PARP-2 mediates cardiomyocyte aging and damage induced by doxorubicin through SIRT1 Inhibition

Doxorubicin (DOX) is an anthracycline antibiotic used as an antitumor treatment. However, its clinical application is limited due to severe side effects such as cardiotoxicity. In recent years, numerous studies have demonstrated that cellular aging has become a therapeutic target for DOX-induced cardiomyopathy. However, the underlying mechanism and specific molecular targets of DOX-induced cardiomyocyte aging remain unclear. Poly (ADP-ribose) polymerase (PARP) is a family of protein post-translational modification enzymes in eukaryotic cells, including 18 members. PARP-1, the most well-studied member of this family, has become a potential molecular target for the prevention and treatment of various cardiovascular diseases, such as DOX cardiomyopathy and heart failure. PARP-1 and PARP-2 share 69% homology in the catalytic regions. However, they do not entirely overlap in function. The role of PARP-2 in cardiovascular diseases, especially in DOX-induced cardiomyocyte aging, is less studied. In this study, we found for the first time that down-regulation of PARP-2 can inhibit DOX-induced cellular aging in cardiomyocytes. On the contrary, overexpression of PARP-2 can aggravate DOX-induced cardiomyocyte aging and injury. Further research showed that PARP-2 inhibited the expression and activity of SIRT1, which in turn was involved in the development of DOX-induced cardiomyocyte aging and injury. Our findings provide a preliminary experimental basis for establishing PARP-2 as a new target for preventing and treating DOX cardiomyopathy and related drug development.

Multi-scale characterization and analysis of cellular viscoelastic mechanical phenotypes by atomic force microscopy

The viscoelasticity of cells serves as a biomarker that reveals changes induced by malignant transformation, which aids the cytological examinations. However, differences in the measurement methods and parameters have prevented the consistent and effective characterization of the viscoelastic phenotype of cells. To address this issue, nanomechanical indentation experiments were conducted using an atomic force microscope (AFM). Multiple indentation methods were applied, and the indentation parameters were gradually varied to measure the viscoelasticity of normal liver cells and cancerous liver cells to create a database. This database was employed to train machine-learning algorithms in order to analyze the differences in the viscoelasticity of different types of cells and as well as to identify the optimal measurement methods and parameters. These findings indicated that the measurement speed significantly influenced viscoelasticity and that the classification difference between the two cell types was most evident at 5 μm/s. In addition, the precision and the area under the receiver operating characteristic curve were comparatively analyzed for various widely employed machine-learning algorithms. Unlike previous studies, this research validated the effectiveness of measurement parameters and methods with the assistance of machine-learning algorithms. Furthermore, the results confirmed that the viscoelasticity obtained from the multiparameter indentation measurement could be effectively used for cell classification. RESEARCH HIGHLIGHTS: This study aimed to analyze the viscoelasticity of liver cancer cells and liver cells. Different nano-indentation methods and parameters were used to measure the viscoelasticity of the two kinds of cells. The neural network algorithm was used to reverse analyze the dataset, and the methods and parameters for accurate classification and identification of cells are successfully found.

Accurate and fast localization of EBSD pattern centers for screen moving technology

The authors of this study develop an accurate and fast method for the localization of the pattern centers (PCs) in the electron backscatter diffraction (EBSD) technique by using the model of deformation of screen moving technology. The proposed algorithm is divided into two steps: (a) Approximation: We use collinear feature points to obtain the initial value of the coordinates of the PC and the zoom factor. (b) Subdivision: We then construct a deformation function containing the three parameters to be solved, select a large region for global registration, use the inverse compositional Gauss-Newton (ICGN) to optimize the objective function, and obtain the results of iteration of the PC and the zoom factor. The proposed algorithm was applied to simulated patterns, and yielded an accuracy of measurement of the PCs that was better than 4.6×10-6 of their resolution while taking only 0.2 s for computations. Moreover, the proposed algorithm has a large radius of convergence that makes it robust to the initial estimate. We also discuss the influence of factors of mechanical instability on its results of calibration during the insertion of the detector, and show that errors in measurements caused by the tilt motion of the camera are related only to the tilt angle of its motion and the detector distance, and are unrelated to the distance moved by it.

Atypical characteristic changes of surface morphology and structural covariance network in developmental dyslexia

BACKGROUND

Developmental dyslexia (DD) is a neurodevelopmental disorder that is characterized by difficulties with all aspects of information acquisition in the written word, including slow and inaccurate word recognition. The neural basis behind DD has not been fully elucidated.

METHOD

The study included 22 typically developing (TD) children, 16 children with isolated spelling disorder (SpD), and 20 children with DD. The cortical thickness, folding index, and mean curvature of Broca's area, including the triangular part of the left inferior frontal gyrus (IFGtriang) and the opercular part of the left inferior frontal gyrus, were assessed to explore the differences of surface morphology among the TD, SpD, and DD groups. Furthermore, the structural covariance network (SCN) of the triangular part of the left inferior frontal gyrus was analyzed to explore the changes of structural connectivity in the SpD and DD groups.

RESULTS

The DD group showed higher curvature and cortical folding of the left IFGtriang than the TD group and SpD group. In addition, compared with the TD group and the SpD group, the structural connectivity between the left IFGtriang and the left middle-frontal gyrus and the right mid-orbital frontal gyrus was increased in the DD group, and the structural connectivity between the left IFGtriang and the right precuneus and anterior cingulate was decreased in the DD group.

CONCLUSION

DD had atypical structural connectivity in brain regions related to visual attention, memory and which might impact the information input and integration needed for reading and spelling.

Meta-analysis of impacts of microplastics on plant heavy metal(loid) accumulation

The co-occurrence of microplastics (MPs) and heavy metal(loid)s (HMs) has attracted growing scientific interest because of their wide distribution and environmental toxicity. Nevertheless, the interactions between MPs and HMs in soil-plant systems remain unclear. We conducted a meta-analysis with 3226 observations from 87 independent studies to quantify the impact of MPs addition on the plant biomass and HMS accumulation. Co-occurrence of MPs and HMs (except for As) induced synergistic toxicity to plant growth. MPs promoted their uptake in the shoot by 11.0% for Cd, 30.0% for Pb, and 47.1% for Cu, respectively. In contrast, MPs caused a significant decrease (22.6%, 17.9-26.9%) in the shoot As accumulation. The type and dose of MPs were correlated with the accumulation of HMs. MPs increased available concentrations of Cd, Pb, and Cu, but decreased available As concentration in soils. Meanwhile, MPs addition significantly lowered soil pH. These findings may provide explanations for MPs-mediated effects on influencing the accumulation of HMs in plants. Using a machine learning approach, we revealed that soil pH and total HMs concentration are the major contributors affecting their accumulation in shoot. Overall, our study indicated that MPs may increase the environmental risks of HMs in agroecosystems, especially metal cations.

Low-Temperature Oxidation of Methane on Rutile TiO2(110): Identifying the Role of Surface Oxygen Species

Understanding the microkinetic mechanism underlying photocatalytic oxidative methane (CH4) conversion is of significant importance for the successful design of efficient catalysts. Herein, CH4 photooxidation has been systematically investigated on oxidized rutile(R)-TiO2(110) at 60 K. Under 355 nm irradiation, the C-H bond activation of CH4 is accomplished by the hole-trapped dangling OTi- center rather than the hole-trapped Ob- center via the Eley-Rideal reaction pathway, producing movable CH3• radicals. Subsequently, movable CH3• radicals encounter an O/OH species to form CH3O/CH3OH species, which could further dissociate into CH2O under irradiation. However, the majority of the CH3• radical intermediate is ejected into a vacuum, which may induce radical-mediated reactions under ambient conditions. The result not only advances our knowledge about inert C-H bond activation but also provides a deep insight into the mechanism of photocatalytic CH4 conversion, which will be helpful for the successful design of efficient catalysts.

Medium-Entropy Monosilicates Deliver High Corrosion Resistance to Calcium-Magnesium Aluminosilicate Molten Salt

For decreasing the global cost of corrosion, it is essential to understand the intricate mechanisms of corrosion and enhance the corrosion resistance of materials. However, the ambiguity surrounding the dominant mechanism of calcium-magnesium aluminosilicate (CMAS) molten salt corrosion in extreme environments hinders the mix-and-matching of the key rare earth elements for increasing the resistance of monosilicates against corrosion of CMAS. Herein, an approach based on correlated electron microscopy techniques combined with density functional theory calculations is presented to elucidate the complex interplay of competing mechanisms that control the corrosion of CMAS of monosilicates. These findings reveal a competition between thermodynamics and kinetics that relies on the temperatures and corrosion processes. Innovative medium-entropy monosilicates with exceptional corrosion resistance even at 1500 °C are developed. This is achieved by precisely modulating the radii of rare earth ions in monosilicates to strike a delicate balance between the competition in thermodynamics and kinetics. After 50 and 100 h of corrosion, the thinnest reactive layers are measured to be only 28.8 and 35.4 µm, respectively.

