Associations of thyroid feedback quantile-based index with diabetes in euthyroid adults in the United States and China

BACKGROUND

We aimed to investigate the associations between thyroid hormone sensitivity indices and diabetes in euthyroid adults in the United States and China.

METHODS

2296 euthyroid adults from the NHANES in the United States and 8319 euthyroid adults from the SPEED-Shunde in China were involved. The thyroid sensitivity indices, namely TFQIFT4 and TFQIFT3, were calculated. Multivariable logistic regression, restricted cubic spline analysis, and general ordinal logit regression were utilized.

RESULTS

In the NHANES, compared with participants in quartile 1st (Q1), those in Q4 of TFQIFT3 (OR 2.12, 95% CI (1.18, 3.81)) and those in Q3 of TFQIFT4 (OR 2.31, 95% CI (1.18, 4.53)) (both P for trend < 0.05) were associated with a greater prevalence of diabetes. In the SPEED-Shunde, compared with participants in Q1, those in Q4 of TFQIFT3 had a greater prevalence of diabetes (OR 1.36, 95% CI (1.11, 1.66) (P for trend < 0.05), while no significant associations between TFQIFT4 and diabetes were found.

CONCLUSIONS

TFQIFT3 was associated with a higher prevalence of diabetes both in the United States and China. However, TFQIFT4 was only associated with a higher prevalence of diabetes in the United States, not in China. Further prospective cohort studies are necessary to validate these findings.

Development of a LC-QTOF-MS based dilute-and-shoot approach for the botanical discrimination of honeys

Honeys of particular botanical origins can be associated with premium market prices, a trait which also makes them susceptible to fraud. Currently available authenticity testing methods for botanical classification of honeys are either time-consuming or only target a few "known" types of markers. Simple and effective methods are therefore needed to monitor and guarantee the authenticity of honey. In this study, a 'dilute-and-shoot' approach using liquid chromatography (LC) coupled to quadrupole time-of-flight-mass spectrometry (QTOF-MS) was applied to the non-targeted fingerprinting of honeys of different floral origin (buckwheat, clover and blueberry). This work investigated for the first time the impact of different instrumental conditions such as the column type, the mobile phase composition, the chromatographic gradient, and the MS fragmentor voltage (in-source collision-induced dissociation) on the botanical classification of honeys as well as the data quality. Results indicated that the data sets obtained for the various LC-QTOF-MS conditions tested were all suitable to discriminate the three honeys of different floral origin regardless of the mathematical model applied (random forest, partial least squares-discriminant analysis, soft independent modelling by class analogy and linear discriminant analysis). The present study investigated different LC-QTOF-MS conditions in a "dilute and shoot" method for honey analysis, in order to establish a relatively fast, simple and reliable analytical method to record the chemical fingerprints of honey. This approach is suitable for marker discovery and will be used for the future development of advanced predictive models for honey botanical origin.

[Methylthioadenosine phosphorylase and p16 as surrogate diagnostic markers for CDKN2A homozygous deletion in brain tumors]

Objective: To examine whether immunohistochemistry of methylthioadenosine phosphorylase (MTAP) and p16 could be used to predict the CDKN2A status in various brain tumors. Methods: A total of 118 cases of IDH-mutant astrocytomas, 16 IDH-wildtype glioblastoma, 17 polymorphic xanthoastrocytoma (PXA) and 20 meningiomas diagnosed at Xuanwu Hospital, Capital Medical University, Beijing, China from November 2017 to October 2023 were collected and analyzed. The CDKN2A status was detected by using fluorescence in situ hybridization or next-generation sequencing. Expression of MTAP and p16 proteins was detected with immunohistochemistry. The association of loss of MTAP/p16 expression with CDKN2A homozygous/heterozygous deletion was examined. Results: Among the 118 cases of IDH-mutant astrocytoma, 13 cases showed homozygous deletion of CDKN2A. All of them had no expression of MTAP while 9 cases had no expression of p16. Among the 16 cases of IDH wild-type glioblastoma, 6 cases showed homozygous deletion of CDKN2A. All 6 cases had no expression of MTAP, while 3 of these cases had no expression of p16 expression. Among the 17 PXA cases, 4 cases showed homozygous deletion of CDKN2A, and the expression of MTAP and p16 was also absent in these 4 cases. Among the 20 cases of meningiomas, 4 cases showed homozygous deletion of CDKN2A. Their expression of MTAP and p16 was also absent. Among the four types of brain tumors, MTAP was significantly correlated with CDKN2A homozygous deletion (P<0.05), with a sensitivity of 100%. However, it was only significantly correlated with the loss of heterozygosity (LOH) of CDKN2A in astrocytomas (P<0.001). P16 was associated with CDKN2A homozygous deletion in IDH-mutant astrocytoma and PXA (P<0.001), but not with the LOH of CDKN2A. Its sensitivity and specificity were lower than that of MTAP. Conclusions: MTAP could serve as a predictive surrogate for CDKN2A homozygous deletion in adult IDH-mutant astrocytoma, PXA, adult IDH-wildtype glioblastoma and meningioma. However, p16 could only be used in the first two tumor types, and its specificity and sensitivity are lower than that of MTAP.

Biomonitoring of bisphenol A (BPA) and bisphenol analogues in human milk from South Africa and Canada using a modified QuEChERS extraction method

A sensitive modified QuEChERS extraction method was developed to assess the levels of free and conjugated bisphenols (BPs) in human milk collected between 2018 and 2019 from two regions of South Africa (the Limpopo Province Vhembe district, n = 194; Pretoria, n = 193) and Canada (Montreal, n = 207). Total BPA (free and conjugated) and BPS were the predominant bisphenols detected in samples from Vhembe and Pretoria, whereas total BPS was the predominant bisphenol detected in Montreal samples. The levels of total BPA in samples from Vhembe and Pretoria ranged between < MDL-18.61 and Collapse

Key Words

• Biomonitoring
• Bisphenols
• Canada
• Human milk
• Liquid chromatography–mass spectrometry
• South Africa

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MESH Headings

• Humans
• South Africa
• Biological Monitoring
• Milk, Human/chemistry
• Benzhydryl Compounds/analysis
• Canada
• Phenols

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Affiliation(s)

Zhi Hao Chi Department of Food Science and Agricultural Chemistry, McGill University, Montreal, QC, Canada
Lan Liu Department of Food Science and Agricultural Chemistry, McGill University, Montreal, QC, Canada
Jingyun Zheng Department of Food Science and Agricultural Chemistry, McGill University, Montreal, QC, Canada
Lei Tian Department of Food Science and Agricultural Chemistry, McGill University, Montreal, QC, Canada
Jonathan Chevrier Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
Riana Bornman University of Pretoria, Pretoria, South Africa
Muvhulawa Obida University of Pretoria, Pretoria, South Africa
Cynthia Gates Goodyer McGill University Health Centre Research Institute, Montreal, QC, Canada
Barbara F Hales Department of Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
Stéphane Bayen Department of Food Science and Agricultural Chemistry, McGill University, Montreal, QC, Canada.

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Dipeptidyl peptidase 4 inhibitors vs. metformin for new-onset dementia: a propensity score-matched cohort study

BACKGROUND

Hypoglycemic pharmacotherapy interventions for alleviating the risk of dementia remains controversial, particularly about dipeptidyl peptidase 4 (DPP4) inhibitors versus metformin. Our objective was to investigate whether the initiation of DPP4 inhibitors, as opposed to metformin, was linked to a reduced risk of dementia.

METHODS

We included individuals with type 2 diabetes over 40 years old who were new users of DPP4 inhibitors or metformin in the Chinese Renal Disease Data System (CRDS) database between 2009 and 2020. The study employed Kaplan-Meier and Cox regression for survival analysis and the Fine and Gray model for the competing risk of death.

RESULTS

Following a 1:1 propensity score matching, the analysis included 3626 DPP4 inhibitor new users and an equal number of metformin new users. After adjusting for potential confounders, the utilization of DPP4 inhibitors was associated with a decreased risk of all-cause dementia compared to metformin (hazard ratio (HR) 0.63, 95% confidence interval (CI) 0.45-0.89). Subgroup analysis revealed that the utilization of DPP4 inhibitors was associated with a reduced incidence of dementia in individuals who initiated drug therapy at the age of 60 years or older (HR 0.69, 95% CI 0.48-0.98), those without baseline macrovascular complications (HR 0.62, 95% CI 0.41-0.96), and those without baseline microvascular complications (HR 0.67, 95% CI 0.47-0.98).

CONCLUSION

In this real-world study, we found that DPP4 inhibitors presented an association with a lower risk of dementia in individuals with type 2 diabetes than metformin, particularly in older people and those without diabetes-related comorbidities.

