Circulating plasma cells as a predictive biomarker in Multiple myeloma: an updated systematic review and meta-analysis

BACKGROUND

Circulating plasma cells (CPCs) are defined by the presence of peripheral blood clonal plasma cells, which would contribute to the progression and dissemination of multiple myeloma (MM). An increasing number of studies have demonstrated the predictive potential of CPCs in the past few years. Therefore, there is a growing need for an updated meta-analysis to identify the specific relationship between CPCs and the prognosis of MM based on the current research status.

METHODS

The PubMed, Embase, and Cochrane Library databases were screened to determine eligible studies from inception to November 5, 2023. Publications that reported the prognostic value of CPCs in MM patients were included. Hazard ratios (HRs) with 95% confidence intervals (CIs) of overall survival (OS) and progression-free survival (PFS) were extracted to pool the results. Subgroup analyses were performed based on region, sample size, cut-off value, detection time, initial treatment, and data type. The association between CPCs level and clinicopathological characteristics, including the International Staging System (ISS), Revised-ISS (R-ISS), and cytogenetic abnormalities were also evaluated. Statistical analyses were conducted using STATA 17.0 software.

RESULTS

Twenty-two studies with a total of 5637 myeloma patients were enrolled in the current meta-analysis. The results indicated that myeloma patients with elevated CPCs were expected to have a poor OS (HR = 2.19, 95% CI: 1.81-2.66, p < 0.001) and PFS (HR = 2.45, 95% CI: 1.93-3.12, p < 0.001). Subgroup analyses did not alter the prognostic role of CPCs, regardless of region, sample size, cut-off value, detection time, initial treatment, or data type. Moreover, the increased CPCs were significantly related to advanced tumour stage (ISS III vs. ISS I-II: pooled OR = 2.89, 95% CI: 2.41-3.46, p < 0.001; R-ISS III vs. R-ISS I-II: pooled OR = 3.65, 95% CI: 2.43-5.50, p < 0.001) and high-risk cytogenetics (high-risk vs. standard-risk: OR = 2.22, 95% CI: 1.60-3.08, p < 0.001).

CONCLUSION

Our meta-analysis confirmed that the increased number of CPCs had a negative impact on the PFS and OS of MM patients. Therefore, CPCs could be a promising prognostic biomarker that helps with risk stratification and disease monitoring.

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.

Jidong ovarian aging cohort study: Objective, design, and baseline characteristics

BACKGROUND

Ovarian aging is a continuous process comprising a gradual decrease in both the quantity and the quality of the oocytes. A decline in ovarian function leads to chronic disease and physiological problems. The aim of this study is to establish a cohort for the purpose of examining the ovarian aging process and its relationship with health status and quality of life in women across all age groups.

METHOD

This protocol outlines a community-based, prospective long-term observational study involving 1676 women recruited from Caofeidian District in Tangshan City, Hebei Province, China. Data are gathered by the administration of questionnaires, doing physical examinations, performing blood biochemistry tests, and measuring levels of female hormones. The primary outcomes will be the occurrence of cardiovascular disease, osteoporosis, hypertension, diabetes, hyperlipidemia, and other chronic diseases, assessed according to established diagnostic criteria for each disease. The secondary outcome will be the decline in quality of life during the follow-up period, assessed by the modified Kupperman Index. The study comprises a baseline cross-sectional assessment and a follow-up evaluation. The participants will undergo face-to-face interviews as part of their regular medical examinations until 2026 or until the occurrence of outcome events.

DISCUSSION

The results of the prospective study will indicate the association between ovarian aging and the prevalence of chronic diseases as well as diminished quality of life among women across different age categories.

[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.

Co-delivery of fucoxanthin and Twist siRNA using hydroxyethyl starch-cholesterol self-assembled polymer nanoparticles for triple-negative breast cancer synergistic therapy

INTRODUCTION

Triple-negative breast cancer (TNBC) represents the most aggressive subtype of breast cancer with an extremely dismal prognosis and few treatment options. As a desmoplastic tumor, TNBC tumor cells are girdled by stroma composed of cancer-associated fibroblasts (CAFs) and their secreted stromal components. The rapidly proliferating tumor cells, together with the tumor stroma, exert additional solid tissue pressure on tumor vasculature and surrounding tissues, severely obstructing therapeutic agent from deep intratumoral penetration, and resulting in tumor metastasis and treatment resistance.

OBJECTIVES

Fucoxanthin (FX), a xanthophyll carotenoid abundant in marine algae, has attracted widespread attention as a promising alternative candidate for tumor prevention and treatment. Twist is a pivotal regulator of epithelial to mesenchymal transition, and its depletion has proven to sensitize antitumor drugs, inhibit metastasis, reduce CAFs activation and the following interstitial deposition, and increase tumor perfusion. The nanodrug delivery system co-encapsulating FX and nucleic acid drug Twist siRNA (siTwist) was expected to form a potent anti-TNBC therapeutic cyclical feedback loop.

METHODS AND RESULTS

Herein, our studies constituted a novel self-assembled polymer nanomedicine (siTwist/FX@HES-CH) based on the amino-modified hydroxyethyl starch (HES-NH2) grafted with hydrophobic segment cholesterol (CH). The MTT assay, flow cytometry apoptosis analysis, transwell assay, western blot, and 3D multicellular tumor spheroids growth inhibition assay all showed that siTwist/FX@HES-CH could kill tumor cells and inhibit their metastasis in a synergistic manner. The in vivo anti-TNBC efficacy was demonstrated that siTwist/FX@HES-CH remodeled tumor microenvironment, facilitated interstitial barrier crossing, killed tumor cells synergistically, drastically reduced TNBC orthotopic tumor burden and inhibited lung metastasis.

CONCLUSION

Systematic studies revealed that this dual-functional nanomedicine that targets both tumor cells and tumor microenvironment significantly alleviates TNBC orthotopic tumor burden and inhibits lung metastasis, establishing a new paradigm for TNBC therapy.

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.

Ruxolitinib-loaded cytokine nanosponge alleviated the cytokine storm and dampened macrophage overactivation for the treatment of hemophagocytic lymphohistiocytosis

Hemophagocytic lymphohistiocytosis (HLH) is a life-threatening clinical syndrome characterized by a positive feedback loop between cytokine storm and macrophages and lymphocytes overactivation, which could serve as a valid therapeutic target for HLH treatment. In this study, the clinically extensively used JAK1/2 inhibitor ruxolitinib was encapsulated into macrophage membrane-coated nanoparticles (M@NP-R) with high drug-loading efficiency for targeted HLH treatment. In vitro and in vivo studies demonstrated that M@NP-R not only efficiently adsorbed extracellular proinflammation cytokines, like IFN-γ and IL-6 to alleviate the cytokine storm, but also effectively dampened macrophage activation and proliferation by intracellular JAK/STAT signaling pathway inhibition. M@NP-R treatment significantly ameliorated the clinical and laboratory manifestations of HLH in mouse models, including trilineage cytopenia, hypercytokinemia, organomegaly, hepatorenal dysfunction, and tissue inflammation. Importantly, M@NP-R significantly enhanced the survival of the lethal HLH mice. Altogether, M@NP-R successfully blocked the positive feedback loop between the cytokine storm and macrophage overactivation by depleting extracellular inflammatory cytokines and inhibiting the intracellular JAK/STAT signaling pathway, both of which worked synergistically in HLH treatment. As ruxolitinib has already been extensively used in clinics with favorable safety, and M@NP is biodegradable and highly biocompatible, M@NP-R has good prospects for clinical translation.

