CT quantification of pulmonary vessels in lung aging

AIM

To evaluate the effect of aging on pulmonary vessels based on computed tomography (CT) quantification and analyse the correlation between quantitative pulmonary vascular volume and pulmonary function during aging.

MATERIALS AND METHODS

A total of 330 healthy adult volunteers, including 161 men (53 aged 20-39 years, 61 aged 40-59 years, and 47 aged ≥60 years) and 169 women (53 aged 20-39 years, 63 aged 40-59 years, and 53 aged ≥60 years) were recruited in this study. AVIEW software was used to quantitatively measure pulmonary vascular volume, including pulmonary total blood vessel volume (TBV) and small blood vessel volume with a cross-sectional area of <5 mm2 (BV5). Pulmonary vascular volume parameters were standardised using the ratio of vascular volume to the body surface area (BSA; TBV/BSA and BV5/BSA). Subsequently, the effect of aging on the pulmonary vessels was analysed.

RESULTS

The pulmonary vascular volume parameters TBV/BSA and BV5/BSA of the whole lung, right lung, and left lung decreased significantly with increasing age (p<0.05). Additionally, TBV/BSA and BV5/BSA of the whole lung were higher in men than in women. The declining trend of pulmonary vascular volume was consistent in men and women and increased with age.

CONCLUSIONS

The pulmonary vascular volume parameters, TBV/BSA and BV5/BSA, decreased with age and were weakly positively correlated with pulmonary function.

Intellectual landscapes and emerging trends of non-steroidal mineralocorticoid receptor antagonists: a bibliometric and visual analysis

PURPOSE

With increasing studies, non-steroidal mineralocorticoid receptor (MR) antagonists have been increasingly recognized as a major novel dimension in cardiorenal disease therapy. This bibliometric analysis aimed to uncover current research status and identify future research directions in the study of non-steroidal MR antagonists to inform subsequent investigations.

METHODS

Relevant English-language literature was retrieved from the Science Citation Index Expanded of the Web of Science Core Collection on January 10, 2024. Analyses of countries, institutions, authors, journals, documents, cited references and keywords were performed by the CiteSpace and VOSviewer software.

RESULTS

Overall, 498 documents, including 297 articles and 201 reviews, were included and analyzed. The United States (n = 188), Bayer AG (n = 78), and Professor Peter Kolkhof (n = 59) were the most prolific country, institution, and author in this field, respectively. Cluster analysis of cited references identified major clusters like cardiovascular disease, chronic kidney disease, and omecamtiv mecarbil. Keyword analysis indicated that sodium-glucose transport protein (SGLT)-2 inhibitors, pharmacotherapy, clinical trial, and guideline have emerged recently.

CONCLUSION

The field of non-steroidal MR antagonists is gradually gaining momentum as a novel pharmacotherapy in cardiorenal diseases, especially diabetic kidney disease, hypertension, and heart failure. Future studies will focus on add-on pharmacotherapy by combining non-steroidal MR antagonists with SGLT-2 inhibitors and the development and publication of clinical guidelines to facilitate patient management.

Discovery of a novel SHP2 allosteric inhibitor using virtual screening, FMO calculation, and molecular dynamic simulation

CONTEXT

SHP2 is a non-receptor protein tyrosine phosphatase to remove tyrosine phosphorylation. Functionally, SHP2 is an essential bridge to connect numerous oncogenic cell-signaling cascades including RAS-ERK, PI3K-AKT, JAK-STAT, and PD-1/PD-L1 pathways. This study aims to discover novel and potent SHP2 inhibitors using a hierarchical structure-based virtual screening strategy that combines molecular docking and the fragment molecular orbital method (FMO) for calculating binding affinity (referred to as the Dock-FMO protocol). For the SHP2 target, the FMO method prediction has a high correlation between the binding affinity of the protein-ligand interaction and experimental values (R2 = 0.55), demonstrating a significant advantage over the MM/PBSA (R2 = 0.02) and MM/GBSA (R2 = 0.15) methods. Therefore, we employed Dock-FMO virtual screening of ChemDiv database of ∼2,990,000 compounds to identify a novel SHP2 allosteric inhibitor bearing hydroxyimino acetamide scaffold. Experimental validation demonstrated that the new compound (E)-2-(hydroxyimino)-2-phenyl-N-(piperidin-4-ylmethyl)acetamide (7188-0011) effectively inhibited SHP2 in a dose-dependent manner. Molecular dynamics (MD) simulation analysis revealed the binding stability of compound 7188-0011 and the SHP2 protein, along with the key interacting residues in the allosteric binding site. Overall, our work has identified a novel and promising allosteric inhibitor that targets SHP2, providing a new starting point for further optimization to develop more potent inhibitors.

METHODS

All the molecular docking studies were employed to identify potential leads with Maestro v10.1. The protein-ligand binding affinities of potential leads were further predicted by FMO calculations at MP2/6-31G* level using GAMESS v2020 system. MD simulations were carried out with AmberTools18 by applying the FF14SB force field. MD trajectories were analyzed using VMD v1.9.3. MM/GB(PB)SA binding free energy analysis was carried out with the mmpbsa.py tool of AmberTools18. The docking and MD simulation results were visualized through PyMOL v2.5.0.

Study of the f_{0}(980) and f_{0}(500) Scalar Mesons through the Decay D_{s}^{+}→π^{+}π^{-}e^{+}ν_{e}

Using e^{+}e^{-} collision data corresponding to an integrated luminosity of 7.33  fb^{-1} recorded by the BESIII detector at center-of-mass energies between 4.128 and 4.226 GeV, we present an analysis of the decay D_{s}^{+}→π^{+}π^{-}e^{+}ν_{e}, where the D_{s}^{+} is produced via the process e^{+}e^{-}→D_{s}^{*±}D_{s}^{∓}. We observe the f_{0}(980) in the π^{+}π^{-} system and the branching fraction of the decay D_{s}^{+}→f_{0}(980)e^{+}ν_{e} with f_{0}(980)→π^{+}π^{-} measured to be (1.72±0.13_{stat}±0.10_{syst})×10^{-3}, where the uncertainties are statistical and systematic, respectively. The dynamics of the D_{s}^{+}→f_{0}(980)e^{+}ν_{e} decay are studied with the simple pole parametrization of the hadronic form factor and the Flatté formula describing the f_{0}(980) in the differential decay rate, and the product of the form factor f_{+}^{f_{0}}(0) and the c→s Cabibbo-Kobayashi-Maskawa matrix element |V_{cs}| is determined for the first time to be f_{+}^{f_{0}}(0)|V_{cs}|=0.504±0.017_{stat}±0.035_{syst}. Furthermore, the decay D_{s}^{+}→f_{0}(500)e^{+}ν_{e} is searched for the first time but no signal is found. The upper limit on the branching fraction of D_{s}^{+}→f_{0}(500)e^{+}ν_{e}, f_{0}(500)→π^{+}π^{-} decay is set to be 3.3×10^{-4} at 90% confidence level.

MicroRNA-150-5P regulates Th1/Th2 cytokines expression levels by targeting EGR2 in allergic rhinitis

BACKGROUND

MiR-150-5p is one of the miRNAs in the expression profile of miRNAs, and in many previous studies, it has been shown that miR-150-5p may play an important role in peripheral blood dendritic cells (DCs) of allergic rhinitis (AR) patients. We sought to investigate the role and mechanism of miR-150-5p in regulating DC function by modulating EGR2 and influencing T cell derivation to promote AR development.

METHODS

The expression of miR-150-5p and EGR2 in AR patients was examined by real-time quantitative polymerase chain reaction (qRT-PCR), the expression of IL-4 cytokines in the supernatant of AR patients was tested by enzyme-linked immunosorbent assay (ELISA), and the expression of eosinophils in the supernatant of AR patients was measured by HE staining. The expression of EGR2 was detected by immunohistochemistry and fluorescent m-immunohistochemistry.

RESULTS

MiR-150-5p expression was up-regulated and EGR2 expression was down-regulated in peripheral blood DCs from AR patients. miR-150-5p upregulated DCs, which promoted T-cell differentiation. miR-150-5p further regulated EGR2, which suppressed DCs and caused alteration of T-cell differentiation, in turn triggering the occurrence of AR.

CONCLUSION

MiR-150-5p and its target gene EGR2 are involved in the development of AR, and DCs foster T-cell differentiation in peripheral blood of AR patients.

Predictive value of crossover sign for outcome of ultrasound-guided vacuum aspiration in women with Cesarean scar pregnancy

OBJECTIVE

To examine the value of the crossover sign (COS) in predicting treatment outcome in women with a Cesarean scar pregnancy (CSP) who were treated with ultrasound-guided vacuum aspiration.

METHODS

This was a retrospective cohort study of women with CSP who underwent ultrasound-guided vacuum aspiration. Based on the relationship between the gestational sac, Cesarean scar and anterior wall of the uterus, CSPs were classified by COS type. Analysis was conducted to investigate the association between COS type (COS-1, COS-2) and treatment outcome. The incidence of treatment failure, retained pregnancy tissue, secondary therapy and bleeding ≥ 200 mL were analyzed.

RESULTS

In total, 181 eligible patients with CSP, including 90 (49.7%) women with COS-1 and 91 (50.3%) women with COS-2, were analyzed. COS-1 patients had a higher incidence of treatment failure compared with COS-2 patients (25.6% vs 8.8%; P = 0.003), as well as higher rates of retained pregnancy tissue (18.9% vs 6.6%; P = 0.013), secondary therapy (20.0% vs 6.6%; P = 0.002) and bleeding of ≥ 200 mL (13.3% vs 4.4%; P = 0.034). COS-1 and a large gestational sac (30.1-50.0 mm or >50.0 mm in diameter) were associated independently with increased risk of treatment failure (odds ratio, 4.57 (95% CI, 1.66-12.56); P = 0.003, 4.34 (95% CI, 1.35-13.94); P = 0.014 and 10.50 (95% CI, 2.54-43.46); P = 0.001, respectively).

CONCLUSIONS

Ultrasound evaluation of the relationship between the gestational sac and the endometrial line (COS classification) in women with CSP may help to predict treatment outcome among those undergoing vacuum aspiration. Among COS-1 patients, especially those with a gestational sac diameter of >30.0 mm, vacuum aspiration may be discouraged. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

High MCM6 expression promotes proliferation and correlates with poor prognosis in triple-negative breast cancer

OBJECTIVE

Triple-negative breast cancer (TNBC) is an aggressive subtype with a poor prognosis. Minichromosome maintenance genes (MCM2-7) crucial for DNA replication are significant biomarkers for various tumor types; however, their roles in TNBC remain underexplored.

