An improved method for generating human spinal cord neural stem cells

Neural stem cells have exhibited efficacy in pre-clinical models of spinal cord injury (SCI) and are on a translational path to human testing. We recently reported that neural stem cells must be driven to a spinal cord fate to optimize host axonal regeneration into sites of implantation in the injured spinal cord, where they subsequently form neural relays across the lesion that support significant functional improvement. We also reported methods of deriving and culturing human spinal cord neural stem cells derived from embryonic stem cells that can be sustained over serial high passage numbers in vitro, providing a potentially optimized cell source for human clinical trials. We now report further optimization of methods for deriving and sustaining cultures of human spinal cord neural stem cell lines that result in improved karyotypic stability while retaining anatomical efficacy in vivo. This development improves prospects for safe human translation.

The complexity of glucose time series is associated with short- and long-term mortality in critically ill adults: a multi-center, prospective, observational study

BACKGROUND

The wealth of data taken from continuous glucose monitoring (CGM) remains to be fully used. We aimed to evaluate the relationship between a promising new CGM metric, complexity of glucose time series index (CGI), and mortality in critically ill patients.

METHODS

A total of 293 patients admitted to mixed medical/surgical intensive care units from 5 medical centers in Shanghai were prospectively included between May 2020 and November 2021. CGI was assessed using intermittently scanned CGM, with a median monitoring period of 12.0 days. Outcome measures included short- and long-term mortality.

RESULTS

During a median follow-up period of 1.7 years, a total of 139 (47.4%) deaths were identified, of which 73 (24.9%) occurred within the first 30 days after ICU admission, and 103 (35.2%) within 90 days. The multivariable-adjusted HRs for 30-day mortality across ascending tertiles of CGI were 1.00 (reference), 0.68 (95% CI 0.38-1.22) and 0.36 (95% CI 0.19-0.70), respectively. For per 1-SD increase in CGI, the risk of 30-day mortality was decreased by 51% (HR 0.49, 95% CI 0.35-0.69). Further adjustment for HbA1c, mean glucose during hospitalization and glucose variability partially attenuated these associations, although the link between CGI and 30-day mortality remained significant (per 1-SD increase: HR 0.57, 95% CI 0.40-0.83). Similar results were observed when 90-day mortality was considered as the outcome. Furthermore, CGI was also significantly and independently associated with long-term mortality (per 1-SD increase: HR 0.77, 95% CI 0.61-0.97).

CONCLUSIONS

In critically ill patients, CGI is significantly associated with short- and long-term mortality.

Single organ metastatic sites in non-small cell lung cancer: Patient characteristics, treatment patterns and outcomes from a large retrospective Canadian cohort

BACKGROUND

There is a paucity of information about the characteristics, treatment patterns, and outcomes of non-small cell lung cancer (NSCLC) patients with single organ metastasis (SOM).

METHODS

This retrospective cohort study includes all patients with a diagnosis of stage IV NSCLC diagnosed from 2014 to 2016 and treated at Princess Margaret Cancer Centre. We compared baseline characteristics and patterns of metastatic sites between patients with SOM versus multiple (M)OM. Additionally, we identified treatment modalities and outcomes for patients with SOM. Cox multivariable models (MVA) were utilized to evaluate differences in overall survival (OS) between the SOM and MOM cohorts.

RESULTS

Of 893 pts analyzed, 457 (51 %) had SOM, while 436 (49 %) had MOM at initial diagnosis. Demographics were comparable between the two groups. Brain was the most common site of metastasis for SOM patients. When compared to the MOM group, the SOM group had lower percentages of liver and adrenal metastases. Amongst SOM patients, 54 % received single modality treatment, and 20 % did not receive any treatment for their SOM. In MVA, patients with liver (HR 2.4), bone (HR 1.8), and pleural (HR 1.7) metastasis as their SOM site had the worst outcomes, with median OS of 6.8 months, 12.1 months, and 13.0 months respectively. Patients with SOM had a significantly improved median OS compared to those with MOM (15.9 months vs. 10.6 months; HR 0.56, 95 % CI 0.47-0.66, p < 0.001).

CONCLUSION

In NSCLC patients who presented with SOM, survival correlated with the initial organ involved and was better overall compared to patients with MOM. SOM NSCLC may benefit from specific management strategies and SOM patients could be considered as a specific subgroup for survival analyses in observational and non-randomized interventional studies. In clinical trials, SOM can be considered as a stratification factor in the future.

Effects of 3-nitrooxypropanol (3-NOP, Bovaer®10) and whole cottonseed on milk production and enteric methane emissions from dairy cows under Swiss management conditions

The objective of this study was to determine the potential effect and interaction of 3- nitrooxypropanol (3-NOP; Bovaer®) and whole cottonseed (WCS) on lactational performance, and enteric methane (CH4) emission of dairy cows. A total of 16 multiparous cows, including 8 Holstein Friesian (HF) and 8 Brown Swiss (BS) [224 ± 36 d in milk, 26 ± 3.7 kg milk yield], were used in a split-plot design, where the main plot was the breed of cows. Within each subplot, cows were randomly assigned to a treatment sequence in a replicated 4 × 4 Latin Square design with 2 × 2 factorial arrangements of treatments with 4, 24-d periods. The experimental treatments were: 1) Control (basal TMR), 2) 3-NOP (60 mg/kg TMR DM), 3) WCS (5% TMR DM), and 4) 3-NOP + WCS. The treatment diets were balanced for ether extract, crude protein, and NDF contents (4%, 16%, and 43% of TMR DM, respectively). The basal diets were fed twice daily at 0800 and 1800 h. Dry matter intake (DMI) and milk yield were measured daily, and enteric gas emissions were measured (using the GreenFeed system) during the last 3 d of each 24-d experimental period when animals were housed in tie stalls. There was no difference in DMI on treatment level, whereas the WCS treatment increased ECM yield and milk fat yield. There was no interaction of 3-NOP and WCS for any of the enteric gas emission parameters, but 3-NOP decreased CH4 production (g/d), CH4 yield (g/kg DMI), and CH4 intensity (g/kg ECM) by 13, 14 and 13%, respectively. Further, an unexpected interaction of breed by 3-NOP was observed for different enteric CH4 emission metrics: HF cows had a greater CH4 mitigation effect compared with BS cows for CH4 production (g/d; 18 vs. 8%), CH4 intensity (g/kg MY; 19% vs. 3%) and CH4 intensity (g/kg ECM; 19 vs. 4%). Hydrogen production was increased by 2.85 folds in HF and 1.53 folds in BS cows receiving 3-NOP. Further, there was a 3-NOP ' Time interaction for both breeds. In BS cows, 3-NOP tended to reduce CH4 production by 18% at around 4 h after morning feeding but no effect was observed at other time points. In HF cows, the greatest mitigation effect of 3-NOP (29.6%) was observed immediately after morning feeding and it persisted at around 23% to 26% for 10 h until the second feed provision, and 3 h thereafter, in the evening. In conclusion, supplementing 3-NOP at 60 mg/kg DM to a high fiber diet resulted in 18 to 19% reduction in enteric CH4 emission in Swiss Holstein Friesian cows. The lower response to 3-NOP by BS cows was unexpected and has not been observed in other studies. These results should be interpreted with caution due to low number of cows per breed. Lastly, supplementing WCS at 5% of DM improved ECM and milk fat yield but did not enhance CH4 inhibition effect of 3-NOP of dairy cows.

Association of serum 25-hydroxyvitamin D with cardiovascular mortality and kidney outcome in patients with early stages of CKD

PURPOSE

While serum 25-hydroxyvitamin D (25[OH]D) deficiency is prevalent in chronic kidney disease (CKD), the effects of 25(OH)D deficiency on cardiovascular mortality and kidney outcomes in patients with early-stage CKD remain incompletely understood.

METHODS

This multicenter retrospective cohort study included adult patients with stages 1-3 CKD from 19 medical centers across China between January 2000 and May 2021. The primary outcome was cardiovascular mortality. The secondary study outcome included CKD progression (defined as a sustained > 40% eGFR decrease from baseline or progress to end-stage kidney disease), and annual percentage change of eGFR.

RESULTS

Of 9229 adults with stages 1-3 CKD, 27.0% and 38.9% had severe (< 10 ng/mL) and moderate (10 to < 20 ng/mL) serum 25(OH)D deficiency, respectively. Compared with patients having 25(OH)D ≥ 20 ng/mL, a significantly higher risk of cardiovascular mortality (hazard ratio [HR] 1.90, 95% CI 1.37-2.63), CKD progression (HR 2.20, 95% CI 1.68-2.88), and a steeper annual decline in eGFR (estimate - 7.87%; 95% CI - 10.24% to - 5.51% per year) was found in those with serum 25(OH)D < 10 ng/mL. Similar results were obtained in subgroups and by sensitivity analyses.

CONCLUSIONS

25(OH)D deficiency is associated with increased risks of cardiovascular mortality and CKD progression in patients with early-stage CKD. Studies are needed to determine whether early intervention for 25(OH)D deficiency could improve the prognosis of patients with early-stage CKD.

