Effect of long-term serum sodium levels on the prognosis of patients on maintenance hemodialysis

Abnormal serum Na (SNa) levels are common in patients with chronic kidney disease (CKD) which is associated with increased morbidity and mortality. There are relatively few studies on the effect of SNa indicators on the prognosis of patients undergoing maintenance hemodialysis (MHD). We aim to investigate the effect of long-term SNa levels on the survival and prognosis of patients undergoing hemodialysis (HD). Newly entered HD patients in the registration system of Zhejiang Provincial Dialysis Quality Control Center between January 1, 2010 and December 31, 2019 were included and followed up until December 31, 2020. Multiple sodium levels were collected from patients, defining long-term SNa as the mean of multiple SNa, according to which patients were grouped, with the prognostic differences between subgroups compared by Kaplan-Meier modeling and multifactorial Cox regression modeling. Finally, a total of 21,701 patients were included in this study and Cox regression showed that decreased SNa levels (Na < 135 mmol/L, HR = 1.704, 95% CI 1.408-2.063, p < 0.001; 135≦Na≦137.5 mmol/L, HR = 1.127,95% CI 1.016-1.250, p = 0.024) and elevated SNa levels (142.5 < Na≦145mmol/L, HR = 1.198, 95% CI 1.063-1.350, p = 0.003; Na > 145mmol/L, HR = 2.150, 95% CI 1.615-2.863, p < 0.001) were all independent risk factors for all-cause mortality in MHD patients.

Prevalence and characteristics of ertapenem-mono-resistant isolates among carbapenem-resistant Enterobacterales in China

Carbapenem-resistant Enterobacterales (CRE), specifically those resistant to only ertapenem among carbapenems (ETP-mono-resistant), are increasingly reported, while the optimal therapy options remain uncertain. To investigate the prevalence and characteristics of ETP-mono-resistant CRE, CRE strains were systematically collected from 102 hospitals across China between 2018 and 2021. A 1:1 randomized matching study was conducted with ETP-mono-resistant strains to meropenem- and/or imipenem-resistant (MEM/IPM-resistant) strains. Antimicrobial susceptibility testing, whole-genome sequencing, carbapenem-hydrolysing activity and the expression of carbapenemase genes were determined. In total, 18.8% of CRE strains were ETP-mono-resistant, with relatively low ertapenem MIC values. ETP-mono-resistant strains exhibited enhanced susceptibility to β-lactams, β-lactam/β-lactamase inhibitor combinations, levofloxacin, fosfomycin, amikacin and polymyxin than MEM/IPM-resistant strains (P < 0.05). Phylogenetic analysis revealed high genetic diversity among ETP-mono-resistant strains. Extended-spectrum β-lactamases (ESBLs) and/or AmpC, as well as porin mutations, were identified as potential major mechanisms mediating ETP-mono-resistance, while the presence of carbapenemases was found to be the key factor distinguishing the carbapenem-resistant phenotypes between the two groups (P < 0.001). Compared with the MEM/IPM-resistant group, limited carbapenemase-producing CRE (CP-CRE) strains in the ETP-mono-resistant group showed a significantly lower prevalence of ESBLs and porin mutations, along with reduced expression of carbapenemase. Remarkably, spot assays combined with modified carbapenem inactivation method indicated that ETP-mono-resistant CP-CRE isolates grew at meropenem concentrations eightfold above their corresponding MIC values, accompanied by rapidly enhanced carbapenem-hydrolysing ability. These findings illustrate that ETP-mono-resistant CRE strains are relatively prevalent and that caution should be exercised when using meropenem alone for treatment. The detection of carbapenemase should be prioritized.

Chromosomal integration and plasmid fusion occurring in ST20 carbapenem-resistant Klebsiella pneumoniae isolates coharboring blaNDM-1 and blaIMP-4 induce resistance transmission and fitness variation

To investigate the epidemiology of ST20 carbapenem-resistant Klebsiella pneumoniae (CRKP) in China, and further explore the genomic characteristics of blaIMP-4 and blaNDM-1 coharboring isolates and plasmid contributions to resistance and fitness. Seven ST20 CRKP isolates were collected nationwide, and antimicrobial susceptibility testing was performed. Antimicrobial resistance genes, virulence genes, and plasmid replicons were identified via whole-genome sequencing, and clonality assessed via core-genome multilocus sequence typing. Furthermore, we found four dual-metallo-β-lactamases (MBL)-harbouring isolates, the gene location was detected by Southern blotting, and plasmid location analysis showed that blaIMP-4 was located on a separate plasmid, a self-conjugative fusion plasmid, or the bacterial chromosome. These isolates were subjected to long-read sequencing, the presence of blaIMP-4 in different locations was identified by genomic comparison, and transposon units were detected via inverse PCR. We subsequently found that blaIMP-4 on the fusion plasmid and bacterial chromosome was formed via intact plasmid recombination by the IS26 and ltrA, respectively, and the circular transposon unit was related to cointegration, however, blaIMP-4 in different locations did not affect the gene stability. The blaNDM-1-harbouring plasmid contributed to the increased resistance to β-lactams and shortened survival lag time which was revealed in plasmid cured isolates. In summary, the K. pneumoniae ST20 clone is a high-risk resistant clone. With the use of ceftazidime/avibactam, MBL-positive isolates, especially dual-MBL-harbouring isolates, should be given additional attention.

The functions of exosomes targeting astrocytes and astrocyte-derived exosomes targeting other cell types

Astrocytes are the most abundant glial cells in the central nervous system; they participate in crucial biological processes, maintain brain structure, and regulate nervous system function. Exosomes are cell-derived extracellular vesicles containing various bioactive molecules including proteins, peptides, nucleotides, and lipids secreted from their cellular sources. Increasing evidence shows that exosomes participate in a communication network in the nervous system, in which astrocyte-derived exosomes play important roles. In this review, we have summarized the effects of exosomes targeting astrocytes and the astrocyte-derived exosomes targeting other cell types in the central nervous system. We also discuss the potential research directions of the exosome-based communication network in the nervous system. The exosome-based intercellular communication focused on astrocytes is of great significance to the biological and/or pathological processes in different conditions in the brain. New strategies may be developed for the diagnosis and treatment of neurological disorders by focusing on astrocytes as the central cells and utilizing exosomes as communication mediators.

Trace the origin of yak meat in Xizang based on stable isotope combined with multivariate statistics

In this study, the feasibility of tracing the origin of yak meat in Xizang Autonomous Region based on stable isotope combined with multivariable statistics was researched. The δ13C, δ15N, δ2H and δ18O in yak meat were determined by stable isotope ratio mass spectrometry, and the data were analyzed by analysis of variance, fisher discriminant analysis (FDA), back propagation (BP) neural network and orthogonal partial least squares discrimination analysis (OPLS-DA). The results showed that the δ13C, δ15N, δ2H and δ18O had significant differences among different origins (P < 0.05). The overall original correct discrimination rate of fisher discriminant analysis was 89.7 %, and the correct discrimination rate of cross validation was 88.2 %. The correct classification rate of BP neural network based on training set was 93.38 %, and the correct classification rate of BP neural network based on test set was 89.83 %. The OPLS-DA model interpretation rate parameter R2Y was 0.67, the model prediction rate parameter Q2 was 0.409, which could distinguish yak meat from seven different producing areas in Xizang Autonomous Region. The results showed that the origin of yak meat in Xizang Autonomous Region can be traced based on stable isotope combined with multivariate statistics.

Systemic evaluation of novel acaricide hexythiazox for bioactivity improvement and risk reduction at the enantiomer level

Traditional risk assessments of chiral pesticides mainly depend on racemic form, which is often incomprehensive. This study conducted systemic investigations on the bioactivity, toxicity, and ecotoxicological effects of hexythiazox (HTZ) at the enantiomer level. The elution order and absolute configuration of HTZ enantiomers were determined. (4R, 5R)-(+)-HTZ exhibited 708 and 1719 times higher bioactivity against Tetranychus cinnabarinus and Tetranychus urticae eggs than (4S, 5S)-(-)-HTZ, respectively. Molecular docking indicated greater interactions between (4R, 5R)-(+)-HTZ and chitin synthase leading to higher bioactivity of (4R, 5R)-(+)-HTZ. However, (4S, 5S)-(-)-HTZ induced greater changes in protein and malondialdehyde content, and antioxidant and detoxification enzyme activities than (4R, 5R)-(+)-HTZ in earthworms. Furthermore, integrated biomarker response results indicated (4S, 5S)-(-)-HTZ exhibited higher toxic effects on earthworms than (4R, 5R)-(+)-HTZ. Finally, significant differentially expressed genes (DEGs) were observed in earthworms after exposure to (4R, 5R)-(+)-HTZ and (4S, 5S)-(-)-HTZ, respectively. These DEGs were mainly enriched in glycolysis/gluconeogenesis and purine metabolism pathways in earthworms. Additionally, six metabolism pathways were also enriched, including pyruvate metabolism, fatty acid biosynthesis, oxidative phosphorylation, citric acid cycle, fatty acid degradation, and ATP-binding cassette transporters. These findings suggest that earthworms exhibited enantiomer-specific responses to (4R, 5R)-(+)-HTZ and (4S, 5S)-(-)-HTZ. This study provides systemic insight into the toxicity mechanism of HTZ at the enantiomer level and the potential to develop (4R, 5R)-(+)-HTZ as a high-efficiency and low-risk pesticide.

