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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