Associations of ambient temperature and total cloud cover during pregnancy with newborn vitamin D status

OBJECTIVES

We aimed to estimate the effects of temperature and total cloud cover before birth on newborn vitamin D status.

STUDY DESIGN

Prospective birth cohort.

METHODS

This study included 2055 mother-newborn pairs in Wuhan, Hubei province, China. The data of temperature and total cloud cover from 30 days before birth were collected, and cord blood 25-hydroxyvitamin D [25(OH)D] were determined. Restricted cubic spline regression models, multiple linear regression models, and logistic regression models were applied to estimate the associations.

RESULTS

A "J" shaped curve was observed between temperature and vitamin D status, and an inverse "J" shaped curve was observed between total cloud cover and vitamin D status. Compared to the fourth quartile (75-100th percentile, Q4) of average temperature (30 days before birth), the odds ratio (OR) for Q1 (0-25th percentile) associated with the vitamin D deficiency occurrence (<20 ng/mL) was 3.63 (95% CI, 1.54, 8.65). Compared to Q1 of the average total cloud cover (30 days before birth), the OR associated with the occurrence of vitamin D deficiency was 2.38 (95% CI, 1.63, 3.50) for the Q4.

CONCLUSIONS

Low temperature and high cloud cover before delivery were significantly associated with an increased probability of vitamin D deficiency in newborns. The findings suggested that pregnancy women lacking sufficient sunlight exposure still need vitamin D supplement to overcome the potential vitamin D deficiency status.

Determination of Spin-Parity Quantum Numbers of X(2370) as 0^{-+} from J/ψ→γK_{S}^{0}K_{S}^{0}η^{'}

Based on (10087±44)×10^{6}  J/ψ events collected with the BESIII detector, a partial wave analysis of the decay J/ψ→γK_{S}^{0}K_{S}^{0}η^{'} is performed. The mass and width of the X(2370) are measured to be 2395±11(stat)_{-94}^{+26}(syst)  MeV/c^{2} and 188_{-17}^{+18}(stat)_{-33}^{+124}(syst)  MeV, respectively. The corresponding product branching fraction is B[J/ψ→γX(2370)]×B[X(2370)→f_{0}(980)η^{'}]×B[f_{0}(980)→K_{S}^{0}K_{S}^{0}]=(1.31±0.22(stat)_{-0.84}^{+2.85}(syst))×10^{-5}. The statistical significance of the X(2370) is greater than 11.7σ and the spin parity is determined to be 0^{-+} for the first time. The measured mass and spin parity of the X(2370) are consistent with the predictions of the lightest pseudoscalar glueball.

[Current status of pubertal sexual characteristics development of 2 704 girls aged 6-18 years in Tongzhou District of Beijing]

Objective: To understand the current status of pubertal sexual characteristics development of girls aged 6-18 years in Tongzhou District of Beijing and to compare the differences in sexual characteristics development among girls characterized as thin, normal, overweight, and obese. Methods: A cross-sectional survey was conducted among 2 844 girls aged 6-18 years in Tongzhou District of Beijing from September 2022 to July 2023. The developmental stages of breast and pubic hair were assessed on site, and menarche status was inquired. Weight and height were measured. The girls were subsequently characterized into thin, normal, overweight and obese groups. Basic information (including family and personal history) was obtained through questionnaires. Probit probability unit regression was applied to calculate the age of each Tanner stage of sexual characteristics development and the age of menarche. The χ2 test was applied to compare the counting data between two or multiple groups. Results: A total of 2 844 girls were surveyed and 2 704 girls met the inclusion criteria, resulting in a valid response rate of 95.1%. Among these girls, 1 105 (40.9%) were aged 6-9 years, 1 053 (38.9%) were aged 10-13 years, and 546 (20.2%) were aged 14-18 years. The of height-for-age Z-score (HAZ), weight-for-age Z-score (WAZ), and body mass index-for-age Z-score (BAZ) were 0.46(-0.23,1.16), 0.69(-0.16,1.67), and 0.67(-0.27,1.73) respectively. The prevalences of thin, overweight, and obesity were respectively 1.7% (45/2 704), 17.3% (467/2 704), and 19.9% (538/2 704), respectively. There were 45 girls in the thin group, 1 654 girls in the normal weight group, 1 005 girls in the overweight and obesity group. The age of Tanner stage breast 2 (B2), Tanner stage pubic hair 2 (P2), and menarche was 9.0 (95%CI 8.9-9.1), 10.5 (95%CI 10.4-10.6), and 11.4 (95%CI 11.3-1.5) years, respectively. The current status of breast and pubic hair maturity in girls with pubertal development shows that 64.6% (1 211/1 874) of these girls had breast development preceding pubic hair development, 32.4% (607/1 874) had concurrent breast and pubic hair development, and 3.0% (56/1 874) had pubic hairs development preceding breast development. The interval age between B2 and B5 was 4.7 (95%CI 4.6-4.8) years, between P2 and P5 was 4.5 (95%CI 4.4-4.6) years, and between B2 and menarche was 2.4 (95%CI 2.3-2.5) years. The ages of sexual characteristics development in overweight and obese groups were earlier than that in normal and thin groups. The ages of B2 in thin, normal, overweight, and obese groups were 10.0 (95%CI 9.5-10.6), 9.3 (95%CI 9.2-9.4), and 8.6 (95%CI 8.4-8.7) years, respectively. The age of menarche in thin, normal, overweight, and obese groups were 13.1 (95%CI 12.4-13.7), 11.6 (95%CI 11.4-11.7), and 11.1 (95%CI 11.0-11.2) years, respectively. The interval ages between B2 and B5 and between P2 and P5 was 4.5 and 4.1 years, respectively in the overweight and obese groups, and those in normal group and thin group was 4.7 and 4.5 years, 4.6 and 4.7 years, respectively. Conclusions: The ages of sexual characteristics development and menarche tend in Tongzhou District of Beijing to be earlier than that being reported of Beijing's survey 20 years ago. Girls characterized as overweight and obese not only start puberty at an earlier age than girls of normal weight, but also have a shorter developmental process.

[Clinical characteristics of children with severe SARS-CoV-2 infection in Yunnan]

Objective: To investigate the clinical characteristics of 130 children with severe SARS-CoV-2 infection in Yunnan province after the relaxation of non-pharmaceutical interventions, and analyze the risk factors for mortality. Methods: This study is a retrospective case summary that analyzed the demographic data, underlying diseases, clinical diagnoses, disease outcomes, and laboratory results of 130 children with severe COVID-19 infections admitted to nine top-tier hospitals in Yunnan Province from December 2022 to March 2023. According to the prognosis, the patients were divided into survival group and death group. The clinical and laboratory data between the two groups were compared, and the risk factors of death were evaluated. The χ2 test and Mann-Whitney U test were employed to compare between groups, while Spearman correlation test and multiple Logistic regression were used to analyze the risk factors for death. The predictive value of independent risk factors was evaluated by receiver operating characteristic curve. Results: The 130 severe patients included 80 males and 50 females with an onset age of 28.0 (4.5, 79.5) months. There were 97 cases in the survival group and 33 cases in the death group with no significant differences in gender and age between the two groups (P>0.05). Twenty-five cases (19.2%) out of the 130 patients had underlying diseases, and the number with underlying diseases was significantly higher in death group than in survival group (36.4% (12/33) vs. 13.4%(13/97), χ2=8.36, P=0.004). The vaccination rate in the survival group was significantly higher than that in the death group (86.1% (31/36) vs. 7/17, χ2=9.38, P=0.002). A total of 42 cases (32.3%) of the 130 patients were detected to be infected with other pathogens, but there was no significant difference in the incidence of co-infection between the death group and the survival group (39.3%(13/33) vs. 29.9% (29/97), χ2=1.02, P>0.05). Among the 130 cases, severe respiratory cases were the most common 66 cases (50.8%), followed by neurological severe illnesses 34 cases (26.2%) and circulatory severe 13 cases (10%). Compared to the survival group, patients in the death group had a significantly higher levels of neutrophil, ferritin, procalcitonin, alanine aminotransferase, lactate dehydrogenase, creatine kinase isoenzyme, B-type natriuretic peptide, interleukin-6 and 10 (6.7 (4.0, 14.0) vs. 3.0 (1.6, 7.0)×109/L, 479 (298, 594) vs. 268 (124, 424) μg/L, 4.8 (1.7, 10.6) vs. 2.0 (1.1, 3.1) μg/L, 66 (20, 258) vs. 23 (15, 49) U/L, 464 (311, 815) vs. 304 (252, 388) g/L, 71(52, 110) vs. 24(15, 48) U/L, 484 (160, 804) vs. 154 (26, 440) ng/L, 43 (23, 102) vs. 19 (13, 27) ng/L, 216 (114, 318) vs. 86 (45, 128) ng/L, Z=-4.21, -3.67, -3.76, -3.31, -3.75, -5.74, -3.55, -4.65, -5.86, all P<0.05). The correlated indexes were performed by multivariate Logistic regression and the results showed that vaccination was a protective factor from death in severe cases (OR=0.01, 95%CI 0-0.97, P=0.049) while pediatric sequential organ failure assessment (PSOFA) (OR=3.31, 95%CI 1.47-7.47, P=0.004), neutrophil-to-lymphocyte ratio (NLR) (OR=1.56, 95%CI 1.05-2.32, P=0.029) and D dimer (OR=1.49, 95%CI 1.00-1.02, P=0.033) were independent risk factors for death (all P<0.05). The area under the curve of the three independent risk factors for predicting death were 0.86 (95%CI 0.79-0.94), 0.89 (95%CI 0.84-0.95) and 0.87 (95%CI 0.80-0.94), all P<0.001, and the cut-off values were 4.50, 3.66 and 4.69 mg/L, respectively. Conclusions: Severe SARS-CoV-2 infection can occur in children of all ages, primarily affecting the respiratory system, but can also infect the nervous system, circulatory system or other systems. Children who died had more severe inflammation, tissue damage and coagulation disorders. The elevations of PSOFA, NLR and D dimer were independent risk factors for death in severe children.