MATERIALS AND METHODS

We utilized four TNBC-related GEO databases to examine MCM2-7 gene expression and predict its prognosis in TNBC, performing single-cell analysis and GSEA to discover MCM6's potential function. The Cancer Dependency Map gene effect scores and CCK8 assay were used to assess MCM6's impact on TNBC cell proliferation. The correlations between MCM6 expression, immune infiltrates, and immune cells were also analyzed. WGCNA and LASSO Cox regression built a risk score model predicting TNBC patient survival based on MCM6-related gene expression.

RESULTS

MCM2-7 gene expression was higher in TNBC tissues compared to adjacent normal tissues. High MCM6 expression correlated with shorter TNBC patient survival time. GSEA and single-cell analysis revealed a relationship between elevated MCM6 expression and the cell cycle pathway. MCM6 knockdown inhibited TNBC cell proliferation. A risk model featuring MCM6, CDC23, and CCNB1 effectively predicts TNBC patient survival.

CONCLUSIONS

MCM6 overexpression in TNBC links to a worse prognosis and reduced cell proliferation upon MCM6 knockdown. We developed a risk score model based on MCM6-related genes predicting TNBC patient prognosis, potentially assisting future treatment strategies.

Effects of electroacupuncture on chronic urinary retention after pelvic or lumbosacral tumor resection surgeries: a retrospective cohort study

Background

Chronic urinary retention (CUR) resulting from lower motor neuron lesions (LMNL) is a medical condition secondary to pelvic or lumbosacral tumor resection surgeries. Electroacupuncture (EA) is proved to be effective and safe in treating certain lower urinary tract disorders. However, the clinical benefit and optimal duration of EA treatment for CUR following LMNL remain unknown.

Methods

Using a retrospective cohort design, 20 eligible patients diagnosed with CUR resulting from LMNL secondary to pelvic or lumbosacral tumor resection surgeries were included from March 1, 2017, to June 30, 2020. The patients were treated by EA three times a week for 2 to 12 weeks and followed up for 24 weeks after treatment. The electric stimulators with a 5-Hz continuous wave (5-10 mA intensity) were separately connected to bilateral Ciliao (BL32), bilateral Zhongliao (BL33), and bilateral Huiyang (BL35), and stimulators with a 10-Hz continuous wave (1-2 mA intensity) were connected to bilateral Sanyinjiao (SP6). Current intensity was adjusted according to the patients' individual tolerance. The median follow-up was 32 weeks (range, 26-36 weeks). Responders were defined as patients whose post-void residuals (PVR) reduced by 50% or more from baseline. Adverse event was recorded.

Results

Totally 20 patients [mean (standard deviation) age, 48.1 (15.5) years; 9 men (45.0%); 11 women (55.0%)] were included. Of the 20 patients, 14 (70.0%) had responded to EA treatment and stopped catheterization for achieving satisfactory spontaneous urination (PVR <100 mL without complications), 7 (35.0%) had complete resolution (90-100% reduction in PVR from baseline), and 13 (65.0%) scored 1 (much better) or 2 (moderately better) in the Patient Global Impression of Improvement (PGI-I) assessment. Moreover, 6 (30.0%) patients had responded within 4 weeks of EA treatment. According to Kaplan-Meier survival curve, we found that more than 50% patients could respond to EA treatment within 8 weeks or longer. None of the responders had ever experienced relapse in 24 weeks after EA treatment ended. None of the patients manifested urinary tract infection (UTI), newly diagnosed hydroureter or hydronephrosis. One patient diagnosed with hydronephrosis at baseline recovered after 12-week EA treatment. Two patients with UTI at baseline were prescribed antibiotics and did not present UTI again during the follow-up.

Conclusions

EA could be a promising treatment option for CUR caused by LMNL following pelvic or lumbosacral tumor resection surgeries, with long-term effects and a good safety profile. The optimal duration of EA should be of 8 weeks at least. But this was a retrospective cohort study of a small sample size, so future studies are needed to investigate EA in larger populations in randomized controlled trials.

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

Key Words Collapse

MESH Headings Collapse

Grants Collapse

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
F De Mori University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
B Ding Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Jinan, Jinan 250022, People's Republic of China
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
M Y 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 University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
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
W X Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China
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
J Jackson Indiana University, Bloomington, Indiana 47405, USA
S Jaeger Bochum Ruhr-University, D-44780 Bochum, Germany
S Janchiv Institute of Physics and Technology, Peace Avenue 54B, Ulaanbaatar 13330, Mongolia
J H Jeong Chung-Ang University, Seoul, 06974, Republic of Korea
Q Ji Institute of High Energy Physics, Beijing 100049, People's Republic of China
Q P Ji Henan Normal University, Xinxiang 453007, People's Republic of China
X B Ji 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 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
Y Y Ji Shandong University, Jinan 250100, People's Republic of China
X Q Jia Shandong University, Jinan 250100, People's Republic of China
Z K Jia 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 J Jiang Wuhan University, Wuhan 430072, People's Republic of China
P C Jiang Peking University, Beijing 100871, People's Republic of China
S S Jiang Liaoning Normal University, Dalian 116029, People's Republic of China
T J Jiang Hangzhou Normal University, Hangzhou 310036, People's Republic of China
X S Jiang 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 Jiang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J B Jiao Shandong University, Jinan 250100, People's Republic of China
Z Jiao Huangshan College, Huangshan 245000, People's Republic of China
S Jin Nanjing University, Nanjing 210093, People's Republic of China
Y Jin University of Jinan, Jinan 250022, People's Republic of China
M Q Jing 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 M Jing University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
T Johansson Uppsala University, Box 516, SE-75120 Uppsala, Sweden
X Kui Institute of High Energy Physics, Beijing 100049, People's Republic of China
S Kabana Instituto de Alta Investigación, Universidad de Tarapacá, Casilla 7D, Arica 1000000, Chile
N Kalantar-Nayestanaki University of Groningen, NL-9747 AA Groningen, The Netherlands
X L Kang China University of Geosciences, Wuhan 430074, People's Republic of China
X S Kang Liaoning University, Shenyang 110036, People's Republic of China
M Kavatsyuk University of Groningen, NL-9747 AA Groningen, The Netherlands
B C Ke Zhengzhou University, Zhengzhou 450001, People's Republic of China
V Khachatryan Indiana University, Bloomington, Indiana 47405, USA
A Khoukaz University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
R Kiuchi Institute of High Energy Physics, Beijing 100049, People's Republic of China
R Kliemt GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
O B Kolcu Turkish Accelerator Center Particle Factory Group, Istinye University, 34010 Istanbul, Turkey
B Kopf Bochum Ruhr-University, D-44780 Bochum, Germany
M Kuessner Bochum Ruhr-University, D-44780 Bochum, Germany
A Kupsc National Centre for Nuclear Research, Warsaw 02-093, Poland Uppsala University, Box 516, SE-75120 Uppsala, Sweden
W Kühn Justus-Liebig-Universitaet Giessen, II. Physikalisches Institut, Heinrich-Buff-Ring 16, D-35392 Giessen, Germany
J J Lane University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
P Larin Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
A Lavania Indian Institute of Technology Madras, Chennai 600036, India
L Lavezzi University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
T T Lei 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 H Lei 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 Leithoff Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
M Lellmann Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
T Lenz Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
C Li Qufu Normal University, Qufu 273165, People's Republic of China
C Li Nankai University, Tianjin 300071, People's Republic of China
C H Li Liaoning Normal University, Dalian 116029, People's Republic of China
Cheng 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
D M Li Zhengzhou University, Zhengzhou 450001, People's Republic of China
F Li 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
G Li Institute of High Energy Physics, Beijing 100049, People's Republic of China
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
H J Li Henan Normal University, Xinxiang 453007, People's Republic of China
H N Li South China Normal University, Guangzhou 510006, People's Republic of China
Hui Li Nankai University, Tianjin 300071, People's Republic of China
J R Li Tsinghua University, Beijing 100084, People's Republic of China
J S Li Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
J W Li Shandong University, Jinan 250100, People's Republic of China
Ke Li Institute of High Energy Physics, Beijing 100049, People's Republic of China
L J 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
L K Li Institute of High Energy Physics, Beijing 100049, People's Republic of China
Lei Li Renmin University of China, Beijing 100872, People's Republic of China
M H Li Nankai University, Tianjin 300071, People's Republic of China
P R Li Lanzhou University, Lanzhou 730000, People's Republic of China
Q X Li Shandong University, Jinan 250100, People's Republic of China
S X Li Fudan University, Shanghai 200433, People's Republic of China
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
W G Li Institute of High Energy Physics, Beijing 100049, People's Republic of China
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
Xiaoyu 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
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
Z X Li Guangxi University, Nanning 530004, People's Republic of China
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
Y F Liang Sichuan University, Chengdu 610064, People's Republic of China
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
L Z Liao Shandong University, Jinan 250100, People's Republic of China
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
A Limphirat Suranaree University of Technology, University Avenue 111, Nakhon Ratchasima 30000, Thailand
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
T Lin Institute of High Energy Physics, Beijing 100049, People's Republic of China
B J Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
B X Liu Wuhan University, Wuhan 430072, People's Republic of China
C Liu Jilin University, Changchun 130012, People's Republic of China
C X Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
F H Liu Shanxi University, Taiyuan 030006, People's Republic of China
Fang Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
Feng Liu Central China Normal University, Wuhan 430079, People's Republic of China
G M Liu South China Normal University, Guangzhou 510006, People's Republic of China
H Liu Lanzhou University, Lanzhou 730000, People's Republic of China
H B Liu Guangxi University, Nanning 530004, People's Republic of China
H M Liu 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
Huanhuan Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
Huihui Liu Henan University of Science and Technology, Luoyang 471003, People's Republic of China
J 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
J Y Liu 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
K Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
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

Collapse

Genome-wide association study and genomic prediction for resistance to brown planthopper in rice

The brown planthopper (BPH) is the most destructive insect pest that threatens rice production globally. Developing rice varieties incorporating BPH-resistant genes has proven to be an effective control measure against BPH. In this study, we assessed the resistance of a core collection consisting of 502 rice germplasms by evaluating resistance scores, weight gain rates and honeydew excretions. A total of 117 rice varieties (23.31%) exhibited resistance to BPH. Genome-wide association studies (GWAS) were performed on both the entire panel of 502 rice varieties and its subspecies, and 6 loci were significantly associated with resistance scores (P value < 1.0e-8). Within these loci, we identified eight candidate genes encoding receptor-like protein kinase (RLK), nucleotide-binding and leucine-rich repeat (NB-LRR), or LRR proteins. Two loci had not been detected in previous study and were entirely novel. Furthermore, we evaluated the predictive ability of genomic selection for resistance to BPH. The results revealed that the highest prediction accuracy for BPH resistance reached 0.633. As expected, the prediction accuracy increased progressively with an increasing number of SNPs, and a total of 6.7K SNPs displayed comparable accuracy to 268K SNPs. Among various statistical models tested, the random forest model exhibited superior predictive accuracy. Moreover, increasing the size of training population improved prediction accuracy; however, there was no significant difference in prediction accuracy between a training population size of 737 and 1179. Additionally, when there existed close genetic relatedness between the training and validation populations, higher prediction accuracies were observed compared to scenarios when they were genetically distant. These findings provide valuable resistance candidate genes and germplasm resources and are crucial for the application of genomic selection for breeding durable BPH-resistant rice varieties.