Processes and mechanisms in remediation of aqueous chromium contamination by sulfidated nano-scale zerovalent iron (S-nZVI): Experimental and computational investigations

Sulfidated nano-scale zerovalent iron (S-nZVI) has emerged as an advanced functional nanomaterial for efficiently remediating Cr(VI) contamination in aqueous environments. However, there is an insufficient understanding of its coherent process, removal pathway, and hydrochemical reactive mechanisms, presenting potential challenges for its future environmental applications. To address this gap, this study successfully synthesized S-nZVI through a chemical precipitation method and effectively applied it for the removal of Cr(VI). Additional characterization revealed that the removal of Cr(VI) followed a sequence of rapid chemisorption and intraparticle diffusion processes, concomitant with an increase in pH and a decrease in oxidation-reduction potential. The remediation mechanism encompassed a synergistic reduction of Cr(VI) to Cr(III) and simultaneous immobilization via Cr2FeO4 coprecipitation. The highest Cr(VI) removal capacity of 75 mg/g was attained during dynamic removal experiments in the sand column packed with S-nZVI. Further computational analysis, employing density functional theory calculations based on the experimental data, revealed the involvement of multiple molecular orbitals of Cr(VI) in the removal process. It also elucidated a step-by-step reduction pathway for Cr(VI) characterized by decreasing free energy. These findings provide evidence-based insights into Cr(VI) remediation using S-nZVI and can serve as valuable technical support for future environmental management of heavy metals.

Integrative metabolome and transcriptome profiling provide insights into elucidation of the synthetic mechanisms of phenolic compounds in Yunnan hulled wheat (Triticum aestivum ssp. yunnanense King)

BACKGROUND

Yunnan hulled wheat grains (YHWs) have abundant phenolic compounds (PCs). However, a systematic elucidation of the phenolic characteristics and molecular basis in YHWs is currently lacking. The aim of the study, for the first time, was to conduct metabolomic and transcriptomic analyses of YHWs at different developmental stages.

RESULTS

A total of five phenolic metabolite classes (phenolic acids, flavonoids, quinones, lignans and coumarins, and tannins) and 361 PCs were identified, with flavonoids and phenolic acids being the most abundant components. The relative abundance of the identified PCs showed a dynamic decreasing pattern with grain development, and the most significant differences in accumulation were between the enlargement and mature stage, which is consistent with the gene regulation patterns of the corresponding phenolic biosynthesis pathway. Through co-expression and co-network analysis, PAL, HCT, CCR, F3H, CHS, CHI and bZIP were identified and predicted as candidate key enzymes and transcription factors.

CONCLUSION

The results broaden our understanding of PC accumulation in wheat whole grains, especially the differential transfer between immature and mature grains. The identified PCs and potential regulatory factors provide important information for future in-depth research on the biosynthesis of PCs and the improvement of wheat nutritional quality. © 2024 Society of Chemical Industry.

A review regarding the article 'Supervised exercise training in heart failure with preserved ejection fraction: A systematic review and meta-analysis of randomized controlled trials'

Heart failure is a clinical syndrome with different etiologies and phenotypes. For all forms, supervised exercise training and individual physical activity are class IA recommendations in current guidelines. Over the past two decades it has become recognized that physical deconditioning may play a key role in the progression of symptoms and poor outcomes. A number of prior studies have assessed the ability of exercise training to improve functional capacity in patients with HF. Most of these previous studies showed positive effects of exercise training on exercise capacity, quality of life, and biomarkers and observed relatively few complications during training. These studies also suggested that exercise training might improve survival and decrease HF hospitalizations. Despite the failure of pharmacological interventions to reduce mortality and hospitalization in HFpEF, exercise training has shown efficacy in improving maximal exercise capacity, assessed through peak oxygen consumption (peak V˙o2), in clinically stable patients. Nonetheless, there remains a safety concern regarding exercise training in HF. Although the complication rate for patients participating in cardiac rehabilitation has been reported to be extremely low, the complication rate for HF patients in clinical trials of exercise training has been substantially higher. One potential reason is the 100-fold increased risk for myocardial infarction and 50-fold increased risk of sudden death that exercisers, who are habitually sedentary, experience when initiating exercise training. Additionally, comprehensive insights into the potential effects of exercise training, not only on critical clinical outcomes such as hospitalization, mortality, and cardiovascular events but also on cost-effectiveness, require longer-term trials.

Herombopag for the treatment of persistent thrombocytopenia following hematopoietic stem cell transplantation

Allogeneic hematopoietic stem cell transplantation (Allo-HSCT) stands as a pivotal treatment for hematologic malignancies, often considered the sole effective treatment option. A frequent complication following allo-HSCT is poor graft function (PGF), with one of its primary manifestations being persistent thrombocytopenia (PT), comprising prolonged isolated thrombocytopenia (PIT) and secondary failure of platelet recovery (SFPR). Conventional treatment methods have had poor efficacy and a high transplantation-associated mortality rate. In recent years, the efficacy of eltrombopag has been reported in the treatment of post-transplantation PT, and additional thrombopoietin receptor agonists (TPO-RA) have been developed. Herombopag is a next-generation TPO-RA which has strong proliferation-promoting effects on human TPO-R-expressing cells (32D-MPL) and hematopoietic progenitor cells in vitro. We reviewed eighteen patients with transplantation-associated thrombocytopenia who received herombopag when eltrombopag was ineffective or poorly tolerated and evaluated its efficacy including effects on survival. Herombopag was administered at a median time of 197 days post-transplantation. Six patients achieved complete response (CR), with a median time to CR of 56 days. Five patients achieved partial response (PR), and the median time to PR was 43 days. Seven patients were considered to have no response (NR). The overall response (OR) rate was 61.1%, and the cumulative incidence (CI) of OR was 90.2%. No patients developed herombopag-associated grade 3-4 toxicity. The median follow-up period was 6.5 months. Twelve patients survived and six patients died, with an overall survival rate of 66.7%. This is the first study to demonstrate the efficacy and safety of herombopag in transplantation-associated thrombocytopenia after failing eltrombopag, introducing a new approach in the treatment of PT following allo-HSCT.

SUGAR: Spherical ultrafast graph attention framework for cortical surface registration

Cortical surface registration plays a crucial role in aligning cortical functional and anatomical features across individuals. However, conventional registration algorithms are computationally inefficient. Recently, learning-based registration algorithms have emerged as a promising solution, significantly improving processing efficiency. Nonetheless, there remains a gap in the development of a learning-based method that exceeds the state-of-the-art conventional methods simultaneously in computational efficiency, registration accuracy, and distortion control, despite the theoretically greater representational capabilities of deep learning approaches. To address the challenge, we present SUGAR, a unified unsupervised deep-learning framework for both rigid and non-rigid registration. SUGAR incorporates a U-Net-based spherical graph attention network and leverages the Euler angle representation for deformation. In addition to the similarity loss, we introduce fold and multiple distortion losses to preserve topology and minimize various types of distortions. Furthermore, we propose a data augmentation strategy specifically tailored for spherical surface registration to enhance the registration performance. Through extensive evaluation involving over 10,000 scans from 7 diverse datasets, we showed that our framework exhibits comparable or superior registration performance in accuracy, distortion, and test-retest reliability compared to conventional and learning-based methods. Additionally, SUGAR achieves remarkable sub-second processing times, offering a notable speed-up of approximately 12,000 times in registering 9,000 subjects from the UK Biobank dataset in just 32 min. This combination of high registration performance and accelerated processing time may greatly benefit large-scale neuroimaging studies.

Changed nocturnal levels of stress-related hormones couple with sleep-wake states in the patients with chronic insomnia disorder: A clinical pilot study

OBJECTIVES

To explore the relationship between nocturnal levels of stress-related hormones and different sleep-wake states in chronic insomnia disorder (CID) patients.

