Comparison of various excimer laser (EL) combination therapies for vitiligo: a systematic review and network meta-analysis

AIM

This study aimed to compare the efficacy and safety of excimer laser (EL)-based combination regimens in improving repigmentation.

METHODS

A comprehensive search was conducted in PubMed, Web of Science, Cochrane Library, and Embase on July 1, 2023, to include randomized controlled trials of EL combination treatments for vitiligo that met the criteria. The primary outcome measure was a repigmentation rate ≥ 75%, and the secondary outcome measures were a repigmentation rate of ≤ 25% and adverse events.

RESULTS

Eleven studies involving 348 patients were included. Network Meta-Analysis showed that EL combined with antioxidants (SUCRA = 98.8%), EL combined with calcipotriol (SUCRA = 59.8%) and EL combined with tacalcitol (SUCRA = 59.6%) were the three optimal interventions achieving repigmentation rates ≥ 75%. EL alone (SUCRA = 77.6%), EL combined with tacalcitol (SUCRA = 61.7%) and EL combined with antioxidants (SUCRA = 57.2%) were the three interventions with the highest rates of treatment failure. Adverse events in all groups mainly included erythema, burning sensation and hyperpigmentation. Based on the results of the current study, EL combination therapies were safe with mild adverse events.

CONCLUSION

EL combined with antioxidants was the preferred regimen for vitiligo, whereas EL alone was the regimen with the highest rate of treatment failure in vitiligo.

Detailed operational building data for six office rooms in Denmark: Occupancy, indoor environment, heating, ventilation, lighting and room control monitoring with sub-hourly temporal resolution

The operational building data presented in this paper has been collected from six office rooms located in an office building (research and educational purposes) located on the main campus of Aalborg University in Denmark. The dataset consists of measurements of occupancy, indoor environmental quality, room-level and system-level heating, ventilation and lighting operation at a 5 min resolution. The indoor environmental quality and building system data were collected from the building management system. The occupancy level in each monitored room is established from the computer vision-based analysis of wall-mounted camera footage of each office. The number of people present in the room is estimated using the YOLOv5s image recognition algorithm. The present dataset can be used for occupancy analysis, indoor environmental quality investigations, machine learning, and model predictive control.

Phosphorus recovery from sewage sludge via Mg-air battery system

A pressing issue in contemporary society is the resource scarcity of phosphorus. Operating on the principle of electrochemical reactions between Mg as the anode and oxygen from air as the cathode, Mg-air batteries (MAB) have been employed to provide new prospects for phosphorus recovery in struvite form. Different phosphorus concentrations and reaction time impact struvite generation in MAB systems; however, the exact mechanism has rarely been investigated. We investigated how varying the initial phosphorus concentration and the reaction time affects phosphorus recovery, electricity generation, and the efficiency of struvite production in MAB. Additionally, we examine the impact of solid carbon sources on phosphorus transformation in sludge. The findings revealed that the incorporation of solid carbon sources facilitated the release of phosphate by changing phosphorus speciation. The electrolyte derived from the conditioned sludge filtrate exhibited a remarkable phosphorus removal efficiency of 91.7 % within 1 h, yielding the highest struvite purity of ∼70 %, whereas that using raw sludge filtrate or extending the reaction time was found to be less effective, even reducing struvite formation. Furthermore, different electrolytes influence the system's ability to passivate anode, and electrolytes with higher phosphorus concentrations have better electricity production performance. The results by Visual MINTEQ model confirmed that longer reaction times and lower initial phosphorus concentrations can negatively affect struvite formation by introducing Mg3(PO4)2 and Mg(OH)2. The integration of agricultural waste as carbon sources with MAB for phosphorus recovery represents a potential methodology for struvite recuperation from sewage sludge, thereby heralding a sustainable strategy for resource recovery.

IFT27 regulates the long-term maintenance of photoreceptor outer segments in zebrafish

Approximately a quarter of Retinitis Pigmentosa (RP) is caused by mutations in transport-related genes in cilia. IFT27 (Intraflagellar Transport 27), a core component of the ciliary intraflagellar transport (IFT) system, has been implicated as a significant pathogenic gene in RP. The pathogenic mechanisms and subsequent pathology related to IFT27 mutations in RP are largely obscure. Here, we utilized TALEN technology to create an ift27 knockout (ift27-/-) zebrafish model. Electroretinography (ERG) detection showed impaired vision in this model. Histopathological examinations disclosed that ift27 mutations cause progressive degeneration of photoreceptors in zebrafish, and this degeneration was late-onset. Immunofluorescence labeling of outer segments showed that rods degenerated before cones, aligning with the conventional characterization of RP. In cultured human retinal pigment epithelial cells, we found that IFT27 was involved in maintaining ciliary morphology. Furthermore, decreased IFT27 expression resulted in the inhibition of the Hedgehog (Hh) signaling pathway, including decreased expression of key factors in the Hh pathway and abnormal localization of the ciliary mediator Gli2. In summary, we generated an ift27-/- zebrafish line with retinal degeneration which mimicked the symptoms of RP patients, highlighting IFT27's integral role in the long-term maintenance of cilia via the Hh signaling pathway. This work may furnish new insights into the treatment or delay of RP caused by IFT27 mutations.

A validation of register-derived diagnoses of interstitial lung disease in patients with inflammatory arthritis: data from the NOR-DMARD study

OBJECTIVE

There is a lack of knowledge concerning the validity of the interstitial lung disease (ILD) diagnoses used in epidemiological studies on rheumatic diseases. This paper seeks to verify register-derived ILD diagnoses using chest computed tomography (CT) and medical records as a gold standard.

METHOD

The Norwegian Anti-Rheumatic Drug Register (NOR-DMARD) is a multicentre prospective observational study of patients with inflammatory arthritis who start treatment with disease-modifying anti-rheumatic drugs. NOR-DMARD is linked to the Norwegian Patient Registry (NPR) and Cause of Death Registry. We searched registers for ILD coded by ICD-10 J84 or J99 among patients with rheumatoid arthritis, psoriatic arthritis, or spondyloarthritis. We extracted chest CT reports and medical records from participating hospitals. Two expert thoracic radiologists scored examinations to confirm the ILD diagnosis. We also searched medical records to find justifications for the diagnosis following multidisciplinary evaluations. We calculated the positive predictive values (PPVs) for ILD across subsets.

RESULTS

We identified 71 cases with an ILD diagnosis. CT examinations were available in 65/71 patients (91.5%), of whom ILD was confirmed on CT in 29/65 (44.6%). In a further 10 patients, medical records confirmed the diagnosis, giving a total of 39/71 verified cases. The PPV of a register-derived ILD diagnosis was thus 54.9%. In a subset of patients who had received an ILD code at two or more time-points and had a CT scan taken within a relevant period, the PPV was 72.2%.

CONCLUSION

The validity of register-based diagnoses of ILD must be carefully considered in epidemiological studies.

[Epidemiological investigation of iron deficiency among preschool children in 10 provinces, autonomous regions, or municipalities in China]

Objective: To understand the current status of anemia, iron deficiency, and iron-deficiency anemia among preschool children in China. Methods: A cross-sectional study was conducted with a multi-stage stratified sampling method to select 150 streets or townships from 10 Chinese provinces, autonomous regions, or municipalities (East: Jiangsu, Zhejiang, Shandong, and Hainan; Central: Henan; West: Chongqing, Shaanxi, Guizhou, and Xinjiang; Northeast: Liaoning). From May 2022 to April 2023, a total of 21 470 children, including community-based children aged 0.5 to<3.0 years receiving child health care and kindergarten-based children aged 3.0 to<7.0 years, were surveyed. They were divided into 3 age groups: infants (0.5 to<1.0 year), toddlers (1.0 to<3.0 years), and preschoolers (3.0 to<7.0 years). Basic information such as sex and date of birth of the children was collected, and peripheral blood samples were obtained for routine blood tests and serum ferritin measurement. The prevalence rates of anemia, iron deficiency, and iron-deficiency anemia were analyzed, and the prevalence rate differences were compared among different ages, sex, urban and rural areas, and regions using the chi-square test. Results: A total of 21 460 valid responses were collected, including 10 780 boys (50.2%). The number of infants, toddlers, and preschoolers were 2 645 (12.3%), 6 244 (29.1%), and 12 571 (58.6%), respectively. The hemoglobin level was (126.7±14.8) g/L, and the serum ferritin level was 32.3 (18.5, 50.1) μg/L. The overall rates of anemia, iron deficiency, and iron-deficiency anemia were 10.4% (2 230/21 460), 28.3% (6 070/21 460), and 3.9% (845/21 460), respectively. The prevalence rate of anemia was higher for boys than for girls (10.9% (1 173/10 780) vs. 9.9% (1 057/10 680), χ2=5.58, P=0.018), with statistically significant differences in the rates for infants, toddlers and preschoolers (18.0% (475/2 645), 10.6% (662/6 244), and 8.7% (1 093/12 571), respectively, χ2=201.81, P<0.01), and the rate was significantly higher for children in rural than that in urban area (11.8% (1 516/12 883) vs. 8.3% (714/8 577), χ2=65.54, P<0.01), with statistically significant differences in the rates by region (χ2=126.60, P<0.01), with the highest rate of 15.8% (343/2 173) for children in Central region, and the lowest rate of 5.3% (108/2 053) in Northeastern region. The prevalence rates of iron deficiency were 33.8% (895/2 645), 32.2% (2 011/6 244), and 25.2% (3 164/12 571) in infants, toddlers, and preschoolers, respectively, and 30.0% (3 229/10 780) in boys vs. 26.6% (2 841/10 680) in girls, 21.7% (1 913/8 821), 40.0% (870/2 173), 27.1% (2 283/8 413), 48.9% (1 004/2 053) in Eastern, Central, Western, and Northeastern regions, respectively, and each between-group showed a significant statistical difference (χ2=147.71, 29.73, 773.02, all P<0.01). The prevalence rate of iron-deficiency anemia showed a significant statistical difference between urban and rural areas, 2.9% (251/8 577) vs. 4.6% (594/12 883) (χ2=38.62, P<0.01), while the difference in iron deficiency prevalence was not significant (χ2=0.51, P=0.476). Conclusions: There has been a notable improvement in iron deficiency and iron-deficiency anemia among preschool children in China, but the situation remains concerning. Particular attention should be paid to the prevention and control of iron deficiency and iron-deficiency anemia, especially among infants and children in the Central, Western, and Northeastern regions of China.

