Low-abundance proteins-based label-free SERS approach for high precision detection of liver cancer with different stages

BACKGROUND

Surface-enhanced Raman scattering (SERS) technology have unique advantages of rapid, simple, and highly sensitive in the detection of serum, it can be used for the detection of liver cancer. However, some protein biomarkers in body fluids are often present at ultra-low concentrations and severely interfered with by the high-abundance proteins (HAPs), which will affect the detection of specificity and accuracy in cancer screening based on the SERS immunoassay. Clearly, there is a need for an unlabeled SERS method based on low abundance proteins, which is rapid, noninvasive, and capable of high precision detection and screening of liver cancer.

RESULTS

Serum samples were collected from 60 patients with liver cancer (27 patients with stage T1 and T2 liver cancer, 33 patients with stage T3 and T4 liver cancer) and 40 healthy volunteers. Herein, immunoglobulin and albumin were separated by immune sorption and Cohn ethanol fractionation. Then, the low abundance protein (LAPs) was enriched, and high-quality SERS spectral signals were detected and obtained. Finally, combined with the principal component analysis-linear discriminant analysis (PCA-LDA) algorithm, the SERS spectrum of early liver cancer (T1-T2) and advanced liver cancer (T3-T4) could be well distinguished from normal people, and the accuracy rate was 98.5% and 100%, respectively. Moreover, SERS technology based on serum LAPs extraction combined with the partial least square-support vector machine (PLS-SVM) successfully realized the classification and prediction of normal volunteers and liver cancer patients with different tumor (T) stages, and the diagnostic accuracy of PLS-SVM reached 87.5% in the unknown testing set.

SIGNIFICANCE

The experimental results show that the serum LAPs SERS detection combined with multivariate statistical algorithms can be used for effectively distinguishing liver cancer patients from healthy volunteers, and even achieved the screening of early liver cancer with high accuracy (T1 and T2 stage). These results showed that serum LAPs SERS detection combined with a multivariate statistical diagnostic algorithm has certain application potential in early cancer screening.

CT venography combined with ultrasound-guided minimally invasive treatment for recurrent varicose veins: a pilot paired-design clinical trial

AIM

To compare 1-year outcomes of computed tomography venography (CTV) combined with ultrasound-guided minimally invasive treatment with ascending phlebography and ultrasound-guided treatment for recurrent varicose veins.

MATERIALS AND METHODS

Consecutive patients with unilateral recurrent varicose veins were matched by gender, age, C classification, and degree of obesity, and randomised in a 1:1 ratio to receive either CTV (CTV group) or ascending phlebography (control group) combined with ultrasound-guided minimally invasive treatment. Patients were followed up by clinical and ultrasound examination. Follow-up was scheduled at 1 week, and 3, 6, and 12 months. The primary outcome measure was the Venous Clinical Severity Score (VCSS) at 12 months. Measures of secondary outcome included Chronic Insufficiency Venous International Questionnaire-20 (CIVIQ-20) score, recurrence of varicose vein or ulcer during 12 months, ulcer healing time, detection and location of treated veins.

RESULTS

Eighty patients were enrolled. Median VCSS in the CTV group was lower than it in the control group (p=0.04) and the CIVIQ-20 score was higher than the control group (p=0.02). By 12 months, no symptomatically recurrent varicose veins or ulcers had occurred. The ulcer healing time in CTV group was shorter (p<0.01). A greater number of patients had treated veins detected using CTV than by ascending venography (p=0.01), especially among patients with recurrence reflux veins in the groin, perineum, and vulva (p<0.01).

CONCLUSION

CTV combined with ultrasound may be more helpful than ascending phlebography combined with ultrasound to improve treatment efficacy for recurrent varices. These results should be verified by an future study with more patients and long-term follow-up.

Sub-region based radiomics analysis for prediction of isocitrate dehydrogenase and telomerase reverse transcriptase promoter mutations in diffuse gliomas

AIM

To enhance the prediction of mutation status of isocitrate dehydrogenase (IDH) and telomerase reverse transcriptase (TERT) promoter, which are crucial for glioma prognostication and therapeutic decision-making, via sub-regional radiomics analysis based on multiparametric magnetic resonance imaging (MRI).

MATERIALS AND METHODS

A retrospective study was conducted on 401 participants with adult-type diffuse gliomas. Employing the K-means algorithm, tumours were clustered into two to four subregions. Sub-regional radiomics features were extracted and selected using the Mann-Whitney U-test, Pearson correlation analysis, and least absolute shrinkage and selection operator, forming the basis for predictive models. The performance of model combinations of different sub-regional features and classifiers (including logistic regression, support vector machines, K-nearest neighbour, light gradient boosting machine, and multilayer perceptron) was evaluated using an external test set.

RESULTS

The models demonstrated high predictive performance, with area under the receiver operating characteristic curve (AUC) values ranging from 0.918 to 0.994 in the training set for IDH mutation prediction and from 0.758 to 0.939 for TERT promoter mutation prediction. In the external test sets, the two-cluster radiomics features and the logistic regression model yielded the highest prediction for IDH mutation, resulting in an AUC of 0.905. Additionally, the most effective predictive performance with an AUC of 0.803 was achieved using the four-cluster radiomics features and the support vector machine model, specifically for TERT promoter mutation prediction.

CONCLUSION

The present study underscores the potential of sub-regional radiomics analysis in predicting IDH and TERT promoter mutations in glioma patients. These models have the capacity to refine preoperative glioma diagnosis and contribute to personalised therapeutic interventions for patients.

[Cancer survival analysis in Tianjin, 2010 to 2016]

Objective: Survival analysis of cancers' incidence data in Tianjin from 2010 to 2016 was conducted to provide the basis for formulating and evaluating regional health policies on cancer prevention and treatment. Methods: Registration data in Tianjin were used between January 1, 2010 to December 31, 2016 and patients were followed-up till 31 December, 2021. Life-table method was used to calculate the observed survival rate and Edered Ⅱ was used to calculate the relative survival rate. The data were stratified by year, gender, age group and cancer sites. Difference in survival curves between group was analyzed by Kaplan-Meier method and Log rank test. Joinpoint regression model was used to analyze the trend change. Results: The 5-year relative survival rates of cancer were 41.92% to 53.65% from 2010 to 2016 for residents in Tianjin, with an increasing trend (t=4.81, P=0.005), and the average was 48.56%. The survival rate of females was higher than that of males (57.71%vs. 39.20%), and the survival rate of urban residents was higher than that of rural residents (49.38% vs. 47.24%). The 5-year relative survival rates were 63.14%, 78.39%, 58.25% and 32.67% in 0-14, 15-44, 45-64 and 65 and above age groups, respectively. The median relative survival times of all cancer were 2.34 to 6.00 years from 2010 to 2016 in Tianjin, with an increasing trend (t=3.86, P=0.012). The average of median relative survival times was 4.11 years. The median survival time of females was longer than that of males (11.99 years vs. 2.03 years), and the time of urban residents were longer than that of rural residents (4.60 years vs. 3.43 years). The median relative survival time were 12.07, 11.92 and 1.34 years in 15-44, 45-64 and 65 and above age groups, respectively. Conclusions: The cumulative survival rate of cancer increased significantly from 2010 to 2016 in Tianjin, indicating that the prevention and treatment effect of cancer is obvious. The focus should be on male, rural areas, higher age group, and targeted prevention and treatment measures should be taken to lung, esophagus, liver, gallbladder and pancreatic cancer.

Improving chilling tolerance of peanut seedlings by enhancing antioxidant-modulated ROS scavenging ability, alleviating photosynthetic inhibition, and mobilizing nutrient absorption

Peanut production is threatened by climate change. Damage to seedlings from low temperatures in early spring can limit yield. Plant adaptations to chilling stress remain unclear in peanut seedlings. It is essential to understand how peanut acquires chilling tolerance. We evaluated effects of chilling stress on growth and recovery of peanut seedlings. We compared and analysed biological characteristics, antioxidants, photosynthesis, biochemical and physiological responses, and nutrient absorption at varying levels of chilling. Compared with chilling-sensitive FH18, the reduced impact of chilling stress on chilling-tolerant NH5 was associated with reduced ROS accumulation, higher ascorbate peroxidase activity and soluble sugar content, lower soluble protein content, and smaller reductions in nutrient content during stress. After removal of chilling stress, FH18 had significant accumulation of O2 •- and H2O2, which decreased photosynthesis, nutrient absorption, and transport. ROS-scavenging reduced damage from chilling stress, allowed remobilization of nutrients, improved chilling tolerance, and restored plant functioning after chilling stress removal. These findings provide a reference for targeted research on peanut seedling tolerance to chilling and lay the foundation for bioinformatics-based research on peanut chilling tolerance mechanisms.

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.

