang University of Science and Technology of China, Hefei 230026, People's Republic of China
H C Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H H Zhang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
H H Zhang Jilin University, Changchun 130012, People's Republic of China
H Q Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H Y Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
J Zhang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
J Zhang Zhengzhou University, Zhengzhou 450001, People's Republic of China
J J Zhang Shanxi Normal University, Linfen 041004, People's Republic of China
J L Zhang Henan University, Kaifeng 475004, People's Republic of China
J Q Zhang Nanjing Normal University, Nanjing 210023, People's Republic of China
J W Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J X Zhang Lanzhou University, Lanzhou 730000, People's Republic of China
J Y Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
J Z Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Jianyu Zhang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L M Zhang Tsinghua University, Beijing 100084, People's Republic of China
L Q Zhang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
Lei Zhang Nanjing University, Nanjing 210093, People's Republic of China
P Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Q Y Zhang Liaoning Normal University, Dalian 116029, People's Republic of China Zhengzhou University, Zhengzhou 450001, People's Republic of China
Shuihan Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Shulei Zhang Hunan University, Changsha 410082, People's Republic of China
X D Zhang North China Electric Power University, Beijing 102206, People's Republic of China
X M Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
X Y Zhang Shandong University, Jinan 250100, People's Republic of China
Y Zhang University of South China, Hengyang 421001, People's Republic of China
Y Zhang University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
Y T Zhang Zhengzhou University, Zhengzhou 450001, People's Republic of China
Y H Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Yan Zhang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Yao Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z D Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z H Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z L Zhang Jilin University, Changchun 130012, People's Republic of China
Z Y Zhang Nankai University, Tianjin 300071, People's Republic of China
Z Y Zhang Wuhan University, Wuhan 430072, People's Republic of China
G Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China
J Y Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J Z Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Lei Zhao State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Ling Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China
M G Zhao Nankai University, Tianjin 300071, People's Republic of China
R P Zhao University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S J Zhao Zhengzhou University, Zhengzhou 450001, People's Republic of China
Y B Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y X Zhao Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z G Zhao State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
A Zhemchugov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
B Zheng University of South China, Hengyang 421001, People's Republic of China
J P Zheng Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
W J Zheng Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y H Zheng University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B Zhong Nanjing Normal University, Nanjing 210023, People's Republic of China
X Zhong Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
H Zhou Shandong University, Jinan 250100, People's Republic of China
L P Zhou Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Zhou Wuhan University, Wuhan 430072, People's Republic of China
X K Zhou Central China Normal University, Wuhan 430079, People's Republic of China
X R Zhou State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X Y Zhou Liaoning Normal University, Dalian 116029, People's Republic of China
Y Z Zhou Fudan University, Shanghai 200433, People's Republic of China
J Zhu Nankai University, Tianjin 300071, People's Republic of China
K Zhu Institute of High Energy Physics, Beijing 100049, People's Republic of China
K J Zhu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L Zhu Jilin University, Changchun 130012, People's Republic of China
L X Zhu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S H Zhu University of Science and Technology Liaoning, Anshan 114051, People's Republic of China
S Q Zhu Nanjing University, Nanjing 210093, People's Republic of China
T J Zhu Fudan University, Shanghai 200433, People's Republic of China
W J Zhu Fudan University, Shanghai 200433, People's Republic of China
Y C Zhu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Z A Zhu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J H Zou Institute of High Energy Physics, Beijing 100049, People's Republic of China
J Zu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China

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Enhancing real-time cell culture monitoring: Automated Raman model optimization with Taguchi method

Raman spectroscopy has found widespread usage in monitoring cell culture processes both in research and practical applications. However, commonly, preprocessing methods, spectral regions, and modeling parameters have been chosen based on experience or trial-and-error strategies. These choices can significantly impact the performance of the models. There is an urgent need for a simple, effective, and automated approach to determine a suitable procedure for constructing accurate models. This paper introduces the adoption of a design of experiment (DoE) method to optimize partial least squares models for measuring the concentration of different components in cell culture bioreactors. The experimental implementation utilized the orthogonal test table L25 (56 ). Within this framework, five factors were identified as control variables for the DoE method: the window width of Savitzky-Golay smoothing, the baseline correction method, the order of preprocessing steps, spectral regions, and the number of latent variables. The evaluation method for the model was considered as a factor subject to noise. The optimal combination of levels was determined through the signal-to-noise ratio response table employing Taguchi analysis. The effectiveness of this approach was validated through two cases, involving different cultivation scales, different Raman spectrometers, and different analytical components. The results consistently demonstrated that the proposed approach closely approximated the global optimum, regardless of data set size, predictive components, or the brand of Raman spectrometer. The performance of models recommended by the DoE strategy consistently surpassed those built using raw data, underscoring the reliability of models generated through this approach. When compared to exhaustive all-combination experiments, the DoE approach significantly reduces calculation times, making it highly practical for the implementation of Raman spectroscopy in bioprocess monitoring.

Exploring metabolic responses and pathway changes in CHO-K1 cells under varied aeration conditions and copper supplementations using 1 H NMR-based metabolomics

The optimization of bioprocess for CHO cell culture involves careful consideration of factors such as nutrient consumption, metabolic byproduct accumulation, cell growth, and monoclonal antibody (mAb) production. Valuable insights can be obtained by understanding cellular physiology to ensure robust and efficient bioprocess. This study aims to improve our understanding of the CHO-K1 cell metabolism using 1 H NMR-based metabolomics. Initially, the variations in culture performance and metabolic profiles under varied aeration conditions and copper supplementations were thoroughly examined. Furthermore, a comprehensive metabolic pathway analysis was performed to assess the impact of these conditions on the implicated pathways. The results revealed substantial alterations in the pyruvate metabolism, histidine metabolism, as well as phenylalanine, tyrosine and tryptophan biosynthesis, which were especially evident in cultures subjected to copper deficiency conditions. Conclusively, significant metabolites governing cell growth and mAb titer were identified through orthogonal partial least square-discriminant analysis (OPLS-DA). Metabolites, including glycerol, alanine, formate, glutamate, phenylalanine, and valine, exhibited strong associations with distinct cell growth phases. Additionally, glycerol, acetate, lactate, formate, glycine, histidine, and aspartate emerged as metabolites influencing cell productivity. This study demonstrates the potential of employing 1 H NMR-based metabolomics technology in bioprocess research. It provides valuable guidance for feed medium development, feeding strategy design, bioprocess parameter adjustments, and ultimately the enhancement of cell proliferation and mAb yield.

Development of an in-line Raman analytical method for commercial-scale CHO cell culture process monitoring: Influence of measurement channels and batch number on model performance

The mammalian cell culture process is a key step in commercial therapeutic protein production and needs to be monitored and controlled due to its complexity. Raman spectroscopy has been reported for cell culture process monitoring by analysis of many important parameters. However, studies on in-line Raman monitoring of the cell culture process were mainly conducted on small or pilot scale. Developing in-line Raman analytical methods for commercial-scale cell culture process monitoring is more challenging. In this study, an in-line Raman analytical method was developed for monitoring glucose, lactate, and viable cell density (VCD) in the Chinese hamster ovary (CHO) cell culture process during commercial production of biosimilar adalimumab (1500 L). The influence of different Raman measurement channels was considered to determine whether to merge data from different channels for model development. Raman calibration models were developed and optimized, with minimum root mean square error of prediction of 0.22 g L-1 for glucose in the range of 1.66-3.53 g L-1 , 0.08 g L-1 for lactate in the range of 0.15-1.19 g L-1 , 0.31 E6 cells mL-1 for VCD in the range of 0.96-5.68 E6 cells mL-1 on test sets. The developed analytical method can be used for cell culture process monitoring during manufacturing and meets the analytical purpose of this study. Further, the influence of the number of batches used for model calibration on model performance was also studied to determine how many batches are needed basically for method development. The proposed Raman analytical method development strategy and considerations will be useful for monitoring of similar bioprocesses.

Q-TWiST analysis of survival benefits with brigatinib versus crizotinib in patients with anaplastic lymphoma kinase-positive non-small cell lung cancer based on results of the ALTA-1L trial

OBJECTIVES

The ALTA-1L phase 3 open-label trial demonstrated increased progression-free survival (PFS) with brigatinib versus crizotinib in patients with anaplastic lymphoma kinase-positive (ALK-positive) locally advanced or metastatic non-small cell lung cancer (NSCLC) previously untreated with ALK-targeted therapy. This post-hoc analysis of data from the ALTA-1L trial used the quality-adjusted (QA) time without symptoms of disease or toxicity (Q-TWiST) methodology to compare the QA survival benefit of brigatinib versus crizotinib in this patient population.