Tanshinone IIA destabilizes SLC7A11 by regulating PIAS4-mediated SUMOylation of SLC7A11 through KDM1A, and promotes ferroptosis in breast cancer

INTRODUCTION

Breast cancer (BC) is the most common malignancy in women with unfavorite prognosis.

OBJECTIVES

Tanshinone IIA (Tan IIA) inhibits BC progression, however, the underlying mechanism remains largely undefined.

METHODS

The cytotoxicity of Tan IIA was assessed by CCK-8 and LDH assays. Ferroptosis was monitored by the level of MDA, Fe2+, lipid ROS and GSH. IHC and western blot were employed to detect the localization and expression of SLC7A11, PIAS4, KDM1A and other key molecules. The SUMOylation of SLC7A11 was detected by Ni-beads pull-down assay and co-IP. Luciferase and ChIP assays were employed to detect the direct association between KDM1A and PIAS4 promoter. The proliferative and metastatic properties of BC cells were assessed by colony formation, CCK-8 and Transwell assays, respectively. The in vitro findings were verified in xenograft and lung metastasis models.

RESULTS

Tan IIA promoted ferroptosis by suppressing SLC7A11 in BC cells. Silencing of PIAS4 or KDM1A inhibited cell growth and metastasis in BC. Mechanistically, PIAS4 facilitated the SUMOylation of SLC7A11 via direct binding to SLC7A11, and KDM1A acted as a transcriptional activator of PIAS4. Functional studies further revealed that Tan IIA decreased KDM1A expression, thus suppressing PIAS4 expression transcriptionally. The inhibition of PIAS4-dependent SUMOylation of SLC7A11 further induced ferroptosis, thereby inhibiting proliferation and metastasis in BC.

CONCLUSION

Tan IIA promoted ferroptosis and inhibited tumor growth and metastasis via suppressing KDM1A/PIAS4/SLC7A11 axis.

Upregulated miR-374a-5p drives psoriasis pathogenesis through WIF1 downregulation and Wnt5a/NF-κB activation

BACKGROUND

Psoriasis is a chronic, inflammatory skin disease. MicroRNAs (miRNAs) are an abundant class of non-coding RNA molecules. Recent studies have shown that multiple miRNAs are abnormally expressed in patients with psoriasis. The upregulation of miR-374a-5p has been associated with psoriasis severity. However, the specific role of miR-374a-5p in the pathogenesis of psoriasis remain unclear.

METHODS

qRT-PCR was employed to validate the expression of miR-374a-5p in psoriatic lesions and in a psoriasis-like cell model constructed using a mixture of M5 (IL-17A, IL-22, OSM, IL-1α, and TNF-α). HaCaT cells were transfected with miR-374a-5p mimic/inhibitor, and assays including EdU, CCK-8, and flow cytometry were conducted to evaluate the effect of miR-374a-5p on cell proliferation. The expression of inflammatory cytokines IL-1β, IL-6, IL-8, and TNF-α was verified by qRT-PCR. Bioinformatics analysis and dual-luciferase reporter gene assay were performed to detect the downstream target genes and upstream transcription factors of miR-374a-5p, followed by validation of their expression through qRT-PCR and Western blotting. A psoriasis-like mouse model was established using imiquimod cream topical application. The psoriasis area and severity index scoring, hematoxylin-eosin histology staining, and Ki67 immunohistochemistry were employed to validate the effect of miR-374a-5p on the psoriatic inflammation phenotype after intradermal injection of miR-374a-5p agomir/NC. Additionally, the expression of pathway-related molecules and inflammatory factors such as IL-1β, IL-17a, and TNF-α was verified by immunohistochemistry.

RESULTS

Upregulation of miR-374a-5p was observed in psoriatic lesions and the psoriasis-like cell model. In vitro experiments demonstrated that miR-374a-5p not only promoted the proliferation of HaCaT cells but also upregulated the expression of inflammatory cytokines, including IL-1β, IL-6, IL-8, and TNF-α. Furthermore, miR-374a-5p promoted skin inflammation and epidermal thickening in the Imiquimod-induced psoriasis-like mouse model. Mechanistic studies revealed that miR-374a-5p led to downregulation of WIF1, thereby activating the Wnt5a/NF-κB signaling pathway. The transcription factor p65 encoded by RELA, as a subunit of NF-κB, further upregulated the expression of miR-374a-5p upon activation. This positive feedback loop promoted keratinocyte proliferation and abnormal inflammation, thereby facilitating the development of psoriasis.

CONCLUSION

Our findings elucidate the role of miR-374a-5p upregulation in the pathogenesis of psoriasis through inhibition of WIF1 and activation of the Wnt5a/NF-κB pathway, providing new potential therapeutic targets for psoriasis.

Zinc remodels mitochondrial network through SIRT3/Mfn2-dependent mitochondrial transfer in ameliorating spinal cord injury

Spinal cord injury (SCI) is a traumatic neuropathic condition that results in motor, sensory and autonomic dysfunction. Mitochondrial dysfunction caused by primary trauma is one of the critical pathogenic mechanisms. Moderate levels of zinc have antioxidant effects, promote neurogenesis and immune responses. Zinc normalises mitochondrial morphology in neurons after SCI. However, how zinc protects mitochondria within neurons is unknown. In the study, we used transwell culture, Western blot, Quantitative Real-time Polymerase Chain Reaction (QRT-PCR), ATP content detection, reactive oxygen species (ROS) activity assay, flow cytometry and immunostaining to investigate the relationship between zinc-treated microglia and injured neurons through animal and cell experiments. We found that zinc promotes mitochondrial transfer from microglia to neurons after SCI through Sirtuin 3 (SIRT3) regulation of Mitofusin 2 protein (Mfn2). It can rescue mitochondria in damaged neurons and inhibit oxidative stress, increase ATP levels and promote neuronal survival. Therefore, it can improve the recovery of motor function in SCI mice. In conclusion, our work reveals a potential mechanism to describe the communication between microglia and neurons after SCI, which may provide a new idea for future therapeutic approaches to SCI.

The enchanting canvas of CAR technology: Unveiling its wonders in non-neoplastic diseases

Chimeric antigen receptor (CAR) T cells have made a groundbreaking advancement in personalized immunotherapy and achieved widespread success in hematological malignancies. As CAR technology continues to evolve, numerous studies have unveiled its potential far beyond the realm of oncology. This review focuses on the current applications of CAR-based cellular platforms in non-neoplastic indications, such as autoimmune, infectious, fibrotic, and cellular senescence-associated diseases. Furthermore, we delve into the utilization of CARs in non-T cell populations such as natural killer (NK) cells and macrophages, highlighting their therapeutic potential in non-neoplastic conditions and offering the potential for targeted, personalized therapies to improve patient outcomes and enhanced quality of life.