Effects of Tirofiban on Neurological Deterioration in Patients With Acute Ischemic Stroke: A Randomized Clinical Trial

Importance

Evidence supports using antiplatelet therapy in patients with acute ischemic stroke. However, neurological deterioration remains common under the currently recommended antiplatelet regimen, leading to poor clinical outcomes.

Objective

To determine whether intravenous tirofiban administered within 24 hours of stroke onset prevents early neurological deterioration in patients with acute noncardioembolic stroke compared with oral aspirin.

Design, Setting, and Participants

This investigator-initiated, multicenter, open-label, randomized clinical trial with blinded end-point assessment was conducted at 10 comprehensive stroke centers in China between September 2020 and March 2023. Eligible patients were aged 18 to 80 years with acute noncardioembolic stroke within 24 hours of onset and had a National Institutes of Health Stroke Scale (NIHSS) score of 4 to 20.

Intervention

Patients were assigned randomly (1:1) to receive intravenous tirofiban or oral aspirin for 72 hours using a central, web-based, computer-generated randomization schedule; all patients then received oral aspirin.

Main Outcome

The primary efficacy outcome was early neurological deterioration (increase in NIHSS score ≥4 points) within 72 hours after randomization. The primary safety outcome was symptomatic intracerebral hemorrhage within 72 hours after randomization.

Results

A total of 425 patients were included in the intravenous tirofiban (n = 213) or oral aspirin (n = 212) groups. Median (IQR) age was 64.0 years (56.0-71.0); 124 patients (29.2%) were female, and 301 (70.8%) were male. Early neurological deterioration occurred in 9 patients (4.2%) in the tirofiban group and 28 patients (13.2%) in the aspirin group (adjusted relative risk, 0.32; 95% CI, 0.16-0.65; P = .002). No patients in the tirofiban group experienced intracerebral hemorrhage. At 90-day follow-up, 3 patients (1.3%) in the tirofiban group and 3 (1.5%) in the aspirin group died (adjusted RR, 1.15; 95% CI, 0.27-8.54; P = .63), and the median (IQR) modified Rankin scale scores were 1.0 (0-1.25) and 1.0 (0-2), respectively (adjusted odds ratio, 1.28; 95% CI, 0.90-1.83; P = .17).

Conclusions and Relevance

In patients with noncardioembolic stroke who were seen within 24 hours of symptom onset, tirofiban decreased the risk of early neurological deterioration but did not increase the risk of symptomatic intracerebral hemorrhage or systematic bleeding.

Trial Registration

ClinicalTrials.gov Identifier: NCT04491695.

Thermo-growing ion clusters enabled healing strengthening and tough adhesion for highly reliable skin electronics

Self-healing and self-adhesion capacities are essential for many modern applications such as skin-interfaced electronics for improving longevity and reliability. However, the self-healing efficiency and adhesive toughness of most synthetic polymers are limited to their original network, making reliability under dynamic deformation still challenging. Herein, inspired by the growth of living organisms, a highly stretchable supramolecular elastomer based on thermo-responsive ion clusters and a dynamic polysulfide backbone was developed. Attributed to the synergic growth of ion clusters and dynamic exchange of disulfide bonds, the elastomer exhibited unique healing strengthening (healing efficiency >200%) and thermo-enhanced tough adhesion (interfacial toughness >500 J m-2) performances. To prove its practical application in highly reliable skin electronics, we further composited the elastomer with a zwitterion to prepare a highly conductive ionic elastomer and applied it in wearable strain sensing and long-term electrophysiological detection. This work provides a new avenue to realize high reliability in skin interfaced electronics.

[The mechanical ventilation guided by electrical impedance tomography in acute respiratory distress syndrome]

Acute respiratory distress syndrome (ARDS) is a common and critical clinical condition characterized by diffuse damage to the lung interstitium, alveoli, and increased permeability of pulmonary blood vessels. CT can be used to assess the imaging features, severity, and prediction of ARDS, but it requires patient transportation to the CT room and is only a static examination. Electrical impedance tomography (EIT) is an increasingly widely used monitoring tool in clinical applications in recent years. It enables continuous real-time assessment of lung ventilation distribution at the bedside and has high clinical value in optimizing mechanical ventilation parameters for critically ill patients. This article introduces the basic principles of EIT and how to better utilize EIT technology to guide mechanical ventilation treatment for ARDS patients.

Clinical effects of combined use of carbamazepine and amitriptyline in the treatment of diabetic neuropathy with concurrent diabetic foot

OBJECTIVE

This study aims to analyze the clinical effects of combining carbamazepine and amitriptyline in the treatment of diabetic neuropathy with concurrent diabetic foot.

METHODS

A total of 120 diabetic neuropathy patients treated at our hospital from June 2022 to November 2023 were included in the study. Patients meeting the inclusion criteria were registered, and their basic data were collected. The patients were randomly divided into two groups: the control group treated with amitriptyline and the study group treated with a combination of carbamazepine and amitriptyline.

RESULTS

The study group demonstrated significantly better clinical efficacy compared to the control group (p < 0.05). There were no significant differences in psychological status and pain perception before treatment between the two groups (p > 0.05). However, post-treatment, the study group showed improved psychological status, reduced pain perception, and overall better quality of life in both physiological and psychological dimensions compared to the control group (p < 0.05).

CONCLUSION

The combined use of carbamazepine and amitriptyline in the treatment of diabetic neuropathy with concurrent diabetic foot yields positive clinical outcomes. It effectively alleviates symptoms, improves psychological well-being, reduces pain sensation, and enhances overall quality of life. These findings can guide physicians in adopting a more evidence-based treatment approach and provide patients with more effective individualized treatment strategies.

[New 2023 global definition of acute respiratory distress syndrome: progress and limitation]

Acute respiratory distress syndrome (ARDS) presents a challenge in clinical diagnosis as it lacks a definitive gold standard. Over the past 55 years, there have been several revisions to the definition of ARDS. With the progress of clinical practice and scientific research, the limitations of the "Berlin definition" have become increasingly evident. In response to these changes, the 2023 global definition of ARDS aims to address these issues by expanding the diagnostic targets, chest imaging, and methods for assessing hypoxia. Additionally, the new definition increases the diagnostic criteria to accommodate resource-constrained settings. The expansion facilitates early identification and treatment interventions for ARDS, thereby advancing epidemiological and clinically related research. Nevertheless, the broad nature of this revision may include patients who do not actually have ARDS, thus raising the risk of false-positive diagnoses. Therefore, additional verification is crucial to ascertain the validity and accuracy of the 2023 global definition of ARDS.

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

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

Clinical Analysis of Risk Factors and Perinatal Outcomes in Recurrent Pre-Eclampsia with Severe Features

To analyze the differences in risk factors and pregnancy outcomes between recurrent and initial pre-eclampsia(PE) with severe features. Data from recurrent (n = 128) and initial (n = 904) PE with severe features who terminated their pregnancy or gave birth at 20 weeks of gestation or later at the tertiary teaching hospital (Women's Hospital of Nanjing Medical University, Nanjing Women and Children's Healthcare Hospital) from January 2016 to December 2022 were collected. Risk factors for recurrent PE with severe features and differences in pregnancy outcomes between the two groups were assessed using the chi-square test, student t-test, or nonparametric test. Independent risk factors for recurrent PE with severe features were further analyzed by logistic regression. (1) Logistic regression analysis identified 3 independent risk factors for recurrent PE with severe features: history of cesarean section, rural residence and chronic hypertension. In addition, assisted reproductive technology (ART) is an independent risk factor for initial PE with severe features; (2) The incidence of oligohydramnios, chorioamnionitis, preterm birth, stillbirth, fetal growth restriction (FGR) and abnormal umbilical blood flow was higher in the recurrent PE with severe features group than in the initial PE with severe features group(P < 0.05). In contrast, the incidence of premature rupture of membrane (PROM) and postpartum hemorrhage (PPH) was higher in the group of initial PE with severe features(P < 0.05); (3) In the recurrent PE with severe features group, gestational age(GA) of birth and birth weight were lower than those in the initial PE with severe features group(P < 0.05). Also, the incidence of mild asphyxia, the rate of neonatal intensive care unit (NICU) hospitalization, length of stay in NICU, and the rate of abandoning treatment in the recurrent PE with severe features group were higher than those in the initial PE with severe features group(P < 0.05). 3 independent risk factors was identified for recurrent PE with severe features: history of cesarean section, rural residence and chronic hypertension. Women with recurrent PE with severe features are more likely to have adverse perinatal outcomes than those with initial PE with severe features.

[Comparison of the microbiota diversity between autogenous and anautogenous Culex pipiens pallens]

OBJECTIVE

To investigate the microbiota composition and diversity between autogenous and anautogenous Culex pipiens pallens, so as to provide insights into unraveling the pathogenesis of autogeny in Cx. pipiens pallens.

