Manganese induces oxidative damage in the hippocampus by regulating the expression of oxidative stress-related genes via modulation of H3K18 acetylation

Prolonged exposure to manganese (Mn) contributes to hippocampal Mn accumulation, which leads to neurodegenerative diseases called manganese poisoning. However, the underlying molecular mechanisms remain unclear and there are no ideal biomarkers. Oxidative stress is the essential mechanisms of Mn-related neurotoxicity. Furthermore, histone acetylation has been identified as being engaged in the onset and development of neurodegenerative diseases. Therefore, the work aims to understand the molecular mechanisms of oxidative damage in the hippocampus due to Mn exposure from the aspect of histone acetylation modification and to assess whether H3K18 acetylation (H3K18ac) modification level in peripheral blood reflect Mn-induced oxidative damage in the hippocampus. Here, we randomly divided 60 male rats into four groups and injected them intraperitoneally with sterile pure water and MnCl2 ⋅4H2 O (5, 10, and 15 mg/kg) for 16 weeks, 5 days a week, once a day. The data confirmed that Mn exposure down-regulated superoxide dismutase activity and glutathione level as well as up-regulated malondialdehyde level in the hippocampus and plasma, and that there was a positive correlation between these indicators in the hippocampus and plasma. Besides, we noted that Mn treatment upregulated H3K18ac modification levels in the hippocampus and peripheral blood and that H3K18ac modification levels correlated with oxidative stress. Further studies demonstrated that Mn treatment decreased the amounts of H3K18ac enrichment in the manganese superoxide dismutase (SOD2) and glutathione transferase omega 1 (GSTO1) gene promoter regions, contributing to oxidative damage in the hippocampus. In short, our results demonstrate that Mn induces oxidative damage in the hippocampus by inhibiting the expression of SOD2 and GSTO1 genes via modulation of H3K18ac. In assessing Mn-induced hippocampal neurotoxicity, oxidative damage in plasma may reflect hippocampal oxidative damage in Mn-exposed groups.

Evaluation of brain iron deposition in different cerebral arteries of acute ischaemic stroke patients using quantitative susceptibility mapping

AIM

To investigate differences in iron deposition between infarct and normal cerebral arterial regions in acute ischaemic stroke (AIS) patients using quantitative susceptibility mapping (QSM).

MATERIALS AND METHODS

Forty healthy controls and 40 AIS patients were recruited, and their QSM images were obtained. There were seven regions of interest (ROIs) in AIS patients, including the infarct regions of responsible arteries (R1), the non-infarct regions of responsible arteries (R2), the contralateral symmetrical sites of lesions (R3), and the non-responsible cerebral arterial regions (R4, R5, R6, R7). For the healthy controls, the cerebral arterial regions corresponding to the AIS patient group were selected as ROIs. The differences in corresponding ROI susceptibilities between AIS patients and healthy controls and the differences in susceptibilities between infarcted and non-infarct regions in AIS patients were compared.

RESULTS

The susceptibilities of infarct regions in AIS patients were significantly higher than those in healthy controls (p<0.0001). There was no significant difference in non-infarct regions between the two groups (p>0.05). The susceptibility of the infarct regions in AIS patients was significantly higher than those of the non-infarct region of responsible artery and non-responsible cerebral arterial regions (p<0.01).

CONCLUSIONS

Abnormal iron deposition detected by QSM in the infarct regions of AIS patients may not affect iron levels in the non-infarct regions of responsible arteries and normal cerebral arteries, which may open the door for potential new diagnostic and treatment strategies.

OTU deubiquitinase 7B facilitates the hyperthermia-induced inhibition of lung cancer progression through enhancing Smac-mediated mitochondrial dysfunction

Hyperthermia, as an adjuvant therapy, has shown promising anti-tumor effects. Ovarian tumor domain-containing 7B (OTUD7B) is a deubiquitinating enzyme that is frequently found in a variety of cancers. The aim of this study is to investigate the role of OTUD7B in lung cancer hyperthermia and the underlying mechanism. A549 and CALU-3 cells were respectively exposed to 42 or 44°C for the indicated times (0, 1, 3, or 6 h) followed by incubation at 37°C for 24 h. We found a temperature- and time-dependent decrease in cell viability and an increase in apoptosis levels. Compared with 0 h, heat treatment for 3 h inhibited the proliferation and invasion of A549 cells, reduced the expression levels of mitochondrial membrane potential, IAP family members (cIAP-1 and XIAP) proteins and ubiquitination of Smac, and increased Smac protein expression. Treatment with 10 μM Smac mimic BV6 further enhanced the anti-tumor effect of hyperthermia. Next, co-IP validation showed that OTUD7B interacted with Smac and stabilized Smac through deubiquitination. OTUD7B overexpression induced damage in A549 and CALU-3 cells, while silencing OTUD7B caused opposite effects. Overexpressing OTUD7B enhanced the anti-cancer effect of hyperthermia, while si-OTUD7B reversed the anti-cancer effect of hyperthermia, which was verified in the xenograft tumor model in nude mice. Taken together, OTUD7B may serve as a potential anticancer factor with potential clinical efficacy in the thermotherapeutic treatment of lung cancer.

Analysis of China's PM2.5 and ozone coordinated control strategy based on the observation data from 2015 to 2020

The coordinated control of PM2.5 and ozone has become the strategic goal of national air pollution control. Considering the gradual decline in PM2.5 concentration and the aggravation of ozone pollution, a better understanding of the coordinated control of PM2.5 and ozone is urgently needed. Here, we collected and sorted air pollutant data for 337 cities from 2015 to 2020 to explore the characteristics of PM2.5 and ozone pollution based on China's five major air pollution regions. The results show that it is necessary to continue to strengthen the emission reduction in PM2.5 and ozone precursors, and control NOx and VOCs while promoting a dramatic emission reduction in PM2.5. The primary method of curbing ozone pollution is to strengthen the emission control of VOCs, with a long-term strategy of achieving substantial emission reductions in NOx, because VOCs and NOx are also precursors to PM2.5; hence, their reductions also contribute to the reduction in PM2.5. Therefore, the implementation of a multipollutant emission reduction control strategy aimed at the prevention and control of PM2.5 and ozone pollution is the only means to realize the coordinated control of PM2.5 and ozone.

Sarcoma Cells Secrete Hypoxia-Modified Collagen VI to Weaken the Lung Endothelial Barrier and Promote Metastasis

Intratumoral hypoxia correlates with metastasis and poor survival in patients with sarcoma. Using an impedance sensing assay and a zebrafish intravital microinjection model, we demonstrated here that the hypoxia-inducible collagen-modifying enzyme lysyl hydroxylase PLOD2 and its substrate collagen type VI (COLVI) weaken the lung endothelial barrier and promote transendothelial migration. Mechanistically, hypoxia-induced PLOD2 in sarcoma cells modified COLVI, which was then secreted into the vasculature. Upon reaching the apical surface of lung endothelial cells, modified COLVI from tumor cells activated integrin β1 (ITGβ1). Furthermore, activated ITGβ1 colocalized with Kindlin2, initiating their interaction with F-actin and prompting its polymerization. Polymerized F-actin disrupted endothelial adherens junctions and induced barrier dysfunction. Consistently, modified and secreted COLVI was required for the late stages of lung metastasis in vivo. Analysis of patient gene expression and survival data from The Cancer Genome Atlas (TCGA) revealed an association between the expression of both PLOD2 and COLVI and patient survival. Furthermore, high levels of COLVI were detected in surgically resected sarcoma metastases from patient lungs and in the blood of tumor-bearing mice. Together, these data identify a mechanism of sarcoma lung metastasis, revealing opportunities for therapeutic intervention.

SIGNIFICANCE

Collagen type VI modified by hypoxia-induced PLOD2 is secreted by sarcoma cells and binds to integrin β1 on endothelial cells to induce barrier dysfunction, which promotes sarcoma vascular dissemination and metastasis.

Rational construction of visible-light-driven perylene diimides/Fe2O3@C derived from MIL-88A (Fe) heterojunction with S-scheme electron transfer pathway to activate peroxymonosulfate for degradation of antibiotics

The novel composite photocatalytic material perylene diimides/Fe2O3@C (PDIs/Fe2O3@C) was constructed by a simple hydrothermal-calcination method and an oil bath method. 20 % PDIs/Fe2O3@C displayed a 16.4-fold increase in the rate of tetracycline (TC) removal over Fe2O3@C at 8 min. The main factor that enhanced photocatalytic performance was due to the combination of PDIs with Fe2O3@C, which effectively improved the phenomenon during the self-assembly of highly agglomerative PDIs, increased the specific surface area of Fe2O3@C, exposed more reaction sites, and promoted the activation of peroxymonosulfate (PMS) by Fe2+/Fe3+; and secondly, the composite of two different materials, both organic and inorganic, which effectively promoted the photogenerated electron transfer and the separation of electron-hole pairs, the a new S-scheme electron transport pathway is formed, which effectively promoted the photogenerated electron transfer as well as the e--h+ separation, which was more favorable for the activation of PMS. The whole reaction pathway and product toxicity were thoroughly evaluated by Fukui function calculations, Liquid Chromatograph Mass Spectrometer (LC-MS), and Toxicity Estimation Software Tool (T.E.S.T.) simulation results, which demonstrated the rationality of the degradation pathway and the greatly reduced product toxicity. Moreover, the composites were effective and versatile for all other antibiotics (chlortetracycline (CTC), ciprofloxacin (CIP) and sulfadiazine (SDZ)). As an advanced oxidation process, the activation of PDIs/Fe2O3@C under visible light shows its potential application in pollutant degradation, which provides new perspectives and ideas for further effective treatment of real wastewater.