PATIENTS AND METHODS

The Q-TWiST analysis was performed using final (January 29, 2021) individual patient-level blinded independent review committee (BIRC)- and investigator-assessed survival data for brigatinib (n = 137) and crizotinib (n = 138) in adult patients (N = 275) with ALK-positive locally advanced or metastatic NSCLC previously untreated with ALK-targeted therapy. Q-TWiST was compared between the two treatments. Subgroup analyses were performed in patients stratified by various clinicopathological characteristics, including presence or absence of brain metastases at baseline.

RESULTS

Brigatinib was associated with significantly longer time without symptoms of disease or toxicity (P < 0.001) than crizotinib, with significantly greater Q-TWiST (mean [SE] months: BIRC-assessed, 28.2 [1.2] versus 25.1 [1.1], P = 0.045; investigator-assessed, 28.5 [1.2] versus 24.8 [1.1], P = 0.018). Relative gains in Q-TWiST with brigatinib compared to crizotinib were clinically meaningful (BIRC-assessed, 10.4%; investigator-assessed, 12.3%). Patients with brain metastases at baseline receiving brigatinib had significantly greater Q-TWiST (mean [SE] months: BIRC-assessed, 29.0 [1.9] versus 19.0 [1.9], P = 0.0001) than those receiving crizotinib.

CONCLUSION

First-line brigatinib treatment was associated with significant and clinically meaningful gains in Q-TWiST compared to crizotinib in patients with ALK-positive locally advanced or metastatic NSCLC, supporting the results of the ALTA-1L trial and brigatinib as a safe and effective first-line treatment for ALK-positive NSCLC.

[Observation on the efficacy of different targets low-frequency repetitive transcranial magnetic stimulation for the treatment of tremor-dominant subtypes of Parkinson's disease]

Objective: To analyze the efficacy of different targets low-frequency repetitive transcranial magnetic stimulation (rTMS) for the treatment of tremor Parkinson's disease(PD). Method: A total of 82 patients with primary PD who were admitted to the Department of Neurology, Xin Hua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine from April 1, 2020 to March 31, 2021 were prospectively collected. According to the clinical characteristics of major movement disorders, 82 patients with tremor type (TD) were selected to enroll.The patients were randomly divided into 3 groups at a 1∶1∶1 ratio according to the randomized coding sequence of the trial: the primary motor cortex (M1) group with 26 cases, the cerebellum group with 26 cases and the dual-site (M1, cerebellum) group with 30 cases. All patients were treated with 1 Hz low-frequency stimulation of the corresponding target once a day for 5 days a week for 2 weeks, a total of 10 times; The dosage remained unchanged during the treatment for all groups. Before and after 2 weeks' treatment, the patients were assessed with the Unified PD Rating Scale (UPDRS) and PD Quality of Life Questionnaire-39 (PDQ-39) without medication. Cortical excitability, namely transcranial magnetic stimulation motor evoked potential (TMS-MEP), [including resting motor threshold (rMT) and active motor threshold (aMT) examinations], timed up and go (TUG) and electromyographic tremor were conducted. Result: There were 82 patients, 39 males and 43 females, with an average age of (67±8) years. Before the treatment, there was no statistically significant difference in the evaluation indicators among the three groups (all P>0.05). After the treatment, the differences of the UPDRS-Ⅲ score [(38.9±2.5) vs (29.2±3.6) ], UPDRS tremor score [(23.7±2.1) vs (14.6±3.1) ], TUG time [(44.8±3.1) s vs (33.7±4.1) s], tremor amplitude [(480±126) μV vs (276±94) μV], PDQ-39 score [(51±13) vs (45±13) ], rMT [(36±17)% vs (43±13)%], and aMT [(26±16)% vs (31±12)%] were statistically significant (all P<0.01) from those before the treatment. There was no statistical difference in the above factors between the M1 group and cerebellum group (all P>0.05). There was no statistically significant difference in tremor peak frequency among the three groups before and after the treatment (all P>0.05). Conclusions: Dual-site low-frequency rTMS can improve PD tremor, while M1 or cerebellar low-frequency rTMS does not significantly improve PD tremor. Its mechanism may be to improve PD tremor symptoms by regulating cortical excitability.

Detection, survival, and persistence of Staphylococcus capitis NRCS-A in neonatal units in England

BACKGROUND

The multidrug-resistant Staphylococcus capitis clone, NRCS-A, is increasingly associated with late-onset sepsis in low birthweight newborns in neonatal intensive care units (NICUs) in England and globally. Understanding where this bacterium survives and persists within the NICU environment is key to developing and implementing effective control measures.

AIM

To investigate the potential for S. capitis to colonize surfaces within NICUs.

METHODS

Surface swabs were collected from four NICUs with and without known NRCS-A colonizations/infections present at the time of sampling. Samples were cultured and S. capitis isolates analysed via whole-genome sequencing. Survival of NRCS-A on plastic surfaces was assessed over time and compared to that of non-NRCS-A isolates. The bactericidal activity of commonly used chemical disinfectants against S. capitis was assessed.

FINDINGS

Of 173 surfaces sampled, 40 (21.1%) harboured S. capitis with 30 isolates (75%) being NRCS-A. Whereas S. capitis was recovered from surfaces across the NICU, the NRCS-A clone was rarely recovered from outside the immediate neonatal bedspace. Incubators and other bedside equipment were contaminated with NRCS-A regardless of clinical case detection. In the absence of cleaning, S. capitis was able to survive for three days with minimal losses in viability (<0.5 log10 reduction). Sodium troclosene and a QAC-based detergent/disinfectant reduced S. capitis to below detectable levels.

CONCLUSION

S. capitis NRCS-A can be readily recovered from the NICU environment, even in units with no recent reported clinical cases of S. capitis infection, highlighting a need for appropriate national guidance on cleaning within the neonatal care environment.

A Metastatic Pulmonary Sarcomatoid Carcinoma Patient Harboring KIF5B-RET Fusion Responds to First-Line Pralsetinib Treatment: A Case Report

Pulmonary sarcomatoid carcinoma (PSC) is a rare subtype of non-small cell lung cancer (NSCLC), accounting for about 1% of cases. These tumors are characterized by their high malignancy and frequent resistance to chemotherapy, resulting in a worse prognosis compared to other NSCLC subtypes. Currently, there is no established therapeutic strategy for PSC. Recent advancements in targeted therapies have led to the development of ret proto-oncogene (RET) inhibitors, such as selpercatinib and pralsetinib, which have been approved for the treatment of RET fusion-positive NSCLC patients. Despite their effectiveness in RET fusion-positive NSCLC is observed, the efficacy of these inhibitors in PSC remains unclear. In this context, we present a case of metastatic PSC harboring de novo KIF5B-RET fusion. The patient responded to first-line trametinib treatment. These findings suggest that RET inhibitors could be a potential treatment option for metastatic PSC patients with RET fusion-positive tumors.

Invasive acupuncture for gastroparesis after thoracic or abdominal surgery: a systematic review and meta-analysis

OBJECTIVES

This meta-analysis aimed to systematically evaluate the efficacy of acupuncture in treating postsurgical gastroparesis syndrome (PGS) after thoracic or abdominal surgery.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

Twelve databases (PubMed, Embase, Cochrane Library Cochrane Central Register of Controlled Trials (CENTRAL), Medline (Ovid) (from 1946), Web of Science, EBSCO, Scopus, Open Grey, China National Knowledge Infrastructure (CNKI), Wanfang Database, Chinese Scientific Journals Database (VIP) and China Biology Medicine disc (CBM)) and three registration websites (WHO International Clinical Trials Registry Platform (ICTRP), ClinicalTrials.gov, and Chinese Clinical Trial Registry (ChiCTR)) were searched from the inception to September 2022, and citations of the included literature were screened.

ELIGIBILITY CRITERIA

All randomised controlled trials addressing invasive acupuncture for PGS.

DATA EXTRACTION AND SYNTHESIS

Key information on the included studies was extracted by two reviewers independently. Risk ratio (RR) with 95% CI was used for categorical data, and mean difference with 95% CI for continuous data. The quality of evidence was assessed using Grading of Recommendations Assessment, Development and Evaluation. Outcomes were conducted with trial sequential analysis (TSA).