Novel alternative tools for metastatic pheochromocytomas/paragangliomas prediction

OBJECTIVE

The existing prediction models for metastasis in pheochromocytomas/paragangliomas (PPGLs) showed high heterogeneity in different centers. Therefore, this study aimed to establish new prediction models integrating multiple variables based on different algorithms.

DESIGN AND METHODS

Data of patients with PPGLs undergoing surgical resection at the Peking Union Medical College Hospital from 2007 to 2022 were collected retrospectively. Patients were randomly divided into the training and testing sets in a ratio of 7:3. Subsequently, decision trees, random forest, and logistic models were constructed for metastasis prediction with the training set and Cox models for metastasis-free survival (MFS) prediction with the total population. Additionally, Ki-67 index and tumor size were transformed into categorical variables for adjusting models. The testing set was used to assess the discrimination and calibration of models and the optimal models were visualized as nomograms. Clinical characteristics and MFS were compared between patients with and without risk factors.

RESULTS

A total of 198 patients with 59 cases of metastasis were included and classified into the training set (n = 138) and testing set (n = 60). Among all models, the logistic regression model showed the best discrimination for metastasis prediction with an AUC of 0.891 (95% CI, 0.793-0.990), integrating SDHB germline mutations [OR: 96.72 (95% CI, 16.61-940.79)], S-100 (-) [OR: 11.22 (95% CI, 3.04-58.51)], ATRX (-) [OR: 8.42 (95% CI, 2.73-29.24)] and Ki-67 ≥ 3% [OR: 7.98 (95% CI, 2.27-32.24)] evaluated through immunohistochemistry (IHC), and tumor size ≥ 5 cm [OR: 4.59 (95% CI, 1.34-19.13)]. The multivariate Cox model including the above risk factors also showed a high C-index of 0.860 (95% CI, 0.810-0.911) in predicting MFS after surgery. Furthermore, patients with the above risk factors showed a significantly poorer MFS (P ≤ 0.001).

CONCLUSIONS

Models established in this study provided alternative and reliable tools for clinicians to predict PPGLs patients' metastasis and MFS. More importantly, this study revealed for the first time that IHC of ATRX could act as an independent predictor of metastasis in PPGLs.

Wildfire particulate exposure and risks of preterm birth and low birth weight in the Southwestern United States

OBJECTIVES

Wildfire air pollution is a growing concern on human health. The study aims to assess the associations between wildfire air pollution and pregnancy outcomes in the Southwestern United States.

STUDY DESIGN

This was a retrospective cohort study.

METHODS

Birth records of 627,404 singleton deliveries in 2018 were obtained in eight states of the Southwestern United States and were linked to wildfire-sourced fine particulate matter (PM2.5) and their constituents (black carbon [BC] and organic carbon [OC]) during the entire gestational period. A double-robust logistic regression model was used to assess the associations of wildfire-sourced PM2.5 exposures and preterm birth and term low birth weight, adjusting for non-fire-sourced PM2.5 exposure and individual- and area-level confounder variables.

RESULTS

Wildfire-sourced PM2.5 contributed on average 15% of the ambient total PM2.5 concentrations. For preterm birth, the strongest association was observed in the second trimester (odds ratio [OR]: 1.06, 95% confidence interval [CI]: 1.05-1.07 for PM2.5; 1.06, 95% CI: 1.05-1.07 for BC; 1.04, 95% CI: 1.03-1.05 for OC, per interquartile range increment of exposure), with higher risks identified among non-smokers or those with low socio-economic status. For term low birth weight, the associations with wildfire-sourced PM2.5 exposures were consistently elevated for all trimesters except for the exposure averaged over the entire gestational period. Overall, the associations between wildfire-sourced PM2.5 and pregnancy outcomes were stronger than those with total PM2.5.

CONCLUSIONS

Wildfire-sourced PM2.5 and its constituents are linked to higher risks of preterm birth and term low birth weight among a significant US population than the effects of ambient total PM2.5.

[The efficiency of total endoscopic surgery with preferential incision of the tendon sheath and traditional open surgery for the treatment of peroneal tendondislocation]

Objective: To compare the outcomes between the patients of peroneal tendon dislocation treated by either total endoscopic surgery with preferential incision of the tendon sheath or traditional open surgery. Methods: This is a retrospective cohort study. The clinical data of 45 patients with peroneal tendon dislocation were operated on Department of Sports Medicine and Joint Surgery, Nanjing First Hospital from July 2016 to June 2020. There were 26 males and 19 females,aged (31.2±9.3) years (range: 17 to 45 years). Among them,23 patients underwent open peroneal tendon groove deepening followed by tendon sheath repair(traditional open group), and the other 22 patients underwent similar operations but all-endoscopically with preferential incision of peroneal tendon sheath(total endoscopic group). The perioperative data of patients were collected, and pain visual analogue score (VAS) was used to evaluate the pain changes before and after surgery and during the follow-up period, and the American Orthopaedic Foot and Ankle Society anklehindfoot scale (AOFAS-AH), range of motion (ROM), the MOS item short form health survey (SF)-36, and the homemade questionnaire of patient satisfaction were used to evaluate the patients' outcomes after the operation, and CT scan was carried out to observe the deepening of the fibular groove and MRI to observe the status of the peroneal tendon and sheath during the follow-up. Independent sample t test, Wilcoxon rank sum test and repeated measure ANOVA were used for comparison of quantitative data between groups. Chi-square test,Mann-whitney U or Fisher exact test was used for comparison of classified data, respectively;and paired sample t test was used for comparison of quantitative data before and after surgery in groups. Results: There was no statistically significant difference between the two groups of patients in terms of gender, age, disease duration, side of injury, and injury typing (all P>0.05). There was no significant difference between the two groups in terms of operation time((47.9±5.4)minutes vs. (47.2±6.3)minutes;t=0.402, P=0.690), but the incision length ((2.17±0.35)cm vs. 5.97±0.42)cm;t=32.892,P<0.01)and hospitalization time ((4.0±1.7)days vs. (7.6±3.6)days;t=4.249,P<0.01) were significantly shorter in the total endoscopic group than those in the traditional open group. All patients were followed up for more than 12 months, and the follow-up time was (19.2±3.9) months (range: 12 to 24 months). The total endoscopic group showed a significant increase in VAS, AOFAS scores, SF-36 scores and patient satisfaction rate at 3 months postoperatively and the last follow-up (all P<0.05). Three months after surgery, the ROM in the total endoscope group was higher than that in the traditional group ((62.14±1.46) ° vs. (53.13±1.52) °;t=20.315, P<0.01), and there was no significant difference between the two groups at the last follow-up ((63.18±1.10) ° vs. (63.48±2.43) °;t=0.531, P=0.599). Conclusion: Total endoscopic surgery with preferential incision of the tendon sheath has the advantages of minimally invasivenessas compared with traditional open surgery with faster recovery and better outcome.

