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The surface strengthening treatment of 304 stainless steel was carried out by using ultrasonic nanocrystal surface modification （UNSM） technology， and the effect of UNSM on the microstructure and mechanical performance of stainless steel was studied. The microstructure evolution and plastic deformation mechanism before and after UNSM treatment were analyzed by transmission electron microscopy （TEM）， electron backscattered diffraction （EBSD） and other characterization methods. The effects of UNSM on surface roughness， microhardness and residual stress of materials were studied by using white light interferometry and microhardness testing. The results show that after UNSM treatment， 304 stainless steel undergoes severe plastic deformation on the surface， resulting in an increase in dislocation density and significant grain refinement， and nanocrystals are generated on the near-surface region， forming a hardened layer about 300 μm deep. The deformation induces austenite-to-martensite transformation and introduces the residual compressive stress of about 900 MPa on the surface. The microhardness of the material increases from 238HV to 511HV， and the surface integrity is significantly improved.

Abstract Background Sjögren’s Syndrome (SS) is a rare chronic autoimmune disorder primarily affecting adult females, characterized by chronic inflammation and salivary and lacrimal gland dysfunction. It is often associated with systemic lupus erythematosus, rheumatoid arthritis and kidney disease, which can lead to increased mortality. Early diagnosis is critical, but traditional methods for diagnosing SS, mainly through histopathological evaluation of salivary gland tissue, have limitations. Methods The study used 100 labial gland biopsy, creating whole-slide images (WSIs) for analysis. The proposed model, named Cell-tissue-graph-based pathological image analysis model (CTG-PAM) and based on graph theory, characterizes single-cell feature, cell-cell feature, and cell-tissue feature. Building upon these features, CTG-PAM achieves cellular-level classification, enabling lymphocyte recognition. Furthermore, it leverages connected component analysis techniques in the cell graph structure to perform SS diagnosis based on lymphocyte counts. Findings CTG-PAM outperforms traditional deep learning methods in diagnosing SS. Its area under the receiver operating characteristic curve (AUC) is 1.0 for the internal validation dataset and 0.8035 for the external test dataset. This indicates high accuracy. The sensitivity of CTG-PAM for the external dataset is 98.21%, while the accuracy is 93.75%. In comparison, the sensitivity and accuracy for traditional deep learning methods (ResNet-50) are lower. The study also shows that CTG-PAM’s diagnostic accuracy is closer to skilled pathologists compared to beginners. Interpretation Our findings indicate that CTG-PAM is a reliable method for diagnosing SS. Additionally, CTG-PAM shows promise in enhancing the prognosis of SS patients and holds significant potential for the differential diagnosis of both non-neoplastic and neoplastic diseases. The AI model potentially extends its application to diagnosing immune cells in tumor microenvironments.

Abstract Background The coronavirus disease 2019 (COVID-19) rapidly spreads worldwide and causes more suffering. The relation about the aggravation of inguinal pain and COVID-19 was unclear in patients with total hip arthroplasty (THA). This study aimed to evaluate the risk of groin pain aggravation in short-term THA patients after COVID-19. Methods Between 2020 and 2022, 129 patients with THA who were affected COVID-19 were enrolled. A short-standardized questionnaire was administered during follow-up to inquire about the aggravation of groin ache before and after SARS-COV-2 affection. Furthermore, we evaluated the potential association between the presence of increased pain and various factors, including age, gender, body mass index, diagnosis, and length of hospital stay. Results The case-crossover study revealed an increased risk of inguinal soreness aggravation when comparing 8 weeks after COVID-19 with 12 weeks before COVID-19 (Relative risk [RR], 9.5; 95% Confidence intervals [CI], 2.259–39.954). For COVID-19 positive patients, multivariate analysis showed length of stay was an independent factor significantly associated with increased risk of aggravation of groin pain (Odds ratio [OR], 1.26; 95%CI, 1.03–1.55, p = 0.027). Conclusion This study confirms the association between COVID-19 and the exacerbation of soreness in the groin region in THA patients and extended length of stay is a possible contributing factor. This study expands the current literature by investigating the risk of aggravation of inguinal pain in patients with THA after COVID-19, providing valuable insights into postoperative outcomes in this specific population. Trial registration This retrospective study was approved by the Institutional Review Board of Shanghai general hospital (No.2023-264).

Machine learning (ML) provides a new surrogate method for investigating groundwater flow dynamics in unsaturated soils. Traditional pure data-driven methods (e.g. deep neural network, DNN) can provide rapid predictions, but they do require sufficient on-site data for accurate training, and lack interpretability to the physical processes within the data. In this paper, we provide a physics and equality-constrained artificial neural network (PECANN), to derive unsaturated infiltration solutions with a small amount of initial and boundary data. PECANN takes the physics-informed neural network (PINN) as a foundation, encodes the unsaturated infiltration physical laws (i.e. Richards equation, RE) into the loss function, and uses the augmented Lagrangian method to constrain the learning process of the solutions of RE by adding stronger penalty for the initial and boundary conditions. Four unsaturated infiltration cases are designed to test the training performance of PECANN, i.e. one-dimensional (1D) steady-state unsaturated infiltration, 1D transient-state infiltration, two-dimensional (2D) transient-state infiltration, and 1D coupled unsaturated infiltration and deformation. The predicted results of PECANN are compared with the finite difference solutions or analytical solutions. The results indicate that PECANN can accurately capture the variations of pressure head during the unsaturated infiltration, and present higher precision and robustness than DNN and PINN. It is also revealed that PECANN can achieve the same accuracy as the finite difference method with fewer initial and boundary training data. Additionally, we investigate the effect of the hyperparameters of PECANN on solving RE problem. PECANN provides an effective tool for simulating unsaturated infiltration.

Abstract Background Controversy remains over whether different surgical approaches exert an impact on the component positioning in total hip arthroplasty. We conducted a retrospective study to reveal the long-term position of prostheses in the first group of patients in China who underwent direct anterior hip arthroplasty. Methods Collected were data from 350 patients who underwent direct anterior hip arthroplasty between 2008 and 2013, including demographic information, imaging data, Harris hip scores, and surgical complications. Variables, measured radiographically or by CT, included hip offset, leg length discrepancy, component position, and stability within one week after surgery and at the last follow-up. The data were subjected to statistical analysis by using paired t-tests and Pearson chi-square tests. Results Data were harvested by follow-up and self-reported questionnaires. The postoperative follow-up lasted for 13.1 years on average (minimum, 10 years; maximum, 15 years), and the overall survival rate of hip prostheses was 96.3%. The mean Harris score at the final follow-up was 91.8 points. After excluding patients with significant preoperative hip deformities, the incidence of postoperative limb inequality (> 5 mm) was 4.9% at the last follow-up, and the incidence of hip offset discrepancy (> 5 mm) was 14.6%. The overall proportion of the acetabular components located in the Lewinnek safe zone was 77.7%, whereas the proportion of femoral prostheses in the safe zone ( 2 mm was 5.1%. Conclusion Direct anterior approach hip arthroplasty could achieve excellent component positioning and long-term prosthesis survival in patients without severe hip deformities.