RESULTS

Fifteen studies with 759 patients met the inclusion criteria. Subgroup analyses revealed that compared with the drug group, the drug and acupuncture group had a greater positive effect on the total effective rate (TER) (nine trials, n=427; RR=1.20; 95% CI 1.08 to 1.32; P-heterogeneity=0.20, I2=28%, p=0.0004) and the recovery rate (RCR) (six trials, n = 294; RR = 1.61; 95% CI 1.30 to 1.98; P-heterogeneity=0.29, I2=19%, p<0.0001) of PGS after abdominal surgery. However, acupuncture showed no significant advantages in terms of the TER after thoracic surgery (one trial, p=0.13) or thoracic/abdominal surgery-related PGS (two trials, n = 115; RR=1.18; 95% CI 0.89 to 1.57; P-heterogeneity=0.08, I2=67%, p=0.24) and the RCR after thoracic/abdominal surgery (two trials, n=115; RR=1.40; 95% CI 0.97 to 2.01; P-heterogeneity=0.96, I2=0%, p=0.07). The quality of evidence for TER and RCR was moderate certainty. Only one study reported an acupuncture-related adverse event, in the form of mild local subcutaneous haemorrhage and pain that recovered spontaneously. TSA indicated that outcomes reached a necessary effect size except for clinical symptom score.

CONCLUSION

Based on subgroup analysis, compared with the drug treatment, acupuncture combined drug has significant advantages in the treatment of PGS associated with abdominal surgery, but not with thoracic surgery.

PROSPERO REGISTRATION NUMBER

CRD42022299189.

1 H NMR-based process understanding and biochemical marker identification methodology for monitoring CHO cell culture process during commercial-scale manufacturing

Controlling the process of CHO cell fed-batch culture is critical for biologics quality control. However, the biological complexity of cells has hampered the reliable process understanding for industrial manufacturing. In this study, a workflow was developed for the consistency monitoring and biochemical marker identification of the commercial-scale CHO cell culture process through ¹ H NMR assisted with multivariate data analysis (MVDA). Firstly, a total of 63 metabolites were identified in this study object in ¹ H NMR spectra of the CHO cell-free supernatants. Secondly, multivariate statistical process control (MSPC) charts were used to evaluate process consistency. According to MSPC charts, the batch-to-batch quality consistency was high, indicating the CHO cell culture process at the commercial scale was well-controlled and stable. Then, the biochemical marker identification was provided through orthogonal partial least square discriminant analysis (OPLS-DA) based S-line plots during the cell logarithmic expansion, stable growth, and decline phases. Identified biochemical markers of the three cell growth phases were as follows: L-glutamine, pyroglutamic acid, 4-hydroxyproline, choline, glucose, lactate, alanine, and proline were of the logarithmic growth phase; isoleucine, leucine, valine, acetate, and alanine were of the stable growth phase; acetate, glycine, glycerin, and gluconic acid were of the cell decline phase. Additional potential metabolic pathways that might influence the cell culture phase transitions were demonstrated. The workflow proposed in this study demonstrates that the combination of MVDA tools and ¹ H NMR technology is highly appealing to the research of the biomanufacturing process, and applies well to provide guidance in future work on consistency evaluation and biochemical marker monitoring of the production of other biologics. This article is protected by copyright. All rights reserved.

[Analysis of 7 cases of pediatric acute myeloid leukemia with DEK-NUP214 fusion gene]

Objective: To investigate the clinical features, treatment regime, and outcome of pediatric acute myeloid leukemia (AML) with DEK-NUP214 fusion gene. Methods: The clinical data, genetic and molecular results, treatment process and survival status of 7 cases of DEK-NUP214 fusion gene positive AML children admitted to the Pediatric Blood Diseases Center of Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences from May 2015 to February 2022 were analyzed retrospectively. Results: DEK-NUP214 fusion gene positive AML accounted for 1.02% (7/683) of pediatric AML diagnosed in the same period, with 4 males and 3 females. The age of disease onset was 8.2 (7.5, 9.5) years. The blast percentage in bone marrow was 0.275 (0.225, 0.480), and 6 cases were M5 by FAB classification. Pathological hematopoiesis was observed in all cases except for one whose bone marrow morphology was unknown. Three cases carried FLT3-ITD mutations, 4 cases carried NRAS mutations, and 2 cases carried KRAS mutations. After diagnosis, 4 cases received IAE induction regimen (idarubicin, cytarabine and etoposide), 1 case received MAE induction regimen (mitoxantrone, cytarabine and etoposide), 1 case received DAH induction regimen (daunorubicin, cytarabine and homoharringtonine) and 1 case received DAE induction regimen (daunorubicin, cytarabine and etoposide). Complete remission was achieved in 3 cases after one course of induction. Four cases who did not achieved complete remission received CAG (aclarubicin, cytarabine and granulocyte colony-stimulating factor), IAH (idarubicin, cytarabine and homoharringtonine), CAG combined with cladribine, and HAG (homoharringtonine, cytarabine and granulocyte colony-stimulating factor) combined with cladribine reinduction therapy, respectively, all 4 cases reached complete remission. Six patients received hematopoietic stem cell transplantation (HSCT) after 1-2 sessions of intensive consolidation treatment, except that one case was lost to follow-up after complete remission. The time from diagnosis to HSCT was 143 (121, 174) days. Before HSCT, one case was positive for flow cytometry minimal residual disease and 3 cases were positive for DEK-NUP214 fusion gene. Three cases accepted haploid donors, 2 cases accepted unrelated cord blood donors, and 1 case accepted matched sibling donor. The follow-up time was 20.4 (12.9, 53.1) months, the overall survival and event free survival rates were all 100%. Conclusions: Pediatric AML with DEK-NUP214 fusion gene is a unique and rare subtype, often diagnosed in relatively older children. The disease is characterized with a low blast percentage in bone marrow, significant pathological hematopoiesis and a high mutation rate in FLT3-ITD and RAS genes. Low remission rate by chemotherapy only and very high recurrence rate indicate its high malignancy and poor prognosis. Early HSCT after the first complete remission can improve its prognosis.

Short- and long-term outcomes of laparoscopic versus open pelvic exenteration for locally advanced rectal cancer: a single-center propensity score matching analysis

BACKGROUND

Research on short-term outcomes and long-term oncological results of laparoscopic pelvic exenteration (LPE) for locally advanced rectal cancer (LARC) is still limited. The purpose of this study was to compare the outcomes of LPE and open pelvic exenteration (OPE).

METHODS

Between January 2010 and December 2019, consecutive LARC patients who underwent radical pelvic exenteration at Peking University First Hospital were enrolled. Groups were matched at a 1:1 ratio using propensity score matching. The primary endpoints were 3 year overall survival (OS) and disease-free survival (DFS). The secondary endpoints were postoperative short-term outcomes.

RESULTS

There were 144 patients (68 males and 76 females, median age 58.5 [range 27.0-86.0] years). After matching, patients were stratified into LPE (n = 48) and OPE (n = 48) groups (LPE: 24 males and 24 females, median age 57.0 [range 27.0-81.0] years; OPE: 26 males and 22 females, median age 58.0[range 36.0-80.0] years). There were no significant differences on baseline data between the two groups. Compared with the OPE group, the LPE group had a significantly lower estimated blood loss (200 vs 500 ml, p = 0.003), less overall postoperative complications (12/48 vs 25/48, p = 0.006), less surgical site infection (8/48 vs 20/48, p = 0.007), shorter length of stay (12 vs. 15 days, p = 0.005), but similar operative time (344 vs. 360 min, p = 0.493). The pathological R0 resection rate (98.0% vs. 93.7%, p = 0.610), 3 year local recurrence (18.4% vs. 23.5, p = 0.140), 3 year OS (74.6% vs. 65.5%, p = 0.290) and 3-year DFS (60.0% vs. 50.3%, p = 0.208) were similar between the two groups. Shorter distance from anal verge (HR = 0.92, p = 0.042), (y) pT4b (HR = 2.45, p = 0.023), (y)pN1-2 (HR = 2.42, p = 0.004) and positive CRM (HR = 6.23, p = 0.004) were independent prognostic risks for 3 year DFS.

CONCLUSIONS

LPE can be performed safely and has certain short-term advantages over OPE, most notably less blood loss and surgical site infection. However, LPE does not improve long-term oncological outcomes.