ENO1 deletion potentiates ferroptosis and decreases glycolysis in colorectal cancer cells via AKT/STAT3 signaling

Colorectal cancer (CRC) is one of the most prevailing and lethal forms of cancer globally. α-enolase (ENO1) has been well documented to be involved in the progression and drug resistance of CRC. The present study was designed to specify the role of ENO1 in major events during the process of CRC and to introduce its latent functional mechanism. ENO1 expression was determined by western blot analysis. Extracellular acidification rates were assessed using an XF96 extracellular flux analyzer. Glucose uptake, lactic acid production, total iron levels and ferroptosis-related markers were examined with corresponding kits. A dichlorodihydrofluorescein diacetate probe measured intracellular reactive oxygen species content. Western blotting detected the expression of glycolysis- and ferroptosis-related proteins. CCK-8 and EdU staining assays assessed cell proliferation. In the current study, ENO1 was highly expressed in CRC cells. Knockdown of ENO1 markedly reduced the glycolysis and accelerated the ferroptosis in CRC cells. Moreover, the inhibitory effects of WZB117, a specific inhibitor of glycolysis-related glucose transporter type 1, on CRC cell proliferation were further enhanced by ENO1 interference. In addition, silencing of ENO1 inactivated the AKT/STAT3 signaling. The AKT activator SC79 partially reversed the effects of ENO1 deficiency on the AKT/STAT3 signaling, glycolysis, proliferation as well as ferroptosis in CRC cells. In summary, inactivation of AKT/STAT3 signaling mediated by ENO1 inhibition might boost the ferroptosis and suppress the glycolysis in CRC cells.

Microwave Ablation versus Surgical Resection for US-detected Multifocal T1N0M0 Papillary Thyroid Carcinoma: A 10-Center Study

Background Microwave ablation (MWA) is currently under preliminary investigation for the treatment of multifocal papillary thyroid carcinoma (PTC) and has shown promising treatment efficacy. Compared with surgical resection (SR), MWA is minimally invasive and could preserve thyroid function. However, a comparative analysis between MWA and SR is warranted to draw definitive conclusions. Purpose To compare MWA and SR for preoperative US-detected T1N0M0 multifocal PTC in terms of overall and 1-, 3-, and 5-year progression-free survival rates and complication rates. Materials and Methods In this retrospective study, 775 patients with preoperative US-detected T1N0M0 multifocal PTC treated with MWA or SR across 10 centers between May 2015 and December 2021 were included. Propensity score matching (PSM) was performed for patients in the MWA and SR groups, followed by comparisons between the two groups. The primary outcomes were overall and 1-, 3-, and 5-year progression-free survival (PFS) rates and complication rates. Results After PSM, 229 patients (median age, 44 years [IQR 36.5-50.5 years]; 179 female) in the MWA group and 453 patients (median age, 45 years [IQR 37-53 years]; 367 female) in the SR group were observed for a median of 20 months (range, 12-74 months) and 26 months (range, 12-64 months), respectively. MWA resulted in less blood loss, shorter incision length, and shorter procedure and hospitalization durations (all P < .001). There was no evidence of differences in overall and 1-, 3-, or 5-year PFS rates (all P > .05) between MWA and SR (5-year rate, 77.2% vs 83.1%; P = .36) groups. Permanent hoarseness (2.2%, P = .05) and hypoparathyroidism (4.0%, P = .005) were encountered only in the SR group. Conclusion There was no evidence of a significant difference in PFS rates between MWA and SR for US-detected multifocal T1N0M0 PTC, and MWA resulted in fewer complications. Therefore, MWA is a feasible option for selected patients with multifocal T1N0M0 PTC. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Georgiades in this issue.

Comprehensive Analysis of Predictive Value and the potential therapeutic target of NLRP3 inflammasome in glioma based on tumor microenvironment

BACKGROUND

Glioma exhibits high recurrence rates and poor prognosis. The nucleotide-binding oligomerization domain-like receptor protein 3 (NLRP3) inflammasome plays a crucial role in inflammation. There is a lack of research exploring the NLRP3 in glioma.

METHODS

We used several databases, networks, Western blotting, multiple immunofluorescence staining to analyze the role of NLRP3 in inflammatory tumor microenvironment (TME).

RESULTS

NLRP3 is higher-expression in glioma with a low mutation load. NLRP3 expression is linked to the infiltration of immune cells, chemokines, immunomodulators, and the TME. Signaling pathways, co-expression genes and interacting proteins contribute to the up-regulation of NLRP3. Patients responding to immunotherapy positively tend to have lower NLRP3 expression relating to the overall survival based on nomogram. Sensitivity to molecular medicines is observed in relation to NLRP3.

CONCLUSION

The NLRP3 inflammasome plays a pivotal role in TME which could serve as a higher predictive value biomarker and therapeutic target for glioma treatment.

Floating-Point Approximation Enabling Cost-Effective and High-Precision Digital Implementation of FitzHugh-Nagumo Neural Networks

The study of neuron interactions and hardware implementations are crucial research directions in neuroscience, particularly in developing large-scale biological neural networks. The FitzHugh-Nagumo (FHN) model is a popular neuron model with highly biological plausibility, but its complexity makes it difficult to apply at scale. This paper presents a cost-saving and improved precision approximation algorithm for the digital implementation of the FHN model. By converting the computational data into floating-point numbers, the original multiplication calculations are replaced by adding the floating-point exponent part and fitting the mantissa part with piecewise linear. In the hardware implementation, shifters and adders are used, greatly reducing resource overhead. Implementing FHN neurons by this approximation calculations on FPGA reduces the normalized root mean square error (RMSE) to 3.5% of the state-of-the-art (SOTA) while maintaining a performance overhead ratio improvement of 1.09 times. Compared to implementations based on approximate multipliers, the proposed method achieves a 20% reduction in error at the cost of a 2.8% increase in overhead.This model gained additional biological properties compared to LIF while reducing the deployment scale by only 9%. Furthermore, the hardware implementation of nine coupled circular networks with eight nodes and directional diffusion was carried out to demonstrate the algorithm's effectiveness on neural networks. The error decreased to 60% compared to the single neuron of the SOTA. This hardware-friendly algorithm allows for the low-cost implementation of high-precision hardware simulation, providing a novel perspective for studying large-scale, biologically plausible neural networks.

Enhancement of functional properties, digestive properties, and in vitro digestion product physiological activity of extruded corn gluten meal by enzymatic modification

BACKGROUND

Enzymatic modification is an effective means of improving the functional properties, digestive properties, and in vitro digestion product physiological activity of proteins, thus significantly expanding protein uses in various food applications.

RESULTS

In this study, the addition of chymotrypsin (CT) at pH 9.0 and 11.0 was found to significantly improve the functional properties (solubility, foaming properties, water holding capacity, oil holding capacity, etc.) and digestive properties of extruded corn gluten meal (ECGM). Similar changes were observed when treating ECGM with glutaminase, protein glutaminase, and papain. These changes were likely due to the increase in number of carboxyl groups and the multiple effects of change in protein net charge and conformation caused by enzymatic deamidation. Of note, ECGM deamidated by CT showed the highest degree of deamidation, solubility, and gastrointestinal digestibility at pH 11.0, up to 44.92%, 43.75%, and 82.22%, respectively. In addition, CT-ECGM digestion product exhibited strong antioxidant activity and potential to promote alcohol metabolism in both a static digestion model and dynamic digestion model, even comparable to commercial corn peptides (CCP), while being inexpensive and of low bitterness compared to CCP. Meanwhile, the physiological activity enhanced as the molecular weight of digestion product decreased with the digested component having strongest activity.

CONCLUSION

This study may promote the application of ECGM as a food component in the food industry or even as a substitute for CCP. © 2023 Society of Chemical Industry.

Pharmacokinetics, Bioequivalence, and Safety Evaluation of Two Oral Formulations of Calcium Dobesilate Capsules in Healthy Chinese Volunteers Under Fasting and Fed Conditions

To determine the pharmacokinetics (PK), safety, and bioequivalence profiles of 0.5-g calcium dobesilate capsules in both fasting and fed states for the test drug and reference drug. A randomized-sequence, single-dose, open-label, 2-period crossover study was conducted in fasted and fed healthy Chinese volunteers (Chinese Clinical Trials Registry identifier: CTR202000268-01). The fasting and fed studies, both involving 24 subjects, were conducted. A single dosage of either the reference or the test preparation was given to each eligible subject in a 1:1 ratio, followed by a 7-day rest interval before the administration of the alternative formulation. After taking the capsules, plasma samples were taken for 48 hours, and using liquid chromatography-tandem mass spectrometry, the calcium dobesilate level was determined. The PK parameters evaluated in the study included the maximum serum concentration (Cmax), area under the plasma concentration-time curve (AUC) from time 0 to the last quantifiable concentration, AUC from time 0 to infinity, half-life, time to Cmax, and terminal elimination rate constant. In addition, the safety evaluation encompassed monitoring fluctuations in vitals (temperature, pulse, and blood pressure) and laboratory tests (urinalysis, hepatic function, blood biochemistry, and hematology), as well as recording the emergence of adverse events (AEs). The geometric mean ratio (GMR) of the test/reference medications was used to assess bioequivalence by determining if the 90% confidence intervals of the GMR fell within the predefined range of 80%-125%. AEs were assessed as safety end points. The study included 48 healthy Chinese volunteers (with n = 24 each for the fasting and the fed conditions), and no subjects dropped out for any reason. The differences in the PK metrics for the test and reference drugs for both conditions were insignificant (P > .05). For bioequivalence, irrespective of whether the food was consumed or not, the range of the 90% confidence intervals of the GMR for Cmax, AUC from time 0 to the last quantifiable concentration, and AUC from time 0 to infinity was between 80% and 125%. In the experiment, no serious AEs were recorded. Our findings revealed that the calcium dobesilate capsules used as the reference and the test drugs were both bioequivalent. Irrespective of whether the healthy Chinese volunteers consumed food or not, the PK and safety profiles were comparable.