METHODS

Thirty-three CID patients and 34 good sleepers were enrolled and completed assessment of sleep log, Pittsburgh Sleep Quality Index and Insomnia Severity Index. During a-overnight polysomnography monitoring, the patients' vein bleeds were continually collected at different time points (pre-sleep, deep-sleep, 5-min or 30-min waking, and morning waking-up). The control subjects' bleeds were collected only at 22:00 and morning waking-up. The serum hormones were detected using enzyme-linked immunosorbent assay.

RESULTS

Compared with at pre-sleep, the level of cortisol was significantly higher at morning waking-up respectively in two-group subjects (Ps < 0.001), with insignificant inter-group differences in cortisol, corticotropin releasing hormone and copeptin at the two time-points. In the patients, the nocturnal secretion curves of three hormones were similar, with the highest concentration at morning waking-up, followed by 30-min waking, 5-min waking, pre-sleep, and deep-sleep. The patients' cortisol (Z = 79.192, P < 0.001) and copeptin (Z = 12.333, P = 0.015) levels were statistically different at different time-points, with higher cortisol at morning waking-up relative to deep-sleep, pre-sleep and 5-min waking (Ps < 0.05), and at 30-min waking relative to deep-sleep and pre-sleep (Ps < 0.05), and higher copeptin at morning waking-up relative to deep-sleep (P < 0.05).

CONCLUSIONS

In CID, the nocturnal wakes were instantaneously accompanied by high level, and deep sleep was accompanied by the lowest levels, of stress-related hormones, especially in cortisol, supporting the insomniac hypothesis of increased nocturnal pulse-release of cortisol.

Regulatory T cells suppress myeloma-specific immunity during autologous stem cell mobilization and transplantation

ABSTRACT

Autologous stem cell transplantation (ASCT) is the standard of care consolidation therapy for eligible patients with myeloma but most patients eventually progress, an event associated with features of immune escape. Novel approaches to enhance antimyeloma immunity after ASCT represent a major unmet need. Here, we demonstrate that patient-mobilized stem cell grafts contain high numbers of effector CD8 T cells and immunosuppressive regulatory T cells (Tregs). We showed that bone marrow (BM)-residing T cells are efficiently mobilized during stem cell mobilization (SCM) and hypothesized that mobilized and highly suppressive BM-derived Tregs might limit antimyeloma immunity during SCM. Thus, we performed ASCT in a preclinical myeloma model with or without stringent Treg depletion during SCM. Treg depletion generated SCM grafts containing polyfunctional CD8 T effector memory cells, which dramatically enhanced myeloma control after ASCT. Thus, we explored clinically tractable translational approaches to mimic this scenario. Antibody-based approaches resulted in only partial Treg depletion and were inadequate to recapitulate this effect. In contrast, a synthetic interleukin-2 (IL-2)/IL-15 mimetic that stimulates the IL-2 receptor on CD8 T cells without binding to the high-affinity IL-2Ra used by Tregs efficiently expanded polyfunctional CD8 T cells in mobilized grafts and protected recipients from myeloma progression after ASCT. We confirmed that Treg depletion during stem cell mobilization can mitigate constraints on tumor immunity and result in profound myeloma control after ASCT. Direct and selective cytokine signaling of CD8 T cells can recapitulate this effect and represent a clinically testable strategy to improve responses after ASCT.

Superconductivity in Ca-intercalated bilayer graphene: C2CaC2

The deposition and intercalation of metal atoms can induce superconductivity in monolayer and bilayer graphenes. For example, it has been experimentally proved that Li-deposited graphene is a superconductor with critical temperature Tc of 5.9 K, Ca-intercalated bilayer graphene C6CaC6 and K-intercalated epitaxial bilayer graphene C8KC8 are superconductors with Tc of 2-4 K and 3.6 K, respectively. However, the Tc of them are relatively low. To obtain higher Tc in graphene-based superconductors, here we predict a new Ca-intercalated bilayer graphene C2CaC2, which shows higher Ca concentration than the C6CaC6. It is proved to be thermodynamically and dynamically stable. The electronic structure, electron-phonon coupling (EPC) and superconductivity of C2CaC2 are investigated based on first-principles calculations. The EPC of C2CaC2 mainly comes from the coupling between the electrons of C-pz orbital and the high- and low-frequency vibration modes of C atoms. The calculated EPC constant λ of C2CaC2 is 0.75, and the superconducting Tc is 18.9 K, which is much higher than other metal-intercalated bilayer graphenes. By further applying -4% biaxial compressive strain to C2CaC2, the Tc can be boosted to 26.6 K. Thus, the predicted C2CaC2 provides a new platform for realizing superconductivity with the highest Tc in bilayer graphenes.

Analysing influencing factors and correlation paths of learning burnout among secondary vocational students in the context of social media: An integrated ISM-MICMAC approach

By analysing the factors influencing secondary vocational students' learning burnout in the context of social media, this study unearthed the underlying causes of learning burnout. It also determined the correlation paths among the factors influencing learning burnout, providing references for educational and pedagogical improvement. This contributes to preventing secondary vocational students' learning burnout and enhancing learning efficiency in secondary vocational schools. Combined with previous research results and a theoretical basis, this study identifies 10 influencing factors employing the Delphi method, and uses Interpretative Structural Modelling (ISM) and Matrice d' Impacts Croisés Multiplication Appliqués à un Classement (MICMAC) to elucidate the relationship between influencing factors of learning burnout among secondary vocational students in the context of social media. This study also constructs a corresponding mechanism model and subsequently proposes prevention and improvement strategies. The results show that the overdevelopment of social media, as driving factors, has the greatest impact on secondary vocational students' learning burnout. Simultaneously, it takes the lead in addressing cognitive bias among students, decreased self-control, and low learning efficiency, factors that contribute to learning burnout. This is particularly beneficial in alleviating the degree of learning burnout among secondary vocational students in the context of social media and improves overall learning outcomes for these students. The hierarchical structure and correlation paths identified in this study offer robust invaluable guidance for developing a scientific program to address the problem of learning burnout among this demographic. This includes implementing related educational practises, thereby reducing the unpredictability of the practical applications.

Urban-rural disparities in depressive symptoms and related factors among offspring of advanced maternal mothers: A national cross-sectional study in China

BACKGROUND

Urbanization remains an important factor affecting the mental health of people. Currently, there is insufficient research on depression among the offspring of women who have children at an advanced maternal age (AMA). The objective of this study is to investigate the urban and rural disparities in depressive symptoms among the offspring of AMA and related influencing factors.

METHODS

In this study, a total of 1273 offspring born to AMA were selected from the fourth wave of the China Health and Retirement Longitudinal Study (CHARLS) database. The questionnaire used in the study included demographic information and the Center for Epidemiologic Studies Depression Scale (CESD). Chi-square tests and logistic regression analysis were employed to examine the urban-rural differences.

RESULTS

The likelihood of developing depressive symptoms of the offspring of AMA in rural areas was 1.633 times higher (AOR = 1.633; 95%CI: 1.211-2.202) compared to their urban counterparts. Several factors including gender, education, marital status, disability, chronic diseases, and sleep duration showed a linkage with depression among rural based offspring of AMA. For the urban offspring of AMA, only disability and sleep duration exhibited a significant association with depressive symptoms.

LIMITATIONS

This study cannot establish a causal relationship between depressive symptoms and the influencing factors for the offspring of AMA. Age range and variables of the respondents needs further consideration.

CONCLUSIONS

This study has brought to light the elevated risk of depressive symptoms among rural-based offspring of AMA. Thus, emphasizing the urgent need for targeted interventions and measures to address the negative impact.

Role of reactive oxygen species in myelodysplastic syndromes

Reactive oxygen species (ROS) serve as typical metabolic byproducts of aerobic life and play a pivotal role in redox reactions and signal transduction pathways. Contingent upon their concentration, ROS production not only initiates or stimulates tumorigenesis but also causes oxidative stress (OS) and triggers cellular apoptosis. Mounting literature supports the view that ROS are closely interwoven with the pathogenesis of a cluster of diseases, particularly those involving cell proliferation and differentiation, such as myelodysplastic syndromes (MDS) and chronic/acute myeloid leukemia (CML/AML). OS caused by excessive ROS at physiological levels is likely to affect the functions of hematopoietic stem cells, such as cell growth and self-renewal, which may contribute to defective hematopoiesis. We review herein the eminent role of ROS in the hematological niche and their profound influence on the progress of MDS. We also highlight that targeting ROS is a practical and reliable tactic for MDS therapy.

