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Abstract This study aims to develop a deep learning (DL) model based on whole-slide images (WSIs) to predict the pathological stage of clear cell renal cell carcinoma (ccRCC). The histopathological images of 513 ccRCC patients were downloaded from The Cancer Genome Atlas (TCGA) database and randomly divided into training set and validation set according to the ratio of 8∶2. The CLAM algorithm was used to establish the DL model, and the stability of the model was evaluated in the external validation set. DL features were extracted from the model to construct a prognostic risk model, which was validated in an external dataset. The results showed that the DL model showed excellent prediction ability with an area under the curve (AUC) of 0.875 and an average accuracy score of 0.809, indicating that the model could reliably distinguish ccRCC patients at different stages from histopathological images. In addition, the prognostic risk model constructed by DL characteristics showed that the overall survival rate of patients in the high-risk group was significantly lower than that in the low-risk group (P = 0.003), and AUC values for predicting 1-, 3- and 5-year overall survival rates were 0.68, 0.69 and 0.69, respectively, indicating that the prediction model had high sensitivity and specificity. The results of the validation set are consistent with the above results. Therefore, DL model can accurately predict the pathological stage and prognosis of ccRCC patients, and provide certain reference value for clinical diagnosis.

Abstract Background Scientific evidence clearly demonstrates that inhaling the smoke from the combustion of cigarettes is responsible for most of the harm caused by smoking, and not the nicotine. However, a majority of U.S. adults who smoke inaccurately believe that nicotine causes cancer which may be a significant barrier, preventing switching to potentially reduced risk, non-combustible products like electronic nicotine delivery systems (ENDS) and smokeless tobacco (ST). We assessed the population health impact associated with nicotine perceptions. Methods Using a previously validated agent-based model to the U.S. population, we analyzed nationally representative data from the Population Assessment of Tobacco and Health (PATH) study to estimate base case rates of sustained (maintained over four waves) cessation and switching to non-combustible product use, by sex. Nicotine perception scenarios were determined from PATH data. The overall switch rate from smoking in Wave 4 to non-combustible product use in Wave 5 (3.94%) was stratified based on responses to the nicotine perception question “Do you believe nicotine is the chemical that causes most of the cancer caused by smoking cigarettes?”, (four-item scale from “Definitely not” to “Definitely yes”). The relative percent change between the overall and stratified rates, corresponding to each item, was used to adjust the base case rates of switching, to determine the impact, if all adults who smoke exhibited switching behaviors based on responses to the nicotine perceptions question. The public health impact of nicotine perceptions was estimated as the difference in all-cause mortality between the base case and the four nicotine perception scenarios. Results Switch rates associated with those who responded, “Definitely not” (8.39%) resulted in a net benefit of preventing nearly 800,000 premature deaths over an 85-year period. Conversely switch rates reflective of those who responded, “Definitely yes” (2.59%) resulted in a net harm of nearly 300,000 additional premature deaths over the same period. Conclusions Accurate knowledge regarding the role of nicotine is associated with higher switch rates and prevention of premature deaths. Our findings suggest that promoting public education to correct perceptions of harm from nicotine has the potential to benefit public health.

Umami peptides, the flavor compounds mainly derived from natural proteins, provide a pleasant taste for humans and exhibit a variety of biological activities, such as antioxidant and lipid-lowering properties. Marine proteins, which serve as excellent sources of umami peptides, have become a focal point of research. This review introduces the research progress on reported marine umami peptides. Firstly, it discusses the structural characteristics of umami peptides and the mechanism behind their formation to create an umami taste. It then presents several commonly used techniques for preparing and regulating umami peptides while summarizing the advantages and disadvantages of each technique. Finally, this review describes the potential application prospects for core technologies within Industry 4.0—such as molecular simulation, artificial intelligence, big data analysis, cloud computing, and blockchain technology—which could bring new opportunities for the development of marine umami peptides.

Abstract Enhancing influenza vaccine cross-protection is imperative to alleviate the significant public health burden of influenza. Heterologous sequential immunization may synergize diverse vaccine formulations and routes to improve vaccine potency and breadth. Here we investigate the effects of immunization strategies on the generation of cross-protective immune responses in female Balb/c mice, utilizing mRNA lipid nanoparticle (LNP) and protein-based PHC nanoparticle vaccines targeting influenza hemagglutinin. Our findings emphasize the crucial role of priming vaccination in shaping Th bias and immunodominance hierarchies. mRNA LNP prime favors Th1-leaning responses, while PHC prime elicits Th2-skewing responses. We demonstrate that cellular and mucosal immune responses are pivotal correlates of cross-protection against influenza. Notably, intranasal PHC immunization outperforms its intramuscular counterpart in inducing mucosal immunity and conferring cross-protection. Sequential mRNA LNP prime and intranasal PHC boost demonstrate optimal cross-protection against antigenically drifted and shifted influenza strains. Our study offers valuable insights into tailoring immunization strategies to optimize influenza vaccine effectiveness.

Deep learning has been extensively applied to medical image segmentation, resulting in significant advancements in the field of deep neural networks for medical image segmentation since the notable success of U-Net in 2015. However, the application of deep learning models to ocular medical image segmentation poses unique challenges, especially compared to other body parts, due to the complexity, small size, and blurriness of such images, coupled with the scarcity of data. This article aims to provide a comprehensive review of medical image segmentation from two perspectives: the development of deep network structures and the application of segmentation in ocular imaging. Initially, the article introduces an overview of medical imaging, data processing, and performance evaluation metrics. Subsequently, it analyzes recent developments in U-Net-based network structures. Finally, for the segmentation of ocular medical images, the application of deep learning is reviewed and categorized by the type of ocular tissue.

Abstract Background There is limited data on the 2-year outcomes of transapical transcatheter edge-to-edge repair (TA-TEER) using the ValveClamp in patients with severe primary mitral regurgitation (MR) and its impact on myocardial deformation. Methods From July 2018 to March 2021, 53 patients with symptomatic severe primary MR underwent TA-TEER were enrolled. The endpoint was the composite of all-cause mortality, recurrent 3 + or 4 + MR, or need for mitral surgery. Results Among the 53 patients who had successfully ValveClamp implantation, 8(15.1%) reached the composite endpoint. Significant improvement in left ventricular (LV) end-diastolic volume, pulmonary artery systolic pressure, NYHA functional class, and MR severity were observed (P

Indole-3-carbinol (I3C), a compound found in cruciferous vegetables, has shown significant efficacy in treating both cancerous and non-cancerous diseases. Its primary derivative, 3,3′-diindolylmethane (DIM), formed during digestion, also exhibits similar therapeutic benefits. In liver disorders, I3C and DIM exhibit dual roles by inhibiting and promoting hepatocellular carcinoma (HCC) and providing relief for nonmalignant liver diseases, such as acute liver injury (ALI), hepatic fibrosis, nonalcoholic fatty liver disease (NAFLD), and alcohol-related liver disease (ALD). Mechanistically, I3C and DIM modulate various pathophysiological processes, including cell proliferation, apoptosis, oxidative stress, and lipogenesis. This review aims to enhance researchers' understanding of the regulatory roles of I3C and DIM in these liver diseases and explore the potential of plant-derived substances in liver disease treatment.

Existing methods for matching multi-modal remote sensing images (MRSI) demonstrate considerable adaptability. However, high-precision matching for rectification remains challenging due to differing imaging mechanisms in cross-modal remote sensing images, leading to numerous non-repeated detailed feature points. Additionally, assuming linear transformations between images conflicts with the complex aberrations present in remote sensing images, limiting matching accuracy. This paper aims to elevate matching accuracy by implementing a detailed texture removal strategy that effectively isolates repeatable structural features. Subsequently, we construct a radiation-invariant similarity function within a generalized gradient framework for least-squares matching, specifically designed to mitigate nonlinear geometric and radiometric distortions across MRSIs. Comprehensive qualitative and quantitative evaluations across multiple datasets, employing substantial manual checkpoints, demonstrate that our method significantly enhances matching accuracy for image data involving multiple modal combinations and outperforms the current state-of-the-art solutions in matching accuracy. Additionally, rectification experiments employing WorldView and TanDEM-X images validate our method’s ability to achieve a matching accuracy of 1.05 pixels, thereby indicating its practical utility and generalization capacity. Access to experiment-related data and codes will be provided at https://github.com/LiaoYF001/refinement/.