Prevalence and anatomic associations of middle mesial canals and isthmi in mandibular first molars: A CBCT-based study in a Northern Chinese population

This study investigated the prevalence of the middle mesial canal (MMC) and isthmus in a northern Chinese subpopulation using cone-beam computed tomography (CBCT). CBCT images of 1060 mandibular first molars (MFMs) were analysed. Data analysis was performed using the chi-square test, t-test, and multiple logistic regression analysis (p < 0.05). The prevalence of MMC and isthmus was 15.2% and 40.6%, respectively. The average dentinal thickness in the danger zone was 1.61 ± 0.14 mm. Patients younger than 40 years were two times more likely to have MMC (odds ratio [OR] = 2.204). Additionally, for every 1 mm reduction in the MB-ML orifice distance, the likelihood of detection of MMC in MFM nearly doubled (OR = 1.738). Furthermore, MFMs with MB-ML isthmus were five times more likely to exhibit MMC than those without it (OR = 4.756). The findings revealed that the prevalence of MMC and isthmus in MFMs is high and suggested that anatomical and demographic variables can serve as valuable indicators for clinicians in anticipating their presence.

Perspectives on the Novel Multifunctional Nerve Guidance Conduits: From Specific Regenerative Procedures to Motor Function Rebuilding

Peripheral nerve injury potentially destroys the quality of life by inducing functional movement disorders and sensory capacity loss, which results in severe disability and substantial psychological, social, and financial burdens. Autologous nerve grafting has been commonly used as treatment in the clinic; however, its rare donor availability limits its application. A series of artificial nerve guidance conduits (NGCs) with advanced architectures are also proposed to promote injured peripheral nerve regeneration, which is a complicated process from axon sprouting to targeted muscle reinnervation. Therefore, exploring the interactions between sophisticated NGC complexes and versatile cells during each process including axon sprouting, Schwann cell dedifferentiation, nerve myelination, and muscle reinnervation is necessary. This review highlights the contribution of functional NGCs and the influence of microscale biomaterial architecture on biological processes of nerve repair. Progressive NGCs with chemical molecule induction, heterogenous topographical morphology, electroactive, anisotropic assembly microstructure, and self-powered electroactive and magnetic-sensitive NGCs are also collected, and they are expected to be pioneering features in future multifunctional and effective NGCs.

The modified Rajan's heart failure risk score predicts all-cause mortality in patients hospitalized for heart failure with reduced ejection fraction: a retrospective cohort study

Background

The dimensionless Rajan's heart failure (R-hf) risk score was proposed to predict all-cause mortality in patients hospitalized with chronic heart failure (HF) and reduced ejection fraction (EF) (HFrEF).

Purpose

To examine the association between the modified R-hf risk score and all-cause mortality in patients with HFrEF.

Methods

Retrospective cohort study included adults hospitalized with HFrEF, as defined by clinical symptoms of HF with biplane EF less than 40% on transthoracic echocardiography, at a tertiary centre in Dalian, China, between 1 November 2015, and 31 October 2019. All patients were followed up until 31 October 2020. A modified R-hf risk score was calculated by substituting brain natriuretic peptide (BNP) for N-terminal prohormone of BNP (NT-proBNP) using EF× estimated glomerular filtration rate (eGFR)× haemoglobin (Hb))/BNP. The patients were stratified into tertiles according to the R-hf risk score. The measured outcome was all-cause mortality. The score performance was assessed using C-statistics.

Results

A total of 840 patients were analyzed (70.2% males; mean age, 64±14 years; median (interquartile range) follow-up 37.0 (27.8) months). A lower modified R-hf risk score predicted a higher risk of all-cause mortality, independent of sex and age [1st tertile vs. 3rd tertile: adjusted hazard ratio (aHR), 3.46; 95% CI: 2.11-5.67; P<0.001]. Multivariate Cox regression analysis indicated that a lower modified R-hf risk score was associated with increased cumulative all-cause mortality [univariate: (1st tertile vs. 3rd tertile: aHR, 3.45; 95% CI: 2.11-5.65; P<0.001) and multivariate: (1st tertile vs. 3rd tertile: aHR 2.21, 95% CI: 1.29-3.79; P=0.004)]. The performance of the model, as reported by C-statistic was 0.67 (95% CI: 0.62-0.72).

Conclusion

The modified R-hf risk score predicted all-cause mortality in patients hospitalized with HFrEF. Further validation of the modified R-hf risk score in other cohorts of patients with HFrEF is needed before clinical application.

Comparison of Omicron breakthrough infection versus monovalent SARS-CoV-2 intramuscular booster reveals differences in mucosal and systemic humoral immunity

Our understanding of the quality of cellular and humoral immunity conferred by COVID-19 vaccination alone versus vaccination plus SARS-CoV-2 breakthrough (BT) infection remains incomplete. While the current (2023) SARS-CoV-2 immune landscape of Canadians is complex, in late 2021 most Canadians had either just received a third dose of COVID-19 vaccine, or had received their two-dose primary series and then experienced an Omicron BT. Herein we took advantage of this coincident timing to contrast cellular and humoral immunity conferred by three doses of vaccine versus two doses plus BT. Our results show thatBT infection induces cell-mediated immune responses to variants comparable to an intramuscular vaccine booster dose. In contrast, BT subjects had higher salivary immunoglobulin (Ig)G and IgA levels against the Omicron spike and enhanced reactivity to the ancestral spike for the IgA isotype, which also reacted with SARS-CoV-1. Serumneutralizing antibody levels against the ancestral strain and the variants were also higher after BT infection. Our results support the need for the development of intranasal vaccines that could emulate the enhanced mucosal and humoral immunity induced by Omicron BT without exposing individuals to the risks associated with SARS-CoV-2 infection.

Alveoli-Mimetic Synergistic Liquid and Solid Thermal Conductive Interface as a Novel Strategy for Designing High-Performance Thermal Interface Materials

Thermal interface materials (TIMs) are in desperate desire with the development of the modern electronic industry. An excellent TIM needs desired comprehensive properties including but not limited to high thermal conductivity, low Yong's modulus, lightweight, as well as low price. However, as is typically the case, those properties are naturally contradictory. To tackle such dilemmas, a strategy of construction high-performance TIM inspired by alveoli is proposed. The material design includes the self-alignment of graphite into 3D interconnected thermally conductive networks by polydimethylsiloxane beads (PBs) -the alveoli; and a small amount of liquid metal (LM) - capillary networks bridging the PBs and graphite network. Through the delicate structural regulation and the synergistic effect of the LM and solid graphite filler, superb thermal conductivity (9.98 ± 0.34 W m-1 K-1) can be achieved. The light emitting diode (LED) application and their performance in the central processing unit (CPU) heat dispersion manifest the TIM developed in the work has stable thermal conductivity for long-term applications. The thermally conductive, soft, and lightweight composites are believed to be high-performance silicone bases TIMs for advanced electronics.

[Retrospective analysis of perioperative anaphylactic shock induced by cefuroxime]

This study investigated the characteristics and frequency of perioperative anaphylactic shock induced by cefuroxime, so as to provide a reference for the safe and rational use of cefuroxime in the perioperative period. Cases of perioperative anaphylactic shock caused by cefuroxime in our hospital from 2011 to 2021 were extracted from the Adverse Drug Reaction Monitoring System. Literature reporting adverse drug reactions (ADR) including cefuroxime-induced anaphylactic shock in perioperative settings was collected from the CNKI, VIP, Wanfang, PubMed, and Web of Science databases from their respective inception to May 2022. Statistical analysis was performed for all cases of cefuroxime-induced perioperative anaphylactic shock. A total of 31 patients were included [13 men (48.1%) and 14 women (51.9%)], most of whom were over 60 years old (n=16, 59.3%); 9 (29.0%) patients had a history of drug allergy; 5 (16.1%) patients had received skin tests, but with negative results; 28 (90.3%) patients received treatment intravenously; 22 (71.0%) patients were treated after anesthesia. For 20 (64.5%) patients the ADR occurred within 10 minutes after anesthesia. The main manifestations were hypotension, dyspnea, rash, and tachycardia. For all patients, symptoms resolved after withdrawal of the drug and active rescue, and there were no deaths. A history of allergy and skin test findings may have limitations in predicting perioperative anaphylactic shock caused by cefuroxime; greater vigilance should be exercised when using cefuroxime in the perioperative period. Close monitoring is recommended for patients undergoing treatment with cefuroxime. Rescue therapy should be administered for allergic shock, and suitable response measures must be taken in a timely manner to ensure the safety of patients.

