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Diabetes mellitus (DM) is a chronic metabolic disorder that can affect the balance of bone metabolism and bone microenvironment, leading to impaired fracture healing. There are several underlying mechanisms which contributing to the impaired diabetic bone microenvironment such as hyperglycemia, the production of advanced glycation end products (AGEs), inflammation, and oxidative stress, etc. Recent studies have achieved great progress in developing novel smart biomaterials in improving the diabetic bone microenvironment to promote diabetic fracture healing. In this paper, we reviewed the mechanisms on DM-induced impaired fracture healing. Meanwhile, we also summarized the smart biomaterials used to improve the local microenvironment of diabetic fractures healing, which provides a novel perspective for the future treatment of fractures in diabetic patients.

Abstract Thyroid nodule (TN) has been becoming a great concern worldwide due to its high incidence. Although some studies have reported associations between trace elements exposure and the risk of TNs, the linkage was not inconclusive. The present study aimed to identify the association of selected serum trace elements (Ca, Mg, V, Fe, Co, Cu, Zn, Se, Mn and Mo) with TNs among general adults. A cross-sectional study was conducted in January 2021 in Chengdu, China. 1282 subjects completed the questionnaire and gave at least one human biological material after an overnight fast, venous blood, and urine, including 377 TN participants defined through ultrasound. Various trace elements in serum specimens were determined by inductively coupled plasma mass spectrometry. Thyroid functions were tested by chemiluminescent microparticle immunoassay (CMIA). The associations between trace elements levels and the risk of TNs were examined by restricted cubic splines (RCS) regression and bayesian kernel machine regression (BKMR) models. TNs were more common in females (P 0.05) and BKMR models. FT3 or T4 (total or free) increased significantly with increasing total trace elements mixture levels. In TI-RADS-4 TN subjects, TPO-Ab level increased significantly with increasing total trace elements mixture levels in the high-dose range. Ca, Zn, Mo at their 75th percentile showed positive individual effects on TPO-Ab, which was examined to be interactive. The detection of trace elements for TNs in general adults may be of no significance, but once individuals classified as TI-RADS-4 TNs are detected with abnormal TPO-Ab, Ca, Zn and Mo level are recommended to measure. The substantive association on it still needs to be continuously explored in the future.

IntroductionThe extent to which sinks are contaminated by carbapenem-resistant Acinetobacter baumannii (CRAB) in intensive care units (ICUs) and the association between these contaminated sinks and hospital-acquired CRAB infections during the non-cluster period remains largely unknown. Here, we performed a prospective multicenter study in 16 ICUs at 11 tertiary hospitals in Chengdu, China.MethodsWe sampled sinks, collected CRAB clinical isolates, and conducted whole-genome sequencing and analysis.ResultsA total of 789 swabs were collected from 158 sinks, and 16 CRAB isolates were recovered from 16 sinks, resulting in a contamination rate of 10.16%. Twenty-seven clinical isolates were collected during the study period. The majority (97.67%, 42/43) of the CRAB isolates belonged to ST2, and 36 (83.72%) of them had both blaOXA-23 and blaOXA-66. The 43 strains belonged to 12 clones. One certain clone caused multiple contaminations of seven sinks in one GICU. Two clones of ST2 blaOXA-23 and blaOXA-66-carrying sink strains were likely the sources of the two clusters in the two GICUs, respectively. Five ST2 blaOXA-23-carrying isolates were found to be common clones but were recovered from two hospitals.ConclusionThe contamination rate of CRAB in handwashing sinks is high in some local ICUs, and the contaminated sinks can serve as environmental reservoirs for CRAB clusters.

Abstract Backgrounds The ossification of the ligamentum flavum (OLF) is one of the major causes of thoracic myelopathy. Previous studies indicated there might be a potential link between metabolic disorder and pathogenesis of OLF. The aim of this study was to determine the potential role of metabolic disorder in the pathogenesis of OLF using the strict bioinformatic workflow for metabolism‐related genes and experimental validation. Methods A series of bioinformatic approaches based on metabolism‐related genes were conducted to compare the metabolism score between OLF tissues and normal ligamentum flavum (LF) tissues using the single sample gene set enrichment analysis. The OLF‐related and metabolism‐related differentially expressed genes (OMDEGs) were screened out, and the biological functions of OMDEGs were explored, including the Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes enrichment analysis, and protein–protein interaction. The competing endogenous RNA (ceRNA) network based on pairs of miRNA‐hub OMDEGs was constructed. The correlation analysis was conducted to explore the potential relationship between metabolic disorder and immunity abnormality in OLF. In the end, the cell experiments were performed to validate the roles of GBE1 and TNF‐α in the osteogenic differentiation of LF cells. Results There was a significant difference of metabolism score between OLF tissues and normal LF tissues. Forty‐nine OMDEGs were screened out and their biological functions were determined. The ceRNA network containing three hub OMDEGs and five differentially expressed miRNAs (DEmiRNAs) was built. The correlation analysis between hub OMDEGs and OLF‐related infiltrating immune cells indicated that metabolic disorder might contribute to the OLF via altering the local immune status of LF tissues. The cell experiments determined the important roles of GBE1 expression and TNF‐α in the osteogenic differentiation of LF cells. Conclusions This research, for the first time, preliminarily illustrated the vital role of metabolic disorder in the pathogenesis of OLF using strict bioinformatic algorithms and experimental validation for metabolism‐related genes, which could provide new insights for investigating disease mechanism and screening effective therapeutic targets of OLF in the future.

During the excavation process of deep rock masses, the surrounding rock stress field forms alternating zones of stress increase (loading zones) and stress decrease (unloading zones) near the excavation surface, which can easily trigger instability and failure in high-stress rock masses. Despite numerous studies that have been conducted on rock loading and unloading failures, the understanding of rock failure mechanisms under complex true triaxial stress paths remains insufficient. This study focuses on cubic granite specimens from the Linglong gold mine in Shandong. To comprehensively investigate the strength and failure characteristics of granite samples under complex true triaxial loading and unloading paths, true triaxial loading tests under different lateral stresses were conducted, followed by further true triaxial unloading tests in different unloading directions. The experimental results reveal that with the increase of intermediate principal stress, the failure mode of granite under true triaxial loading condition changes from shear-tensile failure to tensile failure, and the true triaxial loading failure strength increases first and then decreases slowly. Under the same intermediate and minimum principal stress conditions, the true triaxial unloading failure strength of granite is smaller than its loading failure strength, and the Mogi strength formula fits well with the true triaxial unloading strength under the minimum principal stress condition. This study provides an essential theoretical basis and guidance for controlling and designing the stability of deep rock masses in engineering applications.

Abstract We revisit HHH model [1] and extend the ansatz of matter fields to being of depending on a spatial dimension except the holographic direction. Despite homogeneous solutions of ground and excited states, especially for the excited states, there also exists solutions where the translational invariance is broken. It is worth mentioning that no periodic sources are assigned to the matter fields, so the translational symmetry is broken spontaneously. We investigate how the new solutions and the condensates of excited states develop with the change of temperature. Moreover, since this kind of condensate will decrease at certain temperature and eventually vanish at sufficiently low temperature, we also study the relation between this interval and length of lattice. Besides, we compare the free energies of non-translational invariant solutions and those of translational invariance in the HHH model, and find that the free energies of the former situations are lower.

Abstract Background Image denoising technology removes noise from the corrupted image by utilizing different features between image and noise. Convolutional neural network (CNN)-based algorithms have been the concern of the recent progress on diverse image restoration problems and become an efficient solution in image denoising. Objective Although a quite number of existing CNN-based image denoising methods perform well on the simplified additive white Gaussian noise (AWGN) model, their performance often degrades severely on the real-world noisy images which are corrupted by more complicated noise. Methods In this paper, we utilized the multi-task learning (MTL) framework to integrate multiple loss functions for collaborative training of CNN. This approach aims to improve the denoising performance of CNNs on real-world images with non-Gaussian noise. Simultaneously, to automatically optimize the weights of individual sub-tasks within the MTL framework, we incorporated a self-learning weight layer into the CNN. Results Extensive experiments demonstrate that our approach effectively enhances the denoising performance of CNN-based image denoising algorithms on real-world images. It reduces excessive image smoothing, improves quantitative metrics, and enhances visual quality in the restored images. Conclusion Our method shows the effectiveness of the improved performance of denoising CNNS for real-world image denoising processing.