[Prognostic nutritional index application value for acute-on-chronic liver failure co-infection]

Objective: To explore the predictive value of the prognostic nutritional index (PNI) in concurrently infected patients with acute-on-chronic liver failure (ACLF). Methods: 220 cases with ACLF diagnosed and treated at the First Affiliated Hospital of Xi'an Jiaotong University from January 2011 to December 2016 were selected. Patients were divided into an infection and non-infection group according to whether they had co-infections during the course of the disease. Clinical data differences were compared between the two groups of patients. Binary logistic regression analysis was used to screen out influencing factors related to co-infection. The receiver operating characteristic curve was used to evaluate the predictive value of PNI for ACLF co-infection. The measurement data between groups were compared using the independent sample t-test and the Mann-Whitney U rank sum test. The enumeration data were analyzed using the Fisher exact probability test or the Pearson χ(2) test. The Pearson method was performed for correlation analysis. The independent risk factors for liver failure associated with co-infection were analyzed by multivariate logistic analysis. Results: There were statistically significant differences in ascites, hepatorenal syndrome, PNI score, and albumin between the infection and the non-infection group (P < 0.05). Among the 220 ACLF cases, 158 (71.82%) were infected with the hepatitis B virus (HBV). The incidence rate of infection during hospitalization was 69.09% (152/220). The common sites of infection were intraabdominal (57.07%) and pulmonary infection (29.29%). Pearson correlation analysis showed that PNI and MELD-Na were negatively correlated (r = -0.150, P < 0.05). Multivariate logistic analysis results showed that low PNI score (OR=0.916, 95%CI: 0.865~0.970), ascites (OR=4.243, 95%CI: 2.237~8.047), and hepatorenal syndrome (OR=4.082, 95%CI : 1.106~15.067) were risk factors for ACLF co-infection (P < 0.05). The ROC results showed that the PNI curve area (0.648) was higher than the MELD-Na score curve area (0.610, P < 0.05). The effectiveness of predicting infection risk when PNI was combined with ascites and hepatorenal syndrome complications was raised. Patients with co-infections had a good predictive effect when PNI ≤ 40.625. The sensitivity and specificity were 84.2% and 41.2%, respectively. Conclusion: Low PNI score and ACLF co-infection have a close correlation. Therefore, PNI has a certain appraisal value for ACLF co-infection.

A three-dimensional (3D) liver-kidney on a chip with a biomimicking circulating system for drug safety evaluation

The liver and kidney are the major detoxifying organs in the human body and play an important role in pharmacokinetics. Drug-induced hepatotoxicity and nephrotoxicity can cause irreversible damage to the liver and kidney and are a major cause of drug failure in later stages. Both animal models and conventional cell culture have a number of limitations, such as animal ethics and gene mismatching and there is an urgent need to develop a new drug toxicity evaluation approach. In this paper, a 3D liver-kidney on a chip with a biomimicking circulating system (LKOCBCS) was constructed to obtain kidney and liver models in vitro for drug safety evaluation. LKOCBCS, which has a parallel circulating system mimicking biological circulation, consists of 3D biomimetic tissue of liver lobules similar to that of the human liver constructed by 3D bioprinting and renal proximal tubule barriers fabricated by ultrafast laser assisted etching. The proposed LKOCBCS facilitates the communication between the liver and the kidney, including the exchange of nutrients, compounds, and metabolites. The results revealed that the glucose concentration and cell metabolism stabilized after 7 days. A dynamically repeated low-dose administration of cyclosporine A (CsA) was fed to the system, and hepatotoxicity and nephrotoxicity were observed on day 3 according to the changes in toxicity markers. The high levels of drug induced biomarkers expressed in LKOCBCS indicate that this system is more sensitive than the monoculture liver chip and it is highly potential in replacing animal models for effective drug toxicity screening.

Investigation of the ΔI=1/2 Rule and Test of CP Symmetry through the Measurement of Decay Asymmetry Parameters in Ξ^{-} Decays

Using (10087±44)×10^{6}  J/ψ events collected with the BESIII detector, numerous Ξ^{-} and Λ decay asymmetry parameters are simultaneously determined from the process J/ψ→Ξ^{-}Ξ[over ¯]^{+}→Λ(pπ^{-})π^{-}Λ[over ¯](n[over ¯]π^{0})π^{+} and its charge-conjugate channel. The precisions of α_{Λ0} for Λ→nπ^{0} and α[over ¯]_{Λ0} for Λ[over ¯]→n[over ¯]π^{0} compared to world averages are improved by factors of 4 and 1.7, respectively. The ratio of decay asymmetry parameters of Λ→nπ^{0} to that of Λ→pπ^{-}, ⟨α_{Λ0}⟩/⟨α_{Λ-}⟩, is determined to be 0.873±0.012_{-0.010}^{+0.011}, where the first and the second uncertainties are statistical and systematic, respectively. The ratio is smaller than unity more than 5σ, which signifies the existence of the ΔI=3/2 transition in Λ for the first time. Besides, we test for CP symmetry in Ξ^{-}→Λπ^{-} and in Λ→nπ^{0} with the best precision to date.

Factors related to acupuncture response in patients with chronic prostatitis/chronic pelvic pain syndrome: secondary analysis of a randomized controlled trial

PURPOSE

Acupuncture has been recommended as an effective therapy to improve symptoms of chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS). We conducted this secondary analysis to explore the factors that may influence the response of patients with CP/CPPS to acupuncture.

METHODS

This secondary analysis was based on a randomized controlled trial demonstrating the efficacy of acupuncture among patients with CP/CPPS. Responder is defined as a patient with a decrease of  ≥ 6 points in National Institutes of Health Chronic Prostatitis Symptom Index (NIH-CPSI) total score from baseline at the 32 week. 206 patients who received acupuncture treatment and completed 32-week follow-up were included in this secondary analysis. Descriptive statistics were used to describe the demographic and clinical characteristics of both responders and non-responders in acupuncture group. Logistic regression analysis with bootstrapping was made to identify potential factors that contributed to the effectiveness of acupuncture for treating CP/CPPS. Responders and non-responders were listed as dependent variables.

RESULTS

In this study, 130 (63.11%) patients were assessed as responders. The results showed that men with non-sedentariness (OR 4.170 [95%CI 1.837 to 9.463; P = 0.001]), non-smoking habit (OR 2.824 [95%CI 1.453 to 5.487; P = 0.002]), without comorbidity (OR 8.788 [95%CI 1.912 to 40.295; P = 0.005]), and severe NIH-CPSI total score (OR 0.227 [95%CI 0.114 to 0.450; P < 0.0001]) benefited more from acupuncture intervention.

CONCLUSION

CP/CPPS patients who are active, non-smokers, without comorbidity, and had severe symptoms may be more likely to respond to acupuncture.

Observation of D_{s}^{+}→η^{'}μ^{+}ν_{μ}, Precision Test of Lepton Flavor Universality with D_{s}^{+}→η^{(')}l^{+}ν_{l}, and First Measurements of D_{s}^{+}→η^{(')}μ^{+}ν_{μ} Decay Dynamics

By analyzing 7.33  fb^{-1} of e^{+}e^{-} annihilation data collected at center-of-mass energies between 4.128 and 4.226 GeV with the BESIII detector, we report the observation of the semileptonic decay D_{s}^{+}→η^{'}μ^{+}ν_{μ}, with a statistical significance larger than 10σ, and the measurements of the D_{s}^{+}→ημ^{+}ν_{μ} and D_{s}^{+}→η^{'}μ^{+}ν_{μ} decay dynamics for the first time. The branching fractions of D_{s}^{+}→ημ^{+}ν_{μ} and D_{s}^{+}→η^{'}μ^{+}ν_{μ} are determined to be (2.235±0.051_{stat}±0.052_{syst})% and (0.801±0.055_{stat}±0.028_{syst})%, respectively, with precision improved by factors of 6.0 and 6.6 compared to the previous best measurements. Combined with the results for the decays D_{s}^{+}→ηe^{+}ν_{e} and D_{s}^{+}→η^{'}e^{+}ν_{e}, the ratios of the decay widths are examined both inclusively and in several ℓ^{+}ν_{ℓ} four-momentum transfer ranges. No evidence for lepton flavor universality violation is found within the current statistics. The products of the hadronic form factors f_{+,0}^{η^{(')}}(0) and the c→s Cabibbo-Kobayashi-Maskawa matrix element |V_{cs}| are determined. The results based on the two-parameter series expansion are f_{+,0}^{η}(0)|V_{cs}|=0.452±0.010_{stat}±0.007_{syst} and f_{+,0}^{η^{'}}(0)|V_{cs}|=0.504±0.037_{stat}±0.012_{syst}, which help to constrain present models on f_{+,0}^{η^{(')}}(0). The forward-backward asymmetries are determined to be ⟨A_{FB}^{η}⟩=-0.059±0.031_{stat}±0.005_{syst} and ⟨A_{FB}^{η^{'}}⟩=-0.064±0.079_{stat}±0.006_{syst} for the first time, which are consistent with the theoretical calculation.

Risk factors and prediction models for bronchiolitis obliterans after severe adenoviral pneumonia

Severe adenoviral pneumonia (SAP) can cause post-infectious bronchiolitis obliterans (PIBO) in children. We aimed to investigate the relevant risk factors for PIBO and develop a predictive nomogram for PIBO in children with SAP. This prospective study analysed the clinical data of hospitalised children with SAP and categorised them into the PIBO and non-PIBO groups. Least absolute shrinkage and selection operator (LASSO) regressions were applied to variables that exhibited significant intergroup differences. Logistic regression was adopted to analyse the risk factors for PIBO. Additionally, a nomogram was constructed, and its effectiveness was assessed using calibration curves, C-index, and decision curve analysis. A total of 148 hospitalised children with SAP were collected in this study. Among them, 112 achieved favourable recovery, whereas 36 developed PIBO. Multivariable regression after variable selection via LASSO revealed that aged < 1 year (OR, 2.38, 95% CI, 0.82-6.77), admission to PICU (OR, 24.40, 95% CI, 7.16-105.00), long duration of fever (OR, 1.16, 95% CI, 1.04-1.31), and bilateral lung infection (OR, 8.78, 95% CI, 1.32-195.00) were major risk factors for PIBO. The nomogram model included the four risk factors: The C-index of the model was 0.85 (95% CI, 0.71-0.99), and the area under the curve was 0.85 (95% CI, 0.78-0.92). The model showed good calibration with the Hosmer-Lemeshow test (χ2 = 8.52, P = 0.38) and was useful in clinical settings with decision curve analysis.