Objective Design The diet self-management scale (DSE-MS) items were identified and evaluated by literature and guideline review, expert consultation and pilot testing. Factor structure was tested by randomly dividing the sample into two equal groups for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). The scale was further tested for internal consistency, convergent validity, discriminant validity and known-group validity.Setting A city in China.Participants A total of 724 urban residents from a city in China participated in this study in 2023.Results The final DSE-MS included 45 items and showed good internal consistency with a Cronbach’s alpha coefficient of 0.961. EFA identified 6 dimensions explaining 67.89% of the total variance: cognition (11 items), microenvironment (12 items), macroenvironment (4 items), safety management (7 items), nutrition management (6 items) and environment management (5 items). The CFA showed a good model fit of the six-dimension scale: χ2/df=1.8878, root mean square error of approximate=0.0496, Comparing Fit Index=0.9425, Incremental Fit Index=0.9428, Tucker-Lewis Index=0.9369 and standard root mean square residual=0.0525. Convergent validity was supported by high composite reliability (CR=0.89–0.96) and average variance extracted (AVE=0.58–0.72), while discriminant validity was confirmed by higher AVE estimates than the squared interconstruct correlations. The DSE-MS scores varied by various sample characteristics, verifying the known-group validity.Conclusion The 6-factor 45-item DSE-MS had good psychometric properties assessing diet self-management among urban citizens. The scale can guide future intervention programmes to improve diet self-management and evaluate the intervention effect. Further validation of the DSE-MS in other populations in other areas is needed to verify its wide application.

Creation of novel fluorescent nanomaterials with high photoluminescence quantum yield (PLQY) will lead to high-efficiency luminescent solar concentrators (LSCs). Herein, two kinds of carbon dots (CDs), e.g., blue and green emissive CDs and one novel carbon nanorings (NRs) with yellow emission are synthesized by the solvothermal method. Diaminobenzenesulfonic acid is used as one main precursor to create blue, green emission carbon dots (B/GCDs) and yellow emission carbon nanorings (YCNRs). Importantly, the PLQYs of B/GCDs and YCNRs are 59.8±3.2 %, 12.2±0.7 % and 63.2±1.5 %, respectively. Moreover, B/GCDs and YCNRs are used as fluorescent nanomaterials to construct laminated LSCs with single-layer and double-layer through one-step photopolymerization technique. For single-layer laminated LSCs, YCNRs-based LSCs have the maximum external optical efficiency (ηopt), up to 3.05 %. Furthermore, for double-layer laminated LSCs, the LSCs based on BCDs and YCNRs achieve the highest ηopt of 4.09 %. Compared to single-layer laminated LSCs, the double-layer laminated LSCs show an increase of 34 % in ηopt. Their internal quantum efficiency (ηint) of 29.88 % and external quantum efficiency (ηext) of 25.18 % are obtained, respectively. The presented values are just for single-wavelength excitation. Thus, the synthesis of novel YCNRs with high PLQYs contributes to the optical performances at a large level. Remarkably, the optimal double-layer laminated LSCs show good transparency.

Streptococcus pneumoniae, a gram-positive bacterium, is responsible for diverse infections globally, and its antibiotic resistance presents significant challenges to medical advancements. It is imperative to employ various strategies to identify antibiotics. 3-oxoacyl-[acyl-carrier-protein] reductase (FabG) is a key component in the type II fatty acid synthase (FAS II) system, which is a developing target for new anti-streptococcal drugs. We first demonstrated the function of SpFabG in vivo and in vitro and the 2 Å SpFabG structure was elucidated using X-ray diffraction technique. It was observed that the NADPH binding promotes the transformation from tetramers to dimers in solution, suggesting dimers but not tetramer may be the active conformation. By comparing the structures of FabG homologues, we have identified the conserved tetramerization site and further confirmed the mechanism that the tetramerization site mutation leads to a loss of function and destabilization through mutagenesis experiments. Starting from 533,600 compounds, we proceeded with a sequential workflow involving pharmacophore-based virtual screening, molecular docking, and binding energy calculations. Combining all the structural analysis, we identified L1, L2 and L5 as a promising candidate for SpFabG inhibitor, based on the most stable binding mode in comparison to other evaluated inhibitors.

Abstract Self‐supervised neural language models have recently achieved unprecedented success from natural language processing to learning the languages of biological sequences and organic molecules. These models have demonstrated superior performance in the generation, structure classification, and functional predictions for proteins and molecules with learned representations. However, most of the masking‐based pre‐trained language models are not designed for generative design, and their black‐box nature makes it difficult to interpret their design logic. Here a Blank‐filling Language Model for Materials (BLMM) Crystal Transformer is proposed, a neural network‐based probabilistic generative model for generative and tinkering design of inorganic materials. The model is built on the blank‐filling language model for text generation and has demonstrated unique advantages in learning the “materials grammars” together with high‐quality generation, interpretability, and data efficiency. It can generate chemically valid materials compositions with as high as 89.7% charge neutrality and 84.8% balanced electronegativity, which are more than four and eight times higher compared to a pseudo‐random sampling baseline. The probabilistic generation process of BLMM allows it to recommend materials tinkering operations based on learned materials chemistry, which makes it useful for materials doping. The model is applied to discover a set of new materials as validated using the Density Functional Theory (DFT) calculations. This work thus brings the unsupervised transformer language models based generative artificial intelligence to inorganic materials. A user‐friendly web app for tinkering materials design has been developed and can be accessed freely at www.materialsatlas.org/blmtinker.

Abstract In recent years, knowledge graph technology has been widely applied in various fields such as intelligent auditing, urban transportation planning, legal research, and financial analysis. In traditional auditing methods, there are inefficiencies in data integration and analysis, making it difficult to achieve deep correlation analysis and risk identification among data. Additionally, decision support systems in the auditing process may face issues of insufficient information interpretability and limited predictive capability, thus affecting the quality of auditing and the scientificity of decision-making. However, knowledge graphs, by constructing rich networks of entity relationships, provide deep knowledge support for areas such as intelligent search, recommendation systems, and semantic understanding, significantly improving the accuracy and efficiency of information processing. This presents new opportunities to address the challenges of traditional auditing techniques. In this paper, we investigate the integration of intelligent auditing and knowledge graphs, focusing on the application of knowledge graph technology in auditing work for power engineering projects. We particularly emphasize mainstream key technologies of knowledge graphs, such as data extraction, knowledge fusion, and knowledge graph reasoning. We also introduce the application of knowledge graph technology in intelligent auditing, such as improving auditing efficiency and identifying auditing risks. Furthermore, considering the environment of cloud-edge collaboration to reduce computing latency, knowledge graphs can also play an important role in intelligent auditing. By integrating knowledge graph technology with cloud-edge collaboration, distributed computing and data processing can be achieved, reducing computing latency and improving the response speed and efficiency of intelligent auditing systems. Finally, we summarize the current research status, outlining the challenges faced by knowledge graph technology in the field of intelligent auditing, such as scalability and security. At the same time, we elaborate on the future development trends and opportunities of knowledge graphs in intelligent auditing.