Histone methyltransferase SETD2 is required for proper hippocampal lamination and neuronal maturation

Proper formation of the hippocampus is crucial for the brain to execute memory and learning functions. However, many questions remain regarding how pyramidal neurons (PNs) of the hippocampus mature and precisely position. Here we revealed that Setd2, the methyltransferase for histone 3 lysine 36 trimethylation (H3K36me3), is essential for the precise localization and maturation of PNs in the hippocampal CA1. The ablation of Setd2 in neural progenitors leads to irregular lamination of the CA1 and increased numbers of PNs in the stratum oriens. Setd2 deletion in postmitotic neurons causes mislocalization and immaturity of CA1 PNs. Transcriptome analyses revealed that SETD2 maintains the expressions of clustered protocadherin (cPcdh) genes. Together, Setd2 is required for proper hippocampal lamination and maturation of CA1 PNs.

[Corrigendum] Upregulation of centromere protein M promotes tumorigenesis: A potential predictive target for cancer in humans

Subsequently to the publication of this paper, the authors' have realized that Fig. 2A on p. 3927 was published featuring an error; specifically, there was an unintentional duplication of one of the representative images chosen for the figure (the same image was selected to represent the 'BRCA/Normal' and 'Tumor/LUSC' experiments). Additionally, the sample numbers in Fig. 2A were also incorrect. The correct sample numbers are as follows: 3 samples of breast tissue, 12 samples of breast cancer tissue, 3 samples of normal cervical tissue, 9 samples of CESC, 7 samples of LIHC, 3 samples of normal lung tissue and 6 samples of LUSC. These errors were due to negligence during the storage of HPA database images.  The revised version of Fig. 2, showing the correct data for the 'Tumor/LUSC' experiment in Fig. 2A (where the error occurred), is shown on the next page. Note that this error did not significantly affect either the results or the conclusions reported in this paper, and all the authors agree to the corrigendum. The authors do stress the importance of a larger sample size to ascertain statistically significant differences in CENPM protein expression, predominantly localized in the cell nucleus. Furthermore, the authors thank the Editor of Molecular Medicine Reports for allowing them the opportunity to publish this corrigendum, and apologize to the readership for any inconvenience caused. [Molecular Medicine Reports 22: 3922‑3934, 2020; DOI: 10.3892/mmr.2020.11461].

Expression of chicken epidermal growth factor (cEGF) in Escherichia coli regulates the microflora structure of the duodenum to improve growth performance and intestinal morphogenesis in broilers

1. A study used gene synthesis to obtain the functional domains of chicken epidermal growth factor (cEGF) and examined their impact on broiler growth performance, small intestinal morphology, digestive enzyme activities in the intestinal contents and the structure of duodenal microflora.2. The pET-32a-cEGF recombinant expression vector was constructed. The specific band at 26 KDa was shown by SDS-PAGE analysis and WB results. The purified protein content was shown to be 1687 μg/ml by assay.3. A total of 180 healthy, one-day-old Arbor Acres male, white-feathered broilers were randomly divided into three dietary treatment groups (six replicate pens, 10 birds per replicate): A control diet (ND); cEGF diet (cEGF), control supplemented with 250 mg/kg cEGF and the control diet (CD) supplemented with 250 mg/kg chlortetracycline.4. The results showed that feeding the cEGF and CD diet reduced FCR of broilers aged 1-21 d, average daily feed intake (ADFI) at 22-42 d, and the FCR in the whole period (1-42 d; p < 0.05). Compared with the ND group, the cEGF diet increased duodenal α-amylase and alkaline phosphatase activities in the 1-21 d, duodenal lipase, alkaline phosphatase, and ileal alkaline phosphatase activities in the post-period and increased villus height in the duodenum and ileum (p < 0.05). In addition, the ACE and Chao1 index for the birds fed cEGF were higher than the ND group (p < 0.05). At the phyla level, Firmicutes and Proteobacteria were dominant in all groups. At the genus level, the dominant genus was Lactobacillus. The LEfSe analysis showed that the cEGF group was enriched by 11 species including Brevibacillus, Eisenbergiella, Cloacibacterium, Butyricoccus spp.5. The addition of 250 mg/kg cEGF to the diet can increase growth performance by improving intestinal development and digestive enzyme activity, which may be related to the duodenal intestinal microflora. Therefore, cEGF is an effective alternative to antibiotics in broiler farming.

Arginine-assembly as NO nano-donor prevents the negative feedback of macrophage repolarization by mitochondrial dysfunction for cancer immunotherapy

Repolarizing the tumor-associated macrophages (TAMs) towards the antitumoral M1-like phenotype has been a promising approach for cancer immunotherapy. However, the anti-cancer immune response is severely limited mainly by the repolarized M1-like macrophages belatedly returning to the M2-like phenotype (i.e., negative feedback). Inspired by nitric oxide (NO) effectively preventing repolarization of inflammatory macrophages in inflammatory diseases, herein, we develop an arginine assembly, as NO nano-donor for NO generation to prevent the negative feedback of the macrophage repolarization. The strategy is to first apply reversible tagging of hydrophobic terephthalaldehyde to create an arginine nano-assembly, and then load a toll-like receptor 7/8 agonist resiquimod (R848) (R848@Arg). Through this strategy, a high loading efficiency of 40 % for the arginine and repolarization characteristics for TAMs can be achieved. Upon the macrophage repolarization by R848, NO can be intracellularly generated from the released arginine by the upregulated inducible nitric oxide synthase. Mechanistically, NO effectively prevented the negative feedback of the repolarized macrophage by mitochondrial dysfunction via blocking oxidative phosphorylation. Notably, R848@Arg significantly increased the tumor inhibition ratio by 3.13-fold as compared to the free R848 by maintaining the M1-like phenotype infiltrating into tumor. The Arg-assembly as NO nano-donor provides a promising method for effective repolarization of macrophages.

[Establishment and application of intrahepatic cholangiocarcinoma organoid models]

Intrahepatic cholangiocarcinoma(ICC) refers to cholangiocarcinomas originating from the secondary bile ducts within the liver and their branches.As a prevalent malignancy of the liver,the diagnosis and treatment of ICC pose significant challenges due to the high heterogeneity of the tumor and its propensity to develop drug resistance.Traditional drug screening and tumor mechanism studies have been confined to two-dimensional cell line cultures and patient-derived xenograft(PDX) models.However,cell lines cannot fully recapitulate the tumor heterogeneity,and PDX models have limitations such as high costs and time consumption,making them less practical for widespread clinical application.To address the limitations of these models,organoid models have been developed based on two-dimensional cell cultures.Organoid models combine the advantages of both aforementioned culture methods and offer unique strengths in cancer research.They provide a new perspective for studying the development and treatment of tumors. In this review, the focus is primarily on the latest advances in the field of organoids of ICC,with a particular emphasis on existing culture protocols and their potential applications in precision medicine and the establishment of biobanks.

SPCS, a Novel Classifier System Based on Senescence Axis Regulators Reveals Tumor Microenvironment Heterogeneity and Guides Frontline Therapy for Clear Cell Renal Carcinoma

RATIONALE

The emerging evidence suggested that senescence regulator genes were involved in multi cancers, which may be utilized as new targets for cancers. However, the dysregulation and clinical impact of senescence regulator genes in clear cell renal cell cancer (ccRCC) were still in foggy.

METHODS

Using multiomics data from TCGA-KIRC and other datasets, we comprehensively investigated the function of senescence regulator genes in ccRCC. ccRCC patients could be remodeled into 2 significant different groups basing on senescence regulators expression: senescence-pattern cancer subtype1 (SPCS1) and subtype2 (SPCS2). We further explored clinical characteristics, functional analysis, tumor immune microenvironment, immunotherapy response, genomic mutation and drug sensitivity between the 2 subtypes. Besides, senescence-pattern related risk model was established to determine the patient's prognosis of ccRCC. Finally, the overview of MECP2 function was investigated in multi cancers.

RESULTS

ccRCC patients could be divided into SPCS1 (normal aging group) and SPCS2 (Aging disorder group). The 2 subtypes showed significant different clinical characteristics and biological process in ccRCC. SPCS2, an aggressive subtype, comprised higher clinical stage and worse prognosis of ccRCC patients. SPCS2 subtype indicated activated oncogenic signaling pathway and metabolic signatures to prompt cancer expansion. SPCS2 subgroup owned immunocompromised status, which induced immune dysfunction and low ICI therapy response. The genome-copy numbers of SPCS2, including arm-gain and arm-loss was significantly more frequent than SPCS1. In addition, the 2 subtypes argue contrasting drug sensitivity profiles in clinical specimens and matched cell lines. Finally, we constructed a prognostic risk model consisted of each subtype's leading biomarkers, which exerted a satisfied performance for ccRCC patients.

CONCLUSION

Senescence regulator-related signature could modify functional pathways and tumor immune microenvironment by genome mutation and pathway interaction. Senescence regulator-related molecular subtype strengthen the understanding of ccRCC' characterization and guide clinical treatment. Targeting senescence regulators may be regard as a proper way in ccRCC.