GTPase activity of porcine Mx1 plays a dominant role in inhibiting the N-Nsp9 interaction and thus inhibiting PRRSV replication

Porcine Mx1 is a type of interferon-induced GTPase that inhibits the replication of certain RNA viruses. However, the antiviral effects and the underlying mechanism of porcine Mx1 for porcine reproductive and respiratory syndrome virus (PRRSV) remain unknown. In this study, we demonstrated that porcine Mx1 could significantly inhibit PRRSV replication in MARC-145 cells. By Mx1 segment analysis, it was indicated that the GTPase domain (68-341aa) was the functional area to inhibit PRRSV replication and that Mx1 interacted with the PRRSV-N protein through the GTPase domain (68-341aa) in the cytoplasm. Amino acid residues K295 and K299 in the G domain of Mx1 were the key sites for Mx1-N interaction while mutant proteins Mx1(K295A) and Mx1(K299A) still partially inhibited PRRSV replication. Furthermore, we found that the GTPase activity of Mx1 was dominant for Mx1 to inhibit PRRSV replication but was not essential for Mx1-N interaction. Finally, mechanistic studies demonstrated that the GTPase activity of Mx1 played a dominant role in inhibiting the N-Nsp9 interaction and that the interaction between Mx1 and N partially inhibited the N-Nsp9 interaction. We propose that the complete anti-PRRSV mechanism of porcine Mx1 contains a two-step process: Mx1 binds to the PRRSV-N protein and subsequently disrupts the N-Nsp9 interaction by a process requiring the GTPase activity of Mx1. Taken together, the results of our experiments describe for the first time a novel mechanism by which porcine Mx1 evolves to inhibit PRRSV replication.

IMPORTANCE

Mx1 protein is a key mediator of the interferon-induced antiviral response against a wide range of viruses. How porcine Mx1 affects the replication of porcine reproductive and respiratory syndrome virus (PRRSV) and its biological function has not been studied. Here, we show that Mx1 protein inhibits PRRSV replication by interfering with N-Nsp9 interaction. Furthermore, the GTPase activity of porcine Mx1 plays a dominant role and the Mx1-N interaction plays an assistant role in this interference process. This study uncovers a novel mechanism evolved by porcine Mx1 to exert anti-PRRSV activities.

Characterisation of the mitochondrial genome and phylogenetic analysis of Toxocara apodemi (Nematoda: Ascarididae)

We first sequenced and characterised the complete mitochondrial genome of Toxocara apodeme, then studied the evolutionary relationship of the species within Toxocaridae. The complete mitochondrial genome was amplified using PCR with 14 specific primers. The mitogenome length was 14303 bp in size, including 12 PCGs (encoding 3,423 amino acids), 22 tRNAs, 2 rRNAs, and 2 NCRs, with 68.38% A+T contents. The mt genomes of T. apodemi had relatively compact structures with 11 intergenic spacers and 5 overlaps. Comparative analyses of the nucleotide sequences of complete mt genomes showed that T. apodemi had higher identities with T. canis than other congeners. A sliding window analysis of 12 PCGs among 5 Toxocara species indicated that nad4 had the highest sequence divergence, and cox1 was the least variable gene. Relative synonymous codon usage showed that UUG, ACU, CCU, CGU, and UCU most frequently occurred in the complete genomes of T. apodemi. The Ka/Ks ratio showed that all Toxocara mt genes were subject to purification selection. The largest genetic distance between T. apodemi and the other 4 congeneric species was found in nad2, and the smallest was found in cox2. Phylogenetic analyses based on the concatenated amino acid sequences of 12 PCGs demonstrated that T. apodemi formed a distinct branch and was always a sister taxon to other congeneric species. The present study determined the complete mt genome sequences of T. apodemi, which provide novel genetic markers for further studies of the taxonomy, population genetics, and systematics of the Toxocaridae nematodes.

Metal-Oxo Electronic Tuning via In Situ CO Decoration for Promoting Methane Conversion to Oxygenates over Single-Atom Catalysts

Direct methane conversion (DMC) to oxygenates at low temperature is of great value but remains challenging due to the high energy barrier for C-H bond activation. Here, we report that in situ decoration of Pd1-ZSM-5 single atom catalyst (SAC) by CO molecules significantly promoted the DMC reaction, giving the highest turnover frequency of 207 h-1 ever reported at room temperature and ~100 % oxygenates selectivity with H2O2 as oxidant. Combined characterizations and DFT calculations illustrate that the C-atom of CO prefers to coordinate with Pd1, which donates electrons to the Pd1-O active center (L-Pd1-O, L=CO) generated by H2O2 oxidation. The correspondingly improved electron density over Pd-O pair renders a favorable heterolytic dissociation of C-H bond with low energy barrier of 0.48 eV. Applying CO decoration strategy to M1-ZSM-5 (M=Pd, Rh, Ru, Fe) enables improvement of oxygenates productivity by 3.2-11.3 times, highlighting the generalizability of this method in tuning metal-oxo electronic structure of SACs for efficient DMC process.

Ginseng Glucosyl Oleanolate from Ginsenoside Ro, Exhibited Anti-Liver Cancer Activities via MAPKs and Gut Microbiota In Vitro/Vivo

Ginseng is widely recognized for its diverse health benefits and serves as a functional food ingredient with global popularity. Ginsenosides with a broad range of pharmacological effects are the most crucial active ingredients in ginseng. This study aimed to derive ginseng glucosyl oleanolate (GGO) from ginsenoside Ro through enzymatic conversion and evaluate its impact on liver cancer in vitro and in vivo. GGO exhibited concentration-dependent HepG2 cell death and markedly inhibited cell proliferation via the MAPK signaling pathway. It also attenuated tumor growth in immunocompromised mice undergoing heterograft transplantation. Furthermore, GGO intervention caused a modulation of gut microbiota composition by specific bacterial populations, including Lactobacillus, Bacteroides, Clostridium, Enterococcus, etc., and ameliorated SCFA metabolism and colonic inflammation. These findings offer promising evidence for the potential use of GGO as a natural functional food ingredient in the prevention and treatment of cancer.

Chromosome-level genome assembly and characterization of the Calophaca sinica genome

Calophaca sinica is a rare plant endemic to northern China which belongs to the Fabaceae family and possesses rich nutritional value. To support the preservation of the genetic resources of this plant, we have successfully generated a high-quality genome of C. sinica (1.06 Gb). Notably, transposable elements (TEs) constituted ~73% of the genome, with long terminal repeat retrotransposons (LTR-RTs) dominating this group of elements (~54% of the genome). The average intron length of the C. sinica genome was noticeably longer than what has been observed for closely-related species. The expansion of LTR-RTs and elongated introns emerged had the largest influence on the enlarged genome size of C. sinica in comparison to other Fabaceae species. The proliferation of TEs could be explained by certain modes of gene duplication, namely, whole genome duplication (WGD) and dispersed duplication (DSD). Gene family expansion, which was found to enhance genes associated with metabolism, genetic maintenance, and environmental stress resistance, was a result of transposed duplicated genes (TRD) and WGD. The presented genomic analysis sheds light on the genetic architecture of C. sinica, as well as provides a starting point for future evolutionary biology, ecology, and functional genomics studies centered around C. sinica and closely-related species.