Summary: While the benefits of physical activity on mental health are well-known, systematic reviews and meta-analyses on its impact on mental illness in adults with COVID-19 are scarce. This study of 25 randomized controlled trials shows that physical activity significantly reduces anxiety (standardized mean difference [SMD] = −0.915; 95% confidence interval [CI] = −1.182 to −0.648; I2 = 82.0%; p 60 min for depression, yield the best outcomes. These results highlight the specific effectiveness of physical activity in alleviating anxiety and depression in COVID-19 patients.

Lipid metabolism disorders lead to lipotoxicity. The hyperlipidemia-induced early stage of renal injury mainly manifests as podocyte damage. CD36 mediates fatty acid uptake and the subsequent accumulation of toxic lipid metabolites, resulting in podocyte lipotoxicity.

Exploring deep learning and its influencing factors is receiving increasing attention. This study examines influencing factors (i.e. self-efficacy, learning engagement, and teacher-student interaction) of high school students' English deep learning in blending courses. Research hypotheses are proposed, and a structural equation model (SEM) of influencing factors of deep learning is built. A sample size of 225 participants was recruited on a voluntary and anonymous basis from a high school in Southwestern China for this study. We find: (1) self-efficacy, learning engagement and teacher-student interaction all predict and have positive impacts on English deep learning; (2) self-efficacy has a positive impact on learning engagement and teacher-student interaction; (3) teacher-student interaction has a positive impact on learning engagement. The findings underscore the importance of self-efficacy, learning engagement, and teacher-student interaction for EFL high school learners to achieve deep learning in a blending context. Drawn from these findings, pedagogical implications for promoting these learners' deep learning are provided.

This work proposes a recorded recurrent twin delayed deep deterministic (RRTD3) policy gradient algorithm to solve the challenge of constructing guidance laws for intercepting endoatmospheric maneuvering missiles with uncertainties and observation noise. The attack-defense engagement scenario is modeled as a partially observable Markov decision process (POMDP). Given the benefits of recurrent neural networks (RNNs) in processing sequence information, an RNN layer is incorporated into the agent’s policy network to alleviate the bottleneck of traditional deep reinforcement learning methods while dealing with POMDPs. The measurements from the interceptor’s seeker during each guidance cycle are combined into one sequence as the input to the policy network since the detection frequency of an interceptor is usually higher than its guidance frequency. During training, the hidden states of the RNN layer in the policy network are recorded to overcome the partially observable problem that this RNN layer causes inside the agent. The training curves show that the proposed RRTD3 successfully enhances data efficiency, training speed, and training stability. The test results confirm the advantages of the RRTD3-based guidance laws over some conventional guidance laws.

In many cases, the 'labeled' outcome is difficult to observe and may require a complicated or expensive procedure, and the predictor information is easy to be obtained. We propose a semi-supervised estimator for the one-dimensional varying coefficient regression model which improves the conventional supervised estimator by using the unlabeled data efficiently. The semi-supervised estimator is proposed by introducing the intercept model and its asymptotic properties are proven. The Monte Carlo simulation studies and a real data example are conducted to examine the finite sample performance of the proposed procedure.

Abstract Single-cell technologies enable the dynamic analyses of cell fate mapping. However, capturing the gene regulatory relationships and identifying the driver factors that control cell fate decisions are still challenging. We present CEFCON, a network-based framework that first uses a graph neural network with attention mechanism to infer a cell-lineage-specific gene regulatory network (GRN) from single-cell RNA-sequencing data, and then models cell fate dynamics through network control theory to identify driver regulators and the associated gene modules, revealing their critical biological processes related to cell states. Extensive benchmarking tests consistently demonstrated the superiority of CEFCON in GRN construction, driver regulator identification, and gene module identification over baseline methods. When applied to the mouse hematopoietic stem cell differentiation data, CEFCON successfully identified driver regulators for three developmental lineages, which offered useful insights into their differentiation from a network control perspective. Overall, CEFCON provides a valuable tool for studying the underlying mechanisms of cell fate decisions from single-cell RNA-seq data.

Semi-macrosegregation in continuously cast blooms and its associated carbide banding defects in hot-rolled bars were investigated to explore the characteristics, evolution, and control methods in high-carbon chromium bearing steel. It was found that the semi-macrosegregation exists only in the central equiaxed crystal zone of the blooms, and C, Cr solute elements inside show typical spot-like positive segregation. Carbide banding appears as white bright bands along the rolling direction, composed of high number density M3C carbide particles, with a significant effect on the mechanical properties of the rolled products. The hereditary evolution from semi-macrosegregation to carbide banding was established by means of banded segregation. Morphologically, normal semi-macrosegregation and carbide-typed semi-macrosegregation are formed based on whether Cr-rich primary carbides are generated. These two types of semi-macrosegregation evolved into two kinds of carbide banding respectively, that is, dark bands and bright bands defined according to the ability to reflect light under OM. To alleviate the carbide banding defect in rolling products of high-carbon chromium bearing steel, the morphology and area ratio of equiaxed crystals in continuous cast blooms should be modified. It is observed that the equiaxed crystal ratio in the bloom can be decreased from 62.5 % to 28 % by increasing liquid steel superheat and inhibiting superheat dissipation in the mold through casting practices. Additionally, the morphology of equiaxed crystals is related to the as-cast solidification structure, and when the equiaxed crystal ratio is large, the equiaxed crystals turn out to be coarser ones with only cellular primary dendrite; when the columnar crystals are longer, however, the equiaxed crystals appear as a dendrite structure with fine secondary dendrites. As a result, semi-macrosegregation was improved obviously with the fine dendrite structure splitting the local residual liquid phase, leading to the decrease of the maximum width of carbide banding from 268.41 μm to 73.72 μm, and the increase of mechanical properties in the rolled bar of high-carbon chromium bearing steel.

