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Abstract Exploring efficient and easily implementable prenatal screening strategies aims at birth defect prevention and control. However, there have been limited economic evaluations of non-invasive prenatal screening (NIPS) strategies in China. Furthermore, these studies were predominantly confined to local or geographically proximate provinces and lacked universality and representativeness. This study assesses the health economics of current prenatal screening strategies and NIPS as first-line screening programs, analyzing their efficacy to determine an optimal strategy. From the perspective of health economics, cost-effectiveness, cost-benefit, and single-factor sensitivity were conducted for five different screening strategies using a decision tree model. Among pregnant women aged

Abstract Introduction Repeat transcranial magnetic stimulation (rTMS) demonstrates beneficial effects for stroke patients, though its efficacy varies due to the complexity of patient conditions and disease progression. Unsupervised machine learning could be the optimal solution for identifying target patients for transcranial magnetic stimulation treatment. Methods We collected data from ischaemic stroke patients treated with rTMS. Unsupervised machine learning methods, including K-means and Hierarchical Clustering, were used to explore the clinical characteristics of patients suitable for rTMS. We then utilized a prospective observational cohort to validate the effect of selected characteristics. For the validated cohort, outcomes included the presence of motor evoked potentials (MEP), favorable functional outcomes (FFO), and changes in the Fugl-Meyer Assessment (FMA) at 3 and 6 months. Results Hierarchical clustering methods revealed that patients in the better prognosis group were more likely to take statins. The validated cohort was grouped based on statin intake. Patients taking statins exhibited a higher rate of MEP (p = 0.006), a higher rate of FFO at 3 months (p = 0.003) and 6 months (p = 0.021), and a more significant change in FMA (p

Abstract Background Pathogenic (P) copy number variants (CNVs) may be associated with second-trimester ultrasound soft markers (USMs), and noninvasive prenatal screening (NIPS) can enable interrogate the entire fetal genome to screening of fetal CNVs. This study evaluated the clinical application of NIPS for detecting CNVs among fetuses with USMs in pregnant women not of advanced maternal age (AMA). Results Fetal aneuploidies and CNVs were identified in 6647 pregnant women using the Berry Genomics NIPS algorithm.Those with positive NIPS results underwent amniocentesis for prenatal diagnosis. The NIPS and prenatal diagnosis results were analyzed and compared among different USMs. A total of 96 pregnancies were scored positive for fetal chromosome anomalies, comprising 37 aneuploidies and 59 CNVs. Positive predictive values (PPVs) for trisomy 21, trisomy 18, trisomy 13, and sex chromosome aneuploidies were 66.67%, 80.00%, 0%, and 30.43%, respectively. NIPS sensitivity for aneuploidies was 100%. For CNVs, the PPVs were calculated as 35.59% and false positive rate of 0.57%. There were six P CNVs, two successfully identified by NIPS and four missed, of which three were below the NIPS resolution limit and one false negative. The incidence of aneuploidies was significantly higher in fetuses with absent or hypoplastic nasal bone, while that of P CNVs was significantly higher in fetuses with aberrant right subclavian artery (ARSA), compared with other groups. Conclusions NIPS yielded a moderate PPV for CNVs in non-AMA pregnant women with fetal USM. However, NIPS showed limited ability in identifying P CNVs. Positive NIPS results for CNVs emphasize the need for further prenatal diagnosis. We do not recommend the use of NIPS for CNVs screening in non-AMA pregnant women with fetal USM, especially in fetuses with ARSA.

BackgroundRecurrence can worsen conditions and increase mortality in ICH patients. Predicting the recurrence risk and preventing or treating these patients is a rational strategy to improve outcomes potentially. A machine learning model with improved performance is necessary to predict recurrence.MethodsWe collected data from ICH patients in two hospitals for our retrospective training cohort and prospective testing cohort. The outcome was the recurrence within one year. We constructed logistic regression, support vector machine (SVM), decision trees, Voting Classifier, random forest, and XGBoost models for prediction.ResultsThe model included age, NIHSS score at discharge, hematoma volume at admission and discharge, PLT, AST, and CRP levels at admission, use of hypotensive drugs and history of stroke. In internal validation, logistic regression demonstrated an AUC of 0.89 and precision of 0.81, SVM showed an AUC of 0.93 and precision of 0.90, the random forest achieved an AUC of 0.95 and precision of 0.93, and XGBoost scored an AUC of 0.95 and precision of 0.92. In external validation, logistic regression achieved an AUC of 0.81 and precision of 0.79, SVM obtained an AUC of 0.87 and precision of 0.76, the random forest reached an AUC of 0.92 and precision of 0.86, and XGBoost recorded an AUC of 0.93 and precision of 0.91.ConclusionThe machine learning models performed better in predicting ICH recurrence than traditional statistical models. The XGBoost model demonstrated the best comprehensive performance for predicting ICH recurrence in the external testing cohort.

ObjectiveThe structural response and dynamic structural collapse mode of a container ship in waves are studied by fully considering the effects of load nonlinearity and surface nonlinearity.MethodsFirst, a hydrodynamic model of a container ship is established based on the CFD platform, and the overset grid method is used to realize matching between the dynamic boundary grid of the hull hydrodynamic model and the Euler grid in the far-field fluid domain. The volume of fluid method is used to simulate the nonlinearity of the free surface in the whole fluid domain, and the N–S equations in three directions are solved in the whole flow field domain so as to solve the nonlinear wave load of the ship in real time. A nonlinear finite element model of the ship that can simulate its collapse behavior is then established, and the time-domain collapse response of the ship including plasticity and buckling is calculated based on the explicit dynamic nonlinear finite element method (FEM). Finally, the transmission of fluid pressure and node displacement between the hydrodynamic model and structural finite element model on the wet surface are realized, and two-way iterative coupling between the CFD solver and nonlinear finite element solver is carried out to calculate the nonlinear wave load and structural collapse response during the structural collapse of a 4600 TEU container ship in real time. ResultsUnder extreme waves, the upper to middle structures of the container ship are widely plastic, the main deck, side plate, deck longitudinal, side longitudinal and other components have typical yield instability under the action of wave loads, the deck longitudinal, side longitudinal and other structural members have serious lateral instability, and the hull structure loses its bearing capacity.ConclusionThe proposed CFD nonlinear FEM can accurately solve the structural response and dynamic collapse mode, making it viable as a new approach to studying the collapse response of ship structures.

