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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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.

Associations of Circulating Estrogens and Estrogen Metabolites with Fecal and Oral Microbiome in Postmenopausal Women in the Ghana Breast Health Study

The human fecal and oral microbiome may play a role in the etiology of breast cancer through modulation of endogenous estrogen metabolism. This study aimed to investigate associations of circulating estrogens and estrogen metabolites with the fecal and oral microbiome in postmenopausal African women. A total of 117 women with fecal (N = 110) and oral (N = 114) microbiome data measured by 16S rRNA gene sequencing, and estrogens and estrogen metabolites data measured by liquid chromatography tandem mass spectrometry were included. The outcomes were measures of the microbiome and the independent variables were the estrogens and estrogen metabolites. Estrogens and estrogen metabolites were associated with the fecal microbial Shannon index (global P < 0.01). In particular, higher levels of estrone (β = 0.36, P = 0.03), 2-hydroxyestradiol (β = 0.30, P = 0.02), 4-methoxyestrone (β = 0.51, P = 0.01), and estriol (β = 0.36, P = 0.04) were associated with higher levels of the Shannon index, while 16alpha-hydroxyestrone (β = -0.57, P < 0.01) was inversely associated with the Shannon index as indicated by linear regression. Conjugated 2-methoxyestrone was associated with oral microbial unweighted UniFrac as indicated by MiRKAT (P < 0.01) and PERMANOVA, where conjugated 2-methoxyestrone explained 2.67% of the oral microbial variability, but no other estrogens or estrogen metabolites were associated with any other beta diversity measures. The presence and abundance of multiple fecal and oral genera, such as fecal genera from families Lachnospiraceae and Ruminococcaceae, were associated with several estrogens and estrogen metabolites as indicated by zero-inflated negative binomial regression. Overall, we found several associations of specific estrogens and estrogen metabolites and the fecal and oral microbiome. IMPORTANCE Several epidemiologic studies have found associations of urinary estrogens and estrogen metabolites with the fecal microbiome. However, urinary estrogen concentrations are not strongly correlated with serum estrogens, a known risk factor for breast cancer. To better understand whether the human fecal and oral microbiome were associated with breast cancer risk via the regulation of estrogen metabolism, we conducted this study to investigate the associations of circulating estrogens and estrogen metabolites with the fecal and oral microbiome in postmenopausal African women. We found several associations of parent estrogens and several estrogen metabolites with the microbial communities, and multiple individual associations of estrogens and estrogen metabolites with the presence and abundance of multiple fecal and oral genera, such as fecal genera from families Lachnospiraceae and Ruminococcaceae, which have estrogen metabolizing properties. Future large, longitudinal studies to investigate the dynamic changes of the fecal and oral microbiome and estrogen relationship are needed.

Abstract 3055: Associations of the mucosal microbiome and circulating bile acids with colorectal adenoma among average-risk women

Background: The gut microbiome (GM) and the metabolites it produces and regulates, such as bile acids (BAs), may act individually or interact to influence colorectal cancer development. We used data from a multi-center screening study to investigate the associations of the normal colon tissue microbiome and circulating BAs with colorectal adenoma.

Methods: We used data from the multi-center Colorectal Neoplasia Screening with Colonoscopy in Average-Risk Women Regional Navy/Army Medical Centers study (CONCeRN), comprising women undergoing complete colonoscopies. We individually matched women with adenoma (n=165 cases) to those without adenoma (n=311 controls). We extracted DNA using the Animal Tissue DNA Extraction Kit (AutoGene) and sequenced the V4 region of the 16S rRNA gene to characterize normal colonic mucosal bacteria, including alpha and beta diversity, taxonomic abundance, and co-abundance groups (CAGs). Fasting serum collected before colonoscopy underwent targeted quantitative liquid chromatography-tandem mass spectrometry to measure 13 primary and secondary BAs. We used multivariable conditional logistic regression, partial Pearson’s correlations, and linear regression to estimate interrelationships among the GM, BAs, and adenoma.