CONCLUSION

Age < 1 year, PICU admission, long fever duration, and bilateral lung infection are independent risk factors for PIBO in children with SAP. The nomogram model may aid clinicians in the early diagnosis and intervention of PIBO.

WHAT IS KNOWN

• Adenoviruses are the most common pathogens associated with PIBO. • Wheezing, tachypnoea, hypoxemia, and mechanical ventilation are the risk factors for PIBO.

WHAT IS NEW

• Age < 1 year, admission to PICU, long duration of fever days, and bilateral lung infection are independent risk factors for PIBO in children with SAP. • A prediction model presented as a nomogram may help clinicians in the early diagnosis and intervention of PIBO.

Quantitative structure-property relationship modelling on autoignition temperature: evaluation and comparative analysis

The autoignition temperature (AIT) serves as a crucial indicator for assessing the potential hazards associated with a chemical substance. In order to gain deeper insights into model performance and facilitate the establishment of effective methodological practices for AIT predictions, this study conducts a benchmark investigation on Quantitative Structure-Property Relationship (QSPR) modelling for AIT. As novelties of this work, three significant advancements are implemented in the AIT modelling process, including explicit consideration of data quality, utilization of state-of-the-art feature engineering workflows, and the innovative application of graph-based deep learning techniques, which are employed for the first time in AIT prediction. Specifically, three traditional QSPR models (multi-linear regression, support vector regression, and artificial neural networks) are evaluated, alongside the assessment of a deep-learning model employing message passing neural network architecture supplemented by graph-data augmentation techniques.

Differentiation of Crohn's disease, ulcerative colitis, and intestinal tuberculosis by dual-layer spectral detector CT enterography

AIM

To investigate the value of radiological features and energy spectrum quantitative parameters in the differential diagnosis of Crohn's disease (CD), ulcerative colitis (UC), and intestinal tuberculosis (ITB) by dual-layer spectral detector computed tomography (CT) enterography (CTE).

MATERIALS AND METHODS

Clinical and CTE data were collected from 182 patients with CD, 29 with UC, and 51 with ITB. CT images were obtained at the enteric phases and portal phases. The quantitative energy spectrum parameters were iodine density (ID), normalised ID (NID), virtual non-contrast (VNC) value, and effective atomic number (Z-eff). The area under curve (AUC) of the receiver operating characteristic curve (ROC) was calculated.

RESULTS

The vascular comb sign (p=0.009) and enlarged lymph nodes (p=0.001) were more common in patients with CD than UC or ITB. In the differentiation of moderate-severe active CD from UC, enteric phase NID (AUC, 0.938; p<0.001) and portal phase Z-eff (AUC, 0.925; p<0.001) had the highest accuracy, which were compared separately. In the differentiation of moderate-severe active CD from ITB, enteric phase NID (AUC, 0.906; p<0.001) and portal phase Z-eff (AUC, 0.947; p<0.001) had the highest accuracy; however, the AUC value was highest when the four parameters are combined (AUC, 0.989; p<0.001; AUC, 0.986; p<0.001; AUC, 0.936; p<0.001; and AUC, 0.986; p<0.001).

CONCLUSION

The present study shows that the combined strategies of four parameters have higher sensitivity and specificity in differentiating CD, UC, and ITB, and may play a key role in guiding treatment.

Estimation and projection of the burden of hearing loss in China: findings from the Global Burden of Disease Study 2019

OBJECTIVES

The comprehensive description of hearing loss in China and the shifting pattern remain unclear. We conducted the study to estimate the burden of hearing loss in China and project the trends from 2020 to 2034.

STUDY DESIGN AND METHOD

Data on the disease burden of hearing loss were extracted from the Global Burden of Disease Study 2019. Estimated annual percentage changes (EAPCs) were calculated to quantify the trends of the age-standardized rates. Projections of hearing loss burden were made until 2034 using Bayesian age-period-cohort analysis.

RESULTS

In China, prevalent cases of hearing loss increased from 224.4 million in 1990 to 426.5 million in 2019, representing an increase of 90.1 %. The age-standardized prevalence rate of hearing loss ranged from 22,592.8/100,000 in 1990-22,612.4/100,000 in 2019, with an EAPC of 0.003 %, representing a stable trend. Of the category of hearing loss, mild hearing loss accounted for the highest proportion, with 331.4 million people. More than 95 million people had moderate-to-complete hearing loss. Moreover, hearing loss was mostly attributable to age-related and other factors for adults and otitis media for children younger than 10 years. Based on the projection results, there will be 561 million people (40.1 % of the total population) have hearing loss by 2034.

CONCLUSIONS

The prevalent cases of hearing loss in China substantially increased over the last 30 years. Over two in five Chinese people will have hearing loss by 2034, thus suggesting more solutions should be established to reduce the burden of hearing loss.

Determination of the Σ^{+} Timelike Electromagnetic Form Factors

Based on data samples collected with the BESIII detector at the BEPCII collider, the process e^{+}e^{-}→Σ^{+}Σ[over ¯]^{-} is studied at center-of-mass energies sqrt[s]=2.3960, 2.6454, and 2.9000 GeV. Using a fully differential angular description of the final state particles, both the relative magnitude and phase information of the Σ^{+} electromagnetic form factors in the timelike region are extracted. The relative phase between the electric and magnetic form factors is determined to be sinΔΦ=-0.67±0.29(stat)±0.18(syst) at sqrt[s]=2.3960  GeV, ΔΦ=55°±19°(stat)±14°(syst) at sqrt[s]=2.6454  GeV, and 78°±22°(stat)±9°(syst) at sqrt[s]=2.9000  GeV. For the first time, the phase of the hyperon electromagnetic form factors is explored in a wide range of four-momentum transfer. The evolution of the phase along with four-momentum transfer is an important input for understanding its asymptotic behavior and the dynamics of baryons.

[Research progress on the role of hepatitis B virus DNA integration in chronic hepatitis B]

Chronic hepatitis B virus (HBV) infection will greatly contribute to raising the occurrence probability of cirrhosis and hepatocellular carcinoma in patients. Although existing antiviral treatment regimens have a certain effect on delaying disease progression and improving prognosis, it is still not effective in attaining functional cures. Hepatitis B virus DNA integration may be one of the reasons for this phenomenon. Therefore, this paper reviews the possible mechanisms of HBV DNA integration in maintaining chronic inflammation of the liver, evading existing antiviral treatment methods, and inducing hepatocellular carcinoma so as to further deepen the understanding of the role of HBV DNA integration in the occurrence and development of chronic hepatitis B, providing ideas and references for formulating better treatment strategies.

Individualised neoantigen therapy mRNA-4157 (V940) plus pembrolizumab versus pembrolizumab monotherapy in resected melanoma (KEYNOTE-942): a randomised, phase 2b study

BACKGROUND

Checkpoint inhibitors are standard adjuvant treatment for stage IIB-IV resected melanoma, but many patients recur. Our study aimed to evaluate whether mRNA-4157 (V940), a novel mRNA-based individualised neoantigen therapy, combined with pembrolizumab, improved recurrence-free survival and distant metastasis-free survival versus pembrolizumab monotherapy in resected high-risk melanoma.

METHODS

We did an open-label, randomised, phase 2b, adjuvant study of mRNA-4157 plus pembrolizumab versus pembrolizumab monotherapy in patients, enrolled from sites in the USA and Australia, with completely resected high-risk cutaneous melanoma. Patients with completely resected melanoma (stage IIIB-IV) were assigned 2:1 to receive open-label mRNA-4157 plus pembrolizumab or pembrolizumab monotherapy. mRNA-4157 was administered intramuscularly (maximum nine doses) and pembrolizumab intravenously (maximum 18 doses) in 3-week cycles. The primary endpoint was recurrence-free survival in the intention-to-treat population. This ongoing trial is registered at ClinicalTrials.gov, NCT03897881.

FINDINGS

From July 18, 2019, to Sept 30, 2021, 157 patients were assigned to mRNA-4157 plus pembrolizumab combination therapy (n=107) or pembrolizumab monotherapy (n=50); median follow-up was 23 months and 24 months, respectively. Recurrence-free survival was longer with combination versus monotherapy (hazard ratio [HR] for recurrence or death, 0·561 [95% CI 0·309-1·017]; two-sided p=0·053), with lower recurrence or death event rate (24 [22%] of 107 vs 20 [40%] of 50); 18-month recurrence-free survival was 79% (95% CI 69·0-85·6) versus 62% (46·9-74·3). Most treatment-related adverse events were grade 1-2. Grade ≥3 treatment-related adverse events occurred in 25% of patients in the combination group and 18% of patients in the monotherapy group, with no mRNA-4157-related grade 4-5 events. Immune-mediated adverse event frequency was similar for the combination (37 [36%]) and monotherapy (18 [36%]) groups.

INTERPRETATION

Adjuvant mRNA-4157 plus pembrolizumab prolonged recurrence-free survival versus pembrolizumab monotherapy in patients with resected high-risk melanoma and showed a manageable safety profile. These results provide evidence that an mRNA-based individualised neoantigen therapy might be beneficial in the adjuvant setting.

FUNDING

Moderna in collaboration with Merck Sharp & Dohme, a subsidiary of Merck & Co, Rahway, NJ, USA.

Discovery of New Isotopes ^{160}Os and ^{156}W: Revealing Enhanced Stability of the N=82 Shell Closure on the Neutron-Deficient Side

Using the fusion-evaporation reaction ^{106}Cd(^{58}Ni,4n)^{160}Os and the gas-filled recoil separator SHANS, two new isotopes _{76}^{160}Os and _{74}^{156}W have been identified. The α decay of ^{160}Os, measured with an α-particle energy of 7080(26) keV and a half-life of 201_{-37}^{+58}  μs, is assigned to originate from the ground state. The daughter nucleus ^{156}W is a β^{+} emitter with a half-life of 291_{-61}^{+86}  ms. The newly measured α-decay data allow us to derive α-decay reduced widths (δ^{2}) for the N=84 isotones up to osmium (Z=76), which are found to decrease with increasing atomic number above Z=68. The reduction of δ^{2} is interpreted as evidence for the strengthening of the N=82 shell closure toward the proton drip line, supported by the increase of the neutron-shell gaps predicted in theoretical models.