Targeting Trop2 by Bruceine D suppresses breast cancer metastasis by blocking Trop2/β-catenin positive feedback loop

INTRODUCTION

Tumor-associated calcium signal transducer 2 (Trop2) has been used as a transport gate for cytotoxic agents into cells in antibody-drug conjugate designs because of its expression in a wide range of solid tumors. However, the specific role of Trop2 itself in breast cancer progression remains unclear and small molecules targeting Trop2 have not yet been reported.

OBJECTIVES

To screen small molecules targeting Trop2, and to reveal its pharmacological effects and the molecular mechanisms of action.

METHODS

Small molecule targeting Trop2 was identified by cell membrane chromatography, and validated by cellular thermal shift assay and point mutation analyses. We investigated the pharmacological effects of Trop2 inhibitor using RNA-seq, human foreskin fibroblast (HFF)-derived extracellular matrix (ECM), Matrigel drop invasion assays, colony-forming assay, xenograft tumor model, 4T1 orthotopic metastasis model and 4T1 experimental metastasis model. The molecular mechanism was determined using immunoprecipitation, mass spectrometry, immunofluorescence, immunohistochemistry and Western blotting.

RESULTS

Here we identified Bruceine D (BD) as the inhibitor of Trop2, and demonstrated anti-metastasis effects of BD in breast cancer. Notably, Lys307 and Glu310 residues of Trop2 acted as critical sites for BD binding. Mechanistically, BD suppressed Trop2-induced cancer metastasis by blocking the formation of Trop2/β-catenin positive loop, in which the Trop2/β-catenin complex prevented β-catenin from being degraded via the ubiquitin-proteosome pathway. Destabilized β-catenin caused by BD reduced nucleus translocation, leading to the reduction of transcription of Trop2, the reversal of epithelial-mesenchymal transition (EMT) process, and the inhibition of ECM remodeling, further inhibiting cancer metastasis. Additionally, the inhibitory effects of BD on lung metastatic colonization and the beneficial effects of BD on prolongation of survival were validated in 4T1 experimental metastasis model.

CONCLUSIONS

These results support the tumor-promoting role of Trop2 in breast cancer by stabilizing β-catenin in Trop2/β-catenin positive loop, and suggest Bruceine D as a promising candidate for Trop2 inhibition.

Gene expression prognostic of early relapse risk in low-risk B-cell acute lymphoblastic leukaemia in children

ETV6::RUNX1 is the most common fusion gene in childhood acute lymphoblastic leukaemia (ALL) and is associated with favorable outcomes, especially in low-risk children. However, as many as 10% of children relapse within 3 years, and such early relapses have poor survival. Identifying children at risk for early relapse is an important challenge. We interrogated data from 87 children with low-risk ETV6::RUNX1-positive B-cell ALL and with available preserved bone marrow samples (discovery cohort). We profiled somatic point mutations in a panel of 559 genes and genome-wide transcriptome and single-nucleotide variants. We found high TIMD4 expression (> 85th-percentile value) at diagnosis was the most important independent prognostic factor of early relapse (hazard ratio [HR] = 5.07 [1.76, 14.62]; p = 0.03). In an independent validation cohort of low-risk ETV6::RUNX1-positive B-cell ALL (N = 68) high TIMD4 expression at diagnosis had an HR = 4.78 [1.07, 21.36] (p = 0.04) for early relapse. In another validation cohort including 78 children with low-risk ETV6::RUNX1-negative B-cell ALL, high TIMD4 expression at diagnosis had an HR = 3.93 [1.31, 11.79] (p = 0.01). Our results suggest high TIMD4 expression at diagnosis in low-risk B-cell ALL in children might be associated with high risk for early relapse.

Efficacy, safety, and survival findings after long-term follow-up of ZGJAK002: A phase 2 study comparing jaktinib at 100 mg twice daily (BID) and 200 mg once daily (QD) in patients with myelofibrosis

Jaktinib, a novel JAK and ACVR1 inhibitor, has exhibited promising results in treating patients with myelofibrosis (MF). ZGJAK002 is a Phase 2 trial aimed to assess the efficacy and safety of jaktinib 100 mg BID (N = 66) and 200 mg QD (N = 52) in JAK inhibitor-naive patients with intermediate- or high-risk MF. We herein present the long-term data with a median follow-up of 30.7 months. At data cutoff, 30.3% of patients in 100 mg BID and 28.8% in 200 mg QD were still continuing their treatment. The 100 mg BID group displayed a numerically higher best spleen response compared with the 200 mg QD group (69.7% vs. 46.2%), with 50.4% from the BID and 51.2% from the QD group maintaining spleen responses over 120 weeks. The 36-month survival rates were 78.2% in BID and 73.6% in QD group. The tolerability of jaktinib remained well, and common grade ≥3 adverse drug reactions included anemia (15.2% vs. 21.2%), thrombocytopenia (15.2% vs. 11.5%), and infectious pneumonia (10.6% vs. 1.9%) in BID and QD groups, respectively. By comparing the two groups, the incidence of adverse events (AEs) were similar, except for drug-related serious AEs (24.2% vs. 9.6%) and AEs leading to treatment discontinuation (15.2% vs. 7.7%), which were higher in BID group. The percentages of AEs resulting in death were comparable, with 6.1% in BID and 5.8% in QD group. These analyses further support the long-term durable efficacy and acceptable safety of jaktinib at 100 mg BID and 200 mg QD doses for treating MF.

Machine learning models developed and internally validated for predicting chronicity in pediatric immune thrombocytopenia

BACKGROUND

Primary immune thrombocytopenia (ITP) in children is typically self-limiting; however, 20% to 30% of patients may experience prolonged thrombocytopenia lasting over a year. The challenge is predicting chronicity to ensure personalized treatment approaches.

OBJECTIVES

To address this issue, we developed and internally validated 4 machine learning (ML) models using demographic and immunologic characteristics to predict the likelihood of chronicity.

METHODS

The present study was conducted at Beijing Children's Hospital from June 2018 to December 2021, aiming to develop predictive models for determining the chronicity of pediatric ITP. Four ML models, based on a logistic regression classifier, random forest classifier, eXtreme Gradient Boosting (XGBoost), and support vector machine, were employed. These models used a set of 16 variables, including 14 immunologic and 2 demographic predictors. The performance evaluation criteria included prediction accuracy, precision, recall, F1 score, and area under the receiver operating characteristic curve (AUROC).

RESULTS

Data were collected from 662 patients who were randomly assigned to either a training dataset or a testing dataset using a random number generator. Among them, 26.5% had chronic disease. All models performed well, with AUROC values ranging from 0.81 to 0.84, and XGBoost was selected for its highest AUROC score and interpretability in constructing the predictive model. Age, T helper 17, T helper 17-to-regulatory T cell ratio, T helper 1, and double-negative T cells were identified as significant predictors by the XGBoost algorithm.

CONCLUSION

We developed a precise predictive model for chronicity in pediatric ITP using ML during the initial phase. The XGBoost model achieved high predictive accuracy by using individual patient clinical parameters and demonstrated commendable interpretability.

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.