[Analysis of factors influencing the efficacy and prognosis of surgical treatment for primary malignant pelvic bone tumors]

Objective: To analyze the prognostic factors and the influence of surgical margin to prognosis. Methods: A retrospective analysis was performed for 208 pelvic tumors who received surgical treatment from January 2000 to December 2017 in our instituition. Survival analysis was performed using the Kaplan-Meier method and Log rank test, and impact factor analysis was performed using Cox regression models. Results: There were 183 initial patients and 25 recurrent cases. According to Enneking staging, 110 cases were stage ⅠB and 98 cases were stage ⅡB. 19 lesions were in zone Ⅰ, 1 in zone Ⅱ, 15 in zone Ⅲ, 29 in zone Ⅰ+Ⅱ, 71 in zone Ⅱ+Ⅲ, 29 in zone Ⅰ+Ⅳ, 35 in zone Ⅰ+Ⅱ+Ⅲ, 3 in zone Ⅰ+Ⅱ+Ⅳ, and 6 in zone Ⅰ+Ⅱ+Ⅲ+Ⅳ. Surgical margins including Intralesional excision in 7 cases, contaminated margin in 21 cases, marginal resection in 67 cases, and wide resection in 113 cases. Local recurrence occurred in 37 cases (17.8%), 25 cases were performed by reoperation and 12 cases received amputation finally. The 5-year recurrence rate of marginal resection was higher than wide resection (P＜0.05), and the recurrence-free survival rate of marginal resection was lower than wide resection (P＜0.05). There was significant differences in recurrence rate and recurrence-free survival rate between R0 and R1 resection (P＜0.05). 92 cases were not reconstructed and 116 cases were reconstructed after pelvic surgery. At the last follow-up, 63 patients (30.3%) died, and the 5-year, 10-year and 15-year survival rates were 70.4%, 66.8% and 61.3%, respectively. The 5-year survival rate of stage ⅠB and ⅡB tumor was 90.4% and 46.8%, respectively. There were 29 cases had postoperative wound complications (13.8%), 1 case with pelvic organ injury. The final function was evaluated in 132 patients, with an average MSTS score of 25.1±3.6. Cox multivariate analysis showed that surgical staging, R0/R1 margin and metastasis were independent prognostic factors for pelvic tumors. Conclusions: The safe surgical margin is the key factor for recurrence-free of pelvic tumor. The survival rate of stage ⅡB pelvic tumors was significantly lower than that of stage ⅠB tumors. Wound infection is the main postoperative complication. Surgical staging, R0/R1 margin and metastasis were independent prognostic factors of pelvic tumors.

[The Miao medicine Sidaxue alleviates rheumatoid arthritis in rats possibly by downregulating matrix metalloproteinases]

OBJECTIVE

To explore the inhibitory effect of Sidaxue, a traditional Miao herbal medicine formula, on articular bone and cartilage destruction and synovial neovascularization in rats with collagen-induced arthritis (CIA).

METHODS

In a SD rat model of CIA, we tested the effects of daily gavage of Sidaxue at low, moderate and high doses (10, 20, and 40 g/kg, respectively) for 21 days, with Tripterygium glycosides (GTW) as the positive control, on swelling in the hind limb plantar regions by arthritis index scoring. Pathologies in joint synovial membrane of the rats were observed with HE staining, and serum TNF-α and IL-1β levels were detected with ELISA. The expressions of NF-κB p65, matrix metalloproteinase 1 (MMP1), MMP2 and MMP9 at the mRNA and protein levels in the synovial tissues were detected using real-time PCR and Western blotting. Network pharmacology analysis was conducted to identify the important target proteins in the pathways correlated with the therapeutic effects of topical Sidaxue treatment for RA, and the core target proteins were screened by topological analysis.

RESULTS

Treatment with GTW and Sidaxue at the 3 doses all significantly alleviated plantar swelling, lowered arthritis index scores, improved cartilage and bone damage and reduced neovascularization in CIA rats (P<0.05), and the effects of Sidaxue showed a dose dependence. Both GTW and Sidaxue treatments significantly lowered TNF-α, IL-1β, NF-κB p65, MMP1, MMP2, and MMP9 mRNA and protein expressions in the synovial tissues of CIA rats (P<0.05). Network pharmacological analysis identified MMPs as the core proteins associated with topical Sidaxue treatment of RA.

CONCLUSION

Sidaxue alleviates articular bone and cartilage damages and reduces synovial neovascularization in CIA rats possibly by downregulating MMPs via the TNF-α/IL-1β/NF-κB-MMP1, 2, 9 signaling pathway, and MMPs probably plays a key role in mediating the effect of Sidaxue though the therapeutic pathways other than oral administration.

Isolated non-immune mediated second-degree atrioventricular block in fetus: natural history and predictive factors for spontaneous recovery

OBJECTIVES

To uncover the clinical course of fetal isolated non-immune mediated second-degree AVB and determine the factors associated with the spontaneous recovery for fetal non-immune second-degree atrioventricular block (AVB).

METHODS

A total of 20 fetuses with isolated, non-immune mediated second-degree AVB were prospectively recruited between 2014 and 2022. These fetuses were divided into the spontaneous recovery group (n=12) and the non-spontaneous recovery group (n=8). Maternal and fetal basic characteristics, intrauterine and postnatal outcomes were compared between groups.

RESULTS

Twelve fetuses restored 1:1 atrioventricular conduction in utero and did not recur during the postnatal follow-up period. The residual eight fetuses maintained as second-degree AVB and six of them were aborted due to parental request in utero. Of the two live children with second-degree AVB, one of them progressed to complete AVB at the latest follow up at the age of 34 months, but without any symptoms, heart enlargement or dysfunction. The residual one progressed to complete AVB and was finally diagnosed with type 2 long-QT syndrome. Fetuses in the spontaneous recovery group presented with earlier gestational age at diagnosis (20.0[17.0-26.0] vs. 24.5[18.0-35.0] weeks, p=0.004) and higher atrial rate (147[130-160] vs 138.00[125.00-149.00] bpm, p=0.006) in comparison with the non-spontaneous recovery group. A cut-off value of 22.5 weeks of gestational age and 144 bpm of atrial rate at diagnosis could predict the failure of spontaneous recovery, with sensitivities of 87.5%, 75%, and specificities of 92.0%, 87.5%, respectively.

CONCLUSIONS

The outcome of fetal non-immune second-degree AVB was favorable. Earlier gestational age at diagnosis and higher atrial rate were related to spontaneous reversion for isolated non-immune-mediated second-degree AVB. However, prenatal gene test should be performed for those with persistent AVB to exclude the heritable disorders including LQTS. These findings may provide important references for clinical management and prenatal counseling. This article is protected by copyright. All rights reserved.

Observation of Structures in the Processes e^{+}e^{-}→ωχ_{c1} and ωχ_{c2}

We present measurements of the Born cross sections for the processes e^{+}e^{-}→ωχ_{c1} and ωχ_{c2} at center-of-mass energies sqrt[s] from 4.308 to 4.951 GeV. The measurements are performed with data samples corresponding to an integrated luminosity of 11.0  fb^{-1} collected with the BESIII detector operating at the Beijing Electron Positron Collider storage ring. Assuming the e^{+}e^{-}→ωχ_{c2} signals come from a single resonance, the mass and width are determined to be M=(4413.6±9.0±0.8)  MeV/c^{2} and Γ=(110.5±15.0±2.9)  MeV, respectively, which is consistent with the parameters of the well-established resonance ψ(4415). In addition, we also use one single resonance to describe the e^{+}e^{-}→ωχ_{c1} line shape and determine the mass and width to be M=(4544.2±18.7±1.7)  MeV/c^{2} and Γ=(116.1±33.5±1.7)  MeV, respectively. The structure of this line shape, observed for the first time, requires further understanding.

Airway Reactance Predicts Static Lung Hyperinflation in Severe Asthma

BACKGROUND AND OBJECTIVES

Background: Static lung hyperinflation (SLH) measured using body plethysmography in patients with asthma is associated with poor outcomes. The severity of SLH may be associated with small airway dysfunction (SAD), which can be measured using impulse oscillometry (IOS). Objective: This study aims to determine the correlation between SLH and SAD in patients with severe asthma and assess the improvement in SLH and SAD with treatment.

METHODS

We analyzed data from patients who were enrolled in the Taiwan Severe Asthma Registry, which comprises a prospective observational cohort. Plethysmography and IOS were performed regularly. The relationship between spirometry and IOS parameters was determined. Changes in the clinical outcomes in response to treatment were analyzed.

RESULTS

Of 107 patients with severe asthma, 83 (77.6%) had SLH based on an increased residual volume to total lung capacity ratio (RV/ TLC). Most patients were older women with worse pulmonary function and SAD than those without SLH. SAD, defined as increased airway resistance/reactance, was significantly correlated with SLH. Airway reactance at 5 Hz (X5) ≤-0.21 kPa/(L/s) detected SLH with an area under the receiver operating characteristic curve of 0.84 (P<.0001; sensitivity, 85.2%; and specificity, 83.3%). After 12 months, patients who received add-on biologics (vs those who did not) had significantly reduced exacerbations, fractional exhaled nitric oxide level, and blood eosinophil counts, as well as improved forced expiratory volume in the first second, X5, and a trend toward reduced RV/TLC ratio.

CONCLUSIONS

In severe asthma, airway reactance (X5) could be a novel parameter for assessing SLH.

Coupled-Channel Analysis of the χ_{c1}(3872) Line Shape with BESIII Data

We perform a study of the χ_{c1}(3872) line shape using the data samples of e^{+}e^{-}→γχ_{c1}(3872), χ_{c1}(3872)→D^{0}D[over ¯]^{0}π^{0}, and π^{+}π^{-}J/ψ collected with the BESIII detector. The effects of the coupled channels and the off-shell D^{*0} are included in the parametrization of the line shape. The line shape mass parameter is obtained to be M_{X}=(3871.63±0.13_{-0.05}^{+0.06})  MeV. Two poles are found on the first and second Riemann sheets corresponding to the D^{*0}D[over ¯]^{0} branch cut. The pole location on the first sheet is much closer to the D^{*0}D[over ¯]^{0} threshold than the other, and is determined to be 7.04±0.15_{-0.08}^{+0.07}  MeV above the D^{0}D[over ¯]^{0}π^{0} threshold with an imaginary part -0.19±0.08_{-0.19}^{+0.14}  MeV.