Pine wilt disease (PWD) is one of the most destructive diseases for pine trees, causing a significant effect on ecological resources. The identification of PWD-infected trees is an effective approach for disease control. However, the effects of complex environments and the multi-scale features of PWD trees hinder detection performance. To address these issues, this study proposes a detection model based on PWD-YOLOv8 by utilizing aerial images. In particular, the coordinate attention (CA) and convolutional block attention module (CBAM) mechanisms are combined with YOLOv8 to enhance feature extraction. The bidirectional feature pyramid network (BiFPN) structure is used to strengthen feature fusion and recognition capability for small-scale diseased trees. Meanwhile, the lightweight FasterBlock structure and efficient multi-scale attention (EMA) mechanism are employed to optimize the C2f module. In addition, the Inner-SIoU loss function is introduced to seamlessly improve model accuracy and reduce missing rates. The experiment showed that the proposed PWD-YOLOv8n algorithm outperformed conventional target-detection models on the validation set (mAP@0.5 = 94.3%, precision = 87.9%, recall = 87.0%, missing rate = 6.6%; model size = 4.8 MB). Therefore, the proposed PWD-YOLOv8n model demonstrates significant superiority in diseased-tree detection. It not only enhances detection efficiency and accuracy but also provides important technical support for forest disease control and prevention.

There are a large number of rock landslide disasters developed in the first section of the Three Gorges Reservoir area, many of which are very hidden and have not been identified. In this paper, taking the left bank of Xietan River in the first section of the Three Gorges Reservoir as the study area, taking the only bedding rock landslide in Kamenziwan as an example, the failure mode of bedding rock landslide in this area is summarized on the basis of analyzing its genesis mechanism. Nine evaluation index factors, including elevation, slope aspect, slope, relief, plane curvature, profile curvature, formation lithology, distance from river and distance from road, as well as suspected hidden danger points of landslide disaster are determined. These hidden danger points are taken as landslide samples. Automatic Landside Susceptibility Assessment Model (ALSA) was used to carry out landslide Susceptibility zoning in the study area. Finally, ROC curve and field review were used to verify the reliability of the evaluation results. The prediction results show that the extremely high and highly prone areas of bedding rock landslides in the study area are distributed in a plane shape, mainly concentrated in the middle Jurassic Upper Shaximiao Formation purplish red mudstone intercalated sandstone, and the northwest slope direction near the reservoir bank area. Field verification shows that the results of prone zoning are consistent with the distribution law of landslide failure mode, indicating that the landslide susceptibility results obtained by selecting landslide samples based on landslide failure mode can also reflect the spatial distribution law of landslide probability in the study area on the whole, and can be used as a substitute scheme in the absence of accurate landslide samples. The above research results provide theoretical support and scientific basis for selecting landslide samples to carry out vulnerability assessment based on landslide failure mode.

Linear discriminant analysis (LDA) is one of the most widely used methods in discriminant classification and pattern recognition. However, with the rapid development of information science and technology, the dimensionality of collected data is high or ultrahigh, which causes the failure of LDA. To address this issue, a feature screening procedure based on the Fisher's linear projection and the marginal score test is proposed to deal with the ultrahigh-dimensional binary classification problem. The sure screening property is established to ensure that the important features could be retained and the irrelevant predictors could be eliminated. The finite sample properties of the proposed procedure are assessed by Monte Carlo simulation studies and a real-life data example.

Sulfate is quite crucial to influence Tc/Re volatilization during nuclear liquid waste vitrification, but the mechanism of the effect is not yet clear. In this paper, the simplified SiO2-KReO4-K2SO4 system was used to study the effect of sulfate on Re volatilization during vitrification, and the heat-treated samples were characterized with XRD, TG-DSC and ICP-AES. The results show that sulfate hardly effects Re volatilization, when the mass ratio of Re to S ≥1.80; whereas, when the mass ratio of Re to S

Abstract Aims/Introduction In this study, we aimed to investigate the relationships between gamma‐glutamyl transferase (GGT) and fasting blood glucose (FBG) during a 6‐year follow‐up study of participants, and to determine whether GGT is a risk factor for FBG. Materials and Methods A total of 1,369 individuals from the health examination survey in the urban area of Xuzhou, central China, were followed up for 6 years. The patients were divided into four groups based on their baseline GGT levels (in quartiles). The one‐way analysis of variance (anova) method was used to compare the differences between the variables and baseline. The relationship between GGT and FBG levels was investigated using repeated measurements anova. Results The grouping of baseline GGT levels affected the changes in blood glucose during the 6‐year follow‐up study. In the GGT quartile subgroups, the annual mean increase in FBG levels showed a positive relationship with baseline GGT levels. This trend was even more aggregated in the highest baseline GGT group. Interactions among time course, baseline FBG and GGT groups in different participants together affected the change of FBG levels during the follow‐up period. The repeated measures anova suggested that different baseline GGT groups were still significantly associated with increased FBG levels. GGT is a risk factor that affects FBG levels(P

Federated learning (FL) can guarantee privacy by allowing local users only upload their training models to central server (CS). However, the existence of Byzantine or inactive users may cause model corruption or inactively participation in FL. In this paper, a game theory based detection and incentive method is designed for Byzantine and inactive users. Specifically, a differential aggregate gradient descent (DAGD) algorithm is adopted to improve the stability and fasten the convergence. Then the loss function is modified by considering Byzantine and inactive users. For Byzantine users, a random Euclidean distance (RED) voting method is designed to identify Byzantine users, and after identification, Byzantine users are motivated by the game theory. For inactive users, when inactive users are detected by contribution, Nash equilibrium with a mixed strategy is used to solve the problem of inactive users’ participation in FL. Extensive experimental results show that Byzantine users and inactive users can be detected and motivated by the algorithm. Meanwhile, compared with other methods, the accuracy of the optimized model is improved with reduced training time.

Abstract Background Previous studies have indicated an association between birth weight (BW) and type 2 diabetes mellitus (T2DM), but few studies have explored this relationship under different conditions of obesity in adulthood. Methods A total of 4,005 individuals from ten provinces of China were randomly selected to participate in this study. We used a questionnaire to collect age, BW, current weight, height, T2DM history, age at T2DM diagnosis, and other variables. The participants were divided into three groups were according to BW trisection (BW ≤ 2500 g for the lower BW group, 2500 g 3500 g for the higher BW group). The cutoff of overweight and obesity were 25 kg/m2 and 28 kg/m2, respectively. Results The prevalence rates of T2DM among women with lower BW, normal BW and higher BW were 5.2%, 3.6% and 2.0%, respectively. The obesity prevalence rates in the lower BW, normal BW and higher BW groups were 8.1%, 6.7% and 9.0%, respectively. In the obese population, we did not find a relationship between BW and T2DM, but in the nonobese population, we found that with increasing BW, the risk of developing T2DM was reduced. Obese status in adulthood modified the association between BW and the risk of T2DM. Conclusion There is a “U” shape association between BW and risk of adulthood obesity in Chinese women, but this trend is not existed between BW and risk of developing T2DM. In non-overweight females, the risk of developing T2DM decreased with increasing BW, but this trend was not observed in overweight females.