METHODS

Autogenous and anautogenous adult Cx. pipiens pallens samples were collected at 25 ℃, and the hypervariable regions of the microbial 16S ribosomal RNA (16S rRNA) gene was sequenced on the Illumina NovaSeq 6000 sequencing platform. The microbiota abundance and diversity were evaluated using the alpha diversity index, and the difference in the microbiota structure was examined using the beta diversity index. The microbiota with significant differences in the abundance between autogenous and anautogenous adult Cx. pipiens pallens samples was identified using the linear discriminant analysis effect size (LEfSe).

RESULTS

The microbiota in autogenous and anautogenous Cx. pipiens pallens samples belonged to 18 phyla, 28 classes, 70 orders, 113 families, and 170 genera, and the dominant phyla included Proteobacteria, Bacteroidetes, and so on. At the genus level, Wolbachia was a common dominant genus, and the relative abundance was (77.6 ± 11.3)% in autogenous Cx. pipiens pallens samples and (47.5 ± 8.5)% in anautogenous mosquito samples, while Faecalibaculum (0.4% ± 0.1%), Dubosiella (0.5% ± 0.0%) and Massilia (0.5% ± 0.1%) were specific species in autogenous Cx. pipiens pallens samples. Alpha diversity analysis showed that higher Chao1 index and ACE index in autogenous Cx. pipiens pallens samples than in anautogenous samples (both P values > 0.05), and lower Shannon index (P > 0.05) and Simpson index (P < 0.05) in autogenous Cx. pipiens pallens samples than in anautogenous samples. LEfSe analysis showed a total of 48 significantly different taxa between autogenous and anautogenous Cx. pipiens pallens samples (all P values < 0.05).

CONCLUSIONS

There is a significant difference in the microbiota diversity between autogenous and anautogenous Cx. pipiens pallens.

Analysis of the Correlation Between Dynamic Characteristics and Symptoms of Gastroesophageal Reflux Disease

OBJECTIVE

To investigate the esophageal motility characteristics of gastroesophageal reflux disease (GERD) and their relationship with symptoms.

PATIENTS AND METHODS

We examined 101 patients diagnosed with GERD by endoscopy and divided them into 3 groups as follows: nonerosive reflux disease (NERD), reflux esophagitis, and Barrett esophagus. Esophageal high-resolution manometry and the GERD Questionnaire were used to investigate the characteristics of esophageal dynamics and symptoms. In addition, the reflux symptom index was completed and the patients were divided into 7 groups according to symptoms. We then determined the correlation between dynamic esophageal characteristics and clinical symptoms.

RESULTS

Upper (UES) and lower (LES) esophageal sphincter pressures and the 4-second integrated relaxation pressure in the RE group were lower than those in the NERD group. The 4-second integrated relaxation pressure in the Barrett esophagus group was also lower than that in the NERD group. In the analysis of extraesophageal symptoms, high-resolution manometry showed significant differences in UES pressures among all groups. Further subgroup analysis showed that compared with the group without extraesophageal symptoms, the UES pressure of the groups with pharyngeal foreign body sensation, throat clearing, and multiple extraesophageal symptoms was lower.

CONCLUSIONS

As GERD severity increases, motor dysfunction of the LES and esophageal body gradually worsens, and the LES plays an important role in GERD development. Decreased UES pressure plays an important role in the occurrence of extraesophageal symptoms, which is more noticeable in patients with pharyngeal foreign body sensation and throat clearing.

A comprehensive review on the inherent and enhanced antifouling mechanisms of hydrogels and their applications

Biofouling remains a persistent challenge within the domains of biomedicine, tissue engineering, marine industry, and membrane separation processes. Multifunctional hydrogels have garnered substantial attention due to their complex three-dimensional architecture, hydrophilicity, biocompatibility, and flexibility. These hydrogels have shown notable advances across various engineering disciplines. The antifouling efficacy of hydrogels typically covers a range of strategies to mitigate or inhibit the adhesion of particulate matter, biological entities, or extraneous pollutants onto their external or internal surfaces. This review provides a comprehensive review of the antifouling properties and applications of hydrogels. We first focus on elucidating the fundamental principles for the inherent resistance of hydrogels to fouling. This is followed by a comprehensive investigation of the methods employed to enhance the antifouling properties enabled by the hydrogels' composition, network structure, conductivity, photothermal properties, release of reactive oxygen species (ROS), and incorporation of silicon and fluorine compounds. Additionally, we explore the emerging prospects of antifouling hydrogels to alleviate the severe challenges posed by surface contamination, membrane separation and wound dressings. The inclusion of detailed mechanistic insights and the judicious selection of antifouling hydrogels are geared toward identifying extant gaps that must be bridged to meet practical requisites while concurrently addressing long-term antifouling applications.

Multicenter registry study of cerebral venous thrombosis in china (RETAIN-CH): Rationale and design

BACKGROUND AND RATIONALE

Cerebral venous thrombosis (CVT) is a rare cerebrovascular disorder that mainly affects young and middle-aged adults. Epidemiological data on the incidence, risk factors, diagnosis, treatment, and prognosis of CVT are lacking in China. In addition, there is a lack of evidence from large, multicenter, real-world studies on the efficacy and safety of endovascular.

AIM

To understand the incidence, diagnosis and treatment status of CVT in China and to estimate the effectiveness and safety of endovascular treatment in the real-world.

METHODS

A multicenter, retrospective observational cohort study will be conducted on CVT patient records from 104 hospitals, between January 1, 2018 and June 30, 2022, identified using a 2-stage cluster sampling design based on per capita gross domestic product. Each enrolled participant is required to complete a further follow-up, which includes the current situation and the assessment at 3 and 12 months after discharge.

STUDY OUTCOMES

The outcomes of this study will include the current status of the incidence, pathogenesis, etiology, clinical symptoms, diagnosis, and treatment of CVT in China, as well as the effectiveness and safety of endovascular treatment in the real-world.

DISCUSSION

Results from this study will provide evidence on the incidence, specific risk factors, symptomatic and imaging features, and clinical outcomes of CVT in China as well as indicate whether endovascular treatment is superior to medical management alone for patients with acute CVT in the real-world.

TRIAL REGISTRATION

http://www.

CLINICALTRIALS

gov.

IDENTIFIER

NCT05448248.

Clinicopathological features and prognostic factors of salivary gland myoepithelial carcinoma: institutional experience of 42 cases

Myoepithelial carcinoma (MECA) is a rare type of carcinoma for which the clinicopathological features and prognostic factors have not yet been fully clarified. A retrospective study of 42 patients diagnosed with salivary gland MECA was performed, focusing on the clinicopathological features and prognostic factors. Of the 42 patients, 20 died of cancer, 20 lived without tumour, one lived with distant metastasis, and one was lost to follow-up. Overall, 69.0% had tumour recurrence, 16.7% had cervical nodal metastasis, and 21.4% had distant metastasis. The 5-year overall survival rate was 70.2%. Kaplan-Meier analysis revealed that patients with pathological positive lymph nodes (pN+), multiple recurrences of tumour, and higher histological grade had worse overall survival. Multivariate Cox analysis indicated pN+ and higher histological grade to be independent predictors of decreased survival. The 5-year overall survival rate in the pN0 group was 87.5%, while that in the pN+ group was 28.6%. In conclusion, myoepithelial carcinoma can be defined as a tumour with a high incidence of recurrence and poor prognosis, especially in pN+ patients. Pathological positive lymph nodes and histological grade may serve as predictors of survival.

Intratumoral and Peritumoral Multiparametric MRI-Based Radiomics Signature for Preoperative Prediction of Ki-67 Proliferation Status in Glioblastoma: A Two-Center Study

RATIONALE AND OBJECTIVES

To assess the predictive ability of intratumoral and peritumoral multiparametric magnetic resonance imaging (MRI)-based radiomics signature (RS) for preoperative prediction of Ki-67 proliferation status in glioblastoma. MATERIALS AND METHODS: A total of 205 patients with glioblastoma at two institutions were retrospectively analyzed. Data from institution 1 (n = 158) were used to develop the predictive model, and as an internal test dataset, data from institution 2 (n = 47) constitute the external test dataset. Feature selection was performed using spearman correlation coefficient, univariate ranking method, and the least absolute shrinkage and selection operator algorithm. RSs were established using a logistic regression algorithm. The predictive performance of the RSs was assessed using calibration curve, decision curve analysis (DCA), and area under the curve (AUC).