Observation of the Anomalous Shape of X(1840) in J/ψ→γ3(π^{+}π^{-}) Indicating a Second Resonance Near pp[over ¯] Threshold

Using a sample of (10087±44)×10^{6}  J/ψ events, which is about 45 times larger than that was previously analyzed, a further investigation on the J/ψ→γ3(π^{+}π^{-}) decay is performed. A significant distortion at 1.84  GeV/c^{2} in the line shape of the 3(π^{+}π^{-}) invariant mass spectrum is observed for the first time, which could be resolved by two overlapping resonant structures, X(1840) and X(1880). The new state X(1880) is observed with a statistical significance larger than 10σ. The mass and width of X(1880) are determined to be 1882.1±1.7±0.7  MeV/c^{2} and 30.7±5.5±2.4  MeV, respectively, which indicates the existence of a pp[over ¯] bound state.

First Measurement of η Meson Production in Neutrino Interactions on Argon with MicroBooNE

We present a measurement of η production from neutrino interactions on argon with the MicroBooNE detector. The modeling of resonant neutrino interactions on argon is a critical aspect of the neutrino oscillation physics program being carried out by the DUNE and Short Baseline Neutrino programs. η production in neutrino interactions provides a powerful new probe of resonant interactions, complementary to pion channels, and is particularly suited to the study of higher-order resonances beyond the Δ(1232). We measure a flux-integrated cross section for neutrino-induced η production on argon of 3.22±0.84(stat)±0.86(syst) 10^{-41}  cm^{2}/nucleon. By demonstrating the successful reconstruction of the two photons resulting from η production, this analysis enables a novel calibration technique for electromagnetic showers in GeV accelerator neutrino experiments.

[Development and validation of a persistent postural-perceptual dizziness screening questionnaire]

Objective: To develop a simple screening questionnaire for persistent postural-perceptual dizziness (PPPD) and evaluate its screening ability. Methods: A convenience sample of 296 individuals who met the inclusion criteria between November 2021 and January 2023 were prospectively selected for three rounds of screening at the Vertigo Specialty Clinic of the Department of Otorhinolaryngology-Head and Neck Surgery in the First Hospital of Shanxi Medical University. In conjunction with expert opinion and statistical analysis, the first and second rounds of screening were used to modify and finalize the questionnaire entries, and the third round of screening was used to evaluate the questionnaire's screening ability. Independent sample t-test was used for inter group comparison, reliability and validity indicators were employed to screen and evaluate questionnaire entries, and the receiver operating characteristic (ROC) curve was plotted to determine the optimal cut-off value and corresponding sensitivity and specificity. Results: The final PPPD screening questionnaire entries included 21 items. In evaluating the reliability of this questionnaire, the Cronbach's alpha coefficient was 0.831, the half folding coefficient was 0.742, the content validity was 0.86, and the Kaiser-Meyer-Olkin (KMO) value in the structural validity was 0.811. Additionally, there were six factors with characteristic root>1 and a cumulative contribution rate of 62.62%. The area under the ROC curve of the screening questionnaire was 0.935 (95%CI: 0.877-0.992), and the optimal cut-off value was 8.5, with a sensitivity of 85.0%, a specificity of 85.5%, and a Kappa value of 0.653. Conclusion: The PPPD simple screening questionnaire designed in this study has a high sensitivity and specificity, making it a useful tool for identifying PPPD patients.

[Effect of LAG3 deficiency on natural killer cell function and hepatic fibrosis in mice infected with Echinococcus multilocularis]

OBJECTIVE

To investigate the effect of LAG-3 deficiency (LAG3-/-) on natural killer (NK) cell function and hepatic fibrosis in mice infected with Echinococcus multilocularis.

METHODS

C57BL/6 mice, each weighing (20 ± 2) g, were divided into the LAG3-/- and wild type (WT) groups, and each mouse in both groups was inoculated with 3 000 E. multilocularis protoscoleces via the hepatic portal vein. Mouse liver and spleen specimens were collected 12 weeks post-infection, sectioned and stained with sirius red, and the hepatic lesions and fibrosis were observed. Mouse hepatic and splenic lymphocytes were isolated, and flow cytometry was performed to detect the proportions of hepatic and splenic NK cells, the expression of CD44, CD25 and CD69 molecules on NK cell surface, and the secretion of interferon γ (IFN-γ), tumor necrosis factor α (TNF-α), interleukin (IL)-4, IL-10 and IL-17A.

RESULTS

Sirius red staining showed widening of inflammatory cell bands and hyperplasia of fibrotic connective tissues around mouse hepatic lesions, as well as increased deposition of collagen fibers in the LAG3-/-group relative to the WT group. Flow cytometry revealed lower proportions of mouse hepatic (6.29% ± 1.06% vs. 11.91% ± 1.85%, P < 0.000 1) and splenic NK cells (4.44% ± 1.22% vs. 5.85% ± 1.10%, P > 0.05) in the LAG3-/- group than in the WT group, and the mean fluorescence intensity of CD44 was higher on the surface of mouse hepatic NK cells in the LAG3-/- group than in the WT group (t = -3.234, P < 0.01), while no significant differences were found in the mean fluorescence intensity of CD25 or CD69 on the surface of mouse hepaticNK cells between the LAG3-/- and WT groups (both P values > 0.05). There were significant differences between the LAG3-/- and WT groups in terms of the percentages of IFN-γ (t = -0.723, P > 0.05), TNF-α (t = -0.659, P > 0.05), IL-4 (t = -0.263, P > 0.05), IL-10 (t = -0.455, P > 0.05) or IL-17A secreted by mouse hepatic NK cells (t = 0.091, P > 0.05), and the percentage of IFN-γ secreted by mouse splenic NK cells was higher in the LAG3-/- group than in the WT group (58.40% ± 1.64% vs. 50.40% ± 4.13%; t = -4.042, P < 0.01); however, there were no significant differences between the two groups in terms of the proportions of TNF-α (t = -1.902, P > 0.05), IL-4 (t = -1.333, P > 0.05), IL-10 (t = -1.356, P > 0.05) or IL-17A secreted by mouse splenic NK cells (t = 0.529, P > 0.05).

CONCLUSIONS

During the course of E. multilocularis infections, LAG3-/- promotes high-level secretion of IFN-γ by splenic NK cells, which may participate in the reversal the immune function of NK cells, resulting in aggravation of hepatic fibrosis.

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.

Punicalagin attenuates isoproterenol-induced myocardial infarction through nuclear factor erythroid 2-related factor 2/silent information regulator transcript-1-mediated inhibition of inflammation and cardiac stress markers in experimental animal models

Myocardial infarction (MI) is a significant global health issue and the leading cause of death. Myocardial infarction (MI) is characterized by events such as damage to heart cells and stress generated by inflammation. Punicalagin (PCN), a naturally occurring bioactive compound found in pomegranates, exhibits a diverse array of pharmacological effects against many disorders. This study aimed to assess the preventive impact of PCN, with its potential anti-inflammatory and antioxidant properties, on myocardial injury caused by isoproterenol (ISO) in rats and elucidate the possible underlying mechanisms. Experimental rats were randomly categorized into four groups: control group (fed a regular diet for 15 days), PCN group (orally administered PCN at 50 mg/kg body weight (b.w.) for 15 days), ISO group (subcutaneously administered ISO (85 mg/kg b.w.) on days 14 and 15 to induce MI), and PCN+ISO group (orally preadministered PCN (50 mg/kg b.w.) for 15 days and administered ISO (85 mg/kg b.w.) on days 14 and 15). The rat cardiac tissue was then investigated for cardiac marker, oxidative stress marker, and inflammatory marker expression levels. PCN prevented ISO-induced myocardial injury, suppressing the levels of creatine kinase-myocardial band, C-reactive protein, homocysteine, cardiac troponin T, and cardiac troponin I in the rats. Moreover, PCN treatment reversed (P<0.01) the ISO-induced increase in blood pressure, attenuated lipid peroxidation markers, and depleted both enzymatic and nonenzymatic markers in the rats. Additionally, PCN inhibited (P<0.01) ISO-induced overexpression of oxidative stress markers (p-38, p-c-Jun N-terminal kinase, and p-extracellular signal-regulated kinase 1), inflammatory markers (nuclear factor-kappa B, tumor necrosis factor-alpha, and interleukin-6), and matrix metalloproteinases and decreased the levels (P<0.01) of apoptosis proteins in the rats. Nuclear factor erythroid 2-related factor 2/silent information regulator transcript-1 (Nrf2/Sirt1) is a major cellular defense protein that regulates and scavenges oxidative toxic substances through apoptosis. Therefore, overexpression of Nrf2/Sirt1 to inhibit inflammation and oxidative stress is considered a novel target for preventing MI. PCN also significantly enhanced the expression of Nrf2/Sirt1 in ISO-induced rats. Histopathological analyses of cardiac tissue revealed that PCN treatment exhibited a protective effect on the heart tissue, mitigating damage. These findings show that by activating the Nrf2/Sirt1 pathway, PCN regulates oxidative stress, inflammation, and apoptosis, hence providing protection against ISO-induced myocardial ischemia.