Abstract Hepatic ischemia–reperfusion injury (HIRI) elicits an immune-inflammatory response that may result in hepatocyte necrosis and apoptosis, ultimately culminating in postoperative hepatic dysfunction and hepatic failure. The precise mechanisms governing the pathophysiology of HIRI remain incompletely understood, necessitating further investigation into key molecules and pathways implicated in disease progression to guide drug discovery and potential therapeutic interventions. Gene microarray data was downloaded from the GEO expression profile database. Integrated bioinformatic analyses were performed to identify HIRI signature genes, which were subsequently validated for expression levels and diagnostic efficacy. Finally, the gene expression was verified in an experimental HIRI model and the effect of anti-IL17A antibody intervention in three time points (including pre-ischemic, post-ischemic, and at 1 h of reperfusion) on HIRI and the expression of these genes was investigated. Bioinformatic analyses of the screened characterized genes revealed that inflammation, immune response, and cell death modulation were significantly associated with HIRI pathophysiology. CCL2, BTG2, GADD45A, FOS, CXCL10, TNFRSF12A, and IL-17 pathway were identified as key components involved in the HIRI. Serum and liver IL-17A expression were significantly upregulated during the initial phase of HIRI. Pretreatment with anti-IL-17A antibody effectively alleviated the damage of liver tissue, suppressed inflammatory factors, and serum transaminase levels, and downregulated the mRNA expression of CCL2, GADD45A, FOS, CXCL10, and TNFRSF12A. Injection of anti-IL17A antibody after ischemia and at 1 h of reperfusion failed to demonstrate anti-inflammatory and attenuating HIRI benefits relative to earlier intervention. Our study reveals that the IL-17 pathway and related genes may be involved in the proinflammatory mechanism of HIRI, which may provide a new perspective and theoretical basis for the prevention and treatment of HIRI.

BackgroundAortic regurgitation (AR) may lead to right ventricular dysfunction (RVD), but the prognostic value of RVD in patients undergoing transcatheter aortic valve replacement (TAVR) remains unclear. Our goal was to evaluate the clinical implications, predictors and prognostic significance of RVD in patients with pure AR after TAVR.MethodsIn this multicentre prospective study, patients undergoing TAVR were included between January 2019 and April 2021. The patients were divided into four groups according to the results of transthoracic echocardiography pre- and post-TAVR. The primary end point was 2-year all-cause mortality.ResultsA total of 648 patients were divided into four groups: 325 patients (54.3%) in the no RVD group; 106 patients (17.7%) in the new-onset RVD group; 73 patients (12.2%) in the normalized RVD group; and 94 patients (15.7%) in the residual RVD group. At the 2-year follow-up, there were significant differences in all-cause mortality among the four groups (5.2%, 12.3%, 11.0% and 17.0%, respectively; p

Background and aims: Tumor recurrence significantly affects the prognostic outcomes for liver cancer patients following liver transplantation. However, existing predictive models often neglect the inclusion of body composition indicators. Hence, this research aimed to investigate the significance of the psoas muscle index (PMI) in evaluating the post-transplant prognosis of liver cancer. Methods: A retrospective analysis was conducted on liver cancer patients who underwent liver transplantation surgery. Imaging analysis was performed using CT data to calculate PMI based on the left and right psoas muscle areas. Subsequently, the patients were categorized into PMI-Low and PMI-High groups using the established cut-off values. Univariate and multivariate analyses were performed using Cox proportional hazards regression to assess the correlation between PMI and clinical outcomes, and a nomogram was constructed accordingly. Results: Among the 225 patients included in the analysis, the PMI-High group exhibited significantly improved overall survival (P

Background Electronic health (eHealth) has been widely adopted in chronic disease management. Prior studies focused on time-based reminders as a cue to facilitate behavior change intentions, ignoring the development of automatic cue-behavior associations via other cue types. Objective Hence, this study utilized avatar appearance as a visual-based cue to help establish the automatic association between appearance transformation and health behavior to form habits without intention. Methods To better understand users’ attitudes and experiences toward applying changes in avatar appearance to develop cue-behavior associations for hypertensive patients. Fifteen participants were recruited in a 14-day experiment. After excluding one participant who dropped out of the experiment, others were randomly assigned to two groups. One group consisted of a visual-based cue (a virtual plant) and basic behavior change techniques (BCTs). The other group only included basic BCTs. Attitudes and experience outcomes were collected by interview, and qualitative data were analyzed using thematic analysis. Results 57% of participants had been diagnosed with hypertension for more than five years, and more than 50% of participants have experience using mobile apps or wearables. 66% of participants did physical activity more than three times every week. The result shows that tailored time-based reminders, blood pressure monitoring, and daily dietary intake were the most attractive features. Additionally, hypertensive participants have positive attitudes toward avatar appearance as a visual-based cue to develop cue-behavior association, which enhances self-management motivation. Conclusion This study proposes a visual-based cue design for habit formation and conducts a qualitative method to explore hypertensive patients’ perceptions. The findings offer insights from user's perspectives into hypertensive patients’ attitudes toward visual-based cues and perception of the connection between avatar appearance and health behavior for self-management. Subsequent discussions present eHealth design guidelines of habit formation from intention, automatic cue-behavior association, and self-management perspectives.

Inhomogeneous damping distribution leads to the occurrence of complex modes of structures. Complex modes' vibration and acoustic radiation characteristics are different from real modes. This paper studies the complex modal characteristics of inhomogeneously damped plates for forced vibration. We used the state space method to obtain the complex eigenvalues and eigenvectors of the vibration system and calculated the relationship between modal coordinates and frequency under unit force. The results show that the absolute values of the modal coordinates of two conjugate complex eigenvectors are different, and the structure's vibration is dominated by the eigenvectors corresponding to eigenvalues with positive imaginary parts near natural frequencies. When investigating the structural sound radiation in the vibration mode space, each mode's acoustic radiation is not independent but coupled with each other. The influence of the coupling between complex modes on the calculation results of the acoustic radiation power of the plate is studied in this paper. The results show that the coupling effect between modes cannot be ignored when calculating the acoustic radiation power based on complex modes. Finally, the error of calculating acoustic power using the decoupling approximation instead of the complex mode method is analyzed.

Abstract Background Geographic access to food may affect dietary choices and health outcomes, but the strength and direction of associations may depend on the operationalization of exposure measures. We aimed to systematically review the literature on up-to-date evidence on the association between food environment exposures based on Global Positioning System (GPS) and diet-related and cardiometabolic health outcomes. Methods The databases PubMed, Embase.com, APA PsycInfo (via Ebsco), Cinahl (via Ebsco), the Web of Science Core Collection, Scopus, and the International Bibliography of the Social Sciences (via ProQuest) were searched from inception to October 31, 2022. We included studies that measured the activity space through GPS tracking data to identify exposure to food outlets and assessed associations with either diet-related or cardiometabolic health outcomes. Quality assessment was evaluated using the criteria from a modified version of the Newcastle–Ottawa Scale (NOS) for cross-sectional studies. We additionally used four items from a quality assessment tool to specifically assess the quality of GPS measurements. Results Of 2949 studies retrieved, 14 studies fulfilled our inclusion criteria. They were heterogeneous and represent inconsistent evidence. Yet, three studies found associations between food outlets and food purchases, for example, more exposure to junk food outlets was associated with higher odds of junk food purchases. Two studies found associations between greater exposure to fast food outlets and higher fast food consumption and out of three studies that investigated food environment in relation to metabolic outcomes, two studies found that higher exposure to an unhealthy food environment was associated with higher odds of being overweight. Conclusions The current and limited evidence base does not provide strong evidence for consistent associations of GPS-based exposures of the food environment with diet-related and cardiometabolic health outcomes.

Blastocystis sp. is a prevalent protistan parasite found globally in the gastrointestinal tract of humans and various animals. This review aims to elucidate the advancements in research on axenic isolation techniques for Blastocystis sp. and their diverse applications. Axenic isolation, involving the culture and isolation of Blastocystis sp. free from any other organisms, necessitates the application of specific media and a series of axenic treatment methods. These methods encompass antibiotic treatment, monoclonal culture, differential centrifugation, density gradient separation, micromanipulation and the combined use of culture media. Critical factors influencing axenic isolation effectiveness include medium composition, culture temperature, medium characteristics, antibiotic type and dosage and the subtype (ST) of Blastocystis sp. Applications of axenic isolation encompass exploring pathogenicity, karyotype and ST analysis, immunoassay, characterization of surface chemical structure and lipid composition and understanding drug treatment effects. This review serves as a valuable reference for clinicians and scientists in selecting appropriate axenic isolation methods.