[Effects of mechanical tension on the formation of hypertrophic scars in rabbit ears and transforming growth factor-β1/Smad signaling pathway]

Objective: To explore the effects of mechanical tension on the formation of hypertrophic scars in rabbit ears and transforming growth factor-β1 (TGF-β1)/Smad signaling pathway. Methods: The experimental research method was adopted. Six New Zealand white rabbits, male or female, aged 3-5 months were used and 5 full-thickness skin defect wounds were made on the ventral surface of each rabbit ear. The appearance of all rabbit ear wounds was observed on post surgery day (PSD) 0 (immediately), 7, 14, 21, and 28. On PSD 28, the scar formation rate was calculated. Three mature scars in the left ear of each rabbit were included in tension group and the arch was continuously expanded with a spiral expander. Three mature scars in the right ear of each rabbit were included in sham tension group and only the spiral expander was sutured without expansion. There were 18 scars in each group. After mechanical tension treatment (hereinafter referred to as treatment) for 40 days, the color and texture of scar tissue in the two groups were observed. On treatment day 40, the scar elevation index (SEI) was observed and calculated; the histology was observed after hematoxylin eosin staining, and the collagen morphology was observed after Masson staining; mRNA expressions of TGF-β1, Smad3, collagen Ⅰ, collagen Ⅲ, and α-smooth muscle actin (α-SMA) in scar tissue were detected by real-time fluorescence quantitative reverse transcription polymerase chain reaction; and the protein expressions of TGF-β1, collagen Ⅰ, collagen Ⅲ, and α-SMA, and phosphorylation level of Smad3 in scar tissue were detected by Western blotting. The number of samples of each group in the experiments was 3. Data were statistically analyzed with independent sample t test. Results: On PSD 0, 5 fresh wounds were formed on all the rabbit ears; on PSD 7, the wounds were scabbed; on PSD 14, most of the wounds were epithelialized; on PSD 21, all the wounds were epithelialized; on PSD 28, obvious hypertrophic scars were formed. The scar formation rate was 75% (45/60) on PSD 28. On treatment day 40, the scar tissue of rabbit ears in tension group was more prominent than that in sham tension group, the scar tissue was harder and the color was more ruddy; the SEI of the scar tissue of rabbit ears in tension group (2.02±0.08) was significantly higher than 1.70±0.08 in sham tension group (t=5.07, P<0.01). On treatment day 40, compared with those in sham tension group, the stratum corneum of scar tissue became thicker, and a large number of new capillaries, inflammatory cells, and fibroblasts were observed in the dermis, and collagen was more disordered, with nodular or swirling distribution in the scar tissue of rabbit ears in tension group. On treatment day 40, the mRNA expressions of TGF-β1, Smad3, collagen Ⅰ, collagen Ⅲ, and α-SMA in the scar tissue of rabbit ears in tension group were respectively 1.81±0.25, 5.71±0.82, 7.86±0.56, 4.35±0.28, and 5.89±0.47, which were significantly higher than 1.00±0.08, 1.00±0.12, 1.00±0.13, 1.00±0.14, and 1.00±0.14 in sham tension group (with t values of 5.36, 9.82, 20.60, 18.26, and 17.13, respectively, all P<0.01); the protein expressions of TGF-β1, collagen Ⅰ, collagen Ⅲ, and α-SMA, and phosphorylation level of Smad3 in the scar tissue of rabbit ears in tension group were respectively 0.865±0.050, 0.895±0.042, 0.972±0.027, 1.012±0.057, and 0.968±0.087, which were significantly higher than 0.657±0.050, 0.271±0.029, 0.631±0.027, 0.418±0.023, and 0.511±0.035 in sham tension group (with t values of 5.08, 21.27, 15.55, 16.70, and 8.40, respectively, all P<0.01). Conclusions: Mechanical tension can inhibit the regression of hypertrophic scars in rabbit ears through stimulating the hyperplasia of scars, inhibiting the normal arrangement of dermal collagen fibers, and intensifying the deposition of collagen fibers, and the mechanism may be related to the activation of TGF-β1/Smad signaling pathway by mechanical tension.

Comparison of suspected and confirmed internal EVD-related infections: a prospective multi-centre U.K. observational study

Background

Diagnosis of internal external ventricular drain (EVD)-related infections (iERI) is an area of diagnostic difficulty. Empiric treatment is often initiated on clinical suspicion. There is limited guidance around antimicrobial management of confirmed versus suspected iERI.

Methods

Data on patients requiring EVD insertion were collected from 21 neurosurgical units in the United Kingdom from 2014 to 2015. Confirmed iERI was defined as clinical suspicion of infection with positive cerebrospinal fluid (CSF) culture and/or Gram stain. Cerebrospinal fluid, blood, and clinical parameters and antimicrobial management were compared between the 2 groups. Mortality and Modified Rankin Scores were compared at 30 days post-EVD insertion.

Results

Internal EVD-related infection was suspected after 46 of 495 EVD insertions (9.3%), more common after an emergency insertion. Twenty-six of 46 were confirmed iERIs, mostly due to Staphylococci (16 of 26). When confirmed and suspected infections were compared, there were no differences in CSF white cell counts or glucose concentrations, nor peripheral blood white cell counts or C-reactive protein concentrations. The incidence of fever, meningism, and seizures was also similar, although altered consciousness was more common in people with confirmed iERI. Broad-spectrum antimicrobial usage was prevalent in both groups with no difference in median duration of therapy (10 days [interquartile range {IQR}, 7–24.5] for confirmed cases and 9.5 days [IQR, 5.75–14] for suspected, P = 0.3). Despite comparable baseline characteristics, suspected iERI was associated with lower mortality and better neurological outcomes.

Conclusions

Suspected iERI could represent sterile inflammation or lower bacterial load leading to false-negative cultures. There is a need for improved microbiology diagnostics and biomarkers of bacterial infection to permit accurate discrimination and improve antimicrobial stewardship.

P11.24.A The anisotropic component of the decomposed diffusion tensor predicts overall survival in patients with glioblastoma

Background

The diffusion tensor can be decomposed into isotropic (DTI-p) and anisotropic (DTI-q) components (Peña et al., 2006). Regions of DTI-q abnormality have a high tumour burden and increased surgical resection of abnormal DTI-q positively correlates with overall survival (OS) (Yan et al., 2017). We aimed to establish if median voxel DTI-q (MVQ) or a distribution measure of DTI-q (DMQ) could act as a neuro-imaging biomarker, to predict OS in patients with glioblastoma.

Material and Methods

Diffusion tensor decomposition was used to create DTI-p and DTI-q maps, using FSL software (FMRIB Software Library, Oxford). MVQ and DMQ (the 95th centile minus the 5th centile of the DTI-q distribution, divided by the MVQ) were calculated from the preoperative whole brain (WB), contrast-enhancing (CE), and non-contrast-enhancing (NCE) hemisphere DTI-q maps, using fslstats, for 33 patients with glioblastoma. Using R Studio, multiple linear regression (MLR) models were computed to establish if MVQ or DMQ of the WB, CE and NCE hemispheres or age, significantly predicts OS. The Breusch-Pagan Test, on package “lmtest” in R, was calculated for all MLR models, to determine if heteroscedasticity was present and, if so, bootstrapped multiple regression (BMR) models were computed using package “boot” in R.

Results

Evidence for heteroscedasticity was present in MLRs that modelled the relationship between DMQ of WB, age, and OS (BP = 6.032, p = 0.014) and DMQ of CE hemisphere, age, and OS (BP = 7.163, p = 0.028). In the BMR of WB DMQ, age, and OS, the 95% bias-corrected accelerated confidence intervals (BCa-CI) for the WB DMQ regression coefficient was 133.5 - 1851.4 and included the WB DMQ estimated coefficient of 803.9. The BMR of CE hemisphere DMQ, age, and OS, computed a 95% BCa-CI for the CE hemisphere DMQ coefficient of 101.8 - 1579.6, containing the CE hemisphere DMQ coefficient estimate of 612.414. For both BMRs, the 95% BCa-CI for age coefficients crossed a value of 0. From computed MLR models, WB MVQ (t = -2.569, p = 0.015), CE hemisphere MVQ (t = -2.143, p = 0.040), NCE hemisphere MVQ (t = -2.567, p = 0.015) and NCE hemisphere DMQ (t = 2.39, p = 0.024) were significant predictors of OS. Age did not significantly predict OS in any models and was not significantly related to WB, CE and NCE hemisphere MVQ or DMQ.

Conclusion

WB, CE and NCE hemisphere MVQ and DMQ predict OS in our tested subgroup of patients with glioblastoma. Age is not a significant predictor of OS and does not significantly correlate with MVQ or DMQ. The median value and distribution spread of DTI-q may act as a prognostic biomarker in glioblastoma, facilitating patient stratification.

P15.07.A Predicting sites of local tumour progression - what should be our imaging biomarker?