BackgroundCoronary artery bypass grafting using radial artery grafts (RA-CABG) has improved long-term outcomes. However, major adverse cardiovascular events (MACE-4, including all-cause death, myocardial infarction, stroke, and repeat revascularization) after RA-CABG still occur and the predictors remain uncertain. This study aimed to detect independent risk factors of MACE-4 after RA-CABG.MethodsThis is a retrospective case-control study (NCT04935086) conducted among patients who underwent primary isolated RA-CABG between 2009 and 2019 in our center. Baseline characteristics, procedure characteristics, and medication use were compared to identify the independent predictors of MACE-4, all-cause death, and myocardial infarction (MI) with univariate and then multivariate logistic regression.ResultsA total of 370 patients were analyzed using a mean follow-up duration of 48.8 ± 41.0 months. MACE-4, all-cause death, and MI occurred in 102 (27.6%), 27 (7.3%), and 66 patients (17.8%), respectively. Multivariate analysis revealed prior MI (OR = 2.12, 95%CI 1.05–4.25, P = 0.04) and RA to the left anterior descending artery (LAD) (non-left internal mammary artery to LAD) (OR = 4.87, 95%CI 1.41–16.82, P = 0.01) as independent predictors of MACE-4 after surgery. Female (OR = 4.53, 95%CI 1.06–19.41, P = 0.04), left ventricular ejection fraction (LVEF)

ObjectiveCell-free DNA (cfDNA) is a useful biomarker in various clinical contexts. Herein, we aimed to identify maternal characteristics and pregnancy outcomes associated with a failed NIPS test due to high cfDNA concentrations.MethodsA retrospective study of cases with high plasma cfDNA concentration in pregnant women in which NIPS test was performed (from 174,318 cases). We reported the detection of 126 cases (118 with complete clinical information) in which the high amount of cfDNA did not allow the performance of NIPS and study the possible causes of this result.Results622 (0.35%) of 174,318 pregnant women had failed the NIPS test, including 126 (20.3%) cases with high plasma cfDNA concentrations. The failed NIPS due to high plasma cfDNA concentrations was associated with maternal diseases and treatment with low-molecular-weight heparin (LMWH). Further follow-up of the 118 pregnant women in the case group revealed that the pregnancy outcomes included 31 premature deliveries, 21 abortions. The cfDNA concentrations of pregnant women with preterm deliveries were 1.15 (0.89, 1.84), which differed significantly from those who had full-term deliveries.ConclusionsAmong pregnant women with high cfDNA concentrations, systemic autoimmune diseases, pregnancy complications and LMWH were associated with increased incidence of failed NIPS test. High maternal cfDNA concentrations may not be associated with chromosomal abnormalities in the fetus. However, they should be alerted to the possibility of preterm births and stillbirths. Further clinical studies on pregnant women with high cfDNA concentrations are required.

BackgroundGenetic factors are important causes of birth defects. Noninvasive prenatal screening (NIPS) is widely used for prenatal screening of trisomy 21, trisomy 18, and trisomy 13, which are the three most common fetal aneuploidies. Fetal fraction refers to the proportion of cell-free fetal DNA in maternal plasma, which can influence the accuracy of NIPS. Elucidating the factors that influence fetal fraction can provide guidance for the interpretation of NIPS results and genetic counseling. However, there is currently no broad consensus on the known factors that influence fetal fraction.ObjectiveThe study aimed to explore the maternal and fetal factors influencing fetal fraction.MethodsA total of 153,306 singleton pregnant women who underwent NIPS were included. Data on gestational age; maternal age; body mass index (BMI); z-scores for chromosomes 21, 18, and 13; and fetal fraction in NIPS were collected from the study population, and the relationships between fetal fraction and these factors were examined. The relationship between fetal fraction and different fetal trisomy types was also analyzed.ResultsThe results showed that the median gestational age, maternal age, and BMI of the pregnant women were 18 (16, 20) weeks, 29 (25, 32) years, and 22.19 (20.40, 24.24) kg/m2, respectively. The median fetal fraction was 11.62 (8.96, 14.7)%. Fetal fraction increased with gestational age and decreased with maternal age and BMI (P

Morphology engineering has been developed as one of the most widely used strategies for improving the performance of electrocatalysts. However, the harsh reaction conditions and cumbersome reaction steps during the nanomaterials synthesis still limit their industrial applications. Herein, one-dimensional (1D) novel-segmented PtTe porous nanochains (PNCs) were successfully synthesized by the template methods assisted by Pt autocatalytic reduction. The PtTe PNCs consist of consecutive mesoporous architectures that provide a large electrochemical surface area (ECSA) and abundant active sites to enhance methanol oxidation reaction (MOR). Furthermore, 1D nanostructure as a robust sustaining frame can maintain a high mass/charge transfer rate in a long-term durability test. After 2,000 cyclic voltammetry (CV) cycles, the ECSA value of PtTe PNCs remained as high as 44.47 m2·gPt–1, which was much larger than that of commercial Pt/C (3.95 m2·gPt–1). The high catalytic activity and durability of PtTe PNCs are also supported by CO stripping test and density functional theory calculation. This autocatalytic reduction-assisted synthesis provides new insights for designing efficient low-dimensional nanocatalysts.

In the natural and social systems of the real world, various network can be seen everywhere. The world where people live can be seen as a combination of network with different dimensions. Link prediction formalizes the interaction behavior between people. Traditional link prediction methods mainly study the user behavior of static social network. This article studied the dynamic graph representation learning so as to put forward an improved link prediction model in dynamic social network. Besides, the interactions in the real world can be multiple, links at different moments may have different meanings. The proposed model firstly solved the problem of link prediction on multiple kinds of edges. The whole embedding of each node is separated into two parts, basic embedding and edge embedding. Then the proposed model selected time slices for dynamic social network to get the graph embeddings in different snapshots. What's more, the t+1 time step embedding vector was used to validate t time step prediction effect and the proposed model performed better than traditional graph representation learning methods.