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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A preliminary understanding of the relationship between synthetic phenolic antioxidants and early pregnancy loss: Uncovering the potential molecular mechanisms

Mounting evidence suggests that environmental pollutants may affect reproductive health, potentially leading to adverse outcomes like pregnancy loss. However, it remains unclear whether exposure to synthetic phenolic antioxidants (SPAs) correlates with early pregnancy loss (EPL). This study explores SPA exposure's link to EPL and its potential molecular mechanisms. From 2021 to 2022, 265 early pregnant women (136 serum and 129 villus samples) with and without EPL were enrolled. We quantified 17 SPAs in serum and chorionic villus, with AO1010, AO3114, BHT, AO2246, and BHT-Q frequently being detected, suggesting their ability to cross the placental barrier. AO1135 showed a positive relationship with EPL in sera, indicating a significant monotonic dose-response relationship (p-trend <0.001). BHT-Q exhibited a similar relationship with EPL in villi. Inhibitory effects of BHT-Q on estradiol (E2) were observed. Molecular docking revealed SPA-protein interactions involved in E2 synthesis. SPA-induced EPL might occur with specific serum levels of AO1135 and certain villus levels of AO1010, BHT-Q, and AO2246. BHT-Q emerges as a potential biomarker for assessing EPL risk. This study provides insights into understanding of the exposure to SPAs and potential adverse outcomes in pregnant women.

[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.

Hematologic health services and practical characteristics: report of a nationwide survey among Chinese hematologists

BACKGROUND

In the past 40 years, China has experienced tremendous economic development, but the current situation of hematologists has rarely been reported. A landscape survey of human resources is essential for healthcare development and policy formulation in the future.

METHODS

The Chinese Society of Hematology initiated a survey of Chinese hematologists in mainland China for evaluating demographic and practice characteristics. Respondents were anonymous, and there were no limitations regarding their age, sex, etc. RESULTS: Totally 2032 hematologists responded, with a median age bracket of 36-45 years. Respondents were well engaged into subspecialties, and 28.1% acquired doctorates of philosophy. Hematopoietic cell transplantation (HCT) centers have been established all over China. Higher-GDP regions reported more advantages, including bigger scale of transplant centers (P < 0.001), younger age structure (P = 0.039), better education qualifications (P = 0.001) and less turnover intentions (P = 0.004), despite of increased risk of medical disputes (P = 0.028). Although females accounted for 65.5% of hematologists, males were older (P < 0.001), and had more senior professional titles (P < 0.001), academic positions (P < 0.001), opportunities for continuing education (P < 0.001), and paper publishing in the recent two years (P = 0.001). For turnover intention, the higher GDP regions led to an independently reduced risk (HR = 0.673, 95%CI [0.482-0.940], P = 0.020), whereas medical disputes resulted in an increased the risk (HR = 2.037, 95%CI [1.513-2.743], P < 0.001). Considering the impact of the COVID-19 pandemic, majority of respondents believed that the decline in patient visits and delay in treatment was within 30%. 67.9% of respondents reported a decrease of the use of bone marrow as grafts but 18.8% reported an increase of cord blood units. 35.0% of the respondents switched their daily work to support the anti-epidemic medical activities.

CONCLUSIONS

We concluded the discipline of hematology in China has flourished in recent years with a young workforce, while regional economic and gender disparities warrant further continuous optimization. Joint efforts against the impact of COVID-19 are needed in the post-pandemic era.

Amyloid β oligomer induces cerebral vasculopathy via pericyte-mediated endothelial dysfunction

BACKGROUND

Although abnormal accumulation of amyloid beta (Aβ) protein is thought to be the main cause of Alzheimer's disease (AD), emerging evidence suggests a pivotal vascular contribution to AD. Aberrant amyloid β induces neurovascular dysfunction, leading to changes in the morphology and function of the microvasculature. However, little is known about the underlying mechanisms between Aβ deposition and vascular injuries. Recent studies have revealed that pericytes play a substantial role in the vasculopathy of AD. Additional research is imperative to attain a more comprehensive understanding.

METHODS

Two-photon microscopy and laser speckle imaging were used to examine cerebrovascular dysfunction. Aβ oligomer stereotactic injection model was established to explain the relationship between Aβ and vasculopathy. Immunofluorescence staining, western blot, and real-time PCR were applied to detect the morphological and molecular alternations of pericytes. Primary cultured pericytes and bEnd.3 cells were employed to explore the underlying mechanisms.

RESULTS

Vasculopathy including BBB damage, hypoperfusion, and low vessel density were found in the cortex of 8 to 10-month-old 5xFAD mice. A similar phenomenon accompanied by pericyte degeneration appeared in an Aβ-injected model, suggesting a direct relationship between Aβ and vascular dysfunction. Pericytes showed impaired features including low PDGFRβ expression and increased pro-inflammatory chemokines secretion under the administration of Aβ in vitro, of which supernatant cultured with bEND.3 cells led to significant endothelial dysfunction characterized by TJ protein deficiency.

CONCLUSIONS

Our results provide new insights into the pathogenic mechanism underlying Aβ-induced vasculopathy. Targeting pericyte therapies are promising to ameliorate vascular dysfunction in AD.

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.

Predicting the risk of pulmonary infection in patients with chronic kidney failure: A-C2GH2S risk score-a retrospective study

PURPOSE

The objective of this study is to investigate the associated risk factors of pulmonary infection in individuals diagnosed with chronic kidney disease (CKD). The primary goal is to develop a predictive model that can anticipate the likelihood of pulmonary infection during hospitalization among CKD patients.

METHODS

This retrospective cohort study was conducted at two prominent tertiary teaching hospitals. Three distinct models were formulated employing three different approaches: (1) the statistics-driven model, (2) the clinical knowledge-driven model, and (3) the decision tree model. The simplest and most efficient model was obtained by comparing their predictive power, stability, and practicability.

RESULTS

This study involved a total of 971 patients, with 388 individuals comprising the modeling group and 583 individuals comprising the validation group. Three different models, namely Models A, B, and C, were utilized, resulting in the identification of seven, four, and eleven predictors, respectively. Ultimately, a statistical knowledge-driven model was selected, which exhibited a C-statistic of 0.891 (0.855-0.927) and a Brier score of 0.012. Furthermore, the Hosmer-Lemeshow test indicated that the model demonstrated good calibration. Additionally, Model A displayed a satisfactory C-statistic of 0.883 (0.856-0.911) during external validation. The statistical-driven model, known as the A-C2GH2S risk score (which incorporates factors such as albumin, C2 [previous COPD history, blood calcium], random venous blood glucose, H2 [hemoglobin, high-density lipoprotein], and smoking), was utilized to determine the risk score for the incidence rate of lung infection in patients with CKD. The findings revealed a gradual increase in the occurrence of pulmonary infections, ranging from 1.84% for individuals with an A-C2GH2S Risk Score ≤ 6, to 93.96% for those with an A-C2GH2S Risk Score ≥ 18.5.

CONCLUSION

A predictive model comprising seven predictors was developed to forecast pulmonary infection in patients with CKD. This model is characterized by its simplicity, practicality, and it also has good specificity and sensitivity after verification.

[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.

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

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

Identification of genes and key pathways underlying the pathophysiological association between sarcopenia and chronic obstructive pulmonary disease

PURPOSE

Chronic obstructive pulmonary disease (COPD) patients are likely to develop sarcopenia, while the exact mechanism underlying the association between sarcopenia and COPD is still not clear. This cohort study aims to explore the genes, signaling pathways, and transcription factors (TFs) that are related to the molecular pathogenesis of sarcopenia and COPD.