Fine-grained remote sensing ship detection is crucial in a variety of fields, such as ship safety, marine environmental protection, and maritime traffic management. Despite recent progress, current research suffers from the following three major challenges: insufficient features representation, conflicts in shared features, and inappropriate anchor labeling strategy, which significantly impede accurate fine-grained ship detection. To address these issues, we propose FineShipNet as a solution. Specifically, we first propose a novel blend synchronization module, which aims to facilitate the coutilization of semantic information from top-level and bottom-level features and minimize information redundancy. Subsequently, the blend feature maps are fed into a novel polarized feature focusing module, which decouples the features used in classification and regression to create task-specific discriminating features maps. Meanwhile, we adopt the adaptive harmony anchor labeling and propose a novel metric, harmony score, to choose high-quality anchors that can effectively capture the discriminating features of the target. Extensive experiments on four fine-grained remote sensing ship datasets (HRSC2016, DOSR, FGSD2021, and ShipRSImageNet) demonstrate that our FineShipNet outperforms current state-of-the-art object detection methods, achieving superior performance with mean average precision scores of 81.3%, 68.5%, 85.7%, and 63.9%, respectively.

Recently, significant advancements have been achieved in optimizing algorithms for synthetic aperture radar (SAR) ship detection. Nevertheless, two challenges still impede further research as follows. 1) Mainstream methods, whether anchor-free or anchor-based, adhere to a dense paradigm, leading to substantial redundancy and limited adaptability. 2) Ship targets in SAR images exhibit large shape variations and scale differences, making it difficult to efficiently extract key features from background clutter. To tackle the aforementioned problems, we propose DETR with Concentrated dEformable Attention (DCEA), a query-based method for end-to-end optimization of the current pipeline. First, for the irregular shapes and sparse distribution of ships, the concentrated deformable attention is introduced to model the spatial positions of targets, simulating their geometric transformations with precision. Second, an attentionwise propagation module is designed to integrate local fine-grained information with global semantic information, improving the detection performance for objects across diverse scales. Finally, due to the lack of information exchange between object queries, a dimensionwise information mixing module is employed to incorporate key information from various dimensions to enhance their representation capability. To validate the superior performance of DCEA, we conduct extensive experiments on multiple public datasets, achieving mean average precision scores of 0.991, 0.929, and 0.962 on the SSDD, HRSID, and SAR-Ship-Dataset, respectively, with a model size of only 14.34M parameters and 44.4 giga floating point operations.

We study the nonstationary stochastic Multi-Armed Bandit (MAB) problem in which the distributions of rewards associated with arms are assumed to be time-varying and the total variation in the expected rewards is subject to a variation budget. The regret of a policy is defined by the difference in the expected cumulative reward obtained using the policy and using an oracle that selects the arm with the maximum mean reward at each time. We characterize the performance of the proposed policies in terms of the worst-case regret, which is the supremum of the regret over the set of reward distribution sequences satisfying the variation budget. We design Upper-Confidence Bound (UCB)-based policies with three different approaches, namely, periodic resetting, sliding observation window, and discount factor, and show that they are order-optimal with respect to the minimax regret, i.e., the minimum worst-case regret achieved by any policy. We also relax the sub-Gaussian assumption on reward distributions and develop robust versions of the proposed policies that can handle heavy-tailed reward distributions and maintain their performance guarantees.

Abstract Background The increasing prevalence of neurocognitive disorders (NCDs) in the aging population worldwide has become a significant concern due to subjectivity of evaluations and the lack of precise diagnostic methods and specific indicators. Developing personalized diagnostic strategies for NCDs has therefore become a priority. Results Multimodal electroencephalography (EEG) data of a matched cohort of normal aging (NA) and NCDs seniors were recorded, and their faecal samples and urine exosomes were collected to identify multi-omics signatures and metabolic pathways in NCDs by integrating metagenomics, proteomics, and metabolomics analysis. Additionally, experimental verification of multi-omics signatures was carried out in aged mice using faecal microbiota transplantation (FMT). We found that NCDs seniors had low EEG power spectral density and identified specific microbiota, including Ruminococcus gnavus, Enterocloster bolteae, Lachnoclostridium sp. YL 32, and metabolites, including L-tryptophan, L-glutamic acid, gamma-aminobutyric acid (GABA), and fatty acid esters of hydroxy fatty acids (FAHFAs), as well as disturbed biosynthesis of aromatic amino acids and TCA cycle dysfunction, validated in aged mice. Finally, we employed a support vector machine (SVM) algorithm to construct a machine learning model to classify NA and NCDs groups based on the fusion of EEG data and multi-omics profiles and the model demonstrated 92.69% accuracy in classifying NA and NCDs groups. Conclusions Our study highlights the potential of multi-omics profiling and EEG data fusion in personalized diagnosis of NCDs, with the potential to improve diagnostic precision and provide insights into the underlying mechanisms of NCDs. Video Abstract

Abstract Background To investigate the trends in health-related quality of life (HRQoL) among hepatitis C virus (HCV) patients and to assess the longitudinal impact of antiviral therapy on their well-being. Methods In this prospective multicenter observational study in adults with HCV infection, sociodemographic, clinical characteristics and EQ-5D questionnaires were collected. Generalized estimating equation (GEE) models were used to assess the associations between these variables and changes in HRQoL over time. Results 456 patients were included, with a median age of 46.5 (36.5–57.0) years, of which 262 (57.5%) were males and 44 (9.6%) had cirrhosis. 335 patients (73.5%) receiving antiviral therapy and 61.8% achieved sustained virologic response (SVR). The baseline EQ-5D utility and EQ-VAS were 0.916 ± 0.208 and 80.6 ± 13.0. In multivariable analysis of GEE estimation, achieving SVR24 was positively associated with EQ-5D utility (p = 0.000) and EQ-VAS (p = 0.000) over time. Age and income were shown to be significant predictors of EQ-5D utility, while gender, age and genotype were associated with EQ-VAS over time. Conclusions SVR improved long-term HRQoL in HCV patients in the first few years following viral clearance. Certain sociodemographic factors, such as gender, age, income as well as genotype, significantly influenced long-term changes in patients’ quality of life. Trial registration NCT01594554. Registration date: 09/05/2012.

As the speed of high-speed trains increases, the vehicle’s lateral stability steadily deteriorates. There have been observations of abnormal vibrations in the tail car, particularly on certain sections of the railway line. This study built a high-speed train aerodynamic simulation model for a three-car consist, and a multibody dynamics simulation model for an eight-car consist based on numerical simulations of train aerodynamics and multibody dynamics. It investigated both steady and unsteady aerodynamic loads, flow field characteristics, and the dynamic performance of vehicles under varied aerodynamic loads at 400 km/h. The results indicate that the aerodynamic loads generated during high-speed train operation exhibit highly unsteady characteristics. Steady aerodynamic loads have a relatively minor impact on the vehicle’s dynamic performance, whereas unsteady loads exert a more significant influence. Under unsteady aerodynamic forces, the tail car experiences severe lateral vibrations. The lateral stability index, displacement, velocity, and acceleration of the tail car under unsteady conditions were measured at 2.26, 7.54 mm, and 0.53 m/s2, respectively. These values represent increases of over 17.71%, 148.84%, and 111.24%, respectively, compared to the steady loads. Large oscillation amplitudes result in more significant lateral displacements and accelerations of the vehicle. This phenomenon is a crucial factor contributing to the “tail swing” effect observed in high-speed trains. This study emphasizes the importance of considering unsteady aerodynamic effects in assessing the lateral stability of high-speed trains and highlights the significance of mitigating the adverse impacts of such dynamic responses, particularly in the tail car.

Background: Transcatheter aortic valve replacement (TAVR) is a challenge for patients with aortic regurgitation (AR) and a large annulus. Our goal was to evaluate the clinical outcomes and predictors of transapical TAVR in AR patients with a large annulus and noncalcification and the feasibility and safety of 3-dimensional printing (3DP) in the preprocedural simulation. Methods: Patients with a large annulus (diameter >29 mm) were enrolled and divided into the simulation (n = 43) and the nonsimulation group (n = 82). Surgeons used the specific 3DP model of the simulation group to simulate the main steps before the procedure and to refit the transcatheter heart valve (THV) according to the simulated results. Results: The average annular diameter of the overall cohort was 29.8 ± 0.7 mm. Compared with the nonsimulation group, the simulation group used a higher proportion of extra oversizing for THVs (97.6% vs. 85.4%, p = 0.013), and the coaxiality performance was better (9.7 ± 3.9° vs. 12.7 ± 3.8°, p < 0.001). Both THV displacement and ≥ mild paravalvular leakage (PVL) occurred only in the nonsimulation group (9.8% vs. 0, p < 0.001; 9.8% vs. 0, p < 0.001). Multivariate regression analysis showed that extra oversizing, coaxial angle and annulus diameter were independent predictors of THV displacement and ≥ mild PVL, respectively. Conclusions: Based on 3DP guidance, transapical TAVR using extra oversizing was safe and feasible for patients with noncalcified AR with a large annulus. Extra oversizing and coaxial angle were predictors of postprocedural THV displacement and ≥ mild PVL in such patients.