Residue Degradation and Risk Assessment of Difenoconazole and Its Metabolite during Tea Growing, Processing and Brewing by Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry Determination

Residue dissipation and risk assessment of difenoconazole and its metabolite difenoconazole-alcohol during tea growing, processing, and brewing was first investigated by ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). The limits of quantification for both difenoconazole and difenoconazole-alcohol were 0.001 mg/kg in fresh tea leaves and tea, and 0.0002 mg/L in tea infusion. In field trials, the dissipation half-lives of difenoconazole in fresh tea leaves was 1.77 days. After spraying, the residues of difenoconazole-alcohol increased and then gradually dissipated like difenoconazole. After 14 days, the dissipation rates of difenoconazole and difenoconazole-alcohol reached 99%. When fresh tea leaves were harvested on different days, the total processing factors (PFs) of difenoconazole and difenoconazole-alcohol for green tea were 0.86-1.05 and 0.78-0.85, respectively, while the total PFs for black tea were 0.83-1.13 and 0.82-1.66, respectively. Metabolism of difenoconazole was accelerated during tea processing. When brewing black tea, the leaching rates (LRs) of difenoconazole and difenoconazole-alcohol were 8.4-17.9% and 31.8-38.9%, respectively, while when brewing green tea, the LRs were 15.4-23.5% and 30.4-50.6%, respectively. The LRs of difenoconazole and difenoconazole-alcohol in black tea were higher than those in green tea. The potential threat to human health for dietary intake of difenoconazole and difenoconazole-alcohol residues from tea consumption is negligible. However, the dietary risk of difenoconazole in fruits and vegetables that are essential for daily diets is concerning, with a risk probability of 158%.

Trust as a catalyst: revealing the impact of government trust and professional trust on public health policy compliance during a pandemic

BACKGROUND

Existing research has extensively explored the relationship between government trust and compliance behaviour, but significant controversies exist. Some studies suggest a strong positive correlation between the two. Other studies have found that government trust hinders compliance behaviour. However, during the pandemic, the effectiveness of public health policies largely depends on the public's compliance with these policies. To examine the aforementioned controversies, this study utilizes survey data on the Chinese population during the COVID-19 period to explore the relationship between compliance with public health policies and government trust.

METHODS

The study conducted a questionnaire survey of 1,395 individuals from 25 provinces in China from mid-November to mid-December 2022. Firstly, we categorized the public's compliance behaviour with public health policies based on the results of factor analysis. Subsequently, we examined the impact of government trust and professional trust on compliance behaviour with public health policies by constructing a structural equation model.

RESULTS

Based on the results of factor analysis, we classified public adherence to public health policies into protective compliance and restrictive compliance. Results from the structural equation model show a positive correlation between the public's trust in the government and both protective and restrictive compliance, with a stronger influence on protective compliance. Government trust also exerts a positive impact on restrictive compliance behaviour through professional trust. Additionally, the study indicates a significant positive correlation between the public's professional trust and restrictive compliance, while it does not significantly affect protective compliance. Moreover, the public from rural areas demonstrates a greater willingness to adhere to both types of public health policies. Married individuals exhibit a stronger inclination toward protective compliance, while females show a stronger tendency toward restrictive compliance.

CONCLUSION

The study revealed a significant positive impact of government trust and professional trust on compliance behaviour with public health policies during the COVID-19 pandemic, refuting any negative correlation between government trust and compliance behaviour. Normative motivations for compliance behaviour had a substantial impact on adherence. These findings offer valuable insights for future public health crisis management and public policy formulation.

Dihydroergotamine protects against ischemic stroke by modulating microglial/macrophage polarization and inhibiting inflammation in mice

OBJECTIVES

The search for drugs that can protect the brain tissue and reduce nerve damage in acute ischemic stroke has emerged as a research hotspot. We investigated the potential protective effects and mechanisms of action of dihydroergotamine against ischemic stroke.

METHODS

C57BL/6 mice were subjected to middle cerebral artery occlusion (MCAO), and dihydroergotamine at a dose of 10 mg/kg/day was intraperitoneally injected for 14 days. Adhesive removal and beam walking tests were conducted 1, 3, 5, 7, 10, and 14 days after MCAO surgery. Thereafter, the mechanism by which dihydroergotamine regulates microglia/macrophage polarization and inflammation and imparts ischemic stroke protection was studied using enzyme-linked immunosorbent assay, immunofluorescence staining, and western blotting.

RESULTS

From the perspective of a drug repurposing strategy, dihydroergotamine was found to inhibit oxygen-glucose deprivation damage to neurons, significantly improve cell survival rate, and likely exert a protective effect on ischemic brain injury. Dihydroergotamine significantly improved neural function scores and survival rates and reduced brain injury severity in mice. Furthermore, dihydroergotamine manifests its protective effect on ischemic brain injury by reducing the expression of TNF-α and IL-1β in mouse ischemic brain tissue, inhibiting the polarization of microglia/macrophage toward the M1 phenotype and promoting polarization toward the M2 phenotype.

CONCLUSION

This study is the first to demonstrate the protective effect of dihydroergotamine, a first-line treatment for migraine, against ischemic nerve injury in vitro and in vivo.

Inhibition of Anaplastic Lymphoma Kinase Protects From Ischemic Stroke

BACKGROUND

Ischemic stroke is often accompanied by oxidative stress and inflammatory response, both of which work synergistically to exacerbate the disruption of the blood-brain barrier and ischemic brain injury. ALK (anaplastic lymphoma kinase), a cancer-associated receptor tyrosine kinase, was found to play a role in oxidative stress and inflammation. In this study, we investigated the role of ALK inhibition in a murine model of ischemic stroke.

METHODS

Focal cerebral ischemia was induced by temporary occlusion of the right middle cerebral artery in mice with a filament. The ALK inhibitor alectinib was administered following the stroke. ALOX15 (arachidonic acid 15-lipoxygenase) was overexpressed by adenovirus injection. The immunohistochemistry, Western blot, oxidative stress, inflammation, blood-brain barrier leakage, infarct volume, and functional outcomes were determined.

RESULTS

We found that the expression of ALK was markedly increased in the neurovascular unit after cerebral ischemia. Treatment with the ALK inhibitor alectinib reduced the accumulation of reactive oxygen species, lipid peroxidation, and oxidative DNA, increased the vascular levels of antioxidant enzymes, inactivated the vascular NLRP3 (nucleotide-binding oligomerization domain-like receptor protein 3) inflammasome pathway, and reduced vascular inflammation (ICAM-1 [intercellular adhesion molecule-1] and MCP-1 [monocyte chemoattractant protein-1]) after ischemia. Moreover, alectinib reduced the loss of cerebrovascular integrity and blood-brain barrier damage, consequently decreasing brain infarction and neurological deficits. Furthermore, alectinib reduced stroke-evoked ALOX15 expression, whereas virus-mediated overexpression of ALOX15 abolished alectinib-dependent inhibition of oxidative stress and vascular inflammation, blood-brain barrier protection, and neuroprotection, suggesting the protective effects of alectinib for stroke may involve ALOX15.

CONCLUSIONS

Our findings demonstrated that alectinib protects from stroke by regulating ischemic signaling cascades and suggest that ALK may be a novel therapeutic target for ischemic stroke.

Vof16-miR-185-5p-GAP43 network improves the outcomes following spinal cord injury via enhancing self-repair and promoting axonal growth

INTRODUCTION

Self-repair of spinal cord injury (SCI) has been found in humans and experimental animals with partial recovery of neurological functions. However, the regulatory mechanisms underlying the spontaneous locomotion recovery after SCI are elusive.

AIMS

This study was aimed at evaluating the pathological changes in injured spinal cord and exploring the possible mechanism related to the spontaneous recovery.

RESULTS

Immunofluorescence staining was performed to detect GAP43 expression in lesion site after spinal cord transection (SCT) in rats. Then RNA sequencing and gene ontology (GO) analysis were employed to predict lncRNA that correlates with GAP43. LncRNA smart-silencing was applied to verify the function of lncRNA vof16 in vitro, and knockout rats were used to evaluate its role in neurobehavioral functions after SCT. MicroRNA sequencing, target scan, and RNA22 prediction were performed to further explore the underlying regulatory mechanisms, and miR-185-5p stands out. A miR-185-5p site-regulated relationship with GAP43 and vof16 was determined by luciferase activity analysis. GAP43-silencing, miR-185-5p-mimic/inhibitor, and miR-185-5p knockout rats were also applied to elucidate their effects on spinal cord neurite growth and neurobehavioral function after SCT. We found that a time-dependent increase of GAP43 corresponded with the limited neurological recovery in rats with SCT. CRNA chip and GO analysis revealed lncRNA vof16 was the most functional in targeting GAP43 in SCT rats. Additionally, silencing vof16 suppressed neurite growth and attenuated the motor dysfunction in SCT rats. Luciferase reporter assay showed that miR-185-5p competitively bound the same regulatory region of vof16 and GAP43.

CONCLUSIONS

Our data indicated miR-185-5p could be a detrimental factor in SCT, and vof16 may function as a ceRNA by competitively binding miR-185-5p to modulate GAP43 in the process of self-recovery after SCT. Our study revealed a novel vof16-miR-185-5p-GAP43 regulatory network in neurological self-repair after SCT and may underlie the potential treatment target for SCI.