Background

Glioblastoma is the most aggressive primary brain tumour diagnosed in adults. Despite intensive treatment of maximal safe resection and chemoradiotherapy, the prognosis remains grim due to invasive tumour cells. Current treatment and standard imaging methods are highly limited in terms of managing these invasive cells as they are often located outside the area of surgical resection and are generally resistant to chemoradiation. DTI appears to be a promising tool for imaging tumour cell invasion and predicting the site of recurrence especially when decomposed into its anisotropic (q) and isotropic (p) components. The aim of this study is to investigate the sensitivity of imaging biomarkers as predictors of recurrence.

Material and Methods

All pre-op and recurrence sequences were co-registered to the pre-op post-contrast T1-weighted images as reference. Co-registration of images was performed using FSL and ANTs. The ROIs for 49 patients with a primary diagnosis of GBM were segmented using 3DSlicer. Each voxel was assigned to one of four status: true negative, false negative, true positive and false positive. Sensitivity and specificity between the pre-op ROIs and the progression region were calculated using FSL. The significance of the differences in sensitivity and specificity between the ROIs was computed in MATLAB.

Results

The sensitivity for the contrast enhancing region was 48.77 ± 26.13 (Mean ± SD) and 62.40 ± 23.07 (Mean ± SD). The abnormal q alone has a significantly greater sensitivity than the contrast enhancing region (t = -2.7327, df = 96, p-value = 0.0075). The sensitivity for the ROI of combined contrast enhancement and abnormal q was 65.86 ± 23.29 (Mean ± SD). There is an even more significant increase in sensitivity when the contrast enhancing ROI is combined with abnormal q region (t = -3.4133, df = 96, p-value = 9.4123e-04) compared to when it is alone. There was no statistical difference in the specificities of the different ROIs.

Conclusion

Current surgical and radiation volumes focus solely on pre-op contrast enhancement. However, these results suggest that combining the abnormal q with the standard contrast enhancing region is a more sensitive predictor of tumour recurrence than contrast enhancement alone, while still retaining high specificity. The higher sensitivity is an indicator of correct identification of tumour recurrence while the high specificity correctly identifies normal brain, or non-recurrent regions. These results are currently being prospectively assessed in a multi-centre study (PRaM-GBM).

Cost-effectiveness analyses of denosumab for osteoporosis: a systematic review

This paper systematically reviewed and assessed all retrievable pharmacoeconomic studies on denosumab for the treatment of osteoporosis. Denosumab was more cost-effective in patients with older age, prior fracture experience, lower BMD T-scores, and more risk factors. ESCEO-IOF guidelines were more applicable to improve the quality of pharmacoeconomic studies in osteoporosis.

INTRODUCTION

There are many pharmacoeconomic studies on denosumab for osteoporosis. However, the corresponding reviews are outdated or incomplete and need to be updated and refined. This article aims to systematically review and evaluate all retrievable pharmacoeconomic studies of denosumab for osteoporosis.

METHODS

A systematic literature search was performed utilizing PubMed, EMBASE(Ovid), Proquest(EconLit), Chongqing VIP, WanFang Database, and Chinese National Knowledge Infrastructure to identify full-text articles published before September 2021. The quality of full-text articles was evaluated by the Consolidated Health Economic Evaluation Reporting Standards(CHEERS) and the European Society for Clinical and Economic Aspects of Osteoporosis, Osteoarthritis and Musculoskeletal Diseases International Osteoporosis Foundation guideline(ESCEO-IOF).

RESULTS

In total, 21 full-text articles were eligible for inclusion. Denosumab for postmenopausal osteoporosis was not dominant compared to zoledronate and teriparatide. However, denosumab was dominant compared with strontium ranelate, raloxifene, and ibandronate in patients over 65 years. The probabilities of denosumab being cost-effective or dominant were more than 85% compared with no treatment and risedronate in patients aged over 70 years. Compared to alendronate, the highest rate of denosumab dominance occurred in patients aged 65 to 75 years, at about 65%. Most of the articles had higher CHEERS scores than ESCEO-IOF scores (converted into percentages).

CONCLUSIONS

The cost-effectiveness of denosumab for the treatment of osteoporosis was influenced by multiple factors. Generally, denosumab was more cost-effective in patients with older age, prior fracture experience, lower BMD T-scores, and more risk factors. ESCEO-IOF guidelines were more applicable to improve the transparency, generalization, and quality of pharmacoeconomic studies in osteoporosis.

[Preliminary experience of surgical treatment for torus tubarius hypertrophy in children]

Objective: To assess the incidence of symptomatic torus tubarius hypertrophy (TTH) in recurred OSA in children, and to explore the preliminary experience of partial resection of TTH assisted with radiofrequency ablation. Methods: From January 2004 to February 2020, 4 922 children, who diagnosed as OSA and received adenotonsillectomy at the Department of Otolaryngology, The 4th Medical Center of the PLA General Hospital, were retrospectively reviewed. There were 3 266 males and 1 656 females, the age ranged from 1 to 14 years old(median age of 5.0 years). Twenty-two cases were identified with recurrence of OSA syndrome, and the clinical data, including sex, age of primary operation, age of recurrence and presentation, and opertation methods were analyzed. Follow-up was carried out by outpatient visit or telephone. Graphpad prism 5.0 software was used for statistical analysis. Results: Twenty-two cases were identified as recurred OSA and received revised surgery in 4 922 cases. Among these 22 cases, 11 cases were diagnosed as TTH resulting in an incidence of 2.23‰(11/4 922), 1 case was cicatricial adhesion on tubal torus (0.20‰, 1/4 922), 10 cases were residual adenoid combined with tubal tonsil hypertrophy (2.03‰, 10/4 922). Median age of primary operation was 3.0 years (range:2.4 to 6.0 years) in 11 TTH cases. Recurrent interval varied from 2 months to 5.5 years (2.4±1.9 years) after first operation. Age of revised partial resection of TTH was 7.0±2.7 years (range: 4.0 to 12.0 years). Average time interval between primary operation and revised operation was 3.5±2.1 years (range: 0.5 to 6.0 years). Individualized treatments were carried out based on partial resection of TTH assisted with radiofrequency ablation. All of 11 cases received satisfied therapeutic results without nasopharyngeal stenosis occured. Twenty-two cases were followed up for 1.6 to 13 years (median follow-up time was 6.2 years). Conclusions: TTH contributed to recurred OSA in child. TTH might be misdiagnosed as tubal tonsil hypertrophy. Partial resection of TTH assisted with radiofrequency ablation was a safty and effective treatment.

[The clinical efficacy of the stratification medical treatment based on the risk estimation of motor complications in Parkinson's disease]

Objectives: To evaluate the clinical efficacy of the stratification medical treatment based on the motor complications risk estimation in improving the quality of life, motor symptoms and delaying the motor complications in Parkinson's patients. Methods: Outpatients and inpatients from Xinhua Hospital, Shanghai Jiao Tong University, were recruited between November 2019 and June 2020. The participants were all clinically diagnosed with PD and treated with anti-PD medications, but had no history of motor complications, with the 8-item Parkinson's disease questionnaire summary index (PDQ-8 SI)>18.59. At baseline, the demographic characteristics, PD medical history, levodopa dosage (LD) and levodopa equivalent dosage (LED) were collected, and the evaluation of PDQ-8, Unified Parkinson's disease rating scale (UPDRS)-Ⅱ and Ⅲ, Hoehn and Yahr (H&Y) grade, Hamilton anxiety scale-14 (HAMA-14), Hamilton depression scale-24 (HAMD-24), mini-mental state examination (MMSE), Pittsburgh sleep quality index (PSQI), and Epworth sleepiness scale (ESS) tools was accomplished in all participants. Meanwhile, a Parkinson's disease risk estimation scale for motor complications was used to assess patients' risk of motor complications, and thus the medication was stratified in PD patients accordingly. During the 6-month and 12-month follow-ups, the evaluation of the above-mentioned parameters was repeated in all participants. At the 3-month and 9-month follow-ups, the information of anti-PD medications, the occurrence of motor complications (motor fluctuations and dyskinesia) and adverse drug reactions were recorded, and PDQ-8 was also evaluated. Results: Two hundred and fifty-one patients completed the 1-year follow-up, with 135 males and 116 females. At baseline, the median age of the patients was 66 (60, 71) years and the median PDQ-8 SI was 31.2 (21.9, 40.6). Additionally, 15.9% (40/251) of the patients were at high risk of motor fluctuation, and 7.2% (18/251) were at high risk of dyskinesia. There were significant differences in the age of onset, disease duration, PD treatment duration, the scores of UPDRS-Ⅱ and Ⅲ, H&Y Grade, and PDQ-8 SI among PD patients of different risk groups (all P<0.05). In the 12th month, the median of PDQ-8 SI, Δ PDQ-8 SI and Δ UPDRS-Ⅲ was 12.5 (9.4, 18.8), -15.6 (-21.9, -9.4) and -9(-16, -4), respectively, which was statistically different from that of baseline (all P<0.05). The change of UPDRS-Ⅱ scores in the group with high risk of motor fluctuation was statistically different from that in the groups with low and moderate risk (P<0.05). The changes of PSQI score, LD and LED in the group with high risk of dyskinesia was statistically different from those in the groups with low and moderate risk (all P<0.05). During the follow-up, the incidence of motor fluctuation and dyskinesia was 9.56% (24/251) and 5.97% (15/251), respectively. Conclusion: The stratification medical treatment might have a positive intervention effect on promoting a better quality of life, improving motor symptoms and delaying motor complications in PD patients.