RESULTS

In the RSs based on single-parametric (contrast-enhanced T1-weighted image, T2-weighted image, or apparent diffusion coefficient maps), the AUCs of intratumoral, peritumoral, and combined area (intratumoral and peritumoral) were 0.60-0.67, with no significant difference among them. The RSs that using multiparametric features (integrating the previously mentioned three sequences) showed improved AUC compared to the single-parametric RSs; AUC reached 0.75-0.89. Among them, the multiparametric RS based on radiomics features of the combined area (Multi-Com) exhibited the highest performance, with an internal test dataset AUC of 0.89 (95% confidence interval (CI) 0.75-1.00) and an external test dataset AUC of 0.88 (95% CI 0.78-0.97). The calibration curve and DCA display RS (Multi-Com) have good calibration ability and clinical applicability.

CONCLUSION

The multiparametric MRI-based RS combining intratumoral and peritumoral features can serve as a noninvasive and effective tool for preoperative assessment of Ki-67 proliferation status in glioblastoma.

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

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

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

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

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Affiliation(s)

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

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Association of metabolic syndrome and sarcopenia with all-cause and cardiovascular mortality: a prospective cohort study based on the NHANES

Background

Metabolic syndrome (MetS) and sarcopenia (SP) have emerged as significant public health concerns in contemporary societies, characterized by shared pathophysiological mechanisms and interrelatedness, leading to profound health implications. In this prospective cohort study conducted within a US population, we aimed to examine the influence of MetS and SP on all-cause and cardiovascular mortality.

Methods

This study analyzed data from the National Health and Nutrition Examination Survey (NHANES) III for the years 1999-2006 and 2011-2018, and death outcomes were ascertained by linkage to National Death Index (NDI) records through December 31, 2019. Cox proportional hazard models were used to estimate hazard ratios (HRs) and 95% confidence intervals (95% CIs) for all-cause and cardiovascular mortality. In addition, subgroup and sensitivity analyses were conducted to test the robustness of the results.

Results

Over a median follow-up period of 13.3 years (95% CI: 12.8-13.8), 1714 deaths were observed. The groups characterized by MetS-/SP+, MetS+/SP-, and MetS+/SP+ exhibited higher all-cause mortality rates in comparison to the MetS-/SP- group, with the MetS+/SP+ group (HR 1.76, 95% CI: 1.37-2.25) displaying the highest all-cause mortality. Increased cardiovascular mortality was observed in the MetS+/SP- (HR 1.84, 95% CI: 1.24-2.72), and MetS+/SP+ groups (HR 2.39, 95% CI: 1.32-4.35) compared to the MetS-/SP- group, whereas it was not statistically significant in the MetS-/SP+ group. However, among males and individuals aged < 60, the presence of both MetS and SP (MetS+/SP+ group) was found to be significantly associated with a higher risk of all-cause and cardiovascular mortality.

Conclusion

The coexistence of MetS and SP increased the risk of all-cause and cardiovascular mortality, particularly in males and in nonelderly populations. Individuals with either MetS or SP may require more careful management to prevent the development of other diseases and thereby reduce mortality.

Air enema reduction versus hydrostatic enema reduction for intussusceptions in children: A systematic review and meta-analysis

OBJECTIVES

We conducted a comprehensive meta-analysis to compare the effectiveness and safety of fluoroscopy-guided air enema reduction (FGAR) and ultrasound-guided hydrostatic enema reduction (UGHR) for the treatment of intussusception in pediatric patients.

METHODS

A systematic review and meta-analysis were conducted on retrospective studies obtained from various databases, including PUBMED, MEDLINE, Cochrane, Google Scholar, China National Knowledge Infrastructure (CNKI), WanFang, and VIP Database. The search included publications from January 1, 2003, to March 31, 2023, with the last search done on Jan 15, 2023.

RESULTS

We included 49 randomized controlled studies and retrospective cohort studies involving a total of 9,391 patients, with 4,841 in the UGHR and 4,550 in the FGAR. Specifically, UGHR exhibited a significantly shorter time to reduction (WMD = -4.183, 95% CI = (-5.402, -2.964), P < 0.001), a higher rate of successful reduction (RR = 1.128, 95% CI = (1.099, 1.157), P < 0.001), and a reduced length of hospital stay (WMD = -1.215, 95% CI = (-1.58, -0.85), P < 0.001). Furthermore, UGHR repositioning was associated with a diminished overall complication rate (RR = 0.296, 95% CI = (0.225, 0.389), P < 0.001) and a lowered incidence of perforation (RR = 0.405, 95% CI = (0.244, 0.670), P < 0.001).

CONCLUSION

UGHR offers the benefits of being non-radioactive, achieving a shorter reduction time, demonstrating a higher success rate in repositioning in particular, resulting in a reduced length of postoperative hospital stay, and yielding a lower overall incidence of postoperative complications, including a reduced risk of associated perforations.

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

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

Prevalence and risk factors of MAFLD and its metabolic comorbidities in community-based adults in China: A cross-sectional study

AIMS

There is a growing interest in the co-management of metabolic dysfunction-associated fatty liver disease (MAFLD) and its metabolic comorbidities. However, there is insufficient epidemiological data regarding MAFLD and its metabolic comorbidities in China. This study aims to investigate the prevalence and risk factors of MAFLD and its metabolic comorbidities.

METHODS

9171 participants were recruited in this cross-sectional study, utilizing a multistage, stratified sampling method. All participants underwent a comprehensive assessment. The diagnosis of MAFLD was based on vibration-controlled transient elastography (VCTE). The prevalence of MAFLD and its metabolic comorbidities was calculated. Binary and ordinary logistic regressions were conducted.

RESULTS

The overall weighted prevalence of MAFLD was 21.18%. Of the 2081 adults with MAFLD, 1866 (89.67%) had more than one metabolic comorbidity, and only 215 (10.33%) did not have comorbidity. Among the population with MAFLD, the prevalence of dyslipidemia, hypertension, hyperuricemia, and diabetes was 67.47%, 43.73%, 39.10%, and 33.88%, respectively. Advanced age, male gender, overweight/obesity, excessive alcohol consumption, and elevated HOMA-IR levels were positively correlated with the number of MAFLD-related metabolic comorbidities.

CONCLUSIONS

A significant proportion of individuals diagnosed with MAFLD presented with metabolic comorbidities. Therefore, engaging in the co-management of MAFLD and its metabolic comorbidities is imperative.

Contrast-enhanced CT radiomics for preoperative prediction of stage in epithelial ovarian cancer: a multicenter study

BACKGROUND

Preoperative prediction of International Federation of Gynecology and Obstetrics (FIGO) stage in patients with epithelial ovarian cancer (EOC) is crucial for determining appropriate treatment strategy. This study aimed to explore the value of contrast-enhanced CT (CECT) radiomics in predicting preoperative FIGO staging of EOC, and to validate the stability of the model through an independent external dataset.

METHODS

A total of 201 EOC patients from three centers, divided into a training cohort (n = 106), internal (n = 46) and external (n = 49) validation cohorts. The least absolute shrinkage and selection operator (LASSO) regression algorithm was used for screening radiomics features. Five machine learning algorithms, namely logistic regression, support vector machine, random forest, light gradient boosting machine (LightGBM), and decision tree, were utilized in developing the radiomics model. The optimal performing algorithm was selected to establish the radiomics model, clinical model, and the combined model. The diagnostic performances of the models were evaluated through receiver operating characteristic analysis, and the comparison of the area under curves (AUCs) were conducted using the Delong test or F-test.

RESULTS

Seven optimal radiomics features were retained by the LASSO algorithm. The five radiomics models demonstrate that the LightGBM model exhibits notable prediction efficiency and robustness, as evidenced by AUCs of 0.83 in the training cohort, 0.80 in the internal validation cohort, and 0.68 in the external validation cohort. The multivariate logistic regression analysis indicated that carcinoma antigen 125 and tumor location were identified as independent predictors for the FIGO staging of EOC. The combined model exhibited best diagnostic efficiency, with AUCs of 0.95 in the training cohort, 0.83 in the internal validation cohort, and 0.79 in the external validation cohort. The F-test indicated that the combined model exhibited a significantly superior AUC value compared to the radiomics model in the training cohort (P < 0.001).

CONCLUSIONS

The combined model integrating clinical characteristics and radiomics features shows potential as a non-invasive adjunctive diagnostic modality for preoperative evaluation of the FIGO staging status of EOC, thereby facilitating clinical decision-making and enhancing patient outcomes.

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

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

Effectiveness of Continuous Care Interventions in Elderly Patients with High-Risk Pressure Ulcers and Impact on Patients' Activities of Daily Living

Objective

To explore the influence of continuous care interventions on the incidence of pressure ulcers in elderly patients with high-risk pressure ulcers and their activities of daily living (ADL).