METHODS

According to the strict inclusion criteria, two gene sets (GSE8479 for sarcopenia and GSE76925 for COPD) were obtained from the Gene Expression Omnibus (GEO) platform. Overlapping differentially expressed genes (DEGs) in sarcopenia and COPD were detected, and comprehensive bioinformatics analysis was conducted, including functional annotation, enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG), construction of a protein-protein interaction (PPI) network, co-expression analysis, identification and validation of hub genes, and TFs prediction and verification.

RESULTS

In total, 118 downregulated and 92 upregulated common DEGs were detected. Functional analysis revealed that potential pathogenesis involves oxidoreductase activity and ferroptosis. Thirty hub genes were detected, and ATP metabolic process and oxidative phosphorylation were identified to be closely related to the hub genes. Validation analysis revealed that SAA1, C3, and ACSS2 were significantly upregulated, whereas ATF4, PPARGC1A, and MCTS1 were markedly downregulated in both sarcopenia and COPD. In addition, six TFs (NFKB1, RELA, IRF7, SP1, MYC, and JUN) were identified to regulate the expression of these genes, and SAA1 was found to be coregulated by NFKB1 and RELA.

CONCLUSION

This study uncovers potential common mechanisms of COPD complicated by sarcopenia. The hub gene SAA1 and the NF-κB signaling pathway could be involved, and oxidative phosphorylation and ferroptosis might be important contributors to this comorbidity.

Lower urinary albumin-to-creatinine ratio predicted all-cause and cardiovascular mortality in Chinese population with diabetes and prediabetes-The Shanghai Changfeng cohort study

INTRODUCTION

Elevated urinary albumin-to-creatinine ratio (UACR) was associated with increased mortality in general population and diabetic patients. However, whether the association remains similar in the subjects with different status of glucose metabolism was unclear. The optimal level of UACR in predicting mortality also remained unknown. This study aims to investigate the relationship between UACR with all-cause and cardiovascular mortality in population with different status of glucose metabolism and explore the predictive cutoff point of UACR.

METHODS

Six thousand three hundred and eighty-six community-dwelling individuals aged ≥45 years were enrolled and followed for an average of 5.3 years. Cox proportional hazards model was performed to analysis the association of baseline UACR and all-cause as well as cardiovascular mortality according to the status of glucose metabolism. Receiver operating characteristic curve was plotted to explore the optimal predictive cutoff point of UACR.

RESULTS

With UACR increasing, both the prevalence of all-cause and cardiovascular death increased. Cox analyses showed baseline UACR independently predicted the risk of all-cause and cardiovascular mortality in the patients with prediabetes mellitus (pre-DM) and diabetes mellitus (DM) but not in subjects with normal glucose tolerance (NGT). When divided by quartiles of UACR, the cumulative survival rate decreased acrossing the quartiles. Compared to the subjects with lowest quartile of UACR, participants with UACR ≥7.40 mg/gCr had a higher risk of all-cause mortality, and participants with UACR ≥16.60 mg/gCr had an increased risk of cardiovascular mortality in all hyperglycemia subjects. The optimal predictive cutoff point of UACR was about 17 mg/gCr.

CONCLUSION

UACR was an independent predictor of all-cause and cardiovascular mortality in population with pre-DM and DM but not in the subjects with NGT. The optimal predictive cutoff point of UACR is about 17 mg/gCr, which was far below the diagnostic cutoff point of microalbuminuria. Earlier interventions of albuminuria should be initiated from very early stage of hyperglycemia to reduce the burden of death in all patients whose glucose metabolism are impaired.

Targeting autophagy overcomes cancer-intrinsic resistance to CAR-T immunotherapy in B-cell malignancies

BACKGROUND

Chimeric antigen receptor T (CAR-T) therapy has substantially revolutionized the clinical outcomes of patients with hematologic malignancies, but the cancer-intrinsic mechanisms underlying resistance to CAR-T cells remain yet to be fully understood. This study aims to explore the molecular determinants of cancer cell sensitivity to CAR-T cell-mediated killing and to provide a better understanding of the underlying mechanisms and potential modulation to improve clinical efficacy.

METHODS

The human whole-genome CRISPR/Cas9-based knockout screening was conducted to identify key genes that enable cancer cells to evade CD19 CAR-T-cell-mediated killing. The in vitro cytotoxicity assays and evaluation of tumor tissue and bone marrow specimens were further conducted to confirm the role of the key genes in cancer cell susceptibility to CAR-T cells. In addition, the specific mechanisms influencing CAR-T cell-mediated cancer clearance were elucidated in mouse and cellular models.

RESULTS

The CRISPR/Cas9-based knockout screening showed that the enrichment of autophagy-related genes (ATG3, BECN1, and RB1CC1) provided protection of cancer cells from CD19 CAR-T cell-mediated cytotoxicity. These findings were further validated by in vitro cytotoxicity assays in cells with genetic and pharmacological inhibition of autophagy. Notably, higher expression of the three autophagy-related proteins in tumor samples was correlated with poorer responsiveness and worse survival in patients with relapsed/refractory B-cell lymphoma after CD19 CAR-T therapy. Bulk RNA sequencing analysis of bone marrow samples from B-cell leukemia patients also suggested the clinical relevance of autophagy to the therapeutic response and relapse after CD19 CAR-T cell therapy. Pharmacological inhibition of autophagy and knockout of RB1CC1 could dramatically sensitize tumor cells to CD19 CAR-T cell-mediated killing in mouse models of both B-cell leukemia and lymphoma. Moreover, our study revealed that cancer-intrinsic autophagy mediates evasion of CAR-T cells via the TNF-α-TNFR1 axis-mediated apoptosis and STAT1/IRF1-induced chemokine signaling activation.

CONCLUSIONS

These findings confirm that autophagy signaling in B-cell malignancies is essential for the effective cytotoxic function of CAR-T cells and thereby pave the way for the development of autophagy-targeting strategies to improve the clinical efficacy of CAR-T cell immunotherapy.

Prognostic analysis of Yes-associated protein 1 in patients with colorectal cancer. A systematic review and meta-analysis

BACKGROUND

colorectal cancer (CRC) is the most common carcinoma worldwide, but a lack of effective prognostic markers limits clinical diagnosis and treatment. Yes-associated protein 1 (YAP1) is an effector of the HIPPO-pathway, which plays a critical role in cancer development and prognosis, including CRC. However, previous reports have suggested that it plays a dual role in CRC.

METHODS

a meta-analysis using RevMan 5.4 and Stata 14.0 was performed to evaluate the relationship between YAP1 and clinical outcomes of CRC, after searching for eligible studies in the PubMed, Web of Science and Embase databases. Online datasets GEPIA and LOGpc were also used to calculate survival results and for comparison with the meta-analysis results. Besides, "DESeq" packages were used for the expression analysis of YAP1 from the TCGA dataset.