With the deepening of the coupling degree between power network and natural gas network, the traditional independent optimal scheduling of transmission and distribution integrated electricity–gas systems cannot make full use of the flexible control ability of various resources and the complementary support capabilities between different energy systems. Therefore, this paper puts forward a cooperative optimization scheduling model of transmission–distribution​ integrated electricity–gas systems (TD-IEGS). The coordinated operation of multi-energy coupling devices including gas-fired units, combined heat and power (CHP) units and electric boilers (EB) are considered in this paper to enhance system operation flexibility and economy. The second order cone relaxation (SOCR) method is utilized to tackle the non-linear power and gas flow constraints, and the proposed mixed-integer non-linear programming co-optimization scheduling model is transformed into a mixed integer second order cone programming model (MISOCP). Case studies demonstrate that compared with the independent operation mode, the proposed co-optimization scheduling model of TD-IEGS can reduce total operation cost by about 6.7% under 50% wind power penetration level. Moreover, the co-optimization of TD-IEGS can promote wind power utilization especially in a high share of wind energy.

ABSTRACT: Objectives: Carbapenem-resistant Acinetobacter baumannii (CRAB) is a prevalent pathogen contributing to hospital infections. Pulsed-field gel electrophoresis (PFGE), multilocus sequence typing (MLST) and core-genome MLST (cgMLST) are frequently used methods to illuminate the nosocomial transmission of CRAB. In this study, we compared the discriminatory power of the three typing methods. Methods: Antimicrobial susceptibility tests were performed by the broth microdilution and Vitek2 methods. PFGE, MLST and cgMLST were conducted to determine the clonality and phylogenetic relationship of the strains. Whole-genome sequence data were acquired by an Illumina HiSeq 2000, and cgMLST was analysed by the Ridom SeqSphere+ v.7.2.3 software. Results: A total of 149 carbapenem-resistant A. baumannii isolates had 15 different PFGE profiles (A-O type), and 73 of the isolates had related subtypes (A1 and A2), accounting for the majority of type A isolates. The maximum-likelihood phylogenetic analysis based on the cgMLST genes grouped the same PFGE clonal pattern A into nine different clusters. ST_Pasteur grouped all the strains into ST2, whereas ST_Oxford grouped the PFGE clonal pattern A isolates into six STs. In addition, the gdhB allele in the ST_Oxford scheme had two copies in five strains, which complicated the ST_Oxford typing. Conclusions: cgMLST was more discriminant than PFGE and MLST. CgMLST is the most suitable and comprehensive method for genotyping A. baumannii in surveillance and epidemiological research.

The unpredictable biological behavior and tumor heterogeneity of metastatic renal cell carcinoma (mRCC) cause significant differences in axitinib efficacy. The aim of this study is to establish a predictive model based on clinicopathological features to screen patients with mRCC who can benefit from axitinib treatment. A total of 44 patients with mRCC were enrolled and divided into the training set and validation set. In the training set, variables related with the therapeutic efficacy of second-line treatment with axitinib were screened through univariate Cox proportional hazards regression and least absolute shrinkage and selection operator analyses. A predictive model was subsequently established to assess the therapeutic efficacy of second-line treatment with axitinib. The predictive performance of the model was evaluated by analyzing the concordance index and time-dependent receiver operating characteristic, calibration, and decision curves. The accuracy of the model was similarly verified in the validation set. The International Metastatic RCC Database Consortium (IMDC) grade, albumin, calcium, and adverse reaction grade were identified as the best predictors of the efficacy of second-line axitinib treatment. Adverse reaction grade was an independent prognostic index that correlated with the therapeutic effects of second-line treatment with axitinib. Concordance index value of the model was 0.84. Area under curve values for the prediction of 3-, 6-, and 12-month progression-free survival after axitinib treatment were 0.975, 0.909, and 0.911, respectively. The calibration curve showed a good fit between the predicted and actual probabilities of progression-free survival at 3, 6, and 12 months. The results were verified in the validation set. Decision curve analysis revealed that the nomogram based on a combination of four clinical parameters (IMDC grade, albumin, calcium, and adverse reaction grade) had more net benefit than adverse reaction grade alone. Our predictive model can be useful for clinicians to identify patients with mRCC who can benefit from second-line treatment with axitinib.

Expansion-break connecting rods are high-end products for automotive precision transmission. They need to have high strength, high toughness, and brittle cleavage fracture characteristics during cracking processing. Continuous casting production of nonquenched and tempered steel for high-carbon, sulfur-containing, free-cutting, and expanding connecting rods is the current trend for efficient production. Based on the typical bloom continuous casting process and analysis of the as-cast structure and composition uniformity, the common unqualified fracture morphology due to as-cast hereditary factors were studied. Taking the typical German C70S6 steel as an example, a 250 mm × 280 mm section-curved continuous caster was adopted to study the as-cast macrostructure and dendrite morphology of the bloom casting with popular mold electromagnetic stirring. Moreover, the chemical distribution at different crystal regions was studied. Results show that the common center shrinkage defects of high-carbon steel continuous casting blooms are under control, which are beneficial to improve the qualified rate of internal flaw detection for their subsequent hot-rolled bars. However, it is found that there are obvious negative segregation white bands of carbon and sulfur and the deflection of columnar crystals in the solidification front of the initial solidified shell. Both the image analysis of the metallographic sample and solidification simulation by a phase-field method show that this columnar crystal has countercurrent growth characteristics. In addition, its deflection angle is the result of the primary dendrite tip growing in the opposite direction to the swirling fluid flow. In the center of the narrow surface, the measured primary dendrite deflection angle of the columnar crystal region is between −7° and 27°. EDS was used to further detect the distribution of the main alloying elements Si, Mn, and Mo in the different crystal regions of the bloom casting, revealing the segregation characteristics of the as-cast product and differences of each solute element. Finally, the heredity of this as-cast structure and composition segregation on the structure of subsequent hot-rolled bars and connecting rods was discussed. Moreover, the influence of the fracture inconsistency of its expansion and fracture processing were explored. It is pointed out that the as-cast quality control from the very beginning of casting has special significance in meeting the requirement of both processing and service properties of these high-grade high strength low alloy steels.