Abstract Background To identify independent factors of cognitive frailty (CF) and construct a nomogram to predict cognitive frailty risk in patients with lung cancer receiving drug therapy. Methods In this cross‐sectional study, patients with lung cancer undergoing drug therapy from October 2022 to July 2023 were enrolled. The data collected includes general demographic characteristics, clinical data characteristics and assessment of tools for cognitive frailty and other factors. Logistic regression was harnessed to determine the influencing factors, R software was used to establish a nomogram model to predict the risk of cognitive frailty. The enhanced bootstrap method was employed for internal verification of the model. The performance of the nomogram was evaluated by using calibration curves, the area under the receiver operating characteristic curve, and decision curve analysis. Results A total of 372 patients were recruited, with a cognitive frailty prevalence of 56.2%. Age, education background, diabetes mellitus, insomnia, sarcopenia, and nutrition status were identified as independent factors. Then, a nomogram model was constructed and patients were classified into high‐ and low‐risk groups with a cutoff value of 0.552. The internal validation results revealed good concordance, calibration and discrimination. The decision curve analysis presented prominent clinical utility. Conclusions The prevalence of cognitive frailty was higher in lung cancer patients receiving drug therapy. The nomogram could identify the risk of cognitive frailty intuitively and simply in patients with lung cancer, so as to provide references for early screening and intervention for cognitive frailty at the early phases of drug treatment.

Abstract Intervertebral disc degeneration (IDD) is an important pathological basis for degenerative spinal diseases and is involved in mitophagy dysfunction. However, the molecular mechanisms underlying mitophagy regulation in IDD remain unclear. This study aimed to clarify the role of DJ-1 in regulating mitophagy during IDD pathogenesis. Here, we showed that the mitochondrial localization of DJ-1 in nucleus pulposus cells (NPCs) first increased and then decreased in response to oxidative stress. Subsequently, loss- and gain-of-function experiments revealed that overexpression of DJ-1 in NPCs inhibited oxidative stress-induced mitochondrial dysfunction and mitochondria-dependent apoptosis, whereas knockdown of DJ-1 had the opposite effect. Mechanistically, mitochondrial translocation of DJ-1 promoted the recruitment of hexokinase 2 (HK2) to damaged mitochondria by activating Akt and subsequently Parkin-dependent mitophagy to inhibit oxidative stress-induced apoptosis in NPCs. However, silencing Parkin, reducing mitochondrial recruitment of HK2, or inhibiting Akt activation suppressed DJ-1-mediated mitophagy. Furthermore, overexpression of DJ-1 ameliorated IDD in rats through HK2-mediated mitophagy. Taken together, these findings indicate that DJ-1 promotes HK2-mediated mitophagy under oxidative stress conditions to inhibit mitochondria-dependent apoptosis in NPCs and could be a therapeutic target for IDD.

Abstract C-type lectin-like receptor-2 (CLEC-2) is a member of the C-type lectin superfamily of cell surface receptors. The first confirmed endogenous and exogenous ligands of CLEC-2 are podoplanin and rhodocytin, respectively. CLEC-2 is expressed on the surface of platelets, which participates in platelet activation and aggregation by binding with its ligands. CLEC-2 and its ligands are involved in pathophysiological processes, such as atherosclerosis, cancer, inflammatory thrombus status, maintenance of vascular wall integrity, and cancer-related thrombosis. In the last 5 years, different anti- podoplanin antibody types have been developed for the treatment of cancers, such as glioblastoma and lung cancer. New tests and new diagnostics targeting CLEC-2 are also discussed. CLEC-2 mediates thrombosis in various pathological states, but CLEC-2-specific deletion does not affect normal hemostasis, which would provide a new therapeutic tool for many thromboembolic diseases. The CLEC-2-podoplanin interaction is a target for cancer treatment. CLEC-2 may be applied in clinical practice and play a therapeutic role.

This study designs an energy management strategy for fuel cell hybrid vehicles aimed to alleviating the performance degradation of Proton Exchange Membrane Fuel Cells (PEMFC), fuel consumption, and battery State of Charge (SOC) fluctuations during vehicle operation. The research focuses on the decrease in Electrochemical Surface Area (ECSA) due to Platinum (Pt) catalyst degradation, which is a key factor influencing PEMFC durability. An ECSA model based on the Pt dissolution mechanism is established, and a dynamic condition accelerated stress test (DC-AST) is designed to specifically investigate the impacts of key parameters, such as cycle period, amplitude, and duty cycle on the degradation of ECSA. This reveals the association between ECSA degradation and irregular potential fluctuations. A hierarchical power allocation strategy is then proposed, combining the global search capabilities of optimization algorithms with heuristic strategies in light of these features. The upper level employs Pontryagin’s Minimum Principle (PMP) strategy for global power allocation optimization, while the lower level uses a satisfaction-based heuristic strategy that is tailored to the ECSA degradation characteristics, to dynamically adjust power output. Simulation results demonstrate that, compared to traditional rule-based strategies and classical PMP-based strategies, the proposed energy management strategy more effectively mitigates ECSA degradation of PEMFC under typical vehicle conditions.

Abstract Background To ensure emergency infection prevention and control (IPC) can be fully supervised and monitored in coronavirus disease (COVID-19) epidemic period, a three-level inspector mechanism called "Internal self-check, Departmental cross-check, and Verification of outstanding key and difficult issues" was established in southwest China. The present study aimed to explore the effectiveness of inspector mechanism for the emergency IPC. Methods A self-control real-world study was conducted during COVID-19 epidemic period from 2020 to 2022. An innovative designed mobile phone application was used to realize paperless information transmission and data management. Data were compared between inspection levels using SPSS 19.0 software. Results A total of 2,800,132 supervision records were collected, including 149,137 comprehensive epidemic IPC projects, 1,410,093 personal protective equipment (PPE) use, 1,223,595 wearing and removing process of PPE and 17,307 ultraviolet light-detectable fluorescent (UV/F) surface marker. During the study period, the inspectors and subjects explored many optimized IPC measures. The compliance rate of check items has exceeded 98%, and internal self-check has a statistically significant higher rate than departmental cross-check (99.95% versus 98.74%, χ 2 = 26111.479, P < 0.001). Compare with the failure rate in internal self check, the failure rate of PPE usage and wearing/removing process was statistically higher in departmental cross-check (χ 2 = 1957.987, P < 0.001, χ 2 = 465.610, P < 0.001, respectively). The overall clearance rate of UV/F surface markers is 87.88%, but there is no statistically significant difference over the three years of the present study (F = 2.902, P = 0.071). Conclusions Inspector mechanism for the emergency IPC completed an incredible inspection workload and offered creative assistance to combat the COVID-19 outbreak. These methods and accumulated experiences should be helpful for us to strengthen IPC for future epidemic.

Differential beamforming has attracted much research since it can utilize an array with a small aperture size to form frequency-invariant beampatterns and achieve high directional gains. It has recently been applied to the loudspeaker line array to produce a broadside frequency-invariant radiation pattern. However, designing steerable frequency-invariant beampatterns for the loudspeaker line array has yet to be explored. This paper proposes a method to design a steerable differential beamformer with a loudspeaker line array. We first determine the target differential beampatterns according to the desired direction, the main lobe width, and the beampattern order. Then, we transform the target beampattern into the modal domain for representation. The Jacobi-Anger expansion is subsequently used to design the beamformer so that the resulting beampattern matches the target differential beampattern. Furthermore, based on the criterion of minimizing the mean square error between the synthesized beampattern and the ideal one, a multi-constraint optimization problem, which compromises between the robustness and the mean square error, is formulated to calculate the optimal desired weighting vector. Simulations and experimental results show that the proposed method can achieve steerable frequency-invariant beamforming from 300 Hz–4 kHz.