Functional Characterization of Sesquiterpene Synthases and P450 Enzymes in Flammulina velutipes for Biosynthesis of Spiro [4.5] Decane Terpene

Flammulina velutipes, a popular edible mushroom, contains sesquiterpenes with potential health benefits. We characterized 12 sesquiterpene synthases and one P450 enzyme in F. velutipes using Aspergillus oryzae as a heterologous expression system, culminating in the biosynthesis of 16 distinct sesquiterpene compounds. An enzyme encoded by the axeB gene responsible for the synthesis of the spiro [4.5] decane compound axenol was discovered, and the mechanism of spirocycle formation was elucidated through quantum mechanical calculations. Furthermore, we delineated the role of a P450 enzyme colocated with AxeB in producing the novel compound 3-oxo-axenol. Our findings highlight the diverse array of sesquiterpene skeletons and functional groups biosynthesized by these enzymes in F. velutipes and underscore the effectiveness of the A. oryzae system as a heterologous host for expressing genes in the Basidiomycota genome. These insights into the biosynthesis of bioactive compounds in F. velutipes have significant implications for functional food and drug development.

Antibacterial, biocompatible, and mineralization-inducing properties of calcium silicate-based cements

BACKGROUND

Different pulp capping materials have different origins and compositions, require different preparations, and may vary in their bioactive properties.

AIM

The purpose of this study was to evaluate the antibacterial activity, biocompatibility, and mineralization-inducing potential of calcium silicate-based pulp capping materials.

DESIGN

Six contemporary calcium silicate-based cements, ProRoot MTA, MTA Angelus, Biodentine, EndoSequence, NeoMTA 2, and NeoPutty, were evaluated. The antibacterial effects of these materials against Streptococcus mutans UA159 and Enterococcus faecalis ATCC 29212 were determined by the agar diffusion assay and the direct culture test. The biocompatibility and mineralization-inducing potential of these materials in preodontoblastic 17IIA11 cells were evaluated by the MTT assay and by Alizarin Red S staining, respectively.

RESULTS AND CONCLUSION

In agar diffusion test, only Biodentine showed distinct antibacterial effects against S. mutans. All the tested materials, however, showed antibacterial effects against S. mutans and E. faecalis in the direct culture test, with Biodentine showing the strongest growth inhibition against both S. mutans and E. faecalis. All the tested materials showed acceptable biocompatibility and mineralization-supporting potential in our experimental conditions. In summary, ProRoot MTA, MTA Angelus, Biodentine, EndoSequence, NeoMTA 2, and NeoPutty demonstrated acceptable in vitro antimicrobial, biocompatible, and mineralization-supporting properties.

Can Arthrogenic Muscle Inhibition Exist in Peroneal Muscles Among People with Chronic Ankle Instability? A Cross-sectional Study

BACKGROUND

Ankle sprains lead to an unexplained reduction of ankle eversion strength, and arthrogenic muscle inhibition (AMI) in peroneal muscles is considered one of the underlying causes. This study aimed to observe the presence of AMI in peroneal muscles among people with chronic ankle instability (CAI).

METHODS

Sixty-three people with CAI and another sixty-three without CAI conducted maximal voluntary isometric contraction (MVIC) and superimposed burst (SIB) tests during ankle eversion, then fifteen people with CAI and fifteen without CAI were randomly invited to repeat the same tests to calculate the test-retest reliability. Electrical stimulation was applied to the peroneal muscles while the participants were performing MVIC, and the central activation ratio (CAR) was obtained by dividing MVIC torque by the sum of MVIC and SIB torques, representing the degree of AMI.

RESULTS

The intra-class correlation coefficients were 0.77 (0.45-0.92) and 0.92 (0.79-0.97) for the affected and unaffected limbs among people with CAI, and 0.97 (0.91-0.99) and 0.93 (0.82-0.97) for the controlled affected and unaffected limbs among people without CAI; Significant group × limb interaction was detected in the peroneal CAR (p = 0.008). The CARs were lower among people with CAI in the affected and unaffected limbs, compared with those without CAI (affected limb = 82.54 ± 9.46%, controlled affected limb = 94.64 ± 6.37%, p < 0.001; unaffected limb = 89.21 ± 8.04%, controlled unaffected limb = 94.93 ± 6.01%, p = 0.016). The CARs in the affected limbs were lower than those in the unaffected limbs among people with CAI (p = 0.023). No differences between limbs were found for CAR in the people without CAI (p = 0.10).

CONCLUSIONS

Bilateral AMI of peroneal muscles is observed among people with CAI. Their affected limbs have higher levels of AMI than the unaffected limbs.

Mgp High-Expressing MSCs Orchestrate the Osteoimmune Microenvironment of Collagen/Nanohydroxyapatite-Mediated Bone Regeneration

Activating autologous stem cells after the implantation of biomaterials is an important process to initiate bone regeneration. Although several studies have demonstrated the mechanism of biomaterial-mediated bone regeneration, a comprehensive single-cell level transcriptomic map revealing the influence of biomaterials on regulating the temporal and spatial expression patterns of mesenchymal stem cells (MSCs) is still lacking. Herein, the osteoimmune microenvironment is depicted around the classical collagen/nanohydroxyapatite-based bone repair materials via combining analysis of single-cell RNA sequencing and spatial transcriptomics. A group of functional MSCs with high expression of matrix Gla protein (Mgp) is identified, which may serve as a pioneer subpopulation involved in bone repair. Remarkably, these Mgp high-expressing MSCs (MgphiMSCs) exhibit efficient osteogenic differentiation potential and orchestrate the osteoimmune microenvironment around implanted biomaterials, rewiring the polarization and osteoclastic differentiation of macrophages through the Mdk/Lrp1 ligand-receptor pair. The inhibition of Mdk/Lrp1 activates the pro-inflammatory programs of macrophages and osteoclastogenesis. Meanwhile, multiple immune-cell subsets also exhibit close crosstalk between MgphiMSCs via the secreted phosphoprotein 1 (SPP1) signaling pathway. These cellular profiles and interactions characterized in this study can broaden the understanding of the functional MSC subpopulations at the early stage of biomaterial-mediated bone regeneration and provide the basis for materials-designed strategies that target osteoimmune modulation.

Total ginsenosides decrease Aβ production through activating PPARγ

INTRODUCTION

Total ginsenosides (TG), the major active constituents of ginseng, have been proven to be beneficial in treatment of Alzheimer's disease (AD). However, the underlying mechanism of TG remains unclear.

METHODS

APP/PS1 mice and N2a/APP695 cells were used as in vivo and in vitro model, respectively. Morris water maze (MWM) was used to investigate behavioral changes of mice; neuronal pathological changes were assessed by hematoxylin and eosin (H&E) and nissl staining; immunofluorescence staining was used to examine amyloid beta (Aβ) deposition; Western blotting and quantitative real-time polymerase chain reaction (qRT-PCR) were used to examine the expression of relative amyloidogenic genes and proteins. Moreover, the antagonist of PPARγ, GW9662, was used to determine whether the effects of TG on Aβ production were associated with PPARγ activity.

RESULTS

TG treatment increased the spatial learning and memory abilities of APP/PS1 mice while decreasing the Aβ accumulation in the cortex and hippocampus. In N2a/APP695 cells, TG treatment attenuated the secretion of Aβ1-40 and Aβ1-42 acting as an PPARγ agonist by inhibiting the translocation of NF-κB p65. Additionally, TG treatment also decreased the expression of amyloidogenic pathway related gene BACE1, PS1 and PS2.

CONCLUSIONS

TG treatment reduced the production of Aβ both in vivo and in vitro. Activating PPARγ might be a potential therapeutic target of TG in facilitating Aβ clearance and ameliorating cognitive deficiency in APP/PS1 mice.

Addition of ruxolitinib to standard graft-versus-host disease prophylaxis for allogeneic stem cell transplantation in aplastic anemia patients

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) offers rapid hematopoietic and immune reconstitution for aplastic anemia (AA). As a non-malignant disorder, attenuation of GVHD remains a clinical priority in AA patients. Our study sought to investigate the safety and efficacy of the prophylactic use of ruxolitinib in allogeneic HSCT. A total of 35 AA patients were retrospectively consecutively treated with allo-HSCT whereby ruxolitinib was added to the standard GVHD prophylaxis regimen (rux group). The addition of peri-transplant ruxolitinib did not impact the engraftment and graft function, while better recovery of CD4+ Tregs in the rux group was observed. Interestingly, the rux group demonstrated significantly lower incidence of bacterial/fungal infections (17.14% vs 45.71%). Compared to the control group, the rux group exhibited significantly lower incidence of moderate to severe aGVHD (17.1% vs 48.6%) with a trend toward lower severe aGVHD (8.6% vs 20%) and cGVHD (26.2 vs 38.3). The rux group also demonstrated a trend toward higher GVHD and failure-free survival (GFFS: 85.7% vs 68.6%) and lower TRM (2.9% vs 14.3%). Addition of ruxolitinib to standard GVHD prophylaxis regimen, thus, represents a safe and highly efficient method for the attenuation of GVHD with better outcome of allo-HSCT.

