An alkali-extracted neutral heteropolysaccharide from Phellinus nigricans used as an immunopotentiator in immunosuppressed mice by activating macrophages

A neutral heteropolysaccharide (PNANb) was isolated with alkali (0.1 M NaOH) from mycelia of Phellinus nigricans, and the structure, immunostimulating activity and some of the underlying molecular mechanisms of action of PNANb were explored in the current study. PNANb (14.95 kDa) predominantly consisted of Gal, Glc, and Man with minor Fuc. GC-MS and NMR analyses indicated that the backbone of PNANb was mainly composed of 6-α-Galp, 2,6-α-Galp with minor 3,6-β-Glcp, which was substituted with complex side chains at C-2 of 2,6-α-Galp and C-3 of 3,6-β-Glcp. Notably, PNANb (50 or 100 mg/kg) possessed immunoprotective effects in cyclophosphamide (Cy)-induced immunosuppressed C57BL/6 mice, which was supported by evidence including the enhancement of spleen and thymus indices, levels of serum immunoglobulins (IgG, IgM) and cytokines (IFN-γ, IL-2, IL-4, IL-10), and macrophage activity. However, the immunostimulation effects of PNANb were decreased when macrophages were depleted, underscoring the essential role of macrophages in the beneficial effects of PNANb in Cy-induced immunosuppressed mice. Further investigations in vitro indicated that PNANb activated macrophages through MAPK/NF-κB signaling pathways mediated by Toll-like receptor 4. Therefore, PNANb can serve as a prospective immunopotentiator in immunosuppression.

A chitosan-camouflaged nanomedicine triggered by hierarchically stimuli to release drug for multimodal imaging-guided chemotherapy of breast cancer

Breast cancer remains one of the most intractable diseases, especially the malignant form of metastasis, with which the cancer cells are hard to track and eliminate. Herein, the common known carbohydrate polymer chitosan (CS) was innovatively used as a shelter for the potent tumor-killing agent. The designed nanoparticles (NPs) not only enhance the solubility of hydrophobic paclitaxel (PTX), but also provide a "hide" effect for cytotoxic PTX in physiological condition. Moreover, coupled with the photothermal (PTT) properties of MoS2, results in a potent chemo/PTT platform. The MoS2@PTX-CS-K237 NPs have a uniform size (135 ± 17 nm), potent photothermal properties (η = 31.5 %), and environment-responsive (low pH, hypoxia) and near infrared (NIR) laser irradiation-triggered PTX release. Through a series of in vitro and in vivo experiments, the MoS2@PTX-CS-K237 showed high affinity and specificity for breast cancer cells, impressive tumor killing capacity, as well as the effective inhibitory effect of metastasis. Benefit from the unique optical properties of MoS2, this multifunctional nanomedicine also exhibited favorable thermal/PA/CT multimodality imaging effect on tumor-bearing mice. The system developed in this work represents the advanced design concept of hierarchical stimulus responsive drug release, and merits further investigation as a potential nanotheranostic platform for clinical translation.

Model construction and application for predicting pre-eclampsia by Sonoclot coagulation analyzer

Maternal age has significantly increased among Chinese women, thereby posing risk of pregnancy-related complications. Preeclampsia is a leading cause of maternal and perinatal morbidity and mortality, and coagulation analysis in conjunction with clinical signs and symptoms are generally used for its diagnosis with limited efficacy. Sonoclot coagulation analyzer is effective in assessing coagulation function used during cerebral surgery and cardiovascular surgery. However, its use has not been explored in preeclampsia. Here, we investigated the potential use of Sonoclot in diagnosing preeclampsia in obstetrics cases. Subjects meeting the screening criteria were divided either into a test group or a control group, according to whether they were preeclamptic or not. We recorded the Sonoclot-derived coagulation and the routine coagulation parameters including platelet function (PF), activated clotting time (ACT) and clot rate (CR), prothrombin time (PT), activated partial thromboplastin time (APTT), thrombin time (TT), fibrinogen (FIB), and platelet count. Regression analysis was done on the relevant parameters to assess the feasibility of Sonoclot analyzer in preeclampsia diagnosis. In parallel, changes in preeclampsia lncRNAs was also evaluated. Significant differences were recorded in PT and ACT between the two groups. In the monovariant logistic regression, PT and ACT appeared to be reliable predictor variables. In the multinomial logistic regression, a total of five regression steps were performed with decreasing AIC values. The K-fold cross validation resulted in an accuracy rate (ACC) of 77.5%, a false positive rate of 16.4%, and a false negative rate of 33.2%. lncRNAs ANRIL and HOXD-AS1 were found deregulated. Our findings indicate that Sonoclot may be useful for diagnosis of preeclampsia in obstetrics.

Geographical traceability of soybean: An electronic nose coupled with an effective deep learning method

The quality of soybeans is correlated with their geographical origin. It is a common phenomenon to replace low-quality soybeans from substandard origins with superior ones. This paper proposes the adaptive convolutional kernel channel attention network (AKCA-Net) combined with an electronic nose (e-nose) to achieve soybean quality traceability. First, the e-nose system is used to collect soybean gas information from different origins. Second, depending on the characteristics of the gas information, we propose the adaptive convolutional kernel channel attention (AKCA) module, which focuses on key gas channel features adaptively. Finally, the AKCA-Net is proposed, which is capable of modeling deep gas channel interdependency efficiently, realizing high-precision recognition of soybean quality. In comparative experiments with other attention mechanisms, AKCA-Net demonstrated superior performance, achieving an accuracy of 98.21%, precision of 98.57%, and recall of 98.60%. In conclusion, the combination of the AKCA-Net and e-nose provides an effective strategy for soybean quality traceability.

The association between endothelial function and autoimmune thyroiditis induced by iodine excess

BACKGROUND

Iodine excess (IE) intake leads to lymphocyte dysfunction and contributes to autoimmune thyroiditis (AIT). Abnormal thyroid function is associated with adverse cardiovascular events, endothelial dysfunction is often an early pathophysiological feature in most cardiovascular disease. However, the relationship between iodine and the cardiovascular system is currently unclear. Therefore, the aim of this study was to investigate the effects of IE on endothelial function in mouse model.

METHODS

A total of 24 NOD.H-2h4 mice were randomly divided into different groups. A sodium iodide (NaI) group supplied with 0.05% NaI water for 8 weeks. Serum levels of tumor necrosis factors α (TNFα), interleukin-6 (IL-6) and C-reactive Protein (CRP), as well as endothelin-1 (ET-1), von Willebrand factor (VWF) and thrombomodulin (THBD) were detected by Elisa. In addition, the mRNA and protein expression of these genes were measured by RT-PCR and Western blotting.

RESULTS

Here, we found the urinary iodine concentration (UIC) was higher in the NaI group compared to the control group. Serum levels of ET-1, VWF, and THBD were also significantly lower in the NaI group, however, CRP serum levels are significantly increased. In aorta, the mRNA and protein expression of ET-1, VWF, THBD were downregulated, however, the expression of IL-6, CRP and TNFα mRNA and protein were upregulated in the NaI group. A correlation analysis showed negative correlation between UIC with ET-1, VWF, and THBD, similarly, negative correlation between CRP with THBD was observed. In addition, positive correlations between UIC with CRP.

CONCLUSION

Collectively, in the NOD.H-2h4 mice, IE supplementation had a suppressive effect on endothelial function, and this inhibition maybe due to the increase expression of inflammatory cytokines.

Novel chiral matrine derivatives as potential antitumor agents: Design, synthesis and biological evaluation

Since the thalidomide incident, research on chiral drugs has escalated immensely. Differences in drug configuration can lead to significant variations in therapeutic efficacy. Matrine, a natural product esteemed for its low toxicity and high water solubility, has garnered significant attention in research endeavors. Nonetheless, its precise target has proven elusive. In this study, we designed and synthesized a novel chiral matrine derivative. Their cytotoxicity against three types of tumor cells was assessed. Comparing the newly synthesized derivatives to the parent matrine, most compounds exhibited significantly enhanced inhibitory effects on cancer cells. Among them, Q12 exhibited the highest activity, with IC50 values of 8.31 μM against rat glioma cells C6, 6.3 μM against human liver cancer cells HepG2 and 7.14 μM against human gastric cancer cells HGC-27, meanwhile showing low toxicity. Based on IC50 values, we constructed a preliminary structure-activity relationship (SAR). Compound Q12 significantly suppressed the cloning and migration of HepG2 cells. Further mechanistic studies indicated that Q12 inhibited Topo I in HepG2 cells, leading to DNA damage, induction of G0/G1 cell cycle arrest and ultimately causing apoptosis. The molecular docking experiments provided a rational binding mode of Q12 with the Topo I-DNA complex. In vivo, experiments demonstrated that Q12 exhibited a higher tumor growth inhibition rate (TGI) compared to the positive control drug Lenvatinib, while maintaining good safety. In summary, it suggests that Topo I might be a potential target for matrine and Q12 represents a promising candidate for cancer treatment.

Fungi and tumors: The role of fungi in tumorigenesis (Review)

Fungi inhabit different anatomic sites in the human body. Advances in omics analyses of host‑microbiome interactions have tremendously improved our understanding of the effects of fungi on human health and diseases such as tumors. Due to the significant enrichment of specific fungi in patients with malignant tumors, the associations between fungi and human cancer have attracted an increasing attention in recent years. Indeed, cancer type‑specific fungal profiles have been found in different tumor tissues. Importantly, fungi also influence tumorigenesis through multiple factors, such as host immunity and bioactive metabolites. Microbiome interactions, host factors and fungal genetic and epigenetic factors could be involved in fungal enrichment in tumor tissues and/or in the conversion from a commensal fungus to a pathogenic fungus. Exploration of the interactions of fungi with the bacterial microbiome and the host may enable them to be a target for cancer diagnosis and treatment. In the present review, the associations between fungi and human cancer, cancer type‑specific fungal profiles and the mechanisms by which fungi cause tumorigenesis were discussed. In addition, possible factors that can lead to the enrichment of fungi in tumor tissues and/or the conversion of commensal fungi to pathogenic fungi, as well as potential therapeutic and preventive strategies for tumors based on intratumoral fungi were summarized.

Contamination status, partitioning behavior, ecological risks assessment of legacy and emerging per- and polyfluoroalkyl substances in a typical heavily polluted semi-enclosed bay, China

The contaminant status, spatial distribution, partitioning behavior, and ecological risks of 26 legacy and emerging perfluoroalkyl and polyfluoroalkyl substances (PFASs) in Laizhou Bay, China were investigated. The concentrations of ∑PFASs in surface and bottom seawater ranged from 37.2 to 222 ng/L and from 34.2 to 305 ng/L with an average of 116 ± 62.7 and 138 ± 93.8 ng/L, respectively. There were no significant differences in the average concentrations between the surface and bottom seawater (P > 0.05). Perfluorooctanoic acid (PFOA) and short-chain PFASs dominated the composition of PFASs in seawater. The concentrations of ∑PFASs in sediments ranged from 0.997 to 7.21 ng/g dry weight (dw), dominated by perfluorobutane sulfonate (PFBS), perfluorobutanoic acid (PFBA), and long-chain PFASs. The emerging alternatives of perfluoro-1-butane-sulfonamide (FBSA) and 6:2 fluorotelomer sulfonic acid (6:2 FTSA) were detected for the first time in Laizhou Bay. The ∑PFASs in seawater in the southwest of the bay were higher than those in the northeast of the bay. The ∑PFASs in sediments in the northeast sea area were higher than those in the inner area of the bay. Log Kd and log Koc values increased with increasing carbon chain length for PFASs compounds. Ecological risk assessments indicated a low ecological risk associated with HFPO-DA but a moderate risk associated with PFOA contamination in Laizhou Bay. Positive matrix factorization (PMF) analysis revealed that fluoropolymer manufacturing, metal plating plants, and textile treatments were identified as major sources contributing to PFASs contamination.

Association of Neutrophil-to-Lymphocyte Ratio With All-Cause Mortality in the Elderly Population in China: A Retrospective Cohort Study

Neutrophil-to-lymphocyte ratio (NLR) is an emerging systemic inflammation marker associated with disease progression and mortality in patients. However, there is limited research on the predictive value of NLR in the general population. This study aimed to investigate the relationship between NLR and all-cause mortality in an elderly Chinese population. A retrospective cohort study was conducted based on health examination in a community in Shanghai, China, between 2015 and 2020. Among 6364 participants (aged ≥ 55 years), a total of 169 (2.66%) participants died during a median follow-up period of 5.37 years. The median NLR was 1.63 (interquartile range: 1.29, 2.11). Multivariate analysis revealed that the upper 2 quartiles of NLR were positively associated with all-cause mortality (Q3 vs Q1: hazard ratio [HR] = 1.82, 95% confidence interval [CI]: 1.07-3.09; Q4 vs Q1: HR = 2.22, 95% CI: 1.34-3.68, P for trend <.001). The stratified and interaction analyses showed that age, sex, body mass index (BMI), history of diabetes, or history of hypertension did not significantly modify the association between NLR and all-cause mortality. Elevated NLR was independently associated with an increased risk of all-cause mortality in the elderly Chinese population.

Intestinal Mucosal Immune Barrier: A Powerful Firewall Against Severe Acute Pancreatitis-Associated Acute Lung Injury via the Gut-Lung Axis

The pathogenesis of severe acute pancreatitis-associated acute lung injury (SAP-ALI), which is the leading cause of mortality among hospitalized patients in the intensive care unit, remains incompletely elucidated. The intestinal mucosal immune barrier is a crucial component of the intestinal epithelial barrier, and its aberrant activation contributes to the induction of sustained pro-inflammatory immune responses, paradoxical intercellular communication, and bacterial translocation. In this review, we firstly provide a comprehensive overview of the composition of the intestinal mucosal immune barrier and its pivotal roles in the pathogenesis of SAP-ALI. Secondly, the mechanisms of its crosstalk with gut microbiota, which is called gut-lung axis, and its effect on SAP-ALI were summarized. Finally, a number of drugs that could enhance the intestinal mucosal immune barrier and exhibit potential anti-SAP-ALI activities were presented, including probiotics, glutamine, enteral nutrition, and traditional Chinese medicine (TCM). The aim is to offer a theoretical framework based on the perspective of the intestinal mucosal immune barrier to protect against SAP-ALI.