Glycemia and Gluconeogenesis With Metformin and Liraglutide: A Randomized Trial in Youth-onset Type 2 Diabetes

OBJECTIVE

Elevated rates of gluconeogenesis are an early pathogenic feature of youth-onset type 2 diabetes (Y-T2D), but targeted first-line therapies are suboptimal, especially in African American (AA) youth. We evaluated glucose-lowering mechanisms of metformin and liraglutide by measuring rates of gluconeogenesis and β-cell function after therapy in AA Y-T2D.

METHODS

In this parallel randomized clinical trial, 22 youth with Y-T2D-age 15.3 ± 2.1 years (mean ± SD), 68% female, body mass index (BMI) 40.1 ± 7.9 kg/m2, duration of diagnosis 1.8 ± 1.3 years-were randomized to metformin alone (Met) or metformin + liraglutide (Lira) (Met + Lira) and evaluated before and after 12 weeks. Stable isotope tracers were used to measure gluconeogenesis [2H2O] and glucose production [6,6-2H2]glucose after an overnight fast and during a continuous meal. β-cell function (sigma) and whole-body insulin sensitivity (mSI) were assessed during a frequently sampled 2-hour oral glucose tolerance test.

RESULTS

At baseline, gluconeogenesis, glucose production, and fasting and 2-hour glucose were comparable in both groups, though Met + Lira had higher hemoglobin A1C. Met + Lira had a greater decrease from baseline in fasting glucose (-2.0 ± 1.3 vs -0.6 ± 0.9 mmol/L, P = .008) and a greater increase in sigma (0.72 ± 0.68 vs -0.05 ± 0.71, P = .03). The change in fractional gluconeogenesis was similar between groups (Met + Lira: -0.36 ± 9.4 vs Met: 0.04 ± 12.3%, P = .9), and there were no changes in prandial gluconeogenesis or mSI. Increased glucose clearance in both groups was related to sigma (r = 0.63, P = .003) but not gluconeogenesis or mSI.

CONCLUSION

Among Y-T2D, metformin with or without liraglutide improved glycemia but did not suppress high rates of gluconeogenesis. Novel therapies that will enhance β-cell function and target the elevated rates of gluconeogenesis in Y-T2D are needed.

Crystalline hydrogen-bonded organic framework for air-tolerant triplet-triplet annihilation upconversion

A hydrogen-bonded organic framework (HOF) consisting of a 9,10-diphenylanthracene carboxylic derivative, DPACOOH, was developed for solid state triplet-triplet annihilation upconversion (TTA-UC). The HOF sample shows a 70% increase in upconversion quantum yield and a lower threshold value of 126.0 mW cm-2 compared to those of the disordered powder sample, due to a 43% longer triplet diffusion length in HOF than that in the powder sample.

Body mass index, waist circumference, and mortality in subjects older than 80 years: a Mendelian randomization study

BACKGROUND AND AIMS

Emerging evidence has raised an obesity paradox in observational studies of body mass index (BMI) and health among the oldest-old (aged ≥80 years), as an inverse relationship of BMI with mortality was reported. This study was to investigate the causal associations of BMI, waist circumference (WC), or both with mortality in the oldest-old people in China.

METHODS

A total of 5306 community-based oldest-old (mean age 90.6 years) were enrolled in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) between 1998 and 2018. Genetic risk scores were constructed from 58 single-nucleotide polymorphisms (SNPs) associated with BMI and 49 SNPs associated with WC to subsequently derive causal estimates for Mendelian randomization (MR) models. One-sample linear MR along with non-linear MR analyses were performed to explore the associations of genetically predicted BMI, WC, and their joint effect with all-cause mortality, cardiovascular disease (CVD) mortality, and non-CVD mortality.

RESULTS

During 24 337 person-years of follow-up, 3766 deaths were documented. In observational analyses, higher BMI and WC were both associated with decreased mortality risk [hazard ratio (HR) 0.963, 95% confidence interval (CI) 0.955-0.971 for a 1-kg/m2 increment of BMI and HR 0.971 (95% CI 0.950-0.993) for each 5 cm increase of WC]. Linear MR models indicated that each 1 kg/m2 increase in genetically predicted BMI was monotonically associated with a 4.5% decrease in all-cause mortality risk [HR 0.955 (95% CI 0.928-0.983)]. Non-linear curves showed the lowest mortality risk at the BMI of around 28.0 kg/m2, suggesting that optimal BMI for the oldest-old may be around overweight or mild obesity. Positive monotonic causal associations were observed between WC and all-cause mortality [HR 1.108 (95% CI 1.036-1.185) per 5 cm increase], CVD mortality [HR 1.193 (95% CI 1.064-1.337)], and non-CVD mortality [HR 1.110 (95% CI 1.016-1.212)]. The joint effect analyses indicated that the lowest risk was observed among those with higher BMI and lower WC.

CONCLUSIONS

Among the oldest-old, opposite causal associations of BMI and WC with mortality were observed, and a body figure with higher BMI and lower WC could substantially decrease the mortality risk. Guidelines for the weight management should be cautiously designed and implemented among the oldest-old people, considering distinct roles of BMI and WC.

Cellular nucleus image-based smarter microscope system for single cell analysis

Cell imaging technology is undoubtedly a powerful tool for studying single-cell heterogeneity due to its non-invasive and visual advantages. It covers microscope hardware, software, and image analysis techniques, which are hindered by low throughput owing to abundant hands-on time and expertise. Herein, a cellular nucleus image-based smarter microscope system for single-cell analysis is reported to achieve high-throughput analysis and high-content detection of cells. By combining the hardware of an automatic fluorescence microscope and multi-object recognition/acquisition software, we have achieved more advanced process automation with the assistance of Robotic Process Automation (RPA), which realizes a high-throughput collection of single-cell images. Automated acquisition of single-cell images has benefits beyond ease and throughout and can lead to uniform standard and higher quality images. We further constructed a single-cell image database-based convolutional neural network (Efficient Convolutional Neural Network, E-CNN) exceeding 20618 single-cell nucleus images. Computational analysis of large and complex data sets enhances the content and efficiency of single-cell analysis with the assistance of Artificial Intelligence (AI), which breaks through the super-resolution microscope's hardware limitation, such as specialized light sources with specific wavelengths, advanced optical components, and high-performance graphics cards. Our system can identify single-cell nucleus images that cannot be artificially distinguished with an accuracy of 95.3%. Overall, we build an ordinary microscope into a high-throughput analysis and high-content smarter microscope system, making it a candidate tool for Imaging cytology.

Long-term exposure to fine particulate matter constituents and cognitive impairment among older adults: An 18-year Chinese nationwide cohort study

BACKGROUND

Although growing evidence has shown independent links of long-term exposure to fine particulate matter (PM2.5) with cognitive impairment, the effects of its constituents remain unclear. This study aims to explore the associations of long-term exposure to ambient PM2.5 constituents' mixture with cognitive impairment in Chinese older adults, and to further identify the main contributor.

METHODS

15,274 adults ≥ 65 years old were recruited by the Chinese Longitudinal Healthy Longevity Study (CLHLS) and followed up through 7 waves during 2000-2018. Concentrations of ambient PM2.5 and its constituents (i.e., black carbon [BC], organic matter [OM], ammonium [NH4+], sulfate [SO42-], and nitrate [NO3-]) were estimated by satellite retrievals and machine learning models. Quantile-based g-computation model was employed to assess the joint effects of a mixture of 5 PM2.5 constituents and their relative contributions to cognitive impairment. Analyses stratified by age group, sex, residence (urban vs. rural), and region (north vs. south) were performed to identify vulnerable populations.

RESULTS

During the average 3.03 follow-up visits (89,296.9 person-years), 4294 (28.1%) participants had developed cognitive impairment. The adjusted hazard ratio [HR] (95% confidence interval [CI]) for cognitive impairment for every quartile increase in mixture exposure to 5 PM2.5 constituents was 1.08 (1.05-1.11). BC held the largest index weight (0.69) in the positive direction in the qg-computation model, followed by OM (0.31). Subgroup analyses suggested stronger associations in younger old adults and rural residents.