Platelet-derived TGF-β1 induces functional reprogramming of myeloid-derived suppressor cells in immune thrombocytopenia

Platelet α-granules are rich in TGF-β1 which is associated with myeloid-derived suppressor cell (MDSC) biology. Responders to thrombopoietin receptor agonists (TPO-RAs) revealed a parallel increase in the number of both platelets and MDSCs. Here, anti-CD61 immune-sensitized splenocytes were transferred into severe combined immunodeficient mice to establish an active murine model of immune thrombocytopenia (ITP). Subsequently, we demonstrated that TPO-RAs augmented the inhibitory activities of MDSCs by arresting plasma cells differentiation, reducing Fas ligand expression on cytotoxic T cells, and re-balancing T cell subsets. Mechanistically, transcriptome analysis confirmed the participation of TGF-β/Smad pathways in TPO-RA-corrected-MDSCs, which was offset by Smad2/3 knockdown. In platelet TGF-β1-deficient mice, TPO-RA-induced amplification and enhanced suppressive capacity of MDSCs was waived. Furthermore, our retrospective data revealed that ITP patients achieving complete platelet response showed superior long-term outcomes compared with those who only reach partial response. In conclusion, we demonstrate that platelet TGF-β1 induces the expansion and functional reprogramming of MDSCs via the TGF-β/Smad pathway. These data indicate that platelet recovery not only serves as an endpoint of treatment response, but also paves the way for immune homeostasis in immune-mediated thrombocytopenia.

Accuracy and consistency of chatbots versus clinicians for answering pediatric dentistry questions: A pilot study

OBJECTIVES

Artificial Intelligence has applications such as Large Language Models (LLMs), which simulate human-like conversations. The potential of LLMs in healthcare is not fully evaluated. This pilot study assessed the accuracy and consistency of chatbots and clinicians in answering common questions in pediatric dentistry.

METHODS

Two expert pediatric dentists developed thirty true or false questions involving different aspects of pediatric dentistry. Publicly accessible chatbots (Google Bard, ChatGPT4, ChatGPT 3.5, Llama, Sage, Claude 2 100k, Claude-instant, Claude-instant-100k, and Google Palm) were employed to answer the questions (3 independent new conversations). Three groups of clinicians (general dentists, pediatric specialists, and students; n = 20/group) also answered. Responses were graded by two pediatric dentistry faculty members, along with a third independent pediatric dentist. Resulting accuracies (percentage of correct responses) were compared using analysis of variance (ANOVA), and post-hoc pairwise group comparisons were corrected using Tukey's HSD method. ACronbach's alpha was calculated to determine consistency.

RESULTS

Pediatric dentists were significantly more accurate (mean±SD 96.67 %± 4.3 %) than other clinicians and chatbots (p < 0.001). General dentists (88.0 % ± 6.1 %) also demonstrated significantly higher accuracy than chatbots (p < 0.001), followed by students (80.8 %±6.9 %). ChatGPT showed the highest accuracy (78 %±3 %) among chatbots. All chatbots except ChatGPT3.5 showed acceptable consistency (Cronbach alpha>0.7).

CLINICAL SIGNIFICANCE

Based on this pilot study, chatbots may be valuable adjuncts for educational purposes and for distributing information to patients. However, they are not yet ready to serve as substitutes for human clinicians in diagnostic decision-making.

CONCLUSION

In this pilot study, chatbots showed lower accuracy than dentists. Chatbots may not yet be recommended for clinical pediatric dentistry.

Machine learning model based on RCA-PDCA nursing methods and differentiating factors to predict hypotension during cesarean section surgery

BACKGROUND

Intraoperative hypotension during cesarean section has become a serious complication for maternal and fetal healthy. It is commonly encountered by subarachnoid anesthesia. However, currently used control methods have varying degrees of side effects, such as drugs. The Root Cause Analysis (RCA) - Plan, Do, Check, Act (PDCA) is a new model of care that identifies the root causes of problems. The study aimed to demonstrate the usefulness of RCA-PDCA nursing methods in preventing intraoperative hypotension during cesarean section and to predict the occurrence of intraoperative hypotension through a machine learning model.

METHODS

Patients who underwent cesarean section at Traditional Chinese Medicine of Southwest Medical University from January 2023 to December 2023 were retrospectively screened, and the data of their gestational times, age, height, weight, history of allergies, intraoperative vital signs, fetal condition, operative time, fluid out and in, adverse effects, use of vasopressor drugs, anxiety-depression-pain scores, and satisfaction were collected and analyzed. The statistically different features were screened and five machine learning models were used as predictive models to assess the usefulness of the RCA-PDCA model of care.

RESULTS

(1) Compared with the general nursing model, the RCA-PDCA nursing model significantly reduces the incidence of intraoperative hypotension and postoperative complications in cesarean delivery, and the patient experience is comfortable and satisfactory. (2) Among the five machine learning models, the RF model has the best predictive performance, and the accuracy of the random forest model in preventing intraoperative hypotension is as high as 90%.

CONCLUSION

Through computer machine learning model analysis, we prove the importance of the RCA-PDCA nursing method in the prevention of intraoperative hypotension during cesarean section, especially the Random Forest model which performed well and promoted the application of artificial intelligence computer learning methods in the field of medical analysis.

Neuroimaging Marker-CT Perfusion of Early Neurological Deterioration in Patients with Minor Stroke and Large Vessel Occlusion after Intravenous Thrombolysis

OBJECTIVE

Early Neurological Deterioration (END) is one of the complications in Acute ischemic stroke (AIS) and relates to prognosis. However, the reason why it occurs is still unclear. Our study is to investigate if CT perfusion (CTP) can predict END in patients with Minor Stroke and Large Vessel Occlusion after Intravenous Thrombolysis (IVT).

METHODS

Patients who underwent IVT with Large Vessel Occlusion were enrolled continuously from January 2021 to August 2023. After evaluating the National Institutes of Health Stroke Scale (NIHSS) score, they were divided into the END group (n=21) and the Non-END group (n=20). Multivariate logistic regression analysis was performed to explore the factors of END. Receiver-operating characteristic (ROC) curve analysis was also used to assess the discriminative ability of CTP in predicting END.

RESULTS

A total of 41 patients (mean age, 62.34 ± 10.82 years, 27 male) were finally included in the analysis; 21 patients had END, and 9 patients underwent Endovascular thrombectomy (EVT). Multivariate logistic regression analysis indicated that rCBV (OR=0.081, 95%CI=0.009- 0.721, p = 0.024) and admission-NIHSS (OR=1.990, 95%CI=1.049-3.772, p = 0.035) were significantly associated with END. The area under the curve (AUC) of rCBV and NIHSS to discriminate END were 0.708 and 0.758. We found patients with END had a higher modified Rankin Scale (mRS) in 3 months.

CONCLUSIONS

The rCBV and NIHSS were associated with post-thrombolysis END and may become reliable markers to predicate END. END might predict a poor 3-month functional outcome.

A novel safer CD19CAR with shRNA interference of IFN-γ can reduce multiple cytokine levels without significantly compromising its killing efficacy

Cytokine release syndrome (CRS) is a great challenge for the application of anti-CD19 CAR-T cell therapy. The aim of this study was to investigate the effect of knocking down interferon gamma (IFN-γ) by shRNA as a potential strategy to reduce the cytokine storms. A newly designed short hairpin interference RNA of IFN-γ (shIFN-γ) in CD19CAR gene was constructed. Several cellular model systems of approach using Nalm-6 cell lines including Nalm-6CD19pos and Nalm-6CD19neg with or without monocytes and endothelial cells were used to analyze the different levels of cytokines after shIFN-γ-anti-CD19CAR-T cell targeted therapy. The activity of this novel CD19CAR-T was evaluated both in vitro and in NSG mouse model. The killing efficacy of shIFN-γ-anti-CD19CAR-T at the E:T ratio of 2:1 was similar to that of regular anti-CD19CAR-T at the E:T ratio of 1:1. The IFN-γ level in the shIFN-γ-anti-CD19CAR-T cell group was (2673.1 ± 307.4) pg/ml at the E:T ratio of 2:1 which was significantly lower than that ((8261.5 ± 345.5) pg/ml) in the regular anti-CD19CAR-T group at the E:T ratio of 1:1. Cytotoxicity experiments in vitro showed significantly reduced concentrations of IFN-γ, IL-6 and TNFα in the shIFN-γ-anti-CD19CAR-T cell group compared to regular anti-CD19CAR-T cell group. Both regular anti-CD19CAR and shIFN-γ-CD19CAR-T exerted bystander killing effect in vitro. We conclude that shIFN-γ-anti-CD19CAR-T cells can reduce the generation of cytokine storms without significantly compromising their therapeutic efficacy in the preclinical setting. In mouse model, 3 × 106 shIFN-γ-anti-CD19CAR-T cells/mouse generated the similar killing efficacy to that with 2 × 106 regular anti-CD19CAR-T cells/mouse.