Results: Multiple genera were statistically significantly associated with adenomas. For example, for the centered log-ratio transformed relative abundance of Phascolarctobacterium, Parvimonas, and Porphymonas, the ORs (95% CIs) were 0.75 (0.60-0.95, P=0.02), 0.71 (0.55-0.92; P=0.01), and 0.67 (0.49-0.93; P=0.02), respectively. A CAG comprising phylum Firmicutes, Actinobacteria, Proteobacteria, and Fusobacteria was less abundant among cases than controls (log2folddiff=-0.80, P=0.002). Alpha and beta diversity associations were close to null and not statistically significant. However, alpha diversity metrics were inversely associated with primary BAs [e.g., for Shannon (R2=-0.13, P=0.01)] and positively associated with secondary BAs [e.g., for Shannon (R2=0.16, P=0.001)]. Though no circulating BAs were statistically significantly associated with adenomas, those in the highest relative to lowest tertile of taurine-conjugated (TC) BAs, primary BAs, and secondary BAs had a 44% (95% CI: 0.85-2.46), 24% (95% CI: 0.73-2.12), and 11% (95% CI: 0.61-2.01) higher odds of adenoma, respectively. We found that TCBAs, primary BAs, and secondary BAs were inversely associated with the abundance of Parvimonas [e.g., for TCBAs (R2=-0.11, P=0.02)] and Porphyromonas [e.g., for TCBAs (R2=-0.11, P=0.02)].

Conclusions: We found short-chain fatty acid-producing and oral-originating bacteria were inversely associated with adenomas. Our oral bacteria findings are contrary to findings from prior studies. Our findings suggest potential interrelationships among the GM and BAs in adenoma development.

Citation Format: Maria F. Gomez, Doratha A. Byrd, Stephanie R. Hogue, Jessica R. Burns, Nathan Smith, Joshua Sampson, Erikka Loftfield, Andrew Warner, Casey Dagnall, Kristine Jones, Belynda Hicks, Yunhu Wan, Youngchul Kim, Jin Xu, Rashmi Sinha, Emily Vogtmann. Associations of the mucosal microbiome and circulating bile acids with colorectal adenoma among average-risk women [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3055.

[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.

Prospective and Cross-sectional Associations of the Rectal Tissue Microbiome with Colorectal Adenoma Recurrence

BACKGROUND

The gut microbiome is plausibly associated with colorectal cancer risk; however, previous studies mostly investigated this association cross-sectionally. We investigated cross-sectional and prospective associations of the rectal tissue microbiome with adenoma recurrence in the Polyp Prevention Trial (PPT).

METHODS

PPT is a 4-year randomized clinical trial of the effect of a dietary intervention on adenoma recurrence among community members. We extracted DNA from rectal biopsies at baseline, end of year 1, and end of year 4 among 455 individuals and sequenced the V4 region of the 16S rRNA gene. At each timepoint, we investigated associations of alpha diversity, beta diversity, and presence and relative abundance of select taxa with adenoma recurrence using multivariable logistic regression.

RESULTS

Variation in beta diversity was primarily explained by subject and minimally by year of collection or time between biopsy and colonoscopy. Cross-sectionally, year 4 alpha diversity was strongly, inversely associated with adenoma prevalence [ORQ3 vs. Q1 Shannon index = 0.40 (95% confidence interval, CI: 0.21-0.76)]. Prospective alpha diversity associations (i.e., baseline/year 1 alpha diversity with adenoma recurrence 3-4 years later) were weak or null, as were cross-sectional and prospective beta diversity-adenoma associations. Bacteroides abundance was more strongly, positively associated with adenoma prevalence cross-sectionally than prospectively.

CONCLUSIONS

Rectal tissue microbiome profiles may be associated with prevalent adenomas, with little evidence supporting prospective associations.

IMPACT

Additional prospective studies, with serial fecal and tissue samples, to explore microbiome-colorectal cancer associations are needed. Eventually, it may be possible to use microbiome characteristics as intervenable risk factors or screening tools.

Stability of the Fecal and Oral Microbiome over 2 Years at -80°C for Multiple Collection Methods

BACKGROUND

In prospective cohorts, biological samples are generally stored over long periods before an adequate number of cases have accrued. We investigated the impact of sample storage at -80°C for 2 years on the stability of the V4 region of the 16S rRNA gene across seven different collection methods (i.e., no additive, 95% ethanol, RNAlater stabilization solution, fecal occult blood test cards, and fecal immunochemical test tubes for feces; OMNIgene ORAL tubes and Scope mouthwash for saliva) among 51 healthy volunteers.