CONCLUSION

Long-term exposure to ambient PM2.5, particularly its constituents BC and OM, is associated with an elevated risk of cognitive impairment onset among Chinese older adults.

Two Key Descriptors for Designing Second Near-Infrared Dyes and Experimental Validation

Second near-infrared (NIR-II) optical imaging technology has emerged as a powerful tool for diagnostic and image-guided surgery due to its higher imaging contrast. However, a general strategy for efficiently designing NIR-II organic molecules is still lacking, because NIR-II dyes are usually difficult to synthesize, which has impeded the rapid development of NIR-II bioprobes. Herein, based on the theoretical calculations on 62 multiaryl-pyrrole (MAP) systems with spectra ranging from the visible to the NIR-II region, a continuous red shift of the spectra toward the NIR-II region could be achieved by adjusting the type and site of substituents on the MAPs. Two descriptors (ΔEgs and μgs) were identified as exhibiting strong correlations with the maximum absorption/emission wavelengths, and the descriptors could be used to predict the emission spectrum in the NIR-II region only if ΔEgs ≤ 2.5 eV and μgs ≤ 22.55 D. The experimental absorption and emission spectra of ten MAPs fully confirmed the theoretical predictions, and biological imaging in vivo of newly designed MAP23-BBT showed high spatial resolution in the NIR-II region in deep tissue angiography. More importantly, both descriptors of ΔEgs and μgs have shown general applicability to most of the reported donor-acceptor-donor-type non-MAP NIR-II dyes. These results have broad implications for the efficient design of NIR-II dyes.

Vectorized dataset of silted land formed by check dams on the Chinese Loess Plateau

Check dams on the Chinese Loess Plateau (CLP) have captured billions of tons of eroded sediment, substantially reducing sediment load in the Yellow River. However, uncertainties persist regarding the precise sediment capture and the role of these dams in Yellow River flow and sediment dynamics due to the lack of available spatial distribution datasets. We produced the first vectorized dataset of silted land formed by check dams on the CLP, combining high-resolution and easily accessible Google Earth images with object-based classification methods. The accuracy of the dataset was verified by 1947 collected test samples, and the producer's accuracy and user's accuracy of the dam lands were 88.9% and 99.5%, respectively. Our dataset not only provides fundamental information for accurately assessing the ecosystem service functions of check dams, but also helps to interpret current changes in sediment delivery of the Yellow River and plan future soil and water conservation projects.

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

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

Bi-directional association between outdoor or social activities and cognitive function: do the PM2.5 exposure catalyze the detrimental inactivity-poor cognition cycle?

BACKGROUND

Previous research has shown that lack of leisure activities, either outdoor or social activities, impedes cognitive function. However, the interrelationship between poor cognition and deficient activities is understudied. In addition, whether exposure to air pollution, such as PM2.5, can accelerate the detrimental 'inactivity-poor cognition' cycle, is worthy of investigation.

METHODS

We used data from the 2008, 2011, 2014, and 2018 waves of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). We assessed the frequency of outdoor or social activities at each wave. The cognitive function was examined using a China-Modified Mini-mental State Examination. We estimated the residential exposure to fine particular matter (PM2.5) via a satellite-based model. We applied cross-lagged panel (CLP) model to examine the bi-directional relationship between outdoor or social activities and cognitive function. We then examined the effect of PM2.5 exposure with sequent cognitive function and activities using generalized estimation equation (GEE) model.

FINDINGS

Overall, we observed significant bi-directional associations between outdoor or social activities and cognitive function. Participants with better cognitive function in the last wave were more likely to engage in outdoor or social activities in the following wave (outdoor activities: β = 0.37, 95% CI [0.27,0.48], P < 0.01; social activities: β = 0.05, 95% CI [0.02,0.09] P < 0.01). Meanwhile, higher engagement in outdoor or social activities in the last wave was associated with more favorable cognitive function in the following wave (outdoor activities: β = 0.06, 95% CI [0.03,0.09], P < 0.01; social activities: β = 0.10, 95% CI [0.03,0.18], P < 0.01). Notably, an increase in PM2.5 exposure during the preceding year was significantly associated with a declining cognitive function (β = -0.05, 95% CI [-0.08,-0.03], P < 0.01), outdoor activities (β = -0.02, 95% CI [-0.04, -0.01], P < 0.01) and social activities (β = -0.02, 95% CI [-0.02, -0.01], P < 0.01) in the current year; the lagged effects of the PM2.5 exposure in the past year of the last wave on activities and cognitive function of the following wave were also observed.

INTERPRETATION

Our findings not only indicate the bi-directional links between the frequency of outdoor or social activities and cognitive function, but also report that PM2.5 exposure plays a role in catalyzing the detrimental inactivity-poor cognition cycle. Future research should investigate whether the policy-driven interventions, such as clean air policies, can break the unfavorable activity-cognition cycle, and thereby promoting health from the dual gains in leisure activities and cognition.

Multiple Force-Triggered Downconverted and Upconverted Emission in Polymers Containing Diels-Alder Adducts

Fluorescent mechanophores can indicate the deformation or damage in polymers. The development of mechanophores with multi-triggered response is of great interest. Herein, Diels-Alder (DA) adducts are incorporated into linear poly(methyl acrylate) PMA-BA and network poly(hexyl methacrylate) (PHMA) as mechanophores to detect the stress caused by ultrasound, freezing, and compression. The DA mechanophores undergo retro-DA reaction to release 9-styrylanthracene chromophore upon applying force, resulting in cyan fluorescence. The dissociation ratio of the DA mechanophore after pulsed ultrasonication of PMA-BA solution for 240 minutes is estimated to be 52 % by absorption spectra and 1H NMR. Additionally, the rate constant of mechanical cleavage is calculated to be 1.2×10-4 min-1⋅kDa-1 with the decrease in molecular weight from 69 to 22 kDa measured by gel permeation chromatography. Freezing of PHMA gels as well as compression of PHMA bulk samples turn-on the DA mechanophores, revealing the microscale fracture. Photon upconversion responses toward various force stimuli are also achieved in both polymer solutions and bulk samples by doping platinum octaethylporphyrin (PtOEP) or palladium meso-tetraphenyltetrabenzoporphyrin (PdTPTBP) sensitizers with multiple excitation wavelengths.

A novel extraction-free dual HiFi-LAMP assay for detection of methicillin-sensitive and methicillin-resistant Staphylococcus aureus

Staphylococcus aureus (S. aureus) is a leading cause of bacteremia and blood stream infections. Methicillin-resistant S. aureus (MRSA) that first appeared in 1961 often caused hospital-acquired infections (HAIs) and community-acquired infections (CAIs) and was associated with high mortality rate. Accurate and rapid point-of-care testing (POCT) of MRSA is crucial for clinical management and treatment of MRSA infections, as well as the prevention and control of HAIs and CAIs. Here, we reported a novel extraction-free dual HiFi-LAMP assay for discriminative detection of methicillin-susceptible S. aureus and MRSA. The dual HiFi-LAMP assay can detect 30 copies/reaction of nuc and mecA genes with detection limits of 147 and 158 copies per 25 µL reaction, respectively. A retrospective clinical evaluation with 107 clinical S. aureus isolates showed both sensitivity and specificity of 100%. A prospective clinical evaluation with 35 clinical samples revealed a specificity of 100% and a sensitivity of 92.3%. The dual HiFi-LAMP assay can detect almost all S. aureus samples (141/142; 99.3%) within 20 min, implying that the entire HiFi-LAMP assay (including sample process) can be completed within 40 min, extremely significantly shorter than 3-5 days by the traditional clinical microbial culture and antibiotic susceptibility testing. The novel extraction-free dual HiFi-LAMP assay can be used as a robust POCT tool to promote precise diagnosis and treatment of MRSA infections in hospitals and to facilitate surveillance of MRSA at hospital and community settings.IMPORTANCEMethicillin-resistant Staphylococcus aureus (MRSA) was associated with high mortality rate and listed as a "priority pathogen" by the World Health Organization. Accurate and rapid point-of-care testing (POCT) of MRSA is critically required for clinical management and treatment of MRSA infections. Some previous LAMP-based POCT assays for MRSA might be questionable due to their low specificity and the lack of appropriate evaluation directly using clinical samples. Furthermore, they are relatively tedious and time-consuming because they require DNA extraction and lack multiplex detection capacity. Here, we reported a novel extraction-free dual HiFi-LAMP assay for discriminative detection of MRSA and methicillin-susceptible S. aureus. The assay has high specificity and sensitivity and can be completed within 40 min. Clinical evaluation with real clinical samples and clinical isolates showed excellent performance with 100% specificity and 92.3%-100% sensitivity. The novel extraction-free assay may be a robust POCT tool to promote precise diagnosis of MRSA infections and facilitate surveillance of MRSA at hospital and community settings.