Impact of endoscopic surveillance on the early diagnosis and endoscopic resection likelihood of gastric cancer

BACKGROUND

Endoscopy could help detect early gastric cancer (EGC) and improve the prognosis of patients. The aim of this study was to analyze the impact of endoscopy and endoscopic surveillance on the early detection of gastric cancer (GC), GC staging, and treatment selection.

METHODS

Patients with GC diagnosed at our center from 2010 to 2022 were retrospectively analyzed and allocated to the short-interval group (had received endoscopy within 3 years before diagnosis), the long-interval group (had received endoscopy more than 3 years before diagnosis), and the unchecked group (had not received endoscopy before diagnosis). The differences in GC staging and treatment modalities among the three groups were analyzed, and the differences in the clinical and pathological features of EGC were further analyzed.

RESULTS

One thousand and twenty-five GC patients were included, with 395 cases of EGC and 630 cases of advanced GC. The proportions of EGC in the short-interval, long-interval, and unchecked groups were 98.0%, 84.2%, and 29.8%, respectively (p < 0.001). Among the 387 lesions of 367 EGC patients were resected by endoscopic submucosal dissection (ESD), 341 (88.1%) exhibited curative resection, and 46 (11.9%) involved noncurative resections. Lesions of EGC differed significantly in diameter, depth of invasion, and curative resection rate (p = 0.033, 0.019, and 0.005, respectively). In the short-interval group, 87.8% of the lesions were ≤ 2 cm, 95.6% of the invasion depths were confined to the mucosal layer, and 96.7% of the eCura scores were A or B. Compared with the unchecked group, they had smaller diameters (RR = 0.419, 95% CI 0.234-0.752), shallower invasion depths (RR = 0.286, 95% CI 0.105-0.777), and a higher curative resection rate (RR = 0.215, 95% CI 0.068-0.676).

CONCLUSION

Endoscopic surveillance at 3-year intervals can help detect EGC, and the EGC lesions found have smaller diameters and shallower depths of invasion, helping improve the curative resection rate of ESD.

The effects of resistance exercises interventions on quality of life and glycemic control in patients with type 2 diabetes: Systematic review and meta-analysis of randomized controlled trials

AIMS

The global burden of diabetes mellitus is escalating rapidly, and the complex comorbidities contribute to a marked decline in patients' quality of life. This review assesses the impact of resistance training on quality of life in type 2 diabetes patients, addressing the ongoing debate over its role in diabetes management and the potential to enhance clinical outcomes.

METHODS

Systematic review and meta-analysis of randomized controlled trials regarding assessing effects from resistance training on quality of life among diabetic patients. We systematically searched PubMed, Medline, Web of Science and Embase and the register of controlled trials searched to February 2021. All included studies were randomized controlled trials in adults with type 2 diabetes that assessed the effect of a resistance training on glycemic control and change in health-related quality of life (HRQOL). The HRQOL was measured by validated questionnaires, covering physical and mental components scores, and pooled standardized effect sizes were calculated. Glycemic control measured by the level of Glycated Hemoglobin (HbA1c), and pooled weighted effect sizes were calculated.

RESULT

9 randomized controlled trials were eligible for the systematic review and our meta-analysis, with 557 participants completed randomized programs data. With random effects meta-analyses model, there was no significant pooled estimate of the standardized mean difference of PCS of quality of life (0.73, 95%CI: -1.19 to 2.65), while in our sensitivity analysis, resistance training had a marginally significantly positive effect on physical components among diabetic patients (0.21, 95%CI: -0.02 to 0.45). Additionally, resistance exercise was non-significantly related with improvement in mental components scores of HRQOL compared with control group (standardized mean difference, (-0.01 (95%CI: -1.25 to 1.23)). With random effects meta-analyses, constructed resistance training intervention did not significantly change the level of HbA1c compared with the control group (-0.22, 95%CI: -0.98 to 0.54) CONCLUSION: The resistance/strength training might have the slight positive effect on the PCS of HRQOL among patients with type 2 diabetes, but was non-significantly beneficial for MCS of HRQOL and glycemic control.

A novel long non-coding RNA SLNCR1 promotes proliferation, migration, and invasion of melanoma via transcriptionally regulating SOX5

Steroid receptor RNA activator (SRA)-like non-coding RNA (SLNCR1) has been implicated in various tumorigenic processes, but the precise regulatory role in melanoma progression remains uncertain. We performed a comprehensive analysis to investigate the prognostic value of SLNCR1 expression in patients with melanoma by TCGA database and melanoma tissue samples via the Kaplan-Meier method. Subsequently, we conducted qRT-PCR and Fluorescence in Situ Hybridization (FISH) assays to identify SLNCR1 expression levels and localization in tissues and cells, respectively. Loss-of-function assays utilizing shRNAs vectors were used to investigate the potential impact of SLNCR1. Our data showed that SLNCR1 is significantly up-regulated in human malignant melanoma tissues and cell lines and functions as an oncogene. Silencing of SLNCR1 suppressed melanoma cell proliferation, migration, invasion, and inhibited tumorigenesis in a mouse xenograft model. Additionally, we employed bioinformatic predictive analysis, combined with dual-luciferase reporter analysis and functional rescue assays, to elucidate the mechanistic target of the SLNCR1/SOX5 axis in melanoma. Mechanistically, we discovered that SLNCR1 promotes EMT of human melanoma by targeting SOX5, as downregulation of SLNCR1 expression leads to a decrease in SOX5 protein levels and inhibits melanoma tumorigenesis. Our research offers promising insights for more precise diagnosis and treatment of human melanoma.

Menopausal age and cardiovascular disease risk in American women: evidence from the National Health and Nutrition Examination Survey

OBJECTIVE

Cardiovascular disease (CVD) is a significant contributor to the deaths of females, and premature menopause adds to the risk of CVD in females. Therefore, our study aimed to investigate the age of menopause and CVD incidence in American females using data from the National Health and Nutrition Examination Survey (NHANES).

METHOD

We analyzed data from 6347 females to investigate the association between menopausal age and the risk of CVD using multivariate logistic regression analysis.

RESULTS

The study found that a later menopausal age reduces the risk of developing CVD (odds ratio [OR] = 0.74, 95% confidence interval [CI] = 0.63 - 0.88, p < 0.001). Moreover, females with early-onset CVD had an increased risk of premature menopause before the age of 40 years (OR = 2.44, 95% CI = 1.60 - 3.72, p < 0.001).

CONCLUSION

Menopausal age is associated with the risk of developing CVD in American females. Specifically, if menopause occurs earlier, there is an increased risk of CVD. Additionally, early-onset CVD significantly raises the risk of premature menopause, which in turn has important implications for female reproductive health.

Cyclodextrins as therapeutic drugs for treating lipid metabolism disorders

OBJECTIVE

This study sought to systematically compare the efficacy and mechanism of cyclodextrins as drug interventions in lipid metabolism diseases, potentially providing ideas for subsequent research directions and clinical applications.

METHODS

We used the bibliometric method for feature mining, applied VOSviewer software for clustering analysis, and applied content analysis for objective descriptions and accurate analysis.

RESULTS

(1) We collected more than 50 studies, which is the basic database of this study. (2) The academic bubble map showed that this research area was popular in the United States. (3) Cluster analysis showed that the intensively studied diseases in this field were Niemann-Pick type C (NPC), atherosclerosis (AS), and obesity. The hot-spot cyclodextrin types were HP-β-CD. (4) Literature measurement revealed the involvement of 15 types of lipid metabolism diseases. Among them, NPC, diabetes, and obesity were studied in clinical trials. Dyslipidemia and AS have been reported relatively more frequently in animal experiments. The studies of cellular experiments provide insight into the molecular mechanisms that intervene in lipid metabolism diseases from multiple perspectives. The exploration of the molecular mechanisms by which cyclodextrins exert their pharmacological effects mainly revolves around lipid metabolism.

CONCLUSION

It is worthwhile to investigate the role and mechanism of cyclodextrins in other lipid metabolism diseases. The potential efficacy evaluation of cyclodextrins as pharmaceutical drugs for oral or injectable formulations is less studied and may become a new focus in the future.

The Profile of Stress and Coping Related to Successful Aging Among Chinese Migrant and Nonmigrant Grandparents

BACKGROUND AND OBJECTIVES

Around eight million older adults have internally migrated to take care of grandchildren in China. This study aimed to explore how Chinese migrant and nonmigrant grandparents perceived successful aging and how they coped with challenges to successful aging.

RESEARCH DESIGN AND METHODS

Based on ecological systems theory, semistructured interviews were conducted among 21 grandparents (12 migrants, median age = 59 years old) from urban areas in China who provided noncustodial grandchild care. Deductive content analysis was employed to examine the data pertaining to the definition of successful aging, the stressors encountered, and coping strategies employed by both groups.