METHODS

Intraclass correlation coefficients (ICC) were calculated for the relative abundance of the top three phyla, the 20 most abundant genera, three alpha-diversity metrics, and the first principal coordinates of three beta-diversity matrices.

RESULTS

The subject variability was much higher than the variability introduced by the sample collection type, and storage time. For fecal samples, microbial stability over 2 years was high across collection methods (range, ICCs = 0.70-0.99), except for the samples collected with no additive (range, ICCs = 0.23-0.83). For oral samples, most microbiome diversity measures were stable over time with ICCs above 0.74; however, ICCs for the samples collected with Scope mouthwash were lower for two alpha-diversity measures, Faith's phylogenetic diversity (0.23) and the observed number of operational taxonomic units (0.23).

CONCLUSIONS

Fecal and oral samples in most used collection methods are stable for microbiome analyses after 2 years at -80°C, except for fecal samples with no additive.

IMPACT

This study provides evidence that samples stored for an extended period from prospective studies are useful for microbiome analyses.

Oral microbiome and risk of incident head and neck cancer: A nested case-control study

OBJECTIVES

This nested case-control study in the NIH-AARP Diet and Health Study was carried out to prospectively investigate the relationship of oral microbiome with head and neck cancer (HNC).

MATERIALS AND METHODS

56 incident HNC cases were identified, and 112 controls were incidence-density matched to cases. DNA extracted from pre-diagnostic oral wash samples was whole-genome shotgun metagenomic sequenced to measure the overall oral microbiome. ITS2 gene qPCR was used to measure the presence of fungi. ITS2 gene sequencing was performed on ITS2 gene qPCR positive samples. We computed taxonomic and functional alpha-diversity and beta-diversity metrics. The presence and relative abundance of groups of red-complex (e.g., Porphyromonas gingivalis) and/or orange-complex (e.g., Fusobacterium nucleatum) periodontal pathogens were compared between cases and controls using conditional logistic regression models and MiRKAT.

RESULTS

Participants with higher taxonomic microbial alpha-diversity had a non-statistically significant decreased risk of HNC. No case-control differences were found for beta diversity by MiRKAT model (all p > 0.05). A greater relative abundance of red-complex periodontal pathogens (OR = 0.51, 95 % CI = 0.26-1.00), orange-complex (OR = 0.38, 95 % CI = 0.18-0.83), and both complexes' pathogens (OR = 0.32, 95 % CI = 0.14-0.75), were associated with reduced risk of HNC. The presence of oral fungi was also strongly associated with reduced risk of HNC compared with controls (OR = 0.39, 95 % CI = 0.17-0.92).

CONCLUSION

Greater taxonomic alpha-diversity, the presence of oral fungi, and the presence or relative abundance of multiple microbial species, including the red- and orange-complex periodontal pathogens, were associated with reduced risk of HNC. Future studies with larger sample sizes are needed to evaluate these associations.

Evaluation of alcohol-free mouthwash for studies of the oral microbiome

Oral bacteria play important roles in human health and disease. Oral samples collected using ethanol-containing mouthwash are widely used for oral microbiome studies. However, ethanol is flammable and not ideal for transportation/storage in large quantities, and some individuals may avoid ethanol due to the burning sensation or due to various personal, medical, religious, and/or cultural factors. Here, we compared ethanol-free and ethanol-containing mouthwashes using multiple microbiome metrics and assessed the stability of the mouthwash samples stored up to 10 days before processing. Forty volunteers provided oral wash samples collected using ethanol-free and ethanol-containing mouthwashes. From each sample, one aliquot was immediately frozen, one was stored at 4°C for 5 days and frozen, while the third aliquot was stored for 5 days at 4°C and 5 days at ambient temperature to mimic shipping delays and then frozen. DNA was extracted, the 16S rRNA gene V4 region was amplified and sequenced, and bioinformatic processing was performed using QIIME 2. Microbiome metrics measured in the two mouthwash types were very similar, with intraclass correlation coefficients (ICCs) for alpha and beta diversity metrics greater than 0.85. Relative abundances of some taxa were significantly different, but ICCs of the top four most abundant phyla and genera were high (> 0.75) for the comparability of the mouthwashes. Stability during delayed processing was also high for both mouthwashes based on alpha and beta diversity measures and relative abundances of the top four phyla and genera (ICCs ≥ 0.90). These results demonstrate ethanol-free mouthwash performs similarly to ethanol-containing mouthwash for microbial analyses, and both mouthwashes are stable for at least 10 days without freezing prior to laboratory processing. Ethanol-free mouthwash is suitable for collecting and shipping oral wash samples, and these results have important implications for planning future epidemiologic studies of the oral microbiome.