Nonlinear error analysis of fast optical delay lines

Optical delay lines have wide applications in terahertz time-domain spectroscopy and optical coherence tomography. In this study, a fast-rotating optical delay line (FRODL) with 24 turntable reflection surfaces was designed. By analyzing the working principle of the FRODL, a mathematical model was established for the nonlinear parameter error of the FRODL delay time. By constructing the polarization Michelson interference system and testing the FRODL structure, the error of actual assembly parameters of the FRODL was approximately 0.015 mm, the actual delay time of the FRODL was greater than 43.5 ps, and the linearity was 99.785%.

[Research progress in clinical diagnosis and treatment of osteosarcoma of the jaw]

Osteosarcoma of the jaw (JOS), is a relatively rare type of osteosarcoma, with a unique pathogenesis and pathological manifestations. The clinical manifestation of JOS is not characteristic, and it often needs to be diagnosed by combining radiological and pathological examination. At present, the conventional treatment of JOS is a comprehensive treatment based on surgery and supplemented by radiotherapy and chemotherapy. Recently, the emergence of new therapies such as immunotherapy, gene therapy, phototherapy and traditional Chinese medicine has provided more choices for treatment and brought new hope to patients with JOS. Therefore, this article summarized the current understanding of diagnosis and the latest treatment development of JOS.

Lactate score classification of hepatocellular carcinoma helps identify patients with tumors that respond to immune checkpoint blockade therapy

PURPOSE

Hepatocellular carcinoma (HCC) responds poorly to immunotherapy, and the durable response rate is 10-20%. Here, we aim to characterize HCC classifications based on lactate genes to identify patients who may benefit from immunotherapy.

METHODS

Lactate-related genes were applied for HCC classification in the current study, and lactate Cluster 1 (LC1) and lactate Cluster 2 (LC2) were defined. Differential genes from LC1 and LC2 helped define the following lactate phenotype clusters: lactate phenotype Cluster 1 (LPC1), lactate phenotype Cluster 2 (LPC2) and lactate phenotype Cluster 3 (LPC3). Based on the cluster annotation, the lactate score was defined and analyzed to evaluate the immunotherapy response.

RESULTS

All the classified clusters were analyzed, and they showed different immune signatures. The survival rate of LPC3 was higher than that of LPC2 (LPC3 vs. LPC2, P = 0.027) and LPC1 (LPC3 vs. LPC1, P = 0.027). Then, the lactate score was annotated and confirmed to be effective in predicting responses to immune checkpoint blockade therapy.

CONCLUSION

In the current study, we developed a classification system for HCC and defined the lactate score, which was validated to be partially effective in estimating responses among tumor patients.

Endosymbiotic Fungal Diversity and Dynamics of the Brown Planthopper across Developmental Stages, Tissues, and Sexes Revealed Using Circular Consensus Sequencing

Endosymbiotic fungi play an important role in the growth and development of insects. Understanding the endosymbiont communities hosted by the brown planthopper (BPH; Nilaparvata lugens Stål), the most destructive pest in rice, is a prerequisite for controlling BPH rice infestations. However, the endosymbiont diversity and dynamics of the BPH remain poorly studied. Here, we used circular consensus sequencing (CCS) to obtain 87,131 OTUs (operational taxonomic units), which annotated 730 species of endosymbiotic fungi in the various developmental stages and tissues. We found that three yeast-like symbionts (YLSs), Polycephalomyces prolificus, Ophiocordyceps heteropoda, and Hirsutella proturicola, were dominant in almost all samples, which was especially pronounced in instar nymphs 4-5, female adults, and the fat bodies of female and male adult BPH. Interestingly, honeydew as the only in vitro sample had a unique community structure. Various diversity indices might indicate the different activity of endosymbionts in these stages and tissues. The biomarkers analyzed using LEfSe suggested some special functions of samples at different developmental stages of growth and the active functions of specific tissues in different sexes. Finally, we found that the incidence of occurrence of three species of Malassezia and Fusarium sp. was higher in males than in females in all comparison groups. In summary, our study provides a comprehensive survey of symbiotic fungi in the BPH, which complements the previous research on YLSs. These results offer new theoretical insights and practical implications for novel pest management strategies to understand the BPH-microbe symbiosis and devise effective pest control strategies.

The first compound heterozygous mutations in SLC12A3 and PDX1 genes: a unique presentation of Gitelman syndrome with distinct insulin resistance and familial diabetes insights

Background

Gitelman Syndrome (GS) patients frequently exhibit disrupted glucose metabolism, attributed to hypokalemia, hypomagnesemia and heightened aldosterone. This study delved into the genetic underpinnings linked to insulin resistance and diabetes in a GS patient, contextualized within his family history.

Methods

The hydrochlorothiazide and furosemide loading test were performed to ascertain the presence of GS. Oral glucose tolerance test (OGTT) evaluated glucose metabolism and insulin sensitivity. Whole-exome sequencing, validated by Sanger sequencing, was employed to confirm gene mutations, which were then tracked among the patient's relatives.

Results

Symptoms and laboratory examination confirmed the clinical diagnosis of GS. Comprehensive whole-exome sequencing, augmented by Sanger sequencing validation, revealed a compound heterozygous mutation within the SLC12A3 gene (c.1108G>C in exon 9, c.676G>A in exon 5 and c.2398G>A in exon 20) in the patient. The OGTT affirmed diabetes and heightened insulin resistance, distinct from previous patients with GS we evaluated. Further genetic analysis identified a missense heterozygous mutation (c.97C>G in exon 1) within the PDX1 gene, inherited from the patient's diabetic mother without GS. Furthermore, the patient's brother, with impaired glucose tolerance but regular potassium levels, also bore this mutation, hinting at additional impacts of the PDX1 gene mutation on glucose metabolism regulation beyond the known impacts of GS.

Conclusion

This study unveils unprecedented compound heterozygous mutations in the SLC12A3 and PDX1 genes in a GS patient. These findings illuminate the potential complex genetic factors influencing glucose metabolism disruptions in GS.

Take-home message

This research uncovers a novel combination of SLC12A3 and PDX1 gene mutations in a Gitelman Syndrome patient, revealing intricate genetic factors that potentially disrupt glucose metabolism and shedding light on familial diabetes links.

First Measurement of the Decay Asymmetry in the Pure W-Boson-Exchange Decay Λ_{c}^{+}→Ξ^{0}K^{+}

Based on 4.4  fb^{-1} of e^{+}e^{-} annihilation data collected at the center-of-mass energies between 4.60 and 4.70 GeV with the BESIII detector at the BEPCII collider, the pure W-boson-exchange decay Λ_{c}^{+}→Ξ^{0}K^{+} is studied with a full angular analysis. The corresponding decay asymmetry is measured for the first time to be α_{Ξ^{0}K^{+}}=0.01±0.16(stat)±0.03(syst). This result reflects the noninterference effect between the S- and P-wave amplitudes. The phase shift between S- and P-wave amplitudes has two solutions, which are δ_{p}-δ_{s}=-1.55±0.25(stat)±0.05(syst)  rad or 1.59±0.25(stat)±0.05(syst)  rad.

[Prevalence of urinary incontinence in middle-aged and elderly adults in 10 areas in China]

Objective: To describe the population and area distribution differences in the prevalence of urinary incontinence in middle-aged and elderly adults in 10 areas in China. Methods: A total of 24 913 participants aged 45-95 years who completed the third resurvey of China Kadoorie Biobank during 2020-2021 were included. The prevalence of urinary incontinence was assessed by an interviewer-administered questionnaire, and urinary incontinence was classified as only stress urinary incontinence, only urgency urinary incontinence and mixed urinary incontinence. The prevalence of urinary incontinence and its subtypes were reported by sex, age and area, and the severity of urinary incontinence and treatment were described. Results: The average age of the participants was (65.4±9.1) years. According to the seventh national census data in 2020, the age-standardized prevalence rates of urinary incontinence was 25.4% in women and 7.0% in men. The age-standardized prevalence rates of only stress, only urgency and mixed incontinence were 1.7%, 4.2% and 1.2% in men and 13.5%, 5.8% and 6.1% in women, respectively. The prevalence rates of urinary incontinence and all subtypes in men and the prevalence of urinary incontinence and all subtypes except only stress urinary incontinence in women all increased with age (P<0.001). After adjusting for age, the prevalence of urinary incontinence in both men and women were higher in rural area than in urban area (P<0.001). The treatment rates in men and women with urinary incontinence were 15.4% and 8.5%, respectively. Conclusions: The prevalence of urinary incontinence was high in middle-aged and elderly adults in China, and the prevalence rate was higher in women than in men, but the treatment rate of urinary incontinence was low.

[A multi-dimensional analysis of pollen broadcasting concerns in Chinese population: a large-scale multi-center cross-sectional survey]

Objective: To investigate the concern about pollen broadcasting in Chinese population from multiple dimensions and to understand the information about allergic rhinitis (AR) in China by analyzing related factors. Methods: From March 1 to September 30, 2022, a large-scale multi-center cross-sectional survey was conducted based on the Questionnaire Star platform in 21 Chinese hospitals. A total of 7 056 subjects from 7 regions in China: Northeast, North, East, Central, South, Southwest, and Northwest China were included. Basic characteristics (including social demographic characteristics and disease characteristics of AR patients), concern about pollen broadcasting, the willingness of pollen-induced AR (PiAR) patients to receive pollen broadcasting, and the treatment satisfaction rate of AR patients were collected. The chi-square test, multivariate linear regression model, and Logistic regression analysis were used to analyze the concern about pollen broadcasting in the Chinese population and related factors from multiple dimensions. Results: Among 7 056 subjects, 23.02% were concerned about pollen broadcasting. Among 3 176 self-reported AR and 1 019 PiAR patients, 25.60% and 39.16% were concerned about pollen broadcasting, respectively, which was higher than that of non-AR or non-PiAR subjects (χ2 value was 21.74 and 175.11, respectively, both P<0.001). Among AR patients, the proportion of spring and autumn allergen-positive patients concerned about pollen broadcasting was higher than that in perennial allergen-positive patients (χ2 value was 20.90 and 19.51, respectively, both P<0.001). The proportion of AR patients with asthma, sinusitis, allergic conjunctivitis, and cardiovascular and cerebrovascular diseases was higher than those without complications (χ2 value was 50.83, 21.97, 56.78, 7.62, respectively, all P<0.05). The proportion of AR patients in North China who could find pollen broadcasting locally was 31.01%, significantly higher than those in other regions (all P<0.05). Multivariate linear regression model analysis showed that among PiAR patients, those with higher per capita household income and higher AR disease cognition levels had been concerned about pollen broadcasting in the past, and those complicated with allergic conjunctivitis had stronger intention to receive pollen broadcasting (B value was 0.24, 0.13, 0.66, 0.47, respectively, all P<0.05). The higher the disease cognition level of PiAR patients, the stronger their willingness to actively participate in treatment (R2=0.72, P<0.001). Only 18.89% of AR patients felt satisfied with the treatment effect. Logistic regression analysis showed that in AR patients, the treatment satisfaction rate was significantly higher among those concerned about pollen broadcasting compared to those who were not (OR=1.83, P<0.001). Conclusions: Currently, the dissemination of pollen broadcasting in China is hindered by various factors such as disease cognition level. The treatment satisfaction among AR patients remains unsatisfactory.