Multimodal brain-controlled system for rehabilitation training: Combining asynchronous online brain-computer interface and exoskeleton

BACKGROUND

Traditional therapist-based rehabilitation training for patients with movement impairment is laborious and expensive. In order to reduce the cost and improve the treatment effect of rehabilitation, many methods based on human-computer interaction (HCI) technology have been proposed, such as robot-assisted therapy and functional electrical stimulation (FES). However, due to the lack of active participation of brain, these methods have limited effects on the promotion of damaged nerve remodeling.

NEW METHOD

Based on the neurofeedback training provided by the combination of brain-computer interface (BCI) and exoskeleton, this paper proposes a multimodal brain-controlled active rehabilitation system to help improve limb function. The joint control mode of steady-state visual evoked potential (SSVEP) and motor imagery (MI) is adopted to achieve self-paced control and thus maximize the degree of brain involvement, and a requirement selection function based on SSVEP design is added to facilitate communication with aphasia patients.

COMPARISON WITH EXISTING METHODS

In addition, the Transformer is introduced as the MI decoder in the asynchronous online BCI to improve the global perception of electroencephalogram (EEG) signals and maintain the sensitivity and efficiency of the system.

RESULTS

In two multi-task online experiments for left hand, right hand, foot and idle states, subject achieves 91.25% and 92.50% best accuracy, respectively.

CONCLUSION

Compared with previous studies, this paper aims to establish a high-performance and low-latency brain-controlled rehabilitation system, and provide an independent and autonomous control mode of the brain, so as to improve the effect of neural remodeling. The performance of the proposed method is evaluated through offline and online experiments.

Jiawei Buzhong Yiqi decoction ameliorates polycystic ovary syndrome via oocyte-granulosa cell communication

ETHNOPHARMACOLOGICAL RELEVANCE

Jiawei Buzhong Yiqi Decoction (JWBZYQ), from records of FuqingzhuNvke, is a classical formula for treating obese women related infertility. JWBZYQ has been shown to be effective in treating polycystic ovary syndrome (PCOS) in both clinical studies and practical practice, with the pharmacological mechanism remaining unknown.

AIM OF THE STUDY

To explore the potential therapeutic effects and mechanistic insights of JWBZYQ in PCOS.

MATERIALS AND METHODS

An overweight PCOS rat model was established via testosterone propionate (TP) injection and 45% high-fat diet (HFD). Then they were categorized into five distinct groups: Control group, Model group, low-dose of JWBZYQ (JWBZYQ1) group, high-dose of JWBZYQ (JWBZYQ2) group, and metformin (Met) group. Body weight, estrous cycle, and sex hormone levels were observed. Hematoxylin-Eosin staining was employed to investigate the histological characteristics of the ovaries. To identify the pathways that changed significantly, transcriptome analysis was performed. The protein and mRNA levels of key molecules in ovarian zona pellucida (ZP) organization, transzonal projections (TZPs) assembly, steroid hormone receptors, and steroidogenesis were assessed using phalloidin staining, immunohistochemistry, Western blot, and polymerase chain reaction.

RESULTS

RNA-seq analysis demonstrated that regulation of hormone secretion, cilium assembly, cell projection assembly, and ZP production may all have crucial impact on the etiology of PCOS and therapeutic effect of JWBZYQ. In particular, PCOS rats exhibited elevated expressions of ZP1-3, which can be reversed by JWBZYQ2 particularly. Simultaneously, TZPs assembly was totally disrupted in PCOS rats, evidenced by the phalloidin staining, upregulated calcium-/calmodulin-dependent protein kinase II beta (CaMKIIβ), and deficient p-CaMKIIβ, myosin X (MYO10), proline-rich tyrosine kinase 2 (PTK2), and Fascin. Nonetheless, JWBZYQ or metformin treatment revived the disturbance, repairing the oocyte-granulosa cell communication, regulating steroidogenesis in PCOS rats. In this way, JWBZYQ and metformin exerted remarkable effects in alleviating altered ovarian morphology and function in PCOS rats, with JWBZYQ2 revealing the best effect.

CONCLUSIONS

JWBZYQ restored the altered ovarian morphology and function by regulating the oocyte-granulosa cell communication, which was related with ZP organization and TZPs assembly in the ovary.

A Multimodal Video-Based AI Biomarker for Aortic Stenosis Development and Progression

Importance

Aortic stenosis (AS) is a major public health challenge with a growing therapeutic landscape, but current biomarkers do not inform personalized screening and follow-up. A video-based artificial intelligence (AI) biomarker (Digital AS Severity index [DASSi]) can detect severe AS using single-view long-axis echocardiography without Doppler characterization.

Objective

To deploy DASSi to patients with no AS or with mild or moderate AS at baseline to identify AS development and progression.

Design, Setting, and Participants

This is a cohort study that examined 2 cohorts of patients without severe AS undergoing echocardiography in the Yale New Haven Health System (YNHHS; 2015-2021) and Cedars-Sinai Medical Center (CSMC; 2018-2019). A novel computational pipeline for the cross-modal translation of DASSi into cardiac magnetic resonance (CMR) imaging was further developed in the UK Biobank. Analyses were performed between August 2023 and February 2024.

Exposure

DASSi (range, 0-1) derived from AI applied to echocardiography and CMR videos.

Main Outcomes and Measures

Annualized change in peak aortic valve velocity (AV-Vmax) and late (>6 months) aortic valve replacement (AVR).

Results

A total of 12 599 participants were included in the echocardiographic study (YNHHS: n = 8798; median [IQR] age, 71 [60-80] years; 4250 [48.3%] women; median [IQR] follow-up, 4.1 [2.4-5.4] years; and CSMC: n = 3801; median [IQR] age, 67 [54-78] years; 1685 [44.3%] women; median [IQR] follow-up, 3.4 [2.8-3.9] years). Higher baseline DASSi was associated with faster progression in AV-Vmax (per 0.1 DASSi increment: YNHHS, 0.033 m/s per year [95% CI, 0.028-0.038] among 5483 participants; CSMC, 0.082 m/s per year [95% CI, 0.053-0.111] among 1292 participants), with values of 0.2 or greater associated with a 4- to 5-fold higher AVR risk than values less than 0.2 (YNHHS: 715 events; adjusted hazard ratio [HR], 4.97 [95% CI, 2.71-5.82]; CSMC: 56 events; adjusted HR, 4.04 [95% CI, 0.92-17.70]), independent of age, sex, race, ethnicity, ejection fraction, and AV-Vmax. This was reproduced across 45 474 participants (median [IQR] age, 65 [59-71] years; 23 559 [51.8%] women; median [IQR] follow-up, 2.5 [1.6-3.9] years) undergoing CMR imaging in the UK Biobank (for participants with DASSi ≥0.2 vs those with DASSi <.02, adjusted HR, 11.38 [95% CI, 2.56-50.57]). Saliency maps and phenome-wide association studies supported associations with cardiac structure and function and traditional cardiovascular risk factors.

Conclusions and Relevance

In this cohort study of patients without severe AS undergoing echocardiography or CMR imaging, a new AI-based video biomarker was independently associated with AS development and progression, enabling opportunistic risk stratification across cardiovascular imaging modalities as well as potential application on handheld devices.

Peripheral nerve injury associated with JEV infection in high endemic regions, 2016-2020: a multicenter retrospective study in China

ABSTRACTPreviously, we have reported a cohort of Japanese encephalitis (JE) patients with Guillain-Barré syndrome. However, the evidence linking Japanese encephalitis virus (JEV) infection and peripheral nerve injury (PNI) remains limited, especially the epidemiology, clinical presentation, diagnosis, treatment, and outcome significantly differ from traditional JE. We performed a retrospective and multicenter study of 1626 patients with JE recorded in the surveillance system of the Chinese Center for Disease Control and Prevention, spanning the years 2016 to 2020. Cases were classified into type 1 and type 2 JE based on whether the JE was combined with PNI or not. A comparative analysis was conducted on demographic characteristics, clinical manifestations, imaging findings, electromyography data, laboratory results, and treatment outcomes. Among 1626 laboratory confirmed JE patients, 230 (14%) were type 2 mainly located along the Yellow River in northwest China. In addition to fever, headache, and disturbance of consciousness, type 2 patients experienced acute flaccid paralysis of the limbs, as well as severe respiratory muscle paralysis. These patients presented a greater mean length of stay in hospital (children, 22 years [range, 1-34]; adults, 25 years [range, 0-183]) and intensive care unit (children, 16 years [range, 1-30]; adults, 17 years [range, 0-102]). The mortality rate was higher in type 2 patients (36/230 [16%]) compared to type 1 (67/1396 [5%]). The clinical classification of the diagnosis of JE may play a crucial role in developing a rational treatment strategy, thereby mitigating the severity of the disease and potentially reducing disability and mortality rates among patients.

Identification of a multidrug resistance genomic island harboring a nonfunctional optrA gene in Campylobacter coli of chicken origin

Campylobacter spp., such as Campylobacter jejuni and Campylobacter coli, are important zoonotic Gram-negative pathogens that cause acute intestinal diseases in humans. In this study, a retrospective analysis was conducted on previously collected Campylobacter isolates from antimicrobial resistance surveillance. A total of 29 optrA-positive C. coli strains were identified and subjected to second-generation sequencing. Multilocus sequence typing and single nucleotide polymorphism analyses demonstrated that the 29 optrA-positive isolates were genetically homogeneous. Notably, among the 29 isolated strains, the ΔoptrA variants exhibit a nonsense mutation at position 979 where the base C is substituted by T, leading to the formation of a premature termination codon. The alignment of sequences and genetic environmental characteristics suggested that ΔoptrA located on a chromosomally carried multidrug-resistant genomic island. There are other resistant genes on the multidrug resistance genomic island, such as aph(2'')-If, aph(3')-III, aadE, tet(O), tet(L), cat, erm(A), optrA and blaOXA-61. As a result, the 29 ΔoptrA-positive strains displayed susceptibility to both florfenicol and linezolid. The ΔoptrA gene is linked to the erm(A) gene, resulting in the formation of translocatable unit (TU) that are encompassed by two copies of IS1216 mobile elements. Multiple occurrences of similar TUs have been documented in numerous C. coli and provided evidence for the significance of TUs in facilitating the transfer of drug resistance genes in C. coli.

Assessing treatment effect heterogeneity in the presence of missing effect modifier data in cluster-randomized trials

Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treatment effects based on pre-specified potential effect modifiers has become a common goal in modern randomized trials. However, when one or more potential effect modifiers are missing, complete-case analysis may lead to bias and under-coverage. While statistical methods for handling missing data have been proposed and compared for individually randomized trials with missing effect modifier data, few guidelines exist for the cluster-randomized setting, where intracluster correlations in the effect modifiers, outcomes, or even missingness mechanisms may introduce further threats to accurate assessment of heterogeneous treatment effect. In this article, the performance of several missing data methods are compared through a simulation study of cluster-randomized trials with continuous outcome and missing binary effect modifier data, and further illustrated using real data from the Work, Family, and Health Study. Our results suggest that multilevel multiple imputation and Bayesian multilevel multiple imputation have better performance than other available methods, and that Bayesian multilevel multiple imputation has lower bias and closer to nominal coverage than standard multilevel multiple imputation when there are model specification or compatibility issues.

Carrageenan/polyvinyl alcohol composite film reinforced with spermidine carbon dots: An active packaging material with dual-mode antibacterial activity

Exploring biopolymer-based antibacterial packaging materials is promising to tackle the issues caused by petroleum plastic pollution and microbial contamination. Herein, a novel packaging material with two antibacterial modes, continuous and efficient, is constructed by dispersing positively charged spermidine carbon dots (Spd-CDs) in a carrageenan/polyvinyl alcohol (CP) composite biopolymer. The obtained nanocomposite film (CP/CDs film) not only gradually releases the ultra-small Spd-CDs but also rapidly generates reactive oxygen species to inhibit the reproduction of E. coli and S. aureus. Benefiting from the complementary advantages of carrageenan and polyvinyl alcohol, as well as the addition of Spd-CDs, the CP/CDs films exhibit high transparency, good mechanical performance, water vapor barrier ability, low migration, etc. The CP/CDs film as a packaging material is validated to be effective in preventing microbial contamination of pork samples. Our prepared nanocomposite film with sustainability and efficient antibacterial properties is expected as food active packaging.

Identification of relevant differential genes to the divergent devel-opment of pectoral muscle in ducks by transcriptomic analysis

Objective

The objective of this study was to identify candidate genes that play im-portant roles in skeletal muscle development in ducks.

Methods

In this study, we investigated the transcriptional sequencing of embryonic pectoral muscles from two specialized line LCA and LCC ducks which were devel-oped from Liancheng White ducks (female) and Cherry Valley ducks (male) F6 hybrid population. In addition, prediction of target genes for the differentially expressed mRNAs was conducted and the enriched gene ontology (GO) terms and Kyoto En-cyclopedia of Genes and Genomes (KEGG) signaling pathways were further analyzed. Finally, a protein-to-protein interaction (PPI) network was analyzed by using the tar-get genes to gain insights into their potential functional association.

Results

A total of 1428 differentially expressed genes (DEGs) with 762 being up-regulated genes and 666 being down-regulated genes in pectoral muscle of LCA and LCC ducks identified by RNA-seq (p < 0.05). Meanwhile, 23 GO terms in the down-regulated genes and 75 GO terms in up-regulated genes were significantly en-riched (p < 0.05). Furthermore, the top 5 most enriched pathways were ECM-receptor interaction, fatty acid degradation, pyruvate degradation, PPAR signaling pathway, and glycolysis/gluconeogenesis. Finally, the candidate genes including Integrin b3 (Itgb3), Pyruvate kinase M1/2 (Pkm), Insulin-like growth factor 1 (Igf1), glu-cose-6-phosphate isomerase（Gpi）, GABA type A receptor-associated protein-like 1(Gabarapl1), and Thyroid hormone receptor beta (Thrb) showed the most expression difference, and then were selected to verification by qRT-PCR. The result of qRT-PCR was consistent with that of transcriptome sequencing.