Rapid urbanization in the Asia-Pacific region is expected to place two-thirds of its population in concrete-dominated urban landscapes by 2050. While diverse architectural facades define the unique appearance of these urban systems. There remains a significant gap in our understanding of the composition, assembly, and ecological potential of microbial communities on building exteriors. Here, we examined bacterial and protistan communities on building surfaces along an urbanization gradient (urban, suburban and rural regions), investigating their spatial patterns and the driving factors behind their presence. A total of 55 bacterial and protist phyla were identified. The bacterial community was predominantly composed of Proteobacteria (33.7% to 67.5%). The protistan community exhibited a prevalence of Opisthokonta and Archaeplastida (17.5% to 82.1% and 1.8% to 61.2%, respectively). The composition and functionality of bacterial communities exhibited spatial patterns correlated with urbanization. In urban buildings, factors such as facade type, light exposure, and building height had comparatively less impact on bacterial composition compared to suburban and rural areas. The highest bacterial diversity and lowest Weighted Average Community Identity (WACI) were observed on suburban buildings, followed by rural buildings. In contrast, protists did not show spatial distribution characteristics related to facade type, light exposure, building height and urbanization level. The distinct spatial patterns of protists were primarily shaped by community diffusion and the bottom-up regulation exerted by bacterial communities. Together, our findings suggest that building exteriors serve as attachment points for local microbial metacommunities, offering unique habitats where bacteria and protists exhibit independent adaptive strategies closely tied to the overall ecological potential of the community.

BackgroundIt has been shown that inflammation may be associated with cognitive impairment (CI). Diet modulates inflammation. However, there is currently a scarcity of epidemiological studies exploring the connection between the inflammatory potential of diet and CI. The objective was to investigate the correlation between the dietary inflammatory index (DII) and cognitive impairment in older adults in the United States.MethodsThe present investigation utilized a cross-sectional dataset obtained from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014. Dietary intake data was used to calculate DII scores, which were then used to categorize participants into quartiles. Participants’ cognitive function was assessed using the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD), Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). Individuals who scored in the lowest quartile on any of these tests were classified as exhibiting low cognitive performance. The association between DII and cognitive impairment was investigated by multivariate logistic regression, smooth curve fitting, and subgroup analysis.ResultsA total of 947 older adults were enrolled in the study. Following the adjustment of confounding variables, DII scores exhibited a significant and positive correlation with low cognitive performance, as measured by AFT (OR 1.15, 95% CI 1.02–1.28, p = 0.02) and the DSST (OR 1.38, 95% CI 1.13–1.68, p = 0.004). Compared with the lowest quartile of DII, the highest weighted odds ratio of cognitive impairment based on AFT was observed in the fourth quartile group (OR 1.89, 95% CI 1.05–3.38, p = 0.03). Similarly, a comparable pattern was evident in DSST (OR 4.30, 95% CI 1.85–9.98, p = 0.003). Additionally, the smooth curve fitting results showed a nonlinear relationship between DII and cognitive decline evaluated by DSST (p for nonlinearity = 0.016). No interaction effects between cognitive impairment and age or gender were observed in relation to all cognitive test scores.ConclusionThis research reveals a positive link between diet with higher inflammatory potential and cognitive decline among elderly individuals in America. However, additional studies on dietary interventions are necessary to explore the cause-and-effect relationship.

Background: Primary insomnia (PI) refers to syndromes of difficulty falling asleep, poor sleep quality, early awakening, and difficulty falling asleep after waking up. Although there have been numerous studies, the specific etiology and pathogenesis of PI are still misunderstanding. In recent years, the gut microbiota has been proved to be involved in the metabolism of many mental disorders. But the specific mechanisms of its involvement in PI have not been fully elucidated. This study aims to explore the relationship between the gut microbiota and the symptoms, cognitive function changes in PI. Methods: In this study, the gut microbiota of PI patients and healthy controls was profiled by performing stool 16s rRNA gene sequencing. The co-occurrence network was constructed by using Weight Gene Co-expression Network Analysis (WGCNA) algorithm. The correlation between gut microbiota associated pathways and traits in PI were predicted. Results: WGCNA results demonstrated several Operational Taxonomic Units (OTU) modules are correlated to symptoms. By using PICRUSt2 software, we predicted the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of microbiota in modules. For instance, sleep efficiency may be correlated with the presence of Insulin signaling pathway, Flavonoid biosynthesis, Ascorbate and aldarate metabolism, Nitrotoluene degradation, Biotin metabolism, RNA polymerase and Chlorocyclohexane and chlorobenzene degradation. Total sleep time may be correlated with the presence of Tyrosine metabolism, Propanoate metabolism, Carbon fixation pathways in prokaryotes, Carotenoid biosynthesis, Systemic lupus erythematosus, Nitrotoluene degradation and Biosynthesis of unsaturated fatty acids. The severity of insomnia may be correlated with Insulin signaling pathway, Flavonoid biosynthesis, Ascorbate and aldarate metabolism, Nitrotoluene degradation, Biotin metabolism and RNA polymerase. Change of name score in Montreal Cognitive Assessment (MoCA) may be correlated with Tyrosine metabolism, Propanoate metabolism, Carbon fixation pathways in prokaryotes, Carotenoid biosynthesis, Systemic lupus erythematosus, Nitrotoluene degradation, Biosynthesis of unsaturated fatty acids, Apoptosis, Steroid hormone biosynthesis, Geraniol degradation, Protein digestion and absorption and Bisphenol degradation in Gut Microbiota (GM). Conclusion: This study revealed the potential relationships between gut microbiota and PI. By using pathway prediction and enrichment analysis, we concluded many metabolic pathways may associated with some important traits of insomnia patients, including sleep efficiency, severe insomnia, total sleep time and change of name score in MoCA. The metabolic pathways include Insulin signaling pathway, Flavonoid biosynthesis, Ascorbate and aldarate metabolism, Nitrotoluene degradation, Biotin metabolism, RNA polymerase and Chlorocyclohexane, chlorobenzene degradation, Tyrosine metabolism, Propanoate metabolism, Carbon fixation pathways in prokaryotes, Carotenoid biosynthesis, Systemic lupus erythematosus, Biosynthesis of unsaturated fatty acids, Apoptosis, Steroid hormone biosynthesis, Geraniol degradation, Protein digestion and absorption and Bisphenol degradation.Our study demonstrated that PI patients demonstrate significant changes in gut microbiota, which will help delineate the relationship between gut microbiota and syndromes of PI.

Abstract Ballistic missile defense systems require accurate target recognition technology. Effective feature extraction is crucial for this purpose. The deep convolutional neural network (CNN) has proven to be an effective method for recognizing high-resolution range profiles (HRRPs) of ballistic targets. It excels in perceiving local features and extracting robust features. However, the standard CNN's fully connected manner results in high computational complexity, which is unsuitable for deployment in real-time missile defense systems with stringent performance requirements. To address the issue of computational complexity in HRRP recognition based on the standard one-dimensional CNN (1DCNN), we propose a lightweight network called group-fusion 1DCNN with layer-wise auxiliary classifiers (GFAC-1DCNN). GFAC-1DCNN employs group convolution (G-Conv) instead of standard convolution to effectively reduce model complexity. Simply using G-Conv, however, may decrease model recognition accuracy due to the lack of information flow between feature maps generated by each G-Conv. To overcome this limitation, we introduce a linear fusion layer to combine the output features of G-Convs, thereby improving recognition accuracy. Additionally, besides the main classifier at the deepest layer, we construct layer-wise auxiliary classifiers for different hierarchical features. The results from all classifiers are then fused for comprehensive target recognition. Extensive experiments demonstrate that GFAC-1DCNN with such simple and effective techniques achieves higher overall testing accuracy than state-of-the-art ballistic target HRRP recognition models, while significantly reducing model complexity. It also exhibits a higher recall rate for warhead recognition compared to other methods. Based on these compelling results, we believe this work is valuable in reducing workload and enhancing missile interception rates in missile defense systems.

Living grass mulching (LMG) is a modern, environmentally friendly, practical, and efficient production management technology that improves the ecological environment, quality, and efficiency of the orchard. However, in arid and semi-arid areas, the effects of different grass species mulching on soil carbon composition, carbon pool stability, and nitrogen content are still unclear. Therefore, in order to explore the impact of different LMG on soil carbon, nitrogen, and its component content, as well as the related soil carbon pool management index in an apple orchard located in the semi-arid region of the Loess Plateau, a control experiment was conducted. The experiment involved different grass species cover treatments on an 11-year-old semi-dwarf Qinguan apple orchard from 2019 to 2022. Soil carbon and nitrogen content were measured under each treatment. The results indicated that the application of LMG treatment and depth of the soil had a significant impact on the soil organic carbon (SOC), particulate organic carbon (POC), inactive organic carbon (NAOC), total nitrogen (TN), and carbon-to-nitrogen ratio (C/N). Planting Vulpia myuros mulches significantly enhanced 39.6% surface soil organic carbon, 61.7% surface particulate organic carbon, 20.3% surface dissolved organic carbon (DOC), 75.8% surface inactive organic carbon, and 20.6% surface soil total nitrogen compared to clean tillage. Mulching treatment with the planting of Vulpia myuros boosted surface soil organic carbon and decreased soil carbon pool activity (CPA) and carbon pool activity index (CPAI), ultimately improving the stability of the soil carbon pool. The findings will have a beneficial impact on improving soil quality, carbon sequestration, and emission reduction in arid and semi-arid regions.