In recent years, suicide has become the leading cause of unnatural death among college students in China. Psychological monitors, as class cadres who manage affairs related to mental health within their classes, are critical in identifying and intervening in psychological crises among their classmates. In China, however, talking about death is a cultural taboo, and many mental health workers have expressed concern about their implementation of interviews about suicide with others. Generally speaking, interviews with suicidal classmates are usually conducted by professional psychological experts and psychological monitors (psychological monitors are non-professional peer counselors in China). Such classmates that have undergone both the aforementioned types of interviews were investigated in this paper. However, this paper focuses on two issues. Firstly, what are the perceptions of classmates who have been interviewed toward the experience of being interviewed by psychological monitors? Secondly, what are the psychological monitors' perceptions of their implementation of interviews about suicide with classmates? In this study, 1,664 classmates who had been interviewed and 1,320 psychological monitors were surveyed by means of an online questionnaire. The results showed that classmates who have been interviewed perceived their experience of being interviewed by a psychological monitor about suicide more positively than the psychological monitors who viewed their implementation of interviews about suicide with classmates. Among the classmates who have been interviewed, three categories of classmates have more positive perceptions of their experience of being interviewed by a psychological monitor about suicide. Category one is males. Category two is those who were willing to seek help from the psychological monitors. Category three is those who had a more correct attitude toward mental illness. Among the psychological monitors, three categories of psychological monitors have more positive perceptions of their implementation of interviews about suicide with classmates. Category one is those who have the experience of implementing interviews with their classmates. Category two is those who have received training. Category three is those who had a more correct attitude toward mental illness. Psychological monitors can interview classmates confidently, and the training of psychological monitors on mental health knowledge should be strengthened in universities.

Summary: Early steps in the endoplasmic reticulum (ER) lumen and cis-Golgi comprise trimming of N-glycans by class I α-mannosidases (MNSs) play crucial roles in root growth and stress response. Herein, we found that the root growth inhibition in the mns1 mns2 mns3 mutant was partially rescued under alkaline condition, and inhibitor treatment to disrupt auxin transport counteracted this alkaline-maintained root growth. Further study showed that indole-3-acetic acid (IAA) levels were undetectable in mns1 mns2 mns3 at normal condition and recovered at alkaline condition, which corroborate our N-glycopeptide profiling, from which N-glycopeptides related with IAA biosynthesis, amino acid conjugates hydrolysis, and response showed differential abundance between normal and alkaline conditions in mns1 mns2 mns3. Overall, our results linked the need for MNSs-mediated N-glycan processing in the ER and cis-Golgi with maintenance of auxin homeostasis and transport in Arabidopsis roots during the response to alkaline stress.

White lupin is a legume crop that develops cluster roots for efficient phosphate acquisition. Here, the authors assemble its triplicated genome, reveal gene expression difference among sub-genomes, and characterize possible pathways related to high phosphorus-use efficiency.

Human activities are usually collective, so clustering has become an important feature of human behavior. This paper studied the evolution of the community in the process of public opinion propagation so as to put forward a public opinion evolution model for the network community number. This study proposed the community number evolution model of public opinion based on stochastic competitive learning, and the proposed model consists of an increase in the number of communities and a decrease in the number of communities. The highlight of this model is that on the one hand, it realizes the research on the evolution of public opinions on the dynamic network; on the other hand, unlike other public opinion evolution models, this model pays attention to the community number increase and decrease rules in the evolution of public opinions. Then, as an extension of the community number evolution model of public opinion, the community number prediction model had been proposed. Based on Twitter data from the 2017 London Bridge attack, the proposed models were validated by experiments. In the verification section of this paper, two methods had been introduced as a comparison. The experimental results show that the community number evolution model of public opinion is correct.

Background: This study aims to evaluate prenatal diagnosis methods following positive noninvasive prenatal screening (NIPS) results.Methods: According to the positive noninvasive prenatal screening results, 926 pregnant women were divided into three groups: main target disease group (high risk for trisomy 21, trisomy 18, or trisomy 13), sex chromosome aneuploidy (SCA) group, and other chromosomal abnormalities group [abnormal Z-scores for chromosomes other than trisomy (T)21/T18/T13 or SCAs]. The verification methods and results were then retrospectively analysed.Results: In the main target disease group, the positive rate of chromosomal abnormalities confirmed by quantitative fluorescence polymerase chain reaction (QF-PCR) was 75.18% (212/282), which was not significantly different from that by karyotyping (79.36%, 173/218) and copy number variation (CNV) detection methods (71.43%, 65/91). The positive rate of additional findings confirmed by karyotyping and copy number variation detection methods in main target disease group was 0.46% (1/218) and 8.79% (8/91), respectively. The positive rate of chromosomal abnormalities confirmed by karyotyping and CNV detection methods were 27.11% (45/166) and 38.46% (95/247) in the SCA group and 4.17% (1/24) and 20% (36/180) in the other chromosomal abnormalities group, respectively. Fetal sex chromosome mosaicism was detected in 16.13% (20/124) of the confirmed SCA cases. There were no significant differences in the detection rates of chromosomal microarray analysis (CMA) and CNV sequencing (CNVseq) among the three groups (p > 0.05).Conclusion: QF-PCR can quickly and accurately identify aneuploidies following NIPS-positive results for common aneuploidy, and in combination with karyotyping and CNV detection techniques can provide more comprehensive results. With the NIPS-positive results for SCA or other abnormalities, CMA and CNVseq may have the same effect on increasing the detection rate. The addition of fluorescence in situ hybridization assay may help to identify true fetal mosaicism.

Introduction. There is a common concern about the pain and rehabilitation of the knee after femoral retrograde intramedullary nailing. It is essential for early postoperative knee function required for physical self-maintenance in daily life. And a favorable rehabilitation of the knee usually promotes the quality of life. However, early rehabilitation is absent or insufficient for many patients in postoperative management. This retrospective study aims to evaluate the effect of early knee function improvement in comparison to postoperative fascia iliaca blocking (FIB) and multimodal drug injection (MDI). Patients and Methods. A retrospective analysis of 41 patients receiving femoral fracture treatment with retrograde intramedullary nailing, was performed during 2018–2020. 19 patients were treated with MDI as postoperative analgesia, and 22 patients were treated with FIB. Rehabilitation started on the first postoperative day and lasted for 3 months. Visual analog scale (VAS), the range of motion (ROM) of the knee, and single assessment numeric evaluation (SANE) were assessed. Results. There was no significant difference shown in any of the demographic, fracture types, and operative time. All patients performed regular and voluntary knee rehabilitation and weight-bearing at home following the instruction from the orthopedic staff. Pain in the FIB group at postoperative 1-day was milder (1.7 ± 1.1), compared with that in the MDI group (2.8 ± 1.3, p=0.038). There was a significant difference in VAS between two groups at postoperative 1-month (p=0.031), with a peak score in the FIB group (3.3 ± 0.9). At postoperative 3-month, both groups had pain relief with similar VAS (p=0.465). The ROM of the knee in both groups was continuously improved during the first three months. The SANE in the MDI group was significantly different compared with FIB at 1-month (p=0.026). However, scores of SANE were similar in both groups at 3 months (p=0.541). All patients were identified as fractures union at 9-month or 12-month follow-up. Conclusion. The knee pain was commonly experienced in this series of retrograde femoral nailings. Both MDI and FIB provided immediate and effective pain control after femoral fracture surgery. MDI was more beneficial to continuous pain control and knee rehabilitation in the early follow-up. The extent of pain relief and knee function improvement reached the same level at postoperative 3-month.