DNA Methylation Analysis Reveals Potential Mechanism in Takifugu rubripes Against Cryptocaryon irritans Infection

Takifugu rubripes (T. rubripes) is a valuable commercial fish, and Cryptocaryon irritans (C. irritans) has a significant impact on its aquaculture productivity. DNA methylation is one of the earliest discovered ways of gene epigenetic modification and also an important form of modification, as well as an essential type of alteration that regulates gene expression, including immune response. To further explore the anti-infection mechanism of T. rubripes in inhibiting this disease, we determined genome-wide DNA methylation profiles in the gill of T. rubripes using whole-genome bisulfite sequencing (WGBS) and combined with RNA sequence (RNA-seq). A total of 4659 differentially methylated genes (DMGs) in the gene body and 1546 DMGs in the promoter between the infection and control group were identified. And we identified 2501 differentially expressed genes (DEGs), including 1100 upregulated and 1401 downregulated genes. After enrichment analysis, we identified DMGs and DEGs of immune-related pathways including MAPK, Wnt, ErbB, and VEGF signaling pathways, as well as node genes prkcb, myca, tp53, and map2k2a. Based on the RNA-Seq results, we plotted a network graph to demonstrate the relationship between immune pathways and functional related genes, in addition to gene methylation and expression levels. At the same time, we predicted the CpG island and transcription factor of four immune-related key genes prkcb and mapped the gene structure. These unique discoveries could be helpful in the understanding of C. irritans pathogenesis, and the candidate genes screened may serve as optimum methylation-based biomarkers that can be utilized for the correct diagnosis and therapy T. rubripes in the development of the ability to resist C. irritans infection.

Development and Validation of Contrast-Enhanced CT-Based Deep Transfer Learning and Combined Clinical-Radiomics Model to Discriminate Thymomas and Thymic Cysts: A Multicenter Study

RATIONALE AND OBJECTIVES

This study aims to evaluate the feasibility and effectiveness of deep transfer learning (DTL) and clinical-radiomics in differentiating thymoma from thymic cysts.

MATERIALS AND METHODS

Clinical and imaging data of 196 patients pathologically diagnosed with thymoma and thymic cysts were retrospectively collected from center 1. (training cohort: n = 137; internal validation cohort: n = 59). An independent external validation cohort comprised 68 thymoma and thymic cyst patients from center 2. Region of interest (ROI) delineation was performed on contrast-enhanced chest computed tomography (CT) images, and eight DTL models including Densenet 169, Mobilenet V2, Resnet 101, Resnet 18, Resnet 34, Resnet 50, Vgg 13, Vgg 16 were constructed. Radiomics features were extracted from the ROI on the CT images of thymoma and thymic cyst patients, and feature selection was performed using intra-observer correlation coefficient (ICC), Spearman correlation analysis, and least absolute shrinkage and selection operator (LASSO) algorithm. Univariate analysis and multivariable logistic regression (LR) were used to select clinical-radiological features. Six machine learning classifiers, including LR, support vector machine (SVM), k-nearest neighbors (KNN), Light Gradient Boosting Machine (LightGBM), Adaptive Boosting (AdaBoost), and Multilayer Perceptron (MLP), were used to construct Radiomics and Clinico-radiologic models. The selected features from the Radiomics and Clinico-radiologic models were fused to build a Combined model. Receiver operating characteristic curve (ROC), calibration curve, and decision curve analysis (DCA) were used to evaluate the discrimination, calibration, and clinical utility of the models, respectively. The Delong test was used to compare the AUC between different models. K-means clustering was used to subdivide the lesions of thymomas or thymic cysts into subregions, and traditional radiomics methods were used to extract features and compare the ability of Radiomics and DTL models to reflect intratumoral heterogeneity using correlation analysis.

RESULTS

The Densenet 169 based on DTL performed the best, with AUC of 0.933 (95% CI: 0.875-0.991) in the internal validation cohort and 0.962 (95% CI: 0.923-1.000) in the external validation cohort. The AdaBoost classifier achieved AUC of 0.965 (95% CI: 0.923-1.000) and 0.959 (95% CI: 0.919-1.000) in the internal and external validation cohorts, respectively, for the Radiomics model. The LightGBM classifier achieved AUC of 0.805 (95% CI: 0.690-0.920) and 0.839 (95% CI: 0.736-0.943) in the Clinico-radiologic model. The AUC of the Combined model in the internal and external validation cohorts was 0.933 (95% CI: 0.866-1.000) and 0.945 (95% CI: 0.897-0.994), respectively. The results of the Delong test showed that the Radiomics model, DTL model, and Combined model outperformed the Clinico-radiologic model in both internal and external validation cohorts (p-values were 0.002, 0.004, and 0.033 in the internal validation cohort, while in the external validation cohort, the p-values were 0.014, 0.006, and 0.015, respectively). But there was no statistical difference in performance among the three models (all p-values <0.05). Correlation analysis showed that radiomics performed better than DTL in quantifying intratumoral heterogeneity differences between thymoma and thymic cysts.

CONCLUSION

The developed DTL model and the Combined model based on radiomics and clinical-radiologic features achieved excellent diagnostic performance in differentiating thymic cysts from thymoma. They can serve as potential tools to assist clinical decision-making, particularly when endoscopic biopsy carries a high risk.

Achyranthes bidentata polysaccharides improve cyclophosphamide-induced adverse reactions by regulating the balance of cytokines in helper T cells

The manuscript aimed to study the immune function maintenance effect of Achyranthes bidentata polysaccharides (ABPs). The mice were divided into the control group, cyclophosphamide-induced (CTX) group, and ABPs-treated (ABP) group. The results showed that, compared with the CTX group, ABPs could significantly improve the spleen index and alleviate the pathological changes in immune organs. Ex vivo study of whole spleen cells, the levels of interleukin-2 (IL-2), interleukin-6 (IL-6), interferon-γ (IFN-γ), and tumor necrosis factor-α (TNF-α) were increased. The proliferation of lymphocytes and the proportion of CD3+CD4+ Th cells in peripheral blood mononuclear cells were increased. The transcription of GATA-3, Foxp3, and ROR γ t were decreased, while the transcription of T-bet was increased. The transcriptome sequencing analysis showed that the differentially expressed genes (DEGs) caused by ABPs-treated were mostly downregulated in CTX-induced mice. The Th2-related genes were significantly enriched in DEGs, with representative genes, including Il4, II13, Il9, etc., while increasing the expression of immune effector genes simultaneously, including Ccl3, Ccr5, and Il12rb2. It was suggested that ABPs possibly regulated the balance of cytokines in helper T cells to ameliorate the immune function of CTX-induced mice.

Effects of fermented cottonseed meal inclusions on growth performance, serum biochemical parameters and hepatic lipid metabolism of geese during 28-70 d of age

The aim of this study was to investigate the effects of solid-state fermented cottonseed meal (FCSM) inclusion levels on the growth performance, serum biochemical parameters and hepatic lipid metabolism in geese from 28 to 70 d of age. A total of 288 twenty-eight-d-old male geese were randomly divided into 4 treatments with FCSM levels of 0, 5, 15 and 25% including 0, 22.74, 67.33, 111.27 mg FG/kg diet, respectively. Each treatment contained 6 replicates and 12 birds per replicate. Treatments of FCSM inclusions from 0 to 25% had no effect on growth rate and feed intake in geese during d 28 to 70. The F/G ratio was increased (P < 0.05) in geese fed the diet with 25% FCSM compared with birds fed the diet with 0% FCSM. Treatment with 25% FCSM levels had no effect on the contents of TC, TG, HDL-C, LDL-C, but increased (P < 0.05) AST and ALT activities in serum of geese at d 70. Treatment with 25% FCSM increased the contents of FG, HDL-C, TC, C18:2n6, C20:4n6 and PUFA and decreased (P < 0.05) the contents of NEFA, SFA, MUFA in liver compared with treatment of 0% FCSM inclusion. Additionally, treatment with 25% FCSM decreased (P < 0.05) the PPARα, AMPK, and LXR mRNA expression related to lipid deposition, and increased (P < 0.05) PPARγ and ACC mRNA expression related to lipolysis in liver compared with birds fed the diet with 0% FCSM. Overall, treatment with 0 to 15% FCSM (<=67.33 mg FG/kg diet) had no adverse effects on the growth performance and lipid metabolism of geese. However, treatment fed 25% FCSM (111.27 mg FG/kg diet) decreased feed efficiency and promoted hepatic lipid deposition associated with the alteration of related gene expression in geese at 28 to 70 d of age.

Transvaginal natural orifice transluminal endoscopic surgery for myomectomy: A more suitable surgical approach for enhanced recovery after surgery

OBJECTIVE

Vaginal natural orifice transluminal endoscopic surgery (vNOTES) is widely recognized for its potential benefits, including reducing post-surgical pain and leaving no discernible scarring. However, the anatomical specificity of the vNOTES approach may elevate the risk of nearby organ damage, such as the rectum and bladder. Thus, this study aims to demonstrate the safety and relative merits of vNOTES over transumbilical laparoendoscopic single-site surgery (LESS).