Sailing speed is a critical factor affecting the ship’s energy consumption and operating costs for a voyage. Inland waterways present a complex navigation environment due to their narrow channels, numerous curved segments, and significant variations in water depth and flow speed. This paper constructs a model of a ship’s energy consumption based on an analysis of ship resistance and the energy transfer relationship of ships. The K-means clustering algorithm is introduced to divide the Yangtze River waterway into multiple segments based on the similarity of navigation environments. Considering the constraints of the ship’s main engine and the desired arrival time, a multi-objective particle swarm optimization (MOPSO) algorithm, improved with cosine decreasing inertial weight and Gaussian random mutation, is employed to optimize segmented navigation speeds to achieve different goals. Finally, four cases are studied with a fully electric ship navigating the reaches of the Yangtze River. The results indicate that the optimized speed can reduce ship energy consumption by up to 6.18% and significantly reduce ship energy consumption and operational costs under different conditions.

The memristor, a novel device, has been widely utilized due to its small size, low power consumption, and memory characteristics. In this paper, we propose a new three-dimensional discrete memristor map based on coupling a one-dimensional chaotic map amplifier with a memristor. Firstly, we analyzed the memristor model to understand its characteristics. Then, a Simulink model for this three-dimensional discrete memristor map was developed. Lastly, the complex dynamical characteristics of the system were analyzed via equilibrium points, bifurcation diagrams, Lyapunov exponent spectra, complexity, and multistability. This study revealed the phenomena of coexisting attractors and hyperchaotic attractors. Simulink modeling confirmed that the discrete memristors effectively enhanced the chaos complexity in the three-dimensional discrete memristor map. This approach addresses the shortcomings of randomness, the lack of ergodicity, and the small key space in a one-dimensional chaotic map, thereby enriching the theoretical analysis and circuit implementation of chaos.

Hainan coconut carving is regarded as an excellent traditional cultural treasure of the Chinese nation, which has a long history with a long history, but the current dissemination of the art of Hainan coconut carving is faced with the dilemma of decline. This paper carries out the construction of a multiple linear regression model, using the least squares method to determine the regression parameters and find the maximum likelihood estimation of the regression parameters. Then, the relevant elements of the art communication path, based on the elements of the construction of the art communication of multiple linear regression model and the production of questionnaires used to obtain the art communication path influence factors data. The least squares and maximum likelihood parameter estimation are numerically simulated, and the art communication path is analyzed using multiple linear regression. The results show that the mean square error of the least squares method is below 0.001 when the sample size of maximum likelihood estimation is increased from 100 to 300, the error rate of different parameters is reduced by 70.83%, and the significance of the influencing factors in the multivariate linear regression model is less than 0.05. Based on the linear regression, we can analyze the influencing factors of the dissemination path of the art of Hainan Coconut Sculpture, which provides a reference to the further expansion of the art of Hainan Coconut Sculpture. It can provide a reference for further expanding the dissemination of Hainan coconut carving art.

Background: In recent years, baroreflex activation therapy (BAT) has been utilized to treat heart failure with reduced ejection fraction (HFrEF). However, the supporting literature on its efficacy and safety is still limited. This investigation elucidates the effects of BAT in HFrEF patients to provide a reference for future clinical applications. Methods: This investigation follows Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines. Relevant investigations on the use of BAT in HFrEF patients were searched and selected from 5 databases, including Web of Science, MEDLINE, PubMed, Embase, and Cochrane Library, from inception to December 2022. The methodological quality of eligible articles was assessed via the Cochrane risk of bias tool, and for meta-analysis, RevMan (5.3) was used. Results: Randomized controlled trials comprising 343 participants were selected for the meta-analysis, which revealed that in HFrEF patients, BAT enhanced the levels of LVEF (MD: 2.97, 95 % CI: 0.53 to 5.41), MLHFQ (MD: −14.81, 95 % CI: −19.57 to −10.06) and 6MWT (MD: 68.18, 95 % CI: 51.62 to 84.74), whereas reduced the levels of LVEDV (MD: −15.79, 95 % CI: −32.96 to 1.37) and DBP (MD: −2.43, 95 % CI: −4.18 to −0.68). Conclusion: It was concluded that BAT is an efficient treatment option for HFrEF patients. However, to validate this investigation, further randomized clinical trials with multiple centers and large sample sizes are needed.

Drought and nitrogen addition are important factors influencing soil microorganisms and changes in the soil environment. In the future, droughts will become more frequent, shorter, and more severe. However, little is known about the soil organic carbon components, enzyme activity, and composition, structure, and functional genes of soil microbial communities under short-term drought and nitrogen application conditions. In this study, we used metagenomics sequencing technology to explore the changes in the composition and functional genes of soil microbial communities under short-term drought, nitrogen application, and their interactions in the artificial grasslands of the Loess Plateau. The results indicated that (I) short-term drought, nitrogen application, and their interactions all increased the particulate organic carbon (POC) content. (II) Short-term drought increased the contents of soil microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN), as well as soil enzyme activity. (III) Short-term drought significantly increased the bacterial alpha diversity, whereas the interactions of short-term drought and nitrogen application enhanced the fungal alpha diversity. (IV) The interactions of short-term drought and nitrogen application inhibited Fusarium to prevent plant diseases. (V) Short-term drought enriched the relative abundance of genes related to carbon cycling and amino acid metabolism, while nitrogen application reduced genes related to carbon cycling but enriched genes related to glycan biosynthesis and metabolism. These results clearly showed that, short-term drought altered the composition and functional genes of soil microbial communities. Our research suggests that in the event of frequent short-term droughts in the future, nitrogen addition can be considered to maintain the diversity of soil microbial communities and sustain soil carbon and nitrogen cycling.

The gait recognition of exoskeletons includes motion recognition and gait phase recognition under various road conditions. The recognition of gait phase is a prerequisite for predicting exoskeleton assistance time. The estimation of real-time assistance time is crucial for the safety and accurate control of lower-limb exoskeletons. To solve the problem of predicting exoskeleton assistance time, this paper proposes a gait recognition model based on inertial measurement units that combines the real-time motion state recognition of support vector machines and phase recognition of long short-term memory networks. A recognition validation experiment was conducted on 30 subjects to determine the reliability of the gait recognition model. The results showed that the accuracy of motion state and gait phase were 99.98% and 98.26%, respectively. Based on the proposed SVM-LSTM gait model, exoskeleton assistance time was predicted. A test was conducted on 10 subjects, and the results showed that using assistive therapy based on exercise status and gait stage can significantly improve gait movement and reduce metabolic costs by an average of more than 10%.

Radar high‐resolution range profile (HRRP) is widely used in radar automatic target recognition due to its advantages such as easy availability, convenient processing, and small storage space. Current recognition methods for HRRP sequences mainly focus on the temporal information of HRRP sequences, which cannot fully utilize the temporal and spatial information contained in HRRP sequences. Moreover, most of these methods fail in long‐range modeling and global information extraction of HRRP sequences. To solve above problems, a HRRP sequence recognition method based on transformer with temporal–spatial fusion and label smoothing (TSF–transformer–LS) is proposed. TSF–transformer–LS contains temporal transformer blocks and spatial transformer blocks, which are used to extract deep global features of HRRP sequences in the time domain and space domain, respectively. Then, an attention fusion mechanism is developed to realize the adaptive fusion of temporal and spatial features. Moreover, label smoothing is used to add noise to sample labels, which can solve the overfitting problem of transformer caused by a large amount of noise hidden in HRRP in real scenes. Experiments on MSTAR, a standard dataset, show that the proposed method outperforms other methods in recognition performance. Furthermore, the effectiveness and interpretability of the method are explored.