The quantity and quality of green space (GS) exposure play an important role in urban residents’ physical and psychological health. However, the current framework for assessing GS quality is primarily based on 2-D remote sensing data and 2.5-D street-view images. Few studies have comprehensively evaluated residential community GSs from an overall 3-D perspective. This study proposes a novel systematic framework for evaluating the quantity and quality of residential GSs based on the generation of a high-resolution 3-D point cloud using Unmanned Aerial Vehicle (UAV)-digital aerial photogrammetry (DAP). Nine indices were proposed: green volume ratio, floor green volume index, green groups diversity index, vegetation diversity index, greenspace fragmentation, average vegetation colour distance, vegetation colour diversity, activity areas ratio, and green cohesion index of activity site. These metrics were calculated using the classified point clouds from four typical Chinese residential communities with different residential greenery types and population densities. The results showed that our method could quantitatively identify the differences in residential GS exposure within urban residential communities. For example, a residential community with a large plant distribution and rich greenery variations had higher greenspace volume ratio and vegetation diversity index values. Our findings suggest that this novel framework, employing cost-effective UAV-DAP, can clearly describe different GS attributes and characteristics, aiding decision-makers and urban planners in comprehensively implementing GS interventions to improve the residents’ quality of life.

Constructing a heterogeneous interface using different components is one of the effective measures to achieve the bifunctionality of nanocatalysts, while synergistic interactions between multiple interfaces can further optimize the performance of single-interface nanocatalysts. The non-precious metal nanocatalysts MoS2/NiSe2/reduced graphene oxide (rGO) bilayer sandwich-like nanostructure with multiple well-defined interfaces is prepared by a simple hydrothermal method. MoS2 and rGO are layered nanostructures with clear boundaries, and the NiSe2 nanoparticles with uniform size are sandwiched between both layered nanostructures. This multiple-interfaced sandwich-like nanostructure is prominent in catalytic water splitting with low overpotential for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) and almost no degradation in performance after a 20 h long-term reaction. In order to simulate the actual overall water splitting process, the prepared nanostructures are assembled into MoS2/NiSe2/rGO||MoS2/NiSe2/rGO modified two-electrode system, whose overpotential is only 1.52 mV, even exceeded that of noble metal nanocatalyst (Pt/C||RuO2~1.63 mV). This work provides a feasible idea for constructing multi-interface bifunctional electrocatalysts using nanoparticle-doped bilayer-like nanostructures.

As the star material among nonprecious electrocatalysts, molybdenum disulfide (MoS2) has received much attention. However, the catalytic inertness of the basal plane, low conductivity of its steady state (2H phase), and the agglomeration of lamellar structures have seriously hindered its catalytic performance. It is very essential to design and modulate the morphology and phase of MoS2 to improve the above issues by synergistic regulation of electrical structure and defect engineering. Herein, MoS2 was delicately composited on CoP nanoframe derived from a typical metal–organic frame nanostructure (ZIF-67), forming the hollow CoP@MoS2 hetero-nanoframe. This exquisite externally layered and internally hollow CoP@MoS2 electrocatalyst demonstrated excellent catalytic performance for the hydrogen evolution reaction, with a low overpotential of 119 mV at 10 mA cm−2, a small Tafel slope of 49 mV dec−1, a large electric double-layer capacitance of 10.28 mF cm−2, and prominent long-term stability. The remarkable catalytic performance of hollow CoP@MoS2 hetero-nanoframe can be attributed to the unique architecture of the hetero-nanostructures, appropriate component ratios, strong interaction between CoP and MoS2, and large-scale defects and disorder. First-principles density-functional theory calculations can prove the above arguments adequately, the hydrogen adsorption free energy of CoP@MoS2 is close to zero.

Background: We aimed to assess the clinical application of noninvasive prenatal screening (NIPS) based on second-trimester ultrasonographic soft markers (USMs) in low-risk pregnant women.Methods: Data of pregnant women between April 2015 and December 2019 were retrospectively analyzed. Pregnant women [age at expected date of confinement (EDC) of

The accumulation of excess white adipose tissue (WAT) has harmful consequences on metabolic health. WAT browning confers beneficial effects on adiposity, insulin resistance, and hyperlipidemia. In this study, it was found out that circNrxn2 sponged miR-103 and enhanced FGF10 levels in HFD mice WAT. We discovered that circNrxn2 promoted WAT browning and mitochondria functions. Furthermore, circNrxn2 also increased M2 macrophage polarization in HFD mouse adipose tissue, and the PPARγ signaling pathway participated in these biological processes. Moreover, eliminating adipose tissue macrophages (ATMs) by clodronate-crippled circNrxn2 promoted WAT browning, and the simulation co-culture of macrophages and adipocytes results suggested that circNrxn2 promoted WAT browning through increasing M2 macrophage polarization. Our finding revealed that circNrxn2 acted as an endogenous miR-103 sponge, blocked miR-103 effects, and relieved its inhibition of FGF10 expression to promote WAT browning through increasing M2 macrophage polarization. This study provides a good therapeutic strategy for treating obesity and improving obesity-related metabolic disorders. Keywords: circNrxn2, miR-103, FGF10, adipose tissue macrophages, white adipose tissue browning

Abstract When the gearbox body interference is connected to the ring gear, prestressing occurs in the ring gear, which has a significant impact on the strength and life of the gear. Research on the prestressing of the inner ring gear is in the preliminary stage, and the distribution rule of the prestressing and the influence of each parameter on the interference prestressing have not been derived. In this paper, based on the method of calculating the prestressing of the thick cylinder in interference fit, the ring gear is found to be equivalent to a thick cylinder, and the distribution rule of prestressing of the ring gear in the interference fit is inferred. Then, by modeling and analyzing the gearbox body and ring gear in the interference fit using ABAQUS, the distribution rule of prestressing the ring gear in the interference fit is obtained through a numerical simulation. Finally, the prestressing of the ring gear in the interference fit is measured using X-ray diffraction, and the distribution rule of prestressing of the ring gear in the interference fit is obtained through analysis. Compared with the distribution rule of prestressing in theory, numerical simulation, and experiment, the theoretical distribution rule of prestressing is amended through a statistical method, and a more accurate formula of prestressing is obtained. Through the calculation of the stress and bending moment in the dangerous section of the ring gear through prestressing, the formula for checking the tooth root flexural fatigue strength in the interference fit prestressing is inferred. This research proposes a tooth root bending strength conditional formula for the inner ring gear of the interference fit, which serves as a guide for the design and production of the actual interference joint inner ring gear.