RESULTS

Findings revealed that both migrant and nonmigrant grandparents placed a higher emphasis on family prosperity than their own physical health, in their definitions of successful aging. Although both groups experienced common stressors in the microsystem (e.g., intergenerational conflicts), migrant grandparents uniquely encountered stressors in the mesosystem (e.g., hardships in their close relationships) and in the macrosystem (e.g., uncertainty in aging preparation). In terms of coping, migrant grandparents exhibited a distinctive pattern of utilizing avoidant coping strategies when navigating intergenerational conflicts and planning for their later life, compared to nonmigrant grandparents.

DISCUSSION AND IMPLICATIONS

Our findings suggested that although migration was not associated with grandparents' definition of successful aging, migrant grandparents encountered specific challenges in achieving successful aging. This highlights the necessity of providing more support to grandparents through the family, community, or state, particularly to those who are migrants.

Compensating losses in polariton propagation with synthesized complex frequency excitation

Surface plasmon polaritons and phonon polaritons offer a means of surpassing the diffraction limit of conventional optics and facilitate efficient energy storage, local field enhancement and highsensitivity sensing, benefiting from their subwavelength confinement of light. Unfortunately, losses severely limit the propagation decay length, thus restricting the practical use of polaritons. While optimizing the fabrication technique can help circumvent the scattering loss of imperfect structures, the intrinsic absorption channel leading to heat production cannot be eliminated. Here, we utilize synthetic optical excitation of complex frequency with virtual gain, synthesized by combining the measurements made at multiple real frequencies, to compensate losses in the propagations of phonon polaritons with dramatically enhanced propagation distance. The concept of synthetic complex frequency excitation represents a viable solution to the loss problem for various applications including photonic circuits, waveguiding and plasmonic/phononic structured illumination microscopy.

Quantitative genetic analysis of attractiveness of yeast products to Drosophila

An attractive perfume is a complex mixture of compounds, some of which may be unpleasant on their own. This is also true for the volatile combinations from yeast fermentation products in vineyards and orchards when assessed by Drosophila. Here we used crosses between a yeast strain with an attractive fermentation profile and another strain with a repulsive one and tested flies responses using a T-maze. QTL analysis reveals allelic variation in four yeast genes, PTC6, SAT4, YFL040W, and ARI1, that modulated expression levels of volatile compounds (assessed by GC-MS) and in different combinations, generated various levels of attractiveness. The parent strain that is more attractive to Drosophila has repulsive alleles at two of the loci while the least attractive parent has attractive alleles. Behavioral assays using artificial mixtures mimicking the composition of odors from fermentation validated the results of GC-MS and QTL mapping, thereby directly connecting genetic variation in yeast to attractiveness in flies. This study can be used as a basis for dissecting the combination of olfactory receptors that mediate the attractiveness/repulsion of flies to yeast volatiles and may also serve as a model for testing the attractiveness of pest species such as Drosophila suzukii to their host fruit.

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

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

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

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

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Affiliation(s)

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

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Extracellular vesicle-encapsulated miR-25-3p promotes epithelial-mesenchymal transition and migration of endometrial epithelial cells by inducing macrophage polarization

The pathogenesis of adenomyosis is closely related to the epithelial-mesenchymal transition and macrophages. MicroRNAs have been extensively investigated in relation to the epithelial-mesenchymal transition in a range of malignancies. However, there is a paucity of research on extracellular vesicles derived from the eutopic endometrium of adenomyosis and their encapsulated microRNAs. In this study, we investigated the role of microRNA-25-3p derived from extracellular vesicles in inducing macrophage polarization and promoting the epithelial-mesenchymal transition in endometrial epithelial cells of patients with adenomyosis and controls. We obtained eutopic endometrial samples and isolated extracellular vesicles from the culture supernatant of primary endometrial cells. Real-time quantitative PCR analysis demonstrated that microRNA-25-3p was highly expressed in extracellular vesicles, as well as in macrophages stimulated by extracellular vesicles from eutopic endometrium of adenomyosis; and macrophages transfected with microRNA-25-3p exhibited elevated levels of M2 markers, while displaying reduced levels of M1 markers. After co-culture with the above polarized macrophages, endometrial epithelial cells expressed higher levels of N-cadherin and Vimentin, and lower protein levels of E-cadherin and Cytokeratin 7. It was revealed that microRNA-25-3p encapsulated in extracellular vesicles from eutopic endometrial cells could induce macrophage polarization toward M2, and the polarized macrophages promote epithelial-mesenchymal transition in epithelial cells. However, in vitro experiments revealed no significant disparity in the migratory capacity of endometrial epithelial cells between the adenomyosis group and the control group. Furthermore, it was observed that microRNA-25-3p-stimulated polarized macrophages also facilitated the epithelial-mesenchymal transition and migration of endometrial epithelial cells within the control group. Thus, the significance of microRNA-25-3p-induced polarized macrophages in promoting the development of adenomyosis is unclear, and macrophage infiltration alone may be adequate for this process. We emphasize the specificity of the local eutopic endometrial microenvironment and postulate its potential significance in the pathogenesis of adenomyosis.

Prediction of riverine daily minimum dissolved oxygen concentrations using hybrid deep learning and routine hydrometeorological data

Dissolved oxygen (DO) depletion is a severe threat to aquatic ecosystems. Hence, using easily available routine hydrometeorological variables without DO as inputs to predict the daily minimum DO concentration in rivers has huge practical significance in the watershed management. The daily minimum DO concentrations at the outlet of the Oyster River watershed in New Hampshire, USA, were predicted by a set of deep learning neural networks using meteorological data and high-frequency water level, water temperature, and specific conductance (CTD) data measured within the watershed. The dependent variable, DO concentration, was measured at the outlet. From April 2013 to March 2018, the dataset was separated into training, validation, and test portions with a ratio of 5:3:3. A Long Short-Term Memory (LSTM) model and a hybrid Convolutional Neural Networks (CNN-LSTM) model were trained and evaluated for predicting the daily minimum DO concentration. The hybrid CNN-LSTM model exhibited the better predictive stability but the comparable accuracy (the mean R2 value = 0.865) compared with the pure LSTM model (the mean R2 value = 0.839). The model performance (both the stability and accuracy) was improved by aggregating the input data frequency from 15 min of raw data to 24 h. Likewise, the modeling performance didn't benefit from including 'forecasted' meteorological data at the predicted time step in the input dataset. This study provided an efficient and low-cost approach to predict the water quality in rivers and streams to realize the scientific watershed management.

Recommended Ten Years' Follow-Up Strategy for Small Hepatocellular Carcinoma after Radiofrequency Ablation: A Cost-Effectiveness Evaluation

OBJECTIVES

An optimal follow-up schedule for small (≤3 cm) hepatocellular carcinoma (HCC) after radiofrequency ablation (RFA) remains unclear in clinical guidelines. We aimed to assess the cost-effectiveness of follow-up strategies in patients with small HCC after RFA.

METHODS

In total, 11,243 patients were collected from global institutions to calculate recurrence rates. Subsequently, a Markov model covering a 10-year period was developed to compare 25 surveillance strategies involving different surveillance techniques (computed tomography [CT], magnetic resonance imaging or ultrasonography [US] and alpha-fetoprotein [AFP]) and intervals (3 or 6 months). The study endpoint was incremental cost-effectiveness ratio (ICER), which represented additional cost per incremental quality-adjusted life-year (QALY). Sensitivity analysis was conducted by varying the values of input parameters to observe the ICER.

RESULTS

In a base case analysis, the dominant strategy was CT every three months during an initial two years, followed by semi-annual CT, and then switch to biannual the combination of US screening and AFP testing after five years (m3_CT-m6_CT-m6_USAFP), with an ICER of $68,570.92 compared to the "not followed" strategy. One-way sensitivity analysis showed the ICER consistently remained below the willingness-to-pay (WTP) threshold of $100,000.00. In a probabilistic sensitivity analysis, m3_CT-m6_CT-m6_USAFP was the most cost-effective approach in 95.6% of simulated scenarios at a WTP threshold.

CONCLUSIONS

For small HCC after RFA, the recommended follow-up strategy is CT, with scans scheduled every three months for the first two years, every six months thereafter, and transition to biannual the combination of US screening and AFP testing after five years.

The LPS-inactivating enzyme acyloxyacyl hydrolase protects the brain from experimental stroke

Blood-brain-barrier (BBB) disruption is a pathological hallmark of ischemic stroke, and inflammation occurring at the BBB contributes to the pathogenesis of ischemic brain injury. Lipopolysaccharide (LPS), a cell wall component of Gram-negative bacteria, is elevated in patients with acute stroke. The activity of LPS is controlled by acyloxyacyl hydrolase (AOAH), a host enzyme that deacylates LPS to inactivated forms. However, whether AOAH influences the pathogenesis of ischemic stroke remain elusive. We performed in vivo experiments to explore the role and mechanism of AOAH on neutrophil extravasation, BBB disruption, and brain infarction. We found that AOAH was upregulated in neutrophils in peri-infarct areas from mice with transient focal cerebral ischemia. AOAH deficiency increased neutrophil extravasation into the brain parenchyma and proinflammatory cytokine production, broke down the BBB and worsened stroke outcomes in mice. These effects require Toll-like receptor 4 (TLR4) because absence of TLR4 or pharmacologic inhibition of TLR4 signaling prevented the exacerbated inflammation and BBB damage in Aoah-/- mice after ischemic stroke. Importantly, neutrophil depletion or inhibition of neutrophil trafficking by blocking LFA-1 integrin dramatically reduced stroke-induced BBB breakdown in Aoah-/- mice. Furthermore, virus-mediated overexpression of AOAH induced a substantial decrease in neutrophil recruitment that was accompanied by reducing BBB damage and stroke volumes. Our findings show the importance of AOAH in regulating neutrophil-dependent BBB breakdown and cerebral infarction. Consequently, strategies that modulate AOAH may be a new therapeutic approach for treatment of ischemic stroke.