Background: At present, the pathogenesis of atherosclerosis has not been fully elucidated, and the diagnosis and treatment face great challenges. Cuproptosis is a novel cell death pattern that might be involved in the development of atherosclerosis. However, no research has reported the correlation between cuproptosis and atherosclerosis. Methods: The differential cuproptosis-related genes (CRGs) between atherosclerosis group and control group (A-CRGs) were discovered via differential expression analysis. The correlation analysis, PPI network analysis, GO, KEGG and GSEA analysis were performed to investigate the function of A-CRGs. The differences of biological function between atherosclerosis group and control group were investigated via immune infiltration analysis and GSVA. The LASSO regression, nomogram and machine learning models were constructed to predict atherosclerosis risk. The atherosclerosis molecular subtypes clusters were discovered via unsupervised cluster analysis. Subsequently, we used the above research methods to analyze the differential CRGs between clusters (M-CRGs) and evaluate the molecular subtypes identification performance of M-CRGs. Finally, we verified the diagnostic value for atherosclerosis and role in cuproptosis of these CRGs through the validation set and in vitro experiments. Results: Five A-CRGs were identified and they were mainly related to the biological function of copper ion metabolism and immune inflammatory response. The diagnostic models and nomogram of atherosclerosis based on 5 A-CRGs indicated that these genes had well diagnostic value. A total of two molecular subtypes clusters were obtained in the atherosclerosis group. There were many differences in biological functions between these two molecular subtypes clusters, such as mitochondrial outer membrane permeabilization and primary immunodeficiency. In addition, 3 M-CRGs were identified in the 2 clusters. Machine learning models and nomogram constructed based on M-CRGs showed that these genes had well molecular subtypes identification efficacy. In the end, the results of in vitro experiment and validation set confirmed the diagnostic value for atherosclerosis and role in cuproptosis of these genes. Conclusion: The cuproptosis may be a potential pathogenesis of atherosclerosis and CRGs may be promising markers for the diagnosis and molecular subtypes identification of atherosclerosis.

Rice paddy soil is a hotspot of antibiotic resistance genes (ARGs) due to the application of organic fertilizers. However, the temporal dynamics of ARGs in rice paddy soil and its flooded water during the growing season remain underexplored. In this study, a microcosm experiment was conducted to explore the ARG profiles in a long term (130 days) flooded two-phase manure-amended soil–water system. By using high-throughput quantitative PCR array, a total of 23–98 and 34–85 ARGs were detected in the soil and overlying water, respectively. Regression analysis exhibited significant negative correlations between ARG profile similarities and flooding duration, indicating that flooding significantly altered the resistome (P

Abstract Integrins are considered the main cell-adhesion transmembrane receptors that play multifaceted roles as extracellular matrix (ECM)-cytoskeletal linkers and transducers in biochemical and mechanical signals between cells and their environment in a wide range of states in health and diseases. Integrin functions are dependable on a delicate balance between active and inactive status via multiple mechanisms, including protein-protein interactions, conformational changes, and trafficking. Due to their exposure on the cell surface and sensitivity to the molecular blockade, integrins have been investigated as pharmacological targets for nearly 40 years, but given the complexity of integrins and sometimes opposite characteristics, targeting integrin therapeutics has been a challenge. To date, only seven drugs targeting integrins have been successfully marketed, including abciximab, eptifibatide, tirofiban, natalizumab, vedolizumab, lifitegrast, and carotegrast. Currently, there are approximately 90 kinds of integrin-based therapeutic drugs or imaging agents in clinical studies, including small molecules, antibodies, synthetic mimic peptides, antibody–drug conjugates (ADCs), chimeric antigen receptor (CAR) T-cell therapy, imaging agents, etc. A serious lesson from past integrin drug discovery and research efforts is that successes rely on both a deep understanding of integrin-regulatory mechanisms and unmet clinical needs. Herein, we provide a systematic and complete review of all integrin family members and integrin-mediated downstream signal transduction to highlight ongoing efforts to develop new therapies/diagnoses from bench to clinic. In addition, we further discuss the trend of drug development, how to improve the success rate of clinical trials targeting integrin therapies, and the key points for clinical research, basic research, and translational research.

Abstract Background Novel biomarkers for personalizing anticoagulation remain undetermined. We aimed to investigate the association of plasma miRNAs with pharmacokinetic–pharmacodynamic (PK-PD) profiles of rivaroxaban. Methods This is a multicenter, exploratory study of miRNAs in a Chinese population. Healthy volunteers and patients receiving rivaroxaban were enrolled in the study. The area under the plasma concentration–time curve from time 0-t h (AUC0-t) and anti-Xa activity at 3 h (AXA3h) were measured in healthy volunteers, and AXA3h was measured in patients. MiRNAs were detected by miRNA microarray in 26 healthy volunteers with 20 mg rivaroxaban, and quantitative reverse transcription polymerase chain reaction was used to exclude undetectable ones. MiR-320a-3p and miR-483-5p were then quantified in 65 healthy volunteers and 71 patients. MiRNA levels at 3 h were compared between high and low AXA3h or AUC0-t subjects and in matched patients with or without bleeding during follow-up. The miRNA targets were predicted by TargetScan, miRTarBase, and miRDB. Validated genes were included in GO enrichment and KEGG analyses. The protein–protein interaction network was established by STRING and visualized by Cytoscape. Results A total of 136 Chinese subjects completed the study. In healthy volunteers taking 15 mg rivaroxaban, the miR-320a level at 3 h was significantly positively correlated with AXA3h and AUC0-t (r = 0.359, p = 0.025; r = 0.370, p = 0.02, respectively). A positive correlation was also observed between miR-483 and AXA3h or AUC0-t (r = 0.372, p = 0.02; r = 0.523, p = 0.001, respectively). MiR-320a and miR-483 levels at 3 h in the higher AUC0-t group were significantly higher than those at 0 h. MiR-483 levels at 3 h may distinguish healthy volunteers with high or low AXA3h or AUC0-t. In the 10 mg fed subgroup, higher 3 h mir-483 levels were also observed compared with the control group. No significant differences were found in the comparisons among patients. Bioinformatic analysis showed that these miRNAs may play a regulatory role by targeting ABCG2, ITGB3, PTEN, MAPK1/3, etc. Conclusions MiR-320a and miR-483 levels were found to be associated with PK and PD profiles of rivaroxaban in healthy Chinese subjects. Further studies are required to verify these findings and explore the mechanisms.

Abstract Background To evaluate the effectiveness of enhanced check to the duration of the central venous catheters associated bloodstream infections (CABSIs), and the impact on infection rates. Methods A before-after, real-world study in six adult intensive care units was conducted. All adult patients who had only one central venous catheter were included during two consecutive periods. The intervention period, added cross-check that all patients with central venous catheter (CVC) need to be performed, and included nurses' checks for insertion practices and doctors' checks for maintenance practices. Propensity scores matching were used to account for potential confounding, and restricted cubic spline was served as visualizing the CABSI risk. Results A total of 2906 patients with 26,157 CVC-days were analyzed. After intervention, the density incidence of CABSI decreased from 10.24 to 6.33/1,000 CVC-days (P

Abstract Convolution neural networks (CNNs) represent one of the workhorses of artificial intelligence applications. As a typical artificial intelligence application, a high‐resolution range profile (HRRP) target recognition method based on CNNs has aroused a lot of research interest. Most CNNs use a relatively small and single‐scale convolution kernel size to control the number of parameters and computational complexity, but recent studies indicate that CNNs with a small kernel size cannot extract enough spatial information, which hurts the recognition performance. Aiming at this problem, this paper proposes a multi‐scale group‐fusion one‐dimensional convolution neural network (MSGF‐1D‐CNN) for HRRP target recognition. MSGF‐1D‐CNN utilises multi‐scale group one‐dimensional convolution (MSG 1D‐Conv) and point‐wise convolution (PW‐Conv) to replace the standard convolution. Multi‐scale group one‐dimensional convolution can significantly reduce complexity and capture the information of targets within HRRP in different levels of detail to enhance feature extraction, while PW‐Conv can realise the fusion of multi‐scale features to help boosting recognition performance. Experiments on five mid‐course ballistic targets in the HRRP dataset show that MSGF‐1D‐CNN has superior recognition performance, and the parameter number of the model is reduced by more than 2.4 times than standard 1D‐CNN. Furthermore, MSGF‐1D‐CNN shows better performance on fine‐grained HRRP target recognition and anti‐noise robustness in most cases.