Antiviral effect of palmatine against infectious bronchitis virus through regulation of NF-κB/IRF7/JAK-STAT signalling pathway and apoptosis

1. Infectious bronchitis virus (IBV), a gamma-coronavirus, can infect chickens of all ages and leads to an acute contact respiratory infection. This study evaluated the anti-viral activity of palmatine, a natural non-flavonoid alkaloid, against IBV in chicken embryo kidney (CEK) cells.2. The half toxic concentration (CC50) of palmatine was 672.92 μM, the half inhibitory concentration (IC50) of palmatine against IBV was 7.76 μM and the selection index (SI) was 86.74.3. Mode of action assay showed that palmatine was able to directly inactivate IBV and inhibited the adsorption, penetration and intracellular replication of IBV.4. Palmatine significantly upregulated TRAF6, TAB1 and IKK-β compared with the IBV-infected group, leading to the increased expressions of pro-inflammatory cytokines IL-1β and TNF-α in the downstream NF-κB signalling pathway.5. Palmatine significantly up-regulated the levels of MDA5, MAVS, IRF7, IFN-α and IFN-β in the IRF7 pathway, inducing type I interferon production. It up-regulated the expression of 2'5'-oligoadenylate synthase (OAS) in the JAK-STAT pathway.6. IBV infection induced cell apoptosis and palmatine-treatment delayed the process of apoptosis by regulation of the expression of apoptosis-related genes (BAX, BCL-2, CASPASE-3 and CASPASE-8).7. Palmatine could exert anti-IBV activity through regulation of NF-κB/IRF7/JAK-STAT signalling pathways and apoptosis, providing a theoretical basis for the utilisation of palmatine to treat IBV infection.

[Surgical treatment and prognosis analysis of hilar cholangiocarcinoma]

Objective: To investigate the surgical treatment effect and prognostic factors of hilar cholangiocarcinoma. Methods: This is an ambispective cohort study. From August 2005 to December 2022,data of 510 patients who diagnosed with hilar cholangiocarcinoma and underwent surgical resection at the Hepatobiliary Center of the First Affiliated Hospital of Nanjing Medical University were retrospectively collected. In the cohort,there were 324 males and 186 females,with an age of (M (IQR)) 63(13)years (range:25 to 85 years). The liver function at admission was Child-Pugh A (343 cases,67.3%) and Child-Pugh B (167 cases,32.7%). Three hundred and seventy-two(72.9%) patients had jaundice symptoms and the median total bilirubin was 126.3(197.6) μmol/L(range: 5.4 to 722.8 μmol/L) at admission. Two hundred and fourty-seven cases (48.4%) were treated with percutaneous transhepatic cholangial drainage or endoscopic nasobiliary drainage before operation. The median bilirubin level in the drainage group decreased from 186.4 μmol/L to 85.5 μmol/L before operation. Multivariate Logistic regression was used to identify the influencing factors for R0 resection,and Cox regression was used to construct multivariate prediction models for overall survival(OS) and disease-free survival(DFS). Results: Among 510 patients who underwent surgical resection,Bismuth-Corlett type Ⅲ-Ⅳ patients accounted for 71.8%,among which 86.1% (315/366) underwent hemi-hepatectomy,while 81.9% (118/144) underwent extrahepatic biliary duct resection alone in Bismuch-Corlett type Ⅰ-Ⅱ patients. The median OS time was 22.8 months, and the OS rates at 1-,3-,5-and 10-year were 72.2%,35.6%,24.8% and 11.0%,respectively. The median DFS time was 15.2 months,and the DFS rates was 66.0%,32.4%,20.9% and 11.0%,respectively. The R0 resection rate was 64.5% (329/510), and the OS rates of patients with R0 resection at 1-,3-,5-and 10-year were 82.5%, 48.6%, 34.4%, 15.2%,respectively. The morbidity of Clavien-Dindo grade Ⅲ-Ⅴ complications was 26.1%(133/510) and the 30-day mortality was 4.3% (22/510). Multivariate Logistic regression indicated that Bismuth-Corlett type Ⅰ-Ⅲ (P=0.009), hemi-hepatectomy and extended resection (P=0.001),T1 and T2 patients without vascular invasion (T2 vs. T1:OR=1.43 (0.61-3.35),P=0.413;T3 vs. T1:OR=2.57 (1.03-6.41), P=0.010;T4 vs. T1, OR=3.77 (1.37-10.38), P<0.01) were more likely to obtain R0 resection. Preoperative bilirubin,Child-Pugh grade,tumor size,surgical margin,T stage,N stage,nerve infiltration and Edmondson grade were independent prognostic factors for OS and DFS of hilar cholangiocarcinoma patients without distant metastasis. Conclusions: Radical surgical resection is necessary to prolong the long-term survival of hilar cholangiocarcinoma patients. Hemi-hepatectomy and extended resection,regional lymph node dissection and combined vascular resection if necessary,can improve R0 resection rate.

Disturbance of mitochondrial dynamics in myocardium of broilers with pulmonary hypertension syndrome

1. The following study investigated the relationship between pulmonary hypertension syndrome (PHS) and mitochondrial dynamics in broiler cardiomyocytes.2. An animal model for PHS was established by injecting broiler chickens with CM-32 cellulose particles. Broiler myocardial cells were cultured under hypoxic conditions to establish an in vitro model. The ascites heart index, histomorphology, mitochondrial ultrastructure, and mitochondrial dynamic-related gene and protein expression were evaluated.3. The myocardial fibres from PHS broilers had wider spaces and were wavy and twisted and the number of mitochondria increased. Compared with the control group, the gene and protein expression levels were decreased for Opa1, Mfn1, and Mfn2 in the myocardium of PHS broilers. The gene and protein expression was significantly increased for Drp1 and Mff.4. This study showed that PHS in broilers may cause myocardial mitochondrial dysfunction, specifically by diminishing mitochondrial fusion and enhancing fission, causing disturbances in the mitochondrial dynamics of the heart.

Feasibility study of the multishot gradient-echo planar imaging sequence in non-enhanced and free-breathing whole-heart magnetic resonance coronary angiography

AIM

To investigate the feasibility of non-enhanced and free-breathing whole-heart magnetic resonance coronary angiography (MRCA) using multishot gradient-echo planar imaging (MSG-EPI).

MATERIALS AND METHODS

In total, 29 healthy volunteers were recruited for free-breathing whole-heart MRCA acquisition using the MSG-EPI sequence and fast gradient echo (GRE) sequence. After the examination, the actual scanning times, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) of the left main (LM) coronary artery, subjective quality scores for each segment, and evaluable length of the coronary artery were recorded and statistically analysed.

RESULTS

There was no significant difference between the SNRLM of the MSG-EPI sequence and fast GRE sequence (p=0.130), but the CNRLM of the MSG-EPI sequence was higher (p=0.001). The subjective quality score of the mid- and distal left anterior descending branch as well as the distal circumflex branch of the coronary artery in the MSG-EPI sequence was higher than that in the fast GRE sequence (p=0.003, 0.001, and 0.003, respectively). The evaluable length of the left anterior descending branch and the circumflex branch was better using the MSG-EPI sequence than that of the fast GRE sequence (p=0.015 and < 0.001, respectively). Moreover, the scanning time of the MSG-EPI sequence was 54.5% less than that of the fast GRE sequence (p<0.001).

CONCLUSION

The MSG-EPI sequence improves the subjective and objective image quality of MRCA as well as reduces the scanning time.

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 soyhulls with different particle size on the growth performance, blood indices and gut microbiota of yellow feather broilers

1. The objective of this study was to determine the effects of soyhulls with different particle sizes on the growth performance, blood indices and gut microbiota of yellow feather broilers.2. Total of 240 healthy, one-day-old, yellow feather broilers were randomly divided into four groups, with six pen replicates within each group and ten birds per pen. The control group birds were fed the basal diet (Control). For the treatment groups, 5% soyhulls with different particle sizes were included in the basal diet. The particle size geometric mean diameters (dgw) of the soyhulls in the three treatment groups were 299.69 μm (LowPS), 489.85 μm (MediumPS) and 734.83 μm (HighPS) with geometric standard deviation (Sgw) 1.75 μm, 1.62 μm and 1.67 μm, respectively.3. Results showed that the growth performance variables and organ indices were not different among the four groups. The MediumPS group had increased TG, T-CHO, ALT, HDL-C, and GSH-PX levels and decreased T-AOC levels, whereas LowPS and HighPS groups had increased HDL-C and GSH-PX levels (p < 0.05). Microbial diversity analysis showed that the intestinal microbiota of yellow feather broilers mainly included Firmicutes and Bacteroidetes. Inclusion of 5% soyhulls with different particle size had no effect on alpha diversity indices of caecal microbiota. The HighPS group had significantly higher relative abundance of Firmicutes spp. and lower Bacteroidetes spp. compared with the LowPS and MediumPS group but this was not different from the Control group. The relative abundance of Cyanobacteria spp. was significantly higher in the HighPS group than the other three groups. LEfSe analysis showed that there were more enriched biomarker taxa in the groups with soyhulls than the control group.4. Overall, the inclusion of soyhulls with different particle sizes had limited effects on growth performance, blood indices and caecal microbiota composition of yellow feather broilers.