Maintaining good connectivity is a major concern when constructing a robust flying mesh network, known as FlyMesh. In a FlyMesh, multiple unmanned aerial vehicles (UAVs) collaborate to provide continuous network service for mobile devices on the ground. To determine the connectivity probability of the aerial link between two UAVs, the Poisson point process (PPP) is used to describe the spatial distribution of UAVs equipped with omnidirectional antennas. However, the PPP fails to reflect the fact that there is a minimum distance restriction between two neighboring UAVs. In this paper, the β-Ginibre point process (β-GPP) is adopted to model the spatial distribution of UAVs, with β representing the repulsion between nearby UAVs. Additionally, a large-scale fading method is used to model the route channel between UAVs equipped with directional antennas, allowing the monitoring of the impact of signal interference on network connectivity. Based on the β-GPP model, an analytical expression for the connectivity probability is derived. Numerical tests are conducted to demonstrate the effects of repulsion factor β, UAV intensity ρ, and beamwidth θ on network connectivity. The results indicate that an increase in UAV intensity decreases network connectivity when the repulsion factor β remains constant. These findings provide valuable insights for enhancing the service quality of the FlyMesh.

Federated learning has become increasingly important for modern machine learning, especially for data privacy sensitive scenarios. Existing federated learning mainly adopts a central server-based network topology, however, the training process of which is susceptible to the central node. To address this problem, this article proposed a decentralized federated learning method based on node selection and knowledge distillation. Specifically, the central node in this method is variable, and it is selected by the indicator interaction between nodes. Meanwhile, the knowledge distillation mechanism is added to make the student model as close as possible to the teacher’s network and ensure the model’s accuracy. The experiments were conducted on the public MNIST, CIFAR-10, and FEMNIST datasets for both the Independent Identically Distribution (IID) setting and the non-IID setting. Numerical results show that the proposed method can achieve an improved accuracy as compared to the centralized federated learning method, and the computing time is reduced greatly with less accuracy loss as compared to the blockchain decentralized federated learning. Therefore, the proposed method guarantees the model effect while meeting the individual model requirements of each node and reducing the running time.

Hypervirulence and carbapenem resistance have emerged as two distinct evolutionary directions for Klebsiella pneumoniae, which pose a great threat in clinical settings. Multiple virulence factors contribute to hypervirulence, and the mechanisms of carbapenem resistance are complicated. However, more and more K. pneumoniae strains have been identified in recent years integrating both phenotypes, resulting in devastating clinical outcomes. Hypervirulent and carbapenem-resistant K. pneumoniae (CR-hvKP) emerged in the early 2010s and thereafter have become increasingly prevalent. CR-hvKP are primarily prevalent in Asia, especially China, but are reported all over the world. Mechanisms for the emergence of CR-hvKP can be summarised by three patterns: (i) carbapenem-resistant K. pneumoniae (CRKP) acquiring a hypervirulent phenotype; (ii) hypervirulent K. pneumoniae (hvKP) acquiring a carbapenem-resistant phenotype; and (iii) K. pneumoniae acquiring both a carbapenem resistance and hypervirulence hybrid plasmid. With their global dissemination, continued surveillance of the emergence of CR-hvKP should be more highly prioritised.

ObjectiveTo investigate the application value of narrative medical model in communication with cancer patients with low education level.MethodsRetrospective analysis was performed on 80 cancer patients with low education level who were hospitalized in West China Hospital of Sichuan University from March 2019 to April 2020. They were randomly divided into control group （n=40） and observation group （n=40）. The control group received conventional nursing intervention， while the observation group received narrative medical nursing mode intervention. Both groups received intervention for 6 months. The Self-rating Anxiety Scale （SAS）， Self-rating Depression Scale （SDS）， Morisky Medication Adherence Scale （MMAS-8）， Social Support Rating Scale （SSRS） and self-designed knowledge of the disease and cognition questionnaire were used for evaluation before and after intervention.ResultsAfter intervention， the SAS and SDS scores of observation group were lower than those of control group （t=7.493， 8.314， P

In this paper, a space-time absolute nodal coordinate formulation cable (SAC) element forming technique based on the Lagrange family of shape functions is proposed. Two distinct SAC elements, each with a distinct spatial shape function, have been generated by this method. Moreover, the external forces such as the bending moment and the air resistance formula have been accounted for. The Lagrange multiplier method, along with the concepts of replacement constraint and supplementary constraint, has been employed to provide a solution for the dynamics of constrained mechanical systems. Additionally, a constraint conversion strategy has been suggested. The solver has been constructed through Hamilton’s law of varying action. The space-time finite element method is used to solve dynamic problems, employing the Newton algorithm and quasi-Newton algorithm. The accuracy and efficiency of the solution has been verified by three simulations and one experiment. The circle-bending static simulation and the double-ended velocity impact dynamic simulation demonstrate the accuracy of the two elements. The correlation between statics and dynamics has been studied for different discretization methods and different solvers’ calculation accuracy and efficiency. Different modeling methods, time steps, order and the application of the quasi-Newton method all have a bearing on the efficiency of the solution. Finally, a comparison with an experiment in the free-pendulum simulation reveals the capability of this model to simulate dynamic problems with air resistance.

Pine wilt disease (PWD) has become increasingly serious recently and causes great damage to the world’s pine forest resources. The use of unmanned aerial vehicle (UAV)-based remote sensing helps to identify pine nematode trees in time and has become a feasible and effective approach to precisely monitor PWD infection. However, a rapid and high-accuracy detection approach has not been well established in a complex terrain environment. To this end, a deep learning-based pine nematode tree identification method is proposed by fusing visible and multispectral imagery. A UAV equipped with a multispectral camera and a visible camera was used to obtain imagery, where multispectral imagery includes six bands, i.e., red, green, blue, near-infrared, red edge and red edge 750 nm. Two vegetation indexes, NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge Index) are extracted as a typical feature according to the reflectance of infected trees in different spectral bands. The YOLOv5 (You Only Look Once v5)-based detection algorithm is adopted and optimized from different aspects to realize the identification of infected pine trees with high detection speed and accuracy. e.g., GhostNet is adopted to reduce the number of model parameters and improve the detection speed; a module combining a CBAM (Convolutional Block Attention Module) and a CA (Coordinate Attention) mechanism is designed to improve the feature extraction for small-scale pine nematode trees; Transformer module and BiFPN (Bidirectional Feature Pyramid Network) structure are applied to improve the feature fusion capability. The experiments show that the mAP@0.5 of the improved YOLOv5 model is 98.7%, the precision is 98.1%, the recall is 97.3%, the average detection speed of single imagery is 0.067 s, and the model size is 46.69 MB. All these metrics outperform other comparison methods. Therefore, the proposed method can achieve a fast and accurate detection of pine nematode trees, providing effective technical support for the control of a pine nematode epidemic.