Adipose inflammation is an important cause for obesity-associated metabolic disorders, including insulin resistance and hypertension. Here we investigated that a circular RNA (circRNA), which we termed circARF3 (ADP-ribosylation factor 3), acts as an endogenous miR-103 sponge to alleviate adipose inflammation by promoting mitophagy. On the other hand, miR-103 aggravated inflammation by inhibiting mitophagy, revealing that miR-103 acts as a positive regulator of adipose inflammation. Furthermore, we found that tumor necrosis factor receptor-associated factor 3 (TRAF3), as a miR-103 downstream target, mediates the functions of miR-103 in adipose inflammation. Overexpressing TRAF3 attenuated miR-103-induced inflammation by accelerating mitophagy. Moreover, we identified that circARF3 blocked miR-103 effects, which resulted in an increase in TRAF3 expression. TRAF3 restrained the nuclear factor κB (NF-κB)-signaling pathway, heightened mitophagy, and suppressed NLRP3 inflammasome activation ultimately. Our data showed that circARF3 acts as an endogenous miR-103 sponge to inhibit mitophagy-mediated adipose inflammation both in vitro and in vivo. These findings disclose a new regulatory pathway for adipose inflammation, which consists of circARF3, miR-103, and TRAF3. This study can be a useful addition to our knowledge, as it provides a new strategy for the prevention of adipose inflammation in obesity disorder. Keywords: circRNA, miR-103, TRAF3, mitophagy, inflammation, adipose tissue

BackgroundOur aim was to provide a theoretical basis for clinicians to conduct genetic counseling and choose further prenatal diagnosis methods for pregnant women who failed non-invasive prenatal screening (NIPS).MethodsA retrospective analysis was performed on pregnant women who had failed NIPS tests.ResultsAmong the 123,291 samples, 394 pregnant women did not obtain valid results due to test failures. A total of 378 pregnant women were available for follow-up, while 16 patients were lost to follow-up. Of these 378, 135 pregnant women chose further prenatal diagnosis through amniocentesis, and one case of dysplasia was recalled for postpartum chromosome testing. The incidence rate of congenital chromosomal abnormalities in those who failed the NIPS was 3.97% (15/378), which was higher than that of the chromosomal abnormalities in the common population (1.8%). Among the pregnant women who received prenatal diagnosis, the positive rates of chromosomal abnormalities in the chromosomal microarray analysis/copy number variation sequencing (CMA/CNV-seq) group and in the karyotyping group were 15.28 and 4.76%, respectively.ConclusionPrenatal diagnosis should be strongly recommended in posttest genetic counseling for pregnant women with NIPS failures. Further, high-resolution detection methods should be recommended for additional prenatal diagnoses.

This Review summarizes the progress in research on the flow structure and aerodynamic characteristics of an airfoil at a low Reynolds number encountered by near-space low-speed aircrafts and micro-air vehicles. The structures of several kinds of laminar separation bubbles and their effect are discussed by drawing on experimental and numerical results reported in the past few decades. The transition process in the separation bubble is detailed from various perspectives, including the receptive, primary instability, secondary instability, and break-down stage. The process of evolution of a coherent vortex structure that may affect the transition is discussed by analyzing the vortex dynamics in the separation bubble. Combined with the flow characteristics at a low Reynolds number and data on the airfoil, aerodynamic characteristics of the airfoil, such as the nonlinear effect and static hysteresis effect at a low angle of attack, are discussed.

Abstract Lead‐free halide double perovskites (HDPs) are promising candidates for high‐performance solar cells because of their environmentally‐friendly property and chemical stability in air. The power conversion efficiency of HDPs‐based solar cells needs to be further improved before their commercialization in the market. It requires a thoughtful understanding of the correlation between their specific structure and property. Here, the structural and optical properties of an important HDP‐based (NH4)2SeBr6 are investigated under high pressure. A dramatic piezochromism is found with the increase in pressure. Optical absorption spectra reveal the pressure‐induced red‐shift in bandgap with two distinct anomalies at 6.57 and 11.18 GPa, and the energy tunability reaches 360 meV within 20.02 GPa. Combined with structural characterizations, Raman and infrared spectra, and theoretical calculations using density functional theory, results reveal that, the first anomaly is caused by the formation of a Br‐Br bond among the [SeBr6]2− octahedra, and the latter is attributed to a cubic‐to‐tetragonal phase transition. These results provide a clear correlation between the chemical bonding and optical properties of (NH4)2SeBr6. It is believed that the proposed strategy paves the way to optimize the optoelectronic properties of HDPs and further stimulate the development of next‐generation clear energy based on HDPs solar cells.

Currently, users’ location information is collected to provide better services or research. Using a central server to collect, store and share location information has inevitable defects in terms of security and efficiency. Using the point-to-point sharing method will result in high computation overhead and communication overhead for users, and the data are hard to be verified. To resolve these issues, we propose a new blockchain-based multi-level location secure sharing scheme. In our proposed scheme, the location data are set hierarchically and shared with the requester according to the user’s policy, while the received data can be verified. For this, we design a smart contract to implement attribute-based access control and establish an incentive and punishment mechanism. We evaluate the computation overhead and the experimental results show that the computation overhead of our proposed scheme is much lower than that of the existing scheme. Finally, we analyze the performances of our proposed scheme and demonstrate that our proposed scheme is, overall, better than existing schemes.