Conclusion

This study provided information of molecular mechanisms underlying the developmental differences in skeletal muscles between specialized duck lines.

Chronic exposure to PM10 induces anxiety-like behavior via exacerbating hippocampal oxidative stress

As one of the most environmental concerns, inhaled particulate matter (PM10) causes numerous health problems. However, the associations between anxiety behavior and toxicity caused by PM10 have rarely been reported so far. To investigate the changes of behavior after PM10 exposure and to identify the potential mechanisms of toxicity, PM10 samples (with doses of 15 mg/kg and 30 mg/kg) were intratracheally instilled into rats to simulate inhalation of polluted air by the lungs. After instillation for eight weeks, anxiety-like behavior was evaluated, levels of oxidative stress and morphological changes of hippocampus were measured. The behavioral results indicated that PM10 exposure induced obvious anxiety-like behavior in the open field and elevated plus maze tests. Both PM10 concentrations tested could increase whole blood viscosity and trigger hippocampal neuronal damage and oxidative stress by increasing superoxide dismutase (SOD) activities and malondialdehyde levels, and decreasing the expressions of antioxidant-related proteins (e.g., nuclear factor erythroid 2-related factor 2 (Nrf2), SOD1 and heme oxygenase 1). Furthermore, through collecting and analyzing questionnaires, the data showed that the participants experienced obvious anxiety-related emotions and negative somatic responses under heavily polluted environments, especially PM10 being the main pollutant. These results show that PM10 exposure induces anxiety-like behavior, which may be related to suppressing the Nrf2/Keap1-SOD1 pathway.

Using routinely collected data to determine care cascades of hypertension and type-2 diabetes management in China: a cross-sectional study

Background

China's National Essential Public Health Service Package (NEPHSP) aims to promote health for all at the primary health care level and includes a focus on hypertension and type-2 diabetes mellitus (T2DM). However, there are limited contemporary data to quantify the care cascades of hypertension and T2DM in primary health care.

Methods

This cross-sectional study involved individual level linkage of routinely collected data from the NEPHSP, health insurance claims and hospital electronic health records, from four diverse regions in China, including Xiling District (central China), Wenchuan County (western), Acheng District and Jiao District (northern). We first compared numbers of people aged ≥35 with a recorded diagnosis of hypertension and T2DM against expected numbers derived from epidemiological data. We then constructed care cascades to assess the percentages (1) enrolled in the NEPHSP, (2) adherent to the follow-up care of NEPHSP, (3) receiving medication treatment, and (4) having hypertension and/or T2DM controlled.

Findings

In the four regions, the total numbers of people aged ≥35 diagnosed of hypertension and T2DM from any data source were 149,176 and 50,828, respectively. This was estimated to be 46.0% (95% confidence interval [CI]: 45.8%-46.2%) and 45.6% (95% CI: 45.3%-45.9%) of the expected totals for hypertension and T2DM, respectively. Among those diagnosed, 65.4% (95% CI: 65.1%-65.6%) with hypertension and 66.1% (95% CI: 65.7%-66.5%) with T2DM were enrolled in the NEPHSP, respectively, in which 54.8% (95% CI: 54.5%-55.2%) with hypertension and 64.7% (95% CI: 64.1%-65.2%) with T2DM were adherent to the required services. Among those enrolled, the overall treatment rates were 70.8% (95% CI: 70.6%-71.1%) for hypertension and 82.2% (95% CI: 81.8%-82.6%) for T2DM. Among those treated, a further 80.9% (95% CI: 80.6%-81.2%) with hypertension and 73.9% (95% CI: 73.3%-74.4%) with T2DM achieved control. These results varied considerably across regions, with the northern sites showing relatively higher enrolment rates while the central site had higher control rates.

Interpretation

Detection and control rates for hypertension and T2DM are suboptimal in these four regions of China. Further strategies are needed to improve people's enrolment in and adherence to the NEPHSP and strengthen care delivery processes. Of note, our estimations of the diagnosis rates for each region are based on national level large epidemiological data. The interpretation of these data needs caution due to potential bias caused by regional variations.

Funding

This study is funded by National Health and Medical Research Council (NHMRC) Global Alliance for Chronic Diseases funding (APP1169757), and National Natural Science Foundation of China (72074065).

Factor structure, measurement invariance, and psychometric properties of the Posttraumatic Cognitions Inventory (PTCI) and its brief version (PTCI-9) in Chinese adolescents and adults

The Posttraumatic Cognitions Inventory (PTCI) is a widely used self-report tool to assess negative posttraumatic cognitions about self, world, and self-blame, but the factor structure remains controversial. Recently, a brief version of the PTCI with nine items (PTCI-9) loading onto three factors has been developed, and an excellent model fit was obtained. In this study, we examined the psychometric properties of the PTCI and PTCI-9 in a large sample of trauma-exposed Chinese adolescents (n = 1,451; mean age = 13.67 years, SD = 1.24) and adults (n = 924; mean age = 39.6 years, SD = 5.43). Confirmatory factor analysis indicated an acceptable model fit for the original PTCI but a better model fit for the PTCI-9. Furthermore, the configural, metric, and scalar invariances of the PTCI-9 were supported across age groups (adolescent and adult), gender (male and female), trauma exposure (direct and indirect), and types of traumatic events (interpersonal and noninterpersonal). The PTCI and PTCI-9 and their subscale scores showed adequate internal consistency reliability in adolescent and adult samples. The PTCI-9 also demonstrated good convergent validity, as demonstrated by statistically significant correlations with PTSD, depression, anxiety, and life satisfaction. Overall, the present study supports the Chinese PTCI and PTCI-9 as valid measures of negative cognitions in both adolescents and adults and makes meaningful comparisons of negative cognitions across gender, trauma exposure, and types of traumatic events. Notably, as a brief and valid tool, the PTCI-9 is suggested to be used in survey and longitudinal studies for adolescents and adults. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Survey data on livelihoods and inputs and outputs of crop production in Quzhou county on the North China Plain

Farmers' decisions on crop choice, management practices, and livelihood strategies are essential to agricultural sustainability. This data article describes three datasets on crop production in Quzhou, a county in the central part of North China Plain. The three datasets cover different scales. The village dataset assembles basic data on all 342 villages of Quzhou county, including information on population, land area, crop grown, labour, irrigation and markets. Data was sourced from the yearbook data of 2017 and a village cadres survey in 2018. The village dataset was used to create a village typology from which 35 villages belonging to seven village types (five for each type) were selected for stratified random sampling to collect information on farm characteristics and cropping practices. We surveyed these 35 villages, interviewing fifteen farmer households per village (525 in total) in 2020. The interviewees represented two farm management models: smallholder farms and business farms. The resulting household dataset provides farm-level data, including demographic data of farming decision-makers and the number of household members, land use and machinery resources, crop production management practices, and government subsidies. The crop-level dataset was derived from the household survey and included input-output inventories for each crop grown during one year on each field greater than 1/30th ha (1/2 mu) on the 525 surveyed farms within a year. This dataset comprises information on cropping practices in 1352 fields. The three datasets provide a basis for analyses on cropping practices and sustainability attributes of farms and crops in a typical agricultural county on the North China Plain.

Rational engineering S1' substrate binding pocket to enhance substrate specificity and catalytic activity of thermal-stable keratinase for efficient keratin degradation

This study reports the rational engineering of the S1' substrate-binding pocket of a thermally-stable keratinase from Pseudomonas aeruginosa 4-3 (4-3Ker) to improve substrate specificity to typical keratinase (K/C > 0.5) and catalytic activity without compromising thermal stability for efficient keratin degradation. Of 10 chosen mutation hotspots in the S1' substrate-binding pocket, the top three mutations M128R, A138V, and V142I showing the best catalytic activity and substrate specificity were identified. Their double and triple combinatorial mutants synergistically overcame limitations of single mutants, fabricating an excellent M128R/A138V/V142I triple mutant which displayed a 1.21-fold increase in keratin catalytic activity, 1.10-fold enhancement in keratin/casein activity ratio, and a 3.13 °C increase in half-inactivation temperature compared to 4-3Ker. Molecular dynamics simulations revealed enhanced flexibility of critical amino acid residues at the substrate access tunnel, improved global protein rigidity, and heightened hydrophobicity within the active site likely underpinned the increased catalytic activity and substrate specificity. Additionally, the triple mutant improved the feather degradation rate by 32.86 % over the wild-type, far exceeding commercial keratinase in substrate specificity and thermal stability. This study exemplified engineering a typical keratinase with enhanced substrate specificity, catalytic activity, and thermal stability from thermally-stable 4-3Ker, providing a more robust tool for feather degradation.

Predictive value of contrast-enhanced ultrasonography and ultrasound elastography for management of BI-RADS category 4 nonpalpable breast masses

PURPOSE

The objective of this study was to investigate the independent risk factors and associated predictive values of contrast-enhanced ultrasound (CEUS), shear wave elastography (SWE), and strain elastography (SE) for high-risk lesions (HRL) and malignant tumors (MT) among nonpalpable breast masses classified as BI-RADS category 4 on conventional ultrasound.

METHODS

This prospective study involved consecutively admitted patients with breast tumors from January 2018, aiming to explore the management of BI-RADS category 4 breast tumors using CEUS and elastography. We conducted a retrospective review of patient data, focusing on those with a history of a nonpalpable mass as the primary complaint. Pathologic findings after surgical resection served as the gold standard. The CEUS arterial-phase indices were analyzed using contrast agent arrival-time parametric imaging processing mode, while quantitative and qualitative indices were examined on ES images. Independent risk factors were identified through binary logistic regression multifactorial analysis. The predictive efficacy of different modalities was compared using a receiver operating characteristics curve. Subsequently, a nomogram for predicting the risk of HRL/MT was established based on a multifactorial logistic regression model.

RESULTS

A total of 146 breast masses from 146 patients were included, comprising 80 benign tumors, 12 HRLs, and 54 MTs based on the final pathology. There was no significant difference in pathologic size between the benign and HRL/MT groups [8.00(6.25,10.00) vs. 9.00(6.00,10.00), P = 0.506]. The diagnostic efficacy of US plus CEUS exceeded that of US plus SWE/SE for BI-RADS 4 nonpalpable masses, with an AUC of 0.954 compared to 0.798/0.741 (P ＜ 0.001). Further stratified analysis revealed a more pronounced improvement for reclassification of BI-RADS 4a masses (AUC: 0.943 vs. 0.762/0.675, P ＜ 0.001) than BI-RADS 4b (AUC:0.950 vs. 0.885/0.796, P＞0.05) with the assistance of CEUS than SWE/SE. Employing downgrade CEUS strategies resulted in negative predictive values ranging from 95.2 % to 100.0 % for BI-RADS 4a and 4b masses. Conversely, using upgrade nomogram strategies, which included the independent predictive risk factors of irregular enhanced shape, poor defined enhanced margin, earlier enhanced time, increased surrounding vessels, and presence of contrast agent retention, the diagnostic performance achieved an AUC of 0.947 with good calibration.

CONCLUSION

After investigating the potential of CEUS and ES in improving risk assessment and diagnostic accuracy for nonpalpable BI-RADS category 4 breast masses, it is evident that CEUS has a more significant impact on enhancing classification compared to ES, particularly for BI-RADS 4a subgroup masses. This finding suggests that CEUS may offer greater benefits in improving risk assessment and diagnostic accuracy for this specific subgroup of breast masses.

Adherence to key recommendations for design and analysis of stepped-wedge cluster randomized trials: A review of trials published 2016-2022

BACKGROUND/AIMS

The stepped-wedge cluster randomized trial (SW-CRT), in which clusters are randomized to a time at which they will transition to the intervention condition - rather than a trial arm - is a relatively new design. SW-CRTs have additional design and analytical considerations compared to conventional parallel arm trials. To inform future methodological development, including guidance for trialists and the selection of parameters for statistical simulation studies, we conducted a review of recently published SW-CRTs. Specific objectives were to describe (1) the types of designs used in practice, (2) adherence to key requirements for statistical analysis, and (3) practices around covariate adjustment. We also examined changes in adherence over time and by journal impact factor.

METHODS

We used electronic searches to identify primary reports of SW-CRTs published 2016-2022. Two reviewers extracted information from each trial report and its protocol, if available, and resolved disagreements through discussion.

RESULTS

We identified 160 eligible trials, randomizing a median (Q1-Q3) of 11 (8-18) clusters to 5 (4-7) sequences. The majority (122, 76%) were cross-sectional (almost all with continuous recruitment), 23 (14%) were closed cohorts and 15 (9%) open cohorts. Many trials had complex design features such as multiple or multivariate primary outcomes (50, 31%) or time-dependent repeated measures (27, 22%). The most common type of primary outcome was binary (51%); continuous outcomes were less common (26%). The most frequently used method of analysis was a generalized linear mixed model (112, 70%); generalized estimating equations were used less frequently (12, 8%). Among 142 trials with fewer than 40 clusters, only 9 (6%) reported using methods appropriate for a small number of clusters. Statistical analyses clearly adjusted for time effects in 119 (74%), for within-cluster correlations in 132 (83%), and for distinct between-period correlations in 13 (8%). Covariates were included in the primary analysis of the primary outcome in 82 (51%) and were most often individual-level covariates; however, clear and complete pre-specification of covariates was uncommon. Adherence to some key methodological requirements (adjusting for time effects, accounting for within-period correlation) was higher among trials published in higher versus lower impact factor journals. Substantial improvements over time were not observed although a slight improvement was observed in the proportion accounting for a distinct between-period correlation.