METHODS

The Longitudinal Vaginal Natural Orifice Transluminal Endoscopic Surgery Study (LovNOTESS), which was conducted in Chengdu, China. A total of 110 patients who underwent myomectomy in vNOTES or LESS from January 2021 to December 2022. This study prospectively collected and compared perioperative and follow-up data of the two groups.

RESULTS

In the vNOTES group, patients had shorter postoperative anal exhaust time, lower pain medications use rate, shorter hospital stay but higher intraoperative conversion rate, and higher postoperative fever rate. vNOTES decreased the anal exhaust time by approximately 8.7 h (95 %CI: -16.182, -1.262, p = 0.007). Moreover, vNOTES reduces pain medication use risk by 73.1 % (OR: 0.269, 95 %CI: 0.172, 0.318, p = 0.016).

CONCLUSION

Relative to LESS, vNOTES can make patients mitigate postoperative discomfort, accelerate the recovery of gastrointestinal function, curtail hospitalization duration, and enable a more rapid return to daily activities in myomectomy. However, vNOTES has a higher risk of surgical conversion and adjacent organ injury. Therefore, larger scale prospective studies are needed to prove its security and promote the widespread application of vNOTES in myomectomy.

Recent advances in the synthesis of single-stranded DNA in vitro

Single-stranded DNA (ssDNA) is the foundation of modern biology, with wide applications in gene editing, sequencing, DNA information storage, and materials science. However, synthesizing ssDNA with high efficiency, high throughput, and low error rate in vitro remains a major challenge. Various methods have been developed for ssDNA synthesis, and some significant results have been achieved. In this review, six main methods were introduced, including solid-phase oligonucleotide synthesis, terminal deoxynucleotidyl transferase-based ssDNA synthesis, reverse transcription, primer exchange reaction, asymmetric polymerase chain reaction, and rolling circle amplification. The advantages and limitations of each method were compared, as well as illustrate their representative achievements and applications. Especially, rolling circle amplification has received significant attention, including ssDNA synthesis, assembly, and application based on recent work. Finally, the future challenges and opportunities of ssDNA synthesis were summarized and discussed. Envisioning the development of new methods and significant progress will be made in the near future with the efforts of scientists around the world.

Neurological and metabolic related pathophysiologies and treatment of comorbid diabetes with depression

BACKGROUND

The comorbidity between diabetes mellitus and depression was revealed, and diabetes mellitus increased the prevalence of depressive disorder, which ranked 13th in the leading causes of disability-adjusted life-years. Insulin resistance, which is common in diabetes mellitus, has increased the risk of depressive symptoms in both humans and animals. However, the mechanisms behind the comorbidity are multi-factorial and complicated. There is still no causal chain to explain the comorbidity exactly. Moreover, Selective serotonin reuptake inhibitors, insulin and metformin, which are recommended for treating diabetes mellitus-induced depression, were found to be a risk factor in some complications of diabetes.

AIMS

Given these problems, many researchers made remarkable efforts to analyze diabetes complicating depression from different aspects, including insulin resistance, stress and Hypothalamic-Pituitary-Adrenal axis, neurological system, oxidative stress, and inflammation. Drug therapy, such as Hydrogen Sulfide, Cannabidiol, Ascorbic Acid and Hesperidin, are conducive to alleviating diabetes mellitus and depression. Here, we reviewed the exact pathophysiology underlying the comorbidity between depressive disorder and diabetes mellitus and drug therapy.

METHODS

The review refers to the available literature in PubMed and Web of Science, searching critical terms related to diabetes mellitus, depression and drug therapy.

RESULTS

In this review, we found that brain structure and function, neurogenesis, brain-derived neurotrophic factor and glucose and lipid metabolism were involved in the pathophysiology of the comorbidity. Obesity might lead to diabetes mellitus and depression through reduced adiponectin and increased leptin and resistin. In addition, drug therapy displayed in this review could expand the region of potential therapy.

CONCLUSIONS

The review summarizes the mechanisms underlying the comorbidity. It also overviews drug therapy with anti-diabetic and anti-depressant effects.

Contrast-enhanced CT-based radiomics differentiate anterior mediastinum lymphoma from thymoma without myasthenia gravis and calcification

AIM

To explore the value of a radiomics model based on enhanced computed tomography (CT) in differentiating anterior mediastinal lymphoma (AML) and thymoma without myasthenia gravis (MG) and calcification.

MATERIALS AND METHODS

The present study analysed patients who were diagnosed histologically with AML and thymoma in three independent institutions. All pre-treatment patients underwent enhanced CT. In the training group of patients from institutions 1 (the First Affiliated Hospital of Kunming Medical University) and 3 (the Yunnan Cancer Hospital), two radiologists independently analysed the enhanced CT images and performed manual segmentation of each tumour. Radiomics features were screened using interobserver interclass coefficient (ICC) analysis, feature correlation analysis, and L1 regularisation. The discriminative efficacy of the logistic regression model was evaluated using receiver operating characteristic (ROC) analysis. Validation group of patients from institution 2 (the Second Affiliated Hospital of Zhejiang University School of Medicine) was used to validate the proposed models.

RESULTS

A total of 114 patients were enrolled in this study and 1,743 radiomics features were extracted from the enhanced CT images. After feature screening, the remaining 37 robust radiomics features were used to construct the model. In the training group, the AUC of the model was 0.987 (95% confidence interval [CI]: 0.976-0.999), the sensitivity, specificity, and accuracy were 0.912, 0.946, and 0.924, respectively. In the validation group, the AUC of the model was 0.798 (95% CI: 0.683-0.913), the sensitivity, specificity, and accuracy were 0.760, 0.700, and 0.743, respectively.

CONCLUSION

The radiomics model created provided effective information to assist in the selection of clinical strategies, thus reducing unnecessary procedures in patients with AML and guiding direct surgery in patients with thymoma to avoid biopsy.

Iodine‑125 seeds combined with carboplatin in the treatment of retroperitoneal metastatic seminoma: A case report and literature review

Testicular seminoma is a relatively rare malignant tumor, with the most common site of recurrence and metastasis being the retroperitoneal lymph nodes. Since seminoma is highly sensitive to radiotherapy and chemotherapy, even if it metastasizes, its cure rate is still >95%. However, the long-term toxicity and side effects of radiotherapy and chemotherapy cannot be ignored. Iodine-125 seeds represent a low-energy radioactive source that kills tumor cells while protecting the surrounding normal tissues, and brachytherapy using iodine-125 seeds has been widely used for the treatment of various malignancies. In addition, carboplatin can be used as an alternative to cisplatin-based combination chemotherapy to reduce the incidence of pulmonary toxicity, neurological damage and renal toxicity. In the present study, a case in which iodine-125 seeds were implanted for the treatment of retroperitoneal metastatic seminoma is reported. The patient was diagnosed with postoperative recurrence of seminoma that metastasized to the retroperitoneal lymph nodes. Since the tumor was large and surrounded blood vessels, surgical intervention and external radiotherapy were not considered. Moreover, considering the potential long-term toxic side effects of standard chemotherapy, a treatment plan for the patient using iodine-125 seed implantation combined with carboplatin (AUC7) therapy was finally formulated. No disease recurrence or toxic reactions occurred during the 3-year follow-up after treatment. The present case therefore demonstrated the antitumor efficacy and reduced toxicity of iodine-125 seeds combined with carboplatin for treating seminoma.

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

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

Genetic characterization of pediatric SARI-associated human adenoviruses in eight Chinese provinces during 2017-2021

Human adenovirus (HAdV) is a significant viral pathogen causing severe acute respiratory infections (SARIs) in children. To improve the understanding of type distribution and viral genetic characterization of HAdV in severe cases, this study enrolled 3404 pediatric SARI cases from eight provinces of China spanning 2017-2021, resulting in the acquisition of 112 HAdV strains. HAdV-type identification, based on three target genes (penton base, hexon, and fiber), confirmed the diversity of HAdV types in SARI cases. Twelve types were identified, including species B (HAdV-3, 7, 55), species C (HAdV-1, 2, 6, 89, 108, P89H5F5, Px1/Ps3H1F1, Px1/Ps3H5F5), and E (HAdV-4). Among these, HAdV-3 exhibited the highest detection rate (44.6%), followed by HAdV-7 (19.6%), HAdV-1 (12.5%), and HAdV-108 (9.8%). All HAdV-3, 7, 55, 4 in this study belonged to dominant lineages circulating worldwide, and the sequences of the three genes demonstrated significant conservation and stability. Concerning HAdV-C, excluding the novel type Px1/Ps3H1F1 found in this study, the other seven types were detected both in China and abroad, with HAdV-1 and HAdV-108 considered the two main types of HAdV-C prevalent in China. Two recombinant strains, including P89H5F5 and Px1/Ps3H1F1, could cause SARI as a single pathogen, warranting close monitoring and investigation for potential public health implications. In conclusion, 5 years of SARI surveillance in China provided crucial insights into HAdV-associated respiratory infections among hospitalized pediatric patients.