Stigma of nursing students towards people with mental illness: Protocol of a mixed-method systematic review

Aims

To synthesize the influencing factors of the stigma of nursing students towards people with mental illness (PMI).

Background

The stigma of nursing students towards PMI may affect their career choices and negatively impact people seeking health services. While many studies have examined the educational aspects of mental health, they often overlook the multiple dimensions of possible factors influencing nursing students' perceptions.

Design

A mixed-method systematic review using the Framework Integrating Normative Influences on Stigma (FINIS).

Methods

We will search six databases, including PubMed, Web of Science, Cochrane Library, EMBASE, CINAHL and PsycINFO. The reference list of the included literature will be thoroughly examined to identify if any additional studies meet the criteria. Two authors will independently screen all titles, abstracts, full text and extract data. The Mixed-method Appraisal Tool will be used to assess quality. The extracted data will be disposed to different levels, including micro (demographic characteristics, disease characteristics), meso (social networks, treatment systems) and macro (media images, national context) to comply with the FINIS.

Expected results

This systematic review aims to comprehensively analyze the influencing factors of the stigma of nursing students towards PMI, providing a reference basis for anti-stigma intervention measures.Prospero registration number: CRD42022374419.

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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Implementing mind mapping in small-group learning to promote student engagement in the medical diagnostic curriculum: a pilot study

BACKGROUND

Medical diagnostics is a pivotal bridge curriculum that receives much less attention from undergraduates in non-clinical medicine health profession programs with less student engagement and poor performance. Mind mapping is an active learning strategy for graphically presenting radiant thinking to culture clinical reasoning. The purpose of this study was to explore whether students' comprehensive diagnostic skills are enhanced through increased student engagement by employing mind mapping.

METHODS

We implemented mind mapping in small-grouped workshops with 86 junior undergraduates from preventive medicine program, for physical diagnostic sessions including physical examination (PE) maneuver, electrocardiogram (ECG) interpretation and medical history collection. We also conducted assessments of the above skills, as well as online surveys regarding their expectation on this course, self-evaluation of mind mapping in teaching and the learning process of all the modules.

RESULTS

Group members employing mind mapping in all PE sessions obtained higher scores in the heart and lung systems during the PE maneuver exam. Similarly, groups that made more in-depth mind maps achieved higher scores on the ECG quiz. In addition, groups displaying mind maps for history taking from normal classes and reformed class exhibited greater completeness of medical history with both standardized patients and real patients, which was consistent with increased collection of accompanying symptoms. Mind mapping was valued by the majority of students for its benefits in terms of acquiring PE maneuver, theoretical knowledge, medical history collection and medical records writing, clinical reasoning, communication skills, sense of teamwork and cooperation, professionalism and humanistic literacy.

DISCUSSION

The visual feature of mind mapping evoked extensive behavioral engagement in all groups, as did cognitive and emotional engagement, as the majority of students expressed their willingness and affective reactions. In the short term, the positive feedbacks encourage growing engagement. The continuous benefits of mind mapping require long-term observation.

Risk factors associated with heatwave mortality in Chinese adults over 65 years

Aging populations are susceptible to heat-related mortality because of physiological factors and comorbidities. However, the understanding of individual vulnerabilities in the aging population is incomplete. In the Chinese Longitudinal Healthy Longevity Survey, we assessed daily heatwave exposure individually for 13,527 participants (median age = 89 years) and 3,249 summer mortalities during follow-up from 2008 to 2018. The mortality risk during heatwave days according to relative temperature is approximately doubled (hazard ratio (HR) range = 1.78-1.98). We found that heatwave mortality risks were increased for individuals with functional declines in mobility (HR range = 2.32-3.20), dependency in activities of daily living (HR range = 2.22-3.27), cognitive impairment (HR = 2.22) and social isolation reflected by having nobody to ask for help during difficulties (HR range = 2.14-10.21). Contrary to current understanding, older age was not predictive of heatwave mortality risk after accounting for individual functional declines; no statistical differences were detected according to sex. Beyond age as a risk factor, our findings emphasize that functional aging is an underlying factor in enhancing heatwave resilience. Assessment of functional decline and implementing care strategies are crucial for targeted prevention of mortality during heatwaves.

Silicon reduces toxicity and accumulation of arsenic and cadmium in cereal crops: A meta-analysis, mechanism, and perspective study

Arsenic (As) and cadmium (Cd) are two toxic metal(loid)s that pose significant risks to food security and human health. Silicon (Si) has attracted substantial attention because of its positive effects on alleviating the toxicity and accumulation of As and Cd in crops. However, our current knowledge of the comprehensive effects and detailed mechanisms of Si amendment is limited. In this study, a global meta-analysis of 248 original articles with over 7000 paired observations was conducted to evaluate Si-mediated effects on growth and As and Cd accumulation in rice (Oryza sativa L.), wheat (Triticum aestivum L.), and maize (Zea mays L.). Si application increases the biomass of these crops under As and/or Cd contamination. Si amendment also decreased shoot As and Cd accumulation by 24.1 % (20.6 to 27.5 %) and 31.9 % (29.0 to 31.9 %), respectively. Furthermore, the Si amendment reduced the human health risks posed by As (2.6 %) and Cd (12.9 %) in crop grains. Si-induced inhibition of Cd accumulation is associated with decreased Cd bioavailability and the downregulation of gene expression. The regulation of gene expression by Si addition was the driving factor limiting shoot As accumulation. Overall, our analysis demonstrated that Si amendment has great potential to reduce the toxicity and accumulation of As and/or Cd in crops, providing a scientific basis for promoting food safety globally.

The influence of respiratory movement on preoperative CT-guided localization of lung nodules

AIM

To evaluate the motion amplitude of lung nodules in different locations during preoperative computed tomography (CT)-guided localization, and the influence of respiratory movement on CT-guided percutaneous lung puncture.

MATERIALS AND METHODS

A consecutive cohort of 398 patients (123 men and 275 women with a mean age of 53.9 ± 10.7 years) who underwent preoperative CT-guided lung nodule localization from May 2021 to Apr 2022 were included in this retrospective study. The respiratory movement-related nodule amplitude in the cranial-caudal direction during the CT scan, characteristics of patients, lesions, and procedures were statistically analyzed. Univariate and multivariate logistic regression analyses were used to evaluate the influence of these factors on CT-guided localization.

RESULTS

The nodule motion distribution showed a statistically significant correlation within the upper/middle (lingular) and lower lobes (p<0.001). Motion amplitude was an independent risk factor for CT scan times (p=0.011) and procedure duration (p=0.016), but not for the technical failure rates or the incidence of complications. Puncture depth was an independent risk factor for the CT scan times, procedure duration, technical failure rates, and complications (p<0.01). Female, prone, and supine (as opposed to lateral) positions were significant protective factors for pneumothorax, while the supine position was an independent risk factor for parenchymal hemorrhage (p=0.025).

CONCLUSION

Respiratory-induced motion amplitude of nodules was greater in the lower lobes, resulting in more CT scan times/radiation dose and longer localization duration, but showed no statistically significant influence on the technical success rates or the incidence of complications during preoperative CT-guided localization.

Developmental Regulation of Alternative Polyadenylation in an Adult Stem Cell Lineage

Co-transcriptional alternate processing of nascent mRNA molecules can make major contributions to cell type specific gene expression programs as proliferating precursor cells initiate terminal differentiation. Alternative Cleavage and Polyadenylation (APA) can result in the production of mRNA isoforms from the same gene locus with either longer or shorter 3'UTRs. In Drosophila spermatogenesis, approximately 500 genes undergo APA as proliferating spermatogonia differentiate into spermatocytes, producing transcript isoforms with shortened 3'UTRs, and resulting in profound stage specific changes in the proteins expressed. The molecular mechanisms that specify usage of upstream polyadenylation sites in spermatocytes are thus key to understanding the changes in cell state. Here, we show that PCF11 and Cbc, the two components of Cleavage factor II (CFII), orchestrate APA switching during Drosophila spermatogenesis. Knockdown of PCF11 or cbc in spermatocytes caused dysregulation of APA, with many transcripts normally cleaved at a proximal site in spermatocytes now cleaved at their distal site, as in spermatogonia. Although PCF11 is widely expressed, cbc is strongly upregulated in spermatocytes. Our findings reveal a developmental mechanism where changes in activity of specific cleavage factors can direct cell type specific APA at selected genes, presenting CFII as a key developmental regulator of APA during spermatogenesis.