IMPORTANCE:. Patients presenting to the emergency department (ED) with hypoxemia often have mixed or uncertain causes of respiratory failure. The optimal treatment for such patients is unclear. Both high-flow nasal cannula (HFNC) and noninvasive ventilation (NIV) are used. OBJECTIVES:. We sought to compare the effectiveness of initial treatment with HFNC versus NIV for acute hypoxemic respiratory failure. DESIGN, SETTING, AND PARTICIPANTS:. We conducted a retrospective cohort study of patients with acute hypoxemic respiratory failure treated with HFNC or NIV within 24 hours of arrival to the University of Michigan adult ED from January 2018 to December 2022. We matched patients 1:1 using a propensity score for odds of receiving NIV. MAIN OUTCOMES AND MEASURES:. The primary outcome was major adverse pulmonary events (28-d mortality, ventilator-free days, noninvasive respiratory support hours) calculated using a win ratio. RESULTS:. A total of 1154 patients were included. Seven hundred twenty-six (62.9%) received HFNC and 428 (37.1%) received NIV. We propensity score matched 668 of 1154 (57.9%) patients. Patients on NIV versus HFNC had lower 28-day mortality (16.5% vs. 23.4%, p = 0.033) and required noninvasive treatment for fewer hours (median 7.5 vs. 13.5, p < 0.001), but had no difference in ventilator-free days (median [interquartile range]: 28 [26, 28] vs. 28 [10.5, 28], p = 0.199). Win ratio for composite major adverse pulmonary events favored NIV (1.38; 95% CI, 1.15–1.65; p < 0.001). CONCLUSIONS AND RELEVANCE:. In this observational study of patients with acute hypoxemic respiratory failure, initial treatment with NIV compared with HFNC was associated with lower mortality and fewer composite major pulmonary adverse events calculated using a win ratio. These findings underscore the need for randomized controlled trials to further understand the impact of noninvasive respiratory support strategies.

Abstract Meconopsis biluoensis, a new species of Papaveraceae in an alpine meadow from Yunnan, Southwest China, is described and illustrated. Morphologically, it resembles Meconopsis georgei, while it is distinct in acaulescent and hispid with clearly expanded bases on the leaves. A genus‐level molecular phylogenetic analysis supported the closest relationship between M. biluoensis and M. georgei. In a finer population‐level molecular phylogenetic analysis using ribosomal DNA (rDNA) and the chloroplast genome, individuals from M. biluoensis and M. georgei were clearly separated, and the extremely short branch length indicated that the two species had a very short differentiation time. The species has currently been assessed as “endangered” (EN) due to its small‐sized population and narrow distribution following the IUCN categories and criteria.

BackgroundMotion tracking technologies serve as crucial links between physical activities and health care insights, facilitating data acquisition essential for analyzing and intervening in physical activity. Yet, systematic methodologies for evaluating motion tracking data, especially concerning user activity recognition in health care applications, remain underreported. ObjectiveThis study aims to systematically review motion tracking in daily living and physical activities, emphasizing the critical interaction among devices, users, and environments from a design perspective, and to analyze the process involved in health care application research. It intends to delineate the design and application intricacies in health care contexts, focusing on enhancing motion tracking data’s accuracy and applicability for health monitoring and intervention strategies. MethodsUsing a systematic review, this research scrutinized motion tracking data and their application in health care and wellness, examining studies from Scopus, Web of Science, EBSCO, and PubMed databases. The review used actor network theory and data-enabled design to understand the complex interplay between humans, devices, and environments within these applications. ResultsOut of 1501 initially identified studies, 54 (3.66%) were included for in-depth analysis. These articles predominantly used accelerometer and gyroscope sensors (n=43, 80%) to monitor and analyze motion, demonstrating a strong preference for these technologies in capturing both dynamic and static activities. While incorporating portable devices (n=11, 20%) and multisensor configurations (n=16, 30%), the application of sensors across the body (n=15, 28%) and within physical spaces (n=17, 31%) highlights the diverse applications of motion tracking technologies in health care research. This diversity reflects the application’s alignment with activity types ranging from daily movements to specialized scenarios. The results also reveal a diverse participant pool, including the general public, athletes, and specialized groups, with a focus on healthy individuals (n=31, 57%) and athletes (n=14, 26%). Despite this extensive application range, the focus primarily on laboratory-based studies (n=39, 72%) aimed at professional uses, such as precise activity identification and joint functionality assessment, emphasizes a significant challenge in translating findings from controlled environments to the dynamic conditions of everyday physical activities. ConclusionsThis study’s comprehensive investigation of motion tracking technology in health care research reveals a significant gap between the methods used for data collection and their practical application in real-world scenarios. It proposes an innovative approach that includes designers in the research process, emphasizing the importance of incorporating data-enabled design framework. This ensures that motion data collection is aligned with the dynamic and varied nature of daily living and physical activities. Such integration is crucial for developing health applications that are accessible, intuitive, and tailored to meet diverse user needs. By leveraging a multidisciplinary approach that combines design, engineering, and health sciences, the research opens new pathways for enhancing the usability and effectiveness of health technologies.

BackgroundOsteopenia and osteoporosis are posing a long-term influence on the aging population’s health contributing to a higher risk of mortality, loss of autonomy, hospitalization, and huge health system costs and social burden. Therefore, more pertinent data are needed to demonstrate the current state of osteoporosis. ObjectiveThis sampling survey seeks to assess the trends in the prevalence of osteopenia and osteoporosis in a Chinese Han population. MethodsA community-based cross-sectional study involving 16,377 participants used a multistage sampling method. Bone mineral density was measured using the quantitative ultrasonic densitometry. Student t test and Mann-Whitney U test were used to test the difference between normally and nonnormally distributed quantitative variables between male and female participants. A chi-square (χ2) test was used to compare categorized variables. Stratified analysis was conducted to describe the prevalence rates of osteoporosis (T score ≤–2.5) and osteopenia (T score –2.5 to –1.0) across age, sex, calcium intake, and menopause. A direct standardization method was used to calculate the age-standardized prevalence rates of osteoporosis and osteopenia. T-score was further categorized into quartiles (T1-T4) by age- and sex-specified groups. ResultsThe prevalence rates of osteopenia and osteoporosis were 40.5% (6633/16,377) and 7.93% (1299/16,377), respectively, and the age-standardized prevalence rates were 27.32% (287,877,129.4/1,053,861,940) and 3.51% (36,974,582.3/1,053,861,940), respectively. There was an increase in osteopenia and osteoporosis prevalence from 21.47% (120/559) to 56.23% (754/1341) and 0.89% (5/559) to 17.23% (231/1341), respectively, as age increased from 18 years to 75 years old. The prevalence rates of osteopenia and osteoporosis were significantly higher in female participants (4238/9645, 43.94% and 1130/9645, 11.72%) than in male participants (2395/6732, 35.58% and 169/6732, 2.51%; P

PurposeThe objective of this study was to create and validate a novel prediction model that incorporated both multi-modal radiomics features and multi-clinical features, with the aim of accurately identifying acute ischemic stroke (AIS) patients who faced a higher risk of poor outcomes.MethodsA cohort of 461 patients diagnosed with AIS from four centers was divided into a training cohort and a validation cohort. Radiomics features were extracted and selected from diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) images to create a radiomic signature. Prediction models were developed using multi-clinical and selected radiomics features from DWI and ADC.ResultsA total of 49 radiomics features were selected from DWI and ADC images by the least absolute shrinkage and selection operator (LASSO). Additionally, 20 variables were collected as multi-clinical features. In terms of predicting poor outcomes in validation set, the area under the curve (AUC) was 0.727 for the DWI radiomics model, 0.821 for the ADC radiomics model, 0.825 for the DWI + ADC radiomics model, and 0.808 for the multi-clinical model. Furthermore, a prediction model was built using all selected features, the AUC for predicting poor outcomes increased to 0.86.ConclusionRadiomics features extracted from DWI and ADC images can serve as valuable biomarkers for predicting poor clinical outcomes in patients with AIS. Furthermore, when these radiomics features were combined with multi-clinical features, the predictive performance was enhanced. The prediction model has the potential to provide guidance for tailoring rehabilitation therapies based on individual patient risks for poor outcomes.