Effectiveness and safety of ivabradine in Chinese patients with chronic heart failure: an observational study

AIMS

A therapeutic strategy for chronic heart failure (HF) is to lower resting heart rate (HR). Ivabradine is a well-known HR-lowering agent, but limited prospective data exist regarding its use in Chinese patients. This study aimed to evaluate the effectiveness and safety of ivabradine in Chinese patients with chronic HF.

METHODS AND RESULTS

This multicentre, single-arm, prospective, observational study enrolled Chinese patients with chronic HF. The primary outcome was change from baseline in HR at 1 and 6 months, measured by pulse counting. Effectiveness was also evaluated using laboratory tests, the Kansas City Cardiomyopathy Questionnaire (KCCQ) clinical summary score (CSS) and overall summary score (OSS), and New York Heart Association (NYHA) class. Treatment-emergent adverse events (TEAEs) were assessed. A post hoc analysis examined the effectiveness and safety of ivabradine combined with an angiotensin receptor-neprilysin inhibitor (ARNI) or beta-blocker. A total of 1003 patients were enrolled [mean age 54.4 ± 15.0 years, 773 male (77.1%), mean baseline HR 88.5 ± 11.3 b.p.m., mean blood pressure 115.7/74.4 ± 17.2/12.3 mmHg, mean left ventricular ejection fraction 30.9 ± 7.6%, NYHA Classes III and IV in 48.8% and 22.0% of patients, respectively]. HR decreased by a mean of 12.9 and 16.1 b.p.m. after 1 and 6 months, respectively (both P < 0.001). At Month 6, improvements in the KCCQ CSS and OSS of ≥5 points were observed in 72.1% and 74.1% of patients, respectively (both P < 0.001). Left ventricular ejection fraction increased by 12.1 ± 11.6 (P < 0.001), and 66.7% of patients showed improvement in NYHA class (P < 0.001). At Month 6, the overall proportion of patients in NYHA Classes III and IV was reduced to 13.5% and 2.1%, respectively. Serum brain natriuretic peptide (BNP) and N-terminal pro-BNP changed by -331.9 ng/L (-1238.6, -134.0) and -1113.8 ng/L (-2202.0, -297.2), respectively (P < 0.001). HR reductions and improvements in NYHA and KCCQ scores with ivabradine were similar with and without use of ARNIs or beta-blockers. Of 498 TEAEs in 296 patients (29.5%), 73 TEAEs in 55 patients (5.5%) were considered related to ivabradine [most frequent sinus bradycardia (n = 7) and photopsia (n = 7)]. TEAEs were reported in a similar number of patients in ARNI and beta-blocker subgroups (21.9-35.6%).

CONCLUSIONS

Ivabradine treatment reduced HR and improved cardiac function and health-related quality of life in Chinese patients with chronic HF. Benefits were seen irrespective of whether or not patients were also taking ARNIs or beta-blockers. Treatment was well tolerated with a similar profile to previous ivabradine studies.

Evolving Therapeutic Landscape of Intracerebral Hemorrhage: Emerging Cutting-Edge Advancements in Surgical Robots, Regenerative Medicine, and Neurorehabilitation Techniques

Intracerebral hemorrhage (ICH) is the most serious form of stroke and has limited available therapeutic options. As knowledge on ICH rapidly develops, cutting-edge techniques in the fields of surgical robots, regenerative medicine, and neurorehabilitation may revolutionize ICH treatment. However, these new advances still must be translated into clinical practice. In this review, we examined several emerging therapeutic strategies and their major challenges in managing ICH, with a particular focus on innovative therapies involving robot-assisted minimally invasive surgery, stem cell transplantation, in situ neuronal reprogramming, and brain-computer interfaces. Despite the limited expansion of the drug armamentarium for ICH over the past few decades, the judicious selection of more efficacious therapeutic modalities and the exploration of multimodal combination therapies represent opportunities to improve patient prognoses after ICH.

Salmonella manipulates macrophage migration via SteC-mediated myosin light chain activation to penetrate the gut-vascular barrier

The intestinal pathogen Salmonella enterica rapidly enters the bloodstream after the invasion of intestinal epithelial cells, but how Salmonella breaks through the gut-vascular barrier is largely unknown. Here, we report that Salmonella enters the bloodstream through intestinal CX3CR1+ macrophages during early infection. Mechanistically, Salmonella induces the migration/invasion properties of macrophages in a manner dependent on host cell actin and on the pathogen effector SteC. SteC recruits host myosin light chain protein Myl12a and phosphorylates its Ser19 and Thr20 residues. Myl12a phosphorylation results in actin rearrangement, and enhanced migration and invasion of macrophages. SteC is able to utilize a wide range of NTPs other than ATP to phosphorylate Myl12a. We further solved the crystal structure of SteC, which suggests an atypical dimerization-mediated catalytic mechanism. Finally, in vivo data show that SteC-mediated cytoskeleton manipulation is crucial for Salmonella breaching the gut vascular barrier and spreading to target organs.

NLRP3-mediated periodontal ligament cell pyroptosis promotes root resorption

AIM

To investigate the mechanisms by which periodontal ligament cells (PDLCs) convert biomechanical stimulation into inflammatory microenvironment inducing root resorption (RR).

MATERIALS AND METHODS

RNA sequencing was employed to explore mechanisms in force-inflammatory signal transduction. Then resorption volume, odontoclastic activity, PDLC pyroptotic ratio and NOD-like receptor protein 3 (NLRP3)-mediated pyroptosis pathway activation were analysed under force and pyroptosis inhibition. Further osteoclast formation, macrophage number and transwell polarization demonstrated the effects of PDLC pyroptosis on osteoclastogenesis and M1 polarization.

RESULTS

RNA sequencing revealed that NLRP3-mediated PDLC pyroptosis induced by Toll-like receptor 4 (TLR4)/nuclear factor kappa B (NFκB)/NLRP3 pathway may be involved in mechano-inflammatory signal transduction. PDLC pyroptosis under force and the expression of NLRP3-mediated pyroptosis pathway in force-enhanced PDLCs were significantly increased, both in vivo and in vitro. MCC950 administration was sufficient to reduce PDLC pyroptosis and alleviate RR, odontoclast formation and M1 polarization in vivo. Further in vitro exploration showed that MCC950 treatment reduced PDLC force-promoted pyroptosis and blocked NLRP3-mediated pyroptosis pathway. Moreover, by treating THP-1 with force-pretreated PDLCs or supernatants, NLRP3-mediated PDLC pyroptotic released products induced osteoclast formation and M1 polarization.

CONCLUSIONS

NLRP3-mediated PDLC pyroptosis promotes RR. PDLCs transmit excessive force into inflammation signals through TLR4/NFκB/NLRP3 pathway, inducing PDLC pyroptosis, which directly promotes odontoclast formation and subsequent RR or promotes M1 polarization to indirectly trigger odontoclastogenesis and RR.

Determinants of depressive symptoms at individual, school and province levels: a national survey of 398,520 Chinese children and adolescents

OBJECTIVES

The aims of this study were to describe the national distribution of depressive symptoms in Chinese children and adolescents, to examine the determinants of depressive symptoms at individual, school and province levels and to assess the gender and age differences in the effect of school factors on depressive symptoms.

STUDY DESIGN

This was a national cross-sectional study.

METHODS

A school-based online survey was conducted in mainland China from between December 1, 2021, and January 1, 2022. A total of 398,520 eligible participants were included in the analysis. School-level data were drawn from students, headteachers and Baidu Maps, and province-level data were obtained from the national human development report. The Patient Health Questionnaire-2 was used to measure depressive symptoms.

RESULTS

Areas with the highest mean scores for depressive symptoms were in the northeastern, inner central and southwestern regions of China. At the individual level, younger age, male sex, being an only child, Han ethnicity, lower body mass index, more days of exercise, less drinking and smoking behaviours, higher subjective family socio-economic status (SES) and popularity in school were related to fewer depressive symptoms; however, objective family SES and maternal education were not related to fewer depressive symptoms. The school-level variables of public status, psychological activities and psychological courses and province-level variable of higher Human Development Index were associated with fewer depressive symptoms. The effect of psychological courses and activities on depressive symptoms was greater in females.

CONCLUSIONS

The results showed multilevel factors related to depressive symptoms and emphasised the importance of implementing school-based psychological activities to ameliorate depressive symptoms in Chinese children and adolescents across age and gender.