CONCLUSIONS

Future methods development should prioritize methods for SW-CRTs with binary or time-to-event outcomes, small numbers of clusters, continuous recruitment designs, multivariate outcomes, or time-dependent repeated measures. Trialists, journal editors, and peer reviewers should be aware that SW-CRTs have additional methodological requirements over parallel arm designs including the need to account for period effects as well as complex intracluster correlations.

Associations of sleep-related variables with reverse dipping patterns of blood pressure in α-synucleinopathies

INTRODUCTION

The reverse dipping blood pressure (BP) pattern is very common in α-synucleinopathies. We aimed to explore the associations of sleep-related variables with abnormal BP circadian rhythms in Parkinson's disease (PD) and multiple system atrophy (MSA).

METHODS

A total of 126 patients, 76 with PD and 50 with MSA, were included. All participants underwent ambulatory BP monitoring and full-night polysomnography (PSG). We analyzed abnormal dipping patterns and sleep-related parameters, including moderate to severe obstructive sleep apnea (OSA), rapid eye movement behavior disorder (RBD), average oxygen saturation (SaO2%), lowest SaO2%, duration of SaO2% <90%, and apnea-hypopnea index (AHI). Binary logistic regression was performed to explore the associations between paraclinical variables, sleep-related variables, and reverse dipping patterns.

RESULTS

Reverse dipping patterns were predominant in patients with PD (58.5 %) and MSA (68.0 %). Patients with MSA had higher AHI, RBD, and lower average SaO2% than those with PD. Taking both diseases together as a whole group of α-synucleinopathies, logistic regression analysis indicates the Hoehn-Yahr stage (odds ratio [OR] = 2.00 for reverse systolic and 2.34 for reverse diastolic dipping patterns), moderate to severe OSA (OR = 2.71 for reverse systolic and 2.53 for reverse diastolic dipping patterns), average SaO2% (OR = 1.35 for reverse systolic dipping patterns), and male sex (OR = 2.70 for reverse diastolic dipping patterns) were independently associated with reverse dipping patterns.

CONCLUSIONS

Reverse dipping patterns were common in patients with PD and MSA. Hoehn-Yahr stage, moderate to severe OSA, average SaO2%, and male sex were associated with reverse dipping patterns in α-synucleinopathy.

Multiply robust generalized estimating equations for cluster randomized trials with missing outcomes

Generalized estimating equations (GEEs) provide a useful framework for estimating marginal regression parameters based on data from cluster randomized trials (CRTs), but they can result in inaccurate parameter estimates when some outcomes are informatively missing. Existing techniques to handle missing outcomes in CRTs rely on correct specification of a propensity score model, a covariate-conditional mean outcome model, or require at least one of these two models to be correct, which can be challenging in practice. In this article, we develop new weighted GEEs to simultaneously estimate the marginal mean, scale, and correlation parameters in CRTs with missing outcomes, allowing for multiple propensity score models and multiple covariate-conditional mean models to be specified. The resulting estimators are consistent provided that any one of these models is correct. An iterative algorithm is provided for implementing this more robust estimator and practical considerations for specifying multiple models are discussed. We evaluate the performance of the proposed method through Monte Carlo simulations and apply the proposed multiply robust estimator to analyze the Botswana Combination Prevention Project, a large HIV prevention CRT designed to evaluate whether a combination of HIV-prevention measures can reduce HIV incidence.

Vitamin D3 mediated peptides-calcium chelate self-assembly: Fabrication, stability and improvement on cellular calcium transport

A self-assembled peptides-calcium-Vitamin D3 ternary delivery system (CSPH-Ca-VD3) was prepared to investigate the promotion of cellular calcium transport. The constructed CSPH-Ca-VD3 nanocomplex exhibited a spherical structure with a size of 135.2 ± 10.2 nm. Based on the thermodynamic calculation of fluorescent spectra, hydrophobic interaction was shown as the major driving force for this nanocomplex structure. CSPH-Ca-VD3 nanocomplex possessed excellent stability during simulated gastrointestinal digestion, contributing to the prevention of acid degradation of VD3 and the enhancement of calcium solubility. Furthermore, the calcium transport efficiency in the form of CSPH-Ca-VD3 (4 mg/mL) across a Caco-2 cells monolayer was significantly increased 2.3-fold compared to that of free Ca2+, mainly attributed to the upregulation in the presence of CSPH-Ca-VD3 of TRPV6, calbindin D9k and PMCA1b expression in Caco-2 cells. The present study provided a basis for developing a novel delivery system of peptides-calcium chelate with the dual effects of VD3 protection and calcium uptake promotion.

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

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

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

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

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Affiliation(s)