Association Between Recombinant Growth Hormone Therapy and All-Cause Mortality and Cancer Risk in Childhood: Systematic Review and Meta-Analysis

OBJECTIVES

The safety of recombinant human growth hormone (rhGH) treatment in childhood and the role of rhGH therapy in promoting tumorigenesis and progression have been the subject of debate for decades. We aimed to systematically assess the relationship between rhGH therapy in children and adolescents and clinical outcomes, including all-cause mortality, cancer mortality, cancer incidence, and risk of the second neoplasm.

METHODS

Literature retrieval, study selection, and data extraction were completed independently and in duplicate. Effect-size estimates are expressed as standardized mortality ratios (SMRs), standardized incidence ratio (SIR), and relative risk (RR) with a 95% CI.

RESULTS

Data from 24 articles, involving 254,776 persons, were meta-analyzed. Overall analyses revealed the association of rhGH therapy was not statistically significant with all-cause mortality (SMR = 1.28; 95% CI: 0.58-2.84; P = 0.547; I ² = 99.2%; Tau² = 2.154) and cancer mortality (SMR = 2.59; 95% CI: 0.55-12.09; P = 0.228; I ² = 96.7%; Tau² = 2.361) and also cancer incidence (SIR = 1.54; 95% CI: 0.68-3.47; P = 0.229; I ² = 97.5%; Tau² = 2.287), yet statistical significance was observed for second neoplasm (RR = 1.77; 95% CI: 1.33-2.35; P = 0.001; I ² = 26.7%; Tau² = 0.055). Differences in the geographic region, gender, treatment duration, mean rhGH dose, overall rhGH exposure dose, and initial disease accounted for heterogeneity in the subgroup analyses.

CONCLUSION

Our findings indicate that the rhGH therapy is not related to all-cause mortality and cancer mortality and cancer incidence, yet it seems to trigger a second tumor risk. Future prospective studies are needed to confirm our findings and answer the more challenging question regarding the optimal dose of rhGH therapy in children and adolescents.

Fate and transport in environmental quality

Changes in pollutant concentrations in environmental media occur both from pollutant transport in water or air and from local processes, such as adsorption, degradation, precipitation, straining, and so on. The terms "fate and transport" and "transport and fate" reflect the coupling of moving with the carrier media and biogeochemical processes describing local transformations or interactions. The Journal of Environmental Quality (JEQ) was one of the first to publish papers on fate and transport (F&T). This paper is a minireview written to commemorate the 50th anniversary of JEQ and show how the research interests, methodology, and public attention have been reflected in fate and transport publications in JEQ during the last 40 years. We report the statistics showing how the representation of different pollutant groups in papers changed with time. Major focus areas have included the effect of solution composition on F&T and concurrent F&T, the role of organic matter, and the relative role of different F&T pathways. The role of temporal and spatial heterogeneity has been studied at different scales. The value of long-term F&T studies and developments in modeling as the F&T research approach was amply demonstrated. Fate and transport studies have been an essential part of conservation measure evaluation and comparison and ecological risk assessment. For 50 years, JEQ has delivered new insights, methods, and applications related to F&T science. The importance of its service to society is recognized, and we look forward to new generations of F&T researchers presenting their contributions in JEQ.

Combination therapy with interleukin-15 and metformin as a synergistic treatment for pancreatic cancer

OBJECTIVE

To investigate the efficacies and mechanisms of combination therapy with interleukin-15 (IL-15) and metformin (Met) on suppressing pancreatic cell proliferation and protecting Panc02-bearing mice.

MATERIALS AND METHODS

MTT assays were applied to assess the inhibitory effects of IL-15 combined Met or Met and IL-15 alone on proliferation of normal HPDE6-C7 and Panc02 cells. After tumor grew up to 150 mm3, the Panc02-bearing xenograft model mice were randomly divided into saline group, IL-15 and Met alone group and combined treatment group (n=8) with the administration of each agent every other day for three weeks, and survival rates were recorded. The changes in tumor size, expression levels of the apoptosis, autophagy as well as Akt/mTOR/STAT3-related factors in tumor tissues were all measured.

RESULTS

MTT assays demonstrated significantly inhibiting efficacy of combination therapy with IL-15 and Met on Panc02 cell proliferation compared to other groups (all p<0.05) with combination index<1 showing evident synergistic effect. Moreover, the apoptosis rate of Panc02 cell under combined treatment were 95.5±3.2% and significantly higher than those of others (all p<0.01). In addition, combined administration remarkably inhibited the growth of pancreatic carcinoma, and improved survival rate of Panc02-bearing model with less body weight loss. Furthermore, combined treatment significantly downregulated anti-apoptotic proteins as well as Akt/mTOR/STAT3 signaling pathway and upregulated autophagy related factors, respectively, compared with those of monotherapy groups in both Panc02 cells and tumor tissues.

CONCLUSIONS

Combined treatment of IL-15 with Met showed synergistic anti-tumor efficacies on Panc02 cells attributing to promotion on apoptosis, autophagy and inhibition on Akt/mTOR/STAT3 signaling-transduction in Panc02-bearing model mice.

The association of metabolic health obesity with incidence of carotid artery plaque in Chinese adults

BACKGROUND AND AIMS

We aimed to evaluate the association between different obese phenotypes with carotid artery plaque (CAP) event.

METHOD AND RESULTS

The current retrospective cohort study was performed in 32,778 Chinese adults (19,221 men and 13,557 women, aged 41.9 ± 11.0 years). Obese phenotypes were assessed based on baseline body mass index (<24.0 vs. ≥24.0 kg/m²) and metabolic characteristics (health vs. unhealth). All the participants were further classified into four groups: metabolic health and normal weight (MHNW), metabolic unhealth and normal weight (MUHNW), metabolic health and overweight (MHO), and metabolic unhealth and overweight (MUHO). Ultrasound B-mode imaging was annually performed to evaluate CAP throughout the study. We have identified 2142 CAP cases during 5-year follow-up. Comparing with the MHNW group, the hazard ratios for the risk of incident CAP was 2.44 (95% CI:1.92 and 3.09) for the MUHNW group, 1.52 (95% CI:1.06 and 2.18) for the MHO group, and 1.8 (95% CI:1.4 and 2.33) for the MUHO group. The association was more pronounced in young adults (<65 y) than that in aged adults (≥65 y). Sensitivity analysis generated similar results with the main analysis.

CONCLUSION

MUHNW, MHO, and MUHO were associated with the risk of CAP.

Systematic analysis of the function and prognostic value of RNA binding proteins in Colon Adenocarcinoma

Background: Abnormal expression of RNA-binding proteins (RBPs) is closely related to tumorigenesis, progression, and prognosis. This study performed systematic bioinformatic analysis of RBPs abnormally expressed in colon adenocarcinoma (COAD) using the Cancer Genome Atlas (TCGA) database to screen prognostic markers and potential therapeutic targets.

Methods: First, the gene expression data from COAD samples were used to screen out differentially expressed RBPs for functional enrichment analysis and to visualize interaction relationships. Second, RBPs that were significantly related to prognosis were screened through univariate and multivariate Cox regression analysis to construct a prognostic model. The prediction performance of the prognostic model was evaluated by survival analysis and receiver operating characteristic (ROC) curve analysis. It addition, it was verified in the test cohort. The Human Protein Atlas (HPA) online database was used to verify the expression levels of RBPs in the prognostic model.