Typical engineering cable structures, such as high-voltage wire and wire rope, usually bring a damping effect which cannot be ignored due to the technological problems of manufacturing. For such problems, especially the damping of cable structures undergoing large displacement and severe deformation, few studies have been reported in the past. In this work, the fractional derivative viscosity model is introduced into the cables described by the absolute nodal coordinate formulation. The computer implementation algorithm of the proposed cable damping model is given based on the three-parameter fractional derivative model. Two numerical examples demonstrate the effectiveness and convergence property of the proposed cable damping model. An experiment is proposed in which a wire is tensioned and released. Configurations are captured by the high-speed camera and compared with the results obtained from the numerical simulation. The agreement of the simulation and experimental results validates the proposed cable damping in application.

The pine wilt disease (PWD) is one of the most dangerous and destructive diseases to coniferous forests. The rapid spread trend and strong destruction directly threaten the security of forests. The complex spread pattern and the hard labor process of diagnosis call for an effective way to detect the infected areas. In this paper, an airborne edge-computing and lightweight deep learning based system are designed for PWD detection by using imagery sensors. Unmanned aerial vehicle (UAV) is firstly utilized to realize a large-scale coverage of forests, which can substantially reduce the hard labor. Except for infected trees, a large number of irrelevant images are also acquired by the UAV, which will overload the burden of process and transmission. Then a lightweight improved YOLOv4-Tiny based method (named as YOLOv4-Tiny-3Layers) is proposed to filter these uninterested images by leveraging the computation capability of edge computing, which can realize a fast coarse-grained detection with a low missing rate. Finally, all the remaining images are transmitted to the ground workstation for the final fine-grained detection. Experimental results show that the proposed system can implement a fast detection with superior performance as compared to other methods, which helps to detect the infected pine trees in a quick manner.

Abstract Since the outbreak of COVID-19, many COVID-19 research studies have proposed different models for predicting the trend of COVID-19. Among them, the prediction model based on mathematical epidemiology (SIR) is the most widely used, but most of these models are adapted in special situations based on various assumptions. In this study, a general adapted time-window based SIR prediction model is proposed, which is characterized by introducing a time window mechanism for dynamic data analysis and using machine learning method predicts the basic reproduction number and the exponential growth rate of the epidemic. We analyzed COVID-19 data from February to July 2020 in seven countries–––China, South Korea, Italy, Spain, Brazil, Germany and France, and the numerical results showed that the framework can effectively measure the real-time changes of the parameters during the epidemic, and error rate of predicting the number of COVID-19 infections in a single day is within 5%.

ABSTRACT Cefiderocol is a novel siderophore cephalosporin exhibiting potent antimicrobial activities. Although cefiderocol has not been approved in China, resistance is emerging. A multicenter study was performed to evaluate the cefiderocol resistance in carbapenem-resistant Klebsiella pneumoniae (CRKP) strains from bloodstream infections in patients with hematologic malignancies in China. Clinical data analysis and whole-genome sequencing were conducted for collected cefiderocol-resistant CRKP strains. CRISPR-Cas9 system was employed to construct site-specific mutagenesis for gene cirA. Plasmid curing and cloning were performed to assess the effect of β-lactamases on cefiderocol resistance. Total 86 CRKP strains were collected. The MICs of cefiderocol ranged from 0.06 to >256 mg/L. Among four cefiderocol-nonsusceptible strains (4/86, 4.7%), two cefiderocol-resistant strains AR8538 (MIC = 32 mg/L) and AR8416 (MIC > 256 mg/L) were isolated from two patients with acute lymphocytic leukemia (frequency of resistance, 2/86, 2.3%). Metallo- and serine-β-lactamase inhibitors addition would decrease the MIC of cefiderocol from 32 to 1 mg/L in AR8538, which harbors blaSHV-12, blaDHA-1, and two copies of blaNDM-1 in different plasmids. Avibactam did not impact cefiderocol susceptibility of AR8416, which produces NDM-5. However, we found a deficient CirA in AR8416. Using the same K serotype strain D3, we proved CirA deficiency or carrying NDM individually reduced cefiderocol susceptibility, but their simultaneously existence rendered a high-level cefiderocol resistance. In summary, the resistance of CRKP against cefiderocol is mediated by multiple factors, including the deficiency of CirA, metallo- or serine-β-lactamases, while a high-level cefiderocol resistance could be rendered by the combined effect of NDM expression and CirA deficiency. IMPORTANCE Cefiderocol-resistant CRKP strains are emerging in bloodstream infections in Chinese patients with hematologic malignancies, although cefiderocol has not been approved for clinical use in China. Our study proved that the resistance of CRKP against cefiderocol is mediated by multiple factors, including the deficiency of CirA, metallo- or serine-β-lactamases, while a high-level cefiderocol resistance could be rendered by the combined effect of NDM expression and CirA deficiency. As NDM production is one of the most critical mechanisms resulting in carbapenem resistance, it would pose great challenges on the clinical efficacy of cefiderocol in future.

Abstract Background Eimeria necatrix, the most highly pathogenic coccidian in chicken small intestines, can cause high morbidity and mortality in susceptible birds and devastating economic losses in poultry production, but the underlying molecular mechanisms in interaction between chicken and E. necatrix are not entirely revealed. Accumulating evidence shows that the long-non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) are key regulators in various infectious diseases. However, the expression profiles and roles of these two non-coding RNAs (ncRNAs) during E. necatrix infection are still unclear. Methods The expression profiles of mRNAs, lncRNAs and circRNAs in mid-segments of chicken small intestines at 108 h post-infection (pi) with E. necatrix were analyzed by using the RNA-seq technique. Results After strict filtering of raw data, we putatively identified 49,183 mRNAs, 818 lncRNAs and 4153 circRNAs. The obtained lncRNAs were classified into four types, including 228 (27.87%) intergenic, 67 (8.19%) intronic, 166 (20.29%) anti-sense and 357 (43.64%) sense-overlapping lncRNAs; of these, 571 were found to be novel. Five types were also predicted for putative circRNAs, including 180 exonic, 54 intronic, 113 antisense, 109 intergenic and 3697 sense-overlapping circRNAs. Eimeria necatrix infection significantly altered the expression of 1543 mRNAs (707 upregulated and 836 downregulated), 95 lncRNAs (49 upregulated and 46 downregulated) and 13 circRNAs (9 upregulated and 4 downregulated). Target predictions revealed that 38 aberrantly expressed lncRNAs would cis-regulate 73 mRNAs, and 1453 mRNAs could be trans-regulated by 87 differentially regulated lncRNAs. Additionally, 109 potential sponging miRNAs were also identified for 9 circRNAs. GO and KEGG enrichment analysis of target mRNAs for lncRNAs, and sponging miRNA targets and source genes for circRNAs identified associations of both lncRNAs and circRNAs with host immune defense and pathogenesis during E. necatrix infection. Conclusions To the best of our knowledge, the present study provides the first genome-wide analysis of mRNAs, lncRNAs and circRNAs in chicken small intestines infected with E. necatrix. The obtained data will offer novel clues for exploring the interaction mechanisms between chickens and Eimeria spp.