Abstract Background The role of obesity in catheter-related bloodstream infection has been reported in several studies, but it is still controversial. We conducted this meta-analysis to summarize existing evidence to assess the relationship between obesity and the risk of catheter-related bloodstream infection. Methods We searched MEDLINE, EMBASE, PubMed and Web of Science for the related studies published before January 2022. Meta-analysis was performed by use of a random-effects model. Results A total of 5 articles were included in this meta-analysis. Patients with body mass index ≥ 25 kg/m2 had an increased risk of catheter-related bloodstream infection (OR 1.75, 95% CI 1.38–2.22) in overall analysis. Further analysis indicated that patients with overweight, obesity and severely obesity were all significantly associated with a higher risk of for catheter-related bloodstream infection (OR 1.51 [1.10–2.08], OR 1.43 [1.12–1.82] and OR 2.74 [1.85–4.05], respectively). Conclusion This meta-analysis provided evidence that obesity was significantly associated with a higher risk of catheter-related bloodstream infection. Close attention should be paid to the complications and prognosis of obese patients with vascular catheterization in clinical work.

Abstract As a special deep learning algorithm, the multilayer extreme learning machine (ML-ELM) has been extensively studied to solve practical problems in recent years. The ML-ELM is constructed from the extreme learning machine autoencoder (ELM-AE), and its generalization performance is affected by the representation learning of the ELM-AE. However, given label information, the unsupervised learning of the ELM-AE is difficult to build the discriminative feature space for classification tasks. To address this problem, a novel Fisher extreme learning machine autoencoder (FELM-AE) is proposed and is used as the component for the multilayer Fisher extreme leaning machine (ML-FELM). The FELM-AE introduces the Fisher criterion into the ELM-AE by adding the Fisher regularization term to the objective function, aiming to maximize the between-class distance and minimize the within-class distance of abstract feature. Different from the ELM-AE, the FELM-AE requires class labels to calculate the Fisher regularization loss, so that the learned abstract feature contains sufficient category information to complete classification tasks. The ML-FELM stacks the FELM-AE to extract feature and adopts the extreme leaning machine (ELM) to classify samples. Experiments on benchmark datasets show that the abstract feature extracted by the FELM-AE is more discriminative than the ELM-AE, and the classification results of the ML-FELM are more competitive and robust in comparison with the ELM, one-dimensional convolutional neural network (1D-CNN), ML-ELM, denoising multilayer extreme learning machine (D-ML-ELM), multilayer generalized extreme learning machine (ML-GELM), and hierarchical extreme learning machine with L21‑norm loss and regularization (H-LR21-ELM).

BackgroundThe pathogenesis of myocardial infarction complicating depression is still not fully understood. Bioinformatics is an effective method to study the shared pathogenesis of multiple diseases and has important application value in myocardial infarction complicating depression.MethodsThe differentially expressed genes (DEGs) between control group and myocardial infarction group (M-DEGs), control group and depression group (D-DEGs) were identified in the training set. M-DEGs and D-DEGs were intersected to obtain DEGs shared by the two diseases (S-DEGs). The GO, KEGG, GSEA and correlation analysis were conducted to analyze the function of DEGs. The biological function differences of myocardial infarction and depression were analyzed by GSVA and immune cell infiltration analysis. Four machine learning methods, nomogram, ROC analysis, calibration curve and decision curve were conducted to identify hub S-DEGs and predict depression risk. The unsupervised cluster analysis was constructed to identify myocardial infarction molecular subtype clusters based on hub S-DEGs. Finally, the value of these genes was verified in the validation set, and blood samples were collected for RT-qPCR experiments to further verify the changes in expression levels of these genes in myocardial infarction and depression.ResultsA total of 803 M-DEGs, 214 D-DEGs, 13 S-DEGs and 6 hub S-DEGs (CD24, CSTA, EXTL3, RPS7, SLC25A5 and ZMAT3) were obtained in the training set and they were all involved in immune inflammatory response. The GSVA and immune cell infiltration analysis results also suggested that immune inflammation may be the shared pathogenesis of myocardial infarction and depression. The diagnostic models based on 6 hub S-DEGs found that these genes showed satisfactory combined diagnostic performance for depression. Then, two molecular subtypes clusters of myocardial infarction were identified, many differences in immune inflammation related-biological functions were found between them, and the hub S-DEGs had satisfactory molecular subtypes identification performance. Finally, the analysis results of the validation set further confirmed the value of these hub genes, and the RT-qPCR results of blood samples further confirmed the expression levels of these hub genes in myocardial infarction and depression.ConclusionImmune inflammation may be the shared pathogenesis of myocardial infarction and depression. Meanwhile, hub S-DEGs may be potential biomarkers for the diagnosis and molecular subtype identification of myocardial infarction and depression.

Objectives: This study aimed to evaluate the risk of venous thrombosis (VTE) associated with Janus kinase (JAK) inhibitors in patients diagnosed with immune-mediated inflammatory diseases.Methods: We conducted a comprehensive search of PUBMED, Cochrane, and Embase databases for randomized controlled trials evaluating venous thromboembolic incidence after administering JAK inhibitors in patients with immune-mediated inflammatory diseases. The studies were screened according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and a meta-analysis was performed.Results: A total of 16 studies, enrolling 17,242 participants, were included in this review. Four approved doses of JAK inhibitors were administered in the included studies. The meta-analysis revealed no significant difference in the incidence of VTE between patients receiving JAK inhibitors, a placebo, or tumor necrosis factor (TNF) inhibitors (RR 0.72, 95% CI (0.33-1.55); RR 0.94, 95%CI (0.33-2.69)). Subgroup analysis showed a lower risk of VTE with lower doses of JAK inhibitors [RR 0.56, 95%CI (0.36-0.88)]. Compared with the higher dose of tofacitinib, the lower dose was associated with a lower risk of pulmonary embolism [RR 0.37, 95%CI (0.18-0.78)].Conclusion: Our meta-analysis of randomized controlled trials observed a potential increase in the risk of VTE in patients with immune-mediated inflammatory diseases treated with JAK inhibitors compared to placebo or tumor necrosis factor inhibitors, though statistical significance was not attained. Notably, a higher risk of pulmonary embolism was observed with high doses of tofacitinib. Our findings provide valuable insights for physicians when evaluating the use of JAK inhibitors for patients with immune-mediated inflammatory diseases.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023382544, identifier CRD42023382544

Elevated temperature and frequent drought events under global climate change may seriously affect soil respiration. However, the underlying mechanism of the effects of warming and drought on soil respiration is not fully understood in the context of the Loess Plateau. This study examined the response of soil respiration (Rs) to multiple factors, including warming (W), drought (P), and their interaction (WP), in the semi-arid grassland of the Loess Plateau in Northwest China. The research period was from May to November 2022, with an open-top heating box used for warming and a rain shelter used for drought. The results showed the following: (1) Rs ranged from 1.67 μmol m−2 s−1 to 4.77 μmol m−2 s−1, with an average of 3.36 ± 0.07 μmol m−2 s−1. The cumulative soil carbon flux ranged from 500.97 g C·m−2 to 566.97 g C·m−2, and the average cumulative soil respiration was 535.28 ± 35.44 g C·m−2. (2) Warming increased Rs by 5.04 ± 3.11%, but drought inhibited Rs by 3.40 ± 3.14%, and the interaction between warming and drought significantly reduced soil respiration by 11.27 ± 3.89%. (3) The content of particulate organic carbon (POC), dissolved organic carbon (DOC), soil organic carbon (SOC), and readily oxidized carbon (ROC) decreased with the increased soil depth. ROC after W and WP treatments was significantly higher than that of the control, and POC after P treatment was significantly higher than CK (p < 0.05). (4) The seasonal variation of soil respiration was positively correlated with soil temperature, soil water content, plant height, and leaf area index (p < 0.05), but the response rules differed during different regeneration periods. Soil water content; soil water content and leaf area index; and soil water content, soil temperature, and leaf area index were the factors that regulated the variation in soil respiration in the first, second, and third regeneration periods, respectively. These results clearly showed the limiting effect of drought stress on the coupling between temperature and soil respiration, especially in semi-arid regions. Collectively, the variations in soil respiration under warming, drought, and their interactions were further regulated by different biotic and abiotic factors. Considering future warming, when coupled with increased drought, our findings indicate the importance of considering the interactive effects of climate change on soil respiration and its components in arid and semi-arid regions over the next decade.