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

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

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

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

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Affiliation(s)

M Ablikim Institute of High Energy Physics, Beijing 100049, People's Republic of China
M N Achasov Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
P Adlarson Uppsala University, Box 516, SE-75120 Uppsala, Sweden
X C Ai Zhengzhou University, Zhengzhou 450001, People's Republic of China
R Aliberti Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
A Amoroso University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
M R An Liaoning Normal University, Dalian 116029, People's Republic of China
Q An State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Y Bai Southeast University, Nanjing 211100, People's Republic of China
O Bakina Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
I Balossino INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
Y Ban Peking University, Beijing 100871, People's Republic of China
H-R Bao University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
V Batozskaya Institute of High Energy Physics, Beijing 100049, People's Republic of China National Centre for Nuclear Research, Warsaw 02-093, Poland
K Begzsuren Institute of Physics and Technology, Peace Avenue 54B, Ulaanbaatar 13330, Mongolia
N Berger Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
M Berlowski National Centre for Nuclear Research, Warsaw 02-093, Poland
M Bertani INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
D Bettoni INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
F Bianchi University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
E Bianco University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
A Bortone University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
I Boyko Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
R A Briere Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
A Brueggemann University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
H Cai Wuhan University, Wuhan 430072, People's Republic of China
X Cai Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
A Calcaterra INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
G F Cao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
N Cao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S A Cetin Turkish Accelerator Center Particle Factory Group, Istinye University, 34010 Istanbul, Turkey
J F Chang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
T T Chang Xinyang Normal University, Xinyang 464000, People's Republic of China
W L Chang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
G R Che Nankai University, Tianjin 300071, People's Republic of China
G Chelkov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
C Chen Nankai University, Tianjin 300071, People's Republic of China
Chao Chen Soochow University, Suzhou 215006, People's Republic of China
G Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China
H S Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
M L Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S J Chen Nanjing University, Nanjing 210093, People's Republic of China
S L Chen North China Electric Power University, Beijing 102206, People's Republic of China
S M Chen Tsinghua University, Beijing 100084, People's Republic of China
T Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X R Chen Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X T Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y B Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y Q Chen Jilin University, Changchun 130012, People's Republic of China
Z J Chen Hunan University, Changsha 410082, People's Republic of China
S K Choi Chung-Ang University, Seoul, 06974, Republic of Korea
X Chu Nankai University, Tianjin 300071, People's Republic of China
G Cibinetto INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
S C Coen Bochum Ruhr-University, D-44780 Bochum, Germany
F Cossio INFN, I-10125, Turin, Italy
J J Cui Shandong University, Jinan 250100, People's Republic of China
H L Dai Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
J P Dai Yunnan University, Kunming 650500, People's Republic of China
A Dbeyssi Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
R E de Boer Bochum Ruhr-University, D-44780 Bochum, Germany
D Dedovich Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
Z Y Deng Institute of High Energy Physics, Beijing 100049, People's Republic of China
A Denig Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
I Denysenko Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
M Destefanis University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
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

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Measurements of All-Particle Energy Spectrum and Mean Logarithmic Mass of Cosmic Rays from 0.3 to 30 PeV with LHAASO-KM2A

We present the measurements of all-particle energy spectrum and mean logarithmic mass of cosmic rays in the energy range of 0.3-30 PeV using data collected from LHAASO-KM2A between September 2021 and December 2022, which is based on a nearly composition-independent energy reconstruction method, achieving unprecedented accuracy. Our analysis reveals the position of the knee at 3.67±0.05±0.15  PeV. Below the knee, the spectral index is found to be -2.7413±0.0004±0.0050, while above the knee, it is -3.128±0.005±0.027, with the sharpness of the transition measured with a statistical error of 2%. The mean logarithmic mass of cosmic rays is almost heavier than helium in the whole measured energy range. It decreases from 1.7 at 0.3 PeV to 1.3 at 3 PeV, representing a 24% decline following a power law with an index of -0.1200±0.0003±0.0341. This is equivalent to an increase in abundance of light components. Above the knee, the mean logarithmic mass exhibits a power law trend towards heavier components, which is reversal to the behavior observed in the all-particle energy spectrum. Additionally, the knee position and the change in power-law index are approximately the same. These findings suggest that the knee observed in the all-particle spectrum corresponds to the knee of the light component, rather than the medium-heavy components.

[Application value of laparoscopic double stapler firings and double stapling technique combined with rectal eversion and total extra-abdominal resection in the sphincter-preserving resection of low rectal cancer]

Objectives: To investigate the application value of laparoscopic double stapler firings and double stapling technique combined with rectal eversion and total extra-abdominal resection (LDER) in the anal preservation treatment of low rectal cancer. Methods: Inclusion criteria: (1) age was 18-70; (2) the distance of the lower tumor edge from the anal verge was 4-5 cm; (3) primary tumor with a diameter ≤3 cm; (4) preoperative staging of T1~2N1~2M0; (5) "difficult pelvis", defined as ischial tuberosity diameter<10 cm or body mass index>25 kg/m2; (6) patients with strong intention for sphincter preservation; (7) no preoperative treatment (e.g., chemotherapy, radiotherapy, molecular targeted therapy, or immunotherapy); (8) no lateral lymph node enlargement; (9) no previous anorectal surgery; (10) patients with good basic condition who could tolerate surgery. Exclusion criteria: (1) previously suffered from malignant tumors of the digestive tract or currently suffering from malignant tumors out of the digestive tract; (2) patients with preoperative anal dysfunction (Wexner score ≥ 10), or fecal incontinence. The specific surgical steps are as follows: the distal end of the rectum was dissected to the level of the interspace between internal and external sphincters of anal canal. Five centimeters proximal to the tumor, the mesorectum was ligated, and a liner stapler was used to transect the rectum. The distal rectum with the tumor were then everted and extracted through the anus. The rectum was transected 0.5-1.0 cm distal to the tumor with a linear stapler. Full thickness suture was used to reinforce the stump of the rectum, which was then brought back into the pelvic cavity. Finally, an end-to-end anastomosis between the colon and the rectum was performed. A retrospective descriptive study was performed of the clinical and pathological data of 12 patients with T1-T2 stage low rectal cancer treated with LDER at Henan Provincial People's Hospital from January 2020 to December 2022. Results: All 12 patients successfully completed LDER with sphincter preservation, without conversion to open surgery or changes in surgical approach. The median surgical time was 272 (155-320) minutes, with a median bleeding volume of 100 (50-200) mL. No protective stoma was performed, and all patients received R0 resection. The average hospital stay was 9 (7-15) days. There were no postoperative anastomotic leakage or perioperative deaths. All 12 patients received postoperative follow-up, with a median follow-up of 12 months (6-36 months) and a Wexner score of 8 (5-14) at 6 months postoperatively. There was no tumor recurrence or metastasis during the follow-up period. Conclusions: LDER is safe and effective for the treatment of low rectal cancer.

Insulin and glycolysis dependency of cardioprotection by nicotinamide riboside

Decreased nicotinamide adenine dinucleotide (NAD+) levels contribute to various pathologies such as ageing, diabetes, heart failure and ischemia-reperfusion injury (IRI). Nicotinamide riboside (NR) has emerged as a promising therapeutic NAD+ precursor due to efficient NAD+ elevation and was recently shown to be the only agent able to reduce cardiac IRI in models employing clinically relevant anesthesia. However, through which metabolic pathway(s) NR mediates IRI protection remains unknown. Furthermore, the influence of insulin, a known modulator of cardioprotective efficacy, on the protective effects of NR has not been investigated. Here, we used the isolated mouse heart allowing cardiac metabolic control to investigate: (1) whether NR can protect the isolated heart against IRI, (2) the metabolic pathways underlying NR-mediated protection, and (3) whether insulin abrogates NR protection. NR protection against cardiac IRI and effects on metabolic pathways employing metabolomics for determination of changes in metabolic intermediates, and 13C-glucose fluxomics for determination of metabolic pathway activities (glycolysis, pentose phosphate pathway (PPP) and mitochondrial/tricarboxylic acid cycle (TCA cycle) activities), were examined in isolated C57BL/6N mouse hearts perfused with either (a) glucose + fatty acids (FA) ("mild glycolysis group"), (b) lactate + pyruvate + FA ("no glycolysis group"), or (c) glucose + FA + insulin ("high glycolysis group"). NR increased cardiac NAD+ in all three metabolic groups. In glucose + FA perfused hearts, NR reduced IR injury, increased glycolytic intermediate phosphoenolpyruvate (PEP), TCA intermediate succinate and PPP intermediates ribose-5P (R5P) / sedoheptulose-7P (S7P), and was associated with activated glycolysis, without changes in TCA cycle or PPP activities. In the "no glycolysis" hearts, NR protection was lost, whereas NR still increased S7P. In the insulin hearts, glycolysis was largely accelerated, and NR protection abrogated. NR still increased PPP intermediates, with now high 13C-labeling of S7P, but NR was unable to increase metabolic pathway activities, including glycolysis. Protection by NR against IRI is only present in hearts with low glycolysis, and is associated with activation of glycolysis. When activation of glycolysis was prevented, through either examining "no glycolysis" hearts or "high glycolysis" hearts, NR protection was abolished. The data suggest that NR's acute cardioprotective effects are mediated through glycolysis activation and are lost in the presence of insulin because of already elevated glycolysis.