Methods

The clinical records of 114 elderly patients at high risk of pressure ulcers (Patients had a Braden pressure ulcer risk score less than or equal to 18 and suffered no pressure pressure ulcers) who attended our hospital from February 2020 to February 2022 were collected for retrospective analysis. Among them, 54 patients treated with conventional nursing interventions were assigned to the control group (CG) (64-91 years old), and the remaining 60 treated with continuous care interventions were assigned to the observation group (OG) (61-91years old). The detection indices and clinical related data were collected from LIS system of our hospital. The self-rating depression scale (SDS) and self-rating anxiety scale (SAS) scores of the two groups were compared before and after the intervention, and the improvement in quality of life and ADL scores were counted. The post-care satisfaction and incidence of pressure ulcers were analyzed, and the caregivers' pressure ulcer care behaviors and pressure ulcer management abilities before and after treatment were compared between both groups of patients.

Results

The SAS and SDS scores were lower in the OG (38.33±3.21 and 39.07±4.15) than in the CG (44.74±4.13 and 45.80±3.92) after the intervention (P < .05); quality of life and ADL were higher in the OG (44.07±7.15 and 58.33±6.21) than in the CG (36.20±6.92 and 45.84±6.12) after the intervention (P < .05). The incidence of pressure ulcers in the OG (18.33%) was lower than that in the CG (37.04%) after the intervention (P < .05); total nursing satisfaction in the OG (91.67%) was higher than that in the CG (74.07%) after the intervention (P < .05), and caregivers' pressure ulcer care behaviors and pressure ulcer management abilities of patients in the OG (36.42±2.41 and 44.07±7.15) were higher than those in the CG (29.23±2.50 and 36.20±6.92) after the intervention (P < .05).

Conclusion

Continuous care interventions can be promoted for elderly patients with high-risk pressure ulcers outside the hospital, which can reduce the incidence of pressure ulcers, negative emotions, related complications and treatment costs, and improve the effective utilization of medical resources. In the future, continuous care guidelines and training plans can be developed for elderly patients with high-risk pressure ulcers, so as to improve the understanding and application ability of health care providers and nurses on intervention measures.

Antimicrobial research of carbohydrate polymer- and protein-based hydrogels as reservoirs for the generation of reactive oxygen species: A review

Reactive oxygen species (ROS) play an important role in biological milieu. Recently, the rapid growth in our understanding of ROS and their promise in antibacterial applications has generated tremendous interest in the combination of ROS generators with bulk hydrogels. Hydrogels represent promising supporters for ROS generators and can locally confine the nanoscale distribution of ROS generators whilst also promoting cellular integration via biomaterial-cell interactions. This review highlights recent efforts and progress in developing hydrogels derived from biological macromolecules with embedded ROS generators with a focus on antimicrobial applications. Initially, an overview of passive and active antibacterial hydrogels is provided to show the significance of proper hydrogel selection and design. These are followed by an in-depth discussion of the various approaches for ROS generation in hydrogels. The structural engineering and fabrication of ROS-laden hydrogels are given with a focus on their biomedical applications in therapeutics and diagnosis. Additionally, we discuss how a compromise needs to be sought between ROS generation and removal for maximizing the efficacy of therapeutic treatment. Finally, the current challenges and potential routes toward commercialization in this rapidly evolving field are discussed, focusing on the potential translation of laboratory research outcomes to real-world clinical outcomes.

Chelatococcus albus sp. nov., a bacterium isolated from hot spring microbial mat

A Gram-stain-negative, non-endospore-forming, motile, short rod-shaped strain, designated SYSU G07232T, was isolated from a hot spring microbial mat, sampled from Rehai National Park, Tengchong, Yunnan Province, south-western China. Strain SYSU G07232T grew at 25-50 °C (optimum, 37 °C), at pH 5.5-9.0 (optimum, pH 6.0) and tolerated NaCl concentrations up to 1.0 % (w/v). Phylogenetic analysis based on the 16S rRNA gene sequences revealed that strain SYSU G07232T showed closest genetic affinity with Chelatococcus daeguensis K106T. The genomic features and taxonomic status of this strain were determined through whole-genome sequencing and a polyphasic approach. The predominant quinone of this strain was Q-10. Major cellular fatty acids comprised C19 : 0 cyclo ω8c and summed feature 8. The whole-genome length of strain SYSU G07232T was 4.02 Mbp, and the DNA G+C content was 69.26 mol%. The average nucleotide identity (ANIm ≤84.85 % and ANIb ≤76.08  %) and digital DNA-DNA hybridization (≤ 21.9 %) values between strain SYSU G07232T and the reference species were lower than the threshold values recommended for distinguishing novel prokaryotic species. Thus, based on the provided phenotypic, phylogenetic, and genetic data, it is proposed that strain SYSU G07232T (=KCTC 8141T=GDMCC 1.4178T) be designated as representing a novel species within the genus Chelatococcus, named Chelatococcus albus sp. nov.

Three dimensional analysis of the exhalation flow in the proximity of the mouth

The human exhalation flow is characterized in this work from the three-dimensional velocimetry results obtained by using the stereo particle image velocimetry (SPIV) measurement technique on the flow emitted from a realistic airway model. For this purpose, the transient exhalation flow through the mouth of a person performing two different breaths corresponding to two metabolic rates, standing relaxed (SR) and walking active (WA), is emulated and studied. To reproduce the flow realistically, a detailed three-dimensional model obtained from computed tomography measurements on real subjects is used. To cope with the variability of the experimental data, a subsequent analysis of the results is performed using the TR-PIV (time resolved particle image velocimetry) technique. Exhalation produces a transient jet that becomes a puff when flow emission ends. Three-dimensional vector fields of the jet velocity are obtained in five equally spaced transverse planes up to a distance of Image 1 from the mouth at equally spaced time instants Image 2 which will be referred to as phases (φ), from the beginning to the end of exhalation. The time evolution during exhalation of the jet area of influence, the velocity field and the jet air entrainment have been characterized for each of the jet cross sections. The importance of the use of realistic airway models for the study of this type of flow and the influence of the metabolic rate on its development are also analyzed. The results obtained contribute to the characterization of the human exhalation as a pathway of the transmission of pathogens such as SARS-CoV-2 virus.

Treatment of Abdominal Desmoplastic Small Round Cell Tumor Induces Acute Myeloid Leukemia-M5: A Case Report and Literature Review

Desmoplastic small round cell tumor (DSRCT) is a rare and highly aggressive malignancy. Most patients are diagnosed at a late stage with poor prognosis. The treatment usually includes combined intensive chemotherapy, cytoreductive surgery, radiotherapy, and targeted therapy. Due to the low incidence rate and dismal survival, there is currently a lack of case reports on DSRCT with concurrent leukemia. We report a case of a young patient who achieved disease stabilization for 14 months after receiving 6 cycles of chemotherapy and whole abdominal radiation therapy (WART), followed by consolidation treatment with anlotinib. However, the treatment was terminated due to the development of Acute Myeloid Leukemia-M5 (AML-M5). Multimodal therapy may provide a survival benefit for rare tumors that lack standard treatment. However, intensive chemotherapy and extensive radiotherapy carry a risk of inducing secondary malignancies. This is the first reported case of concurrent DSRCT and AML-M5 with short intervals between onset.

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

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

Evaluation of the effectiveness of using flipped classroom in puncture skills teaching

BACKGROUND

The effectiveness of flipped classroom (FC) on puncture skills in medical education is still uncertain. This study aimed to assess the role of the FC model in puncture skills and investigate the acceptance and approval of FC among medical students and instructors.

METHODS

A mixed research approach of quasi-experimental research design and descriptive qualitative research was conducted in September 2022 for one month, using an FC teaching method that combined instructional videos and group learning. The study participants were 71 fifth-year medical students from two classes at a Chinese medical school and four instructors. The medical students were randomly divided into two groups: the traditional classroom (TC) group (Group A) and the FC group (Group B). For teaching, Group B used FC, and Group A used PowerPoint-based TC. The effectiveness of the two teaching models was assessed with Objective Structured Clinical Examination (OSCE), and questionnaires were distributed to the medical students and instructors after the assessment. Two independent sample t-tests were used to analyse the differences in demographic data and the OSCE scores of the two groups of medical students.

RESULTS

Group B scored higher in puncture skills than Group A, especially regarding abdominal puncture (p = 0.03), thoracentesis (p < 0.001), bone marrow puncture (p < 0.001) and average performance of puncture skills (p < 0.001). For lumbar puncture, no difference in skill scores was observed between groups A and B (p > 0.409). The medical students thought that the FC improved their self-learning ability and helped them acquire knowledge. Regarding the OSCE of their skills, most medical students thought that it was more innovative and objective than traditional examinations and that it was better for assessing their overall abilities. Both the FC and OSCE were supported by the medical students. The instructors were also satisfied with the students' performance in the FC and supported the teaching model, agreeing to continue using it.