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.

The oral microbiome and breast cancer and nonmalignant breast disease, and its relationship with the fecal microbiome in the Ghana Breast Health Study

The oral microbiome, like the fecal microbiome, may be related to breast cancer risk. Therefore, we investigated whether the oral microbiome was associated with breast cancer and nonmalignant breast disease, and its relationship with the fecal microbiome in a case-control study in Ghana. A total of 881 women were included (369 breast cancers, 93 nonmalignant cases and 419 population-based controls). The V4 region of the 16S rRNA gene was sequenced from oral and fecal samples. Alpha-diversity (observed amplicon sequence variants [ASVs], Shannon index and Faith's Phylogenetic Diversity) and beta-diversity (Bray-Curtis, Jaccard and weighted and unweighted UniFrac) metrics were computed. MiRKAT and logistic regression models were used to investigate the case-control associations. Oral sample alpha-diversity was inversely associated with breast cancer and nonmalignant breast disease with odds ratios (95% CIs) per every 10 observed ASVs of 0.86 (0.83-0.89) and 0.79 (0.73-0.85), respectively, compared to controls. Beta-diversity was also associated with breast cancer and nonmalignant breast disease compared to controls (P ≤ .001). The relative abundances of Porphyromonas and Fusobacterium were lower for breast cancer cases compared to controls. Alpha-diversity and presence/relative abundance of specific genera from the oral and fecal microbiome were strongly correlated among breast cancer cases, but weakly correlated among controls. Particularly, the relative abundance of oral Porphyromonas was strongly, inversely correlated with fecal Bacteroides among breast cancer cases (r = -.37, P ≤ .001). Many oral microbial metrics were strongly associated with breast cancer and nonmalignant breast disease, and strongly correlated with fecal microbiome among breast cancer cases, but not controls.

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).

The Oral Microbiome and Lung Cancer Risk: An Analysis of 3 Prospective Cohort Studies

BACKGROUND

Previous studies suggested associations between the oral microbiome and lung cancer, but studies were predominantly cross-sectional and underpowered.

METHODS

Using a case-cohort design, 1306 incident lung cancer cases were identified in the Agricultural Health Study; National Institutes of Health-AARP Diet and Health Study; and Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Referent subcohorts were randomly selected by strata of age, sex, and smoking history. DNA was extracted from oral wash specimens using the DSP DNA Virus Pathogen kit, the 16S rRNA gene V4 region was amplified and sequenced, and bioinformatics were conducted using QIIME 2. Hazard ratios and 95% confidence intervals were calculated using weighted Cox proportional hazards models.

RESULTS

Higher alpha diversity was associated with lower lung cancer risk (Shannon index hazard ratio = 0.90, 95% confidence interval = 0.84 to 0.96). Specific principal component vectors of the microbial communities were also statistically significantly associated with lung cancer risk. After multiple testing adjustment, greater relative abundance of 3 genera and presence of 1 genus were associated with greater lung cancer risk, whereas presence of 3 genera were associated with lower risk. For example, every SD increase in Streptococcus abundance was associated with 1.14 times the risk of lung cancer (95% confidence interval = 1.06 to 1.22). Associations were strongest among squamous cell carcinoma cases and former smokers.

CONCLUSIONS

Multiple oral microbial measures were prospectively associated with lung cancer risk in 3 US cohort studies, with associations varying by smoking history and histologic subtype. The oral microbiome may offer new opportunities for lung cancer prevention.