[Monitoring and analysis on host animals of hemorrhagic fever with renal syndrome in Henan Province from 2019 to 2022]

Objective: To investigate the distribution and hantavirus (HV) carrying state in host animals of hemorrhagic fever with renal syndrome (HFRS) in Henan Province from 2019 to 2022. Methods: Host animal monitoring was carried out at the monitoring sites of HFRS in Henan Province. The real-time fluorescence quantitative PCR was used to detect hantavirus in rat lungs. The types of hantavirus were analyzed. The positive samples were sequenced and then sequence homology and variation were analyzed. Results: A total of 1 308 rodents were captured from 2019 to 2022, 16 specimens of rat lungs tested positive for hantavirus nucleic acid. The positive rate of HV was 1.22% (16/1 308). According to type, the positive rate of HV in Apodius agrarius was the highest (68.75%, 11/16). According to distribution, the positive rate of HV in field samples was the highest (2.50%, 12/480), and the positive rate of HV in residential samples was 0.53% (4/759). The typing results of 16 positive samples showed that all viruses were hantavirus type Ⅰ (hantaan virus). The positive samples were sequenced and eight S gene fragments (GenBank number: OQ681444-OQ681451) and six M gene fragments (OQ681438-OQ681443) were obtained. The S and M gene fragments were similar to the Shaanxi 84FLi strain and Sichuan SN7 strain. Phylogenetic analysis of S and M gene fragments showed that they all belonged to the hantaan virus-H5 subtype. Amino acid sequence analysis revealed that, compared with the hantaan virus vaccine strain 84FLi, the 74th amino acid encoded by eight S fragments was replaced by aspartamide with serine. Tryptophan was replaced by glycine at the 14th position of Gn region in XC2022047, and isoleucine was replaced by alanine at the 359 position of XC2022022 and XC2022024. Conclusion: The hantavirus carried by host animals in Henan Province from 2019 to 2022 belongs to the type Ⅰ (hantaan virus), and Apodemus agrarius is still the dominant host animal of the hantaan virus. Compared with the vaccine strains, amino acid sites are replaced in the immune epitopes of the S and M gene fragments.

Upregulation of PTPN1 aggravates endotoxemia-induced cardiac dysfunction through inhibiting mitophagy

OBJECTIVES

To investigate the role of protein tyrosine phosphatase non-receptor type 1 (PTPN1) in mitophagy during sepsis and its underlying mechanisms and determine the therapeutic potential of PTPN1 inhibitors in endotoxemia-induced cardiac dysfunction.

METHODS

A mouse model of endotoxemia was established by administering an intraperitoneal injection of lipopolysaccharide (LPS). The therapeutic effect of targeting PTPN1 was evaluated using its inhibitor Claramine (CLA). Mitochondrial structure and function as well as the expression of mitophagy-related proteins were evaluated. Rat H9c2 cardiomyocytes were exposed to mouse RAW264.7 macrophage-derived conditioned medium. Cryptotanshinone, a specific p-STAT3 (Y705) inhibitor, was used to confirm the role of STAT3 in PTPN1-mediated mitophagy following LPS exposure. Electrophoretic mobility shift and dual luciferase reporter assays were performed to discern the mechanisms by which STAT3 regulated the expression of PINK1 and PRKN.

RESULTS

CLA alleviated LPS-induced myocardial damage, cardiac dysfunction, and mitochondrial injury and dysfunction in the mouse heart. PTPN1 upregulation exacerbated LPS-induced mitochondrial injury and dysfunction in H9c2 cardiomyocytes, but inhibited LPS-induced mitophagy. LPS promoted the interaction between PTPN1 and STAT3 and reduced STAT3 phosphorylation at Tyr705 (Y705), which was required to inhibit mitophagy by PTPN1. Upon LPS stimulation, PTPN1 negatively regulated the transcription of PINK1 and PRKN through dephosphorylation of STAT3 at Y705. STAT3 regulated the transcription of PINK1 and PRKN by binding to STAT3-responsive elements in their promoters.

CONCLUSION

PTPN1 upregulation aggravates endotoxemia-induced cardiac dysfunction by impeding mitophagy through dephosphorylation of STAT3 at Y705 and negative regulation of PINK1 and PRKN transcription.

Binding Free Energy Calculation Based on the Fragment Molecular Orbital Method and Its Application in Designing Novel SHP-2 Allosteric Inhibitors

The accurate prediction of binding free energy is a major challenge in structure-based drug design. Quantum mechanics (QM)-based approaches show promising potential in predicting ligand-protein binding affinity by accurately describing the behavior and structure of electrons. However, traditional QM calculations face computational limitations, hindering their practical application in drug design. Nevertheless, the fragment molecular orbital (FMO) method has gained widespread application in drug design due to its ability to reduce computational costs and achieve efficient ab initio QM calculations. Although the FMO method has demonstrated its reliability in calculating the gas phase potential energy, the binding of proteins and ligands also involves other contributing energy terms, such as solvent effects, the 'deformation energy' of a ligand's bioactive conformations, and entropy. Particularly in cases involving ionized fragments, the calculation of solvation free energy becomes particularly crucial. We conducted an evaluation of some previously reported implicit solvent methods on the same data set to assess their potential for improving the performance of the FMO method. Herein, we develop a new QM-based binding free energy calculation method called FMOScore, which enhances the performance of the FMO method. The FMOScore method incorporates linear fitting of various terms, including gas-phase potential energy, deformation energy, and solvation free energy. Compared to other widely used traditional prediction methods such as FEP+, MM/PBSA, MM/GBSA, and Autodock vina, FMOScore showed good performance in prediction accuracies. By constructing a retrospective case study, it was observed that incorporating calculations for solvation free energy and deformation energy can further enhance the precision of FMO predictions for binding affinity. Furthermore, using FMOScore-guided lead optimization against Src homology-2-containing protein tyrosine phosphatase 2 (SHP-2), we discovered a novel and potent allosteric SHP-2 inhibitor (compound 8).

Effects of empagliflozin on progression of chronic kidney disease: a prespecified secondary analysis from the empa-kidney trial

BACKGROUND

Sodium-glucose co-transporter-2 (SGLT2) inhibitors reduce progression of chronic kidney disease and the risk of cardiovascular morbidity and mortality in a wide range of patients. However, their effects on kidney disease progression in some patients with chronic kidney disease are unclear because few clinical kidney outcomes occurred among such patients in the completed trials. In particular, some guidelines stratify their level of recommendation about who should be treated with SGLT2 inhibitors based on diabetes status and albuminuria. We aimed to assess the effects of empagliflozin on progression of chronic kidney disease both overall and among specific types of participants in the EMPA-KIDNEY trial.

METHODS

EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA), and included individuals aged 18 years or older with an estimated glomerular filtration rate (eGFR) of 20 to less than 45 mL/min per 1·73 m2, or with an eGFR of 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher. We explored the effects of 10 mg oral empagliflozin once daily versus placebo on the annualised rate of change in estimated glomerular filtration rate (eGFR slope), a tertiary outcome. We studied the acute slope (from randomisation to 2 months) and chronic slope (from 2 months onwards) separately, using shared parameter models to estimate the latter. Analyses were done in all randomly assigned participants by intention to treat. EMPA-KIDNEY is registered at ClinicalTrials.gov, NCT03594110.

FINDINGS

Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and then followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroups of eGFR included 2282 (34·5%) participants with an eGFR of less than 30 mL/min per 1·73 m2, 2928 (44·3%) with an eGFR of 30 to less than 45 mL/min per 1·73 m2, and 1399 (21·2%) with an eGFR 45 mL/min per 1·73 m2 or higher. Prespecified subgroups of uACR included 1328 (20·1%) with a uACR of less than 30 mg/g, 1864 (28·2%) with a uACR of 30 to 300 mg/g, and 3417 (51·7%) with a uACR of more than 300 mg/g. Overall, allocation to empagliflozin caused an acute 2·12 mL/min per 1·73 m2 (95% CI 1·83-2·41) reduction in eGFR, equivalent to a 6% (5-6) dip in the first 2 months. After this, it halved the chronic slope from -2·75 to -1·37 mL/min per 1·73 m2 per year (relative difference 50%, 95% CI 42-58). The absolute and relative benefits of empagliflozin on the magnitude of the chronic slope varied significantly depending on diabetes status and baseline levels of eGFR and uACR. In particular, the absolute difference in chronic slopes was lower in patients with lower baseline uACR, but because this group progressed more slowly than those with higher uACR, this translated to a larger relative difference in chronic slopes in this group (86% [36-136] reduction in the chronic slope among those with baseline uACR <30 mg/g compared with a 29% [19-38] reduction for those with baseline uACR ≥2000 mg/g; ptrend<0·0001).

INTERPRETATION

Empagliflozin slowed the rate of progression of chronic kidney disease among all types of participant in the EMPA-KIDNEY trial, including those with little albuminuria. Albuminuria alone should not be used to determine whether to treat with an SGLT2 inhibitor.

FUNDING

Boehringer Ingelheim and Eli Lilly.

Impact of primary kidney disease on the effects of empagliflozin in patients with chronic kidney disease: secondary analyses of the EMPA-KIDNEY trial

BACKGROUND

The EMPA-KIDNEY trial showed that empagliflozin reduced the risk of the primary composite outcome of kidney disease progression or cardiovascular death in patients with chronic kidney disease mainly through slowing progression. We aimed to assess how effects of empagliflozin might differ by primary kidney disease across its broad population.

METHODS

EMPA-KIDNEY, a randomised, controlled, phase 3 trial, was conducted at 241 centres in eight countries (Canada, China, Germany, Italy, Japan, Malaysia, the UK, and the USA). Patients were eligible if their estimated glomerular filtration rate (eGFR) was 20 to less than 45 mL/min per 1·73 m2, or 45 to less than 90 mL/min per 1·73 m2 with a urinary albumin-to-creatinine ratio (uACR) of 200 mg/g or higher at screening. They were randomly assigned (1:1) to 10 mg oral empagliflozin once daily or matching placebo. Effects on kidney disease progression (defined as a sustained ≥40% eGFR decline from randomisation, end-stage kidney disease, a sustained eGFR below 10 mL/min per 1·73 m2, or death from kidney failure) were assessed using prespecified Cox models, and eGFR slope analyses used shared parameter models. Subgroup comparisons were performed by including relevant interaction terms in models. EMPA-KIDNEY is registered with ClinicalTrials.gov, NCT03594110.