Observation of WWγ Production and Search for Hγ Production in Proton-Proton Collisions at sqrt[s]=13 TeV

The observation of WWγ production in proton-proton collisions at a center-of-mass energy of 13 TeV with an integrated luminosity of 138  fb^{-1} is presented. The observed (expected) significance is 5.6 (5.1) standard deviations. Events are selected by requiring exactly two leptons (one electron and one muon) of opposite charge, moderate missing transverse momentum, and a photon. The measured fiducial cross section for WWγ is 5.9±0.8(stat)±0.8(syst)±0.7(modeling)  fb, in agreement with the next-to-leading order quantum chromodynamics prediction. The analysis is extended with a search for the associated production of the Higgs boson and a photon, which is generated by a coupling of the Higgs boson to light quarks. The result is used to constrain the Higgs boson couplings to light quarks.

[Surveillance of echinococcosis in Bayingolin Mongol Autonomous Prefecture, Xinjiang Uygur Autonomous Region from 2017 to 2022]

OBJECTIVE

To analyze the echinococcosis surveillance results in Bayingolin Mongol Autonomous Prefecture, Xinjiang Uygur Autonomous Region from 2017 to 2022, so as to provide insights into formulation of echinococcosis control measures in the prefecture.

METHODS

Villagers were randomly sampled using a multistage sampling method from class I and II echinococcosis endemic counties in Bayingolin Mongolian Autonomous Prefecture from 2017 to 2022 for detection of human echinococcosis, while all patients undergoing ultrasound examinations in medical institutions in class III endemic counties received active echinococcosis screening. In addition, livestock in centralized slaughterhouses or slaughtering sites were screened for echinococcosis using the palpation and necropsy method, and fresh domestic dog feces samples were collected from randomly selected dog owners in each administrative village for detection of Echinococcus copro-antigen in domestic dogs. The trends in detection of human and livestock echinococcosis, detection of newly diagnosed human echinococcosis cases and detection of Echinococcus coproantigen in domestic dogs were analyzed in Bayingolin Mongol Autonomous Prefecture from 2017 to 2022.

RESULTS

The mean detection rate of human echinococcosis was 0.13% (540/407 803) in Bayingolin Mongol Autonomous Prefecture from 2017 to 2022, which appeared a tendency towards a decline over years (χ2trend = 1 217.21, P < 0.001), and the highest detection of newly diagnosed echinococcosis cases was seen in Hejing County (0.28%, 191/67 865). The detection of livestock echinococcosis appeared a tendency towards a decline over years from 2017 to 2022 (χ2trend = 147.02, P < 0.001), with the highest detection rate seen in Hejing County (3.44%, 86/2 500), and the detection of Echinococcus copro-antigen in domestic dogs appeared a tendency towards a decline over years from 2017 to 2022 (χ2trend = 302.46, P < 0.001), with the highest detection rate in Qiemo County (2.74%, 118/4 313).

CONCLUSIONS

The detection of human and livestock echinococcosis and dog feces antigens Echinococcus copro-antigen in domestic dogs all appeared a tendency towards a decline in Bayingolin Mongol Autonomous Prefecture, Xinjiang Uygur Autonomous Region from 2017 to 2022; however, there is still a high echinococcosis transmission risk in local areas. Sustainable integrated echinococcosis control is required in Bayingolin Mongol Autonomous Prefecture.

[Influences and mechanism of extracellular vesicles from dermal papilla cells of mice on human hypertrophic scar fibroblasts]

Objective: To investigate the influences and mechanism of extracellular vesicles from dermal papilla cells (DPC-EVs) of mice on human hypertrophic scar fibroblasts (HSFs). Methods: The study was an experimental research. The primary dermal papilla cells (DPCs) of whiskers were extracted from 10 6-week-old male C57BL/6J mice and identified successfully. The DPC-EVs were extracted from the 3rd to 5th passage DPCs by ultracentrifugation, and the morphology was observed through transmission electron microscope and the particle diameter was detected by nanoparticle tracking analyzer (n=3) at 24 h after culture. The 3rd passage of HSFs were divided into DPC-EV group and phosphate buffer solution (PBS) group, which were cultured with DPC-EVs and PBS, respectively. The cell scratch test was performed and cell migration rate at 24 h after scratching was calculated (n=5). The cell proliferation levels at 0 (after 12 h of starvation treatment and before adding DPC-EVs or PBS), 24, 48, 72, and 96 h after culture were detected by using cell counting kit 8 (n=4). The protein expressions of α-smooth muscle actin (α-SMA) and collagen typeⅠ (ColⅠ) in cells at 24 h after culture were detected by immunofluorescence method and Western blotting, and the protein expression of Krüppel-like factor 4 (KLF4) in cells at 24 h after culture was detected by Western blotting. After the 3rd passage of HSFs were cultured with DPC-EVs for 24 h, the cells were divided into blank control group, KLF4 knockdown group, and KLF4 overexpression group according to the random number table. The cells in blank control group were only routinely cultured for 48 h. The cells in KLF4 knockdown group and KLF4 overexpression group were incubated with KLF4 knockdown virus for 24 h, then the cells in KLF4 knockdown group were routinely cultured for 24 h while the cells in KLF4 overexpression group were incubated with KLF4 overexpression virus for 24 h. The protein expressions of KLF4, α-SMA, and ColⅠ in cells were detected by Western blotting at 48 h after culture. Results: At 24 h after culture, the extracted DPC-EVs showed vesicular structure with an average particle diameter of 108.8 nm. At 24 h after scratching, the migration rate of HSFs in PBS group was (54±10)%, which was significantly higher than (29±8)% in DPC-EV group (t=4.37, P<0.05). At 48, 72, and 96 h after culture, the proliferation levels of HSFs in DPC-EV group were significantly lower than those in PBS group (with t values of 4.06, 5.76, and 6.41, respectively, P<0.05). At 24 h after culture, the protein expressions of α-SMA and ColⅠ of HSFs in DPC-EV group were significantly lower than those in PBS group, while the protein expression of KLF4 was significantly higher than that in PBS group. At 48 h after culture, compared with those in blank control group, the protein expression of KLF4 of HSFs in KLF4 knockdown group was down-regulated, while the protein expressions of α-SMA and ColⅠ were both up-regulated; compared with those in KLF4 knockdown group, the protein expression of KLF4 of HSFs in KLF4 overexpression group was up-regulated, while the protein expressions of ColⅠ and α-SMA were down-regulated. Conclusions: The DPC-EVs of mice can inhibit the proliferation and migration of human HSFs and significantly inhibit the expressions of fibrosis markers α-SMA and ColⅠ in human HSFs by activating KLF4.

Diagnostic and prognostic value of parameters of erector spinae in patients with uremic sarcopenia

AIM

This study aimed to investigate whether computed tomography (CT)-measured erector spinae parameters (ESPs) have diagnostic, severity assessment, and prognostic predictive value in uremic sarcopenia (US).

MATERIALS AND METHODS

A total of 202 uremic patients were enrolled and divided into two groups: a control group and a sarcopenia group. Sarcopenia was classified into two types: severe and nonsevere. The area, volume, and density of the erector spinae (ES) were measured using chest CT images, and the relevant ESP, including the erector spinae index (ESI), total erector spinae volume (TESV), erector spinae density (ESD), and erector spinae gauge (ESG) were calculated. The occurrence of adverse events was followed-up for 36 months. The diagnostic value and severity of US were determined using the receiver operating characteristic (ROC) curve. Survival curves diagnosed using CT were plotted and compared with the curve drawn using the gold standard. Cox regression analysis was used to identify independent risk factors associated with survival in US.

RESULTS

With an area under the curve (AUC) of 0.840 and 0.739, the combined ESP has diagnostic value and the ability to assess the severity of US. There was no significant difference in the survival curve between the combined ESP for the diagnosis of US and the gold standard (P > 0.05). ESI is a standalone predictor of survival in patients with US.

CONCLUSION

ESP measured by CT has diagnostic values for US and its severity, as well as being a predictive value for the prognosis of US.