Correlation of allostatic load and perceived stress with clinical features in first-episode schizophrenia

BACKGROUND

Stress plays an important role in the etiology of schizophrenia. However, the mechanisms by which chronic physiological stress and perceived stress relate to the clinical features of schizophrenia may differ. We aimed to elucidate the relationships among chronic physiological stress indexed by allostatic load (AL), perceived stress, and clinical symptoms in individuals with first-episode schizophrenia (FES).

METHODS

Individuals with FES (n = 90, mean age = 28.26years old, 49%female) and healthy controls (111, 28.88, 51%) were recruited. We collected data of 13 biological indicators to calculate the AL index, assessed subjective stress with the Perceived Stress Scale-14 (PSS-14), and compared AL and perceived stress between groups. Patients with FES were also evaluated with the Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale for Schizophrenia (CDSS).

RESULTS

Individuals with FES had higher AL and PSS score than healthy controls. There were no significant correlations between AL and PSS score in either patients or controls. Among individuals with FES, the AL index was associated with the severity of positive symptoms, while the PSS score was positively associated with CDSS score. Both elevated AL and PSS were correlated with the occurrence of schizophrenia.

CONCLUSIONS

Physiological stress, as reflected by AL, may be more related to positive symptoms, while perceived stress appear to be associated with depressive symptoms in individuals with FES. Longitudinal studies are necessary to explore the relationships between interventions for different stressor types and specific clinical outcomes in FES.

Some patients with type 2 diabetes may benefit from intensive glycaemic and blood pressure control: A post-hoc machine learning analysis of ACCORD trial data

AIM

The action to control cardiovascular risk in diabetes (ACCORD) trial showed a neutral average treatment effect of intensive blood glucose and blood pressure (BP) controls in preventing major adverse cardiovascular events (MACE) in individuals with type 2 diabetes. Yet, treatment effects across patient subgroups have not been well understood. We aimed to identify patient subgroups that might benefit from intensive glucose or BP controls for preventing MACE.

MATERIALS AND METHODS

As a post-hoc analysis of the ACCORD trial, we included 10 251 individuals with type 2 diabetes. We applied causal forest and causal tree models to identify participant characteristics that modify the efficacy of intensive glucose or BP controls from 68 candidate variables (demographics, comorbidities, medications and biomarkers) at the baseline. The exposure was (a) intensive versus standard glucose control [glycated haemoglobin (HbA1c) <6.0% vs. 7.0%-7.9%], and (b) intensive versus standard BP control (systolic BP <120 vs. <140 mmHg). The primary outcome was MACE.

RESULTS

Compared with standard glucose control, intensive one reduced MACE in those with baseline HbA1c <8.5% [relative risk (RR): 0.79, 95% confidence interval (CI): 0.67-0.93] and those with estimated glomerular filtration rate ≥106 ml/min/1.73 m2 (RR: 0.74, 95% CI: 0.55-0.99). Intensive BP control reduced MACE in those with normal high-density lipoprotein levels (women >55 mg/dl, men >45 mg/dl; RR: 0.51, 95% CI: 0.34-0.74). Risk reductions were not significant in other patient subgroups.

CONCLUSIONS

Our findings suggest heterogeneous treatment effects of intensive glucose and BP control and could provide biomarkers for future clinical trials to identify more precise HbA1c and BP treatment goals for individualized medicine.

Allogeneic "Zombie Cell" as Off-The-Shelf Vaccine for Postsurgical Cancer Immunotherapy

Allogeneic tumor cell vaccines provide off-the-shelf convenience but lack patient specificity due to heterogeneity in tumor antigens. Here, allogeneic tumor cell corpses are converted into "zombie cells" capable of assimilating heterogeneous tumor by seizing cancer cells and spreading adjuvant infection. This causes pseudo-oncolysis of tumors, transforming them into immunogenic targets for enhanced phagocytosis. It is shown that in postoperative tumor models, localized delivery of premade "zombie cells" through stepwise gelation in resection cavity consolidates tumor surgery. Compared to analogous vaccines lacking "seizing" or "assimilating" capability, "zombie cell" platform effectively mobilizes T cell response against residual tumors, and establishes immunological memory against tumor re-challenge, showing less susceptibility to immune evasion. Despite using allogeneic sources, "zombie cell" platform functions as generalizable framework to produce long-term antitumor immunity in different tumor models, showing comparable effect to autologous vaccine. Together, with the potential of off-the-shelf availability and personalized relevance to heterogenous tumor antigens, this study suggests an alternative strategy for timely therapy after tumor surgery.

IL-6-mediated endothelial injury impairs antiviral humoral immunity after bone marrow transplantation

Endothelial function and integrity are compromised after allogeneic bone marrow transplantation (BMT), but how this affects immune responses broadly remains unknown. Using a preclinical model of CMV reactivation after BMT, we found compromised antiviral humoral responses induced by IL-6 signaling. IL-6 signaling in T cells maintained Th1 cells, resulting in sustained IFN-γ secretion, which promoted endothelial cell (EC) injury, loss of the neonatal Fc receptor (FcRn) responsible for IgG recycling, and rapid IgG loss. T cell-specific deletion of IL-6R led to persistence of recipient-derived, CMV-specific IgG and inhibited CMV reactivation. Deletion of IFN-γ in donor T cells also eliminated EC injury and FcRn loss. In a phase III clinical trial, blockade of IL-6R with tocilizumab promoted CMV-specific IgG persistence and significantly attenuated early HCMV reactivation. In sum, IL-6 invoked IFN-γ-dependent EC injury and consequent IgG loss, leading to CMV reactivation. Hence, cytokine inhibition represents a logical strategy to prevent endothelial injury, thereby preserving humoral immunity after immunotherapy.

DDAH1 recruits peroxiredoxin 1 and sulfiredoxin 1 to preserve its activity and regulate intracellular redox homeostasis

Growing evidence suggests that dimethylarginine dimethylaminohydrolase 1 (DDAH1), a crucial enzyme for the degradation of asymmetric dimethylarginine (ADMA), is closely related to oxidative stress during the development of multiple diseases. However, the underlying mechanism by which DDAH1 regulates the intracellular redox state remains unclear. In the present study, DDAH1 was shown to interact with peroxiredoxin 1 (PRDX1) and sulfiredoxin 1 (SRXN1), and these interactions could be enhanced by oxidative stress. In HepG2 cells, H2O2-induced downregulation of DDAH1 and accumulation of ADMA were attenuated by overexpression of PRDX1 or SRXN1 but exacerbated by knockdown of PRDX1 or SRXN1. On the other hand, DDAH1 also maintained the expression of PRDX1 and SRXN1 in H2O2-treated cells. Furthermore, global knockout of Ddah1 (Ddah1-/-) or liver-specific knockout of Ddah1 (Ddah1HKO) exacerbated, while overexpression of DDAH1 alleviated liver dysfunction, hepatic oxidative stress and downregulation of PRDX1 and SRXN1 in CCl4-treated mice. Overexpression of liver PRDX1 improved liver function, attenuated hepatic oxidative stress and DDAH1 downregulation, and diminished the differences between wild type and Ddah1-/- mice after CCl4 treatment. Collectively, our results suggest that the regulatory effect of DDAH1 on cellular redox homeostasis under stress conditions is due, at least in part, to the interaction with PRDX1 and SRXN1.

Algae removal and algal organic matter chemistry modulated by KMnO4-PAC in simulated karst water

In this study, we examined the modulation of algae removal and algal organic matter (AOM) chemistry by potassium permanganate and poly-aluminum chloride (KMnO4-PAC) in simulated karst water. Specifically, we verified the compositional changes of AOM sourcing from Chlorella sp. and Pseudanabaena sp. in response to the presence of divalent ions (Ca2+ and Mg2+). Aromatic protein and soluble microbial products were identified as the primary AOM components. Divalent ions accelerated dissolved organic carbon (DOC) and UV254 removal, particularly with Pseudanabaena sp. greater than Chlorella sp. (P < 0.05). Surface morphology analysis manifested that the removal of filamentous Pseudanabaena sp. was more feasible in comparison to globular Chlorella sp.. Our results highlight the significance of divalent ions in governing chemical behaviors and subsequent removal of both algae and AOM. This study upscales the understanding of the interactions among divalent ions, algae and AOM during preoxidation and coagulation process in algae-laden karst water.