CONCLUSIONS

This study shows that FC teaching that combines instructional videos and group learning is a reliable and well-received teaching method for puncture skills, which supplements and expands existing teaching methods in the medical field.

Long-term prognostic value of thyroid hormones in left ventricular noncompaction

PURPOSE

Thyroid function is closely related to the prognosis of cardiovascular diseases. This study aimed to explore the predictive value of thyroid hormones for adverse cardiovascular outcomes in left ventricular noncompaction (LVNC).

METHODS

This longitudinal cohort study enrolled 388 consecutive LVNC patients with complete thyroid function profiles and comprehensive cardiovascular assessment. Potential predictors for adverse outcomes were thoroughly evaluated.

RESULTS

Over a median follow-up of 5.22 years, primary outcome (the combination of cardiovascular mortality and heart transplantation) occurred in 98 (25.3%) patients. For secondary outcomes, 75 (19.3%) patients died and 130 (33.5%) patients experienced major adverse cardiovascular events (MACE). Multivariable Cox analysis identified that free triiodothyronine (FT3) was independently associated with both primary (HR 0.455, 95%CI 0.313-0.664) and secondary (HR 0.547, 95%CI 0.349-0.858; HR 0.663, 95%CI 0.475-0.925) outcomes. Restricted cubic spline analysis illustrated that the risk for adverse outcomes increased significantly with the decline of serum FT3. The LVNC cohort was further stratified according to tertiles of FT3 levels. Individuals with lower FT3 levels in the tertile 1 group suffered from severe cardiac dysfunction and remodeling, resulting in higher incidence of mortality and MACE (Log-rank P < 0.001). Subgroup analysis revealed that lower concentration of FT3 was linked to worse prognosis, particularly for patients with left atrial diameter ≥ 40 mm or left ventricular ejection fraction ≤ 35%. Adding FT3 to the pre-existing risk score for MACE in LVNC improved its predictive performance.

CONCLUSION

Through the long-term investigation on a large LVNC cohort, we demonstrated that low FT3 level was an independent predictor for adverse cardiovascular outcomes.

[Clinical study of the efficacies of ruxolitinib plus low-dose PTCY for acute GVHD prevention after haploidentical transplantation in malignant hematological diseases]

Objective: To investigate and verify a novel acute graft versus host disease (aGVHD) prevention protocol in the context of haploidentical hematopoietic stem cell transplantation (haplo-HSCT) . Methods: Patients who underwent haplo-HSCT in our center between January 2022 and December 2022 were included. All patients received reduced doses of cyclophosphamide, Rabbit anti-human tymoglobulin, ruxolitinib, methotrexate, cyclosporine, and MMF to prevent aGVHD. The transplantation outcomes, complications, and survival rate of all patients were investigated. Results: A total of 52 patients with haplo-HSCT were enrolled, 29 (55.8%) male and 23 (44.2%) female, with a median age of 28 (5-59) years. There were 25 cases of acute myeloid leukemia, 17 cases of acute lymphocyte leukemia, 6 cases of myelodysplastic syndrome, 2 cases of chronic myeloid leukemia and 2 cases of myeloproliferative neoplasms. 98.1% of patients had successful engraftment. The incidence of Ⅱ-Ⅳ aGVHD and Ⅲ-Ⅳ aGVHD was 19.2% (95% CI 8.2% -30.3% ) and 7.7% (95% CI 0.2% -15.2% ), respectively. No patients experienced severe gastrointestinal mucositis. The Epstein-Barr virus and CMV reactivation rates were 40.4% and 21.3%, respectively. 9.6% of patients relapsed during followup, with 1-year overall survival, progression-free survival, and non-relapse mortality rates of 86.5% (95% CI 76.9% -96.1% ), 78.8% (95% CI 67.4% -90.3% ) and 11.5% (95% CI 2.6% -20.5% ), respectively. Conclusion: Ruxolitinib combined with a low dose of PTCY is a safe and effective first-line aGVHD prevention strategy.

[Association between waist circumference and ischemic stroke: a prospective study in adults from Qingdao]

Objective: To analyze the association between waist circumference (WC) and ischemic stroke (IS). Methods: The data for the present study were from the prospective cohort study of China Kadoorie Biobank in Qingdao. Using baseline information and IS events of the participants, the Cox proportional hazard regression model and restricted cubic spline (RCS) were used to analyze the association between WC and IS. Results: A total of 33 355 participants were included in the study, with 302 008.88 person-years of follow-up. A total of 1 093 new cases of IS were observed. Multivariate Cox proportional hazard regression model analysis showed that compared to the respondents with normal WC (male <85.0 cm, female <80.0 cm), respondents with excessive WC (male ≥85.0 cm, female ≥80.0 cm) had a 78% higher risk of IS incidence [hazard ratio(HR)=1.78, 95%CI: 1.51-2.10], and the risk increased by 72% (HR=1.72, 95%CI: 1.40-2.12) and 83% (HR=1.83, 95%CI: 1.40-2.39) in men and women. According to the RCS, the increase in WC and the risk of IS showed an "S" trend of nonlinear dose-response relationship. Conclusions: The risk of IS would increase with the WC. Keeping a normal WC is important for preventing IS.

Deficiency of N6-Methyladenosine Demethylase ALKBH5 Alleviates Ultraviolet B Radiation-Induced Chronic Actinic Dermatitis via Regulating Pyroptosis

Pyroptosis is an inflammatory programmed cell death (PCD) and is reported to be associated with N6-methyladenosine (m6A) modification. This study aimed to investigate the mechanism of m6A demethylase AlkB homolog 5 (ALKBH5) in pyroptosis in the process of chronic actinic dermatitis (CAD). Changes of m6A-related genes were evaluated between CAD and normal samples using quantitative reverse-transcription polymerase chain reaction (qRT-PCR). Human keratinocytes (HaCaT cells) exposed to ultraviolet B (UVB; 10, 20, and 30 mJ/cm2), followed by evaluation of cell proliferation, cell apoptosis, inflammatory cytokines (interleukin (IL)-1β, IL-18, and tumor necrosis factor (TNF-α)), and pyroptosis-related proteins (gasdermin D (GSDMD), Caspase-1, and Caspase-4). Small interfering RNA (siRNA) targeting ALKBH5 was transfected into HaCaT cells to assess the effect of si-ALKBH5 on CAD. A CAD mice model was induced after exposure to UVB (250 mJ/cm2 per day) to confirm the role of ALKBH5 in CAD. AKKBH5 was highly expressed in CAD patients. UVB also promoted ALKBH5 expression, increased cell apoptosis, and induced the release of inflammatory cytokines (IL-1β, IL-18, and TNF-α) as well as pyroptosis-related proteins (GSDMD, Caspase-1, and Caspase-4). Silencing ALKBH5 repressed cell apoptosis and suppressed UVB-induced pyroptosis and inflammatory response. Meanwhile, silencing ALKBH5 attenuated UVB-induced skin damage of CAD mice, accompanied with the reduction in expression of inflammatory cytokines and pyroptosis-related proteins. This study helps to further understand the mechanism of ALKBH5 in CAD-induced pyroptosis and provides novel ideas for the research and management of CAD.

Copy Number Alterations Predict Development of OSCC from Oral Leukoplakia

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

Synergistic antitumor efficacy of gemcitabine and cisplatin to induce ferroptosis in pancreatic ductal adenocarcinoma via Sp1-SAT1-polyamine metabolism pathway

PURPOSE

The combination of cisplatin and gemcitabine-based chemotherapy has been recommended as a preferred regimen for pancreatic ductal adenocarcinoma (PDAC) patients with germline-based mutations. However, the underlying mechanism remains poorly elucidated. Therefore, our study aimed to explore the mechanistic basis of the cell-killing activity of gemcitabine plus cisplatin and identify potential therapeutic targets.

METHODS

First, we explored the synergistic cytotoxic effects of gemcitabine and cisplatin on PDAC through in vitro and in vivo experiments. Then, we investigated ferroptosis-related biomarkers, to assess the impact of the combination therapy on ferroptosis. Using bioinformatics methods, we identified SAT1 as a potential key mediator of ferroptosis induced by gemcitabine and cisplatin. We tested the polyamine levels in PDAC cells by LC-MS after overexpressed or knocked down SAT1, and explored the role of polyamines in ferroptosis using exogenous supplementation. Finally, we explored the regulatory effect of Sp1 on SAT1 through ChIP-qPCR and dual-luciferase reporter assay.

RESULTS

Gemcitabine plus cisplatin enhanced cell death and induced ferroptosis in PDAC. This combination upregulated SAT1 transcription by inhibiting Sp1. SAT1 activation promoted the catabolism of spermine and spermidine, leading to iron accumulation and lipid peroxide generation, ultimately resulting in ferroptosis.