M Ablikim Institute of High Energy Physics, Beijing 100049, People's Republic of China
M N Achasov Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
P Adlarson Uppsala University, Box 516, SE-75120 Uppsala, Sweden
X C Ai Zhengzhou University, Zhengzhou 450001, People's Republic of China
R Aliberti Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
A Amoroso University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
M R An Liaoning Normal University, Dalian 116029, People's Republic of China
Q An State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Y Bai Southeast University, Nanjing 211100, People's Republic of China
O Bakina Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
I Balossino INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
Y Ban Peking University, Beijing 100871, People's Republic of China
H-R Bao University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
V Batozskaya Institute of High Energy Physics, Beijing 100049, People's Republic of China National Centre for Nuclear Research, Warsaw 02-093, Poland
K Begzsuren Institute of Physics and Technology, Peace Avenue 54B, Ulaanbaatar 13330, Mongolia
N Berger Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
M Berlowski National Centre for Nuclear Research, Warsaw 02-093, Poland
M Bertani INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
D Bettoni INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
F Bianchi University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
E Bianco University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
A Bortone University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
I Boyko Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
R A Briere Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
A Brueggemann University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
H Cai Wuhan University, Wuhan 430072, People's Republic of China
X Cai Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
A Calcaterra INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
G F Cao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
N Cao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S A Cetin Turkish Accelerator Center Particle Factory Group, Istinye University, 34010 Istanbul, Turkey
J F Chang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
T T Chang Xinyang Normal University, Xinyang 464000, People's Republic of China
W L Chang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
G R Che Nankai University, Tianjin 300071, People's Republic of China
G Chelkov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
C Chen Nankai University, Tianjin 300071, People's Republic of China
Chao Chen Soochow University, Suzhou 215006, People's Republic of China
G Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China
H S Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
M L Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S J Chen Nanjing University, Nanjing 210093, People's Republic of China
S L Chen North China Electric Power University, Beijing 102206, People's Republic of China
S M Chen Tsinghua University, Beijing 100084, People's Republic of China
T Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X R Chen Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X T Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y B Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y Q Chen Jilin University, Changchun 130012, People's Republic of China
Z J Chen Hunan University, Changsha 410082, People's Republic of China
S K Choi Chung-Ang University, Seoul, 06974, Republic of Korea
X Chu Nankai University, Tianjin 300071, People's Republic of China
G Cibinetto INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
S C Coen Bochum Ruhr-University, D-44780 Bochum, Germany
F Cossio INFN, I-10125, Turin, Italy
J J Cui Shandong University, Jinan 250100, People's Republic of China
H L Dai Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
J P Dai Yunnan University, Kunming 650500, People's Republic of China
A Dbeyssi Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
R E de Boer Bochum Ruhr-University, D-44780 Bochum, Germany
D Dedovich Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
Z Y Deng Institute of High Energy Physics, Beijing 100049, People's Republic of China
A Denig Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
I Denysenko Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
M Destefanis University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
F De Mori University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
B Ding Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Jinan, Jinan 250022, People's Republic of China
X X Ding Peking University, Beijing 100871, People's Republic of China
Y Ding Liaoning University, Shenyang 110036, People's Republic of China
Y Ding Jilin University, Changchun 130012, People's Republic of China
J Dong Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
L Y Dong Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
M Y Dong Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Dong Wuhan University, Wuhan 430072, People's Republic of China
M C Du Institute of High Energy Physics, Beijing 100049, People's Republic of China
S X Du Zhengzhou University, Zhengzhou 450001, People's Republic of China
Z H Duan Nanjing University, Nanjing 210093, People's Republic of China
P Egorov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
Y H Fan North China Electric Power University, Beijing 102206, People's Republic of China
J Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
S S Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
W X Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Q Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
R Farinelli INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
L Fava University of Eastern Piedmont, I-15121 Alessandria, Italy INFN, I-10125, Turin, Italy
F Feldbauer Bochum Ruhr-University, D-44780 Bochum, Germany
G Felici INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
C Q Feng State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
J H Feng Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
K Fischer University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
M Fritsch Bochum Ruhr-University, D-44780 Bochum, Germany
C D Fu Institute of High Energy Physics, Beijing 100049, People's Republic of China
J L Fu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y W Fu Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Gao University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y N Gao Peking University, Beijing 100871, People's Republic of China
Yang Gao State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
S Garbolino INFN, I-10125, Turin, Italy
I Garzia INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy University of Ferrara, I-44122 Ferrara, Italy
P T Ge Wuhan University, Wuhan 430072, People's Republic of China
Z W Ge Nanjing University, Nanjing 210093, People's Republic of China
C Geng Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
E M Gersabeck University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
A Gilman University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
K Goetzen GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
L Gong Liaoning University, Shenyang 110036, People's Republic of China
W X Gong Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
W Gradl Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
S Gramigna INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy University of Ferrara, I-44122 Ferrara, Italy
M Greco University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
M H Gu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y T Gu Guangxi University, Nanning 530004, People's Republic of China
C Y Guan Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z L Guan Henan University of Technology, Zhengzhou 450001, People's Republic of China
A Q Guo Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L B Guo Nanjing Normal University, Nanjing 210023, People's Republic of China
M J Guo Shandong University, Jinan 250100, People's Republic of China
R P Guo Shandong Normal University, Jinan 250014, People's Republic of China
Y P Guo Fudan University, Shanghai 200433, People's Republic of China
A Guskov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
J Gutierrez Indiana University, Bloomington, Indiana 47405, USA
T T Han Institute of High Energy Physics, Beijing 100049, People's Republic of China
W Y Han Liaoning Normal University, Dalian 116029, People's Republic of China
X Q Hao Henan Normal University, Xinxiang 453007, People's Republic of China
F A Harris University of Hawaii, Honolulu, Hawaii 96822, USA
K K He Soochow University, Suzhou 215006, People's Republic of China
K L He Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
F H H Heinsius Bochum Ruhr-University, D-44780 Bochum, Germany
C H Heinz Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
Y K Heng Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C Herold Suranaree University of Technology, University Avenue 111, Nakhon Ratchasima 30000, Thailand
T Holtmann Bochum Ruhr-University, D-44780 Bochum, Germany
P C Hong Fudan University, Shanghai 200433, People's Republic of China
G Y Hou Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X T Hou Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y R Hou University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z L Hou Institute of High Energy Physics, Beijing 100049, People's Republic of China
B Y Hu Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
H M Hu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J F Hu South China Normal University, Guangzhou 510006, People's Republic of China
T Hu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y Hu Institute of High Energy Physics, Beijing 100049, People's Republic of China
G S Huang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
K X Huang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
L Q Huang Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X T Huang Shandong University, Jinan 250100, People's Republic of China
Y P Huang Institute of High Energy Physics, Beijing 100049, People's Republic of China
T Hussain University of the Punjab, Lahore-54590, Pakistan
N Hüsken Indiana University, Bloomington, Indiana 47405, USA Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
N In der Wiesche University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
M Irshad State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
J Jackson Indiana University, Bloomington, Indiana 47405, USA
S Jaeger Bochum Ruhr-University, D-44780 Bochum, Germany
S Janchiv Institute of Physics and Technology, Peace Avenue 54B, Ulaanbaatar 13330, Mongolia
J H Jeong Chung-Ang University, Seoul, 06974, Republic of Korea
Q Ji Institute of High Energy Physics, Beijing 100049, People's Republic of China
Q P Ji Henan Normal University, Xinxiang 453007, People's Republic of China
X B Ji Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X L Ji Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y Y Ji Shandong University, Jinan 250100, People's Republic of China
X Q Jia Shandong University, Jinan 250100, People's Republic of China
Z K Jia State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
H J Jiang Wuhan University, Wuhan 430072, People's Republic of China
P C Jiang Peking University, Beijing 100871, People's Republic of China
S S Jiang Liaoning Normal University, Dalian 116029, People's Republic of China
T J Jiang Hangzhou Normal University, Hangzhou 310036, People's Republic of China
X S Jiang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y Jiang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J B Jiao Shandong University, Jinan 250100, People's Republic of China
Z Jiao Huangshan College, Huangshan 245000, People's Republic of China
S Jin Nanjing University, Nanjing 210093, People's Republic of China
Y Jin University of Jinan, Jinan 250022, People's Republic of China
M Q Jing Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X M Jing University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
T Johansson Uppsala University, Box 516, SE-75120 Uppsala, Sweden
X Kui Institute of High Energy Physics, Beijing 100049, People's Republic of China
S Kabana Instituto de Alta Investigación, Universidad de Tarapacá, Casilla 7D, Arica 1000000, Chile
N Kalantar-Nayestanaki University of Groningen, NL-9747 AA Groningen, The Netherlands
X L Kang China University of Geosciences, Wuhan 430074, People's Republic of China
X S Kang Liaoning University, Shenyang 110036, People's Republic of China
M Kavatsyuk University of Groningen, NL-9747 AA Groningen, The Netherlands
B C Ke Zhengzhou University, Zhengzhou 450001, People's Republic of China
V Khachatryan Indiana University, Bloomington, Indiana 47405, USA
A Khoukaz University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
R Kiuchi Institute of High Energy Physics, Beijing 100049, People's Republic of China
R Kliemt GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
O B Kolcu Turkish Accelerator Center Particle Factory Group, Istinye University, 34010 Istanbul, Turkey
B Kopf Bochum Ruhr-University, D-44780 Bochum, Germany
M Kuessner Bochum Ruhr-University, D-44780 Bochum, Germany
A Kupsc National Centre for Nuclear Research, Warsaw 02-093, Poland Uppsala University, Box 516, SE-75120 Uppsala, Sweden
W Kühn Justus-Liebig-Universitaet Giessen, II. Physikalisches Institut, Heinrich-Buff-Ring 16, D-35392 Giessen, Germany
J J Lane University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
P Larin Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
A Lavania Indian Institute of Technology Madras, Chennai 600036, India
L Lavezzi University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
T T Lei State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Z H Lei State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
H Leithoff Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
M Lellmann Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
T Lenz Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
C Li Qufu Normal University, Qufu 273165, People's Republic of China
C Li Nankai University, Tianjin 300071, People's Republic of China
C H Li Liaoning Normal University, Dalian 116029, People's Republic of China
Cheng Li State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
D M Li Zhengzhou University, Zhengzhou 450001, People's Republic of China
F Li Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
G Li Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Li State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
H B Li Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H J Li Henan Normal University, Xinxiang 453007, People's Republic of China
H N Li South China Normal University, Guangzhou 510006, People's Republic of China
Hui Li Nankai University, Tianjin 300071, People's Republic of China
J R Li Tsinghua University, Beijing 100084, People's Republic of China
J S Li Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
J W Li Shandong University, Jinan 250100, People's Republic of China
Ke Li Institute of High Energy Physics, Beijing 100049, People's Republic of China
L J Li Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L K Li Institute of High Energy Physics, Beijing 100049, People's Republic of China
Lei Li Renmin University of China, Beijing 100872, People's Republic of China
M H Li Nankai University, Tianjin 300071, People's Republic of China
P R Li Lanzhou University, Lanzhou 730000, People's Republic of China
Q X Li Shandong University, Jinan 250100, People's Republic of China
S X Li Fudan University, Shanghai 200433, People's Republic of China
T Li Shandong University, Jinan 250100, People's Republic of China
W D Li Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
W G Li Institute of High Energy Physics, Beijing 100049, People's Republic of China
X H Li State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X L Li Shandong University, Jinan 250100, People's Republic of China
Xiaoyu Li Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y G Li Peking University, Beijing 100871, People's Republic of China
Z J Li Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
Z X Li Guangxi University, Nanning 530004, People's Republic of China
C Liang Nanjing University, Nanjing 210093, People's Republic of China
H Liang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H Liang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Y F Liang Sichuan University, Chengdu 610064, People's Republic of China
Y T Liang Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
G R Liao Guangxi Normal University, Guilin 541004, People's Republic of China
L Z Liao Shandong University, Jinan 250100, People's Republic of China
Y P Liao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J Libby Indian Institute of Technology Madras, Chennai 600036, India
A Limphirat Suranaree University of Technology, University Avenue 111, Nakhon Ratchasima 30000, Thailand
D X Lin Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
T Lin Institute of High Energy Physics, Beijing 100049, People's Republic of China
B J Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
B X Liu Wuhan University, Wuhan 430072, People's Republic of China
C Liu Jilin University, Changchun 130012, People's Republic of China
C X Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
F H Liu Shanxi University, Taiyuan 030006, People's Republic of China
Fang Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
Feng Liu Central China Normal University, Wuhan 430079, People's Republic of China
G M Liu South China Normal University, Guangzhou 510006, People's Republic of China
H Liu Lanzhou University, Lanzhou 730000, People's Republic of China
H B Liu Guangxi University, Nanning 530004, People's Republic of China
H M Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Huanhuan Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
Huihui Liu Henan University of Science and Technology, Luoyang 471003, People's Republic of China
J B Liu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
J Y Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
K Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
K Y Liu Liaoning University, Shenyang 110036, People's Republic of China
Ke Liu Henan University of Technology, Zhengzhou 450001, People's Republic of China
L Liu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
L C Liu Nankai University, Tianjin 300071, People's Republic of China
Lu Liu Nankai University, Tianjin 300071, People's Republic of China
M H Liu Fudan University, Shanghai 200433, People's Republic of China
P L Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
Q Liu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S B Liu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
T Liu Fudan University, Shanghai 200433, People's Republic of China
W K Liu Nankai University, Tianjin 300071, People's Republic of China
W M Liu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X Liu Lanzhou University, Lanzhou 730000, People's Republic of China
Y Liu Lanzhou University, Lanzhou 730000, People's Republic of China
Y Liu Zhengzhou University, Zhengzhou 450001, People's Republic of China
Y B Liu Nankai University, Tianjin 300071, People's Republic of China
Z A Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z Q Liu Shandong University, Jinan 250100, People's Republic of China
X C Lou Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
F X Lu Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
H J Lu Huangshan College, Huangshan 245000, People's Republic of China
J G Lu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
X L Lu Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Lu Central South University, Changsha 410083, People's Republic of China
Y P Lu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Z H Lu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C L Luo Nanjing Normal University, Nanjing 210023, People's Republic of China
M X Luo Zhejiang University, Hangzhou 310027, People's Republic of China
T Luo Fudan University, Shanghai 200433, People's Republic of China
X L Luo Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
X R Lyu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y F Lyu Nankai University, Tianjin 300071, People's Republic of China
F C Ma Liaoning University, Shenyang 110036, People's Republic of China
H Ma Yunnan University, Kunming 650500, People's Republic of China
H L Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China
J L Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L L Ma Shandong University, Jinan 250100, People's Republic of China
M M Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Q M Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China
R Q Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Y Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y Ma Peking University, Beijing 100871, People's Republic of China
Y M Ma Institute of Modern Physics, Lanzhou 730000, People's Republic of China
F E Maas Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
M Maggiora University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
S Malde University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
Q A Malik University of the Punjab, Lahore-54590, Pakistan
A Mangoni INFN Sezione di Perugia, I-06100 Perugia, Italy
Y J Mao Peking University, Beijing 100871, People's Republic of China
Z P Mao Institute of High Energy Physics, Beijing 100049, People's Republic of China
S Marcello University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
Z X Meng University of Jinan, Jinan 250022, People's Republic of China
J G Messchendorp GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany University of Groningen, NL-9747 AA Groningen, The Netherlands
G Mezzadri INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
H Miao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
T J Min Nanjing University, Nanjing 210093, People's Republic of China
R E Mitchell Indiana University, Bloomington, Indiana 47405, USA
X H Mo Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B Moses Indiana University, Bloomington, Indiana 47405, USA
N Yu Muchnoi Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
J Muskalla Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
Y Nefedov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
F Nerling Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
I B Nikolaev Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
Z Ning Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
S Nisar COMSATS University Islamabad, Lahore Campus, Defence Road, Off Raiwind Road, 54000 Lahore, Pakistan
Q L Niu Lanzhou University, Lanzhou 730000, People's Republic of China
W D Niu Soochow University, Suzhou 215006, People's Republic of China
Y Niu Shandong University, Jinan 250100, People's Republic of China
S L Olsen University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Q Ouyang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S Pacetti INFN Sezione di Perugia, I-06100 Perugia, Italy University of Perugia, I-06100 Perugia, Italy
X Pan Soochow University, Suzhou 215006, People's Republic of China
Y Pan Southeast University, Nanjing 211100, People's Republic of China
A Pathak Jilin University, Changchun 130012, People's Republic of China
P Patteri INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
Y P Pei State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
M Pelizaeus Bochum Ruhr-University, D-44780 Bochum, Germany
H P Peng State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Y Y Peng Lanzhou University, Lanzhou 730000, People's Republic of China
K Peters GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
J L Ping Nanjing Normal University, Nanjing 210023, People's Republic of China
R G Ping Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S Plura Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
V Prasad Instituto de Alta Investigación, Universidad de Tarapacá, Casilla 7D, Arica 1000000, Chile
F Z Qi Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Qi State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
H R Qi Tsinghua University, Beijing 100084, People's Republic of China
M Qi Nanjing University, Nanjing 210093, People's Republic of China
T Y Qi Fudan University, Shanghai 200433, People's Republic of China
S Qian Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
W B Qian University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C F Qiao University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J J Qin University of South China, Hengyang 421001, People's Republic of China
L Q Qin Guangxi Normal University, Guilin 541004, People's Republic of China
X S Qin Shandong University, Jinan 250100, People's Republic of China
Z H Qin Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
J F Qiu Institute of High Energy Physics, Beijing 100049, People's Republic of China
S Q Qu Tsinghua University, Beijing 100084, People's Republic of China
C F Redmer Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
K J Ren Liaoning Normal University, Dalian 116029, People's Republic of China
A Rivetti INFN, I-10125, Turin, Italy
M Rolo INFN, I-10125, Turin, Italy
G Rong Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Ch Rosner Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
S N Ruan Nankai University, Tianjin 300071, People's Republic of China
N Salone National Centre for Nuclear Research, Warsaw 02-093, Poland
A Sarantsev Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
Y Schelhaas Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
K Schoenning Uppsala University, Box 516, SE-75120 Uppsala, Sweden
M Scodeggio INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy University of Ferrara, I-44122 Ferrara, Italy
K Y Shan Fudan University, Shanghai 200433, People's Republic of China
W Shan Hunan Normal University, Changsha 410081, People's Republic of China
X Y Shan State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
J F Shangguan Soochow University, Suzhou 215006, People's Republic of China
L G Shao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
M Shao State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
C P Shen Fudan University, Shanghai 200433, People's Republic of China
H F Shen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
W H Shen University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Y Shen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B A Shi University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H C Shi State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
J L Shi Fudan University, Shanghai 200433, People's Republic of China
J Y Shi Institute of High Energy Physics, Beijing 100049, People's Republic of China
Q Q Shi Soochow University, Suzhou 215006, People's Republic of China
R S Shi Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Shi Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
J J Song Henan Normal University, Xinxiang 453007, People's Republic of China
T Z Song Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
W M Song Institute of High Energy Physics, Beijing 100049, People's Republic of China Jilin University, Changchun 130012, People's Republic of China
Y J Song Fudan University, Shanghai 200433, People's Republic of China
Y X Song Peking University, Beijing 100871, People's Republic of China
S Sosio University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
S Spataro University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
F Stieler Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
Y J Su University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
G B Sun Wuhan University, Wuhan 430072, People's Republic of China
G X Sun Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Sun University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H K Sun Institute of High Energy Physics, Beijing 100049, People's Republic of China
J F Sun Henan Normal University, Xinxiang 453007, People's Republic of China
K Sun Tsinghua University, Beijing 100084, People's Republic of China
L Sun Wuhan University, Wuhan 430072, People's Republic of China
S S Sun Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
T Sun Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
W Y Sun Jilin University, Changchun 130012, People's Republic of China
Y Sun China University of Geosciences, Wuhan 430074, People's Republic of China
Y J Sun State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Y Z Sun Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z T Sun Shandong University, Jinan 250100, People's Republic of China
Y X Tan State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
C J Tang Sichuan University, Chengdu 610064, People's Republic of China
G Y Tang Institute of High Energy Physics, Beijing 100049, People's Republic of China
J Tang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
Y A Tang Wuhan University, Wuhan 430072, People's Republic of China
L Y Tao University of South China, Hengyang 421001, People's Republic of China
Q T Tao Hunan University, Changsha 410082, People's Republic of China
M Tat University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
J X Teng State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
V Thoren Uppsala University, Box 516, SE-75120 Uppsala, Sweden
W H Tian Shanxi Normal University, Linfen 041004, People's Republic of China
W H Tian Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
Y Tian Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z F Tian Wuhan University, Wuhan 430072, People's Republic of China
I Uman Near East University, Nicosia, North Cyprus, 99138, Mersin 10, Turkey
Y Wan Soochow University, Suzhou 215006, People's Republic of China
S J Wang Shandong University, Jinan 250100, People's Republic of China
B Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China
B L Wang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Bo Wang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
C W Wang Nanjing University, Nanjing 210093, People's Republic of China
D Y Wang Peking University, Beijing 100871, People's Republic of China
F Wang University of South China, Hengyang 421001, People's Republic of China
H J Wang Lanzhou University, Lanzhou 730000, People's Republic of China
J P Wang Shandong University, Jinan 250100, People's Republic of China
K Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
L L Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China
M Wang Shandong University, Jinan 250100, People's Republic of China
Meng Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
N Y Wang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S Wang Fudan University, Shanghai 200433, People's Republic of China
S Wang Lanzhou University, Lanzhou 730000, People's Republic of China
T Wang Fudan University, Shanghai 200433, People's Republic of China
T J Wang Nankai University, Tianjin 300071, People's Republic of China
W Wang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
W Wang University of South China, Hengyang 421001, People's Republic of China
W P Wang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X Wang Peking University, Beijing 100871, People's Republic of China
X F Wang Lanzhou University, Lanzhou 730000, People's Republic of China
X J Wang Liaoning Normal University, Dalian 116029, People's Republic of China
X L Wang Fudan University, Shanghai 200433, People's Republic of China
Y Wang Tsinghua University, Beijing 100084, People's Republic of China
Y D Wang North China Electric Power University, Beijing 102206, People's Republic of China
Y F Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y L Wang Henan Normal University, Xinxiang 453007, People's Republic of China
Y N Wang North China Electric Power University, Beijing 102206, People's Republic of China
Y Q Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Yaqian Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China Hebei University, Baoding 071002, People's Republic of China
Yi Wang Tsinghua University, Beijing 100084, People's Republic of China
Z Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Z L Wang University of South China, Hengyang 421001, People's Republic of China
Z Y Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Ziyi Wang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
D Wei University of Science and Technology Liaoning, Anshan 114051, People's Republic of China
D H Wei Guangxi Normal University, Guilin 541004, People's Republic of China
F Weidner University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
S P Wen Institute of High Energy Physics, Beijing 100049, People's Republic of China
C W Wenzel Bochum Ruhr-University, D-44780 Bochum, Germany
U Wiedner Bochum Ruhr-University, D-44780 Bochum, Germany
G Wilkinson University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
M Wolke Uppsala University, Box 516, SE-75120 Uppsala, Sweden
L Wollenberg Bochum Ruhr-University, D-44780 Bochum, Germany
C Wu Liaoning Normal University, Dalian 116029, People's Republic of China
J F Wu Institute of High Energy Physics, Beijing 100049, People's Republic of China China Center of Advanced Science and Technology, Beijing 100190, People's Republic of China
L H Wu Institute of High Energy Physics, Beijing 100049, People's Republic of China
L J Wu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Wu Fudan University, Shanghai 200433, People's Republic of China
X H Wu Jilin University, Changchun 130012, People's Republic of China
Y Wu University of Science and Technology of China, Hefei 230026, People's Republic of China
Y H Wu Soochow University, Suzhou 215006, People's Republic of China
Y J Wu Institute of Modern Physics, Lanzhou 730000, People's Republic of China
Z Wu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
L Xia State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X M Xian Liaoning Normal University, Dalian 116029, People's Republic of China
T Xiang Peking University, Beijing 100871, People's Republic of China
D Xiao Lanzhou University, Lanzhou 730000, People's Republic of China
G Y Xiao Nanjing University, Nanjing 210093, People's Republic of China
S Y Xiao Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y L Xiao Fudan University, Shanghai 200433, People's Republic of China
Z J Xiao Nanjing Normal University, Nanjing 210023, People's Republic of China
C Xie Nanjing University, Nanjing 210093, People's Republic of China
X H Xie Peking University, Beijing 100871, People's Republic of China
Y Xie Shandong University, Jinan 250100, People's Republic of China
Y G Xie Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y H Xie Central China Normal University, Wuhan 430079, People's Republic of China
Z P Xie State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
T Y Xing Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C F Xu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C J Xu Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
G F Xu Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Y Xu University of Jinan, Jinan 250022, People's Republic of China
Q J Xu Hangzhou Normal University, Hangzhou 310036, People's Republic of China
Q N Xu Inner Mongolia University, Hohhot 010021, People's Republic of China
W Xu Institute of High Energy Physics, Beijing 100049, People's Republic of China
W L Xu University of Jinan, Jinan 250022, People's Republic of China
X P Xu Soochow University, Suzhou 215006, People's Republic of China
Y C Xu Yantai University, Yantai 264005, People's Republic of China
Z P Xu Nanjing University, Nanjing 210093, People's Republic of China
Z S Xu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
F Yan Fudan University, Shanghai 200433, People's Republic of China
L Yan Fudan University, Shanghai 200433, People's Republic of China
W B Yan State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
W C Yan Zhengzhou University, Zhengzhou 450001, People's Republic of China
X Q Yan Institute of High Energy Physics, Beijing 100049, People's Republic of China
H J Yang Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
H L Yang Jilin University, Changchun 130012, People's Republic of China
H X Yang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Tao Yang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Yang Fudan University, Shanghai 200433, People's Republic of China
Y F Yang Nankai University, Tianjin 300071, People's Republic of China
Y X Yang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Yifan Yang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z W Yang Lanzhou University, Lanzhou 730000, People's Republic of China
Z P Yao Shandong University, Jinan 250100, People's Republic of China
M Ye Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
M H Ye China Center of Advanced Science and Technology, Beijing 100190, People's Republic of China
J H Yin Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z Y You Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
B X Yu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C X Yu Nankai University, Tianjin 300071, People's Republic of China
G Yu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J S Yu Hunan University, Changsha 410082, People's Republic of China
T Yu University of South China, Hengyang 421001, People's Republic of China
X D Yu Peking University, Beijing 100871, People's Republic of China
C Z Yuan Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L Yuan Beihang University, Beijing 100191, People's Republic of China
S C Yuan Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Yuan Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z Y Yuan Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
C X Yue Liaoning Normal University, Dalian 116029, People's Republic of China
A A Zafar University of the Punjab, Lahore-54590, Pakistan
F R Zeng Shandong University, Jinan 250100, People's Republic of China
S H Zeng University of South China, Hengyang 421001, People's Republic of China
X Zeng Fudan University, Shanghai 200433, People's Republic of China
Y Zeng Hunan University, Changsha 410082, People's Republic of China
Y J Zeng Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Y Zhai Jilin University, Changchun 130012, People's Republic of China
Y C Zhai Shandong University, Jinan 250100, People's Republic of China
Y H Zhan Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
A Q Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B L Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B X Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
D H Zhang Nankai University, Tianjin 300071, People's Republic of China
G Y Zhang Henan Normal University, Xinxiang 453007, People's Republic of China
H Zhang University of Science and Technology of China, Hefei 230026, People's Republic of China
H C Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H H Zhang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
H H Zhang Jilin University, Changchun 130012, People's Republic of China
H Q Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H Y Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
J Zhang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
J Zhang Zhengzhou University, Zhengzhou 450001, People's Republic of China
J J Zhang Shanxi Normal University, Linfen 041004, People's Republic of China
J L Zhang Henan University, Kaifeng 475004, People's Republic of China
J Q Zhang Nanjing Normal University, Nanjing 210023, People's Republic of China
J W Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J X Zhang Lanzhou University, Lanzhou 730000, People's Republic of China
J Y Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
J Z Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Jianyu Zhang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L M Zhang Tsinghua University, Beijing 100084, People's Republic of China
L Q Zhang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
Lei Zhang Nanjing University, Nanjing 210093, People's Republic of China
P Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Q Y Zhang Liaoning Normal University, Dalian 116029, People's Republic of China Zhengzhou University, Zhengzhou 450001, People's Republic of China
Shuihan Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Shulei Zhang Hunan University, Changsha 410082, People's Republic of China
X D Zhang North China Electric Power University, Beijing 102206, People's Republic of China
X M Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
X Y Zhang Shandong University, Jinan 250100, People's Republic of China
Y Zhang University of South China, Hengyang 421001, People's Republic of China
Y Zhang University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
Y T Zhang Zhengzhou University, Zhengzhou 450001, People's Republic of China
Y H Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Yan Zhang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Yao Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z D Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z H Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z L Zhang Jilin University, Changchun 130012, People's Republic of China
Z Y Zhang Nankai University, Tianjin 300071, People's Republic of China
Z Y Zhang Wuhan University, Wuhan 430072, People's Republic of China
G Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China
J Y Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J Z Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Lei Zhao State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Ling Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China
M G Zhao Nankai University, Tianjin 300071, People's Republic of China
R P Zhao University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S J Zhao Zhengzhou University, Zhengzhou 450001, People's Republic of China
Y B Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y X Zhao Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z G Zhao State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
A Zhemchugov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
B Zheng University of South China, Hengyang 421001, People's Republic of China
J P Zheng Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
W J Zheng Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y H Zheng University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B Zhong Nanjing Normal University, Nanjing 210023, People's Republic of China
X Zhong Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
H Zhou Shandong University, Jinan 250100, People's Republic of China
L P Zhou Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Zhou Wuhan University, Wuhan 430072, People's Republic of China
X K Zhou Central China Normal University, Wuhan 430079, People's Republic of China
X R Zhou State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X Y Zhou Liaoning Normal University, Dalian 116029, People's Republic of China
Y Z Zhou Fudan University, Shanghai 200433, People's Republic of China
J Zhu Nankai University, Tianjin 300071, People's Republic of China
K Zhu Institute of High Energy Physics, Beijing 100049, People's Republic of China
K J Zhu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L Zhu Jilin University, Changchun 130012, People's Republic of China
L X Zhu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S H Zhu University of Science and Technology Liaoning, Anshan 114051, People's Republic of China
S Q Zhu Nanjing University, Nanjing 210093, People's Republic of China
T J Zhu Fudan University, Shanghai 200433, People's Republic of China
W J Zhu Fudan University, Shanghai 200433, People's Republic of China
Y C Zhu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Z A Zhu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J H Zou Institute of High Energy Physics, Beijing 100049, People's Republic of China
J Zu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China