New Structures in the J/ψJ/ψ Mass Spectrum in Proton-Proton Collisions at sqrt[s]=13 TeV

A search is reported for near-threshold structures in the J/ψJ/ψ invariant mass spectrum produced in proton-proton collisions at sqrt[s]=13  TeV from data collected by the CMS experiment, corresponding to an integrated luminosity of 135  fb^{-1}. Three structures are found, and a model with quantum interference among these structures provides a good description of the data. A new structure is observed with a local significance above 5 standard deviations at a mass of 6638_{-38}^{+43}(stat)_{-31}^{+16}(syst)  MeV. Another structure with even higher significance is found at a mass of 6847_{-28}^{+44}(stat)_{-20}^{+48}(syst)  MeV, which is consistent with the X(6900) resonance reported by the LHCb experiment and confirmed by the ATLAS experiment. Evidence for another new structure, with a local significance of 4.7 standard deviations, is found at a mass of 7134_{-25}^{+48}(stat)_{-15}^{+41}(syst)  MeV. Results are also reported for a model without interference, which does not fit the data as well and shows mass shifts up to 150 MeV relative to the model with interference.

[Injury death spectrum in children aged 0-14 years and its urban-rural difference in Tianjin, 1999-2021]

Objective: To analyze the characteristics, change of injury death spectrum in children aged 0-14 years and its urban-rural difference in Tianjin. Methods: The incidence data of injury death in children aged 0-14 years in Tianjin from 1999 to 2021 were collected from the "Population Based Mortality Surveillance System in Tianjin". We calculated constituent ratio, crude and standardized mortality rates in different subgroups of the population and major injury subtypes, and compared the rural-urban differences. The Cochran-Armitage trend test was used to analyze temporal trends in cause-of-death component ratios. Joinpoint regression model was used to calculate average annual percent change (AAPC). Seasonal difference in injury mortality risk were expressed as mortality ratio and 95% confidence interval. Results: From 1999 to 2021, injury was the third cause of death in children aged 0-14 years in Tianjin. The percentage of children who died in health-care facilities in rural area was 31.08%, which was lower than 37.82% in urban area. There was a downward trend in the standardized mortality rate of injury in children (AAPC=-5.54%, P<0.001). The standardized mortality rates of drowning and road traffic injury declined in both urban area and rural area (P<0.001). The mortality rate of accidental poisoning decreased in rural area (AAPC=-8.09%, P<0.001), but showed no significant change trend in urban area (P>0.05). The standardized mortality rate of suicide showed no significant change trend in urban area, but there was an increasing trend in the standardized mortality rate of suicide in rural children aged 10-14 years (AAPC=4.58%). No significant change trend was observed in mortality rate of falls in urban and rural children (P>0.05). The injury mortality rate showed obvious seasonality in children in Tianjin. Overall injury death risk and risk for drowning-caused death were highest in summer in both urban area and rural area. The risk for road traffic injury-related death was highest in autumn in urban area and in summer in rural area. The risk for death caused by accidental poisoning was highest in winter in both urban area and rural area. Conclusions: In recent decades, the injury mortality rate in children aged 0-14 years in Tianjin showed a decreasing trend. There is still a significant difference in the injury mortality level between urban area and rural area, to which close attention needs to be paid in the future policy development.

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.

Multi-scale representation of surface-enhanced Raman spectroscopy data for deep learning-based liver cancer detection

The early detection of liver cancer greatly improves survival rates and allows for less invasive treatment options. As a non-invasive optical detection technique, Surface-Enhanced Raman Spectroscopy (SERS) has shown significant potential in early cancer detection, providing multiple advantages over conventional methods. The majority of existing cancer detection methods utilize multivariate statistical analysis to categorize SERS data. However, these methods are plagued by issues such as information loss during dimensionality reduction and inadequate ability to handle nonlinear relationships within the data. To overcome these problems, we first use wavelet transform with its multi-scale analysis capability to extract multi-scale features from SERS data while minimizing information loss compared to traditional methods. Moreover, deep learning is employed for classification, leveraging its strong nonlinear processing capability to enhance accuracy. In addition, the chosen neural network incorporates a data augmentation method, thereby enriching our training dataset and mitigating the risk of overfitting. Moreover, we acknowledge the significance of selecting the appropriate wavelet basis functions in SERS data processing, prompting us to choose six specific ones for comparison. We employ SERS data from serum samples obtained from both liver cancer patients and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding and healthy volunteers to train and test our classification model, enabling us to assess its performance. Our experimental results demonstrate that our method achieved outstanding performance, surpassing the majority of multivariate statistical analysis and traditional machine learning classification methods, with an accuracy of 99.38 %, a sensitivity of 99.8 %, and a specificity of 97.0 %. These results indicate that the combination of SERS, wavelet transform, and deep learning has the potential to function as a non-invasive tool for the rapid detection of liver cancer.

[The association between portal vein thrombosis and rebleeding after non-urgent endoscopic treatment of esophagogastric varices]

Objective: To investigate the association between portal vein thrombosis and rebleeding after non-urgent endoscopic treatment of esophagogastric varices. Methods: The cirrhotic patients with esophagogastric varices diagnosed in the People's Hospital of Zhengzhou University from January 2017 to March 2023 were retrospectively collected. The patients were divided into thrombotic group and non-thrombotic group according to the presence or absence of portal vein thrombosis. The failure rate of endoscopic treatment and rebleeding rate in different periods were compared between the two groups. Receiver operating characteristic (ROC) curve was used to select the best cutoff value of gastric varicose diameter that affected total rebleeding during follow-up in both groups. The influencing factors of rebleeding within 12 and 36 months in both groups were analyzed, and the influencing factors of rebleeding within 36 months in thrombus group were further analyzed. Results: A total of 106 patients were enrolled, including 53 patients in the thrombotic group [male 37, female 16, aged 18-78 (54±13) years] and 53 patients in the non-thrombotic group [male 37, female 16, aged 27-83 (55±12) years]. The follow-up time of the two groups were (20±15) and (25±15) months, respectively. The total rebleeding rate in the thrombotic group was higher than that in the non-thrombotic group [30.2% (16/53) vs 13.2% (7/53), P˂0.05]. The rebleeding rates within 6, 12, 24 and 36 months in the thrombotic group were higher than those in the non-thrombotic group [18.9% (10/53) vs 5.7% (3/53), 18.9% (10/53) vs 5.7% (3/53), 28.3% (15/53) vs 9.4% (5/53), 30.2% (16/53) vs 11.3% (6/53), all P˂0.05]. The best cut-off value of the diameter of gastric varices that affects the total rebleeding in the two groups was 10.4 mm (10 mm was selected as the best cut-off value for the convenience of practical clinical application). Hemoglobin ˂ 85 g/L (HR=0.202, 95%CI: 0.043-0.953, P=0.043), 10 mm ˂ the diameter of GV ≤ 15 mm (HR=5.321, 95%CI: 1.161-24.390, P=0.031) and endoscopic variceal ligation combined with endoscopic tissue adhesive injection (EVL+ETAI) (HR=7.172, 95%CI: 1.910-26.930, P=0.004) were the risk factors for the first gastroesophageal variceal rebleeding within 12 months after non-urgent endoscopic treatment. EVL+ETAI (HR=3.811, 95%CI: 1.441-10.084, P=0.007) and portal vein thrombosis (HR=4.026, 95%CI: 1.483-10.932, P=0.006) were the risk factors for the first gastroesophageal variceal rebleeding within 36 months after non-urgent endoscopic treatment. The study found that, 10 mm ˂ the diameter of GV ≤ 15 mm (HR=7.503, 95%CI: 1.568-35.890, P=0.012) was the risk factor for rebleeding within 36 months in the thrombotic group. Conclusion: Portal vein thrombosis is a risk factor for rebleeding after non-urgent endoscopic treatment of esophagogastric varices.

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.

Wavelet transform and deep learning-based obstructive sleep apnea detection from single-lead ECG signals

Sleep apnea is a common sleep disorder. Traditional testing and diagnosis heavily rely on the expertise of physicians, as well as analysis and statistical interpretation of extensive sleep testing data, resulting in time-consuming and labor-intensive processes. To address the problems of complex feature extraction, data imbalance, and low model capacity, we proposed an automatic sleep apnea classification model (CA-EfficientNet) based on the wavelet transform, a lightweight neural network, and a coordinated attention mechanism. The signal is converted into a time-frequency image by wavelet transform and put into the proposed model for classification. The effects of input time window, wavelet transform type and data balancing on the classification performance are considered, and a cost-sensitive algorithm is introduced to more accurately distinguish between normal and abnormal breathing events. PhysioNet apnea ECG database was used for training and evaluation. The 3-min Frequency B-Spline wavelets transform of ECG signal was carried out, and Dice Loss was used to train the classification model of sleep breathing. The classification accuracy was 93.44%, sensitivity was 88.9%, specificity was 96.2% and most indexes were better than other related work.