An immunogenomic exome landscape of triple positive primary antiphospholipid patients

Primary antiphospholipid syndrome is characterized by thrombosis and autoantibodies directed against phospholipids or associated proteins. The genetic etiology of PAPS remains unknown. We enrolled 21 patients with thromboembolic events associated to lupus anticoagulant, anticardiolipin and anti β2 glycoprotein1 autoantibodies. We performed whole exome sequencing and a systematic variant-based analysis in genes associated with thrombosis, in candidate genes previously associated with APS or inborn errors of immunity. Data were compared to public databases and to a control cohort of 873 non-autoimmune patients. Variants were identified following a state-of-the-art pipeline. Enrichment analysis was performed by comparing with the control cohort. We found an absence of significant HLA bias and genetic heterogeneity in these patients, including when testing combinations of rare variants in genes encoding for proteins involved in thrombosis and of variants in genes linked with inborn errors of immunity. These results provide evidence of genetic heterogeneity in PAPS, even in a homogenous series of triple positive patients. At the individual scale, a combination of variants may participate to the breakdown of B cell tolerance and to the vessel damage.

Bioinformatics analysis identifies potential autophagy key genes and immune infiltration in preeclampsia

BACKGROUND

Preeclampsia (PE) is a disease that seriously threatens maternal and fetal health. Appropriate autophagy can shield the placenta from oxidative stress, but its role in PE is unclear.

OBJECTIVE

To identify potential autophagy-related genes in PE.

METHODS

Microarray datasets from the Gene Expression Omnibus database, compassing the test dataset GSE10588, along with validation datasets GSE4707 and GSE60438 GPL10558, were utilized. Differentially expressed genes (DEGs) were identified using the limma R package, intersected with autophagy-related genes. Hub genes were obtained using the Cytoscape software and analyzed via gene set enrichment analysis (GSEA). The diagnostic capability of hub genes was evaluated using receiver operating characteristic (ROC) curve analysis. Analysis of immune cell infiltration was conducted using single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT methods. Placental tissues were collected from 10 normal pregnant women and 10 preeclamptic pregnant women, and the expression of hub genes was validated through immunohistochemistry and western blot analysis.

RESULTS

Analysis of the microarray data identified 2224 DEGs, among which 26 were autophagy-related DEGs identified through intersection with autophagy genes. Ten hub genes were identified. Immune cell infiltration analysis suggested the potential involvement of T regulatory cells (Tregs), natural killer cells, neutrophils, and T follicular helper cells in the pathogenesis of PE. ROC curve analysis indicated promising diagnostic capabilities for EGFR and TP53. Additionally, levels of EGFR and TP53 were significantly higher in placental tissue from PE pregnancies compared to normal pregnancies.

CONCLUSION

EGFR and TP53 may play a role in PE by influencing autophagy.

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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Cell surface patching via CXCR4-targeted nanothreads for cancer metastasis inhibition

The binding of therapeutic antagonists to their receptors often fail to translate into adequate manipulation of downstream pathways. To fix this 'bug', here we report a strategy that stitches cell surface 'patches' to promote receptor clustering, thereby synchronizing subsequent mechano-transduction. The "patches" are sewn with two interactable nanothreads. In sequence, Nanothread-1 strings together adjacent receptors while presenting decoy receptors. Nanothread-2 then targets these decoys multivalently, intertwining with Nanothread-1 into a coiled-coil supramolecular network. This stepwise actuation clusters an extensive vicinity of receptors, integrating mechano-transduction to disrupt signal transmission. When applied to antagonize chemokine receptors CXCR4 expressed in metastatic breast cancer of female mice, this strategy elicits and consolidates multiple events, including interception of metastatic cascade, reversal of immunosuppression, and potentiation of photodynamic immunotherapy, reducing the metastatic burden. Collectively, our work provides a generalizable tool to spatially rearrange cell-surface receptors to improve therapeutic outcomes.

Study of High-Transverse-Momentum Higgs Boson Production in Association with a Vector Boson in the qqbb Final State with the ATLAS Detector

This Letter presents the first study of Higgs boson production in association with a vector boson (V=W or Z) in the fully hadronic qqbb final state using data recorded by the ATLAS detector at the LHC in proton-proton collisions at sqrt[s]=13  TeV and corresponding to an integrated luminosity of 137  fb^{-1}. The vector bosons and Higgs bosons are each reconstructed as large-radius jets and tagged using jet substructure techniques. Dedicated tagging algorithms exploiting b-tagging properties are used to identify jets consistent with Higgs bosons decaying into bb[over ¯]. Dominant backgrounds from multijet production are determined directly from the data, and a likelihood fit to the jet mass distribution of Higgs boson candidates is used to extract the number of signal events. The VH production cross section is measured inclusively and differentially in several ranges of Higgs boson transverse momentum: 250-450, 450-650, and greater than 650 GeV. The inclusive signal yield relative to the standard model expectation is observed to be μ=1.4_{-0.9}^{+1.0} and the corresponding cross section is 3.1±1.3(stat)_{-1.4}^{+1.8}(syst)  pb.

Diffusion tensor imaging combined with chemical shift-encoded sequence to quantify the adaptive changes of calf muscles in amateur marathoners

PURPOSE

Calf muscles play an important role in marathon race, and the incidence of injury is high in this process. This study prospectively quantified diffusion tensor metrics, muscle fat fraction (MFF) and cross-sectional area (CSA) of calf muscles induced by endurance exercise in amateur marathoners, and the potential mechanisms underlying the changes in these parameters were analyzed.

METHOD

In this prospective study, 35 marathoners (27 males, 8 females; mean age (standard deviation, SD), 38.92 (4.83) years) and 26 controls (18 males, 8 females; mean age (SD), 38.35 (6.75) years) underwent magnetic resonance imaging (MRI) from September 2022 to March 2023. The diffusion tensor eigenvalues (λ1, λ2, λ3), radial diffusivity (RD), fractional anisotropy (FA), MFF and CSA of calf muscles were compared between marathoners and controls. A binary logistic regression model with gender correction was performed analyze the relationship between marathon exercise and DTI parameters, CSA and MFF of calf muscles.

RESULTS

Interobserver agreement was good (κ = 0.71). The results of binary logistic regression model with gender correction showed that the regression coefficients of FA values in anterior group of calf (AC), soleus (SOL), medial gastrocnemius (MG) and lateral gastrocnemius (LG) were negative, and the odds ratios (OR) were 0.33, 0.45, 0.35, 0.05, respectively (P < 0.05). The OR of RD in SOL and λ2 in external group of calf (EC) were relatively higher, 3.74 and 3.26, respectively (P < 0.05). CSA was greater in SOL of marathoners, with an OR value of 1.00(P < 0.05). The MFF in AC and LG was lower in marathoners and OR of two indexes were -0.69 and -0.59, respectively (P < 0.05).

CONCLUSIONS

Diffusion tensor imaging (DTI) combined with chemical shift-encoded sequence can noninvasively detect and quantify the adaptive changes of calf muscle morphology, microstructure and tissue composition induced by long-term running training in amateur marathoners.

Analysis of Risk Factors for the Association of Sarcopenia in Patients with Type 2 Diabetes Mellitus

Background

Previous studies have shown that the prevalence of sarcopenia in patients with type 2 diabetes mellitus (T2DM) has increased significantly over the years. However, the risk factors for the association of sarcopenia in patients with T2DM are unknown. Therefore, we attempted to investigate the risk factors through measurement and analysis of the patients' data from April 2020 to April 2022.

Methods

A total of 334 hospitalized patients with T2DM were divided into sarcopenia group (n=101) and non-sarcopenia group (n=233). Clinical factors were compared between the two groups and also between the two genders. Receiver operating characteristic curve (ROC) was used to analyze the ROC diagnostic ability of related factors in sarcopenia.

Results

(1) Among the 334 patients, the overall prevalence of sarcopenia was 30.2%; 41.3% in men and 20.1% in women. (2) The multifactorial logistic regression analysis showed that gender (specifically for men; OR=4.997, 95% CI: 2.611-9.564), low body mass index (BMI) (OR=1.525, 95% CI: 1.353-1.718), lower 25(OH)D levels (OR=1.076, 95% CI:1.036-1.117), and lower IGF-1 (OR=1.013, 95% CI:1.006-1.020) were independent risk factors (P < 0.05). (3) ROC curve analysis results showed that BMI, 25 (OH) D, IGF-1, and testosterone (for men) had predictive significance for sarcopenia with T2DM (P < 0.05). However, the AUC of 25 (OH) D, IGF-1 and testosterone (for men) were all <0.7, while the AUC of BMI and the combined factors were all >0.7, has great predictive significance.

Conclusion

The prevalence of sarcopenia in hospitalized patients with T2DM is higher in men than in women. Low BMI and lower serum levels of 25 (OH) D and IGF-1 are risk factors of sarcopenia in patients with T2DM. Low BMI, 25(OH)D, IGF-1, and testosterone (for men) all contributed to the prediction of sarcopenia, among which BMI and combined factors were more significant.