RESULTS

YAP1 was overexpressed in the cancer tissues when compared to normal tissues in patients with CRC from the TCGA database (p = 0.000164) and GEPIA database. A total of 10 studies involving 2305 patients from the literature were selected. Pooled HR indicated that overexpression of YAP1 was associated with poor clinical outcomes (HR = 1.70, 95 % CI: 1.28-2.26, p = 0.0003). Subgroup analysis showed a clear correlation between overexpression of YAP1 and worse survival rate in Chinese patients (HR = 1.94, 95 % CI: 1.40-2.69, p = 0.0001), nuclear YAP1 overexpression (HR = 2.07, 95 % CI: 1.29-3.31, p = 0.003), 60 months of follow-up (HR = 1.89, 95 % CI: 1.30-2.73, p = 0.0008), IHC test (HR = 1.65, 95 % CI: 1.17-2.33, p = 0.005), IHC combined with other tests (HR = 1.77, 95 % CI: 1.13-2.77, p = 0.01) and multivariate analysis (HR = 1.70, 95 % CI: 1.24-2.31, p = 0.0009). Nevertheless, disease-free survival (DFS) showed no significant results in the patients with CRC in our meta-analysis (HR = 1.38, 95 % CI: 0.51-3.75, p = 0.52) as well as in the GEPIA and LOGpc databases. Meanwhile, YAP1 overexpression was also significantly associated with worse overall survival (OS) in GSE17536, GSE40967, GSE29623 and GSE71187.

CONCLUSION

YAP1 overexpression is common in CRC tissues. Overexpression of YAP1 in CRC patients, particularly in the nucleus, might be related to shorter OS, maybe in the early stages. YAP1 could serve as a potential predictor of poor prognosis in CRC.

Stress systems exacerbate the inflammatory response after corneal abrasion in sleep-deprived mice via the IL-17 signaling pathway

Sleep deprivation (SD) has a wide range of adverse health effects. However, the mechanisms by which SD influences corneal pathophysiology and its post-wound healing remain unclear. This study aimed to examine the basic physiological characteristics of the cornea in mice subjected to SD and determine the pathophysiological response to injury after corneal abrasion. Using a multi-platform water environment method as an SD model, we found that SD leads to disturbances of corneal proliferative, sensory, and immune homeostasis as well as excessive inflammatory response and delayed repair after corneal abrasion by inducing hyperactivation of the sympathetic nervous system and hypothalamic-pituitary-adrenal axis. Pathophysiological changes in the cornea mainly occurred through the activation of the IL-17 signaling pathway. Blocking both adrenergic and glucocorticoid synthesis and locally neutralizing IL-17A significantly improved corneal homeostasis and the excessive inflammatory response and delay in wound repair following corneal injury in SD-treated mice. These results indicate that optimal sleep quality is essential for the physiological homeostasis of the cornea and its well-established repair process after injury. Additionally, these observations provide potential therapeutic targets to ameliorate SD-induced delays in corneal wound repair by inhibiting or blocking the activation of the stress system and its associated IL-17 signaling pathway.

Pathogenic recurrent copy number variants in 7,078 pregnancies via chromosomal microarray analysis

OBJECTIVES

To investigate the incidence of pathogenic recurrent CNVs in fetuses with different referral indications and review the intrauterine phenotypic features of each CNV.

METHODS

A total of 7,078 amniotic fluid samples were collected for chromosome microarray analysis (CMA) and cases carrying pathogenic recurrent CNVs were further studied.

RESULTS

The highest incidence of pathogenic recurrent CNVs was 2.25 % in fetal ultrasound anomalies (FUA) group. Moreover, regardless of other indications, pregnant women with advanced maternal age have a lower incidence compared with whom less than 35 years old (p<0.05). In total 1.17 % (83/7,078) samples carried pathogenic recurrent CNVs: 20 cases with 22q11.2 recurrent region (12 microdeletion and eight microduplication), 11 with 1q21.1 (five microdeletion and six microduplication) and 16p13.11 (four microdeletion and seven microduplication), 10 with 15q11.2 recurrent microdeletion, seven with Xp22.31 recurrent microdeletion and 16p11.2 (three microdeletion and four microduplication), four with 7q11.23 (two microdeletion and two microduplication), three with 17p11.2 (three microdeletion), 17p12 (two microdeletion and one microduplication) and 17q12 (two microdeletion and one microduplication). The rest ones were rare in this study.

CONCLUSIONS

Pathogenic recurrent CNVs are more likely to be identified in FUA group. Pregnant women with advanced maternal age have a lower incidence of pathogenic recurrent CNVs. The profile of pathogenic recurrent CNVs between prenatal and postnatal is different, especially in 22q11.2, 1q21.1, 15q13.3 recurrent region and 15q11.2 deletion.

Elevated risk of multimorbidity post-COVID-19 infection: protective effect of vaccination

BACKGROUND

It is unclear how the coronavirus disease 2019 (Covid-19) pandemic has affected multimorbidity incidence among those with one pre-existing chronic condition, as well as how vaccination could modify this association.

AIM

To examine the association of Covid-19 infection with multimorbidity incidence among people with one pre-existing chronic condition, including those with prior vaccination.

DESIGN

Nested case-control study.

METHODS

We conducted a territory-wide nested case-control study with incidence density sampling using Hong Kong electronic health records from public healthcare facilities and mandatory Covid-19 reports. People with one listed chronic condition (based on a list of 30) who developed multimorbidity during 1 January 2020-15 November 2022 were selected as case participants and randomly matched with up to 10 people of the same age, sex and with the same first chronic condition without having developed multimorbidity at that point. Conditional logistic regression was used to estimate adjusted odds ratios (aORs) of multimorbidity.

RESULTS

In total, 127 744 case participants were matched with 1 230 636 control participants. Adjusted analysis showed that there were 28%-increased odds of multimorbidity following Covid-19 [confidence interval (CI) 22% to 36%] but only 3% (non-significant) with prior full vaccination with BNT162b2 or CoronaVac (95% CI -2% to 7%). Similar associations were observed in men, women, older people aged 65 or more, and people aged 64 or younger.

CONCLUSIONS

We found a significantly elevated risk of multimorbidity following a Covid-19 episode among people with one pre-existing chronic condition. Full vaccination significantly reduced this risk increase.

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

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

Different evasion strategies in multiple myeloma

Multiple myeloma is the second most common malignant hematologic malignancy which evolved different strategies for immune escape from the host immune surveillance and drug resistance, including uncontrolled proliferation of malignant plasma cells in the bone marrow, genetic mutations, or deletion of tumor antigens to escape from special targets and so. Therefore, it is a big challenge to efficiently treat multiple myeloma patients. Despite recent applications of immunomodulatory drugs (IMiDS), protease inhibitors (PI), targeted monoclonal antibodies (mAb), and even hematopoietic stem cell transplantation (HSCT), it remains hardly curable. Summarizing the possible evasion strategies can help design specific drugs for multiple myeloma treatment. This review aims to provide an integrative overview of the intrinsic and extrinsic evasion mechanisms as well as recently discovered microbiota utilized by multiple myeloma for immune evasion and drug resistance, hopefully providing a theoretical basis for the rational design of specific immunotherapies or drug combinations to prevent the uncontrolled proliferation of MM, overcome drug resistance and improve patient survival.