A universal system for boosting gene expression in eukaryotic cell-lines

We demonstrate a transcriptional regulatory design algorithm that can boost expression in yeast and mammalian cell lines. The system consists of a simplified transcriptional architecture composed of a minimal core promoter and a synthetic upstream regulatory region (sURS) composed of up to three motifs selected from a list of 41 motifs conserved in the eukaryotic lineage. The sURS system was first characterized using an oligo-library containing 189,990 variants. We validate the resultant expression model using a set of 43 unseen sURS designs. The validation sURS experiments indicate that a generic set of grammar rules for boosting and attenuation may exist in yeast cells. Finally, we demonstrate that this generic set of grammar rules functions similarly in mammalian CHO-K1 and HeLa cells. Consequently, our work provides a design algorithm for boosting the expression of promoters used for expressing industrially relevant proteins in yeast and mammalian cell lines.

Worldwide trends in underweight and obesity from 1990 to 2022: a pooled analysis of 3663 population-representative studies with 222 million children, adolescents, and adults

BACKGROUND

Underweight and obesity are associated with adverse health outcomes throughout the life course. We estimated the individual and combined prevalence of underweight or thinness and obesity, and their changes, from 1990 to 2022 for adults and school-aged children and adolescents in 200 countries and territories.

METHODS

We used data from 3663 population-based studies with 222 million participants that measured height and weight in representative samples of the general population. We used a Bayesian hierarchical model to estimate trends in the prevalence of different BMI categories, separately for adults (age ≥20 years) and school-aged children and adolescents (age 5-19 years), from 1990 to 2022 for 200 countries and territories. For adults, we report the individual and combined prevalence of underweight (BMI <18·5 kg/m2) and obesity (BMI ≥30 kg/m2). For school-aged children and adolescents, we report thinness (BMI <2 SD below the median of the WHO growth reference) and obesity (BMI >2 SD above the median).

FINDINGS

From 1990 to 2022, the combined prevalence of underweight and obesity in adults decreased in 11 countries (6%) for women and 17 (9%) for men with a posterior probability of at least 0·80 that the observed changes were true decreases. The combined prevalence increased in 162 countries (81%) for women and 140 countries (70%) for men with a posterior probability of at least 0·80. In 2022, the combined prevalence of underweight and obesity was highest in island nations in the Caribbean and Polynesia and Micronesia, and countries in the Middle East and north Africa. Obesity prevalence was higher than underweight with posterior probability of at least 0·80 in 177 countries (89%) for women and 145 (73%) for men in 2022, whereas the converse was true in 16 countries (8%) for women, and 39 (20%) for men. From 1990 to 2022, the combined prevalence of thinness and obesity decreased among girls in five countries (3%) and among boys in 15 countries (8%) with a posterior probability of at least 0·80, and increased among girls in 140 countries (70%) and boys in 137 countries (69%) with a posterior probability of at least 0·80. The countries with highest combined prevalence of thinness and obesity in school-aged children and adolescents in 2022 were in Polynesia and Micronesia and the Caribbean for both sexes, and Chile and Qatar for boys. Combined prevalence was also high in some countries in south Asia, such as India and Pakistan, where thinness remained prevalent despite having declined. In 2022, obesity in school-aged children and adolescents was more prevalent than thinness with a posterior probability of at least 0·80 among girls in 133 countries (67%) and boys in 125 countries (63%), whereas the converse was true in 35 countries (18%) and 42 countries (21%), respectively. In almost all countries for both adults and school-aged children and adolescents, the increases in double burden were driven by increases in obesity, and decreases in double burden by declining underweight or thinness.

INTERPRETATION

The combined burden of underweight and obesity has increased in most countries, driven by an increase in obesity, while underweight and thinness remain prevalent in south Asia and parts of Africa. A healthy nutrition transition that enhances access to nutritious foods is needed to address the remaining burden of underweight while curbing and reversing the increase in obesity.

FUNDING

UK Medical Research Council, UK Research and Innovation (Research England), UK Research and Innovation (Innovate UK), and European Union.

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.

Area-level Measures of the Social Environment: Operationalization, Pitfalls, and Ways Forward

People's mental health is intertwined with the social environment in which they reside. This chapter explores approaches for quantifying the area-level social environment, focusing specifically on socioeconomic deprivation and social fragmentation. We discuss census data and administrative units, egocentric and ecometric approaches, neighborhood audits, social media data, and street view-based assessments. We close the chapter by discussing possible paths forward from associations between social environments and health to establishing causality, including longitudinal research designs and time-series social environmental indices.

Tau induces inflammasome activation and microgliosis through acetylating NLRP3

BACKGROUND

Alzheimer's disease (AD) and related Tauopathies are characterised by the pathologically hyperphosphorylated and aggregated microtubule-associated protein Tau, which is accompanied by neuroinflammation mediated by activated microglia. However, the role of Tau pathology in microglia activation or their causal relationship remains largely elusive.

METHODS

The levels of nucleotide-binding oligomerisation domain (NOD)-like receptor pyrin domain containing 3 (NLRP3) acetylation and inflammasome activation in multiple cell models with Tau proteins treatment, transgenic mice with Tauopathy, and AD patients were measured by Western blotting and enzyme-linked immunosorbent assay. In addition, the acetyltransferase activity of Tau and NLRP3 acetylation sites were confirmed using the test-tube acetylation assay, co-immunoprecipitation, immunofluorescence (IF) staining, mass spectrometry and molecular docking. The Tau-overexpressing mouse model was established by overexpression of human Tau proteins in mouse hippocampal CA1 neurons through the adeno-associated virus injection. The cognitive functions of Tau-overexpressing mice were assessed in various behavioural tests, and microglia activation was analysed by Iba-1 IF staining and [18F]-DPA-714 positron emission tomography/computed tomography imaging. A peptide that blocks the interaction between Tau and NLRP3 was synthesised to determine the in vitro and in vivo effects of Tau-NLRP3 interaction blockade on NLRP3 acetylation, inflammasome activation, microglia activation and cognitive function.

RESULTS

Excessively elevated NLRP3 acetylation and inflammasome activation were observed in 3xTg-AD mice, microtubule-associated protein Tau P301S (PS19) mice and AD patients. It was further confirmed that mimics of 'early' phosphorylated-Tau proteins which increase at the initial stage of diseases with Tauopathy, including TauT181E, TauS199E, TauT217E and TauS262E, significantly promoted Tau-K18 domain acetyltransferase activity-dependent NLRP3 acetylation and inflammasome activation in HEK293T and BV-2 microglial cells. In addition, Tau protein could directly acetylate NLRP3 at the K21, K22 and K24 sites at its PYD domain and thereby induce inflammasome activation in vitro. Overexpression of human Tau proteins in mouse hippocampal CA1 neurons resulted in impaired cognitive function, Tau transmission to microglia and microgliosis with NLRP3 acetylation and inflammasome activation. As a targeted intervention, competitive binding of a designed Tau-NLRP3-binding blocking (TNB) peptide to block the interaction of Tau protein with NLRP3 inhibited the NLRP3 acetylation and downstream inflammasome activation in microglia, thereby alleviating microglia activation and cognitive impairment in mice.

CONCLUSIONS

In conclusion, our findings provide evidence for a novel role of Tau in the regulation of microglia activation through acetylating NLRP3, which has potential implications for early intervention and personalised treatment of AD and related Tauopathies.

Effect of Rhodotorula mucilaginosa inoculation on the aroma development of a fermented vegetables simulated system

Fermented vegetables are known for their unique flavors and aromas, which are influenced by the complex microbial processes that occur during fermentation. Rhodotorula mucilaginosa is a red yeast strain that is frequently isolated from fermented vegetables. However, the specific mechanisms underlying their effects on aroma production remain unclear. In this study, a simulated system of vegetables fermented using vegetable juices was used to investigate the effects of R. mucilaginosa inoculation on aroma development. The results demonstrated that this red yeast strain could utilize the nutrients present in the vegetable juices to support its growth and reproduction. Moreover, the inoculation of fermented vegetable juices with this yeast strain led to an increase in the levels of umami amino acids and sweet amino acids. Furthermore, this yeast strain was found able to significantly reduce the content of sulfur-containing compounds, which may decrease the unpleasant odor of fermented vegetables. Additionally, the yeast strain was capable of producing high concentrations of aromatic compounds such as phenylethyl alcohol, methyl 2-methylbutyrate, methyl butyrate, and nonanoic acid in a minimum medium. However, only phenylethyl alcohol has been identified as a core aromatic compound in fermented vegetable juice. The three fermented vegetable juices exhibited significantly different flavor profiles according to comparative analysis. Therefore, the core flavor compounds found in fermented vegetables are primarily derived from the release and modification of endogenous flavors naturally present in the vegetables, facilitated by the yeast during fermentation.