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

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

Isorhamnetin Alleviates Mitochondrial Injury in Severe Acute Pancreatitis via Modulation of KDM5B/HtrA2 Signaling Pathway

Severe acute pancreatitis (SAP), a widespread inflammatory condition impacting the abdomen with a high mortality rate, poses challenges due to its unclear pathogenesis and the absence of effective treatment options. Isorhamnetin (ISO), a naturally occurring flavonoid, demonstrates robust antioxidant and anti-inflammatory properties intricately linked to the modulation of mitochondrial function. However, the specific protective impact of ISO on SAP remains to be fully elucidated. In this study, we demonstrated that ISO treatment significantly alleviated pancreatic damage and reduced serum lipase and amylase levels in the mouse model of SAP induced by sodium taurocholate (STC) or L-arginine. Utilizing an in vitro SAP cell model, we found that ISO co-administration markedly prevented STC-induced pancreatic acinar cell necrosis, primarily by inhibiting mitochondrial ROS generation, preserving ATP production, maintaining mitochondrial membrane potential, and preventing the oxidative damage and release of mitochondrial DNA. Mechanistically, our investigation identified that high-temperature requirement A2 (HtrA2) may play a central regulatory role in mediating the protective effect of ISO on mitochondrial dysfunction in STC-injured acinar cells. Furthermore, through an integrated approach involving bioinformatics analysis, molecular docking analysis, and experimental validation, we uncovered that ISO may directly impede the histone demethylation activity of KDM5B, leading to the restoration of pancreatic HtrA2 expression and thereby preserving mitochondrial function in pancreatic acinar cells following STC treatment. In conclusion, this study not only sheds new light on the intricate molecular complexities associated with mitochondrial dysfunction during the progression of SAP but also underscores the promising value of ISO as a natural therapeutic option for SAP.

Design, synthesis and biological evaluation of matrine contains benzimidazole derivatives as dual TOPOI and PARP inhibitors for cancer therapy

TOPOI inhibitors have long been a focal point in the research and development of antitumor drugs. PARP-1 plays a crucial role in repairing DNA damage induced by TOPOI inhibitors. Thus, concurrent inhibition of TOPOI and PARP-1 has the potential to augment drug activity. Matrine, characterized by low toxicity and good water solubility, offers advantageous properties. In this investigation, a series of benzimidazole matrine derivatives were designed and synthesized using matrine as the lead compound with the aim of developing dual inhibitors targeting both TOPOI and PARP-1. Among these derivatives, Compound B6 exhibited potent inhibitory effects on PARP-1 and TOPOI, effectively suppressing cancer cell proliferation and migration. Mechanistic assessments revealed that B6 induced DNA damage in HGC-27 cells, leading to G0/G1 cell cycle arrest and significant apoptosis. Molecular docking experiments demonstrated that B6 can effectively enter the active pocket of target proteins, where it forms stable hydrogen bonds with amino acid residues. In vivo, experiments demonstrated that B6 exhibited antitumor activity comparable to that of the positive control drug. The tumor growth inhibition rates (TGIs) for irinotecan, B6 and matrine were 87.0%, 75.4% and 9.7%, respectively. Importantly, B6 demonstrated lower toxicity than the positive control drug. Our findings suggest that TOPOI and PARP-1 may represent potential targets for matrine and B6 emerges as a promising candidate for cancer therapy.

How the Water-Sediment Regulation Scheme in the Yellow River affected the estuary ecosystem in the last 10 years?

The Yellow River, renowned as the most sediment-laden river globally, grapples with sediment deposition issues compromising reservoir functionality and elevating downstream riverbeds, posing threats to human life and property safety. In response, the Water-Sediment Regulation Scheme (WSRS) has been innovatively implemented to address these challenges. While effectively mitigating sediment deposition, WSRS has concurrently disrupted the equilibrium of the estuarine ecosystem. This paper addresses the understudied but crucial topic of the interannual impact of WSRS on the estuarine ecosystem. Drawing upon physical, chemical, and biological data gathered through field surveys conducted before, during, and after WSRS from 2011 to 2022, the analysis delves into the interannual changes in the estuarine environment, fish eggs and larvae abundance, and species diversity under the influence of WSRS. The findings reveal an interannual decreasing trend in terrestrial material input due to WSRS, juxtaposed with an interannual increasing trend in fish eggs and larvae around the estuary, as well as the species diversity index. Notably, these trends became more pronounced post-2014. Compared to pre-2014, nutrient concentrations experienced a ~20 % decrease, chlorophyll-a concentration increased by 44 %, fish eggs proliferated approximately 1 time, and the species diversity index transitioned from a declining trend to an ascending trajectory. After 12 years of continuous WSRS implementation, the impact on the estuarine ecosystem has demonstrably diminished. This research aims to furnish reference experience and scientific basis for water and sediment regulation in major rivers around the world in terms of estuarine ecology.

[A multicenter prospective study on early identification of refractory Mycoplasma pneumoniae pneumonia in children]

Objective: To explore potential predictors of refractory Mycoplasma pneumoniae pneumonia (RMPP) in early stage. Methods: The prospective multicenter study was conducted in Zhejiang, China from May 1st, 2019 to January 31st, 2020. A total of 1 428 patients with fever >48 hours to <120 hours were studied. Their clinical data and oral pharyngeal swab samples were collected; Mycoplasma pneumoniae DNA in pharyngeal swab specimens was detected. Patients with positive Mycoplasma pneumoniae DNA results underwent a series of tests, including chest X-ray, complete blood count, C-reactive protein, lactate dehydrogenase (LDH), and procalcitonin. According to the occurrence of RMPP, the patients were divided into two groups, RMPP group and general Mycoplasma pneumoniae pneumonia (GMPP) group. Measurement data between the 2 groups were compared using Mann-Whitney U test. Logistic regression analyses were used to examine the associations between clinical data and RMPP. Receiver operating characteristic (ROC) curves were used to analyse the power of the markers for predicting RMPP. Results: A total of 1 428 patients finished the study, with 801 boys and 627 girls, aged 4.3 (2.7, 6.3) years. Mycoplasma pneumoniae DNA was positive in 534 cases (37.4%), of whom 446 cases (83.5%) were diagnosed with Mycoplasma pneumoniae pneumonia, including 251 boys and 195 girls, aged 5.2 (3.3, 6.9) years. Macrolides-resistant variation was positive in 410 cases (91.9%). Fifty-five cases were with RMPP, 391 cases with GMPP. The peak body temperature before the first visit and LDH levels in RMPP patients were higher than that in GMPP patients (39.6 (39.1, 40.0) vs. 39.2 (38.9, 39.7) ℃, 333 (279, 392) vs. 311 (259, 359) U/L, both P<0.05). Logistic regression showed the prediction probability π=exp (-29.7+0.667×Peak body temperature (℃)+0.004×LDH (U/L))/(1+exp (-29.7+0.667×Peak body temperature (℃)+0.004 × LDH (U/L))), the cut-off value to predict RMPP was 0.12, with a consensus of probability forecast of 0.89, sensitivity of 0.89, and specificity of 0.67; and the area under ROC curve was 0.682 (95%CI 0.593-0.771, P<0.01). Conclusion: In MPP patients with fever over 48 to <120 hours, a prediction probability π of RMPP can be calculated based on the peak body temperature and LDH level before the first visit, which can facilitate early identification of RMPP.