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

AIM

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSIONS

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

Small extracellular vesicles facilitate epithelial-mesenchymal transition in chronic rhinosinusitis with nasal polyps via the miR-375-3p/QKI axis

BACKGROUND

Epithelial-mesenchymal transition (EMT) plays a crucial role in the pathogenesis of chronic rhinosinusitis with nasal polyps (CRSwNP). However, the involvement of small extracellular vesicles (sEVs) in EMT and their contributions to CRSwNP has not been extensively investigated.

METHODS

SEVs were isolated from nasal mucosa through ultracentrifugation. MicroRNA sequencing and reverse-transcription quantitative polymerase chain reaction were employed to analyze the differential expression of microRNAs carried by sEVs. Human nasal epithelial cells (hNECs) were used to assess the EMT-inducing effect of sEVs/microRNAs. EMT-associated markers were detected by western blotting and immunofluorescence. Dual-luciferase reporter assay was performed to determine the target gene of miR-375-3p. MicroRNA mimic, lentiviral, and plasmid transduction were used for functional experiments.

RESULTS

In line with the greater EMT status in eosinophilic CRSwNP (ENP), sEVs derived from ENP (ENP-sEVs) could induce EMT in hNECs. MiR-375-3p was elevated in ENP-sEVs compared to that in control and nonENP. MiR-375- 3p carried by ENP-sEVs facilitated EMT by directly targeting KH domain containing RNA binding (QKI) at seed sequences of 913-919, 1025-1033, and 2438-2444 in 3'-untranslated region. Inhibition of QKI by miR-375-3p overexpression promoted EMT, which could be reversed by restoration of QKI. Furthermore, the abundance of miR-375-3p in sEVs was closely correlated with the clinical symptom score and disease severity.

CONCLUSIONS

MiR-375-3p-enriched sEVs facilitated EMT by suppressing QKI in hNECs. The association of miR-375-3p with disease severity underscores its potential as both a diagnostic marker and a therapeutic target for the innovative management of CRSwNP.

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

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

Review: Mechanism, effectiveness, and the prospects of medicinal plants and their bioactive compounds in lowering ruminants' enteric methane emission

Animal nutritionists continue to investigate new strategies to combat the challenge of methane emissions from ruminants. Medicinal plants (MPs) are known to be beneficial to animal health and exert functional roles in livestock due to their phytogenic compounds with antimicrobial, immunostimulatory, antioxidative, and anti-inflammatory activities. Some MP has been reported to be anti-methanogenic and can effectively lower ruminants' enteric methane emissions. This review overviews trends in MP utilization in ruminants, their bioactivity and their effectiveness in lowering enteric methane production. It highlights the MP regulatory mechanism and the gaps that must be critically addressed to improve its efficacy. MP could reduce enteric methane production by up to 8-50% by regulating the rumen fermentation pathway, directing hydrogen toward propionogenesis, and modifying rumen diversity, structure, and population of the methanogens and protozoa. Yet, factors such as palatability, extraction techniques, and economic implications must be further considered to exploit their potential fully.

MRI radiomics for predicting intracranial progression in non-small-cell lung cancer patients with brain metastases treated with epidermal growth factor receptor tyrosine kinase inhibitors

AIM

To identify clinical and magnetic resonance imaging (MRI) radiomics predictors specialised for intracranial progression (IP) after first-line epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) treatment in non-small-cell lung cancer (NSCLC) patients with brain metastases (BMs).

MATERIALS AND METHODS

Seventy EGFR-mutated NSCLC patients with a total of 212 BMs who received first-line EGFR-TKI therapy were enrolled. Radiomics features were extracted from the BM regions on the pretreatment contrast-enhanced T1-weighted images, and the radiomics score (rad-score) of each BM was established based on the selected features. Furthermore, the mean rad-score derived from the average rad-score of all included BMs in each patient was calculated. Univariate and multivariate logistic regression analyses were performed to identify potential predictors of IP. Prediction models based on different predictors and their combinations were constructed, and nomogram based on the optimal prediction model was evaluated.

RESULTS

Thirty-three (47.1 %) patients developed IP, and the remaining 37 (52.9 %) patients were IP-free. EGFR-19del mutation (OR 0.19, 95 % CI 0.05-0.69), third-generation TKI treatment (OR 0.33, 95 % CI 0.16-0.67) and mean rad-score (OR 5.71, 95 % CI 1.65-19.68) were found to be independent predictive factors. Models based on these three predictors alone and in combination (combined model) achieved AUCs of 0.64, 0.64, 0.74, and 0.86 and 0.64, 0.64, 0.75, and 0.84 in the training and validation sets, respectively, and the combined model demonstrated optimal performance for predicting IP.

CONCLUSIONS

The model integrating EGFR-19del mutation, third-generation TKI treatment and mean rad-score had good predictive value for IP after EGFR-TKI treatment in NSCLC patients with BM.

Effect of COVID-19 infection on pregnant women in plateau regions

OBJECTIVE

The present study aims to explore the effect of COVID-19 infection on pregnant women in plateau regions.

STUDY DESIGN

Data from 381 pregnant women infected with COVID-19 who underwent prenatal examination or treatment at Women and Children's Hospital of Tibet Autonomous Region between January 2020 and December 2022 and 314 pregnant women not infected with COVID-19 were retrospectively collected.

METHODS

The study participants were divided into an infected and non-infected group according to whether they were infected with COVID-19. Basic information (ethnicity, age, body mass index and gestational age [GA]), vaccination status, intensive care unit (ICU) admission and delivery outcomes were compared. Binary logistic regression was used to analyse the influencing factors of ICU admission.

RESULTS

The results revealed significant differences in the GA, vaccination rate, blood pressure, partial pressure of oxygen, white blood cell (WBC) count, ICU admission rate, preeclampsia rate, forearm presentation rate, thrombocytopenia rate, syphilis infection rate and placental abruption rate between the two groups (P < 0.05). A univariate analysis showed that COVID-19 infection, hepatitis B virus infection, the WBC count and hypoproteinaemia were risk factors for ICU admission. The results of the multivariate analysis of the ICU admission of pregnant women showed that COVID-19 infection (odds ratio [OR] = 4.271, 95 % confidence interval [CI]: 3.572-5.820, P < 0.05) was a risk factor for ICU admission and the WBC count (OR = 0.935, 95 % CI: 0.874-0.947, P < 0.05) was a protective factor for ICU admission.

CONCLUSION

Pregnant women are vulnerable to the adverse consequences of COVID-19 infection, and public health measures such as vaccination are needed to protect this population subgroup.

Construct validation of machine learning for accurately predicting the risk of postoperative surgical site infection following spine surgery

BACKGROUND

This study aimed to evaluate the risk factors for machine learning (ML) algorithms in predicting postoperative surgical site infection (SSI) following spine surgery.

METHODS

This prospective cohort study included 986 patients who underwent spine surgery at Taizhou People's Hospital Affiliated to Nanjing Medical University from January 2015 to October 2022. Supervised ML algorithms included support vector machine, logistic regression, random forest, XGboost, decision tree, k-nearest neighbour, and naïve Bayes (NB), which were tested and trained to develop a predicting model. The ML model performance was evaluated from the test dataset. We gradually analysed their accuracy, sensitivity, and specificity, as well as the positive predictive value, negative predictive value, and area under the curve.

RESULTS

The rate of SSI was 9.33%. Using a backward stepwise approach, we identified that the remarkable risk factors predicting SSI in the multi-variate Cox regression analysis were age, body mass index, smoking, cerebrospinal fluid leakage, drain duration and pre-operative albumin level. Compared with other ML algorithms, the NB model had the highest performance in seven ML models, with an average area under the curve of 0.95, sensitivity of 0.78, specificity of 0.88, and accuracy of 0.87.

CONCLUSIONS

The NB model in the ML algorithm had excellent calibration and accurately predicted the risk of SSI compared with the existing models, and might serve as an important tool for the early detection and treatment of SSI following spinal infection.

Global health impacts of ambient fine particulate pollution associated with climate variability

Air pollution is a key global environmental problem raising human health concern. It is essential to comprehensively assess the long-term characteristics of air pollution and the resultant health impacts. We first assessed the global trends of fine particulate matter (PM2.5) during 1980-2020 using a monthly global PM2.5 reanalysis dataset, and evaluated their association with three types of climate variability including El Niño-Southern Oscillation, Indian Ocean Dipole and North Atlantic Oscillation. We then estimated PM2.5-attributable premature deaths using integrated exposure-response functions. Results show a significant increasing trend of ambient PM2.5 during 1980-2020 due to increases in anthropogenic emissions. Ambient PM2.5 caused a total of ∼ 135 million premature deaths globally during the four decades. Occurrence of air pollution episodes was strongly associated with climate variability, which were associated with up to 14 % increase in annual global PM2.5-attributable premature deaths.