Predictors and prognostic implications of left ventricular ejection fraction trajectory improvement in the spectrum of heart failure with reduced and mildly reduced ejection fraction

BACKGROUND

The latest guidelines emphasize the significance of evaluating the left ventricular ejection fraction (LVEF) trajectory in patients with heart failure (HF). Because patients with HF with reduced ejection fraction (HFrEF) and HF with mildly reduced ejection fraction (HFmrEF) have reduction in systolic function, they might be in a trajectory of LVEF improvement after medical and device-based therapies. While previous studies have primarily focused on LVEF improvement in HFrEF, there is limited research on LVEF trajectory improvement across the spectrum of HFrEF and HFmrEF. This study aimed to assess the determinants and prognostic implications of LVEF trajectory improvement in HFrEF and HFmrEF patients.

METHODS

The cohort was classified into the improved group (HFrEF-to-HF with improved ejection fraction (HFimpEF) and HFmrEF-to-HF with preserved ejection fraction (HFpEF)) and the unimproved group (lack of improved group criteria). The primary endpoints were the composite of all-cause mortality or HF hospitalization, all-cause mortality, and HF hospitalization. Predictors of LVEF trajectory improvement were also evaluated.

RESULTS

A total 1303 patients were included in the study (improved/unimproved group: n = 497/806). Cox regression analysis showed that the improved group experienced lower risks of prespecified end points than the unimproved group. Multivariate logistic regression analysis showed that atrial flutter, use of spironolactone, and treatment with catheter ablation were associated with LVEF trajectory improvement, while myocardial infarction, prior percutaneous catheter intervention or coronary artery bypass graft, E/e', and left ventricular end-diastolic diameter were identified as negative predictors of LVEF trajectory improvement. In the improved subgroup, the prognosis for the HFrEF-to-HFimpEF and HFmrEF-to-HFpEF was comparable.

CONCLUSIONS

LVEF trajectory improvement patients had improved clinical outcomes and it was associated with important clinical, baseline cardiac structure and function, and treatment factors. Outcomes were similar in both HFrEF-to-HFimpEF and HFmrEF-to-HFpEF subgroups. These results suggest that emphasis should be placed on LVEF trajectory improvement to improve the outcomes of this population.

State Department of Motor Vehicles Reporting Mandates of Dementia Diagnoses and Dementia Underdiagnosis

Importance

With older drivers representing the fastest growing segment of the driver population and dementia prevalence increasing with age, policymakers face the challenge of balancing road safety and mobility of older adults. In states that require reporting a dementia diagnosis to the Department of Motor Vehicles (DMV), individuals with dementia may be reluctant to disclose symptoms of cognitive decline, and clinicians may be reluctant to probe for those symptoms, which may be associated with missed or delayed diagnoses.

Objective

To assess whether DMV reporting policies for drivers with dementia are associated with primary care clinicians' underdiagnosing dementia.

Design, Setting, and Participants

This cross-sectional study used data from the 100% Medicare fee-for-service program and the Medicare Advantage plans from 2017 to 2019 on 223 036 primary care clinicians with at least 25 Medicare patients. Statistical analysis was performed from July to October 2023.

Exposures

State DMV reporting policies for drivers with dementia.

Main Outcomes and Measures

The main outcome was a binary variable indicating whether the clinician underdiagnosed dementia or not. Each clinician's expected number of dementia cases was estimated using a predictive model based on patient characteristics. Comparing the estimation with observed dementia diagnoses identified clinicians who underdiagnosed dementia vs those who did not, after accounting for sampling errors.

Results

Four states have clinician reporting mandates, 14 have mandates requiring drivers to self-report dementia diagnoses, and 32 states and the District of Columbia do not have explicit requirements. Among primary care clinicians in states with clinician reporting mandates (n = 35 620), 51.4% were female, 91.9% worked in a metropolitan area, and 19.9% of the patient panel were beneficiaries dually eligible for Medicare and Medicaid. Among primary care clinicians in states with patient self-reporting mandates (n = 57 548), 55.7% were female, 83.1% worked in a metropolitan area, and 15.4% of the patient panel were dually eligible for Medicare and Medicaid. Among clinicians in states without mandates, 55.7% were female, 83.0% worked in a metropolitan area, and 14.6% of the patient panel were dually eligible for Medicare and Medicaid. Clinicians in states with clinician reporting mandates had an adjusted 12.4% (95% CI, 10.5%-14.2%) probability of underdiagnosing dementia compared with 7.8% (95% CI, 6.9%-8.7%) in states with self-reporting and 7.7% (95% CI, 6.9%-8.4%) in states with no mandates, an approximately 4-percentage point difference (P < .001).

Conclusions and Relevance

Results of this cross-sectional study of primary care clinicians suggest that mandatory DMV policies for clinicians to report patients with dementia may be associated with a higher risk of missed or delayed dementia diagnoses. Future research is needed to better understand the unintended consequences and the risk-benefit tradeoffs of these policies.

An innovative antibody fusion protein targeting PD-L1, VEGF and TGF-β with enhanced antitumor efficacies

Immunosuppressive pathways in the tumor microenvironment (TME) are inextricably linked to tumor progression. Mono-therapeutics of immune checkpoint inhibitors (ICIs, e.g. antibodies against programmed cell death protein-1/programmed cell death ligand-1, PD-1/PD-L1) is prone to immune escape while combination therapeutics tends to cause high toxicity and side effects. Therefore, using multi-functional molecules to target multiple pathways simultaneously is becoming a new strategy for cancer therapies. Here, we developed a trifunctional fusion protein, DR30206, composed of Bevacizumab (an antibody against VEGF), and a variable domain of heavy chain of heavy chain antibody (VHH) against PD-L1 and the extracellular domain (ECD) protein of TGF-β receptor II (TGF-β RII), which are fused to the N- and C-terminus of Bevacizumab, respectively. The original intention of DR30206 design was to enhance the immune responses pairs by targeting PD-L1 while inhibiting VEGF and TGF-β in the TME. Our data demonstrated that DR30206 exhibits high antigen-binding affinities and efficient blocking capabilities, the principal drivers of efficacy in antibody therapy. Furthermore, the capability of eliciting antibody-dependent cellular cytotoxicity (ADCC) and mixed lymphocyte reaction (MLR) provides a greater possibility to enhance the immune response. Finally, in vivo experiments showed that the antitumor activity of DR30206 was superior to those of monoclonal antibody of PD-L1 or VEGF, PD-L1 and TGF-β bispecific antibody or the combination inhibition of PD-L1 and VEGF. Our findings suggest there is a great potential for DR30206 to become a therapeutic for the treatment of multiple cancer types, especially lung cancer, colon adenocarcinoma and breast carcinoma.

Overexpression of miR-532-5p restrains oxidative stress response of chondrocytes in nontraumatic osteonecrosis of the femoral head by inhibiting ABL1

This study is to probe into the meaning of serum miR-532-5p in nontraumatic osteonecrosis of the femoral head (ONFH), and a molecular mechanism of miR-532-5p in the development of nontraumatic ONFH. This study enrolled 96 patients diagnosed with nontraumatic ONFH and 96 patients with femoral neck fracture. The levels of miR-532-5p, ABL1, MMP-3, MMP-13, and cleaved-caspase3 were determined. Radiographic progression was assessed by ARCO staging system. Visual analog scale (VAS) and Harris hip score (HHS) were employed for evaluation of the symptomatic severity of nontraumatic ONFH. Cell viability and apoptosis in chondrocytes isolated from clinical samples were investigated with CCK-8 and flow cytometry. The levels of lactic dehydrogenase (LDH), superoxide dismutase (SOD), and malondialdehyde (MDA), mitochondrial membrane potential (ΔΨm), and reactive oxygen species (ROS) were determined. miR-532-5p was downregulated in tissues and serum of patients with nontraumatic ONFH, negatively related with ARCO staging and VAS, and positively correlated with HHS. Cell apoptosis, LDH, MDA, and ROS strengthened, while cell viability, ΔΨm, and SOD reduced in chondrocytes of nontraumatic ONFH patients. ABL1 was upregulated in cartilage tissues from nontraumatic ONFH patients. miR-532-5p targeted ABL1, and overexpressed miR-532-5p alleviated nontraumatic ONFH-induced oxidative stress damage of chondrocytes by restraining ABL1. miR-532-5p ameliorated oxidative stress injury in nontraumatic ONFH by inhibiting ABL1.

Bioturbation effect of artificial inoculation on the flavor metabolites and bacterial communities in the Chinese Mao-tofu fermentation

A comparison between artificially inoculated Mao-tofu (CC) and naturally fermented Mao-tofu (MM) indicated that artificially adding Mucor plasmaticus to Mao-tofu dramatically enhanced the essential amino acid (EAA) content, as well as umami and sweet amino acids. Gas chromatography-tandem mass spectrometry (GC-MS/MS) analysis revealed that phenol (3.226 μg/g), 1-octen-3-ol (5.031 μg/g), ethyl heptanoate (1.646 μg/g), and indole (3.422 μg/g) were the key flavor components in Mao-tofu. Unlike MM, CC displayed a substantial increase in esters and a considerable decrease in foul odor substances, including sulfur-containing compounds and indole. Lactococcus raffinolactis, Enterobacter sp. 638, and Streptococcus parauberis KCTC 11537 represented the key bacterial species altering the amino acids and flavor of Mao-tofu according to PacBio single-molecule real-time (SMRT) sequencing and correlation analysis. This study presents the technical feasibility of artificially inoculating Mao-tofu to regulate the core bacterial communities and control the quality of fermented soybean products.