Results: The study identified 181 differentially expressed RBPs and analyzed their interaction and functional enrichment, which were mainly related to non-coding RNA processing, ribosome biogenesis, RNA metabolic processes, RNA phosphodiester bond hydrolysis, and alternative mRNA splicing. Five RBPs related to prognosis were used to construct a prognostic model, and its predictive ability was verified by the test cohort. ROC curve analysis showed that the prognostic model had good sensitivity and specificity. Independent prognostic analysis showed that risk scores could be used as independent prognostic factors for COAD.

Conclusion: This study constructed a reliable prognostic model by analyzing COAD differentially expressed RBPs, facilitating the screening of COAD prognostic markers and therapeutic targets.

Effectiveness assessment of improvement measures in physical protection system monitoring center

The optimization measures in the physical protection system monitoring center of a nuclear power plant include the prioritization of alarm signals, optimization of sound and light alarm form, improvement of the layout of video monitor screen, security training, and strengthening of organizational management. Based on the fuzzy analytic hierarchy process, the influence of these factors on the probability of alert assessment and guard’s respond time in the EASI method are quantitatively analyzed. Making full use of the measures for prioritization of alarm signals can effectively promote the improvement of human-computer interaction efficiency. The degree of influence of the four factors (guarder’s status, decision strategy, guarder’s training and organization management) on guard’s decision-making is roughly the same.

The synergistic antifungal activity of resveratrol with azoles against Candida albicans

Candida albicans is one of the most common clinical pathogenic microorganisms and it is becoming a serious health threat, particularly to immunocompromised populations. Drug resistance of Candida species has also frequently emerged, and combination therapy for fungal infections has attracted considerable attention. In this study, we established the Qinling Mountains myxobacterial secondary metabolites library and a synergic assay in combination with ketoconazole against C. albicans was introduced for metabolites screening. Two active compounds with synergic anticandidal activities were obtained, which were identified as trans-resveratrol and cis-resveratrol. According to our study, resveratrol can reduce the dosage to 1/64 of ketoconazole as well as itraconazole. Furthermore, synergistic anticandidal activity of resveratrol combined with azoles was verified against a panel of clinical C. albicans isolates, and the combination strategy enhanced the azoles susceptibility of three fluconazole-resistant isolates. These findings suggest that resveratrol enhances the efficacy of azoles and provides a promising application in therapy of C. albicans infection.

Effects of the Transverse Instability and Wave Breaking on the Laser-Driven Thin Foil Acceleration

Acceleration of ultrathin foils by the laser radiation pressure promises a compact alternative to the conventional ion sources. Among the challenges on the way to practical realization, one fundamental is a strong transverse plasma instability, which develops density perturbations and breaks the acceleration. In this Letter, we develop a theoretical model supported by three-dimensional numerical simulations to explain the transverse instability growth from noise to wave breaking and its crucial effect on stopping the acceleration. The wave-broken nonlinear mode triggers rapid stochastic heating that finally explodes the target. Possible paths to mitigate this problem for getting efficient ion acceleration are discussed.

Virus shedding dynamics in asymptomatic and mildly symptomatic patients infected with SARS-CoV-2

Objectives

Asymptomatic patients, together with those with mild symptoms of coronavirus disease 2019 (COVID-19), may play an important role in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. However, the dynamics of virus shedding during the various phases of the clinical course of COVID-19 remains unclear at this stage.

Methods

A total of 18 patients found to be positive for SARS-CoV-2 infection by real-time reverse transcription PCR (RT-PCR) assay and admitted to Chongqing University Central Hospital between 29 January and 5 February 2020 were enrolled into this study. Medical data, pulmonary computed tomographic (CT) scan images and RT-PCR results were periodically collected during the patients' hospital stay. All participants were actively followed up for 2 weeks after discharge.

Results

A total of nine (50%) asymptomatic patients and nine (50%) patients with mild symptoms of COVID-19 were identified at admission. Six patients (66.7%) who were asymptomatic at admission developed subjective symptoms during hospitalization and were recategorized as being presymptomatic. The median duration of virus shedding was 11.5, 28 and 31 days for presymptomatic, asymptomatic and mildly symptomatic patients, separately. Seven patients (38.9%) continued to shed virus after hospital discharge. During the convalescent phase, detectable antibodies to SARS-CoV-2 and RNA were simultaneously observed in five patients (27.8%).

Conclusions

Long-term virus shedding was documented in patients with mild symptoms and in asymptomatic patients. Specific antibody production to SARS-CoV-2 may not guarantee virus clearance after discharge. These observations should be considered when making decisions regarding clinical and public health, and when considering strategies for the prevention and control of SARS-CoV-2 infection.

Identification of methylation-driven genes related to the prognosis of papillary renal cell carcinoma: a study based on The Cancer Genome Atlas

Background

Aberrant DNA methylation patterns are involved in the pathogenesis of papillary renal cell carcinoma (pRCC). This study aimed to investigate the potential of methylation-driven genes as biomarkers in determining the prognosis of pRCC by bioinformatics analysis.

Methods

DNA methylation and transcriptome profiling data were downloaded from The Cancer Genome Atlas database. Methylation-driven genes (MDGs) were obtained using MethylMix R package. A Cox regression model was used to screen for pRCC prognosis-related MDGs, and a linear risk model based on MDG methylation profiles was constructed. A combined methylation and gene expression survival analysis was performed to further explore the prognostic value of MDGs independently.

Results

A total of 31 MDGs were obtained. Univariate and multivariate Cox regression analysis identified eight genes (CASP1, CD68, HOXD3, HHLA2, HOXD9, HOXA10-AS, TMEM71, and PLA2G16), which were used to construct a predictive model associated with overall survival in pRCC patients. Combined DNA methylation and gene expression survival analysis revealed that C19orf33, GGT6, GIPC2, HHLA2, HOXD3, HSD17B14, PLA2G16, and TMEM71 were significantly associated with patients’ survival.

Conclusion

Through the analysis of MDGs in pRCC, this study identified potential biomarkers for precision treatment and prognosis prediction, and provided the basis for future research into the molecular mechanism of pRCC.

"Possible mechanisms underlying treatment of Alzheimer's disease with Traditional Chinese Medicine: active components, potential targets and synthetic pathways of Bulao Elixir"

OBJECTIVE

To elucidate the mechanisms underlying the treatment of Alzheimer's disease (AD) with Traditional Chinese Medicine (TCM), by examining the active components, potential targets and synthetic pathways of Bulao Elixir (BLE).

METHODS

The Absorption, Distribution, Metabolism, Excretion (ADME) / Toxicology (T) calculation was used to screen the active components of Bulao Elixir. Based on the TCM Systems Pharmacology Analysis Platform (TCMSP database) and a text mining tool (GoPubMed database), we predicted and screened the active components of Bulao Elixir and its therapeutic targets for AD. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID), we obtained the targets for AD. Cytoscape software was used to establish a network map of the active component-target and target-pathway of Bulao Elixir. Gene function, related biological processes and signaling pathways were analyzed using the DAVID database.

RESULTS

Twelve active components were selected from 196 components of Bulao Elixir. Among 2209 targets, 102 effective targets were selected, and 30 important targets were identified via matching with the disease targets. After further analysis, 14 core targets were identified. Enrichment analysis revealed that most of these important targets were involved in multiple biological processes, including apoptosis, inflammatory reactions, and cell regulation cycles. The synthetic pathways for AD treatment were identified after analyzing and confirming the relevant pathways, providing potentially useful information for diagnosis and treatment methods for AD.

CONCLUSION

The current study elucidated the potential treatment mechanisms of Bulao Elixir in AD using network pharmacology, providing a foundation for further clarification of its treatment targets.

Network pharmacology to dissect the mechanisms of Yinlai Decoction for pneumonia

BACKGROUND

Pneumonia is a common respiratory disorder, which brings an enormous financial burden to the medical system. However, the current treatment options for pneumonia are limited because of drug resistance and side effects. Our previous study preliminarily confirmed that Yinlai Decoction (YD), a common prescription for pneumonia in clinical practice, can regulate the expression of inflammatory factors, but the mechanisms are unknown yet.

METHODS

In our work, a method named network pharmacology was applied, which investigated the underlying mechanisms of herbs based on a variety of databases. We obtained bioactive ingredients of YD on TCMSP database and collected potential targets of these ingredients by target fishing. Then the pneumonia-related targets database was built by TTD, Drugbank, HPO, OMIM, and CTD. Based on the matching targets between YD and pneumonia, the PPI network was built by STRING to analyze the interactions among these targets and then input into Cytoscape for further topological analysis. DAVID and KEGG were utilized for GO and pathway enrichment analysis. Then rat model based on LPS stimulated pneumonia was used to verify the possible mechanism of YD in treating pneumonia.