Abstract 687: The oral microbiome and the risk of head and neck cancer: A nested case-control study in the NIH-AARP

Objective: This nested case-control study in the NIH-AARP Diet and Health Study was carried out to prospectively investigate the relationship of oral microbiome with head and neck cancer.

Methods: Among 34,262 participants with oral wash samples, 60 incident head and neck cancer cases were identified during a mean of 6.25±1.28 years of follow-up. Two controls were matched to each case by age at sample collection, sex, and baseline smoking history, and available controls were not diagnosed with head and neck cancer over follow-up. Whole genome shotgun metagenomic sequencing was used to measure the overall oral microbiome, and ITS2 gene qPCR was used to measure the presence of fungi in pre-diagnostic oral wash samples. ITS2 gene sequencing was performed on ITS2 qPCR positive samples. Taxonomic and functional alpha-diversity and beta-diversity metrics were computed. Presence/absence and relative abundance of groups of red- and/or orange-complex periodontal pathogens were also calculated. Conditional logistic regression models and MiRKAT were used to investigate the case-control associations.

Results: Participants with higher taxonomic microbial alpha-diversity had a decreased risk of head and neck cancer with odds ratios per every 10 observed species of 0.87 (95% CI=0.75-1.004, P=0.057), and per one unit of Faith’s PD and the Shannon index of 0.84 (95% CI=0.72-0.99, P=0.032) and 0.52 (95% CI=0.29-0.94, P=0.032), respectively. No case-control differences were found for beta diversity (all p&gt;0.05). The presence of red-complex periodontal pathogens was non-significantly associated with reduced risk of head and neck cancer (OR=0.41, 95% CI=0.17-1.01, P=0.052). Greater relative abundance of red-complex (OR=0.29, 95% CI=0.09-0.94, P=0.040), orange-complex (OR=0.45, 95% CI=0.22-0.89, P=0.022), and both complexes combined (OR=0.46, 95% CI=0.25-0.82, P=0.009), were associated with reduced risk of head and neck cancer. The presence of oral fungi was also strongly associated with reduced risk of head and neck cancer compared with controls (OR=0.39, 95% CI=0.17-0.87, P=0.022).

Conclusions: Greater taxonomic alpha-diversity and the presence or relative abundance of multiple bacterial or fungal species, including the red- and orange-complex periodontal pathogens, were associated with reduced risk of head and neck cancer.

Citation Format: Zeni Wu, Yongli Han, Yunhu Wan, Xing Hua, Casey L. Dagnall, Kristine Jones, Amy Hutchinson, Belynda D. Hicks, Weiyin Zhou, Linda Liao, Heather Hallen-Adams, Jianxin Shi, Christian C. Abnet, Rashmi Sinha, Anil Chaturvedi, Emily Vogtmann. The oral microbiome and the risk of head and neck cancer: A nested case-control study in the NIH-AARP [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 687.

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.

Comparison of fecal and oral collection methods for studies of the human microbiota in two Iranian cohorts

Background

To initiate fecal and oral collections in prospective cohort studies for microbial analyses, it is essential to understand how field conditions and geographic differences may impact microbial communities. This study aimed to investigate the impact of fecal and oral sample collection methods and room temperature storage on collection samples for studies of the human microbiota.

Results

We collected fecal and oral samples from participants in two Iranian cohorts located in rural Yazd (n = 46) and urban Gonbad (n = 38) and investigated room temperature stability over 4 days of fecal (RNAlater and fecal occult blood test [FOBT] cards) and comparability of fecal and oral (OMNIgene ORAL kits and Scope mouthwash) collection methods. We calculated interclass correlation coefficients (ICCs) based on 3 alpha and 4 beta diversity metrics and the relative abundance of 3 phyla. After 4 days at room temperature, fecal stability ICCs and ICCs for Scope mouthwash were generally high for all microbial metrics. Similarly, the fecal comparability ICCs for RNAlater and FOBT cards were high, ranging from 0.63 (95% CI: 0.46, 0.75) for the relative abundance of Firmicutes to 0.93 (95% CI: 0.89, 0.96) for unweighted Unifrac. Comparability ICCs for OMNIgene ORAL and Scope mouthwash were lower than fecal ICCs, ranging from 0.55 (95% CI: 0.36, 0.70) for the Shannon index to 0.79 (95% CI: 0.69, 0.86) for Bray-Curtis. Overall, RNAlater, FOBT cards and Scope mouthwash were stable up to 4 days at room temperature. Samples collected using FOBT cards were generally comparable to RNAlater while the OMNIgene ORAL were less similar to Scope mouthwash.