Enhanced multi-label cardiology diagnosis with channel-wise recurrent fusion

The timely detection of abnormal electrocardiogram (ECG) signals is vital for preventing heart disease. However, traditional automated cardiology diagnostic methods have the limitation of being unable to simultaneously identify multiple diseases in a segment of ECG signals, and do not consider the potential correlations between the 12-lead ECG signals. To address these issues, this paper presents a novel network architecture, denoted as Branched Convolution and Channel Fusion Network (BCCF-Net), designed for the multi-label diagnosis of ECG cardiology to achieve simultaneous identification of multiple diseases. Among them, the BCCF-Net incorporates the Channel-wise Recurrent Fusion (CRF) network, which is designed to enhance the ability to explore potential correlation information between 12 leads. Furthermore, the utilization of the squeeze and excitation (SE) attention mechanism maximizes the potential of the convolutional neural network (CNN). In order to efficiently capture complex patterns in space and time across various scales, the multi branch convolution (MBC) module has been developed. Through extensive experiments on two public datasets with seven subtasks, the efficacy and robustness of the proposed ECG multi-label classification framework have been comprehensively evaluated. The results demonstrate the superior performance of the BCCF-Net compared to other state-of-the-art algorithms. The developed framework holds practical application in clinical settings, allowing for the refined diagnosis of cardiac arrhythmias through ECG signal analysis.

Elevated MIF identified by multiple cytokine analyses facilitates macrophage M2 polarization contributing to postoperative recurrence in chronic rhinosinusitis with nasal polyps

BACKGROUND

Chronic rhinosinusitis with nasal polyps (CRSwNP) is characterized by tissue heterogeneity and high postoperative recurrence risk. This study aims to employ cytokine analyses to identify serum biomarkers associated with postoperative CRSwNP recurrence and elucidate underlying recurrent mechanisms.

METHODS

A prospective cohort study was conducted on CRSwNP patients undergoing functional endoscopic sinus surgery. Serum and tissue samples were collected and analyzed for multiple cytokines. Participants were followed for 3 years and categorized into recurrent and non-recurrent groups. Cytokine profiles were compared, and potential markers for recurrence were further assessed. Macrophage migration inhibitory factor (MIF) expression in macrophages was modulated, and their polarization and cytokine secretion were assessed.

RESULTS

In the discovery cohort (21 recurrent and 40 non-recurrent patients), circulating cytokine profiles differed significantly, with 8 cytokines showing differential expression between the two groups. Among them, serum eotaxin, MIF, RANTES, and TRAIL exhibited promise in predicting recurrence. In the validation cohort (24 recurrent and 44 non-recurrent patients), serum eotaxin, MIF, and TRAIL levels were higher in recurrent cases. Tissue MIF was elevated in recurrent cases and had a strong predictive value for recurrence. Moreover, tissue MIF was co-expressed with CD206 in recurrent cases. Mechanistically, MIF overexpression promoted macrophage M2 polarization and TGF-β1, CCL-24, and MIF secretion, and MIF recombinant protein facilitated M2 polarization, and TGF-β1 and CCL-24 production, contributing to CRSwNP recurrence.

CONCLUSIONS

Serum-specific cytokine signatures were associated with postoperative recurrence risk in CRSwNP. Elevated MIF enhanced macrophage M2 polarization and cytokine secretion, contributing to the recurrent mechanisms of CRSwNP.

Secreted Clusterin Inhibits Tumorigenesis by Modulating Tumor Cell and Macrophage in Human Meningioma

BACKGROUND

Meningioma is the most common primary intracranial tumor with high frequency of postoperative recurrence, yet the biology of meningioma malignancy process is still obscure.

METHODS

To identify potential therapeutic targets and tumor suppressors, we performed single-cell transcriptome analysis through meningioma malignancy, which included 18 samples spanning normal meninges, benign and high grade in situ tumors, and lung metastases, for extensive transcriptome characterization. Tumor suppressor candidate gene and molecular mechanism were functionally validated at animal model and cellular level.

RESULTS

Comprehensive analysis and validation in mice and clinical cohorts indicated Clusterin (CLU) had suppressive function for meningioma tumorigenesis and malignancy by inducing mitochondria damage and triggering type I interferon pathway dependent on its secreted isoform, and the inhibition effect was enhanced by TNFα as TNFα also induced type I interferon pathway. The expression of CLU was upregulated by histone deacetylase inhibition. Meanwhile, both intra- and extra-cellular CLU overexpression enhanced macrophage polarization towards M1 phenotype and TNFα production, thus promoted tumor killing and phagocytosis.

CONCLUSIONS

CLU might be a key brake of meningioma malignance by synchronous modulating tumor cells and their microenvironment. Our work provides comprehensive insights into meningioma malignancy and a potential therapeutic strategy.

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.

Prognostic Factors and Construction of Nomogram Prediction Model of Lung Cancer Patients Using Clinical and Blood Laboratory Parameters

Objective

This work aimed to explore the prognostic risk factors of lung cancer (LC) patients and establish a line chart prediction model.

Methods

A total of 322 LC patients were taken as the study subjects. They were randomly divided into a training set (n = 202) and a validation set (n = 120). Basic information and laboratory indicators were collected, and the progression-free survival (PFS) and overall survival (OS) were followed up. Single-factor and cyclooxygenase (COX) multivariate analyses were performed on the training set to construct a Nomogram prediction model, which was validated with 120 patients in the validation set, and Harrell's consistency was analyzed.

Results

Single-factor analysis revealed significant differences in PFS (P<0.05) between genders, body mass index (BMI), carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), squamous cell carcinoma antigen (SCCA), treatment methods, treatment response evaluation, smoking status, presence of pericardial effusion, and programmed death ligand 1 (PD-L1) at 0 and 1-50%. Significant differences in OS (P<0.05) were observed for age, tumor location, treatment methods, White blood cells (WBC), uric acid (UA), CA125, pro-gastrin-releasing peptide (ProGRP), SCCA, cytokeratin fragment 21 (CYFRA21), and smoking status. COX analysis identified male gender, progressive disease (PD) as treatment response, and SCCA > 1.6 as risk factors for LC PFS. The consistency indices of the line chart models for predicting PFS and OS were 0.782 and 0.772, respectively.

Conclusion

Male gender, treatment response of PD, and SCCA > 1.6 are independent risk factors affecting the survival of LC patients. The PFS line chart model demonstrates good concordance.

A pivotal role for the IL-1β and the inflammasome in preterm labor

During labor, monocytes infiltrate massively the myometrium and differentiate into macrophages secreting high levels of reactive oxygen species and of pro-inflammatory cytokines (i.e. IL-1β), leading to myometrial contraction. Although IL-1β is clearly implicated in labor, its function and that of the inflammasome complex that cleaves the cytokine in its active form, has never been studied on steps preceding contraction. In this work, we used our model of lipopolysaccharide-induced preterm labor to highlight their role. We demonstrated that IL-1β was secreted by the human myometrium during labor or in presence of infection and was essential for myometrial efficient contractions as its blockage with an IL-1 receptor antagonist (Anakinra) or a neutralizing antibody completely inhibited the induced contractions. We evaluated the implication of the inflammasome on myometrial contractions and differentiation stages of labor onset. We showed that the effects of macrophage-released IL-1β in myometrial cell transactivation were blocked by inhibition of the inflammasome, suggesting that the inflammasome by producing IL-1β was essential in macrophage/myocyte crosstalk during labor. These findings provide novel innovative approaches in the management of preterm labor, specifically the use of an inflammasome inhibitor to block the precursor stages of labor before the acquisition of the contractile phenotype.