METTL14 affects UVB-induced human dermal fibroblasts photoaging via miR-100-3p biogenesis in an m6 A-dependent manner

Exposure to ultraviolet radiation can lead to skin photoaging, which increases the risk of skin tumors. This study aims to investigate how microRNA m6 A modification contributes to skin photoaging. This study found that skin fibroblasts exposed to a single UVB dose of 30 mJ/cm2 exhibited characteristics of photoaging. The m6 A level of total RNA decreased in photoaged cells with a down-regulated level of METTL14, and overexpression of METTL14 displayed a photoprotective function. Moreover, miR-100-3p was a downstream target of METTL14. And METTL14 could affect pri-miR-100 processing to mature miR-100-3p in an m6 A-dependent manner via DGCR8. Furthermore, miR-100-3p targeted at 3' end untranslated region of ERRFI1 mRNA with an inhibitory effect on translation. Additionally, photoprotective effects of overexpression of METTL14 were reversed by miR-100-3p inhibitor or overexpression of ERRFI1. In UVB-induced photoaging of human skin fibroblasts, METTL14-dependent m6 A can regulate miR-100-3p maturation via DGCR8 and affect skin fibroblasts photoaging through miR-100-3p/ERRFI1 axis.

Efficacy and safety of intrathecal pemetrexed for TKI-failed leptomeningeal metastases from EGFR+ NSCLC: an expanded, single-arm, phase II clinical trial

BACKGROUND

This study aimed to evaluate the efficacy and safety of intrathecal pemetrexed (IP) for treating patients with leptomeningeal metastases (LM) from non-small-cell lung cancer (NSCLC) who progressed from epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitor (TKI) treatment in an expanded, prospective, single-arm, phase II clinical study (ChiCTR1800016615).

PATIENTS AND METHODS

Patients with confirmed NSCLC-LM who progressed from TKI received IP (50 mg, day 1/day 5 for 1 week, then every 3 weeks for four cycles, and then once monthly) until disease progression or intolerance. Objectives were to assess overall survival (OS), response rate, and safety. Measurable lesions were assessed by investigator according to RECIST version 1.1. LM were assessed according to the Response Assessment in Neuro-Oncology (RANO) criteria.

RESULTS

The study included 132 patients; 68% were female and median age was 52 years (31-74 years). The median OS was 12 months (95% confidence interval 10.4-13.6 months), RANO-assessed response rate was 80.3% (106/132), and the most common adverse event was myelosuppression (n = 42; 31.8%), which reversed after symptomatic treatment. The results of subgroup analysis showed that absence of brain parenchymal metastasis, good Eastern Cooperative Oncology Group score, good response to IP treatment, negative cytology after treatment, and patients without neck/back pain/difficult defecation had longer survival. Gender, age, previous intrathecal methotrexate/cytarabine, and whole-brain radiotherapy had no significant influence on OS.

CONCLUSIONS

This study further showed that IP is an effective and safe treatment method for the EGFR-TKI-failed NSCLC-LM, and should be recommended for these patients in clinical practice and guidelines.

Preliminary manifestation of the Yangtze River Protection Strategy in improving the carbon sink function of estuary wetlands

In 2016, the Yangtze River Protection Strategy was proposed and a series of measures were applied to restore the health and function of the Yangtze River ecosystem. However, the impact of these measures on the carbon (C) sink capacity of the Yangtze River estuary wetlands has not been exhaustively studied. In this work, the effects of these measures on the C sink capacity of Yangtze River estuary wetlands were examined through the long-term monitoring of C fluxes, soil respiration, plant growth and water quality. The C flux of the Yangtze River estuary wetlands has become increasingly negative after the implementation of these measures, mainly owing to reduction in soil CO2 emission. The decrease in the chemical fertilizer release and returning farmland to wetland had led to the improvement of water quality in the estuary area, which further reduced soil heterotrophic microbial activity, and ultimately decreasing soil CO2 emissions of estuary wetlands.

The lipid globotriaosylceramide promotes germinal center B cell responses and antiviral immunity

Influenza viruses escape immunity owing to rapid antigenic evolution, which requires vaccination strategies that allow for broadly protective antibody responses. We found that the lipid globotriaosylceramide (Gb3) expressed on germinal center (GC) B cells is essential for the production of high-affinity antibodies. Mechanistically, Gb3 bound and disengaged CD19 from its chaperone CD81, permitting CD19 to translocate to the B cell receptor complex to trigger signaling. Moreover, Gb3 regulated major histocompatibility complex class II expression to increase diversity of T follicular helper and GC B cells reactive with subdominant epitopes. In influenza infection, elevating Gb3, either endogenously or exogenously, promoted broadly reactive antibody responses and cross-protection. These data demonstrate that Gb3 determines the affinity and breadth of B cell immunity and has potential as a vaccine adjuvant.

Efficacy and safety of avatrombopag in Chinese children with persistent and chronic primary immune thrombocytopenia: A multicentre observational retrospective study in China

Avatrombopag (AVA) is a novel thrombopoietin receptor agonist (TPO-RA) that has been recently approved as a second-line therapy for immune thrombocytopenia (ITP) in adults; however, its safety and efficacy data in children are lacking. Here, we demonstrated the efficacy and safety of AVA as second-line therapy in children with ITP. A multicentre, retrospective, observational study was conducted in children with persistent or chronic ITP who did not respond to or relapsed from previous treatment and were treated with AVA for at least 12 weeks between August 2020 and December 2022. The outcomes were the responses (defined as achieving a platelet count ≥30 × 109 /L, twofold increase in platelet count from baseline and absence of bleeding), including rapid response within 4 weeks, sustained response at weeks 12 and 24, bleeding control and adverse events (AEs). Thirty-four (18 males) patients with a mean age of 6.3 (range: 1.9-15.3) years were enrolled. The median number of previous treatment types was four (range: 1-6), and 41.2% patients switched from other TPO-RAs. Within 4 weeks, overall response (OR) was achieved in 79.4% patients and complete response (CR, defined as a platelet count ≥100 × 109 /L and the absence of bleeding) in 67.7% patients with a median response time of 7 (range: 1-27) days. At 12 weeks, OR was achieved in 88.2%, CR in 76.5% and sustained response in 44% of patients. At 24 weeks, 22/34 (64.7%) patients who achieved a response and were followed up for 24 weeks were evaluated; 12/22 (54.55%) achieved a sustained response. During AVA therapy, median platelet counts increased by week 1 and were maintained throughout the treatment period. The proportion of patients with grade 1-3 bleeding decreased from 52.95% at baseline to 2.94% at 12 weeks, while concomitant ITP medications decreased from 36.47% at baseline to 8.82% at 12 weeks, with only 9 (26.47%) patients receiving rescue therapy 23 times within 12 weeks. There were 61.8% patients with 59 AEs: 29.8% with Common Terminology Criteria for Adverse Events grade 1 and the rest with grade 2. These findings show that AVA could achieve a rapid and sustained response in children with persistent or chronic ITP as a second-line treatment, with good clinical bleeding control and reduction of concomitant ITP therapy, without significant AEs.