Conclusions

As microbiome measures for feces samples collected using RNAlater, FOBT cards and oral samples collected using Scope mouthwash were stable over four days at room temperature, these would be most appropriate for microbial analyses in these populations. However, one collection method should be consistently since each method may induce some differences.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12866-021-02387-9.

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.

Cigarette Smoking and Opium Use in Relation to the Oral Microbiota in Iran

Cigarettes and opium contain chemicals and particulate matter that may modify the oral microbiota. This study aimed to investigate the association between cigarette and opium use with the oral microbiota. A total of 558 participants were recruited from Iran between 2011 and 2015. Individuals were categorized as never cigarette nor opium users, ever cigarette-only smokers, ever opium-only users, and ever both cigarette and opium users. Participants provided saliva samples for 16S rRNA gene sequencing. Logistic regression, microbiome regression-based kernel association test (MiRKAT), and zero-inflated beta regression models were calculated. For every increase in 10 observed amplicon sequence variants (ASVs), the odds for being a cigarette-only smoker, opium-only user, and both user compared to never users decreased by 9% (odds ratio [OR] = 0.91; 95% confidence interval [95% CI] = 0.86 to 0.97), 13% (OR = 0.87; 95% CI = 0.75 to 1.01), and 12% (OR = 0.88; 95% CI = 0.80 to 0.96), respectively. The microbial communities differed by cigarette and opium use as indicated by MiRKAT models testing the three beta-diversity matrices (P < 0.05 for all). Three genera were less likely and one genus was more likely to be detected in cigarette-only smokers or opium-only users than in never users. The relative abundance of the phylum Actinobacteria (never, 14.78%; both, 21.20%) was higher and the phyla Bacteroidetes (never, 17.63%; both, 11.62%) and Proteobacteria (never, 9.06%; both, 3.70%) were lower in users of both cigarettes and opium, while the phylum Firmicutes (never, 54.29%; opium, 65.49%) was higher in opium-only users. Cigarette and opium use was associated with lower alpha-diversity, overall oral microbiota community composition, and both the presence and relative abundance of multiple taxa. IMPORTANCE Cigarette smoking and opium use are associated with periodontal disease caused by specific bacteria such as Porphyromonas gingivalis, which suggests a link between cigarette smoking and opium use and the oral microbiota. Alterations of the oral microbiota in cigarette smokers compared to nonsmokers have been reported, but this has not been studied across diverse populations. Additionally, the association of opium use with the oral microbiota has not been investigated to date. We conducted this study to investigate differences in the oral microbiota between ever users of cigarettes only, opium only, and both cigarettes and opium and never users of cigarettes and opium in Iran. Lower alpha-diversity, distinct overall oral microbial communities, and the presence and relative abundance of multiple taxa have been found for users of cigarettes and/or opium.

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.

Effects of processed meat and drinking water nitrate on oral and fecal microbial populations in a controlled feeding study

BACKGROUND

One mechanism that can explain the link between processed meat consumption and colorectal cancer (CRC) is the production of carcinogenic N-nitroso compounds (NOCs) in the gastrointestinal tract. Oral and gut microbes metabolize ingested proteins (a source of secondary and tertiary amines and amides) and can reduce nitrate to nitrite, generating potentially carcinogenic NOCs.

OBJECTIVE

We evaluated whether nitrate/nitrite in processed meat or water influences the fecal or salivary microbiota.

DESIGN

In this dietary intervention study, 63 volunteers consumed diets high in conventional processed meats for two weeks, switched to diets high in poultry for two weeks, and then consumed phytochemical-enriched conventional processed or low-nitrite processed meat diets for two weeks. During the intervention, they drank water with low nitrate concentrations and consumed a healthy diet with low antioxidants. Then the volunteers drank nitrate-enriched water for 1 week, in combination with one of the four different diets. We measured creatinine-adjusted urinary nitrate levels and characterized the oral and fecal microbiota using 16S rRNA amplicon sequencing.