Steroid-induced osteonecrosis of the femoral head (ONFH) is a common and devastating bone disorder, which often results in progressive collapse of the femoral head and subsequent osteoarthritis. The proliferation ability and osteogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) play critical roles in maintaining the structural and functional integrity of the femoral head to prevent ONFH. Until now, little has been known about the underlying mechanism of BMSCs differentiation disorder during ONFH progression. Circular RNAs (circRNAs) are considered to be vital non-coding RNAs functionally involved in various human diseases. However, whether and how circRNA regulates the proliferation and osteogenic differentiation of BMSCs in ONFH remain unclear. In this study, we analyzed the circRNA expression profile of five samples of BMSCs in ONFH and five samples of control by using circRNA microarray assays. We identified 182 differentially expressed circRNAs, among which 108 circRNAs were upregulated. We further investigated the effects of a significantly upregulated circRNA, circHGF, on the proliferation and osteogenic differentiation of BMSCs in vitro. Results showed that circHGF suppressed the proliferation and osteogenic differentiation of BMSCs in ONFH by targeting miR-25-3p/SMAD7 axis. Our findings provided a potential diagnostic and therapeutic strategy for ONFH.

Abstract The aim of this study was to evaluate the relationship between polymorphisms in CYP2C19 and the single‐dose pharmacokinetics (PKs) of omeprazole in healthy Chinese volunteers. A 20 mg single dose of omeprazole (Losec) enteric‐coated capsules or tablets was orally administered to 656 healthy subjects from eight subcenters. The polymorphic alleles of CYP2C19*2, *3, and *17 were determined by Sanger sequencing and Agena mass array. Plasma concentrations of omeprazole were determined by high‐performance liquid‐chromatography tandem mass spectrometry. PK parameters of area under the concentration versus time curve (AUC)0‐t, AUC from zero to infinity (AUC0‐∞), maximum plasma concentration (Cmax), and terminal half‐life (t1/2) were significantly influenced by CYP2C19 phenotype (all p

Abstract Introduction Individual variability of rivaroxaban was observed in clinical application. This study aimed to identify genetic variants associated with the variability of pharmacodynamics and bleeding risk of rivaroxbaban in patients with nonvalvular atrial fibrillation (NVAF). Materials and Methods From June 2017, and July 2019, this study enrolled 257 patients with NVAF receiving rivaroxaban. Pharmacodynamics was assessed by determining anti‐Factor Xa (anti‐FXa) level 3 h after rivaroxaban administration as peak concentration. Whole‐exome sequencing was performed to detected single nucleotide polymorphisms (SNPs). This study was registered (NCT03161496). Results The bleeding events within 12 months were significantly related to the peak anti‐FXa level (p = .027). SUSD3 rs76292544 was associated with 12‐month bleeding events (odds ratio [OR]: 4.20, 95% confidence interval [CI]: 2.17–8.14, p = 6.43×10−5). Five SNPs including NCMAP rs4553122 (p = 2.29×10−5), PRF1 rs885821 (p = 7.02×10−5), PRKAG2 rs12703159 (p = 7.97×10−5), PRKAG2 rs13224758 (p = 8.70×10−5), and POU2F3 rs2298579 (p = 8.24×10−5) were associated with peak anti‐FXa level. Genetic variants of 52 SNPs from 36 genes including GOT2 rs14221 and MMP13 rs640198 were potentially related to 12‐month bleeding events caused by rivaroxaban's efficacy. Conclusions Peak anti‐FXa level was associated with risk of bleeding events in patients with NVAF receiving rivaroxaban. SUSD3 rs76292544 was suggestively associated with 12‐month bleeding events and five SNPs (NCMAP rs4553122, PRF1 rs885821, PRKAG2 rs12703159, rs13224758 and POU2F3 rs2298579) were suggestively associated with peak anti‐FXa level.

Background: The regimens of immune checkpoint inhibitors (ICIs) alone or with chemotherapy are emerging as systemic therapy for patients with advanced and metastatic gastrointestinal cancers. However, the risk of treatment-related hematologic toxicity stays unclear.Methods: We enrolled in phase 3 randomized clinical trials (RCTs) comparing PD-1, PD-L1, and CTLA-4 inhibitors in advanced and metastatic gastrointestinal cancers. The incidences of overall treatment-related adverse events (TRAEs), discontinuation, leukopenia, neutropenia, thrombocytopenia, and anemia were extracted for the Bayesian network meta-analysis. Analyses with poor convergence or low incidence were reported as incidences with 95% CIs instead.Results: Sixteen phase 3 RCTs with 9732 patients who received systemic therapy were included. A total of 150 (1.54% [95% CI 1.31–1.80]) treatment-related death events were recorded, whereas 13 (0.13% [95% CI 0.08–0.22]) of them were hematologic. 0.24% (95% CI 0.12–0.48) patients received ICI plus chemotherapy were recorded for hematological deaths, 0.09% (95% CI 0.01–0.23) were for chemotherapy alone, and 0.05% were for ICI alone (95% CI 0.01–0.29). Febrile neutropenia was the most frequent cause of death in ICI with chemotherapy. For grade ≥3 TRAEs, we found nivolumab plus chemotherapy (OR 1.63 [95% CI 0.84–3.17]) had a higher risk than other treatments. Overall, ICI monotherapy led to fewer AEs than chemotherapy-based regimens in the analyses of leukopenia, neutropenia, thrombocytopenia, and anemia. Among the 11 treatments, toripalimab plus chemotherapy possessed the highest risk in any-grade leukopenia (OR 1.84 [95% CI 0.48, 6.82]) and neutropenia (OR 1.71 [95% CI 0.17, 17.40]) respectively. For grade ≥3 hematologic AEs, neutropenia (20.08% [95% CI 18.67–21.56]) related to ICI plus chemotherapy was the most dominant. ICI plus chemotherapy was likely to increase the incidence than dosing these drugs alone.Conclusion: Using ICI alone had a low incidence of causing hematologic mortality and AEs, while the combination with chemotherapy might magnify the side effects. Comprehensively, pembrolizumab plus chemotherapy and sintilimab plus chemotherapy were the safest regimens in terms of leukopenia and neutropenia respectively. This study will guide clinical practice for ICI-based chemotherapy.Systematic Review Registration: PROSPERO, identifier CRD42022380150

Aim: Diabetic nephropathy (DN) is the primary cause of end-stage renal disease worldwide. Although etiology for DN is complex and still needs to be fully understood, lipid metabolism disorder is found to play a role in it. Previously, we found Yishen Huashi (YSHS) granule could inhibit diabetic damage and reduce level of microalbuminuria (mALB) in DN animals. To explore its role and mechanism in lipid metabolism under DN settings, this study was designed. Materials and methods: DN rats were induced by streptozotocin (STZ), HepG2 and CaCO2 cells were applied for in vitro study. Hematoxylin-Eosin (HE), periodic acid–Schiff (PAS) staining, and Transmission Electron Microscopy (TEM) were applied for histological observation; 16s Sequencing was used for intestinal microbiota composition analysis; western blotting (WB) and immunofluorescence were carried out for molecular biological study, and enzyme-linked immunosorbent assay (ELISA) was used for lipid determination. Results: YSHS administration significantly reduced levels of total cholesterol (TC), triglyceride (TG), and low-density lipoprotein (LDL-C), while increased level of high-density lipoprotein (HDL-C); meanwhile, histological changes and steatosis of the liver was ameliorated, integrity of the intestinal barrier was enhanced, and dysbacteriosis within intestinal lumen was ameliorated. Mechanism study found that YSHS modulated mitophagy within hepatocytes and inhibited mTOR/AMPK/PI3K/AKT signaling pathway. Conclusion: In conclusion, we found in the present study that YSHS administration could ameliorate lipid metabolism disorder in DN animals, and its modulation on intestinal-liver axis played a significant role in it.