[Perioperative management of wounds associated with secondary sternal osteomyelitis and/or mediastinitis after sternotomy and its clinical effects]

Objective: To investigate the perioperative management of wounds associated with secondary sternal osteomyelitis and/or mediastinitis after sternotomy, and to evaluate its clinical effects. Methods: This study was a retrospective observational study. From January 2017 to December 2022, 36 patients with wounds associated with secondary sternal osteomyelitis and/or mediastinitis after sternotomy who were conformed to the inclusion criteria were admitted to the Burn Center of PLA of the First Affiliated Hospital of Air Force Medical University, including 23 males and 13 females, aged 25 to 81 years. Preparation for surgery was made. For patients with suspected retrosternal mediastinal abscess cavity, all cancellous bone of the unhealed sternum was bitten off to fully expose the retrosternal mediastinum, remove the source of infection and granulation tissue, and to fill the sternum defect with flipped unilateral pectoralis major muscle. For patients who had no retrosternal mediastinal infection but had fresh granulation tissue in unhealed sternal wounds, the necrotic tissue and a small amount of necrotic sternum were palliatively removed, and bilateral pectoralis major muscles were advanced and abutted to cover the sternal defect. After the skin in the donor area was closed by tension-relieving suture, continuous vacuum sealing drainage was performed, and continuous even infusion and lavage were added 24 hours later. The thorax was fixed with an armor-like chest strap, the patients were guided to breathe abdominally, with both upper limbs fixed to the lateral chest wall using a surgical restraint strap. The bacterial culture results of wound exudation specimens on admission were recorded. The wound condition observed during operation, debridement method, muscle flap covering method, intraoperative bleeding volume, days of postoperative infusion and lavage, lavage solution volume and changes on each day, and postoperative complications and wound healing time were recorded. After discharge, the wound healing quality, thorax shape, and mobility functions of thorax and both upper limbs were evaluated during follow-up. The stability and closure of sternum were observed by computed tomography (CT) reexamination. Results: On admission, among 36 patients, 33 cases were positive and 3 cases were negative in bacterial culture results of wound exudation specimens. Intraoperative observation showed that 26 patients had no retrosternal mediastinal infection but had fresh granulation tissue in unhealed sternal wounds, palliative debridement was performed and bilateral pectoralis major muscles were advanced and abutted to cover the defect. In 10 patients with suspected retrosternal mediastinal abscess cavity, the local sternum was completely removed by bite and the defect was covered using flipped unilateral pectoralis major muscle. During the operation, one patient experienced an innominate vein rupture and bleeding of approximately 3 000 mL during mediastinal exploration, and the remaining patients experienced bleeding of 100-1 000 mL. Postoperative infusion and lavage were performed for 4-7 days, with a lavage solution volume of 3 500-4 500 mL/d. The lavage solution gradually changed from dark red to light red and finally clear. Except for 1 patient who had suture rupture caused by lifting the patient under the armpit during nursing on the 3rd day after surgery, the wounds of the other patients healed smoothly after surgery, and the wound healing time of all patients was 7-21 days. Follow-up for 3 to 9 months after discharge showed that the patient who had suture rupture caused by armpit lifting died due to multiple organ failure. In 1 patient, the armor-like chest strap was removed 2 weeks after surgery, and the shoulder joint movement was not restricted, resulting in local rupture of the suture, which healed after dressing change. The wounds of the remaining patients healed well, and they resumed their daily life. The local skin of patient's pectoralis major muscle defect was slightly sunken and lower than that of the contralateral thorax in the patients undergoing treatment of pectoralis major muscle inversion, while no obvious thoracic deformity was observed in patients undergoing treatment with pectoralis major muscle propulsion and abutment. The chest and upper limb movement in all patients were slightly limited or normal. CT reexamination results of 10 patients showed that the sternum was stable, the local sternum was closed or covered completely with no lacuna or defects. Conclusions: Once the wound associated with secondary sternal osteomyelitis and/or mediastinitis after sternotomy is formed, individualized and precise debridement should be performed as soon as possible, different transfer ways of pectoralis major muscle flap should be chosen to cover the defect, and postoperative continuous infusion and lavage together with strict thorax and shoulder joint restraint and immobilization should be performed. This treatment strategy can ensure good wound healing without affecting the shape and function of the donor area.

Search for Scalar Leptoquarks Produced via τ-Lepton-Quark Scattering in pp Collisions at sqrt[s]=13 TeV

The first search for scalar leptoquarks produced in τ-lepton-quark collisions is presented. It is based on a set of proton-proton collision data recorded with the CMS detector at the LHC at a center-of-mass energy of 13 TeV corresponding to an integrated luminosity of 138  fb^{-1}. The reconstructed final state consists of a jet, significant missing transverse momentum, and a τ lepton reconstructed through its hadronic or leptonic decays. Limits are set on the product of the leptoquark production cross section and branching fraction and interpreted as exclusions in the plane of the leptoquark mass and the leptoquark-τ-quark coupling strength.

Copy Number Alterations Predict Development of OSCC from Oral Leukoplakia

Oral leukoplakia (OLK) is a common type of potentially malignant disorder. Early identification of the malignancy potential leads to a better management of OLK and prediction of development of oral squamous cell carcinoma (OSCC). However, there has been no effective biomarker to assess the risk of malignancy in OLK. Genomic copy number alteration (CNA) is a complex chromosomal structural variation in the genome and has been identified as a potential biomarker in multiple cancers. This study aimed to develop a predictive model for the malignant transformation risk of OLK by copy number analysis. A total of 431 OLK samples with long-term follow-up (median follow-up of 67 mo) from multiple academic centers were analyzed for CNAs. CNA events increased with the severity of hyperplasia, mild dysplasia, moderate dysplasia, and severe dysplasia. More CNA events were present in patients with OLK who later developed OSCC than in those with OLK who did not. By multivariate Cox regression analysis, the OLK of the CNA scorehigh group showed an increased risk of malignant transformation than the CNA scorelow group (P < 0.001). A CNA score model was developed to accurately predict the prognosis (area under the receiver operating characteristic curve [AUC] = 0.879; 95% confidence interval [CI], 0.799-0.959) and was validated using data from 2 external centers (AUC = 0.836, 95% CI, 0.683-0.989; AUC = 0.876, 95% CI, 0.682-1.000), and all of them showed better prediction performances than histopathological grade in assessing the transformation risk of OLK. Furthermore, we performed CNA models among 4 subgroups of OLK with hyperplasia, mild dysplasia, moderate dysplasia, and severe dysplasia and found that CNA score can accurately predict malignant transformation of different subgroups. CNA score may be a useful biomarker to predict malignant transformation of OLK. Subtyping of OLK by the CNA score could contribute to better management of OLK and predicting development of OSCC.

[Effect of dexmedetomidine on the intraoperative and early postoperative complications of patients undergoing orthotopic liver transplantation]

Objective: To investigate the effect of dexmedetomidine on the intraoperative and early postoperative complications of patients undergoing orthotopic liver transplantation. Methods: This is a retrospective cohort study. The clinical data of 399 patients who underwent orthotopic liver transplantation at the First Affiliated Hospital of Nanjing Medical University from January 2016 to September 2020 were retrospectively collected. There were 319 males and 80 females, aged (50.9±10.2) years (range: 10 to 73 years). These patients were divided into the control group (369 cases) and the dexmedetomidine group (30 cases) according to whether dexmedetomidine was continuously pumped intravenously during the operation until the operation ended. The 1∶2 propensity score matching was used to match the preoperative and intraoperative conditions of the two groups of patients, and the caliper width was 0.2. Outcome indicators included intraoperative postreperfusion syndrome, acute kidney injury and pulmonary complications within 7 days after surgery, length of hospital stay, time of stay in ICU, duration of assisted mechanical ventilation, rate of reintubation, 6-month and 1-year survival and recurrence-free survival rate after surgery. The independent sample t test, χ2 test, Mann-Whitney U test or Fisher exact test was used to statistically analyze the data of the two groups of patients, respectively. Survival curves of overall survival and disease-free-survival were plotted by Kaplan-Meier method, and the survival rate and recurrence-free survival rate were compared by Log-rank test. Results: A total of 78 patients were included after propensity score matching, including 26 in the dexmedetomidine group and 52 in the control group. The incidence of acute kidney injury in the dexmedetomidine group within 7 days after surgery was 0 (0/26), significantly lower than that of the control group (21.2%,11/52)(corrected χ2=4.776, P=0.029). There were no significant differences in the incidence of intraoperative postreperfusion syndrome and pulmonary complications within 7 days after surgery, length of hospital stay, ICU time, the duration of assisted mechanical ventilation, rate of reintubation, 6-month and 1-year survival, and recurrence-free survival rate after surgery between the two groups (all P>0.05). Conclusion: Continuous infusion of dexmedetomidine via intravenous pump during operation may be beneficial in reducing the incidence of acute kidney injury within 7 days after orthotopic liver transplantation.

Association of radioactive iodine treatment in differentiated thyroid cancer and cardiovascular death: a large population-based study

PURPOSE

The risk of cardiovascular diseases' death (CVD) in patients with differentiated thyroid cancer (DTC) treated with radioactive iodine (RAI) after surgery has not been adequately studied.

METHODS

Data of DTC patients who received RAI after surgery were retrieved from the Surveillance, Epidemiology, and End Result (SEER) database (2004-2015). Standardized mortality rate (SMR) analysis was used to evaluate the CVD risk in patients with RAI vs general population. A 1:1 propensity score matching (PSM) was applied to balance inter-group bias, and Pearson's correlation coefficient was used to detect collinearity between variables. The Cox proportional hazard model and multivariate competing risk model were utilized to evaluate the impact of RAI on CVD. At last, we curved forest plots to compare differences in factors significantly associated with CVD or cancer-related deaths.