RESULTS

Using linear mixed models, we found that, compared to baseline, urinary nitrate levels were reduced during the phytochemical-enriched low-nitrite meat diet (p-value = 0.009) and modestly during the poultry diet (p-value = 0.048). In contrast, urinary nitrate increased after 1-week of drinking nitrate-enriched water (p-value<10^-5). Nitrate-enriched water, but not processed meats with or without phytochemicals, altered the saliva microbial population (p-value ≤0.001), and significantly increased abundance of 8 bacterial taxa, especially genus Neisseria and other nitrate-reducing taxa. Meats, phytochemicals and nitrate-enriched water had no significant effects on saliva alpha diversity or any diversity parameter measured for the fecal microbiota.

CONCLUSION

These findings support the hypothesis that drinking high nitrate water increases oral nitrate-reducing bacteria, which likely results in increased NOC. However, meat nitrate/nitrite at the levels tested had no effect on either the gut or oral bacteria. CLINICALTRIALS.

GOV IDENTIFIER

NCT04138654.

Associations of fecal microbial profiles with breast cancer and nonmalignant breast disease in the Ghana Breast Health Study

The gut microbiota may play a role in breast cancer etiology by regulating hormonal, metabolic and immunologic pathways. We investigated associations of fecal bacteria with breast cancer and nonmalignant breast disease in a case-control study conducted in Ghana, a country with rising breast cancer incidence and mortality. To do this, we sequenced the V4 region of the 16S rRNA gene to characterize bacteria in fecal samples collected at the time of breast biopsy (N = 379 breast cancer cases, N = 102 nonmalignant breast disease cases, N = 414 population-based controls). We estimated associations of alpha diversity (observed amplicon sequence variants [ASVs], Shannon index, and Faith's phylogenetic diversity), beta diversity (Bray-Curtis and unweighted/weighted UniFrac distance), and the presence and relative abundance of select taxa with breast cancer and nonmalignant breast disease using multivariable unconditional polytomous logistic regression. All alpha diversity metrics were strongly, inversely associated with odds of breast cancer and for those in the highest relative to lowest tertile of observed ASVs, the odds ratio (95% confidence interval) was 0.21 (0.13-0.36; Ptrend < .001). Alpha diversity associations were similar for nonmalignant breast disease and breast cancer grade/molecular subtype. All beta diversity distance matrices and multiple taxa with possible estrogen-conjugating and immune-related functions were strongly associated with breast cancer (all Ps < .001). There were no statistically significant differences between breast cancer and nonmalignant breast disease cases in any microbiota metric. In conclusion, fecal bacterial characteristics were strongly and similarly associated with breast cancer and nonmalignant breast disease. Our findings provide novel insight into potential microbially-mediated mechanisms of breast disease.

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.

Power of Microbiome Beta-Diversity Analyses Based on Standard Reference Samples

A simple method to analyze microbiome beta-diversity computes mean beta-diversity distances from a test sample to standard reference samples. We used reference stool and nasal samples from the Human Microbiome Project and regressed an outcome on mean distances (2 degrees-of-freedom (df) test) or additionally on squares and cross-product of mean distances (5-df test). We compared the power of 2-df and 5-df tests with the microbiome regression-based kernel association test (MiRKAT). In simulations, MiRKAT had moderately greater power than the 2-df test for discriminating skin versus saliva and skin versus nasal samples, but differences were negligible for skin versus stool and stool versus nasal samples. The 2-df test had slightly greater power than MiRKAT for Dirichlet multinomial samples. In associating body mass index with beta-diversity in stool samples from the American Gut Project, the 5-df test yielded smaller P values than MiRKAT for most taxonomic levels and beta-diversity measures. Unlike procedures like MiRKAT that are based on the beta-diversity matrix, mean distances to reference samples can be analyzed with standard statistical tools and shared or meta-analyzed without sharing primary DNA data. Our data indicate that standard reference tests have power comparable to MiRKAT's (and to permutational multivariate analysis of variance), but more simulations and applications are needed to confirm this.

Variation in oral microbiome is associated with future risk of lung cancer among never-smokers

OBJECTIVE

To prospectively investigate whether diversity in oral microbiota is associated with risk of lung cancer among never-smokers.