Bacterial antigens and asthma: a comparative study of common respiratory pathogenic bacteria

Objective: In a previous study we have shown that, in the presence of interleukin (IL)-33, repeated, per-nasal challenge of murine airways with Streptococcus pneumoniae (S. pneumoniae) organisms induces human asthma-like airways inflammation. It is not clear, however, whether this effect is unique or manifest in response to other common respiratory pathogens.Methods: To explore this, airways of BALB/c mice were repeatedly challenged per-nasally with formaldehyde-inactivated bacterial bodies in the presence or absence of murine recombinant IL-33. Serum concentrations of S.pneumoniae, Moraxella catarrhalis (M.catarrhalis) and Haemophilus influenzae (H.influenzae) lysates-specific IgE were measured in patients with asthma and control subjects.Results: We showed that in the presence of IL-33, repeated, per-nasal airways exposure to the bodies of these bacteria induced airways hyperresponsiveness (AHR) in the experimental mice. This was accompanied by cellular infiltration into bronchoalveolar lavage fluid (BALF), eosinophilic infiltration and mucous hypertrophy of the lung tissue, with elevated local expression of some type 2 cytokines and elevated, specific IgG and IgE in the serum. The precise characteristics of the inflammation evoked by exposure to each bacterial species were distinguishable.Conclusions: These results suggest that in the certain circumstances, inhaled or commensal bacterial body antigens of both Gram-positive (S. pneumoniae) and Gram-negative (M. catarrhalis and H. influenzae) respiratory tract bacteria may initiate type 2 inflammation typical of asthma in the airways. In addition, we demonstrated that human asthmatic patients manifest elevated serum concentrations of M.catarrhalis- and H.influenzae-specific IgE.

Comparative brain-wide mapping of ketamine- and isoflurane-activated nuclei and functional networks in the mouse brain

Ketamine (KET) and isoflurane (ISO) are two widely used general anesthetics, yet their distinct and shared neurophysiological mechanisms remain elusive. In this study, we conducted a comparative analysis of the effects of KET and ISO on c-Fos expression across the mouse brain, utilizing hierarchical clustering and c-Fos-based functional network analysis to evaluate the responses of individual brain regions to each anesthetic. Our findings reveal that KET activates a wide range of brain regions, notably in the cortical and subcortical nuclei involved in sensory, motor, emotional, and reward processing, with the temporal association areas (TEa) as a strong hub, suggesting a top-down mechanism affecting consciousness by primarily targeting higher order cortical networks. In contrast, ISO predominantly influences brain regions in the hypothalamus, impacting neuroendocrine control, autonomic function, and homeostasis, with the locus coeruleus (LC) as a connector hub, indicating a bottom-up mechanism in anesthetic-induced unconsciousness. KET and ISO both activate brain areas involved in sensory processing, memory and cognition, reward and motivation, as well as autonomic and homeostatic control, highlighting their shared effects on various neural pathways. In conclusion, our results highlight the distinct but overlapping effects of KET and ISO, enriching our understanding of the mechanisms underlying general anesthesia.

Physicochemical properties and in vitro digestibility of ginseng starches under citric acid-autoclaving treatment

In this study, the effects of citric acid-autoclaving (CA-A) treatment on physicochemical and digestive properties of the native ginseng starches were investigated. The results showed that ginseng starch exhibited a B-type crystal structure with a low onset pasting temperature of 44.23 ± 0.80 °C, but high peak viscosity and setback viscosity of 5897.34 ± 53.72 cP and 692.00 ± 32.36 cP, respectively. The granular morphology, crystal and short-range ordered structure of ginseng starches were destroyed after CA-A treatment. The more short-chain starches were produced, resulting in the ginseng starches solubility increased. In addition, autoclaving, citric acid (CA) and CA-A treatment promoted polymerization and recrystallization of starch molecules, increased the proportion of amylopectin B1, and B3 chains, and improved molecular weight and resistant starch (RS) content of ginseng starches. The most significant multi-scale structural change was induced by CA-A treatment, which reduced the relative crystallinity of ginseng starch from 28.26 ± 0.24 % to 2.75 ± 0.08 %, and increased the content of RS to 54.30 ± 0.14 %. These findings provided a better understanding of the structure and properties of Chinese ginseng starches and offered new ideas for the deep processing of ginseng foods.

Mediating effects of sleep quality between clinical characteristics and quality of life in children with epilepsy: A cross-sectional study from Southwest China

BACKGROUND

Although sleep quality (SQ) reportedly affects the health-related quality of life (QOL) of patients with epilepsy, little is known about the potential association between SQ and QOL, particularly in children with epilepsy (CWE). Our study aimed to investigate the mediating effect of SQ on the QOL of CWE to obtain more information for the prevention and treatment of epilepsy in children.

METHODS

We collected general demographic and clinical data of 212 CWE and 79 controls (children who visited the Health Examination Department), and their guardians were instructed to answer the Children's Sleep Habits Questionnaire (CSHQ) and the optimized Quality of Life in Childhood Epilepsy Questionnaire-16 (QOLCE-16). The t-test, analysis of variance, chi-square test, and Fisher's exact test were used for between group comparisons. The Pearson correlation was used to analyze the correlation between variables. The direct, indirect, and total effects of predictors on the QOL of CWE were estimated based on an adjusted mediation model.

RESULTS

CWE had significantly smaller long-term urban residence rates, less educated guardians, higher total CSHQ score, higher incidence of poor SQ, higher bedtime resistance, more sleep anxiety, worse sleep-disordered breathing, increased parasomnia, more daytime sleepiness, more frequent night waking, and greater sleep onset delay than controls (P < 0.05 for all). The univariable analysis showed significant differences in total CSHQ scores between CWE with different seizure frequency in the last month, whether or not drug-resistant epilepsy (DRE), and with different video electroencephalogram (VEEG) findings (P < 0.05 for all). Differences in QOLCE-16 scores between CWE with different guardian's employment status, age at diagnosis, number of anti-seizure medication (ASM) types, seizure frequency in the last month, DRE status, seizure type, VEEG findings, neuropsychological evaluation findings, magnetic resonance imaging (MRI) findings, and etiology were statistically significant (P < 0.05 for all). The correlation study indicated that the total CSHQ score was negatively correlated with the QOLCE-16 score (P < 0.05). The mediation analysis showed that DRE and VEEG abnormalities had a standardized direct effect on the QOL. Seizure frequency in the last month, DRE, and VEEG abnormalities had an indirect effect on the QOL through SQ, and their mediating effect values of SQ were 31.61 %, 13.45 %, and 14.35 %, respectively.

CONCLUSION

Our findings uncovered the relationship of some clinical characteristics with SQ and QOL and characterized the nature of factors affecting the QOL of CWE. SQ could be a key factor in the prognosis of CWE experiencing epileptic seizures, and more attention should be paid on the management of SQ in interventions for epilepsy.

Molecular and Supramolecular Materials: From Light-Harvesting to Quantum Information Science and Technology

The past two decades have witnessed immense advances in quantum information technology (QIT), benefited by advances in physics, chemistry, biology, and materials science and engineering. It is intriguing to consider whether these diverse molecular and supramolecular structures and materials, partially inspired by quantum effects as observed in sophisticated biological systems such as light-harvesting complexes in photosynthesis and the magnetic compass of migratory birds, might play a role in future QIT. If so, how? Herein, we review materials and specify the relationship between structures and quantum properties, and we identify the challenges and limitations that have restricted the intersection of QIT and chemical materials. Examples are broken down into two categories: materials for quantum sensing where nonclassical function is observed on the molecular scale and systems where nonclassical phenomena are present due to intermolecular interactions. We discuss challenges for materials chemistry and make comparisons to related systems found in nature. We conclude that if chemical materials become relevant for QIT, they will enable quite new kinds of properties and functions.