Alterations in hippocampus-centered morphological features and function of the progression from normal cognition to mild cognitive impairment

Mild cognitive impairment (MCI) is a significant precursor to dementia, highlighting the critical need for early identification of individuals at high risk of MCI to prevent cognitive decline. The study aimed to investigate the changes in brain structure and function before the onset of MCI. This study enrolled 19 older adults with progressive normal cognition (pNC) to MCI and 19 older adults with stable normal cognition (sNC). The gray matter (GM) volume and functional connectivity (FC) were estimated via magnetic resonance imaging during their normal cognition state 3 years prior. Additionally, spatial associations between FC maps and neurochemical profiles were examined using JuSpace. Compared to the sNC group, the pNC group showed decreased volume in the left hippocampus and left amygdala. The significantly positive correlation was observed between the GM volume of the left hippocampus and the MMSE scores after 3 years in pNC group. Besides, it showed that the pNC group had increased FC between the left hippocampus and the anterior-posterior cingulate gyrus, which was significantly correlated with the spatial distribution of dopamine D2 and noradrenaline transporter. Taken together, the study identified the abnormal brain characteristics before the onset of MCI, which might provide insight into clinical research.

Emerging Roles of Macrophage Polarization in Osteoarthritis: Mechanisms and Therapeutic Strategies

Osteoarthritis (OA) is the most common chronic degenerative joint disease in middle-aged and elderly people, characterized by joint pain and dysfunction. Macrophages are key players in OA pathology, and their activation state has been studied extensively. Various studies have suggested that macrophages might respond to stimuli in their microenvironment by changing their phenotypes to pro-inflammatory or anti-inflammatory phenotypes, which is called macrophage polarization. Macrophages accumulate and become polarized (M1 or M2) in many tissues, such as synovium, adipose tissue, bone marrow, and bone mesenchymal tissues in joints, while resident macrophages as well as other stromal cells, including fibroblasts, chondrocytes, and osteoblasts, form the joint and function as an integrated unit. In this study, we focus exclusively on synovial macrophages, adipose tissue macrophages, and osteoclasts, to investigate their roles in the development of OA. We review recent key findings related to macrophage polarization and OA, including pathogenesis, molecular pathways, and therapeutics. We summarize several signaling pathways in macrophage reprogramming related to OA, including NF-κB, MAPK, TGF-β, JAK/STAT, PI3K/Akt/mTOR, and NLRP3. Of note, despite the increasing availability of treatments for osteoarthritis, like intra-articular injections, surgery, and cellular therapy, the demand for more effective clinical therapies has remained steady. Therefore, we also describe the current prospective therapeutic methods that deem macrophage polarization to be a therapeutic target, including physical stimulus, chemical compounds, and biological molecules, to enhance cartilage repair and alleviate the progression of OA.

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.

Psychometric evaluation of the Chinese version of the attitudes, subjective norms, perceived behavioural control and intention to pursue a career in mental health nursing scale

AIM

To adapt culturally and assess the reliability and validity of the Attitudes, Subjective Norms, Perceived Behavioural Control and Intention to Pursue a Career in Mental Health Nursing (ASPIRE) scale among Chinese nursing students.

BACKGROUND

Mental health nurses make up a substantial proportion of the mental health workforce and are pivotal for improving access to professional mental health care. However, a shortage of mental health nurses persists across various countries. Although new nursing graduates have the potential to alleviate the labor shortages, there is currently no standardized tool specifically designed to measure nursing students' intentions to pursue a career in mental health nursing in China.

DESIGN

A quantitative and cross-sectional design.

METHODS

Following email authorization from the author, the Chinese version of the ASPIRE scale was translated following the cross-cultural adaptation process guidelines established by the American Association of Orthopedic Surgeons (AAOS). A questionnaire survey was conducted among 540 nursing students from three universities in Hunan Province from April 2023 to October 2023. Nine experts were invited to evaluate the content equivalence of each item. Item analysis, exploratory factor analysis, confirmatory factor analysis, internal consistency reliability and test-retest reliability were used to test reliability and validity.

RESULTS

The Chinese version of the ASPIRE scale retained 14 items and it has high content validity. Pearson correlation analysis revealed a statistically significant difference in the correlation between the total scores of the items (P < 0.001). Exploratory factor analysis indicated that the one-factor solution explained 65.82% of the total variance and confirmatory factor analysis results indicated a good fit (CMIN/DF = 2.64, RMSEA = 0.079, GFI = 0.908, IFI = 0.964, SRMR = 0.0326). The Cronbach's alpha coefficient for the scale was 0.958 and the test-retest coefficient was 0.783.

CONCLUSION

The Chinese version of the ASPIRE scale has great reliability and validity and can be used for assessing Chinese nursing students' intention to pursue a career in mental health nursing.

Radiofrequency ablation vs. hepatectomy for liver metastases from gastrointestinal stromal tumors

For patients with gastrointestinal stromal tumors (GISTs) and liver metastases, there is still debate about whether radiofrequency ablation (RFA) or hepatectomy is preferable. The present study aimed to compare the clinical outcomes of RFA with hepatectomy in patients with GISTs and liver metastases. The present retrospective study consisted of a cohort of 43 patients who had been diagnosed with liver metastases from GISTs between January 2010 and December 2022. The study included 18 patients who received RFA combined with tyrosine kinase inhibitor (TKI) therapy (RFA group) and 25 patients who underwent hepatectomy combined with TKI therapy (hepatectomy group). For the patients with liver metastases, the progression-free survival (PFS) rates at 1, 3 and 5 years were 66.5, 38.2 and 33.9%, respectively. Notably, patients in the hepatectomy group exhibited significantly improved PFS times compared with those in the RFA group (median PFS, 42.7 months vs. 14.3 months; P=0.034). Furthermore, the time to imatinib treatment failure (TTF) was notably improved in the hepatectomy group compared with that in the RFA group, and this difference was statistically significant (median TTF, 71.1 vs. 38.0 months; P=0.041). However, the overall survival (OS) times of patients who received RFA and those who had hepatectomy did not differ significantly (median OS, not reached vs. not reached, P=0.120). There was no statistically significant distinction in PFS and TTF between patients who underwent hepatectomy combined with postoperative TKI and those who underwent hepatectomy combined with perioperative TKI (median PFS, 29.5 vs. not reached; P=0.520; median TTF, 66.4 months vs. 71.1 months; P=0.430). The univariate and multivariate analyses consistently identified the sole prognostic factor affecting PFS as hepatectomy combined with TKI therapy (hazard ratio, 0.379; 95% CI, 0.159-0.899; P=0.028). In conclusion, hepatectomy combined with TKI therapy improved prognosis for patients with liver metastases to a greater extent than RFA combined with TKI therapy. For this type of patient, hepatectomy may be a preferable option.

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.

Emergence of brain-inspired small-world spiking neural network through neuroevolution

Studies suggest that the brain's high efficiency and low energy consumption may be closely related to its small-world topology and critical dynamics. However, existing efforts on the performance-oriented structural evolution of spiking neural networks (SNNs) are time-consuming and ignore the core structural properties of the brain. Here, we introduce a multi-objective Evolutionary Liquid State Machine (ELSM), which blends the small-world coefficient and criticality to evolve models and guide the emergence of brain-inspired, efficient structures. Experiments reveal ELSM's consistent and comparable performance, achieving 97.23% on NMNIST and outperforming LSM models on MNIST and Fashion-MNIST with 98.12% and 88.81% accuracies, respectively. Further analysis shows its versatility and spontaneous evolution of topologies such as hub nodes, short paths, long-tailed degree distributions, and numerous communities. This study evolves recurrent spiking neural networks into brain-inspired energy-efficient structures, showcasing versatility in multiple tasks and potential for adaptive general artificial intelligence.