DESIGN AND SETTING

A nested case-control study within two prospective cohort studies, the Shanghai Women's Health Study (n=74 941) and the Shanghai Men's Health Study (n=61 480).

PARTICIPANTS

Lifetime never-smokers who had no cancer at baseline. Cases were subjects who were diagnosed with incident lung cancer (n=114) and were matched 1:1 with controls on sex, age (≤2 years), date (≤30 days) and time (morning/afternoon) of sample collection, antibiotic use during the week before sample collection (yes/no) and menopausal status (for women).

MAIN OUTCOMES AND MEASURES

Metagenomic shotgun sequencing was used to measure the community structure and abundance of the oral microbiome in pre-diagnostic oral rinse samples of each case and control. Multivariable logistic regression models were used to estimate the association of lung cancer risk with alpha diversity metrics and relative abundance of taxa. The Microbiome Regression-Based Kernel Association Test (MiRKAT) evaluated the association between risk and the microbiome beta diversity.

RESULTS

Subjects with lower microbiota alpha diversity had an increased risk of lung cancer compared with those with higher microbial alpha diversity (Shannon: p_trend=0.05; Simpson: p_trend=0.04; Observed Species: p_trend=0.64). No case-control differences were apparent for beta diversity (p_MiRKAT=0.30). After accounting for multiple comparisons, a greater abundance of Spirochaetia (OR_low 1.00 (reference), OR_medium 0.61 (95% CI 0.32 to 1.18), OR_high 0.42 (95% CI 0.21 to 0.85)) and Bacteroidetes (OR_low 1.00 (reference), OR_medium 0.66 (95% CI 0.35 to 1.25), OR_high 0.31 (95% CI 0.15 to 0.64)) was associated with a decreased risk of lung cancer, while a greater abundance of the Bacilli class (OR_low 1.00 (reference), OR_medium 1.49 (95% CI 0.73 to 3.08), OR_high 2.40 (95% CI 1.18 to 4.87)) and Lactobacillales order (OR_low 1.00 (reference), OR_medium 2.15 (95% CI 1.03 to 4.47), OR_high 3.26 (95% CI 1.58 to 6.70)) was associated with an increased risk of lung cancer.

CONCLUSIONS

Our prospective study of never-smokers suggests that lower alpha diversity was associated with a greater risk of lung cancer and the abundance of certain specific taxa was associated with altered risk, providing further insight into the aetiology of lung cancer in the absence of active tobacco smoking.

Association of Body Mass Index with Fecal Microbial Diversity and Metabolites in the Northern Finland Birth Cohort

BACKGROUND

Obesity is an established risk factor for multiple cancer types. Lower microbial richness has been linked to obesity, but human studies are inconsistent, and associations of early-life body mass index (BMI) with the fecal microbiome and metabolome are unknown.

METHODS

We characterized the fecal microbiome (n = 563) and metabolome (n = 340) in the Northern Finland Birth Cohort 1966 using 16S rRNA gene sequencing and untargeted metabolomics. We estimated associations of adult BMI and BMI history with microbial features and metabolites using linear regression and Spearman correlations (rs ) and computed correlations between bacterial sequence variants and metabolites overall and by BMI category.

RESULTS

Microbial richness, including the number of sequence variants (rs = -0.21, P < 0.0001), decreased with increasing adult BMI but was not independently associated with BMI history. Adult BMI was associated with 56 metabolites but no bacterial genera. Significant correlations were observed between microbes in 5 bacterial phyla, including 18 bacterial genera, and metabolites in 49 of the 62 metabolic pathways evaluated. The genera with the strongest correlations with relative metabolite levels (positively and negatively) were Blautia, Oscillospira, and Ruminococcus in the Firmicutes phylum, but associations varied by adult BMI category.

CONCLUSIONS

BMI is strongly related to fecal metabolite levels, and numerous associations between fecal microbial features and metabolite levels underscore the dynamic role of the gut microbiota in metabolism.

IMPACT

Characterizing the associations between the fecal microbiome, the fecal metabolome, and BMI, both recent and early-life exposures, provides critical background information for future research on cancer prevention and etiology.

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.