The tissue culture regeneration system of Lupinus albus has always been considered as recalcitrant material due to its genotype-dependent response and low regeneration efficiency that hamper the use of genetic engineering. Establishment of repeatable plant regeneration protocol is a prerequisite tool for successful application of genetic engineering. This aim of this study was to develop standardized, efficient protocol for successful shoot induction from cotyledonary node of white lupin. In this study, 5 day old aseptically cultured seedlings were used to prepare three explants (half cotyledonary node, HCN; whole cotyledonary node, WCN; and traditional cotyledonary node, TCN), cultured on four concentrations of M519 medium (M519, ½ M519, 1/3 M519, and ¼ M519), containing four carbohydrate sources (sucrose, fructose, maltose, and glucose), and stimulated with various combinations of KT (kinetin), and NAA (naphthalene acetic acid) for direct shoot regeneration. High frequency of 80% shoot regeneration was obtained on ½ M519 medium (KT 4.0 mg L−1 + NAA 0.1 mg L−1) by using HCN as an explant. Interestingly, combinations of (KT 4.0 mg L−1 + NAA 0.1 mg L−1 + BAP 1.67 mg L−1), and (KT 2.0 mg L−1 + NAA 0.1 mg L−1) showed similar shoot regeneration frequency of 60%. Augmentation of 0.25 g L−1 activated charcoal (AC) not only reduced browning effect but also improved shoot elongation. Among the all carbohydrate sources, sucrose showed the highest regeneration frequency with HCN. Additionally, 80% rooting frequency was recorded on ½ M519 containing IAA 1.0 mg L−1 + KT 0.1 mg L−1 (indole acetic acid) after 28 days of culturing. The present study describes establishment of an efficient and successful protocol for direct plant regeneration of white lupin from different cotyledonary nodes.

Lipopolysaccharide (LPS) induces rapid increase in systemic inflammatory factors. As adipose tissue is a key contributor to the inflammatory response to numerous metabolic stimuli, it is important to understand the mechanism behind the LPS-induced inflammation in white adipose tissue (WAT). Homeobox a5 (Hoxa5) is an important transcription factor, which is highly expressed in adipose tissue, and its mRNA expression is increased at cold exposure in mice. So far, the function of Hoxa5 in adipose tissue browning has been poorly understood. So, the objective of this study was conducted to determine the role of Hoxa5 in adipose inflammatory response and white adipose browning in mice. LPS-induced inflammatory and cold-induced browning model were conducted. We compared the coordinated role of Hoxa5 in inflammation and thermogenesis of mice adipose. Transcriptional and methylation regulation was determined by luciferase assay, electrophoretic mobility shift assay, and bisulfite conversion experiment. Hoxa5 and tenascin C (TNC) were involved in WAT inflammation and browning in mice with LPS injection. Furthermore, Hoxa5 inhibited the TNC-involved activation of Toll-like receptor (TLR) 4/nuclear factor kappa B (NF-κB) signal pathway and promoted WAT browning. Moreover, we found that a BMP4/Smad1 signal, closely related to browning, was activated by Hoxa5. Hoxa5 relieved adipocyte inflammation by decreasing TNC-mediated TLR4 transducer and activator of the NF-κB pathway. Interestingly, descended methylation level increased Hoxa5 expression in cold exposure. Our findings demonstrated that Hoxa5 alleviated inflammation and enhanced browning of adipose tissue via negative control of TNC/TLR4/NF-κB inflammatory signaling and activating BMP4/Smad1 pathway. These findings indicated a novel potential means for the regulation of inflammation in adipocytes to prevent obesity and other inflammatory diseases.

A polarization-dependent, frequency-selective metamaterial (MM) absorber based on a single-layer patterned resonant structure intended for F frequency band is proposed. The design, fabrication, and measurement for the proposed absorber are presented. The absorber’s absorption properties at resonant frequencies have unique characteristics of a single-band, dual-band, or triple-band absorption for different polarization of the incident wave. The calculated surface current distributions and power loss distribution provide further understanding of physical mechanism of resonance absorption. Moreover, a high absorption for a wide range of TE-polarized oblique incidence was achieved. Hence, the MM structure realized on a highly flexible polyimide film, makingthe absorber suitable for conformal geometry applications. The proposed absorber has great potential in the development of polarization detectors and polarizers.

Abstract Background Heart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there are very few models to predict in-hospital mortality in such elderly patients. We aimed to develop and test an individualized machine learning model to assess risk factors and predict in-hospital mortality in in these patients. Methods From January 2012 to December 2021, this study collected data on elderly patients with heart failure and hypertension from the Chongqing Medical University Medical Data Platform. Least absolute shrinkage and the selection operator was used for recognizing key clinical variables. The optimal predictive model was chosen among eight machine learning algorithms on the basis of area under curve. SHapley Additive exPlanations and Local Interpretable Model-agnostic Explanations was employed to interpret the outcome of the predictive model. Results This study ultimately comprised 4647 elderly individuals with hypertension and heart failure. The Random Forest model was chosen with the highest area under curve for 0.850 (95% CI 0.789–0.897), high accuracy for 0.738, recall 0.837, specificity 0.734 and brier score 0.178. According to SHapley Additive exPlanations results, the most related factors for in-hospital mortality in elderly patients with heart failure and hypertension were urea, length of stay, neutrophils, albumin and high-density lipoprotein cholesterol. Conclusions This study developed eight machine learning models to predict in-hospital mortality in elderly patients with hypertension as well as heart failure. Compared to other algorithms, the Random Forest model performed significantly better. Our study successfully predicted in-hospital mortality and identified the factors most associated with in-hospital mortality.

BackgroundRecent studies have increasingly shown the connection between gut microbiome and gestational diabetes mellitus (GDM). However, no studies have explored the relationship between the gut virome and GDM, and the underlying mechanism remains unknown.MethodsWe performed a nested case-control study within a follow-up cohort, enrolling 51 patients with GDM and 51 healthy controls. Shotgun metagenomics sequencing was used to explore gut virome profiles during early pregnancy.ResultsDiversity analysis revealed no difference in the overall gut virome composition between two groups, however, we found greater abundance of Escherichia phage SH2026Stx1 (Q = 0.23), Enterobacteria phage mEp043 c-1 (Q = 0.21), crAssphage cr50_1 (Q = 0.21), Enterobacteria phage phi80 (Q = 0.21), and Escherichia phage HK106 (Q = 0.23) in GDM patients. Cross-kingdom correlation analysis showed the negative correlation between the gut bacterium Eubacterium eligens and three bacteriophages (Escherichia phage SH2026Stx1, Enterobacteria phage mEp043 c-1, and Escherichia phage HK106) in GDM group (r < 0, P < 0.05). Based on gut microbial features and clinical indicators, we constructed a new prediction model using random forest method for GDM with good predictive performance (AUC of 0.893, 95% CI: 0.736 ∼ 0.990).ConclusionThis study is the first to investigate the relationship between the gut virome and GDM as well as the cross-kingdom correlation between gut viruses and bacteria in GDM. Our findings could enhance strategies for preventing and treating GDM from the perspective of gut microbiome, offering valuable insights into its pathogenesis.