Context Ganoderma lucidum (Leyss.ex Fr.) Karst (Ganodermataceae) is a fungus that has been used in traditional Chinese medicine. Objective This is the first investigation of the lipid-lowering and anti-atherosclerotic effects of Ganoderma lucidum spore ethanol extract (EEG) in hyperlipidemic rabbits. Materials and methods Fifty-four Japanese rabbits were randomly divided into six groups (n = 9): control, model, atorvastatin and three EEG groups (6, 24 and 96 mg/kg/day, p.o.). Control group was administered a normal diet and other groups were administered a high-fat diet to induce hyperlipidaemia and atherosclerosis for 14 weeks. During this time, lipid profiles were recorded; lipid testing and histopathological examination of aorta and liver were conducted. LXRα and its downstream genes expression in the liver and small intestine were examined. The effect of EEG on macrophage cholesterol efflux and ABCA1/G1 expression was observed under silenced LXRα expression. Results EEG reduced serum cholesterol (20.33 ± 3.62 mmol/L vs 34.56 ± 8.27 mmol/L for the model group) and LDL-C, reduced the area of arterial plaques (24.8 ± 10% vs 53.9 ± 15.2% for the model group) and Intima/Medium thickness ratio, increased faecal bile acid content, upregulated LXRα, CYP7A1, ABCA1/G1, ABCG5/G8 expression in the liver, small intestine and macrophages. After silencing LXRα in macrophages, the ability of EEG to promote cholesterol efflux was inhibited. Discussion and conclusion EEG exert lipid-lowering and anti-atherosclerotic effects via upregulating expression of LXRα and downstream genes associated with reverse cholesterol transport and metabolism. However, whether PPARα/γ are involved in the up-regulation of LXR expression by EEG remains to be elucidated.

Diabetes mellitus has become a major global health issue. Currently, the use of antibiotics remains the best foundational strategy in the control of diabetic foot infections. However, the lack of accurate identification of pathogens and the empirical use of antibiotics at early stages of infection represents a non-targeted treatment approach with a poor curative effect that may increase the of bacterial drug resistance. Therefore, the timely identification of drug resistant bacteria is the key to increasing the efficacy of treatments for diabetic foot infections. The traditional identification method is based on bacterial morphology, cell physiology, and biochemistry. Despite the simplicity and low costs associated with this method, it is time-consuming and has limited clinical value, which delays early diagnosis and treatment. In the recent years, MALDI-TOF MS has emerged as a promising new technology in the field of clinical microbial identification. In this study, we developed a strategy for the identification of drug resistance in the diagnosis of diabetic foot infections using a combination of macro-proteomics and MALDI MS analysis. The macro-proteomics result was utilized to determine the differential proteins in the resistance group and the corresponding peptide fragments were used as the finger print in a MALDI MS analysis. This strategy was successfully used in the research of drug resistance in patients with diabetic foot infections and achieved several biomarkers that could be used as a finger print for 4 different drugs, including ceftazidime, piperacillin, levofloxacin, and tetracycline. This method can quickly confirm the drug resistance of clinical diabetic foot infections, which can help aid in the early treatment of patients.

Objective Secondary infection, especially bloodstream infection, is an important cause of death in critically ill patients with COVID-19. We aimed to describe secondary bloodstream infection (SBI) in critically ill adults with COVID-19 in the intensive care unit (ICU) and to explore risk factors related to SBI. Methods We reviewed all SBI cases among critically ill patients with COVID-19 from 12 February 2020 to 24 March 2020 in the COVID-19 ICU of Jingmen First People's Hospital. We compared risk factors associated with bloodstream infection in this study. All SBIs were confirmed by blood culture. Results We identified five cases of SBI among the 32 patients: three with Enterococcus faecium , one mixed septicemia ( E. faecium and Candida albicans ), and one C. parapsilosis . There were no significant differences between the SBI group and non-SBI group. Significant risk factors for SBI were extracorporeal membrane oxygenation, central venous catheter, indwelling urethral catheter, and nasogastric tube. Conclusions Our findings confirmed that the incidence of secondary infection, particularly SBI, and mortality are high among critically ill patients with COVID-19. We showed that long-term hospitalization and invasive procedures such as tracheotomy, central venous catheter, indwelling urethral catheter, and nasogastric tube are risk factors for SBI and other complications.

As a matrix metalloproteinase, the abnormal expression of MMP2 is associated with multiple biological processes, including tissue remodeling and cancer progression. Therefore, spatial analysis of MMP2 protein in tissues can be used as an important approach to evaluate the expression distribution of MMP2 in complex tissue environments, which will help the diagnosis and treatment of various diseases, including tissue or organ injuries. Moreover, this analysis will also help the evaluation of prognoses. However, MMP2 is difficult to be spatially determined by MALDI TOF mass spectrometry due to its large molecular weight (over 72 KD) and low content. Therefore, a new method should be developed to help this detection. Here, we have designed a specific MMP2 probe that closely binds to MMP2 protein in tissue. This probe has a Cl on Tyr at the terminal, which can provide two isotope peaks to help the accuracy quantitative of MMP2 protein. Based on this, we used the probe to determine the spatial expression of MMP2 in the tissues based on MALDI TOF mass spectrometry. This approach may help to study the influence of multifunctional proteases on the degree of malignancy in vivo.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is highly prevalent and poses a significant threat to public health. In critically ill patients, gut colonization is considered to be the reservoir of recurrent CRKP infection. Therefore, eliminating CRKP carriage in the intestine is critical for preventing subsequent CRKP infection. In the present study, Lactobacillus plantarum LP1812, a probiotic that can inhibit CRKP in vitro, was used as a candidate probiotic to investigate its efficacy for CRKP anticolonization. Compared with the control, mice fed with 1×10 8 CFU L. plantarum LP1812 exhibited significant CRKP clearance from 1×10 4 CFU/mg to less than 10 CFU/mg in mice feces. Furthermore, 16S RNA gene sequencing revealed that L. plantarum LP1812 modulated mice microbiota by increasing the relative abundance of the genus Halomanas, Blautia, and Holdemania. Further KEGG pathway enrichment analysis revealed that fatty acid-utilizing bacteria, such as acetate-producing Bacteroidetes and Blautia flourished in mice fed with L. plantarum LP1812. Moreover, we found that the concentration of acetic acid was higher in L. plantarum LP1812, which inhibited the growth of K. pneumoniae strains in vitro. Meanwhile, mice intragastrically administered with acetic acid exhibited significantly increased CRKP elimination in vivo. In conclusion, L. plantarum LP1812 is a potential candidate for intestinal CRKP anticolonization by regulating the intestinal microbiota and inhibiting CRKP via increased acetic acid in the intestinal lumen.