Transcriptomic Profiling of Two Rice Thermo-Sensitive Genic Male Sterile Lines with Contrasting Seed Storability after Artificial Accelerated Aging Treatment

Seed storability has a significant impact on seed vitality and is a crucial genetic factor in maintaining seed value during storage. In this study, RNA sequencing was used to analyze the seed transcriptomes of two rice thermo-sensitive genic male sterile (TGMS) lines, S1146S (storage-tolerant) and SD26S (storage-susceptible), with 0 and 7 days of artificial accelerated aging treatment. In total, 2658 and 1523 differentially expressed genes (DEGs) were identified in S1146S and SD26S, respectively. Among these DEGs, 729 (G1) exhibited similar regulation patterns in both lines, while 1924 DEGs (G2) were specific to S1146S, 789 DEGs (G3) were specific to SD26S, and 5 DEGs (G4) were specific to contrary differential expression levels. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that "translation", "ribosome", "oxidative phosphorylation", "ATP-dependent activity", "intracellular protein transport", and "regulation of DNA-templated transcription" were significantly enriched during seed aging. Several genes, like Os01g0971400, Os01g0937200, Os03g0276500, Os05g0328632, and Os07g0214300, associated with seed storability were identified in G4. Core genes Os03g0100100 (OsPMEI12), Os03g0320900 (V2), Os02g0494000, Os02g0152800, and Os03g0710500 (OsBiP2) were identified in protein-protein interaction (PPI) networks. Seed vitality genes, MKKK62 (Os01g0699600), OsFbx352 (Os10g0127900), FSE6 (Os05g0540000), and RAmy3E (Os08g0473600), related to seed storability were identified. Overall, these results provide novel perspectives for studying the molecular response and related genes of different-storability rice TGMS lines under artificial aging conditions. They also provide new ideas for studying the storability of hybrid rice.

[Clinical characteristics of hospitalized children with respiratory syncytial virus infection and risk prediction of severe illness during the post-COVID-19 era in Kunming]

Objective: To compare the epidemiological and clinical characteristics of hospitalized children with respiratory syncytial virus (RSV) infection in Kunming among the pre-and post-COVID-19 era, and to establish a prediction model for severe RSV infection in children during the post-COVID-19 period. Methods: This was a retrospective study. Clinical and laboratory data were collected from 959 children hospitalized with RSV infection in the Department of Pulmonary and Critical Care Medicine at Kunming Children's Hospital during January to December 2019 and January to December 2023. Patients admitted in 2019 were defined as the pre-COVID-19 group, while those admitted in 2023 were classified as the post-COVID-19 group. Epidemiological and clinical characteristics were compared between the two groups. Subsequently, comparison of the clinical severity among the two groups was performed based on propensity score matching (PSM). Furthermore, the subjects in the post-COVID-19 group were divided into severe and non-severe groups based on clinical severity. Chi-square test and Mann-Whitney U test were used for pairwise comparison between groups, and multivariate Logistic regression was applied for the identification of independent risk factors and construction of the prediction model. The receiver operating characteristic (ROC) curve and calibration curve were employed to evaluate the predictive performance of this model. Results: Among the 959 children hospitalized with RSV infection, there were 555 males and 404 females, with an onset age of 15.4 (7.3, 28.5) months. Of which, there were 331 cases in the pre-COVID-19 group and 628 cases in the post-COVID-19 group. The peak period of RSV hospitalization in the post-COVID-19 group were from May to October 2023, and the monthly number of inpatients for each of these months were as follows: 72 cases (11.5%), 98 cases (15.6%), 128 cases (20.4%), 101 cases (16.1%), 65 cases (10.4%), and 61 cases (9.7%), respectively. After PSM for general data, 267 cases were matched in each group. The proportion of wheezing in the post-COVID-19 group was lower than that in the pre-COVID-19 group (109 cases (40.8%) vs. 161 cases (60.3%), χ2=20.26, P<0.001), while the incidences of fever, tachypnea, seizures, severe case, neutrophil-to-lymphocyte ratio (NLR), C-reactive protein and interleukin-6 levels were all higher than those in the pre-COVID-19 group (146 cases (54.7%) vs. 119 cases (44.6%), 117 cases (43.8%) vs. 89 cases (33.3%), 37 cases (13.9%) vs. 14 cases (5.2%), 69 cases (25.8%) vs. 45 cases (16.9%), 3.6 (1.9, 6.4) vs. 2.3 (1.8, 4.6), 9.9 (7.1, 15.2) vs. 7.8 (4.5, 13.9) mg/L, 20.5 (15.7, 30.4) vs. 17.2 (11.0, 26.9) ng/L, χ2=5.46, 6.36, 11.47, 6.42, Z=4.13, 3.06, 2.96, all P<0.05). There were 252 cases and 107 cases with co-infection in the post-and pre-COVID-19 groups, respectively. The proportion of triple and quadruple infection in the post-COVID-19 group was higher than that in the pre-COVID-19 group (59 cases (23.4%) vs. 13 cases (12.1%), 30 cases (11.9%) vs. 5 cases (4.7%), χ2=5.94, 4.46, both P<0.05). Among the 252 cases with co-infection in post-COVID-19 group, the most prevalent pathogens involving in co-infections, in order, were Mycoplasma pneumoniae 56 cases (22.2%), Influenza A virus 53 cases (21.0%), Rhinovirus 48 cases (19.0%), Parainfluenza virus 35 cases (13.9%), and Adenovirus 28 cases (11.1%).The result of multivariate Logistic regression showed that age (OR=0.70, 95%CI 0.62-0.78, P<0.001), underlying diseases (OR=10.03, 95%CI 4.10-24.55, P<0.001), premature birth (OR=6.78, 95%CI 3.53-13.04, P<0.001), NLR (OR=1.85, 95%CI 1.09-3.15, P=0.023), and co-infection (OR=1.28, 95%CI 1.18-1.38, P<0.001) were independently associated with the development of severe RSV infection in the post-COVID-19 group. The ROC curve of the prediction model integrating the above five factors indicated an area under the curve of 0.85 (95%CI 0.80-0.89, P<0.001), with an optimal cutoff of 0.21, a sensitivity of 0.83 and a specificity of 0.80. The calibration curve showed that the predicted probability in this model did not differ significantly from the actual probability (P=0.319). Conclusions: In the post-COVID-19 era in Kunming, the peak in pediatric hospitalizations for RSV infection was from May to October, with declined incidence of wheezing and increased incidence of fever, tachypnea, seizures, severe cases, and rates of triple and quadruple co-infections. Age, underlying diseases, premature birth, NLR, and co-infection were identified as independent risk factors for severe RSV infection in the post-COVID-19 period. In this study, a risk prediction model for severe pediatric RSV infection was established, which had a good predictive performance.

A Large-Scale Three-Dimensional Apparatus to Study Failure Mechanisms of Rockfalls in Underground Engineering Contexts

Rockfalls are an important factor affecting underground engineering safety. However, there has been limited progress in understanding and predicting these disasters in the past few years. Therefore, a large-scale three-dimensional experimental simulation apparatus to study failure mechanisms of rockfalls occurring during underground engineering was developed. This apparatus, measuring 4 m × 4 m × 3.3 m in size, can achieve vertical and horizontal symmetric loading. It not only simulates the structure and stress environment of a rock mass but also simulates the stepwise excavation processes involved in underground engineering. A complete simulation experiment of rockfalls in an underground engineering context was performed using this apparatus. Dynamic evolution characteristics of block displacement, temperature, natural vibration frequency, and acoustic emissions occurring during rockfalls were studied during the simulation. These data indicate there are several indicators that could be used to predict rockfalls in underground engineering contexts, leading to better prevention and control.

Identification roles of NFE2L3 in digestive system cancers

BACKGROUND

Morbidity and mortality rates of Digestive System Cancers (DSC) continue to pose human lives and health. Nuclear factor erythroid 2-like protein 3 (NFE2L3) is aberrantly expressed in DSC. This study aimed to explore the clinical value and underlying mechanisms of NFE2L3 as a novel biomarker in DSC.

METHODS

We utilized data from databases and clinical gastric cancer specimens to validate the aberrant expression level of NFE2L3 and further assessed the clinical value of NFE2L3. To investigate the potential molecular mechanism of NFE2L3, we analyzed the correlation of NFE2L3 with immune molecular mechanisms, constructed PPI network, performed GO analysis and KEGG analysis, and finally explored the biological function of NFE2L3 in gastric cancer cells.

RESULTS

NFE2L3 expression is up-regulated in DSC and has both prognostic and diagnostic value. NFE2L3 correlates with various immune mechanisms, PPI network suggests proteins interacting with NFE2L3, GSEA analysis suggests potential molecular mechanisms for NFE2L3 to play a role in cancer promotion, and in vitro cellular experiments also confirmed the effect of NFE2L3 on the biological function of gastric cancer cells.

CONCLUSION

Our study confirms the aberrant expression and molecular mechanisms of NFE2L3 in DSC, indicating that NFE2L3 could serve as a novel biomarker for diagnosis and prognosis of DSC.

Broad-spectrum nano-bactericide utilizing antimicrobial peptides and bimetallic Cu-Ag nanoparticles anchored onto multiwalled carbon nanotubes for sustained protection against persistent bacterial pathogens in crops

Worldwide crop yields are threatened by persistent pathogenic bacteria that cause significant damage and jeopardize global food security. Chemical pesticides have shown limited effectiveness in protecting crops from severe yield loss. To address this obstacle, there is a growing need to develop environmentally friendly bactericides with broad-spectrum and sustained protection against persistent crop pathogens. Here, we present a method for preparing a nanocomposite that combines antimicrobial peptides (AMPs) and bimetallic Cu-Ag nanoparticles anchored onto multiwalled carbon nanotubes (MWCNTs). The nanocomposite exhibited dual antibacterial activity by disrupting bacterial cell membranes and splicing nucleic acids. By functionalizing MWCNTs with small AMPs (sAMPs), we achieved enhanced stability and penetration of the nanocomposite, and improved loading capacity of the Cu-Ag nanoparticles. The synthesized MWCNTs&CuNCs@AgNPs@P nanocomposites demonstrated broad-spectrum lethality against both Gram-positive and Gram-negative bacterial pathogens. Glasshouse pot trials confirmed the efficacy of the nanocomposites in protecting rice crops against bacterial leaf blight and tomato crops against bacterial wilt. These findings highlight the excellent antibacterial properties of the MWCNTs&CuNCs@AgNPs@P nanocomposite and its potential to replace chemical pesticides, offering significant advantages for agricultural applications.

Deep learning using contrast-enhanced ultrasound images to predict the nuclear grade of clear cell renal cell carcinoma

PURPOSE

To assess the effectiveness of a deep learning model using contrastenhanced ultrasound (CEUS) images in distinguishing between low-grade (grade I and II) and high-grade (grade III and IV) clear cell renal cell carcinoma (ccRCC).

METHODS

A retrospective study was conducted using CEUS images of 177 Fuhrmangraded ccRCCs (93 low-grade and 84 high-grade) from May 2017 to December 2020. A total of 6412 CEUS images were captured from the videos and normalized for subsequent analysis. A deep learning model using the RepVGG architecture was proposed to differentiate between low-grade and high-grade ccRCC. The model's performance was evaluated based on sensitivity, specificity, positive predictive value, negative predictive value and area under the receiver operating characteristic curve (AUC). Class activation mapping (CAM) was used to visualize the specific areas that contribute to the model's predictions.

RESULTS

For discriminating high-grade ccRCC from low-grade, the deep learning model achieved a sensitivity of 74.8%, specificity of 79.1%, accuracy of 77.0%, and an AUC of 0.852 in the test set.

CONCLUSION

The deep learning model based on CEUS images can accurately differentiate between low-grade and high-grade ccRCC in a non-invasive manner.

A green aqueous binder to enhance the electrochemical performance of Li-rich disordered rock salt cathode material

Li-rich disordered rock-salt oxides (DRX) are considered an attractive cathode material in the future battery field due to their excellent energy density and specific capacity. Nevertheless, anionic redox provides high capacity while causing O2 over-oxidation to O2, resulting in voltage hysteresis and capacity decay. Herein, the crystal structure of Li1.3Mn0.4Ti0.3O1.7F0.3 (LMTOF) cathode is stabilized by using sodium carboxymethylcellulose (CMC) binders replacing traditional polyvinylidene difluoride (PVDF) binders. The electrochemical impedance spectroscopy (EIS) and galvanostatic intermittent titration technique (GITT) reveal that the CMC-based LMTOF electrode has higher electronic conductivity and lithium-ion diffusion kinetics. Moreover, CMC has been demonstrated to improve the O2- reversibility, reduce the amounts of byproducts from electrolyte decomposition and suppress transition metal dissolution by Na+/Li+ exchange reaction. Furthermore, the CMC-based LMTOF electrode also exhibits less volume change upon lithiation/delithiation processes compared to the PVDF-based electrode, resulting in enhanced structural stability during cycling. Benefiting from these features, the CMC binders can effectively improve the cycling life and rate performance of the LMTOF cathode, and the CMC-based LMTOF electrode shows good capacity retention of 94.5 % after 30 cycles at 20 mA/g and 66.7 % after 100 cycles at 200 mA/g. This finding indicates that CMC as a binder can efficiently stabilize the structure and improve the electrochemical performance of Li-rich disordered rock-salt oxides cathode, making it possible for practical Li-ion battery applications.

The Chinese Clinical Sleep Database: An Innovative Database System Includes Large-Scale Clinical Data of Chinese Population

Purpose

In this study, we established the Chinese Clinical Sleep Database (CCSD), aiming to provide a safe, scalable, and user-friendly database that includes high-quality clinical data from Chinese population to facilitate sleep research.