Objectives: To evaluate the performance of noninvasive prenatal screening (NIPS) for the fetal common aneuploidy screening in twin pregnancies. Methods: The data of 5469 women with twin pregnancies were collected in this retrospective observational study between January 2017 and December 2018. Patients underwent NIPS as first-line screening or after standard serum screening for fetal aneuploidy. The performance of NIPS was examined, and a regression analysis was performed to investigate testing failure in cases of low fetal fraction. Results: In this study, 2231 (40.8%) patients opted for NIPS as the primary prenatal screening test, and 3238 (59.2%) opted for serum screening, including 440 patients who opted for NIPS after serum screening. Among the 2671 pregnancies with available NIPS outcomes, 11 cases of aneuploidy were identified, seven of trisomy 21 and four of sex chromosome aneuploidy (SCA). The sensitivity and specificity for trisomy 21 were 100% (95% CI, 56.1–100.0%) and 100% (95% CI, 99.8–100.0%), respectively. The positive predictive value (PPV) for SCA was 40.0% (95% CI, 13.7–72.6%). No false negatives were found, with a negative predictive value (NPV) of 100% (95% CI, 99.8–100.0%) in total. In 32 pregnancies who failed NIPS test without available NIPS outcomes due to low fetal fraction, the regression analysis demonstrated that increasing BMI and assisted reproductive technology treatment were significant independent predictors. Conclusions: NIPS is a high-performing routine primary prenatal screening test in twin pregnancies, with a high PPV and low false positive rate for detecting trisomy 21. It is also useful to identify common sex chromosome aneuploidies in twin pregnancies, with similar performance to that reported in singleton pregnancy. [ABSTRACT FROM AUTHOR]

Summary: We aimed to develop a machine-learning based predictive model to identify 30-day readmission risk in Acute heart failure (AHF) patients. In this study 2232 patients hospitalized with AHF were included. The variance inflation factor value and 5-fold cross-validation were used to select vital clinical variables. Five machine learning algorithms with good performance were applied to develop models, and the discrimination ability was comprehensively evaluated by sensitivity, specificity, and area under the ROC curve (AUC). Prediction results were illustrated by SHapley Additive exPlanations (SHAP) values. Finally, the XGBoost model performs optimally: the greatest AUC of 0.763 (0.703–0.824), highest sensitivity of 0.660, and high accuracy of 0.709. This study developed an optimal XGBoost model to predict the risk of 30-day unplanned readmission for AHF patients, which showed more significant performance compared with traditional logistic regression (LR) model.

Abstract Purpose This research aimed to develop a machine learning model to predict the potential risk of prolonged length of stay in hospital before operation, which can be used to strengthen patient management. Methods Patients who underwent posterior spinal deformity surgery (PSDS) from eleven medical institutions in China between 2015 and 2022 were included. Detailed preoperative patient data, including demographics, medical history, comorbidities, preoperative laboratory results, and surgery details, were collected from their electronic medical records. The cohort was randomly divided into a training dataset and a validation dataset with a ratio of 70:30. Based on Boruta algorithm, nine different machine learning algorithms and a stack ensemble model were trained after hyperparameters tuning visualization and evaluated on the area under the receiver operating characteristic curve (AUROC), precision-recall curve, calibration, and decision curve analysis. Visualization of Shapley Additive exPlanations method finally contributed to explaining model prediction. Results Of the 162 included patients, the K Nearest Neighbors algorithm performed the best in the validation group compared with other machine learning models (yielding an AUROC of 0.8191 and PRAUC of 0.6175). The top five contributing variables were the preoperative hemoglobin, height, body mass index, age, and preoperative white blood cells. A web-based calculator was further developed to improve the predictive model's clinical operability. Conclusions Our study established and validated a clinical predictive model for prolonged postoperative hospitalization duration in patients who underwent PSDS, which offered valuable prognostic information for preoperative planning and postoperative care for clinicians. Trial registration ClinicalTrials.gov identifier NCT05867732, retrospectively registered May 22, 2023, https://classic.clinicaltrials.gov/ct2/show/NCT05867732 .

Metamaterials have experienced rapid development in recent years. Absorbers made of metamaterials play a crucial role in electromagnetic applications and other fields. The design of metamaterials is usually simulated using simulation software and optimized by traditional algorithms, which is time-consuming and difficult to optimize; deep learning, an emerging method, is gradually being used for both forward and inverse design of metamaterials, but few methods can simultaneously generate the geometric structure of metamaterials and meet the requirements of multiple physical spectra. This paper proposes an improved conditional variational autoencoder (conditional VAE), that is composed by an encoder and a decoder. The encoder generates a Gaussian distribution while acting as an inverse generated network to predict the corresponding geometric parameters. The decoder can generate absorption spectra that satisfy the requirements according to the conditions. By adding batch normalization and spectral normalization in network training, the convergence of the neural network is accelerated, and the stability of the network is increased. The results show that the encoder and decoder can accurately predict the geometric parameters and the absorption spectrum according to the conditions, proving the feasibility of the method. A structural sample of an absorber was processed and tested for verification. The method provides an effective way for the target design of absorbers and a new approach for the design of other electromagnetic metamaterials.

In recent years, the production of prepared and frozen foods has increased with economic development. However, during freezing, moisture migration forms ice crystals that damage food structure and reduce quality. This study investigates moisture migration changes in pre-fermented dough during frozen storage and effectiveness of Citrus fibre (CF) and Soya dietary fibre (SDF) on quality improvement. Pre-fermented frozen dough properties were evaluated at different freezing storage days with CF and SDF. Results showed frozen storage reduced water retention, converting deeply bound water to weakly bound and free water. Freezable water content increased significantly from 53% (fresh) to 56.95% (60d-control), forming disruptive ice crystals in gluten protein structure. SDF had superior water flow restriction compared to CF, preventing large ice crystal accumulation, enhancing water-holding capacity, and maintaining gluten protein structure. These findings lay a theoretical foundation for improving quality and industrial applications of pre-fermented frozen dough.