The non-Typhi Salmonella (NTS) infection is critical to children’s health, and the ceftriaxone is the important empirical treatment choice. With the increase resistance rate of ceftriaxone in Salmonella, the molecular epidemiology and resistance mechanism of ceftriaxone-resistant Salmonella needs to be studied. From July 2019 to July 2020, a total of 205 NTS isolates were collected, 195 of which (95.1%) were cultured from stool, but 10 isolates were isolated from an extraintestinal site. Serogroup B accounted for the vast majority (137/205) among the isolates. Fifty-three isolates were resistant to ceftriaxone, and 50 were isolated from children younger than 4years of age. The resistance rates for ceftriaxone, ciprofloxacin, and levofloxacin were significantly higher in younger children than the older children. The resistance genes in the ceftriaxone-susceptible isolates were detected by PCR, and ceftriaxone-resistant Salmonella were selected for further whole-genome sequencing. Whole-genome analysis showed that serotype Typhimurium and its monophasic variant was the most prevalent in ceftriaxone-resistant isolates (37/53), which comprised ST34 (33/53), ST19 (2/53), and ST99 (2/53), and they were close related in the phylogenetic tree. However, the other isolates were diverse, which included one Enteritidis (ST11), one Indiana (ST17), one Derby (ST40), four Kentucky (ST198), two Goldcoast (ST2529, ST358), one Muenster (ST321), one Virchow (ST359), one Rissen (ST469), one Kedougou (ST1543), two Uganda (ST684), and one Kottbus (ST8839). Moreover, CTX-M-55 ESBLs production (33/53) was found to be mainly responsible for ceftriaxone resistance, followed by blaCTX-M-65 (12/53), blaCTX-M-14 (4/53), blaCTX-M-9 (2/53), blaCTX-M-64 (1/53), blaCTX-M-130 (1/53), and blaCMY-2 (1/53). ISEcp1, IS903B, IS Kpn26, IS1F, and IS26 were connected to antimicrobial resistance genes transfer. In conclusion, the dissemination of ESBL-producing Salmonella isolates resulted in an increased prevalence of ceftriaxone resistance in young children. The high rate of multidrug resistance should be given additional attention.

Periprosthetic osteolysis (PPO) along with aseptic loosening (AL) caused by wear particles after artificial joint replacement is the key factor in surgical failure and subsequent revision surgery, however, the precise molecular mechanism underlying PPO remains unclear. Aseptic inflammation triggered by metal particles, resulting in the imbalance between bone formation by osteoblasts and bone resorption by osteoclasts may be the decisive factor. Pyroptosis is a new pro-inflammatory pattern of regulated cell death (RCD), mainly mediated by gasdermins (GSDMs) family, among which GSDMD is the best characterized. Recent evidence indicates that activation of NLRP3 inflammasomes and pyroptosis play a pivotal role in the pathological process of PPO. Here, we review the pathological process of PPO, the molecular mechanism of pyroptosis and the interventions to inhibit the inflammation and pyroptosis of different cells during the PPO. Conclusively, this review provides theoretical support for the search for new strategies and new targets for the treatment of PPO by inhibiting pyroptosis and inflammation.

Klebsiella pneumoniae can cause both hospital- and community-acquired clinical infections. Last-line antibiotics against carbapenem-resistant K. pneumoniae (CRKP), such as ceftazidime/avibactam (CZA) and tigecycline (TGC), remain limited as treatment choices. This study aimed to investigate the mechanisms by which CRKP acquires CZA and TGC resistance in vivo under β-lactam antibiotic and TGC exposure. Three CRKP strains (XDX16, XDX31 and XDX51) were consecutively isolated from an inpatient with a urinary tract infection in two months. PFGE and MLST showed that these strains were closely related and belonged to sequence type (ST) 4496, which is a novel ST closely related to ST11. Compared to XDX16 and XDX31, XDX51 developed CZA and TGC resistance. Sequencing showed that double copies of blaKPC-2 were located on a 108 kb IncFII plasmid, increasing blaKPC-2 expression in XDX51. In addition, ramR was interrupted by Insertion sequence (IS) Kpn14 in XDX51, with this strain exhibiting upregulation of ramA, acrA and acrB expression compared with XDX16 and XDX31. Furthermore, LPS analysis suggested that the O-antigen in XDX51 was defective due to ISKpn26 insertion in the rhamnosyl transferase gene wbbL, which slightly reduced TGC susceptibility. In brief, CZA resistance was caused mainly by blaKPC-2 duplication, and TGC resistance was caused by ramR inactivation with additional LPS changes due to IS element insertion in wbbL. Notably, CRKP developed TGC and CZA resistance within one month under TGC and β-lactam treatment without exposure to CZA. The CRKP clone ST4496 has the ability to evolve CZA and TGC resistance rapidly, posing a potential threat to inpatients during antibiotic treatment.

Carbapenem-resistant Klebsiella pneumoniae (CRKP) is an urgent public health problem worldwide, and its rapid evolution in the clinical environment has been a major concern. A total of 99 CRKP isolates spreading in the intensive care unit (ICU) setting were included and subjected to whole-genome sequencing, and their sequence types (STs), serotype loci, and virulence determinants were screened based on genome data. The phylogenetic structure was reconstructed based on the core genome multilocus sequence typing method. Regions of recombination were assessed. Biofilm formation, serum resistance assays, and a Galleria mellonella infection model were used to evaluate strain virulence. A novel ST, designated ST4496, emerged in the ICU and spread for 6 months before its disappearance. ST4496 was closely related to ST11, with only a single-allele variant, and ST11 is the most dominant clinical clone in China. Recombination events occurred at capsule biosynthesis loci and divided the strains of ST11 and its derivative ST4496 into three clusters, including ST11-KL47, ST11-KL64, and ST4496-KL47. The phylogenetic structure indicated that ST11-KL47 was probably the origin of ST11-related strain evolution and presented more diversity in terms of both sequence similarity and phenotypes. ST4496-KL47 cluster strains presented less virulence than ST11-KL64, which was probably one of the factors preventing the former from spreading widely. In conclusion, ST4496-KL47 was probably derived from ST11-KL47 via intraspecies shifting but was less competitive than ST11-KL64, which also evolved from ST11-KL47 and developed increased virulence via capsule biosynthesis locus recombination. ST11-KL64 has the potential to be the predominant CRKP clone in China.