SC_LPR: Semantically Consistent LiDAR Place Recognition Based on Chained Cascade Network in Long-Term Dynamic Environments

In large-scale long-term dynamic environments, high-frequency dynamic objects inevitably lead to significant changes in the appearance of the scene at the same location at different times, which is catastrophic for place recognition (PR). Therefore, how to eliminate the influence of dynamic objects to achieve robust PR has universal practical value for mobile robots and autonomous vehicles. To this end, we suggest a novel semantically consistent LiDAR PR method based on chained cascade network, called SC_LPR, which mainly consists of a LiDAR semantic image inpainting network (LSI-Net) and a semantic pyramid Transformer-based PR network (SPT-Net). Specifically, LSI-Net is a coarse-to-fine generative adversarial network (GAN) with a gated convolutional autoencoder as the backbone. To effectively address the challenges posed by variable-scale dynamic object masks, we integrate the updated Transformer block with mask attention and gated trident block into LSI-Net. Sequentially, in order to generate a discriminative global descriptor representing the point cloud, we design an encoder with pyramid Transformer block to efficiently encode long-range dependencies and global contexts between different categories in the inpainted semantic image, followed by an augmented NetVALD, a generalized VLAD (Vector of Locally Aggregated Descriptors) layer that adaptively aggregates salient local features. Last but not least, we first attempt to create a LiDAR semantic inpainting dataset, called LSI-Dataset, to effectively validate the proposed method. Experimental comparisons show that our method not only improves semantic inpainting performance by about 6%, but also improves PR performance in dynamic environments by about 8% compared to the representative optimal baseline. LSI-Dataset will be publicly available at https://github.KD.LPR.com/.

Improved Prediction of Epidermal Growth Factor Receptor Status by Combined Radiomics of Primary Nonsmall-Cell Lung Cancer and Distant Metastasis

OBJECTIVES

This study aimed to investigate radiomics based on primary nonsmall-cell lung cancer (NSCLC) and distant metastases to predict epidermal growth factor receptor (EGFR) mutation status.

METHODS

A total of 290 patients (mean age, 58.21 ± 9.28) diagnosed with brain (BM, n = 150) or spinal bone metastasis (SM, n = 140) from primary NSCLC were enrolled as a primary cohort. An external validation cohort, consisting of 69 patients (mean age, 59.87 ± 7.23; BM, n = 36; SM, n = 33), was enrolled from another center. Thoracic computed tomography-based features were extracted from the primary tumor and peritumoral area and selected using the least absolute shrinkage and selection operator regression to build a radiomic signature (RS-primary). Contrast-enhanced magnetic resonance imaging-based features were calculated and selected from the BM and SM to build RS-BM and RS-SM, respectively. The RS-BM-Com and RS-SM-Com were developed by integrating the most important features from the primary tumor, BM, and SM.

RESULTS

Six computed tomography-based features showed high association with EGFR mutation status: 3 from intratumoral and 3 from peritumoral areas. By combination of features from primary tumor and metastases, the developed RS-BM-Com and RS-SM-Com performed well with areas under curve in the training (RS-BM-Com vs RS-BM, 0.936 vs 0.885, P = 0.177; RS-SM-Com vs RS-SM, 0.929 vs 0.843, P = 0.003), internal validation (RS-BM-Com vs RS-BM, 0.920 vs 0.858, P = 0.492; RS-SM-Com vs RS-SM, 0.896 vs 0.859, P = 0.379), and external validation (RS-BM-Com vs RS-BM, 0.882 vs 0.805, P = 0.263; RS-SM-Com vs RS-SM, 0.865 vs 0.816, P = 0.312) cohorts.

CONCLUSIONS

This study indicates that the accuracy of detecting EGFR mutations significantly enhanced in the presence of metastases in primary NSCLC. The established radiomic signatures from this approach may be useful as new predictors for patients with distant metastases.

[Application of interventional respiratory techniques in the treatment of pulmonary bullae:an update]

Pulmonary bullae is a common complication of chronic obstructive pulmonary disease(COPD), causing the deterioration in lung function, leading to aggravated dyspnea and poor quality of life for patients. The traditional therapeutic approach for pulmonary bullae is bullectomy using surgical thoracoscopy. The disadvantage of this approach is the postoperative complications and high risk of recurrence in many patients. In addition, for some patients, due to the patient's physical conditions, such as poor lung function and other diseases, bullectomy could not be used. Therefore, new alternative approaches were urgently needed. In recent years, interventional respiratory technology has been trialed to treat pulmonary bulla all around the world and has achieved great success. In this paper, we reviewed the relevant clinical research progress of interventional respiratory medicine techniques in the treatment of pulmonary bullae.

Anodic Electrodepositing Bioinspired Cu-BDC-NH2@Graphene Oxide Membrane for Efficient Uranium Extraction

The challenge of removing trace levels of heavy metal ions, particularly uranium, from wastewater is a critical concern in environmental management. Uranium, a key element in long-term nuclear power generation, often poses significant extraction difficulties in wastewater due to its low concentration, interference from other ions, and the complexity of aquatic ecosystems. This study introduces an anodic electrodeposited hierarchical porous 2D metal-organic framework (MOF) Cu-BDC-NH2@graphene oxide (GO) membrane for effective uranium extraction by mimicking the function of the superb-uranyl-binding protein. This membrane is characterized by its hierarchical pillared-layer structures resulting from the controlled orientation of Cu-BDC-NH2 MOFs within the laminated GO layers during the electrodeposition process. The integration of amino groups from 2D Cu-BDC-NH2 and carboxylate groups from GO enables a high affinity to uranyl ions, achieving an unprecedented uranium adsorption capacity of 1078.4 mg/g and outstanding selectivity. Our findings not only demonstrate a breakthrough in uranium extraction technology but also pave the way for advancements in water purification and sustainable energy development, proposing a practical and efficient strategy for creating orientation-tunable 2D MOFs@GO membranes tailored for high-efficiency uranium extraction.

MSF-PFP: A Novel Multisource Feature Fusion Model for Protein Function Prediction

Protein function prediction is essential for disease treatment and drug development; yet, traditional biological experimental methods are less efficient in annotating protein function, and existing automated methods fail to fully leverage protein multisource data. Here, we present MSF-PFP, a computational framework that fuses multisource data features to predict protein function with high accuracy. Our framework designs specific models for feature extraction based on the characteristics of various data sources, including a global-local-individual strategy for local location features. MSF-PFP then integrates extracted features through a multisource feature fusion model, ultimately categorizing protein functions. Experimental results demonstrate that MSF-PFP outperforms eight state-of-the-art models, achieving FMax scores of 0.542, 0.675, and 0.624 for the biological process (BP), molecular function (MF), and cellular component (CC), respectively. The source code and data set for MSF-PFP are available at https://swanhub.co/TianGua/MSF-PFP, facilitating further exploration and validation of the proposed framework. This study highlights the potential of multisource data fusion in enhancing protein function prediction, contributing to improved disease therapy and medication discovery strategies.

Maternal KLF17 controls zygotic genome activation by acting as a messenger for RNA Pol II recruitment in mouse embryos

Initiation of timely and sufficient zygotic genome activation (ZGA) is crucial for the beginning of life, yet our knowledge of transcription factors (TFs) contributing to ZGA remains limited. Here, we screened the proteome of early mouse embryos after cycloheximide (CHX) treatment and identified maternally derived KLF17 as a potential TF for ZGA genes. Using a conditional knockout (cKO) mouse model, we further investigated the role of maternal KLF17 and found that it promotes embryonic development and full fertility. Mechanistically, KLF17 preferentially binds to promoters and recruits RNA polymerase II (RNA Pol II) in early 2-cell embryos, facilitating the expression of major ZGA genes. Maternal Klf17 knockout resulted in a downregulation of 9% of ZGA genes and aberrant RNA Pol II pre-configuration, which could be partially rescued by introducing exogenous KLF17. Overall, our study provides a strategy for screening essential ZGA factors and identifies KLF17 as a crucial TF in this process.

Clinical development of MRI-based multi-sequence multi-regional radiomics model to predict lymph node metastasis in rectal cancer

OBJECTIVE

We aim to construct a magnetic resonance imaging (MRI)-based multi-sequence multi-regional radiomics model that will improve the preoperative prediction ability of lymph node metastasis (LNM) in T3 rectal cancer.

METHODS

Multi-sequence MRI data from 190 patients with T3 rectal cancer were retrospectively analyzed, with 94 patients in the LNM group and 96 patients in the non-LNM group. The clinical factors, subjective imaging features, and the radiomic features of tumor and peritumoral mesorectum region of patients were extracted from T2WI and ADC images. Spearman's rank correlation coefficient, Mann-Whitney's U test, and the least absolute shrinkage and selection operator were used for feature selection and dimensionality reduction. Logistic regression was used to construct six models. The predictive performance of each model was evaluated by the receiver operating characteristic curve (ROC). The differences of each model were characterized by area under the curve (AUC) via the DeLong test.

RESULTS

The AUCs of T2WI, ADC single-sequence radiomics model and multi-sequence radiomics model were 0.73, 0.75, and 0.78, respectively. The multi-sequence multi-regional radiomics model with improved performance was created by combining the radiomics characteristics of the peritumoral mesorectum region with the multi-sequence radiomics model (AUC, 0.87; p < 0.01). The AUC of the clinical model was 0.68, and the MRI-clinical composite evaluation model was obtained by incorporating the clinical data with the multi-sequence multi-regional radiomics features, with an AUC of 0.89.