Abstract Background The real-world studies on recurrent venous thromboembolism (VTE) and bleeding events of non-vitamin K antagonist oral anticoagulants (NOACs) in VTE patients have reported conflicting findings. Our study aimed to provide the direct comparison evidence of different NOACs for VTE patients in clinical practice settings. Methods Search of the medical literature was conducted using PubMed, Web of Science, EMBASE, Clinical Trials.gov, and the Cochrane Library from inception to March 22, 2021. Among the 19,996 citations retrieved, a total of 63,144 patients from 6 studies were analyzed. Clinical outcomes included recurrent VTE, death, and different bleeding events. Results Adjusted hazard ratio (HR) analysis suggested that apixaban had significant lower bleeding riskthan rivaroxaban (major, minor and any bleeding: HR = 0.61, 0.56, 0.70; p = 0.008,

Abstract Background Studies show inconsistent results regarding the impact of CYP2C19 genotype on the pharmacodynamics (PD) and clinical outcomes of ticagrelor. With the implementation of genotype-guided individualized antiplatelet therapy, the association between CYP2C19 polymorphism and the efficacy and safety of ticagrelor for patients is still worthy of exploring and studying. Methods This systematic review protocol has been registered in the PROSPERO network (No. CRD 42020158920). Electronic databases of PubMed, EmBase, and the Cochrane Library were systematically searched from inception to January 6th, 2022 to select studies investigating the impact of CYP2C19 genotype on PD and clinical outcomes of ticagrelor. The results were presented as odds ratio (OR) or weight mean difference with its 95% confidence interval (CI) by using the random-effects model. Trial sequential analysis (TSA) was used to control risk of random errors and detect the robustness of outcomes. Results Eight studies recruited a total of 6405 patients treated with ticagrelor. Mostly trials reported no significant effect of any or no CYP2C19 loss-of-function (LOF) allele (*2 or *3) on all the endpoints. Compared with no LOF allele carriers, subgroup analysis suggested any LOF allele in Asian patients was associated with a significant decreased risk of bleeding events (OR: 0.41; 95% CI: 0.22–0.75; P = 0.004). Furthermore, any LOF allele carriers didn’t yield any impact on the risk of MACEs (OR: 1.11; 95% CI: 0.76–1.64; P = 0.586), stroke (OR: 1.71; 95% CI: 0.99–2.96; P = 0.054), definite stent thrombosis (OR: 0.88; 95% CI: 0.17–4.60; P = 0.882), bleeding (OR: 0.63; 95% CI: 0.27–1.46; P = 0.281), myocardial infarction (OR: 0.81; 95% CI: 0.30–2.20; P = 0.682), and revascularization (OR: 0.81; 95% CI: 0.33–2.00; P = 0.649) in all patients. The results of TSA were indicated that more further trials would be required. Conclusions This qualitative and quantitative study suggested Asian patients carrying any CYP2C19 LOF allele might have a lower risk of bleeding events comparing with no LOF allele carriers when treated with ticagrelor. However, we did not prove an important role of CYP2C19 genotype on the risk of PD and clinical endpoints in the whole cohort. In future, more large-scale prospective studies and more different ethnic populations should be included.

Intervertebral disc degeneration (IVDD) is the leading cause of low back pain and Nucleus pulposus cell senescence contributes a lot to its progression. Here, the authors reveal WTAP-mediated m6A modification of lncRNA NORAD promotes IVDD.

Abstract In-vivo estimation of mechanical properties of the myocardium is essential for patient-specific diagnosis and prognosis of cardiac disease involving myocardial remodeling, including myocardial infarction and heart failure with preserved ejection fraction. Current approaches use time-consuming finite-element (FE) inverse methods that involve reconstructing and meshing the heart geometry, imposing measured loading, and conducting computationally expensive iterative FE simulations. In this paper, we propose a machine learning (ML) model that feasibly and accurately predicts passive myocardial properties directly from select geometric, architectural, and hemodynamic measures, thus bypassing exhaustive steps commonly required in cardiac FE inverse problems. Geometric and fiber-orientation features were chosen to be readily obtainable from standard cardiac imaging protocols. The end-diastolic pressure-volume relationship (EDPVR), which can be obtained using a single-point pressure-volume measurement, was used as a hemodynamic (loading) feature. A comprehensive ML training dataset in the geometry-architecture-loading space was generated, including a wide variety of partially synthesized rodent heart geometry and myofiber helicity possibilities, and a broad range of EDPVRs obtained using forward FE simulations. Latin hypercube sampling was used to create 2500 examples for training, validation, and testing. A multi-layer feed-forward neural network (MFNN) was used as a deep learning agent to train the ML model. The model showed excellent performance in predicting stiffness parameters $$a_f$$ a f and $$b_f$$ b f associated with fiber direction ( $$R^2_{a_f}=99.471\%$$ R a f 2 = 99.471 % and $$R^2_{b_f}=92.837\%$$ R b f 2 = 92.837 % ). After conducting permutation feature importance analysis, the ML performance further improved for $$b_f$$ b f ( $$R^2_{b_f}=96.240\%$$ R b f 2 = 96.240 % ), and the left ventricular volume and endocardial area were found to be the most critical geometric features for accurate predictions. The ML model predictions were evaluated further in two cases: (i) rat-specific stiffness data measured using ex-vivo mechanical testing, and (ii) patient-specific estimation using FE inverse modeling. Excellent agreements with ML predictions were found for both cases. The trained ML model offers a feasible technology to estimate patient-specific myocardial properties, thus, bridging the gap between EDPVR, as a confounded organ-level metric for tissue stiffness, and intrinsic tissue-level properties. These properties provide incremental information relative to traditional organ-level indices for cardiac function, improving the clinical assessment and prognosis of cardiac diseases.

Abstract Background The prevention of peripherally inserted central catheters (PICC)-associated BSI and central venous catheters (CVC)-associated BSI have been a topic of national importance in China. Therefore, we aimed to explore the epidemiological characteristics of central line-associated bloodstream infection (CLABSI), and to evaluate whether PICCs were associated with a protective effect for CLABSI. Methods A retrospective cohort study was conducted in teaching hospital in Western China. All adult patients received a CVC or PICC during their hospital stay were included from January 2017 to December 2020. Primary endpoint was CLABSI up to 30 days after CVC or PICC placement. Propensity scores with a 2:1 match was used to account for potential confounders, and restricted cubic spline was used to visualize the risk of CLABSI at different time points during the catheterization. Results A total of 224687 devices (180522 PICCs and 45965 CVCs) in 24879 patients were included. The overall incidence was 1.8 CLABSIs per 1000 catheter-days. The odds ratio (OR) value increased day by day after PICC insertion, reached a relatively high point on the 4th day, and decreased from days 5 through 8. From the 9th day of intubation the OR value began to gradually increase day by day again. After covariate adjustment using propensity scores, CVCs were associated with higher risk of CLABSI (adjHR = 3.27, 95% CI 2.38–4.49) compared with PICCs. Conclusions PICCs have a protective role and the effect of fluctuation curve feature in CLABSI when compared to CVCs, and the first 8 calendar days after CVC insertion are the acute stage of CVC-associated BSI.

Gait models are important for the design and control of lower limb exoskeletons. The inverted pendulum model has advantages in simplicity and computational efficiency, but it also has the limitations of oversimplification and lack of realism. This paper proposes a two-degrees-of-freedom (DOF) inverted pendulum walking model by considering the knee joints for describing the characteristics of human gait. A new parameter, roll factor, is defined to express foot function in the model, and the relationships between the roll factor and gait parameters are investigated. Experiments were conducted to verify the model by testing seven healthy adults at different walking speeds. The results demonstrate that the roll factor has a strong relationship with other gait kinematics parameters, so it can be used as a simple parameter for expressing gait kinematics. In addition, the roll factor can be used to identify walking styles with high accuracy, including small broken step walking at 99.57%, inefficient walking at 98.14%, and normal walking at 99.43%.

Abstract Point cloud processing is a highly challenging task in 3D vision because it is unstructured and unordered. Recently, deep learning has been proven to be quite successful in point cloud recognition, registration, segmentation, etc. Neighborhood search operation is an important component of point cloud deep learning models, and directly affects the performance of the model. In this paper, we propose a learnable neighborhood search method. This method adaptively chooses an appropriate search method based on the characteristics of each point, thus avoiding the disadvantage of selecting the search method manually. We validate the proposed methods on ModelNet40 dataset and ShapeNetPart dataset, and all the chosen models achieved a performance improvement with a maximum improvement of 1.1%. The proposed method is a plug-and-play technique and can be easily integrated into existing methods.