Material and Methods

We collect individual's demographic data, scales, anthropometric measurements, clinical diagnosis, and polysomnography (PSG) recordings from the routine medical process of sleep medicine centers using standardized procedures. The distributed cluster storage technology are utilized to store these data. The structured data are stored in a high-performance MySQL database, while the unstructured data are stored in an object storage service. And we have developed an online data platform to share and manage our data.

Results

The data collection has been conducted in three hospitals. In the preliminary stage of data collection (from October 18, 2022 to September 4, 2023), our database included a total of 1183 patients. Among them, 56.8% were male and their ages ranged from 3 to 88 years. These patients were diagnosed with various types of sleep disorders.

Conclusion

Since the CCSD's inception, it has demonstrated good stability, security, and scalability. As an public database, the CCSD also exhibits user-friendliness. The CCSD contains comprehensive clinical data, which can contribute to the advancement of the diagnosis and treatment strategies for sleep disorders, ultimately promoting sleep health.

Development of a High-throughput Sequencing Platform for Detection of Viral Encephalitis Pathogens Based on Amplicon Sequencing

Objective

Viral encephalitis is an infectious disease severely affecting human health. It is caused by a wide variety of viral pathogens, including herpes viruses, flaviviruses, enteroviruses, and other viruses. The laboratory diagnosis of viral encephalitis is a worldwide challenge. Recently, high-throughput sequencing technology has provided new tools for diagnosing central nervous system infections. Thus, In this study, we established a multipathogen detection platform for viral encephalitis based on amplicon sequencing.

Methods

We designed nine pairs of specific polymerase chain reaction (PCR) primers for the 12 viruses by reviewing the relevant literature. The detection ability of the primers was verified by software simulation and the detection of known positive samples. Amplicon sequencing was used to validate the samples, and consistency was compared with Sanger sequencing.

Results

The results showed that the target sequences of various pathogens were obtained at a coverage depth level greater than 20×, and the sequence lengths were consistent with the sizes of the predicted amplicons. The sequences were verified using the National Center for Biotechnology Information BLAST, and all results were consistent with the results of Sanger sequencing.

Conclusion

Amplicon-based high-throughput sequencing technology is feasible as a supplementary method for the pathogenic detection of viral encephalitis. It is also a useful tool for the high-volume screening of clinical samples.

Application of enhanced recovery after surgery during the perioperative period in children with Meckel's diverticulum-a single-center prospective clinical trial

Background

Enhanced recovery after surgery (ERAS) has been widely used in adult surgery. However, few studies have reported the efficacy of ERAS in paediatric patients with Meckel's diverticulum (MD), the aim of the study was to prospectively evaluate the safety and efficacy of ERAS in treating MD.

Methods

A prospective randomised controlled study of children with MD admitted to our hospital from Jan 1, 2021 to Dec 31, 2023 were conducted, we developed and implemented an ERAS program for this patients. All cases were strictly selected according to the inclusion and exclusion criteria. Among these patients, they were randomly assigned to the ERAS group or the traditional (TRAD) group with random number table row randomization. The main observational indicators were operation time, intraoperative hemorrhage, FLACC pain scale results on 2 h, 6 h, 12 h, 24 h after surgery, length of postoperative stay (LOPS), time to first defecation, time to first eating after surgery, time to discontinuation of intravenous infusion, total treatment cost, incidence of postoperative complications, 30-day readmission rate and parental satisfaction rate.

Results

A total of 50 patients underwent Meckel's diverticulectomy during this period, 7 patients were excluded, 23 patients were assigned to the ERAS group and 20 patients were assigned to the TRAD group. There were no significant differences in demographic data and operation time, intraoperative hemorrhage. The FLACC pain scale results on 2 h, 6 h, 12 h, 24 h after surgery were significantly lower in the ERAS group. The LOPS was 6.17 ± 0.89 days in the ERAS group and 8.30 ± 1.26 days in the TRAD group, resulting in a significantly shorter LOPS in ERAS group. ERAS could also reduce the first postoperative defecation time, the time to first eating after surgery and the time to discontinuation of intravenous infusion. The treatment cost was decreased in the ERAS group. The rate of complications and 30-day readmission were not significantly different between the two groups.

Conclusions

In this single-center study, the ERAS protocol for patients with MD requiring surgery was safe and effective.

Rapid and Precise Differentiation and Authentication of Agricultural Products via Deep Learning-Assisted Multiplex SERS Fingerprinting

Accurate and rapid differentiation and authentication of agricultural products based on their origin and quality are crucial to ensuring food safety and quality control. However, similar chemical compositions and complex matrices often hinder precise identification, particularly for adulterated samples. Herein, we propose a novel method combining multiplex surface-enhanced Raman scattering (SERS) fingerprinting with a one-dimensional convolutional neural network (1D-CNN), which enables the effective differentiation of the category, origin, and grade of agricultural products. This strategy leverages three different SERS-active nanoparticles as multiplex sensors, each tailored to selectively amplify the signals of preferentially adsorbed chemicals within the sample. By strategically combining SERS spectra from different NPs, a 'SERS super-fingerprint' is constructed, offering a more comprehensive representation of the characteristic information on agricultural products. Subsequently, utilizing a custom-designed 1D-CNN model for feature extraction from the 'super-fingerprint' significantly enhances the predictive accuracy for agricultural products. This strategy successfully identified various agricultural products and simulated adulterated samples with exceptional accuracy, reaching 97.7% and 94.8%, respectively. Notably, the entire identification process, encompassing sample preparation, SERS measurement, and deep learning analysis, takes only 35 min. This development of deep learning-assisted multiplex SERS fingerprinting establishes a rapid and reliable method for the identification and authentication of agricultural products.

Cross-sectional study of pharmacovigilance knowledge, attitudes, and practices based on structural equation modeling and network analysis: a case study of healthcare personnel and the public in Yunnan Province

Background

This study focuses on understanding pharmacovigilance knowledge, attitudes, and practices (KAP) in Yunnan Province, employing Structural Equation Modeling (SEM) and network analysis. It aims to evaluate the interplay of these factors among healthcare personnel and the public, assessing the impact of demographic characteristics to inform policy and educational initiatives.

Methods

A cross-sectional survey was conducted in Yunnan, targeting healthcare personnel and the public. Data collection was through questionnaires, with subsequent analysis involving correlation matrices, network visualization, and SEM. The data analysis utilized SPSS 27.0, AMOS 26.0, and Gephi software for network analysis.

Results

This study evaluated pharmacovigilance KAP among 209 public participants and 823 healthcare personnel, uncovering significant differences. Public respondents scored averages of 4.62 ± 2.70 in knowledge, 31.99 ± 4.72 in attitudes, and 12.07 ± 4.96 in practices, while healthcare personnel scored 4.38 ± 3.06, 27.95 ± 3.34, and 7.75 ± 2.77, respectively. Statistically significant correlations across KAP elements were observed in both groups, highlighting the interconnectedness of these factors. Demographic influences were more pronounced among healthcare personnel, emphasizing the role of professional background in pharmacovigilance competency. Network analysis identified knowledge as a key influencer within the pharmacovigilance KAP network, suggesting targeted education as a vital strategy for enhancing pharmacovigilance engagement.

Conclusion

The research reveals a less-than-ideal state of pharmacovigilance KAP among both healthcare personnel and the public in Yunnan, with significant differences between the two groups. SEM and network analysis confirmed a strong positive link among KAP components, moderated by demographics like age, occupation, and education level. These insights emphasize the need to enhance pharmacovigilance education and awareness, thereby promoting safer drug use.

Citric acid-induced photochemical behavior of Cr(III)-substituted ferrihydrite: Fe and Cr release, mineral transformation and reactive oxygen species generation

Cr(III)-substituted ferrihydrite (Fh-Cr(III)) is widespread in the surrounding environment of mining areas. Fh-Cr(III) is unstable and susceptible to the influence of environmental factors, such as dissolved organic matter (DOM) and light, so Cr species embedded in mineral crystal layers are likely to have more profound negative effects on the environment with the photochemical behaviors of minerals. However, the photochemical behaviors of Fh-Cr(III) in the presence of DOM remains poorly understood. For this reason, citric acid (CA) was chosen as the representative DOM to study its combined effects with visible light irradiation on Fh-Cr(III) dissolution and phase transition. The results showed that CA hindered the agglomeration of Fh-Cr(III) particles, thereby slowing the phase transition of Fh-Cr(III). However, CA exacerbated the release of Fe and Cr by maintaining Fh-Cr(III) under unstable crystal structure. Moreover, due to the occurrence of ligand-metal charge transfer (LMCT) in (CA)n-Fh-Cr(III) formed on the Fh-Cr(III) surface, the synergistic effect between CA and light irradiation greatly promoted the dissolution of Fh-Cr(III). In the mixed system of Fh-Cr(III) (3 g/L) and CA (5 mM) at initial pH 3.0, the maximum concentrations of TFe and TCr were 18.17 and 5.68 mg/L after 6 h of light reaction, which were 1.82 and 3.62 times of those in the corresponding system in the darkness. Meanwhile, the Fe(III)/Fe(II) cycling in solution and solid surfaces was affected by various reactive oxygen species (ROS) generated from the LMCT process, in which the photoproduced Fe(II) further accelerated Cr(III) dissolution under acidic solution. The fast release of Cr(III) may pose greater danger to the environment as the more toxic Cr(VI) can be easily formed through the oxidation of dissolved Cr(III). This work offers fresh insights into the migration and transformation of Cr elements in the natural environment.

Mucosa color and size may indicate malignant transformation of chicken skin mucosa-positive colorectal neoplastic polyps

BACKGROUND

Lipid metabolism reprogramming is suspected to exist in pre-cancerous lesions, including colorectal adenoma. Screening colonoscopy frequently reveals chicken skin mucosa (CSM; white or yellow-white speckled mucosa) surrounding colorectal polyps, caused by macrophages engulfing and accumulating the lipids decomposed by colon cells or adjacent tumors. CSM-positive colorectal polyps are associated with various diseases; however, their prognosis varies greatly. Cold snare polypectomy is commonly used to resect lesions up to 10 to 15 mm in diameter without signs of submucosal invasion but is controversial for CSM-positive colorectal polyps. Improved imaging is required to diagnose and treat CSM-positive colorectal polyps.

AIM

To highlight the clinical significance of CSM surrounding colorectal polyps and clarify the associated treatment for endoscopists.

METHODS

This retrospective cohort study included 177 patients with CSM-positive colorectal polyps diagnosed using endoscopy. All patient-related information was extracted from the Goldisc soft-clinic DICOM system or electronic medical record system. Based on the pathological results, patients were classified as non-neoplastic polyps (five juvenile polyps), neoplastic polyps, non-invasive high-grade neoplasia (NHGN), or submucosal invasive carcinoma (SM stage cancer). We analyzed and compared the clinical features, suspected risk factors for malignant transformation of neoplastic polyps, and early infiltration of submucosal carcinoma.

RESULTS

The diameters of NHGN and SM polyps were much smaller than those of neoplastic polyps. Most NHGN polyps had a deeper red mucosal color. On logistic regression analyses, diameter and deeper red mucosal color were independent risk factors for malignant transformation of neoplastic polyps. Type 1 CSM was more common in high-grade intraepithelial neoplasia and SM; type 2 CSM was more common in neoplastic polyps. Logistic regression analyses revealed no significant differences in the malignant transformation of neoplastic polyps or early submucosal invasion of CSM-positive colorectal cancer. Changes in the CSM mucosa surrounding neoplastic polyps and submucosal invasion of colorectal cancer disappeared within 12 months. No tumor recurrence was found during either partial or complete endoscopic resection of the CSM.

CONCLUSION

CSM-positive colorectal polyps > 1 cm in diameter or with deeper red mucosa may be related to NHGN. Resection of CSM surrounding colorectal adenomas did not affect tumor recurrence.

Association between Biomarkers of Phthalate Exposure and Serum Folate Concentrations in Children: A Population-Based Cross-Sectional Study of the NHANES from 2011 to 2016

BACKGROUND

Although adverse health effects of phthalates have been reported, very few studies have assessed the associations between biomarkers of phthalate exposure and serum folate concentrations in children.

OBJECTIVES

We aimed to examine the association between urinary phthalate metabolites, as biomarkers of exposure to phthalates, and total serum folate concentrations in children using national data from the United States.

METHODS

We conducted cross-sectional analyses of 2100 individuals aged 6-18 y enrolled in the National Health and Nutrition Examination Survey, 2011-2016. Multivariable linear regression was applied to examine the relationship between natural logarithm (ln)-transformed urinary phthalate metabolites and serum folate concentrations. The quantile-based g-computation was used to assess the association of urinary phthalate metabolite mixture with serum folate levels. Subgroup analyses were conducted by sex, age, and race/ethnicity, and the interactions were assessed by adding interaction terms of these stratifying variables and phthalates and modeling through the Wald test.

RESULTS

In multiple linear regression models, for participants in the highest tertile of MEHHP, MEOHP, DEHP, MCPP, and MCOP, total serum folate concentrations were 1.566 [β: -1.566; 95% confidence interval: -2.935, -0.196], 1.423 (-1.423; -2.689, -0.157), 1.309 (-1.309; -2.573, -0.044), 1.530 (-1.530; -2.918, -0.142), and 1.381 (-1.381; -2.641, -0.122) ng/mL lower than those in the lowest tertile. The inverse associations were consistent in different subgroups by sex, age, and race/ethnicity (P for interaction ≥0.083 for all). In addition, the phthalate mixture showed a strong inverse correlation with serum folate; a quartile increase in the phthalate mixture on the ln scale was associated with 0.888 (-0.888; -1.677, -0.099) ng/mL decrease in the serum folate.

CONCLUSIONS

Higher concentrations of urinary phthalate metabolites were associated with lower serum folate concentrations in children. Although our findings should be validated through additional population and mechanistic studies, they support a potential adverse effect of phthalate exposure on folate metabolism in children.