RESULTS

Sixty-eight active ingredients, 103 potential targets and 8 related pathways, which likely exert a number of effects, were identified. Three networks were constructed using Cytoscape, which were herb-component-network, YD-pneumonia target network, and herb-component-YD target-pneumonia network. YD was verified to treat LPS-induced pneumonia by regulating the inflammatory factor IL-6, which was a predicted target.

CONCLUSION

Network analysis indicated that YD could alleviate the symptoms and signs of pneumonia through regulating host immune inflammatory response, angiogenesis and vascular permeability, the barrier function of the airway epithelial cells, hormone releasing and cell growth, proliferation, and apoptosis.

Chromosomal abnormalities detected by karyotyping and microarray analysis in twins with structural anomalies

OBJECTIVES

To evaluate the incidence and types of chromosomal abnormalities detected in twins with structural anomalies and compare their distribution according to chorionicity and amnionicity and by structural-anomaly type. The added value of chromosomal microarray analysis (CMA) over conventional karyotyping in twins was also estimated.

METHODS

This was a single-center, retrospective analysis of 534 twin pregnancies seen over an 11-year period, in which one or both fetuses were diagnosed with congenital structural anomalies on ultrasound. The ultrasound findings and invasive prenatal diagnostic results were reviewed. Twin pregnancies were categorized as monochorionic monoamniotic (MCMA), monochorionic diamniotic (MCDA) or dichorionic diamniotic (DCDA). Chromosomal abnormalities detected by G-banding karyotyping and/or CMA were analyzed by chorionicity and amnionicity and by structural-anomaly type.

RESULTS

The 534 twin pairs analyzed comprised 25 pairs of MCMA, 112 pairs of MCDA and 397 pairs of DCDA twins. Of the 549 fetuses affected by structural anomalies, 432 (78.7%) underwent invasive prenatal testing and cytogenetic results were obtained. The incidence of overall chromosomal abnormalities in the DCDA fetuses (25.4%) was higher than that in the MCMA (3.7%) and MCDA (15.3%) fetuses. The incidence of aneuploidy was significantly higher in the DCDA group (22.8%) than in the MCMA (0.0%) and MCDA (12.4%) groups. The incidence of chromosomal abnormalities detected in fetuses, with anomalies of the cardiovascular, faciocervical, musculoskeletal, genitourinary and gastrointestinal systems, was higher in the DCDA group than in the MCDA group. In both the DCDA and MCDA groups, hydrops fetalis was associated with the highest incidence of chromosomal abnormality; of these fetuses, 67.6% had Turner syndrome (45,X). Pathogenic copy-number variations (CNVs) undetectable by karyotyping were identified by CMA in five (2.0%; 95% CI, 0.3-3.7%) DCDA fetuses. No pathogenic CNVs were found in MCMA and MCDA twins.

CONCLUSIONS

Dichorionic twins with structural anomalies have a higher risk of chromosomal abnormalities, especially aneuploidies, than do monochorionic twins. The incremental diagnostic yield of CMA over karyotyping seems to be lower (2.0%) in twins than that reported in singleton pregnancy. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd.

Structural maintenance of chromosomes 2 is identified as an oncogene in bladder cancer in vitro and in vivo

Taurine upregulated gene 1 (TUG1) has been found to promote bladder cancer cell growth in our recent research. In this study, TUG1-depleted bladder cancer cells were used to identify potent players in bladder cancer. Human gene expression arrays were used for transcriptome profiling of TUG1-depleted bladder cancer cells. Cell proliferation was analyzed by MTT assay. Cell apoptosis and cell cycle were analyzed by flow cytometry. Colony formation assay was used to observe the changes of colony formation rates. Xenograft formation assay was performed in nude mice. Immunohistochemical staining was used to test the gene expression levels in tissues from bladder cancer patients. We found that deregulated genes were strongly enriched in cell cycle or pathways in cancer in TUG1-depleted bladder cancer cells. Structural maintenance of chromosomes 2 (SMC2) was inhibited after TUG1 knockdown. The depletion of TUG1 or SMC2 led to G2/M phase arrest in bladder cancer cells. SMC2 depletion inhibited bladder cancer cell proliferation, promoted apoptosis, decreased colony formation, and reduced tumor growth in xenograft nude mice. Overexpression of SMC2 restored the growth of TUG1-depleted cells. The expression levels of SMC2 were higher in human bladder cancer tissues than that in paired normal tissues. Our data suggest that SMC2 is an oncogene in bladder cancer and depletion of SMC2 might have potential therapeutical significance in bladder cancer.

Development and validation of a novel immune-related prognostic model in lung squamous cell carcinoma

Background: The immune system plays an important role in the development of lung squamous cell carcinoma (LUSC). Therefore, immune-related genes (IRGs) expression may be an important predictor of LUSC prognosis. However, a prognostic model based on IRGs that can systematically assess the prognosis of LUSC patients is still lacking. This study aimed to construct a LUSC immune-related prognostic model by using IRGs. Methods: Gene expression data about LUSC were obtained from The Cancer Genome Atlas (TCGA). Differential expression analysis and univariate Cox regression analysis were performed to identify prognostic differentially expressed IRGs. A prognostic model was constructed using the Lasso and multivariate Cox regression analyses. Then we validated the performance of the prognostic model in training and test cohorts. Furthermore, associations with clinical variables and immune infiltration were also analyzed. Results: 593 differentially expressed IRGs were identified, and 8 of them were related to prognosis. Then a transcription factor regulatory network was established. A prognostic model consisted of 4 immune-related genes was constructed by using Lasso and multivariate Cox regression analyses. The prognostic value of this model was successfully validated in training and test cohorts. Further analysis showed that the prognostic model could be used independently to predict the prognosis of LUSC patients. The relationships between the risk score and immune cell infiltration indicated that the model could reflect the status of the tumor immune microenvironment. Conclusions: We constructed a risk model using four PDIRGs that can accurately predict the prognosis of LUSC patients. The risk score generated by this model can be used as an independent prognostic indicator. Moreover, the model can predict the infiltration of immune cells in patients, which is conducive to the prediction of patient sensitivity to immunotherapy.

Quantum Logic Spectroscopy with Ions in Thermal Motion

A mixed-species geometric phase gate has been proposed for implementing quantum logic spectroscopy on trapped ions, which combines probe and information transfer from the spectroscopy to the logic ion in a single pulse. We experimentally realize this method, show how it can be applied as a technique for identifying transitions in currently intractable atoms or molecules, demonstrate its reduced temperature sensitivity, and observe quantum-enhanced frequency sensitivity when it is applied to multi-ion chains. Potential applications include improved readout of trapped-ion clocks and simplified error syndrome measurements for quantum error correction.

LncRNA SNHG8 participates in the development of endometrial carcinoma through regulating c-MET expression by miR-152

OBJECTIVE

To investigate the possible function and mechanism of lncRNA SNHG8 in the pathogenesis of endometrial carcinoma.

PATIENTS AND METHODS

We utilized qRT-PCR to detect the expression of SNHG8 in 60 cases of endometrial carcinoma and 25 cases of normal endometrium; after that, the endometrial carcinoma cell lines were screened. SNHG8 was transfected into endometrial carcinoma cells by Lipofectamine and the proliferative activity of cells was detected by cell counting kit-8 (CCK-8) assay. Bioinformatics methods were used to detect the target microRNA. miR-152 is predicted to bind to SNHG8 and target genes of c-MET. Luciferase reporter assay was performed to detect the relative luciferase activity between miR-152 and c-MET, SNHG8. The interactions between SNHG8, miR-152, and c-MET were further verified by transfection of miR-152 mimics, miR-152 mimics + OE-SNHG8, SNHG8 siRNA, and SNHG8 siRNA + miR-152 inhibitor.

RESULTS

SNHG8 expression in endometrial carcinoma tissue was significantly higher than that in normal endometrium. After transfection with SNHG8 siRNA, the cell viability of AN3CA cells decreased, whereas the activity of Ishikawa was increased after transfection with SNHG8 overexpression plasmid. Bioinformatics predictions and dual luciferase reporter assay illustrated that SNHG8 was bound to miR-152 and miR-152 targeted on c-MET. In addition, miR-152 mimics inhibited the expression of c-MET, and the inhibitory effect was reversed after SNHG8 overexpression. Silencing SNHG8 reduced c-MET expression, and c-MET expression was reversed after addition of miR-152 inhibitor.

CONCLUSIONS

SNHG8 is highly expressed in endometrial carcinoma, and SNHG8 targets c-MET through miR-152 to regulate the proliferation of endometrial cancer cells.