Abstract. To facilitate the achieving of the goal of “eliminating viral hepatitis as a major public health threat by 2030” set by the World Health Organization, the Chinese Society of Hepatology together with the Chinese Society of Infectious Diseases (both are branches of the Chinese Medical Association) organized a panel of experts and updated the guidelines for prevention and treatment of chronic hepatitis B in China (version 2022). With the support of available evidence, this revision of the guidelines focuses on active prevention, large-scale testing, and expansion of therapeutic indication of chronic hepatitis B with the aim of reducing the hepatitis B–related disease burden.

Abstract Background Obesity may increase perioperative mortality of acute Stanford type A aortic dissection (ATAAD). However, the available evidence was limited. This study aimed to systematically review published literatures about body mass index (BMI) and perioperative mortality of ATAAD. Methods Electronic literature search was conducted in PubMed, Medline, Embase and Cochrane Library databases. All observational studies that investigated BMI and perioperative mortality of ATAAD were included. Pooled odds ratio (OR) and 95% confidence interval (CI) were calculated using a random-effects model. Meta-regression analysis was performed to assess the effects of different clinical variables on BMI and perioperative mortality of ATAAD. Sensitivity analysis was performed to determine the sources of heterogeneity. Egger’s linear regression method and funnel plot were used to determine the publication bias. Results A total of 12 studies with 5,522 patients were eligible and included in this meta-analysis. Pooled analysis showed that perioperative mortality of ATAAD increased by 22% for each 1 kg/m2 increase in BMI (OR = 1.22, 95% CI: 1.10–1.35). Univariable meta-regression analysis indicated that age and female gender significantly modified the association between BMI and perioperative mortality of ATAAD in a positive manner (meta-regression on age: coefficient = 0.04, P = 0.04; meta-regression on female gender: coefficient = 0.02, P = 0.03). Neither significant heterogeneity nor publication bias were found among included studies. Conclusions BMI is closely associated with perioperative mortality of ATAAD. Optimal perioperative management needs to be further explored and individualized for obese patient with ATAAD, especially in elderly and female populations. Trial registration PROSPERO (CRD42022358619). Graphical Abstract BMI and perioperative mortality of ATAAD.

Abstract Introduction A pan-genotypic and effective treatment regimen for patients with chronic hepatitis C virus (HCV) infection remains an unmet medical need in China. Alfosbuvir is a novel potent HCV NS5B polymerase inhibitor in development for the treatment of chronic HCV infection. We conducted a phase 3 study to evaluate the efficacy and safety of alfosbuvir in combination with daclatasvir in Chinese patients with HCV infection. Methods All patients received 600 mg alfosbuvir tablets plus 60 mg daclatasvir tablets once daily for 12 weeks. The primary endpoint was sustained virological response 12 weeks after the end of treatment (SVR12). A follow-up visit was done at week 4 and 12, and those who achieved SVR12 were followed up at post-treatment week 24. Results Of the 326 patients who received at least one dose of the study drug, 320 (98.2% [95% confidence interval (CI): 96.5%–99.5%]) achieved sustained virological response at post-treatment week 12 (SVR12), which was superior to the historical SVR12 rate of 88% (p

Objectives Aiming at the problems that the traditional marine main engine fault diagnosis model is difficult to update with real-time data, and the marine main engine has many monitoring points but few fault samples, a fault diagnosis method which can handle unbalanced data and update the model online is proposed. MethodsFirst, principal component analysis (PCA) is used to reduce and extract the features of the monitoring samples to reduce the complexity of the training model, and the SMOTETomek technique is used to construct fault samples to balance the training set. Next, to solve the problem that the diagnosis model is difficult to update in real time, the online sequential extreme learning machine with regularization (OSRELM) model which combines regularization method and can update online is introduced. Finally, the feasibility of the OSRELM model is verified by taking the main engine fuel system as an example, and the effectiveness of the overall model is verified by ablation experiments with unbalanced marine main engine data. ResultsThe results show that the proposed method can improve the diagnostic accuracy by 29.73% on the basis of the original model. ConclusionsThe proposed method has higher diagnostic accuracy, a smaller fluctuation range and better stability than other similar algorithms. In the case of unbalanced data, it still has a strong ability to identify fault samples, providing valuable references for research on marine main engine fault diagnosis.

Abstract Background To assess the prevalence of anemia before and after antiretroviral therapy (ART) initiation and to identify impact of anemia on mortality among HIV-infected patients in China during the Treat-All era. Methods All HIV-infected patients who newly initiated ART between January 1, 2017 and December 31, 2020 were enrolled and followed up to December 31, 2021 in China. We analyzed the prevalence of anemia before and after ART initiation. Generalized estimating equations were fitted to determine factors associated with anemia after ART. Time-dependent cox proportional hazards models were performed to estimate the effect of anemia on death. Results Of 436,658 patients at the baseline of ART initiation, the overall prevalence of anemia was 28.6%. During a median 2.65 (IQR: 1.80–3.51) years of follow-up after ART initiation, 376,325 (86.2%) patients had at least one Hb measurement (a total of 955,300 hemoglobin measurements). The annual prevalence of anemia after ART was 17.0%, 14.1%, 13.4%, 12.6% and 12.7%, respectively. Being anemic at the baseline of ART initiation (adjusted odds ratio, aOR = 6.80, 95% confidence interval (CI): 6.67–6.92) was the strongest factor associated with anemia after ART. Anemia status after ART showed a strong association with death after multivariable adjustment (mild anemia: adjusted hazard ratio (aHR) = 2.65, 95% CI: 2.55–2.76; moderate anemia: aHR = 4.60; 95% CI:4.40–4.81; severe anemia: aHR = 6.41; 95% CI:5.94–6.91). Conclusions In the era of ART universal access, pre-ART anemia was common among HIV-infected patients. Notably, a certain proportion of anemia still persisted after ART, and was significantly associated with death. We recommend strengthening the monitoring of patients at risk of anemia, especially in patients with baseline anemia or during the first year of ART, and timely treatment for correcting anemia.

In recent years, intensive tunnel construction has induced a large number of karst collapses in the Gele Mountain area. However, the distribution, scale, cause and evolution of karst collapses have not been systematically summarized. By means of data collation, drilling data analysis, monitoring data interpretation and other working methods, this study systematically analyzes the development law and formation conditions of karst collapse in the Gelle Mountain region, conducts in-depth discussion of the cave-causing mechanism and evolution process, and comes to the following conclusions. Multiple groups of karst collapse are developed in the wide karst depression, trough or dense area of tectonic fissure covered by Quaternary silty clay in the tunnel site. The multi-stage rock cave and fracture system provide a good spatial development condition for the formation of karst collapse, and the cave-inducing force mainly comes from the change of groundwater dynamic condition caused by tunnel construction. The main mechanism of karst collapse is that tunnel construction changes the local groundwater environment, and the collapse of collapsible geological structures is induced by multi-effect caving in the reconstruction process. The development and evolution of karst collapse includes three stages: incubation, formation and stability. In the incubation period the water-rich karst fissure and cave system are initially exposed during tunnel construction. Space for soil migration and collapse is formed in the surface fissure and cave zone, and the local deformation and fall of overburden occur. In the formation period water diversion and discharge measures are usually adopted in tunnel construction and operation, and karst collapse is eventually formed under the action of multi-effect collapse. In the stable period, after tunnel operation to a certain stage, groundwater environment gradually tends to a new dynamic equilibrium, the existing karst collapse gradually becomes stable, collapse group no longer expands outward. The results can be used as reference for karst collapse monitoring and warning, ecological restoration and tunnel planning.