Exhalomics as a noninvasive method for assessing rumen fermentation in dairy cows: Can exhaled-breath metabolomics replace rumen sampling?

Previously, we used secondary electrospray ionization-mass spectrometry (SESI-MS) to investigate the diurnal patterns and signal intensities of exhaled (EX) volatile fatty acids (VFA) of dairy cows. The current study aimed to validate the potential of an exhalomics approach for evaluating rumen fermentation. The experiment was conducted in a switchback design, with 3 periods of 9 d each, including 7 d for adaptation and 2 d for sampling. Four rumen-cannulated original Swiss Brown (Braunvieh) cows were randomly assigned to 1 of 2 diet sequences (ABA or BAB): (A) low starch (LS; 6.31% starch on a dry matter basis) and (B) high starch (HS; 16.2% starch on a dry matter basis). Feeding was once per day at 0830 h. Exhalome (with the GreenFeed System), and rumen samples were collected 8 times to represent every 3 h of a day, and EX-VFA and ruminal (RM)-VFA were analyzed using SESI-MS and HPLC, respectively. Furthermore, the VFA concentration in the gas phase (HR-VFA) was predicted based on RM-VFA and Henry's Law (HR) constants. No interactions were identified between the types of diets (HS vs. LS) and the measurement methods on daily average VFA profiles (RM vs. EX or HR vs. EX), suggesting a consistent performance among the methods. Additionally, when the 3-h interval VFA data from HS and LS diets were analyzed separately, no interactions were observed between methods and time of day, indicating that the relative daily pattern of VFA molar proportions was similar regardless of the VFA measurement method used. The results revealed that the levels of acetate sharply increased immediately after feeding, trailed by an increase in the acetate:propionate ratio and a steady increase for propionate (2 h after feeding the HS diet, 4 h for LS), and butyrate. This change was more pronounced for the HS diet than the LS diet. However, there was no overall diet effect on the VFA molar proportions, although the measurement methods affected the molar proportions. Furthermore, we observed a strong positive correlation between the levels of RM and EX acetate for both diets (HS: r = 0.84; LS: r = 0.85), RM and EX propionate (r = 0.74), and RM and EX acetate:propionate ratio (r = 0.80). Both EX-VFA and RM-VFA exhibited similar responses to feeding and dietary treatments, suggesting that EX-VFA could serve as a useful proxy for characterizing RM-VFA molar proportions to evaluate rumen fermentation. Similar relationships were observed between RM-VFA and HR-VFA. In conclusion, this study underscores the potential of exhalomics as a reliable approach for assessing rumen fermentation. Moving forward, research should further explore the depth of exhalomics in ruminant studies to provide a comprehensive insight into rumen fermentation metabolites, especially across diverse dietary conditions.

Predictive effect of net water uptake on futile recanalisation in patients with acute large-vessel occlusion stroke

AIM

To determine whether net water uptake (NWU) based on automated software evaluation could predict futile recanalisation in patients with acute anterior circulation large-vessel occlusion (LVO).

MATERIALS AND METHODS

Patients with acute anterior circulation LVO undergoing mechanical thrombectomy in Jinling Hospital were evaluated retrospectively. NWU and other baseline data were evaluated by performing univariate and multivariate analyses. The primary endpoint was 90-day modified Rankin scale score ≥3. A nomogram to predict poor clinical outcomes was developed based on multivariate logistic regression analysis.

RESULTS

Overall, 135 patients who underwent thrombectomy with a TICI grade ≥2b were enrolled. In multivariate logistic regression analysis, the following factors were identified as independent predictors of futile recanalisation: age (odds ratio [OR]: 1.055, 95 % confidence interval [CI]: 1.004-1.110, p=0.035), female (OR: 0.289, 95 % CI: 0.098-0.850, p=0.024), hypertension (OR: 3.182, 95 % CI: 1.160-8.728, p=0.025), high blood glucose level (OR: 1.36, 95 % CI: 1.087-1.701, p=0.007), admission National Institutes of Health Stroke Scale score (OR: 1.082, 95 % CI: 1.003-1.168, p=0.043), and NWU (OR: 1.312, 95 % CI: 1.038-1.659, p=0.023).

CONCLUSIONS

NWU based on Alberta Stroke Program Early Computed Tomography (CT) Score (ASPECTS) could be used to predict the occurrence of futile recanalisation in patients with acute anterior circulation LVO ischaemic stroke.

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

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

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

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

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

Key Words Collapse

MESH Headings Collapse

Grants Collapse

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

Collapse

[Investigation of Anisakis infections in coastal marine fishes and awareness of anisakiasis control knowledge among residents in Yantai City in 2021]

OBJECTIVE

To investigate the prevalence of Anisakis infections in coastal marine fishes and awareness of anisakiasis control knowledge among local residents in Yantai City, Shandong Province in 2021, so as to provide insights into formulation of anisakiasis control interventions.

METHODS

Marine fishes were purchased from Shunxin Port, Yantai City, Shandong Province in November 2021, and the presence of Anisakis was detected in different species of fishes and different fish sites. The correlations between body length and weight of marine fish and intensity of Anisakis infections were examined using Spearman's rank correlation analysis, and the dietary habits and anisakiasis control knowledge were investigated using questionnaire surveys among local residents.

RESULTS

A total of 201 marine fishes belonging to 20 species were dissected, and Anisakis was detected in 77 marine fishes (38.31%) belonging to 11 species (55.00%), with a mean infection intensity of 45.04 parasites per fish (3 468/77). Spearman's rank correlation analysis revealed that the body length (rs = 0.74, P < 0.05) and weight (rs = 0.79, P < 0.01) of the monkfish correlated positively with the intensity of Anisakis infections, and the body length (rs = 0.68, P < 0.05) of the flatfish correlated positively with the intensity of Anisakis infections, while no correlations were examined between the body length or weight of other marine fishes and the intensity of Anisakis infections. Of all respondents, 53.38% men and 56.67% women did not know anisakiasis control knowledge at all, and there was a significant difference in the proportion of respondents using separate chopping boards for raw and cooked food from different villages (χ2 = 17.89, P < 0.01), while there was an age-specific proportion of respondents with habitats of eating raw or semi-raw seafood (χ2 = 28.27, P < 0.01).

CONCLUSIONS

The prevalence and intensity of Anisakis infections were high in coastal marine fishes in Yantai City in 2021, and the awareness of anisakiasis control knowledge was low among local residents. Intensified health education pertaining to anisakiasis control knowledge is recommended to reduce the risk of Anisakis infections.

[Incidence of postoperative complications in Chinese patients with gastric or colorectal cancer based on a national, multicenter, prospective, cohort study]

Objective: To investigate the incidence of postoperative complications in Chinese patients with gastric or colorectal cancer, and to evaluate the risk factors for postoperative complications. Methods: This was a national, multicenter, prospective, registry-based, cohort study of data obtained from the database of the Prevalence of Abdominal Complications After Gastro- enterological Surgery (PACAGE) study sponsored by the China Gastrointestinal Cancer Surgical Union. The PACAGE database prospectively collected general demographic characteristics, protocols for perioperative treatment, and variables associated with postoperative complications in patients treated for gastric or colorectal cancer in 20 medical centers from December 2018 to December 2020. The patients were grouped according to the presence or absence of postoperative complications. Postoperative complications were categorized and graded in accordance with the expert consensus on postoperative complications in gastrointestinal oncology surgery and Clavien-Dindo grading criteria. The incidence of postoperative complications of different grades are presented as bar charts. Independent risk factors for occurrence of postoperative complications were identified by multifactorial unconditional logistic regression. Results: The study cohort comprised 3926 patients with gastric or colorectal cancer, 657 (16.7%) of whom had a total of 876 postoperative complications. Serious complications (Grade III and above) occurred in 4.0% of patients (156/3926). The rate of Grade V complications was 0.2% (7/3926). The cohort included 2271 patients with gastric cancer with a postoperative complication rate of 18.1% (412/2271) and serious complication rate of 4.7% (106/2271); and 1655 with colorectal cancer, with a postoperative complication rate of 14.8% (245/1655) and serious complication rate of 3.0% (50/1655). The incidences of anastomotic leakage in patients with gastric and colorectal cancer were 3.3% (74/2271) and 3.4% (56/1655), respectively. Abdominal infection was the most frequently occurring complication, accounting for 28.7% (164/572) and 39.5% (120/304) of postoperative complications in patients with gastric and colorectal cancer, respectively. The most frequently occurring grade of postoperative complication was Grade II, accounting for 65.4% (374/572) and 56.6% (172/304) of complications in patients with gastric and colorectal cancers, respectively. Multifactorial analysis identified (1) the following independent risk factors for postoperative complications in patients in the gastric cancer group: preoperative comorbidities (OR=2.54, 95%CI: 1.51-4.28, P<0.001), neoadjuvant therapy (OR=1.42, 95%CI:1.06-1.89, P=0.020), high American Society of Anesthesiologists (ASA) scores (ASA score 2 points:OR=1.60, 95% CI: 1.23-2.07, P<0.001, ASA score ≥3 points:OR=0.43, 95% CI: 0.25-0.73, P=0.002), operative time >180 minutes (OR=1.81, 95% CI: 1.42-2.31, P<0.001), intraoperative bleeding >50 mL (OR=1.29,95%CI: 1.01-1.63, P=0.038), and distal gastrectomy compared with total gastrectomy (OR=0.65,95%CI: 0.51-0.83, P<0.001); and (2) the following independent risk factors for postoperative complications in patients in the colorectal cancer group: female (OR=0.60, 95%CI: 0.44-0.80, P<0.001), preoperative comorbidities (OR=2.73, 95%CI: 1.25-5.99, P=0.030), neoadjuvant therapy (OR=1.83, 95%CI:1.23-2.72, P=0.008), laparoscopic surgery (OR=0.47, 95%CI: 0.30-0.72, P=0.022), and abdominoperineal resection compared with low anterior resection (OR=2.74, 95%CI: 1.71-4.41, P<0.001). Conclusion: Postoperative complications associated with various types of infection were the most frequent complications in patients with gastric or colorectal cancer. Although the risk factors for postoperative complications differed between patients with gastric cancer and those with colorectal cancer, the presence of preoperative comorbidities, administration of neoadjuvant therapy, and extent of surgical resection, were the commonest factors associated with postoperative complications in patients of both categories.