CONCLUSIONS

In summary, our findings suggested the gemcitabine and cisplatin combination therapy induced ferroptosis in a GSH-independent manner in PDAC. The combined treatment inhibited Sp1 and upregulated SAT1 transcription, leading to the breakdown of spermine and spermidine. Therefore, targeting SAT1-induced polyamine metabolism may represent a promising therapeutic strategy for PDAC.

The impact of air pollutants on spontaneous abortion: a case-control study in Tongchuan City

OBJECTIVES

Studies related to air pollutants and spontaneous abortion in urban northwestern China are scarce, and the main exposure windows of pollutants acting on pregnant women are unclear.

STUDY DESIGN

Case-control study.

METHODS

Data were collected from pregnant women in Tongchuan City from 2018 to 2019. A total of 289 cases of spontaneous abortion and 1156 cases of full-term labor were included and analyzed using a case-control study. Logistic regression models were developed to explore the relationship between air pollutants and spontaneous abortion after Chi square analysis and Air pollutant description.

RESULTS

O3 (odds ratio [OR] = 1.028) is a risk factor for spontaneous abortion throughout pregnancy. PM2.5 (OR = 1.015), PM10 (OR = 1.010), SO2 (OR = 1.026), and NO2 (OR = 1.028) are risk factors for spontaneous abortion in the 30 days before the last menstrual period. PM2.5 (OR = 1.015), PM10 (OR = 1.013), SO2 (OR = 1.036), and NO2 (OR = 1.033) are risk factors for spontaneous abortion in the 30-60 days before the last menstrual period. PM2.5 (OR = 1.028), PM10 (OR = 1.013), SO2 (OR = 1.035), and NO2 (OR = 1.059) are risk factors for spontaneous abortion in the 60-90 days before the last menstrual period.

CONCLUSION

Exposure to high levels of air pollutants may be a cause of increased risk of spontaneous abortion, especially in the first trimester of the last menstrual period.

Comparison of clinical data between the proximal femoral bionic nail (PFBN) and hip replacement for the treatment of femoral intertrochanteric fracture

OBJECTIVE

The aim of this study was to compare the difference between proximal femoral bionic nail (PFBN) and hip replacement (HR) for femoral intertrochanteric fracture.

MATERIALS AND METHODS

A retrospective analysis of the differences in operative time, length of stay, postoperative Harris score, and postoperative mortality between patients with femoral intertrochanteric fracture treated by PFBN and HR admitted to Jinzhai County People's Hospital from October 2020 to September 2022 was performed.

RESULTS

A total of 56 patients with femoral intertrochanteric fracture, 26 with PFBN and 30 with HR, were included in the study. There were no differences in the length of surgery, pre- and post-operative hemoglobin, or post-operative Harris score at 3 months between the two groups. Compared to the HR group, the PFBN group had a lower total cost, shorter hospital stays, and lower mortality but a longer ambulation time, with a difference of 3.36 weeks.

CONCLUSIONS

PFBN may be a promising new treatment for femoral intertrochanteric fracture.

Toripalimab plus axitinib versus sunitinib as first-line treatment for advanced renal cell carcinoma: RENOTORCH, a randomized, open-label, phase III study

BACKGROUND

Immune checkpoint inhibitors in combination with tyrosine kinase inhibitors are standard treatments for advanced clear cell renal cell carcinoma (RCC). This phase III RENOTORCH study compared the efficacy and safety of toripalimab plus axitinib versus sunitinib for the first-line treatment of patients with intermediate-/poor-risk advanced RCC.

PATIENTS AND METHODS

Patients with intermediate-/poor-risk unresectable or metastatic RCC were randomized in a ratio of 1 : 1 to receive toripalimab (240 mg intravenously once every 3 weeks) plus axitinib (5 mg orally twice daily) or sunitinib [50 mg orally once daily for 4 weeks (6-week cycle) or 2 weeks (3-week cycle)]. The primary endpoint was progression-free survival (PFS) assessed by an independent review committee (IRC). The secondary endpoints were investigator-assessed PFS, overall response rate (ORR), overall survival (OS), and safety.

RESULTS

A total of 421 patients were randomized to receive toripalimab plus axitinib (n = 210) or sunitinib (n = 211). With a median follow-up of 14.6 months, toripalimab plus axitinib significantly reduced the risk of disease progression or death by 35% compared with sunitinib as assessed by an IRC [hazard ratio (HR) 0.65, 95% confidence interval (CI) 0.49-0.86; P = 0.0028]. The median PFS was 18.0 months in the toripalimab-axitinib group, whereas it was 9.8 months in the sunitinib group. The IRC-assessed ORR was significantly higher in the toripalimab-axitinib group compared with the sunitinib group (56.7% versus 30.8%; P < 0.0001). An OS trend favoring toripalimab plus axitinib was also observed (HR 0.61, 95% CI 0.40-0.92). Treatment-related grade ≥3 adverse events occurred in 61.5% of patients in the toripalimab-axitinib group and 58.6% of patients in the sunitinib group.

CONCLUSION

In patients with previously untreated intermediate-/poor-risk advanced RCC, toripalimab plus axitinib provided significantly longer PFS and higher ORR than sunitinib and had a manageable safety profile TRIAL REGISTRATION: ClinicalTrials.gov NCT04394975.

Extracranial malignant rhabdoid tumors in children: high mortality even with the help of an aggressive clinical approach

This paper aims to explore the epidemiology, clinical characteristics, and prognosis of extracranial malignant rhabdoid tumors (eMRTs) in children. A systematic review and meta-analysis of studies published in PUBMED, MEDLINE, Web of Science, Embase, Cochrane, and China National Knowledge Infrastructure (CNKI) was conducted. The search was limited to studies published between Jan 1, 1990 to Dec 31, 2022, with the last search done on Jan 31, 2023. We identified 496 papers through the literature search, and 12 retrospective cohort studies with 398 patients were included. The pooled age at diagnosis for malignant rhabdoid tumor of the kidney (MRTK) was 10.009 months (95%CI (7.542-12.476)), while extracranial malignant rhabdoid tumor (EERT) was 25.917 months (95%CI (17.304-34.530)). Among the 398 patients with eMRTs, chemotherapy treatment rate (86.8% (95%CI (74.4-96.0%))) was more frequently than radiotherapy treatment (45.4% (95%CI (38.1-52.6%))). The rate of metastasis in all patients was 41.4% (95%CI (33.9-48.9%)), in which the lung metastasis was occupied 70.4% (95%CI (58.0-81.6%)). SMARCB1/INI1 mutation was up to 93.2% (95%CI (81.3-99.8%)). The rate of total surgical resection was 50.4% (95%CI (35.2-65.6%)), while pooled proportion of death in all patients was 68.7% (95%CI (56.9-79.5%)).     Conclusion: EMRTs are highly malignant tumors associated with high mortality rates. The loss of SMARCB1/INI1 gene and the protein expression is observed in the vast majority of eMRTs patients. Patients that suffered MRTK are younger than patients with extrarenal EERT and are more prone to lung metastasis, but there is no significant difference in overall survival, possibly due to the higher rate of R0 resection of primary tumors in MRTK.     Trial registration: The study was registered on PROSPERO with registration number CRD42023400985. What is Known: • Malignant rhabdoid tumor (MRT) is a rare and highly malignant tumor that may originate from embryonic stem cells. The incidence of MRT is exceptionally low, estimated at 0.00006%. • Malignant rhabdoid tumor of the kidney (MRTK) and extrarenal extra-cranial malignant rhabdoid tumor (EERT) tend to manifest between 11 to 18 months of age, with a 5-year survival rate of approximately 17%-36%. What is New: • There is no comprehensive meta-analysis or large-scale case series that reported to systematically introduce the eMRTs clinic outcome and prog-nosis based on largely pooled data. • This study performed a meta-analysis through an extensive literature search and clinical data analysis in order to mainly explore the clinical characteris-tics and prognosis of eMRTs, improving the understanding of eMRTs in children..

CT-Based Radiomics Analysis of Different Machine Learning Models for Discriminating the Risk Stratification of Pheochromocytoma and Paraganglioma: A Multicenter Study

RATIONALE AND OBJECTIVES

Using different machine learning models CT-based radiomics to integrate clinical radiological features to discriminating the risk stratification of pheochromocytoma/paragangliomas (PPGLs).

MATERIALS AND METHODS

The present study included 201 patients with PPGLs from three hospitals (training set: n = 125; external validation set: n = 45; external test set: n = 31). Patients were divided into low-risk and high-risk groups using a staging system for adrenal pheochromocytoma and paraganglioma (GAPP). We extracted and selected CT radiomics features, and built radiomics models using support vector machines (SVM), k-nearest neighbors, random forests, and multilayer perceptrons. Using receiver operating characteristic curve analysis to select the optimal radiomics model, a combined model was built using the output of the optimal radiomics model and clinical radiological features, and its accuracy and clinical applicability were evaluated using calibration curves and clinical decision curve analysis (DCA).