Apelin ameliorates sepsis-induced myocardial dysfunction via inhibition of NLRP3-mediated pyroptosis of cardiomyocytes

Sepsis-induced myocardial dysfunction (SMD) is the major cause of death in sepsis. Nucleotide-binding oligomerization domain-like receptor family pyrin domain containing 3 (NLRP3)-mediated pyroptosis contributes to the occurrence and development of SMD. Although Apelin confers direct protection against SMD, the potential mechanisms remain unclear. This study aimed to determine whether Apelin protects against SMD via regulation of NLRP3-mediated pyroptosis of cardiomyocytes. Experimental SMD was induced in wild-type (WT) control mice and Apelin knockout (Apelin-/-) mice by cecal ligation and puncture (CLP). Neonatal mouse cardiomyocytes (NMCs) were treated with lipopolysaccharide (LPS) to simulate the physiological environment of SMD in vitro. The expression of Apelin was greatly decreased in the plasma from septic patients and septic mouse heart. Knockout of Apelin aggravated SMD, evidenced by decreased cardiac function, and increased cardiac fibrosis and NLRP3 inflammasome and pyroptosis levels in CLP-treated Apelin-/- mice compared with WT mice. Overexpression of Apelin activated the AMPK pathway and thereby inhibited NLRP3 inflammasome-mediated pyroptosis of NMCs induced by LPS in vitro These protective effects were partially abrogated by AMPK inhibitor. In conclusion, Apelin attenuated SMD by inhibiting NLRP3-mediated pyroptosis via activation of the AMPK pathway. Apelin may serve as a promising therapeutic target for SMD.

Promising therapeutic targets for ischemic stroke identified from plasma and cerebrospinal fluid proteomes: a multicenter Mendelian randomization study

BACKGROUND

Ischemic stroke (IS) is more common every year, the condition is serious, and have a poor prognosis. New, efficient, and safe therapeutic targets are desperately needed as early treatment especially prevention and reperfusion is the key to lowering the occurrence of poorer prognosis. Generally circulating proteins are attractive therapeutic targets, this study aims to identify potential pharmacological targets among plasma and cerebrospinal fluid (CSF) proteins for the prevention and treatment of IS using a multicenter Mendelian randomization (MR) approach.

METHODS

First, the genetic instruments of 734 plasma and 151 CSF proteins were assessed for causative connections with IS from MEGASTROKE consortium by MR to identify prospective therapeutic targets. Then, for additional validation, plasma proteins from the deCODE consortium and the Fenland consortium, as well as IS GWAS data from the FinnGen cohort, the ISGC consortium and UK biobank, were employed. A thorough evaluation of the aforementioned possible pharmacological targets was carried out using meta-analysis. The robustness of MR results was then confirmed through sensitivity analysis using several techniques, such as bidirectional MR analysis, Steiger filtering, and Bayesian colocalization. Finally, methods like Protein-Protein Interaction (PPI) Networking were utilized to investigate the relationship between putative drug targets and therapeutic agents.

RESULTS

The authors discovered three proteins that may function as promising therapeutic targets for IS and meet the Bonferroni correction ( P <0.05/885=5.65×10 -5 ). Prekallikrein (OR=0.41, 95% CI: 0.27-0.63, P =3.61×10 -5 ), a protein found in CSF, has a 10-fold protective impact in IS, while the plasma proteins SWAP70 (OR=0.85, 95% CI: 0.80-0.91, P =1.64×10 -6 ) and MMP-12 (OR=0.92, 95% CI: 0.89-0.95, P =4.49×10 -6 ) of each SD play a protective role in IS. Prekallikrein, MMP-12, SWAP70 was replicated in the FinnGen cohort and ISGC database. MMP-12 (OR=0.93, 95% CI: 0.91-0.94, P <0.001), SWAP70 (OR=0.92, 95% CI: 0.90-0.94, P <0.001), and prekallikrein (OR=0.53, 95% CI: 0.33-0.72, P <0.001) may all be viable targets for IS, according to the combined meta-analysis results. Additionally, no evidence of reverse causality was identified, and Bayesian colocalization revealed MMP-12 (PPH 4 =0.995), SWAP70 (PPH 4 =0.987), and prekallikrein (PPH 4 =0.894) shared the same variant with IS, supporting the robustness of the aforementioned causation. Prekallikrein and MMP-12 were associated with the target protein of the current treatment of IS. Among them, Lanadelumab, a new drug whose target protein is a prekallikrein, may be a promising new drug for the treatment of IS.

CONCLUSION

The prekallikrein, MMP-12, and SWAP70 are causally associated with the risk of IS. Moreover, MMP-12 and prekallikrein may be treated as promising therapeutic targets for medical intervention of IS.

PARP inhibitors: A review of the pharmacology, pharmacokinetics, and pharmacogenetics

PARP inhibitors have emerged as a promising class of anticancer agents approved for the treatment of ovarian, breast, prostate, and pancreatic cancer. These inhibitors target PARP enzymes involved in DNA repair pathways and exhibit remarkable efficacy in cancers with genetic deficiencies in the homologous recombination pathway responsible for mending DNA double-strand breaks. While all PARP inhibitors demonstrate potent and selective inhibition of PARP1 and PARP2, the key enzymes involved in DNA repair, each agent within the class possesses unique pharmacological profiles distinguishing them from one another. This review aims to comprehensively examine the properties of the entire PARP inhibitor class while emphasizing individual pharmacologic and pharmacokinetic distinctions that inform clinical recommendations. Currently, four agents, namely olaparib, rucaparib, niraparib, and talazoparib, have obtained approval in the United States and Europe. Olaparib, the first approved PARP inhibitor, has been extensively studied and is indicated for a wider range of cancer types. Niraparib and talazoparib, the more recent additions to the PARP inhibitor class, possess the longest half-lives and are formulated for convenient once-daily dosing, alleviating the pill burden for patients when compared to older agents. Moreover, talazoparib undergoes minimal hepatic metabolism, reducing the potential for drug-drug interactions. Notably, niraparib is the sole PARP inhibitor recommended for dose reduction in hepatically impaired populations, whereas talazoparib and olaparib should be dose reduced in renally impaired populations. The mechanisms underlying these dose adjustment recommendations are further explored in this review. Additionally, this review briefly covers veliparib, a PARP inhibitor under development, and two recently approved PARP inhibitors in China, fuzuloparib and pamiparib. Although significant progress has been made in understanding PARP inhibitors, there are several unanswered questions that remain, necessitating further research across a broader spectrum of cancer types within this evolving class of anticancer agents.

Exploring Causal Relationships between Leukocyte Telomere Length, Sex Hormone-Binding Globulin Levels, and Osteoporosis Using Univariable and Multivariable Mendelian Randomization

OBJECTIVE

Recent evidence supports that leukocyte telomere length (LTL) may be positively associated with healthy living and inversely correlated with the risk of age-related diseases, including osteoporosis. Furthermore, it is important to note that sex hormone-binding globulin (SHBG) levels play a crucial role in the regulation of osteoporosis by influencing the availability of sex hormones. Hence, this study holds significant importance as it aims to unravel the roles of LTL and SHBG levels and determine which one acts as a predominant intermediary factor in influencing osteoporosis. Using Mendelian randomization (MR), we can gain valuable insights into the intricate relationships between aging, sex hormones, and bone health.

METHODS

Univariable and multivariable and MR analyses were employed in this study. First, we used genetic variants associated with both LTL, as determined from a study involving 472,174 European participants by Codd et al., and SHBG levels, as identified in a study conducted by Ruth et al. with 370,125 participants, as instrumental variables (IVs). Then we aimed to establish a causal relationship between LTL and SHBG levels and their potential impact on osteoporosis using univariable MR. Finally, we conducted multivariable MR to provide insights into the independent and combined effects of LTL, SHBG levels on osteoporosis risk. We used various MR methods, with the primary analysis employing the inverse-variance weighted (IVW) model.

RESULTS

Univariable MR analysis reveals a potential causal effect of longer LTL on reduced risk of osteoporosis [odds ratio (OR): 0.85; 95% confidence interval (CI): 0.73-0.99; p = 0.03]. Conversely, higher genetically determined SHBG levels affect the risk of osteoporosis positively. (OR: 1.38; 95% CI: 1.09-1.75; p < 0.01). We observed a negative causal effect for LTL on the occurrence of SHBG (OR: 0.96; 95% CI 0.94-0.98, p < 0.01). After adjustment of using multivariable MR, the causal effect of LTL on osteoporosis (OR: 0.92; 95% CI: 0.84-1.03; p = 0.14), and the effect of SHBG on osteoporosis (OR: 1.43; 95% CI: 1.16-1.75; p < 0.01) were observed.

CONCLUSION

Longer LTL may confer a protective effect against osteoporosis. Additionally, the levels of SHBG appear to play a crucial role in mediating the relationship between LTL and osteoporosis. By understanding the interplay between these factors, we can gain valuable insights into the mechanisms underlying bone health and aging and potentially identify new avenues for prevention and intervention strategies.