Screening the optimal housekeeping genes (HKGs) of placenta tissues by RNA-sequence and qRT-PCR throughout gestation in goat (Capra Hircus)

Selection of stable housekeeping genes (HKGs) is very important for accurate calculation of relative expression levels of target genes by quantitative real-time polymerase chain reaction (qRT-PCR). At present, the appropriate HKGs have not been identified in placental tissues throughout the pregnancy of the goat. In our study, 20 HKGs were tentatively selected from RNA-seq data and previous reports. The cycle threshold (Ct) of HKGs was determined by qRT-PCR in trophoblast membrane and cotyledon villus collected from 38 Dazu Black goats on gestation days of 20, 25, 30, 45, 60, 90, 120, and 150 (birth). The expression stability of the HKGs was analyzed by geNorm, Normfinder, Bestkeeper and Delta Ct algorithms, and comprehensively evaluated by ReFinder and ComprFinder. In addition, the optimal HKGs were further verified by placenta-specific genes (SPP1, VEGFA and PAG6). The 16 candidate HKGs (except POP4, TBP, RNF10, UBC) showed a qualified Ct value, less than 28. Among them, YWHAZ, EIF3K and PPIB showed the most stable expression in placental tissues during early, mid-late pregnancy and postpartum, but the least stable expression was B2M at early and mid-late stage, and PPIB at postpartum. After comprehensive analysis, RPLP0, EIF3K and YWHAZ were found to be the most stable placental HKGs throughout pregnancy. The classical HKGs, ACTB, GAPDH and 18S RNA have unstable expressions and even ranked at the bottom of the list from comprehensive index, suggesting an inappropriate for target gene normalization. Taken together, our study confirmed that YWHAZ, EIF3K, HMBS and RPLP0 may be the optimal HKGs in goat placenta at different stage of pregnancy, which provided a valuable reference of HKGs on functional gene expression detection for further research on placenta development and growth in ruminants.

Digital transformation, productive services agglomeration and innovation performance

Innovation is a necessary guarantee for sustainable development. Stepping into the digital age, digital transformation has triggered the innovation revolution. This paper takes 30 provinces in China from 2012 to 2022 as the research sample, we verify whether digital transformation has improved innovation performance. Based on the Solow growth model and agglomeration economics theory, we also explore the moderating role and threshold effect of agglomeration in productive service industry between digital transformation and innovation performance. To achieve this, we apply the methods of machine learning and text analysis to construct an evaluation index of regional digital transformation and measure it. The paper finds that China's digital transformation index is increasing, but there is a digital divide between regions. We also determine that digital transformation significantly and positively contributes to the level of innovation performance. Considering the threshold effect of agglomeration in productive service industry, the impact of digital transformation on innovation performance exhibits non-linear characteristics, As the level of agglomeration continues to exceed the threshold, the innovation-driven effect of digital transformation increases. The research results help clarify the relationship between digital transformation and innovation performance, and provide favorable policy directions for regional governments to identify digital divides and make reasonable industrial layouts. Thus, it can promote the construction of digital China and innovation power, injecting strong innovation force into the realization of SDGs.

Effect of atomic substitution and structure on thermal conductivity in monolayers H-MN and T-MN (M = B, Al, Ga)

Finding materials with suitable thermal conductivity (κ) is crucial for improving energy efficiency, reducing carbon emissions, and achieving sustainability. Atomic substitution and structural adjustments are commonly used methods. By comparing the κ of two different structures of two-dimensional (2D) IIIA-nitrides and their corresponding carbides, we explored whether atomic substitution has the same impact on κ in different structures. All eight materials exhibit normal temperature dependence, with κ decreasing as the temperature rises. Both structures are single atomic layers of 2D materials, forming M-N bonds, with the difference being that H-MN consists of hexagonal rings, while T-MN consists of tetragonal and octagonal rings. 2D IIIA-nitrides provide a good illustration of the impact of atomic substitution and structure on κ. On a logarithmic scale of κ, it approximates two parallel lines, indicating that different structures exhibit similar trends of κ reduction under the same conditions of atomic substitution. We analyzed the mechanisms behind the decreasing trend in κ from a phonon mode perspective. The main reason for the decrease in κ is that heavier atoms lower lattice vibrations, reducing phonon frequencies. Electronegativity increases, altering bonding characteristics and increasing anharmonicity. Reduced symmetry in complex structures decreases phonon group velocities and enhances phonon anharmonicity, leading to decreased phonon lifetimes. It's noteworthy that we found that atomic substitution and structure significantly affect hydrodynamic phonon transport as well. Both complex structures and atomic substitution simultaneously reduce the effects of hydrodynamic phonon transport. By comparing the impact of κ on two different structures of 2D IIIA-nitrides and their corresponding carbides, we have deepened our understanding of phonon transport in 2D materials. Heavier atomic substitution and more complex structures result in reduced κ and decreased hydrodynamic phonon transport effects. This research is likely to have a significant impact on the study of micro- and nanoscale heat transfer, including the design of materials with specific heat transfer properties for future applications.

High-Speed and Ultrasensitive Solar-Blind Ultraviolet Photodetectors Based on In Situ Grown β-Ga2O3 Single-Crystal Films

Deep-level defects in β-Ga2O3 that worsen the response speed and dark current (Id) of photodetectors (PDs) have been a long-standing issue for its application. Herein, an in situ grown single-crystal Ga2O3 nanoparticle seed layer (NPSL) was used to shorten the response time and reduce the Id of metal-semiconductor-metal (MSM) PDs. With the NPSL, the Id was reduced by 4 magnitudes from 0.389 μA to 81.03 pA, and the decay time (τd1/τd2) decreased from 258/1690 to 62/142 μs at -5 V. In addition, the PDs with the NPSL also exhibit a high responsivity (43.5 A W-1), high specific detectivity (2.81 × 1014 Jones), and large linear dynamic range (61 dB) under 254 nm illumination. The mechanism behind the performance improvement can be attributed to the suppression of the deep-level defects (i.e., self-trapped holes) and increase of the Schottky barrier. The barrier height extracted is increased by 0.18 eV compared with the case without the NPSL. Our work contributes to understanding the relationship between defects and the performance of PDs based on heteroepitaxial β-Ga2O3 thin films and provides an important reference for the development of high-speed and ultrasensitive deep ultraviolet PDs.

Precise design of dual active-site catalysts for synergistic catalytic therapy of tumors

A proven and promising method to improve the catalytic performance of single-atom catalysts through the interaction between bimetallic atoms to change the active surface sites or adjust the catalytic sites of reactants is reported. In this work, we used an iron-platinum bimetallic reagent as the metal source to precisely synthesise covalent organic framework-derived diatomic catalysts (FePt-DAC/NC). Benefiting from the coordination between the two metal atoms, the presence of Pt single atoms can successfully regulate Fe-N3 activity. FePt-DAC/NC exhibited a stronger ability to catalyze H2O2 to produce toxic hydroxyl radicals than Fe single-atom catalysts (Fe-SA/NC) to achieve chemodynamic therapy of tumors (the catalytic efficiency improved by 186.4%). At the same time, under the irradiation of an 808 nm laser, FePt-DAC/NC exhibited efficient photothermal conversion efficiency to achieve photothermal therapy of tumors. Both in vitro and in vivo results indicate that FePt-DAC/NC can efficiently suppress tumor cell growth by a synergistic therapeutic effect with photothermally augmented nanocatalytic therapy. This novel bimetallic dual active-site monodisperse catalyst provides an important example for the application of single-atom catalysts in the biomedical field, highlighting its promising clinical potential.