RESULTS

Finally, 13 radiomics features were selected to construct machine learning models. In the radiomics model, the SVM model demonstrated higher accuracy and stability, with an AUC value of 0.915 in the training set, 0.846 in external validation set, and 0.857 in external test set. Combining the outputs of SVM models with two clinical radiological features, a combined model constructed has demonstrated optimal risk stratification ability for PPGLs with an AUC of 0.926 for the training set, 0.883 for the external validation set, and 0.899 for the external test set. The calibration curve and DCA show good calibration accuracy and clinical effectiveness for the combined model.

CONCLUSION

Combined model that integrates radiomics and clinical radiological features can discriminate the risk stratification of PPGLs.

Method for multi-task learning fusion network traffic classification to address small sample labels

In the context of the proliferated evolution of network service types and the expeditious augmentation of network resource deployment, the requisition for copious labeled datasets to facilitate superior performance in traffic classification methods, particularly those hinging on deep learning, is imperative. Nonetheless, the procurement and annotation of such extensive datasets necessitate considerable temporal and human resource investments. In response to this predicament, this work introduces a methodology, termed MTEFU, leveraging a deep learning model-based multi-task learning algorithm, strategically designed to mitigate the reliance on substantial labeled training samples. Multiple classification tasks, encompassing duration, bandwidth size, and business traffic category, are incorporated, with a shared parameter strategy implemented amongst tasks to assure the transference of information across disparate tasks. Employing CNN, SAE, GRU, and LSTM as multi-task learning classification models, training validation and experimental testing were conducted on the QUIC dataset. A comparative analysis with single-task and ensemble learning methods reveals that, in the context of predicting network traffic types, the accuracy derived from the multi-task learning strategy, even with a mere 150 labeled samples, can emulate the 94.67% accuracy achieved through single-task learning with a fully labeled dataset of 6139 samples.

Trends in prevalence of obesity and its association with hypertension across socioeconomic gradients in rural Yunnan Province, China

BACKGROUND

This study aimed to uncover the changing prevalence of obesity and its association with hypertension across socioeconomic gradients in rural southwest China.

METHODS

Data were collected from two cross-sectional health interviews and surveys from 2011 to 2021 among individuals aged ≥ 35 years in rural China. Each participant's height, weight, waist circumference, and blood pressure were measured. The overall prevalence of obesity, central obesity, and hypertension was directly standardized by age based on the total population of the two surveys. Multivariate logistic regression was used to analyze the association between obesity and prevalence of hypertension and an individual socioeconomic position (SEP) index was constructed using principal component analysis.

RESULTS

From 2011 to 2021, the prevalence of obesity, central obesity, and hypertension increased substantially, from 5.9%, 50.2%, and 26.1-12.1%, 58.0%, and 40.4% (P < 0.01), respectively. These increasing rates existed in all subcategories, including sex, age, ethnicity, education, annual household income, access to medical services, and SEP (P < 0.05). In both 2011 and 2021, lower education level and poor access to medical services correlated with higher prevalence of central obesity, while higher SEP correlated with higher prevalence of obesity and central obesity (P < 0.01). Prevalence of obesity was higher in the Han ethnicity participants and individuals with poor access to medical services than in their counterparts (P < 0.01). Whereas the prevalence of central obesity was lower in Han participants than in ethnic minority participants in 2011 (P < 0.01), this trend reversed in 2021 (P < 0.01). A positive relationship between annual household income and prevalence of obesity and central obesity was only found in 2021 (P < 0.01). Obese and centrally obese participants were more likely to be hypertensive in both survey years (P < 0.01).

CONCLUSIONS

Future interventions to prevent and manage obesity in rural China should give increased attention to high income, less educated, poor access to medical services, and high SEP individuals. The implementation of these obesity interventions would also help reduce the prevalence of hypertension.

[Analysis of the incidence and symptomatology of low anterior resection syndrome after laparoscopic anterior resection for rectal cancer]

Objective: This study aims to explore the temporal trend of Low Anterior Resection Syndrome (LARS) and its symptoms after laparoscopic anterior resection for rectal cancer. Methods: A retrospective cohort study design was employed. The study included primary rectal (adenocarcinoma) cancer patients who underwent laparoscopic anterior resection at Tongji Hospital, Huazhong University of Science and Technology, between January 1, 2010, and December 31, 2020. Complete medical records and follow-up data at 3, 6, 9, 12, and 18 months postoperatively were available for all patients. A total of 1454 patients were included, of whom 1094 (75.2%) were aged ≤65 years, and 597 (41.1%) were females. Among them, 1040 cases (71.5%) had an anastomosis-to-anus distance of 0-5cm, and 86 cases (5.9%) received neoadjuvant treatment. All patients completed the Chinese version of the LARS questionnaire and their LARS occurrence and specific symptom information were recorded at 3, 6, 9, 12, and 18 months postoperatively. Considering past literature and clinical experience, further subgroup analyses were performed to explore the potential impact factors on severe LARS, including anastomosis level, preoperative neoadjuvant therapy, postoperative adjuvant therapy, and the presence of preventive stoma. Results: The occurrence rates of LARS at 3, 6, 9, 12, and 18 months postoperatively were 78.5% (1142/1454), 71.4% (1038/1454), 55.0% (799/1454), 45.7% (664/1454), and 45.7% (664/1454), respectively (χ2=546.180, P<0.001). No statistically significant difference was observed between the 12-month and 18-month time points (P>0.05). When compared with the symptoms at 3 months, the occurrence rates of gas incontinence [1.7% (24/1454) vs. 33.9% (493/1454)], liquid stool incontinence [3.9% (56/1454) vs. 41.9% (609/1454)], increased stool frequency [79.6% (1158/1454) vs. 95.9% (1395/1454)], stool clustering [74.3% (1081/1454) vs. 92.9% (1351/1454)], and stool urgency [46.5% (676/1454) vs. 78.7% (1144/1454)] in the LARS symptom spectrum were significantly alleviated at 12 months (all P<0.05) and remained stable beyond 12 months (all P>0.05). With the extension of postoperative time, the incidence rates of severe LARS exhibited a decreasing trend in different subgroups, of anastomosis level, preoperative neoadjuvant therapy, postoperative adjuvant therapy, and the presence of preventive stoma, and reached stability at 12 months postoperatively (all P>0.05). Conclusion: LARS and its specific symptom profile showed a trend of gradual improvement over time up to 1 year postoperatively, and stabilized after more than 1 year. Increased stool frequency and stool clustering are the most common features of abnormal bowel dys function, which improve slowly after surgery.

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

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

[Safety and efficacy of donor-derived chimeric antigen receptor T-cell therapy in patients with relapsed B-cell acute lymphoblastic leukemia after allogeneic hematopoietic stem cell transplantation]

Objective: To investigated the safety and efficacy of donor-derived CD19+ or sequential CD19+ CD22+ chimeric antigen receptor T-cell (CAR-T) therapy in patients with B-cell acute lymphoblastic leukemia (B-ALL) after allogeneic hematopoietic stem cell transplantation (allo-HSCT). Methods: The data of 22 patients with B-ALL who relapsed after allo-HSCT and who underwent donor-derived CAR-T therapy at the Zhujiang Hospital of Southern Medical University and the 920th Hospital of Joint Logistics Support Force of the People's Liberation Army of China from September 2015 to December 2022 were retrospectively analyzed. The primary endpoint was overall survival (OS), and the secondary endpoints were event-free survival (EFS), complete remission (CR) rate, and Grade 3-4 adverse events. Results: A total of 81.82% (n=18) of the 22 patients achieved minimal residual disease-negative CR after CAR-T infusion. The median follow-up time was 1037 (95% CI 546-1509) days, and the median OS and EFS were 287 (95% CI 132-441) days and 212 (95% CI 120-303) days, respectively. The 6-month OS and EFS rates were 67.90% (95% CI 48.30%-84.50%) and 58.70% (95% CI 37.92%-79.48%), respectively, and the 1-year OS and EFS rates were 41.10% (95% CI 19.15%-63.05%) and 34.30% (95% CI 13.92%-54.68%), respectively. Grade 1-2 cytokine release syndrome occurred in 36.36% (n=8) of the patients, and grade 3-4 occurred in 13.64% of the patients (n=3). Grade 2 and 4 graft-versus-host disease occurred in two patients. Conclusion: Donor-derived CAR-T therapy is safe and effective in patients with relapsed B-ALL after allo-HSCT.