Effect of curcumin on malignant hepatocytes and mitochondria studied using atomic force microscopy

Mitochondria are emerging as potential targets for the cancer treatment. In this study, the effects of curcumin on the activity, migration, and mitochondrial membrane potential (MMP) of malignant hepatocytes (SMMC-7721 cells) were determined using cell viability, migration, and MMP assays. Changes in the morphology and biomechanics of SMMC-7721 cells and their mitochondria were studied using both optical microscopy and atomic force microscopy (AFM). The cell survival rate, migration and MMP depended on the concentration of curcumin. Optical microscopy studies showed that curcumin altered the cell morphology. AFM studies showed that the changes in the morphology and nanomechanics of SMMC-7721 cells and their mitochondria, were induced by curcumin. As the concentration of curcumin increased, the cell length, width, and adhesion decreased, but the height, roughness and Young's modulus increased. In contrast, the mitochondrial length, width, height and roughness increased, but the adhesion and Young's modulus decreased. There was a close relationship between mitochondria and cells in terms of function, morphology and biomechanics. This study shows the effects of curcumin on SMMC-7721 cells and their mitochondria from biology and biophysics perspectives. The findings aid in comprehensively understanding the interactions between mitochondria and malignant hepatocytes.

Early recurrence of hepatocellular carcinoma in patients after ablation and resection: A propensity score analysis

BACKGROUND

Early recurrence (ER, recurrence within 2 years) is common in hepatocellular carcinoma (HCC) patients after ablation and resection. We aimed to compare ER and assess the associated risk factors.

METHODS

We collected data from patients underwent resection (1,235) or ablation (517) for early HCC (solitary tumor ≤5 ​cm). Baseline of patients were matched using propensity score matching.

RESULTS

In the matched cohort of 690 patients, the ablation group had a higher ER rate (37.4% vs. 19.4%; P < .001) than the resection group. Patients with ER had worse overall survival (OS) than those without ER in both the ablation (5-year OS: 60.4% vs. 86.7%) and resection groups (5-year OS: 59.2% vs. 88.1%). Ablation treatment was identified as an independent adverse prognostic factor for ER (hazard ratio: 2.751, P < .001). Resection conferred a significant favorable OS than ablation (2-year: 95.4% vs. 90.9%; 5-years: 83.8% vs. 77.0%).

CONCLUSIONS

Resection was superior to ablation in minimizing the risk of ER and offering a better OS for patients with early HCC.

Trends in the prevalence of cognitive impairment at old age in China, 2002-2018

INTRODUCTION

China has the world's largest number of older adults with cognitive impairment (CI). We aimed to examine secular trends in the prevalence of CI in China from 2002 to 2018.

METHODS

Generalized estimating equations (GEE) was used to assess changes in CI trend in 44,154 individuals (72,027 observations) aged 65 to 105 years old.

RESULTS

The prevalence of CI increased from 2002 to 2008 and then decreased until 2018. The age-standardized prevalence increased from 25.7% in 2002, 26.1% in 2005, to 28.2% in 2008, then decreased to 26.0% in 2011, 25.3% in 2014, and 24.9% in 2018. Females and those ≥ 80 years old had greater CI prevalence.

DISCUSSION

The prevalence of CI showed an inverted U shape from early 2000s to late 2010s with a peak in 2008. Follow-up studies are needed to confirm the decreasing trend after 2008 and examine the contributing factors and underlying mechanisms of this trend.

HIGHLIGHTS

Generalized estimating equations (GEE) were used to assess trends of changes in cognitive impairment (CI). CI prevalence in China increased from 2002 to 2008 and then decreased until 2018. Females and those ≥ 80 years old had greater CI prevalence. Stroke, diabetes, and cigarette smoking were risk factors for CI.

Exploiting nonlinear dendritic adaptive computation in training deep Spiking Neural Networks

Inspired by the information transmission process in the brain, Spiking Neural Networks (SNNs) have gained considerable attention due to their event-driven nature. However, as the network structure grows complex, managing the spiking behavior within the network becomes challenging. Networks with excessively dense or sparse spikes fail to transmit sufficient information, inhibiting SNNs from exhibiting superior performance. Current SNNs linearly sum presynaptic information in postsynaptic neurons, overlooking the adaptive adjustment effect of dendrites on information processing. In this study, we introduce the Dendritic Spatial Gating Module (DSGM), which scales and translates the input, reducing the loss incurred when transforming the continuous membrane potential into discrete spikes. Simultaneously, by implementing the Dendritic Temporal Adjust Module (DTAM), dendrites assign different importance to inputs of different time steps, facilitating the establishment of the temporal dependency of spiking neurons and effectively integrating multi-step time information. The fusion of these two modules results in a more balanced spike representation within the network, significantly enhancing the neural network's performance. This approach has achieved state-of-the-art performance on static image datasets, including CIFAR10 and CIFAR100, as well as event datasets like DVS-CIFAR10, DVS-Gesture, and N-Caltech101. It also demonstrates competitive performance compared to the current state-of-the-art on the ImageNet dataset.

An inflammatory gene-related prognostic risk score model for prognosis and immune infiltration in glioblastoma

This study aimed to screen for key genes related to the prognosis of patients with glioblastoma (GBM). First, bioinformatics analysis was performed based on databases such as TCGA and MSigDB. Inflammatory-related genes were obtained from the MSigDB database. The TCGA-tumor samples were divided into cluster A and B groups based on consensus clustering. Multivariate Cox regression was applied to construct the risk score model of inflammatory-related genes based on the TCGA database. Second, to understand the effects of model characteristic genes on GBM cells, U-87 MG cells were used for knockdown experiments, which are important means for studying gene function. PLAUR is an unfavorable prognostic biomarker for patients with glioma. Therefore, the model characteristic gene PLAUR was selected for knockdown experiments. The prognosis of cluster A was significantly better than that of cluster B. The verification results also demonstrate that the risk score could predict overall survival. Although the immune cells in cluster B and high-risk groups increased, no matching survival advantage was observed. It may be that stromal activation inhibits the antitumor effect of immune cells. PLAUR knockdown inhibits tumor cell proliferation, migration, and invasion, and promoted tumor cell apoptosis. In conclusion, a prognostic prediction model for GBM composed of inflammatory-related genes was successfully constructed. Increased immune cell expression may be linked to a poor prognosis for GBM, as stromal activation decreased the antitumor activity of immune cells in cluster B and high-risk groups. PLAUR may play an important role in tumor cell proliferation, migration, invasion, and apoptosis.

Hierarchical rhythmic propagation of corticothalamic interactions for consciousness: A computational study

Clarifying the mechanisms of loss and recovery of consciousness in the brain is a major challenge in neuroscience, and research on the spatiotemporal organization of rhythms at the brain region scale at different levels of consciousness remains scarce. By applying computational neuroscience, an extended corticothalamic network model was developed in this study to simulate the altered states of consciousness induced by different concentration levels of propofol. The cortex area containing oscillation spread from posterior to anterior in four successive time stages, defining four groups of brain regions. A quantitative analysis showed that hierarchical rhythm propagation was mainly due to heterogeneity in the inter-brain region connections. These results indicate that the proposed model is an anatomically data-driven testbed and a simulation platform with millisecond resolution. It facilitates understanding of activity coordination across multiple areas of the conscious brain and the mechanisms of action of anesthetics in terms of brain regions.

Discovery of Novel Phenoxyaryl Pyridones as Bromodomain and Extra-Terminal Domain (BET) Inhibitors with High Selectivity for the Second Bromodomain (BD2) to Potentially Treat Acute Myeloid Leukemia

Bromodomain-selective BET inhibition has emerged as a promising strategy to improve the safety profiles of pan-BET inhibitors. Herein, we report the discovery of potent phenoxyaryl pyridones as highly BD2-selective BET inhibitors. Compound 23 (IC50 = 2.9 nM) exhibited a comparable BRD4 BD2 inhibitory activity relative to 10 (IC50 = 1.0 nM) and remarkably improved selectivity over BRD4 BD1 (23: 2583-fold; 10: 344-fold). This lead compound significantly inhibited the proliferation of acute myeloid leukemia (AML) cell lines through induction of G0/G1 arrest and apoptosis in vitro. Excellent in vivo antitumor efficacy with 23 was achieved in an MV;411 mouse xenograft model. Pleasingly, compound 23 (hERG IC50 > 30 μM) mitigated the inhibition of the human ether-à-go-go-related gene (hERG) ion channel compared with 10 (hERG IC50 = 2.8 μM). This work provides a promising BD2-selective lead for the development of more effective and safe BET inhibitors as anticancer agents.