RESULTS

DTC patients with RAI treatment showed lower SMR for CVD than general population (RAI: SMR = 0.66, 95% CI 0.62-0.71, P < 0.05). After PSM, Cox proportional hazard regression demonstrated a decreased risk of CVD among patients with RAI compared to patients without (HR = 0.76, 95% CI 0.6-0.97, P = 0.029). However, in competing risk regression analysis, there was no significant difference (adjusted HR = 0.82, 95% CI 0.66-1.01, P = 0.11). The independent risk factors associated with CVD were different from those associated with cancer-related deaths.

CONCLUSION

The CVD risk between DTC patients treated with RAI and those who did not was no statistical difference. Noteworthy, they had decreased CVD risk compared with the general population.

Dynamic stabilization of ablative Rayleigh-Taylor instability in the presence of a temporally modulated laser pulse

This paper presents a numeric study of the dynamic stabilization of the ablative Rayleigh-Taylor instability (ARTI) in the presence of a temporally modulated laser pulse. The results show that the specially modulated laser produces a dynamically stabilized configuration near the ablation front. The physical features of the relevant laser-driven parameters in the unperturbed ablative flows have been analyzed to reveal the inherent stability mechanism underlying the dynamically stabilized configuration. A single-mode ARTI for the modulated laser pulse is first compared with that of the unmodulated laser pulse. The results show that the modulated laser stabilizes the surface perturbations and reduces the linear growth rate and enhancement of the cutoff wavelength. For multimode perturbations, the dynamic stabilization effect of the modulated laser pulse contributes to suppress the small-scale structure and reduce the width of the mixing layer. Moreover, the results show that the stabilization effect of the modulated laser pulse decreases as the maximum wavelength increases.

RtNAC055 promotes drought tolerance via a stomatal closure pathway linked to methyl jasmonate/hydrogen peroxide signaling in Reaumuria trigyna

The stomata regulate CO2 uptake and efficient water usage, thereby promoting drought stress tolerance. NAC proteins (NAM, ATAF1/2, and CUC2) participate in plant reactions following drought stress, but the molecular mechanisms underlying NAC-mediated regulation of stomatal movement are unclear. In this study, a novel NAC gene from Reaumuria trigyna, RtNAC055, was found to enhance drought tolerance via a stomatal closure pathway. It was regulated by RtMYC2 and integrated with jasmonic acid signaling and was predominantly expressed in stomata and root. The suppression of RtNAC055 could improve jasmonic acid and H2O2 production and increase the drought tolerance of transgenic R. trigyna callus. Ectopic expression of RtNAC055 in the Arabidopsis atnac055 mutant rescued its drought-sensitive phenotype by decreasing stomatal aperture. Under drought stress, overexpression of RtNAC055 in poplar promoted ROS (H2O2) accumulation in stomata, which accelerated stomatal closure and maintained a high photosynthetic rate. Drought upregulated the expression of PtRbohD/F, PtP5CS2, and PtDREB1.1, as well as antioxidant enzyme activities in heterologous expression poplars. RtNAC055 promoted H2O2 production in guard cells by directly binding to the promoter of RtRbohE, thus regulating stomatal closure. The stress-related genes RtDREB1.1/P5CS1 were directly regulated by RtNAC055. These results indicate that RtNAC055 regulates stomatal closure by maintaining the balance between the antioxidant system and H2O2 level, reducing the transpiration rate and water loss, and improving photosynthetic efficiency and drought resistance.

MdbHLH93 confers drought tolerance by activating MdTyDC expression and promoting dopamine biosynthesis

Dopamine and its biosynthesis-limiting enzyme tyrosine decarboxylase (TyDC) play a vital part in mediating plant growth and the response to drought stress. However, the underlying molecular mechanism remains poorly understood. Here, drought stress markedly induced the expression of Malus domestica bHLH93 (MdbHLH93), the apple basic helix-loop-helix transcription factor. Moreover, MdbHLH93 directly bound to the Malus domestica TyDC (MdTyDC) promoter and positively regulated its expression, which resulted in dopamine synthesis and enhanced drought tolerance. Furthermore, the additive effect of overexpressing MdbHLH93 and MdTyDC simultaneously promoted dopamine synthesis and drought tolerance in apples, while the interference of MdbHLH93 inhibited this effect, indicating that MdTyDC-regulated dopamine synthesis and drought tolerance were positively regulated by MdbHLH93. Taken together, these findings suggest the positive regulation of dopamine accumulation by MdbHLH93 through its transcriptional regulation of MdTyDC and show that increased dopamine content confers drought tolerance in apples.

The fatty acid-related gene signature stratifies poor prognosis patients and characterizes TIME in cutaneous melanoma

BACKGROUND

The aim of this study is to build a prognostic model for cutaneous melanoma (CM) using fatty acid-related genes and evaluate its capacity for predicting prognosis, identifying the tumor immune microenvironment (TIME) composition, and assessing drug sensitivity.

METHODS

Through the analysis of transcriptional data from TCGA-SKCM and GTEx datasets, we screened for differentially expressed fatty acids-related genes (DEFAGs). Additionally, we employed clinical data from TCGA-SKCM and GSE65904 to identify genes associated with prognosis. Subsequently, utilizing all the identified prognosis-related fatty acid genes, we performed unsupervised clustering analysis using the ConsensusClusterPlus R package. We further validated the significant differences between subtypes through survival analysis and pathway analysis. To predict prognosis, we developed a LASSO-Cox prognostic signature. This signature's predictive ability was rigorously examined through multivariant Cox regression, survival analysis, and ROC curve analysis. Following this, we constructed a nomogram based on the aforementioned signature and evaluated its accuracy and clinical utility using calibration curves, cumulative hazard rates, and decision curve analysis. Using this signature, we stratified all cases into high- and low-risk groups and compared the differences in immune characteristics and drug treatment responsiveness between these two subgroups. Additionally, in this study, we provided preliminary confirmation of the pivotal role of CD1D in the TIME of CM. We analyzed its expression across various immune cell types and its correlation with intercellular communication using single-cell data from the GSE139249 dataset.

RESULTS

In this study, a total of 84 DEFAGs were identified, among which 18 were associated with prognosis. Utilizing these 18 prognosis-related genes, all cases were categorized into three subtypes. Significant differences were observed between subtypes in terms of survival outcomes, the expression of the 18 DEFAGs, immune cell proportions, and enriched pathways. A LASSO-Cox regression analysis was performed on these 18 genes, leading to the development of a signature comprising 6 DEFAGs. Risk scores were calculated for all cases, dividing them into high-risk and low-risk groups. High-risk patients exhibited significantly poorer prognosis than low-risk patients, both in the training group (p < 0.001) and the test group (p = 0.002). Multivariate Cox regression analysis indicated that this signature could independently predict outcomes [HR = 2.03 (1.69-2.45), p < 0.001]. The area under the ROC curve for the training and test groups was 0.715 and 0.661, respectively. Combining risk scores with clinical factors including metastatic status and patient age, a nomogram was constructed, which demonstrated significant predictive power for 3  and 5 years patient outcomes. Furthermore, the high and low-risk subgroups displayed differences in the composition of various immune cells, including M1 macrophages, M0 macrophages, and CD8+ T cells. The low-risk subgroup exhibited higher StromalScore, ImmuneScore, and ESTIMATEScore (p < 0.001) and demonstrated better responsiveness to immune therapy for patients with PD1-positive and CTLA4-negative or positive expressions (p < 0.001). The signature gene CD1D was found to be mainly expressed in monocytes/macrophages and dendritic cells within the TIME. Through intercellular communication analysis, it was observed that cases with high CD1D expression exhibited significantly enhanced signal transductions from other immune cells to monocytes/macrophages, particularly the (HLA-A/B/C/E/F)-CD8A signaling from natural killer (NK) cells to monocytes/macrophages (p < 0.01).

CONCLUSIONS

The prognostic signature constructed in this study, based on six fatty acid-related genes, exhibits strong capabilities in predicting patient outcomes, identifying the TIME, and assessing drug sensitivity. This signature can aid in patient risk stratification and provide guidance for clinical treatment strategies. Additionally, our research highlights the crucial role of CD1D in the CM's TIME, laying a theoretical foundation for future related studies.

Search for Inelastic Dark Matter in Events with Two Displaced Muons and Missing Transverse Momentum in Proton-Proton Collisions at sqrt[s]=13 TeV

A search for dark matter in events with a displaced nonresonant muon pair and missing transverse momentum is presented. The analysis is performed using an integrated luminosity of 138  fb^{-1} of proton-proton (pp) collision data at a center-of-mass energy of 13 TeV produced by the LHC in 2016-2018. No significant excess over the predicted backgrounds is observed. Upper limits are set on the product of the inelastic dark matter production cross section σ(pp→A^{'}→χ_{1}χ_{2}) and the decay branching fraction B(χ_{2}→χ_{1}μ^{+}μ^{-}), where A^{'} is a dark photon and χ_{1} and χ_{2} are states in the dark sector with near mass degeneracy. This is the first dedicated collider search for inelastic dark matter.