FINDINGS

Between May 15, 2019, and April 16, 2021, 6609 participants were randomly assigned and followed up for a median of 2·0 years (IQR 1·5-2·4). Prespecified subgroupings by primary kidney disease included 2057 (31·1%) participants with diabetic kidney disease, 1669 (25·3%) with glomerular disease, 1445 (21·9%) with hypertensive or renovascular disease, and 1438 (21·8%) with other or unknown causes. Kidney disease progression occurred in 384 (11·6%) of 3304 patients in the empagliflozin group and 504 (15·2%) of 3305 patients in the placebo group (hazard ratio 0·71 [95% CI 0·62-0·81]), with no evidence that the relative effect size varied significantly by primary kidney disease (pheterogeneity=0·62). The between-group difference in chronic eGFR slopes (ie, from 2 months to final follow-up) was 1·37 mL/min per 1·73 m2 per year (95% CI 1·16-1·59), representing a 50% (42-58) reduction in the rate of chronic eGFR decline. This relative effect of empagliflozin on chronic eGFR slope was similar in analyses by different primary kidney diseases, including in explorations by type of glomerular disease and diabetes (p values for heterogeneity all >0·1).

INTERPRETATION

In a broad range of patients with chronic kidney disease at risk of progression, including a wide range of non-diabetic causes of chronic kidney disease, empagliflozin reduced risk of kidney disease progression. Relative effect sizes were broadly similar irrespective of the cause of primary kidney disease, suggesting that SGLT2 inhibitors should be part of a standard of care to minimise risk of kidney failure in chronic kidney disease.

FUNDING

Boehringer Ingelheim, Eli Lilly, and UK Medical Research Council.

[Anatomical study of the architecture of the cutaneous branch-chained blood vessels in the medial lower leg in adult]

Objective: To investigate the architecture of the cutaneous branch-chained blood vessels in the medial lower leg and provide the anatomical basis for design and clinical application of the cutaneous branch-chained flap from this region. Methods: The experimental research method was used. From March to May 2023, the anatomical study was conducted on the 5 voluntarily donated fresh adult (aged 50 to 70 years, all male) cadaveric specimens from Hangzhou Normal University School of Basic Medical Sciences. The fine anatomy under microscope was performed on each lower leg specimens of 5 corpses (1 lower leg specimen was conducted with digital radiography (DR) scan before fine anatomy), to observe, measure, and record the course of posterior tibial artery, quantity of perforator, the distance between the perforating point of each perforator and the medial condyle of tibia, the external diameter of posterior tibial artery perforator, the length of perforator pedicle, the horizontal distance between the posterior tibial artery perforator and the saphenous nerve, and the course of each perforator within superficial fascia after crossing the deep fascia and the distribution of the cutaneous branch-chains. The DR scan under the perfusion of barium sulfate was conducted in one lower leg specimen to observe the distribution of cutaneous branch-chained vascular network (hereinafter referred to as vascular chain) between perforators. Transparent skin specimen was made from one leg specimen after anatomy to observe the distribution of perforators and vascular chains between perforators. Results: In 5 lower leg specimens, the upper part of posterior tibial artery was located deep in soleus muscle, and the lower part was located between the medial edge of gastrocnemius muscle and flexor digitorum longus muscle. A total of 28 posterior tibial artery perforators were identified, with an average of 5.6 branches in each lower leg. The distance between the perforating point of perforator and the medial condyle of tibia ranged from 6.5 to 36.0 cm, mainly distributed at 22.0 (15.1, 28.1) cm from the medial condyle of tibia, in zones 3 to 6. The external diameters of perforators of posterior tibial arteries were 0.7-1.1 mm. The length of perforator pedicle was 1.0-4.5 cm, and the horizontal distance between the posterior tibial artery perforator and the saphenous nerve was 0.5-3.0 cm. The fine anatomy under microscope showed that the posterior tibial artery perforators had long upward and downward branches after crossing the deep fascia, and the ascending branches and descending branches were anastomosed longitudinally to form the nutrient cutaneous branch-chain in the medial lower leg. DR scan and transparent skin specimen both showed that longitudinal vascular chain was formed between the posterior tibial artery perforators, the transparent skin specimen also showed that longitudinal blood vessel chains included the direct connecting vessels in the adipose layer and the indirect connecting vessels in the subdermal layer. Conclusions: The cutaneous branch-chained vessels in the medial lower leg are constructed by posterior tibial artery perforators, direct connecting vessels, indirect connecting vessels, and traffic branches. The cutaneous branch-chained flap is reliable in terms of vascular anatomy, and can carry the saphenous nerve for partial restoration of its sensation, thus it is an ideal flap.

[Application and evaluation of artificial intelligence TPS-assisted cytologic screening system in urine exfoliative cytology]

Objective: To explore the application of manual screening collaborated with the Artificial Intelligence TPS-Assisted Cytologic Screening System in urinary exfoliative cytology and its clinical values. Methods: A total of 3 033 urine exfoliated cytology samples were collected at the Henan People's Hospital, Capital Medical University, Beijing, China. Liquid-based thin-layer cytology was prepared. The slides were manually read under the microscope and digitally presented using a scanner. The intelligent identification and analysis were carried out using an artificial intelligence TPS assisted screening system. The Paris Report Classification System of Urinary Exfoliated Cytology 2022 was used as the evaluation standard. Atypical urothelial cells and even higher grade lesions were considered as positive when evaluating the recognition sensitivity, specificity, and diagnostic accuracy of artificial intelligence-assisted screening systems and human-machine collaborative cytologic screening methods in urine exfoliative cytology. Among the collected cases, there were also 1 100 pathological tissue controls. Results: The accuracy, sensitivity and specificity of the AI-assisted cytologic screening system were 77.18%, 90.79% and 69.49%; those of human-machine coordination method were 92.89%, 99.63% and 89.09%, respectively. Compared with the histopathological results, the accuracy, sensitivity and specificity of manual reading were 79.82%, 74.20% and 95.80%, respectively, while those of AI-assisted cytologic screening system were 93.45%, 93.73% and 92.66%, respectively. The accuracy, sensitivity and specificity of human-machine coordination method were 95.36%, 95.21% and 95.80%, respectively. Both cytological and histological controls showed that human-machine coordination review method had higher diagnostic accuracy and sensitivity, and lower false negative rates. Conclusions: The artificial intelligence TPS assisted cytologic screening system has achieved acceptable accuracy in urine exfoliation cytologic screening. The combination of manual screening and artificial intelligence TPS assisted screening system can effectively improve the sensitivity and accuracy of cytologic screening and reduce the risk of misdiagnosis.

Clinical characteristics of hypersensitivity pneumonitis: non-fibrotic and fibrotic subtypes

BACKGROUND

The presence of fibrosis is a criterion for subtype classification in the newly updated hypersensitivity pneumonitis (HP) guidelines. The present study aimed to summarize differences in clinical characteristics and prognosis of non-fibrotic hypersensitivity pneumonitis (NFHP) and fibrotic hypersensitivity pneumonitis (FHP) and explore factors associated with the presence of fibrosis.

METHODS

In this prospective cohort study, patients diagnosed with HP through a multidisciplinary discussion were enrolled. Collected data included demographic and clinical characteristics, laboratory findings, and radiologic and histopathological features. Logistic regression analyses were performed to explore factors related to the presence of fibrosis.

RESULTS

A total of 202 patients with HP were enrolled, including 87 (43.1%) NFHP patients and 115 (56.9%) FHP patients. Patients with FHP were older and more frequently presented with dyspnea, crackles, and digital clubbing than patients with NFHP. Serum levels of carcinoembryonic antigen, carbohydrate antigen 125, carbohydrate antigen 153, gastrin-releasing peptide precursor, squamous cell carcinoma antigen, and antigen cytokeratin 21-1, and count of bronchoalveolar lavage (BAL) eosinophils were higher in the FHP group than in the NFHP group. BAL lymphocytosis was present in both groups, but less pronounced in the FHP group. Multivariable regression analyses revealed that older age, <20% of lymphocyte in BAL, and ≥1.75% of eosinophil in BAL were risk factors for the development of FHP. Twelve patients developed adverse outcomes, with a median survival time of 12.5 months, all of whom had FHP.

CONCLUSIONS

Older age, <20% of lymphocyte in BAL, and ≥1.75% of eosinophil in BAL were risk factors associated with the development of FHP. Prognosis of patients with NFHP was better than that of patients with FHP. These results may provide insights into the mechanisms of fibrosis in HP.

Tramadol suppresses growth of orthotopic liver tumors via promoting M1 macrophage polarization in the tumor microenvironment

Tumor-associated macrophages (TAMs) are major infiltrating immune cells in liver cancer. They are polarized to anti-tumor M1 type or tumor-supporting M2 type in a dynamic changing state. Tramadol, a synthetic opioid, exhibits tumor-suppressing effect in several cancers, but whether it plays a role in TAMs polarization is uncertain. In the present study, the potential influence of tramadol on TAMs polarization was explored in liver cancer. An orthotopic murine Hepa 1-6 liver cancer model was constructed. The potential function of tramadol was evaluated by cell viability assay, EdU incorporation assay, flow cytometry, immunofluorescence, quantitative real-time polymerase chain reaction (qRT-PCR), enzyme-linked immunosorbent assay (ELISA) assay, T cell proliferation and suppression assays and western blot. We found that tramadol suppressed proliferation and tumor formation of murine Hepa 1-6 cells in vitro and in vivo. Tramadol reprogramed the immune microenvironment to favor M1 macrophage polarization in orthotopic Hepa 1-6 tumors. Moreover, tramadol facilitated M1 macrophage polarization and inhibited M2 macrophage polarization of bone marrow-derived macrophages (BMDMs) and human THP-1 macrophages in vitro. Furthermore, tramadol-treated BMDMs promoted proliferation and activation of splenic CD4+ and CD8+ T cells. Tramadol induced cellular ROS production and mitochondrial dysfunction of BMDMs. Finally, tramadol activated NF-κB signaling in BMDMs and THP-1 macrophages, while inhibition of NF-κB signaling by JSH-23 attenuated the influence of tramadol on macrophage polarization. In conclusion, these data elucidated a novel anti-tumor mechanism of tramadol in liver cancer. Tramadol might be a promising treatment strategy for liver cancer patients.