LncRNA NEAT1 promotes angiogenesis of retinoblastoma cells through regulation of the miR-106a/HIF-1α axis

Objective

To explore the role and mechanisms of lncRNA nuclear enriched abundant transcript 1 (NEAT1) in angiogenesis of retinoblastoma (RB) cells.

Methods

This study investigated the roles of NEAT1 in RB progression. The RNA expression levels of NEAT1, miR-106a, and hypoxia-inducible factor-1alpha (HIF-1α) examined by quantitative reverse transcription polymerase chain reaction (RT-qPCR) were compared between RB cells and normal retinal pigment epithelial (RPE) cells. The binding sites between NEAT1 and miR-106a, and between miR-106a and HIF-1α were predicted by the TargetScan database and verified using the dual-luciferase reporter assay. By transfection of overexpression plasmid or shRNA of NEAT1, and/or treatment of miR-106a inhibitor or mimics, proliferation, invasion, and angiogenesis of RB cells (measured by the MTT assay, the Transwell assay, and the tube formation assay, respectively) were compared between groups. Group comparisons were analyzed using one-way analysis of variance (ANOVA), and Tukey's post-hoc test was employed for further statistical assessment. P-value less than 0.05 was considered statistically significant.

Results

The RNA expression levels of NEAT1 and HIF-1α were upregulated in RB cells, whereas the expression level of miR-106a was downregulated compared with RPE cells. NEAT1 overexpression or miR-106a knockdown advanced proliferation, invasion, and tube formation of RB cells. As a target of NEAT1, miR-106a could sponge HIF-1α to downregulate HIF-1α expression level. Functional analyses indicated that miR-106a knockdown reversed the inhibitory effects of NEAT1 silencing on the proliferation, invasion, and tube formation of RB cells. Furthermore, miR-106a overexpression suppressed RB cell angiogenesis by downregulating HIF-1α expression level.

Conclusion

NEAT1 promoted proliferation, invasion, and angiogenesis of RB cells through upregulation of HIF-1α expression level by sponging miR-106a, demonstrating that NEAT1 may be a novel target for RB treatment.

Higher serum carotenoid concentrations were associated with the lower risk of cancer-related death: Evidence from the National Health and Nutrition Examination Survey

The study focuses on the association between serum carotenoids and cancer-related death. Using data from the National Health and Nutrition Examination Survey (2001-2006 and 2017-2018), the study encompasses 10,277 participants older than age 20 years, with recorded baseline characteristics and serum carotenoid concentrations (including α-carotene, trans-β-carotene, cis-β-carotene, β-cryptoxanthin, trans-lycopene, and lutein/zeaxanthin). We hypothesized that serum carotenoid concentrations were negatively associated with cancer-related death. The weighted chi-square analyses indicate significant negative correlations between higher serum concentrations of α-carotene, β-cryptoxanthin, trans-lycopene, and total carotenoids, and the risk of cancer-related deaths. Using weighted Cox regression analysis, this study confirms that α-carotene, β-cryptoxanthin, trans-lycopene, and total carotenoids, as continuous or categorical variables, are inversely related to cancer mortality (P < .0001). Furthermore, considering competitive risk events, lower concentrations of serum β-cryptoxanthin (Fine-Gray P = 1.12e-04), trans-lycopene (P = 5.68e-14), and total carotenoids (P = .03) are associated with an increased risk of cancer-related deaths. The research reveals a crucial inverse relationship between serum carotenoid concentrations and cancer-related death.

Amplitude Analysis of the B^{0}→K^{*0}μ^{+}μ^{-} Decay

An amplitude analysis of the B^{0}→K^{*0}μ^{+}μ^{-} decay is presented using a dataset corresponding to an integrated luminosity of 4.7  fb^{-1} of pp collision data collected with the LHCb experiment. For the first time, the coefficients associated to short-distance physics effects, sensitive to processes beyond the standard model, are extracted directly from the data through a q^{2}-unbinned amplitude analysis, where q^{2} is the μ^{+}μ^{-} invariant mass squared. Long-distance contributions, which originate from nonfactorizable QCD processes, are systematically investigated, and the most accurate assessment to date of their impact on the physical observables is obtained. The pattern of measured corrections to the short-distance couplings is found to be consistent with previous analyses of b- to s-quark transitions, with the largest discrepancy from the standard model predictions found to be at the level of 1.8 standard deviations. The global significance of the observed differences in the decay is 1.4 standard deviations.

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.

Competency scale of quality and safety for greenhand nurses: instrument development and psychometric test

BACKGROUND

Guaranteeing nursing service safety and quality is a prioritized issue in the healthcare setting worldwide. However, there still lacks a valid scale to measure the quality and safety competencies of newly graduated nurses globally.

METHODS

This scale was developed in two phases. In Phase One, a literature review and three-round e-Delphi were conducted to generate the initial item pool; while in Phase Two, five experts tested the content validity of the scale. The construct validity was evaluated using confirmatory factor analysis (CFA), and the data were collected among 1,221 newly graduated nursing students between May, 2017 and August, 2017. Finally, the internal consistency reliability and test-retest reliability were tested.

RESULTS

The final version's Competency Scale of Quality and Safety (CSQS) was confirmed by the CFA involving 64 items in six dimensions, including patient-center care, safety, evidence-based practice, collaboration and teamwork, continuous quality improvement, and informatics. The results of data showed that the data supported the modified model of CSQS (Standardized Root Mean Square Residual = 0.03, p = 0.053, Adjusted Goodness of Normed Fit Index = 1.00, Root Mean Square Error of Approximation = 0.007, Fit Index = 0.95, Goodness of Fit Index = 0.97, χ2/df = 1.06), and the standardized factor loadings of items were from 0.59 to 0.74 (p < 0.05). The internal consistency reliability of the total scale was 0.98, and the test-retest reliability was 0.89.

CONCLUSIONS

CSQS was a valid and reliable instrument to measure the safety and quality abilities of greenhand nurses, and could be fully utilized by nursing students, greenhand nurses, nursing educators, as well as hospital nursing managers.

Effects of nurses-led multidisciplinary-based psychological management in spinal surgery: a retrospective, propensity-score-matching comparative study

BACKGROUND

Patients in spine surgery often have emotional disorders which is caused by multi-factors. Therefore, a multidisciplinary and multimodal intervention program is required to improve emotional disorders during the perioperative period. However, related studies were rare. This study aimed to confirm that the multidisciplinary-based psychological management leading by nurses was effective in treating emotional disorders and show the assignments of the members of the multidisciplinary team with the orientations of nurses.

DESIGN

A retrospective, comparative study.

METHOD

This study was a retrospective cohort research and compared the results between the intervention group and control group using the Huaxi Emotional Distress Index (HEI) which was used to evaluate emotional disorders. The intervention group consisted of patients who underwent surgery between January 2018 and December 2020 after psychological management was implemented. The control group consisted of patients with regular care who underwent surgery between January 2015 and December 2017. To improve comparability between the two groups, baseline data from the recruited patients were analyzed using propensity-score-matching (PSM) based on age, sex, marital status, education, and disease region.

RESULTS

A total of 539 (11.5%) people developed emotional disorders, of which 319 (6.8%), 151 (3.2%) and 69 (1.5%) had mild, moderate mood and severe emotional disorders, respectively. 2107 pairs of patients were matched after PSM. Scores of HEI in the intervention group were heightened compared with those in the control group (P<0.001) after matching. Moreover, the incidence of emotional disorders in patients decreased after implementing psychological management (P = 0.001). The severity of emotional disorders was alleviated with statistical significance as well (P = 0.010).

CONCLUSIONS

Nurses-led Multidisciplinary-Based psychological management was able to reduce the incidence of emotional disorders and improve the severity of these in spine surgery patients.

Optimizing Contingency Management with Reinforcement Learning

Contingency Management (CM) is a psychological treatment that aims to change behavior with financial incentives. In substance use disorders (SUDs), deployment of CM has been enriched by longstanding discussions around the cost-effectiveness of prized-based and voucher-based approaches. In prize-based CM, participants earn draws to win prizes, including small incentives to reduce costs, and the number of draws escalates depending on the duration of maintenance of abstinence. In voucher-based CM, participants receive a predetermined voucher amount based on specific substance test results. While both types have enhanced treatment outcomes, there is room for improvement in their cost-effectiveness: the voucher-based system requires enduring financial investment; the prize-based system might sacrifice efficacy. Previous work in computational psychiatry of SUDs typically employs frameworks wherein participants make decisions to maximize their expected compensation. In contrast, we developed new frameworks that clinical decision-makers choose actions, CM structures, to reinforce the substance abstinence behavior of participants. We consider the choice of the voucher or prize to be a sequential decision, where there are two pivotal parameters: the prize probability for each draw and the escalation rule determining the number of draws. Recent advancements in Reinforcement Learning, more specifically, in off-policy evaluation, afforded techniques to estimate outcomes for different CM decision scenarios from observed clinical trial data. We searched CM schemas that maximized treatment outcomes with budget constraints. Using this framework, we analyzed data from the Clinical Trials Network to construct unbiased estimators on the effects of new CM schemas. Our results indicated that the optimal CM schema would be to strengthen reinforcement rapidly in the middle of the treatment course. Our estimated optimal CM policy improved treatment outcomes by 32% while maintaining costs. Our methods and results have broad applications in future clinical trial planning and translational investigations on the neurobiological basis of SUDs.

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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