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

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

Convolutional neural network for identifying common bile duct stones based on magnetic resonance cholangiopancreatography

AIMS

To develop an auto-categorization system based on machine learning for three-dimensional magnetic resonance cholangiopancreatography (3D MRCP) to detect choledocholithiasis from healthy and symptomatic individuals.

MATERIALS AND METHODS

3D MRCP sequences from 254 cases with common bile duct (CBD) stones and 251 cases with normal CBD were enrolled to train the 3D Convolutional Neural Network (3D-CNN) model. Then 184 patients from three different hospitals (91 with positive CBD stone and 93 with normal CBD) were prospectively included to test the performance of 3D-CNN.

RESULTS

With a cutoff value of 0.2754, 3D-CNN achieved the sensitivity, specificity, and accuracy of 94.51%, 92.47%, and 93.48%, respectively. In the receiver operating characteristic curve analysis, the area under the curve (AUC) for the presence or absence of CBD stones was 0.974 (95% CI, 0.940-0.992). There was no significant difference in sensitivity, specificity, and accuracy between 3D-CNN and radiologists. In addition, the performance of 3D-CNN was also evaluated in the internal test set and the external test set, respectively. The internal test set yielded an accuracy of 94.74% and AUC of 0.974 (95% CI, 0.919-0.996), and the external test set yielded an accuracy of 92.13% and AUC of 0.970 (95% CI, 0.911-0.995).

CONCLUSIONS

An artificial intelligence-assisted diagnostic system for CBD stones was constructed using 3D-CNN model for 3D MRCP images. The performance of 3D-CNN model was comparable to that of radiologists in diagnosing CBD stones. 3D-CNN model maintained high performance when applied to data from other hospitals.

[Establishment and validation of prediction models for human leukocyte antigen haplotypes and human leukocyte antigen genotypes]

Objective: To establish prediction models for human leukocyte antigen (HLA) haplotypes and HLA genotypes, and verify the prediction accuracy. Methods: The prediction models were established based on the characteristic of HLA haplotype inheritance and linkage disequilibrium (LD), as well as the invention patents and software copyrights obtained. The models include algorithm and reference databases such as HLA A-C-B-DRB1-DQB1 high-resolution haplotypes database, B-C and DRB1-DQB1 LD database, G group alleles table, and NMDP Code alleles table. The prediction algorithm involves data processing, comparison with reference data, filtering results, probability calculation and ranking, confidence degree estimation, and output of prediction results. The accuracy of the predictions was verified by comparing them with the correct results, and the relationship between prediction accuracy and the probability distribution and confidence degree of the predicted results was analyzed. Results: The HLA haplotypes and genotypes prediction models were established. The prediction algorithm included the prediction of A-C-B-DRB1-DQB1 haplotypes according to HLA-A, B, DRB1, C, DQB1 genotypes, the prediction of C and DQB1 high-resolution results according to A, B and DRB1 high-resolution results, and the prediction of A, B, DRB1, C and DQB1 high resolution results according to the A, B and DRB1 intermediate or low resolution results. Validation results of "Predicting A-C-B-DRB1-DQB1 haplotypes basing on HLA-A, B, DRB1, C, DQB1 genotypes" model: for 787 data, the accuracy was 94.0% (740/787) with 740 correct predictions, 34 incorrect predictions, and 13 instances with no predicted results. For 847 data, the accuracy was 100% (847/847). The 2 411 and 2 594 haplotype combinations predicted from 787 and 847 data were grouped according to confidence degree, the accuracy was 100% (48/48, 114/114) for a confidence degree of 1, 96.2% (303/315) and 97.8% (409/418) for a confidence degree of 2 respectively. Validation results of "Predicting A, B, DRB1 and C, DQB1 high-resolution genotypes basing on HLA-A, B, DRB1 high, intermediate, or low resolution genotypes" model: when predicting C and DQB1 high resolution genotypes basing on A, B, and DRB1 high resolution genotypes, 89.3% (1 459/1 634) of the predictions were correct. The accuracy for the top 2 predicted probability (GPP) ranking was 79.2% (1 156/1 459), and for the top 10, it was 95.0% (1 386/1 459). Furthermore, when GPP≥90% and GPP 50%-90%, the prediction accuracy was 81.3% (209/257) and 72.8% (447/614) respectively. The accuracy of predicting C and DQB1 high resolution genotypes basing on the results of A, B, and DRB1 high resolution genotypes from the China Marrow Donor Program was 87.0% (20/23). The accuracy of predicting A, B, DRB1, C, and DQB1 high resolution genotypes basing on the results of A, B, and DRB1 intermediate or low-resolution genotypes was 70.0% (7/10) and 52.5% (21/40) respectively. When predicting whether the patient is likely to have a HLA 10/10 matched donor, the accuracy of the top 2 GPP combinations with a proportion of ≥50% was 85.7% (6/7). Conclusions: When using A, B, DRB1, C, DQB1 genotypes to predict A-C-B-DRB1-DQB1 haplotype combinations, the results with a confidence degree of 1 and 2 are reliable. When predicting C and DQB1 genotypes according to A, B and DRB1 genotypes, the top 10 results ranked by GPP are reliable, and the top 2 results with GPP≥50% are more reliable.

Dexamethasone relieves the inflammatory response caused by inguinal hernia meshes through miR-155

BACKGROUND

Inguinal hernia is a relatively common condition. Most patients with inguinal hernia require surgery. At present, mesh repair is one of the most effective methods to treat inguinal hernia, but insertion of the mesh can cause inflammation. Dexamethasone (DEX) can treat inflammation, but the mechanism by which DEX alleviates inflammation caused by inguinal hernia mesh placement remains unclear.

METHOD

We randomly divided rats into groups: negative control (NC), inguinal hernia (IH), polypropylene mesh (PM), DEX treatment, and miR-155 treatment groups. RT-qPCR was performed to determine the expression of miR-155. ELISA was implemented to determine the secretion of IL-1β, IL-6, and IL-18. Western blotting was used to detect caspase-1, JAK1, p-JAK1, STAT3, and p-STAT3 expression. A dual-luciferase reporter gene array identified a connection between miR-155 and JAK1.

RESULTS

The results revealed that the expression of miR-155, IL-1β, IL-6, and IL-18 was upregulated in the PM group. After DEX treatment, the secretion of miR-155, caspase-1, IL-1β, IL-6, and IL-18 decreased. Dual luciferase results confirmed that miR-155 induced the targeted downregulation of JAK1, while a miR-155 mimic reversed the therapeutic effect of DEX, and the expression levels of p-JAK1 and p-STAT3 increased.

CONCLUSION

DEX regulates the JAK1/STAT3 signaling pathway through miR-155 to relieve inflammation caused by inguinal hernia meshes.

[Peripheral sterile corneal infiltrates after small incision lenticule extraction]

A 31-year-old female patient with refractive error in both eyes underwent small incision lenticule extraction. On the 4th day after surgery, arc-shaped peripheral corneal infiltrates appeared in the right eye. Tobramycin and dexamethasone eye drops, 0.3% gatifloxacin eye drops, and a corneal bandage lens were applied to the eye. After bacterial infection was ruled out, dexamethasone sodium phosphate was injected subconjunctivally near the corneal lesion. The symptoms improved and the corneal lesion subsided afterwards.

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.