Rosmarinic acid, the active component of Rubi Fructus, induces apoptosis of SGC-7901 and HepG2 cells through mitochondrial pathway and exerts anti-tumor effect

Rosmarinic acid (RA) is a well-known phenolic acid widely present in over 160 species of herbal plants and known to exhibit anti-tumor effects on breast, prostate, and colon cancers in vitro. However, its effect and mechanism in gastric cancer and liver cancer are unclear. Moreover, there is no RA report yet in the chemical constituents of Rubi Fructus (RF). In this study, RA was isolated from RF for the first time, and the effect and mechanism of RA on gastric and liver cancers were evaluated using SGC-7901 and HepG2 cells models. The cells were treated with different concentrations of RA (50, 75, and 100 μg/mL) for 48 h, and the effect of RA on cell proliferation was evaluated by the CCK-8 assay. The effect of RA on cell morphology and mobility was observed by inverted fluorescence microscopy, cell apoptosis and cell cycle were determined by flow cytometry, and the expression of apoptosis-related proteins cytochrome C, cleaved caspase-3, Bax, and Bcl-2 was detected by western blotting. The results revealed that, with an increase in the RA concentration, the cell viability, mobility, and Bcl-2 expression decreased, while the apoptosis rate, Bax, cytochrome C, and cleaved caspase-3 expression increased, and SGC-7901 and HepG2 cells could be induced to arrest their cell cycle in the G0/G1 and S phases, respectively. These results together indicate that RA can induce apoptosis of SGC-7901 and HepG2 cells through the mitochondrial pathway. Thus, this study supplements the material basis of the anti-tumor activity of RF and provides an insight into the potential mechanism of RA-inducing apoptosis of gastric cancer SGC-7901 cells and liver cancer HepG2 cells, thereby facilitating further developmental studies on and the utilization of the anti-tumor activity of RF.

Cuproptosis-related gene LIPT1 as a prognostic indicator in non-small cell lung cancer: Functional involvement and regulation of ATOX1 expression

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-related deaths, necessitating a deeper understanding of novel cell death pathways like cuproptosis. This study explored the relevance of cuproptosis-related genes in NSCLC and their potential prognostic significance. We analyzed the expression of 16 cuproptosis-related genes in 1017 NSCLC tumors and 578 Genotype-Tissue Expression (GTEx) normal samples from The Cancer Genome Atlas (TCGA) to identify significant genes. A risk model and prognostic nomogram were employed to identify the pivotal prognostic gene. Further in vitro experiments were conducted to investigate the functions of the identified genes in NSCLC cell lines. LIPT1, a gene for lipoate-protein ligase 1 enzyme, emerged as the central prognostic gene with decreased expression in NSCLC. Importantly, elevated LIPT1 levels were associated with a favorable prognosis for NSCLC patients. Overexpression of LIPT1 inhibited cell growth and enhanced apoptosis in NSCLC. We confirmed that LIPT1 downregulates the copper chaperone gene antioxidant 1 (ATOX1), thereby impeding NSCLC progression. Our study identified LIPT1 as a valuable prognostic biomarker in NSCLC as it elucidates its tumor-inhibitory role through the modulation of ATOX1. These findings offered insights into the potential therapeutic targeting of LIPT1 in NSCLC, contributing to a deeper understanding of this deadly disease.

The Identification of the Banana Endogenous Reference Gene MaSPS1 and the Construction of Qualitative and Quantitative PCR Detection Methods

Endogenous reference genes play a crucial role in the qualitative and quantitative PCR detection of genetically modified crops. Currently, there are no systematic studies on the banana endogenous reference gene. In this study, the MaSPS1 gene was identified as a candidate gene through bioinformatics analysis. The conservation of this gene in different genotypes of banana was tested using PCR, and its specificity in various crops and fruits was also examined. Southern blot analysis showed that there is only one copy of MaSPS1 in banana. The limit of detection (LOD) test showed that the LOD of the conventional PCR method is approximately 20 copies. The real-time fluorescence quantitative PCR (qPCR) method also exhibited high specificity, with a LOD of approximately 10 copies. The standard curve of the qPCR method met the quantitative requirements, with a limit of quantification (LOQ) of 1.14 × 10-2 ng-about 20 copies. Also, the qPCR method demonstrated good repeatability and stability. Hence, the above results indicate that the detection method established in this study has strong specificity, a low detection limit, and good stability. It provides a reliable qualitative and quantitative detection system for banana.

A Cellular atlas of the human fallopian tube reveals the metamorphosis of secretory epithelial cells during the menstrual cycle and menopause

The fallopian tube, connecting the uterus with the ovary, is a dynamic organ that undergoes cyclical changes and is the site of several diseases, including serous cancer. Here, we use single-cell technologies to construct a comprehensive cell map of healthy pre-menopausal fallopian tubes, capturing the impact of the menstrual cycle and menopause on different fallopian tube cells at the molecular level. The comparative analysis between pre- and post-menopausal fallopian tubes reveals substantial shifts in cellular abundance and gene expression patterns, highlighting the physiological changes associated with menopause. Further investigations into menstrual cycle phases illuminate distinct molecular states in secretory epithelial cells caused by hormonal fluctuations. The markers we identified characterizing secretory epithelial cells provide a valuable tool for classifying ovarian cancer subtypes.

Graphical summary

Graphical summary of results. During the proliferative phase (estrogen high ) of the menstrual cycle, SE2 cells (OVGP1 + ) dominate the fallopian tube (FT) epithelium, while SE1 cells (OVGP1 - ) dominate the epithelium during the secretory phase. Though estrogen levels decrease during menopause, SE post-cells (OVGP1 + , CXCL2 + ) make up most of the FT epithelium.

PANoptosis: Emerging mechanisms and disease implications

PANoptosis, a unique new form of programmed cell death (PCD), is characterized by pyroptosis, apoptosis, and necroptosis, but it cannot be explained by pyroptosis, apoptosis or necroptosis alone. Assembly of the PANoptosome complex is a key feature of PANoptosis. To date, four kinds of PANoptosomes with distinct sensors and regulators have been defined, namely Z-DNA binding protein 1 (ZBP1) PANoptosome, absent in melanoma 2 (AIM2) PANoptosome, receptor-interacting protein kinase 1 (RIPK1) PANoptosome, and nucleotide-binding leucine-rich repeat-containing receptor 12 (NLRP12). Each PANoptosome contains three components: sensors for pathogen-associated molecular patterns (PAMPs) or damage-associated molecular patterns (DAMPs), adaptors as connected bridges, and catalytic effectors or executioners. Mechanistically, different PAMPs or DAMPs are recognized by the sensors in a context-dependent manner, which initiates PANoptosome assembly through adaptors, and ultimately engages synchronous activation of pyroptosis, apoptosis, and necroptosis via different catalytic effectors. Resultantly, PANoptosis is emerged as a prospective and promising therapeutic target for various diseases. This review covers the accumulating evidence about the roles and mechanisms of PANoptosis in innate immunity and discusses the attractive prospect of manipulating PANoptosis as a new treatment for diseases.

Test of CP Symmetry in Hyperon to Neutron Decays

The quantum entangled J/ψ→Σ^{+}Σ[over ¯]^{-} pairs from (1.0087±0.0044)×10^{10}  J/ψ events taken by the BESIII detector are used to study the nonleptonic two-body weak decays Σ^{+}→nπ^{+} and Σ[over ¯]^{-}→n[over ¯]π^{-}. The CP-odd weak decay parameters of the decays Σ^{+}→nπ^{+} (α_{+}) and Σ[over ¯]^{-}→n[over ¯]π^{-} (α[over ¯]_{-}) are determined to be 0.0481±0.0031_{stat}±0.0019_{syst} and -0.0565±0.0047_{stat}±0.0022_{syst}, respectively. The decay parameter α[over ¯]_{-} is measured for the first time, and the accuracy of α_{+} is improved by a factor of 4 compared to the previous results. The simultaneously determined decay parameters allow the first precision CP symmetry test for any hyperon decay with a neutron in the final state with the measurement of A_{CP}=(α_{+}+α[over ¯]_{-})/(α_{+}-α[over ¯]_{-})=-0.080±0.052_{stat}±0.028_{syst}. Assuming CP conservation, the average decay parameter is determined as ⟨α_{+}⟩=(α_{+}-α[over ¯]_{-})/2=-0.0506±0.0026_{stat}±0.0019_{syst}, while the ratios α_{+}/α_{0} and α[over ¯]_{-}/α[over ¯]_{0} are -0.0490±0.0032_{stat}±0.0021_{syst} and -0.0571±0.0053_{stat}±0.0032_{syst}, where α_{0} and α[over ¯]_{0} are the decay parameters of the decays Σ^{+}→pπ^{0} and Σ[over ¯]^{-}→p[over ¯]π^{0}, respectively.

Measurement of Energy-Dependent Pair-Production Cross Section and Electromagnetic Form Factors of a Charmed Baryon

We study the process e^{+}e^{-}→Λ_{c}^{+}Λ[over ¯]_{c}^{-} at twelve center-of-mass energies from 4.6119 to 4.9509 GeV using data samples collected by the BESIII detector at the BEPCII collider. The Born cross sections and effective form factors (|G_{eff}|) are determined with unprecedented precision after combining the single and double-tag methods based on the decay process Λ_{c}^{+}→pK^{-}π^{+}. Flat cross sections around 4.63 GeV are obtained and no indication of the resonant structure Y(4630), as reported by Belle, is found. In addition, no oscillatory behavior is discerned in the |G_{eff}| energy dependence of Λ_{c}^{+}, in contrast to what is seen for the proton and neutron cases. Analyzing the cross section together with the polar-angle distribution of the Λ_{c}^{+} baryon at each energy point, the moduli of electric and magnetic form factors (|G_{E}| and |G_{M}|) are extracted and separated. For the first time, the energy dependence of the form factor ratio |G_{E}/G_{M}| is observed, which can be well described by an oscillatory function.

Effect of online intervention mode on breastfeeding results: a systematic review and meta-analysis

OBJECTIVE

To evaluate the effect of Internet based intervention model on breastfeeding knowledge, attitude, self-confidence and breastfeeding rate.

METHODS

An electronic literature search of PubMed, Embase, Web of Science, Cochrane Library, CINAHL, CNKI, Wanfang, CBM and VIP database was performed using combinations of the keywords "Breastfeeding," "Breastfeeding, Exclusive," "Internet," "Online," and "Website". The retrieval period was from their inception to March 31, 2023. Quality appraisal was performed using the Cochrane 5.1 for randomized controlled trials (RCTs). RevMan5.3 was used for data analysis.

RESULTS

Thirty-two studies were included in the review, with a total of 9514 samples. The results of Meta-analysis showed that, compared with routine nursing, the intervention model based on the Internet can effectively improve breastfeeding knowledge and attitude of pregnant women, improve breastfeeding self-confidence (P < 0.05), and improve the rate of exclusive breastfeeding in the short term (within 6 weeks) and the long-term postpartum (3-6 months) had a positive effect on the rate of exclusive breastfeeding (P < 0.05).

CONCLUSIONS

The Internet breastfeeding intervention model may be an effective intervention to improve the effect of exclusive breastfeeding. In the future, more high-quality, large-sample randomized controlled trials can be carried out to further explore the effect of the Internet intervention model on breastfeeding.