CONCLUSION

The MRI-based multi-sequence multi-regional radiomics model significantly improved the prediction ability of LNM for T3 rectal cancer and could be applied to guide surgical decision-making in patients with T3 rectal cancer.

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.

Profiling the selected hotspots for ear traits in two maize-teosinte populations

KEY MESSAGE

Eight selected hotspots related to ear traits were identified from two maize-teosinte populations. Throughout the history of maize cultivation, ear-related traits have been selected. However, little is known about the specific genes involved in shaping these traits from their origins in the wild progenitor, teosinte, to the characteristics observed in modern maize. In this study, five ear traits (kernel row number [KRN], ear length [EL], kernel number per row [KNR], cob diameter [CD], and ear diameter [ED]) were investigated, and eight quantitative trait loci (QTL) hotspots were identified in two maize-teosinte populations. Notably, our findings revealed a significant enrichment of genes showing a selection signature and expressed in the ear in qbdCD1.1, qbdCD5.1, qbpCD2.1, qbdED1.1, qbpEL1.1, qbpEL5.1, qbdKNR1.1, and qbdKNR10.1, suggesting that these eight QTL are selected hotspots involved in shaping the maize ear. By combining the results of the QTL analysis with data from previous genome-wide association study (GWAS) involving two natural panels, we identified eight candidate selected genes related to KRN, KNR, CD, and ED. Among these, considering their expression pattern and sequence variation, Zm00001d025111, encoding a WD40/YVTN protein, was proposed as a positive regulator of KNR. This study presents a framework for understanding the genomic distribution of selected loci crucial in determining ear-related traits.

Quercetin-loaded mesoporous nano-delivery system remodels osteoimmune microenvironment to regenerate alveolar bone in periodontitis via the miR-21a-5p/PDCD4/NF-κB pathway

BACKGROUND

Impaired osteo-/angiogenesis, excessive inflammation, and imbalance of the osteoimmune homeostasis are involved in the pathogenesis of the alveolar bone defect caused by periodontitis. Unfortunately, there is still a lack of ideal therapeutic strategies for periodontitis that can regenerate the alveolar bone while remodeling the osteoimmune microenvironment. Quercetin, as a monomeric flavonoid, has multiple pharmacological activities, such as pro-regenerative, anti-inflammatory, and immunomodulatory effects. Despite its vast spectrum of pharmacological activities, quercetin's clinical application is limited due to its poor water solubility and low bioavailability.

RESULTS

In this study, we fabricated a quercetin-loaded mesoporous bioactive glass (Quercetin/MBG) nano-delivery system with the function of continuously releasing quercetin, which could better promote the bone regeneration and regulate the immune microenvironment in the alveolar bone defect with periodontitis compared to pure MBG treatment. In particular, this nano-delivery system effectively decreased injection frequency of quercetin while yielding favorable therapeutic results. In view of the above excellent therapeutic effects achieved by the sustained release of quercetin, we further investigated its therapeutic mechanisms. Our findings indicated that under the periodontitis microenvironment, the intervention of quercetin could restore the osteo-/angiogenic capacity of periodontal ligament stem cells (PDLSCs), induce immune regulation of macrophages and exert an osteoimmunomodulatory effect. Furthermore, we also found that the above osteoimmunomodulatory effects of quercetin via macrophages could be partially blocked by the overexpression of a key microRNA--miR-21a-5p, which worked through inhibiting the expression of PDCD4 and activating the NF-κB signaling pathway.

CONCLUSION

In summary, our study shows that quercetin-loaded mesoporous nano-delivery system has the potential to be a therapeutic approach for reconstructing alveolar bone defects in periodontitis. Furthermore, it also offers a new perspective for treating alveolar bone defects in periodontitis by inhibiting the expression of miR-21a-5p in macrophages and thereby creating a favorable osteoimmune microenvironment.

Clinical characteristics and prognosis of pediatric Listeria monocytogenes meningitis based on 10-year data from a large children's hospital in China

This study aims to summarize the clinical characteristics and prognosis of Listeria monocytogenes (LM) meningitis in children in Chongqing, China. A retrospective analysis of the clinical data and follow-up results of 20 LM meningitis patients admitted to the Children's Hospital of Chongqing Medical University (CHCMU) from January 2012 to December 2022 was performed. The LM meningitis incidence rate was between 0 and 14.3 per 100,000 persons. The median age at onset was 8.98 months. There were five neonate cases, which all had perinatal abnormalities. Seven non-neonatal cases (7/15, 46.7%) had a documented history of contaminated food intake. One case had primary immunodeficiency. The most common symptoms were fever (20/20), altered consciousness (19/20), and vomiting (15/20). Seven cases had seizures, eight cases had cranial nerve involvement, eight cases had positive Babinski sign, and 10 cases had positive meningeal signs. The most common complications were hyponatremia (6/20), hypokalemia (6/20), respiratory failure (5/20), subdural effusion (3/20), and hydrocephalus (2/20). Treatment primarily involved monotherapy or combination therapy with meropenem (15/20) and ampicillin (10/20). Fifteen cases were treated with monotherapy or combination therapy using vancomycin. Twelve cases were successfully followed up from 10 months to 9 years and 6 months, and all had favorable long-term outcomes. LM meningitis incidence in children is low and with nonspecific clinical manifestations. Strengthening food hygiene and safety education, and avoiding infections during pregnancy are important to prevent LM infection in neonates and high-risk individuals. Meropenem and ampicillin are the preferred treatments. Early diagnosis and treatment can improve prognosis.IMPORTANCEThe incidence of LM meningitis is extremely low, and there is currently no standardized treatment. We conducted a retrospective analysis of ten years of data from CHCMU regarding diagnosed LM meningitis cases, aiming to provide clinical evidence for the diagnosis and treatment.

[Respiratory virus infection and its influence on outcome in children with septic shock]

Objective: To investigate respiratory virus infection in children with septic shock in pediatric care units (PICU) in China and its influence on clinical outcomes. Methods: The clinical data of children with septic shock in children's PICU from January 2018 to December 2019 in 10 Chinese hospitals were retrospectively collected. They were divided into the pre-COVID-19 and post-COVID-19 groups according to the onset of disease, and the characteristics and composition of respiratory virus in the 2 groups were compared. Matching age, malignant underlying diseases, bacteria, fungi and other viruses, a new database was generated using 1∶1 propensity score matching method. The children were divided into the respiratory virus group and non-respiratory virus group according to the presence or absence of respiratory virus infection; their clinical characteristics, diagnosis, and treatment were compared by t-test, rank sum test and Chi-square test. The correlation between respiratory virus infection and the clinical outcomes was analyzed by logistic regression. Results: A total of 1 247 children with septic shock were included in the study, of them 748 were male; the age was 37 (11, 105) months. In the pre-and post-COVID-19 groups, there were 530 and 717 cases of septic shock, respectively; the positive rate of respiratory virus was 14.9% (79 cases) and 9.8% (70 cases); the seasonal distribution of septic shock was 28.9% (153/530) and 25.9% (185/717) in autumn, and 30.3% (161/530) and 28.3% (203/717) in winter, respectively, and the corresponding positive rates of respiratory viruses were 19.6% (30/153) and 15.7% (29/185) in autumn, and 21.1% (34/161) and 15.3% (31/203) in winter, respectively. The positive rates of influenza virus and adenovirus in the post-COVID-19 group were lower than those in the pre-COVID-19 group (2.1% (15/717) vs. 7.5% (40/530), and 0.7% (5/717) vs. 3.2% (17/530), χ2=21.51 and 11.08, respectively; all P<0.05). Rhinovirus virus were higher than those in the pre-Covid-19 group (1.7% (12/717) vs. 0.2% (1/530), χ2=6.51, P=0.011). After propensity score matching, there were 147 cases in both the respiratory virus group and the non-respiratory virus group. Rate of respiratory failure, acute respiratory distress, rate of disseminated coagulation dysfunction, and immunoglobulin usage of the respiratory virus group were higher than those of non-respiratory virus group (77.6% (114/147) vs. 59.2% (87/147), 17.7% (26/147) vs. 4.1% (6/147), 15.6% (25/147) vs. 4.1% (7/147), and 35.4% (52/147) vs. 21.4% (32/147); χ2=11.07, 14.02, 11.06 and 6.67, all P<0.05); and PICU hospitalization of the former was longer than that of the later (7 (3, 16) vs. 3 (1, 7)d, Z=5.01, P<0.001). Univariate logistic regression analysis showed that the presence of respiratory viral infection was associated with respiratory failure, disseminated coagulation dysfunction, the use of mechanical ventilation, and the use of immunoglobulin and anti-respiratory viral drugs (OR=2.42, 0.22, 0.25, 0.56 and 1.12, all P<0.05). Conclusions: The composition of respiratory virus infection in children with septic shock is different between pre and post-COVID-19. Respiratory viral infection is associated with organ dysfunction in children with septic shock. Decreasing respiratory viral infection through respiratory protection may improve the clinical outcome of these children.