Abstract Self-supervised neural language models have recently found wide applications in the generative design of organic molecules and protein sequences as well as representation learning for downstream structure classification and functional prediction. However, most of the existing deep learning models for molecule design usually require a big dataset and have a black-box architecture, which makes it difficult to interpret their design logic. Here we propose the Generative Molecular Transformer (GMTransformer), a probabilistic neural network model for generative design of molecules. Our model is built on the blank filling language model originally developed for text processing, which has demonstrated unique advantages in learning the “molecules grammars” with high-quality generation, interpretability, and data efficiency. Benchmarked on the MOSES datasets, our models achieve high novelty and Scaf compared to other baselines. The probabilistic generation steps have the potential in tinkering with molecule design due to their capability of recommending how to modify existing molecules with explanation, guided by the learned implicit molecule chemistry. The source code and datasets can be accessed freely at https://github.com/usccolumbia/GMTransformer

This study aimed to understand the dynamic changes in fibrosis and its relationship with the evaluation of post-treatment viral hepatitis using qFibrosis. A total of 158 paired pre- and post-treatment liver samples from patients with chronic hepatitis B (CHB; n = 100) and C (CHC; n = 58) were examined. qFibrosis was employed with artificial intelligence (AI) to analyze the fibrosis dynamics in the portal tract (PT), periportal (PP), midzonal, pericentral, and central vein (CV) regions. All patients with CHB achieved a virological response after 78 weeks of treatment, whereas patients with CHC achieved a sustained viral response after 24 weeks. For patients initially staged as F5/6 (Ishak system) at baseline, the post-treatment cases exhibited a significant reduction in the collagen proportionate area (CPA) (25–69%) and number of collagen strings (#string) (9–72%) across all regions. In contrast, those initially staged as F3/4 at baseline showed a similar CPA and #string trend at 24 weeks. For regression patients, 27 parameters (25 in the CV region) in patients staged as F3/4 and 15 parameters (three in the PT and 12 in the PP regions) in those staged as F5/6 showed significant differences between the CHB and CHC groups at baseline. Following successful antiviral treatment, the pre- and post-treatment liver samples provided quantitative evidence of the heterogeneity of fibrotic features. qFibrosis has the potential to provide new insights into the characteristics of fibrosis regression in both patients with CHB and CHC as early as 24 weeks after antiviral therapy.

The rapid expansion of reservoir fractures and the enlargement of the area affected by working fluids can be accomplished solely through fracturing operations of oilfield working fluids in geological reservoirs. Supercritical CO2 is regarded as an ideal medium for shale reservoir fracturing owing to the inherent advantages of environmental friendliness, excellent capacity, and high stability. However, CO2 gas channeling and complex propagation of fractures in shale reservoirs hindered the commercialization of Supercritical CO2 fracturing technology. Herein, a simulation method for Supercritical CO2 fracturing based on cohesive force units is proposed to investigate the crack propagation behavior of CO2 fracturing technology under different construction parameters. Furthermore, the shale fracture propagation mechanism of Supercritical CO2 fracturing fluid is elucidated. The results indicated that the propagation ability of reservoir fractures and Mises stress are influenced by the fracturing fluid viscosity, fracturing azimuth angle, and reservoir conditions (temperature and pressure). An azimuth angle of 30° can achieve a maximum Mises stress of 3.213 × 107 Pa and a crack width of 1.669 × 10−2 m. However, an apparent viscosity of 14 × 10−6 Pa·s results in a crack width of only 2.227 × 10−2 m and a maximum Mises stress of 4.459 × 107 Pa. Additionally, a weaker fracture propagation ability and reduced Mises stress are exhibited at the fracturing fluid injection rate. As a straightforward model to synergistically investigate the fracture propagation behavior of shale reservoirs, this work provides new insights and strategies for the efficient extraction of shale reservoirs.

In precise space operation tasks, the impact of disturbing torques on the space flexible robotic arm (SFRA) cannot be ignored. Besides, the slender structure of the SFRA is very likely to generate vibration of the robotic arm. These are all potential hidden dangers in space safety. To quantify the potential risk, an accurate dynamics model of the SFRA considering the disturbing torque is built by Lagrange principle and the assumed modal method (AMM). Moreover, the effects of the disturbing torque, modal order and nonlinear terms on the deformation accuracy of the SFRA are compared. It is observed that the simplified dynamics model with neglecting the nonlinear terms (NNTs) has a high model accuracy and be easily solved. Therefore, the NNTs simplified model is chosen for deriving the transfer function of the SFRA. The parameters of the PI controller are adjusted in real time based on fuzzy rules to reduce the tracking error in the SFRA. In addition, the disturbance observer is designed to observe and compensate of the disturbance torque in the SFRA. The control method of adjusting controller parameters with fuzzy rules based on the disturbance observer greatly improves the rotational control accuracy of the SFRA. Finally, the validity of aforementioned control strategy is confirmed by simulation analysis and experimental results.

Background: The improvement rate and predictors of secondary mitral regurgitation in patients with aortic regurgitation undergoing transcatheter aortic valve replacement (TAVR) remain unclear. This study aimed to identify predictors of persistent moderate to severe secondary mitral regurgitation after TAVR in patients with aortic regurgitation by assessing mitral valve geometry with computed tomography (CT). Methods: This retrospective cohort study reviewed 242 consecutive patients with aortic regurgitation who underwent TAVR between May 2014 and December 2022. Patients with primary or less than moderate mitral regurgitation were excluded. Mitral annular dimensions (area, perimeter, anteroposterior, intercommissural, and trigone-to-trigone diameter), mitral valve tenting geometry (mitral valve tenting area [MVTA] and mitral valve tenting height [MVTH]), and papillary muscle displacement were systematically measured at CT. Mitral regurgitation improvement was assessed at 3 months after TAVR by echocardiography. Logistic regression was performed to explore the association of mitral valve geometry with mitral regurgitation improvement after TAVR. Results: A total of 75 patients (mean age, 74 ± 7 years; 32.0% female) with moderate to severe secondary mitral regurgitation were included in the final analysis. Mitral regurgitation improved in 49 patients and remained unchanged in 26 patients. Mitral annular dimensions, including area, perimeter, anteroposterior, and intercommissural diameter, were associated with mitral regurgitation improvement. MVTA and MVTH were risk factors for sustained mitral regurgitation. In addition, QRS duration >120 ms and atrial fibrillation had an impact on the mitral regurgitation improvement. Mitral annular area (odds ratio [OR], 1.41; 95% confidence interval [CI]: 1.05, 1.90; p = 0.02) and MVTA (OR, 7.24; 95% CI: 1.72, 30.44; p = 0.007) were independent predictors of persistent secondary mitral regurgitation after TAVR. Conclusions: Mitral annular area and MVTA were independent predictors of persistent secondary mitral regurgitation after TAVR.

TIR1/AFB proteins are a class of auxin receptors with key roles in plant development and biotic and abiotic stress responses; several have been identified as targets of the auxin-mimicking herbicide picloram. In this study, we identified five putative TIR1/AFB gene family members in the important vegetable crop Solanum melongena (eggplant) and characterized them using bioinformatics tools and gene expression analyses. Phylogenetic analysis of the TIR1/AFBs classified them into three subgroups based on their Arabidopsis and Solanum lycopersicum homologs. AFB6 homologs were present only in S. melongena and S. lycopersicum, whereas AFB2/3 homologs were found only in Arabidopsis. One pair of S. melongena TIR1 homologs were located in syntenic regions in the genome and appeared to have arisen by segmental duplication. Promoter analysis revealed 898 cis-elements in the TIR1/AFB promoters, 125 of which were related to hormones, stress, light, or growth responses, but only SmAFB5 had a cis-acting regulatory element involved in auxin responsiveness (AuxRR-core). RNA sequencing and expression profiling showed that the TIR1/AFB genes were differentially expressed at different growth stages and in response to light, temperature, and drought. Only SmTIR1A expression was significantly induced by picloram treatment and different growth stages. TIR1/AFB expression is regulated by microRNAs (miRNAs) in other plant species, and we identified 6 or 29 miRNAs that potentially targeted the five TIR1/AFB genes on the basis of comparisons with S. lycopersicum and S. tuberosum miRNAs, respectively. Three-dimensional protein structure predictions revealed that all the TIR1/AFB proteins were very similar in structure, differing only in the numbers of alpha helices and in one angle linking an α helix and a β sheet. For measuring the function of TIR1/AFB genes in response to drought, SmAFB5 was selected, and knockdown by virus-induced gene silence (VIGS) 35S::SmAFB5 lines showed resistance to drought compared to controls. These analyses provide insight into the potential functions of TIR1/AFBs during growth and in response to stress; they highlight differences among the SmTIR1/AFBs that may be useful for eggplant breeding.