Fetal macrosomia is defined as a birthweight ≥4000 g and causes harm to pregnant women and fetuses. Studies reported that the maternal intestinal microbiome plays a key role in the establishment, growth, and development of the fetal intestinal microbiome. However, whether there is a relationship between maternal gut microbiota and macrosomia remains unclear. Our study aimed to identify gut microbiota that may be related to the occurrence of macrosomia, explore the possible mechanisms by which it causes macrosomia, and establish a prediction model to determine the feasibility of predicting macrosomia by early maternal gut microbiota. We conducted a nested case-control study based on an early pregnancy cohort (ChiCTR1900020652) in the Maternity and Child Health Hospital of Hunan Province on fecal samples of 93 women (31 delivered macrosomia as the case group and 62 delivered normal birth weight newborns as the control group) collected and included in this study. We performed metagenomic analysis to compare the composition and function of the gut microbiome between cases and controls. Correlation analysis was used to explore the association of differential species and differential functional pathways. A random forest model was used to construct an early pregnancy prediction model for macrosomia. At the species level, there were more Bacteroides salyersiae, Bacteroides plebeius, Ruminococcus lactaris, and Bacteroides ovatus in the intestinal microbiome of macrosomias’ mothers compared with mothers bearing fetuses that had normal birth weight. Functional pathways of the gut microbiome including gondoate biosynthesis, L-histidine degradation III, cis-vaccenate biosynthesis, L-arginine biosynthesis III, tRNA processing, and mannitol cycle, which were more abundant in the macrosomia group. Significant correlations were found between species and functional pathways. Bacteroides plebeius was significantly associated with the pathway of cis-vaccenate biosynthesis (r = 0.28, p = 0.005) and gondoate biosynthesis (r = 0.28, p < 0.001) and Bacteroides ovatus was positively associated with the pathway of cis-vaccenate biosynthesis (r = 0.29, p = 0.005) and gondoate biosynthesis (r = 0.32, p = 0.002). Bacteroides salyersiae was significantly associated with the pathway of cis-vaccenate biosynthesis (r = 0.24, p = 0.018), gondoate biosynthesis (r = 0.31, p = 0.003), and L–histidine degradation III (r = 0.22, p = 0.291). Finally, four differential species and four clinical indicators were included in the random forest model for predicting macrosomia. The areas under the working characteristic curves of the training and validation sets were 0.935 (95% CI: 0.851~0.979) and 0.909 (95% CI: 0.679~0.992), respectively. Maternal gut microbiota in early pregnancy may play an important role in the development of macrosomia and can be used as potential predictors to prevent macrosomia.

Abstract Background High-dose methotrexate (HD-MTX) is a potent chemotherapeutic agent used to treat pediatric acute lymphoblastic leukemia (ALL). HD-MTX is known for cause delayed elimination and drug-related adverse events. Therefore, close monitoring of delayed MTX elimination in ALL patients is essential. Objective This study aimed to identify the risk factors associated with delayed MTX elimination and to develop a predictive tool for its occurrence. Methods Patients who received MTX chemotherapy during hospitalization were selected for inclusion in our study. Univariate and least absolute shrinkage and selection operator (LASSO) methods were used to screen for relevant features. Then four machine learning (ML) algorithms were used to construct prediction model in different sampling method. Furthermore, the performance of the model was evaluated using several indicators. Finally, the optimal model was deployed on a web page to create a visual prediction tool. Results The study included 329 patients with delayed MTX elimination and 1400 patients without delayed MTX elimination who met the inclusion criteria. Univariate and LASSO regression analysis identified eleven predictors, including age, weight, creatinine, uric acid, total bilirubin, albumin, white blood cell count, hemoglobin, prothrombin time, immunological classification, and co-medication with omeprazole. The XGBoost algorithm with SMOTE exhibited AUROC of 0.897, AUPR of 0.729, sensitivity of 0.808, specificity of 0.847, outperforming the other models. And had AUROC of 0.788 in external validation. Conclusion The XGBoost algorithm provides superior performance in predicting the delayed elimination of MTX. We have created a prediction tool to assist medical professionals in predicting MTX metabolic delay.

Abstract The joints between segments represent weak points and introduce discontinuity into structures, therefore they are particularly significant in precast concrete segmental bridges. In this study, a new steel shear key was designed, and 6 full-scale tests were conducted. Various shear keys and joint types were taken as experimental parameters to study crack propagation, failure mode, shear slip, ultimate bearing capacity, and the residual bearing capacity of various joints under direct shear loading. The results show that the stiffness and shear capacity of steel shear keyed joints were higher than concrete key joints, and the structural system was more stable than concrete keyed joints at the moment of cracking. Both the concrete key and steel key epoxied joints suffered direct shear failure. However, different to the concrete epoxied joints which experienced brittle failure, steel key epoxied joints demonstrated a large residual capacity. Based on traditional segmental bridges construction, construction methods involving steel shear keyed joints included short-line match, long-line match, and modular methods are introduced. Finally, the feasibility of steel shear keyed joints construction was verified via engineering tests.

Abstract Objective To accurately screen potential immune cells that can predict the survival of colorectal cancer (CRC) patients and identify related prognostic predictors. Methods The sample data of CRC patients were downloaded from the GEO database as a training set to establish a prognosis-scoring model and screen prognosis-related immune cells. The sample data of CRC patients from the TCGA database were used as the validation set. Simultaneously, cancer tissue samples from 116 patients with CRC diagnosed pathologically in Shanghai Dongfang Hospital were collected to analyze the relationship of prognosis-related immune cells with patients’ survival, and clinical and pathological parameters, and to screen prognostic predictors. Results Prognosis-related immune cells screened from GEO and TCGA databases mainly included Follicular Helper T cells (Tfh), Monocytes and M2 Macrophages. In the training set, the 2,000- and 4,000-day survival rates were 48.3% and 10.7% in the low-risk group (N = 234), and 42.1% and 7.5% in the high-risk group (N = 214), respectively. In the validation set, the 2,000- and 4,000-day survival rates were 34.8% and 8.6% in the low-risk group (N = 187), and 28.9% and 6.1% in the high-risk group (N = 246), respectively. The prognosis of patients in the high-risk group was worse than that in the low-risk group (P