Bogie hunting instability is one of the common faults in railway vehicles. It not only affects ride comfort but also threatens operational safety. Due to the lower operating speed of metro vehicles, their bogie hunting stability is often overlooked. However, as wheel tread wear increases, metro vehicles with high conicity wheel–rail contact can also experience bogie hunting instability. In order to enhance the operational safety of metro vehicles, this paper conducts field tests and simulation calculations to study the bogie hunting instability behavior of metro vehicles and proposes corresponding solutions from the perspective of wheel–rail contact relationships. Acceleration and displacement sensors are installed on metro vehicles to collect data, which are processed in real time in 2 s intervals. The lateral acceleration of the frame is analyzed to determine if bogie hunting instability has occurred. Based on calculated safety indicators, it is determined whether deceleration is necessary to ensure the safety of vehicle operation. For metro vehicles in the later stages of wheel wear (after 300,000 km), the stability of their bogies should be monitored in real time. To improve the stability of metro vehicle bogies while ensuring the longevity of wheelsets, metro vehicle wheel treads should be reprofiled regularly, with a recommended reprofiling interval of 350,000 km.

Abstract Inhibin subunit beta A (INHBA) is a member of the transforming growth factor-beta (TGF-β) superfamily that plays a fundamental role in various cancers. However, a systematic analysis of the exact role of INHBA in patients with gastric cancer (GC) has not yet been conducted. We evaluated the expression levels of INHBA and the correlation between INHBA and GC prognosis in GC. The relationship between INHBA expression, immune infiltration levels, and type markers of immune cells in GC was also explored. In addition, we studied INHBA mutations, promoter methylation, and functional enrichment analysis. Besides, high expression levels of INHBA in GC were significantly related to unfavorable prognosis. INHBA was negatively correlated with B cell infiltration, but positively correlated with macrophage and most anticancer immunity steps. INHBA expression was positively correlated with the type markers of CD8+ T cells, neutrophils, macrophages, and dendritic cells. INHBA has a weak significant methylation level change between tumor and normal tissues and mainly enriched in cancer-related signaling pathways. The present study implies that INHBA may serve as a potential biomarker for predicting the prognosis of patients with GC. INHBA is a promising predictor of immunotherapy response, with higher levels of INHBA indicating greater sensitivity.

Abstract Background Lipid metabolism plays an important role in liver regeneration, but its regulation still requires further research. In this study, lipid metabolites involved in mouse liver regeneration at different time points were sequenced and analyzed to study their influence on liver regeneration and its mechanism. Methods Our experiment was divided into two parts. The first part examined lipid metabolites during liver regeneration in mice. In this part, lipid metabolites were sequentially analyzed in the livers of 70% mouse hepatectomy models at 0, 1, 3and 7 days after operation to find the changes of lipid metabolites in the process of liver regeneration. We screened L-carnitine as our research object through metabolite detection. Therefore, in the second part, we analyzed the effects of carnitine on mouse liver regeneration and lipid metabolism during liver regeneration. We divided the mouse into four groups: control group (70% hepatectomy group); L-carnitine group (before operation) (L-carnitine were given before operation); L-carnitine group (after operation)(L-carnitine were given after operation) and L-carnitine + perhexiline maleate (before operation) group. Weighing was performed at 24 h, 36 and 48 h in each group, and oil red staining, HE staining and MPO staining were performed. Tunnel fluorescence staining, Ki67 staining and serological examination. Results Sequencing analysis of lipid metabolites in 70% of mouse livers at different time points after hepatectomy showed significant changes in carnitine metabolites. The results showed that, compared with the control group the mouse in L-carnitine group (before operation) at 3 time points, the number of fat drops in oil red staining was decreased, the number of Ki67 positive cells was increased, the number of MPO positive cells was decreased, the number of Tunnel fluorescence positive cells was decreased, and the liver weight was increased. Serum enzymes were decreased. Compared with control group, L-carnitine group (after operation) showed similar trends in all indexes at 36 and 48 h as L-carnitine group (before operation). L-carnitine + perhexiline maleate (before operation) group compared with control group, the number of fat drops increased, the number of Ki67 positive cells decreased, and the number of MPO positive cells increased at 3 time points. The number of Tunnel fluorescent positive cells increased and serum enzyme increased. However, both liver weights increased. Conclusion L-carnitine can promote liver cell regeneration by promoting lipid metabolism and reduce aseptic inflammation caused by excessive lipid accumulation.

Abstract Background Dyslipidemia and inflammation are significant factors for the onset of cardiovascular diseases (CVD); however, studies regarding their interactions on the risk of CVD are scarce. This study aimed to assess the interaction of dyslipidemia and high-sensitivity C-reactive protein (hs-CRP) on CVD. Methods This prospective cohort enrolled 4,128 adults at baseline in 2009 and followed them up until May 2022 for collecting CVD events. Cox-proportional hazard regression analysis estimated the hazard ratios (HRs) and 95% confidence intervals (CIs) of the associations of increased hs-CRP (≥ 1 mg/L) and dyslipidemia with CVD. The additive interactions were explored using the relative excess risk of interaction (RERI) and the multiplicative interactions were assessed with HRs (95% CI) while the multiplicative interactions were assessed by the HRs (95% CI) of interaction terms. Results The HRs of the association between increased hs-CRP and CVD were 1.42 (95% CI: 1.14–1.79) and 1.17 (95% CI: 0.89–1.53) among subjects with normal lipid levels and subjects with dyslipidemia, respectively. Stratified analyses by hs-CRP levels showed that among participants with normal hs-CRP ( 2.10 g/L had a significant association with CVD [HR (95% CI): 1.69 (1.14–2.51)]. Interaction analyses showed that increased hs-CRP had multiplicative and additive interactions with LDL-C ≥ 160 mg/dL and non-HDL-C ≥ 190 mg/dL on the risk of CVD [HRs (95%CIs): 0.309 (0.153–0.621), and 0.505 (0.295–0.866); RERIs (95%CIs): -1.704 (-3.430-0.021 and − 0.694 (-1.476-0.089), respectively, all P

Abstract Background Since hepatitis B surface antigen (HBsAg) loss is rarely achieved with nucleos(t)ide analog (NA) treatment, most patients require life-long NA treatment. Previous studies have shown that some patients remain virologically responsive even after NA cessation. However, there is still controversy surrounding whether NA discontinuation increases the HBsAg loss rate. Therefore, this study aimed to assess the cumulative rate of HBsAg loss and identify the predictors of HBsAg loss after NA discontinuation. Methods This multicenter prospective study included HBV e antigen (HBeAg)-positive patients without cirrhosis from 12 hospitals in China who met the inclusion criteria. The enrolled patients stopped NA and were followed up with clinical and laboratory assessments every 3 months for 24 months after NA cessation or until clinical relapse (CR) occurred. Results Overall, 158 patients were classified into two groups. Group A included patients with HBsAg positivity at NA cessation (n = 139), and Group B included patients with HBsAg negativity at NA cessation (n = 19). In Group A, the 12-month and 24-month cumulative rates of HBsAg loss were4.3%and 9.4%, respectively. End of treatment (EOT) HBsAg (hazard ratio (HR) = 0.152, P