Search

Background: Combined first-line therapies have been frequently adopted for patients with anti-N-methyl-D-aspartate receptor (anti-NMDAR) encephalitis. Plasma exchange (PE) or immunoadsorption (IA) was used as an add-on option following initial immunotherapies, including high-dose steroids and intravenous immunoglobulin (IVIG). However, whether a shorter delay of PE or IA can improve the early recovery prognosis of patients with anti-NMDAR encephalitis remains largely unknown. Objective: To compare short-term clinical improvement between patients with early and late initiation of PE or IA in anti-NMDAR encephalitis. Design: A retrospective study was conducted for patients admitted with anti-NMDAR encephalitis between January 2015 and December 2023 ( n = 29), including 21 patients who received PE or IA as synergistic therapies. Methods: The clinical prognosis was compared between the early PE/IA group and the late PE/IA group in the research. Primary outcome included changes in the Clinical Assessment Scale for Autoimmune Encephalitis (∆CASE) at 90 and 120 days after encephalitis onset. Secondary outcomes included changes in the modified Rankin scale (∆mRS) after 90 and 120 days from encephalitis onset, and the length of intensive care unit (ICU) stay for patients with severe anti-NMDAR encephalitis. Results: The ∆CASE scores after 90 and 120 days from encephalitis onset revealed a significant difference between patients with early and late initiation of PE or IA ( p ⩽ 0.05). A significant difference in the ∆mRS was also found between patients with early and late initiation of PE or IA in severe encephalitis ( p ⩽ 0.05). No significant difference was found in the length of ICU admission ( p = 0.101). Conclusion: Our findings emphasize the importance of considering PE or IA as early as feasible for patients with anti-NMDAR encephalitis, even when steroids and IVIG are in use.

ObjectivesTo determine risk factors for the occurrence of adverse outcomes in patients with new-onset refractory status epilepsy (NORSE) and to construct a concomitant nomogram.MethodsSeventy-six adult patients with NORSE who were admitted to the Department of Neurology, First Affiliated Hospital of Sun Yat-sen University between January 2016 and December 2022 were enrolled for the study. Participants were divided into two—those with good and poor functional outcomes—and their pertinent data was obtained from the hospital medical recording system. Univariate analysis was used to identify potential causes of poor outcomes in both groups and a multivariate logistic regression model was used to identify risk factors for the occurrence of poor outcomes. Using the R programming language RMS package, a nomogram was created to predict the occurrence of poor outcomes.ResultsThe NORSE risk of adverse outcome nomogram model included four predictors, namely duration of mechanical ventilation (OR = 4.370, 95% CI 1.221–15.640, p = 0.023), antiviral therapy (OR = 0.045, 95% CI 0.005–0.399, p = 0.005), number of anesthetics (OR = 13.428, 95% CI 2.16–83.48, p = 0.005) and neutrophil count/lymphocyte count ratio (NLR) (OR = 5.248, 95% CI 1.509–18.252, p = 0.009). The nomogram had good consistency and discrimination in predicting risk and can thus assist clinical care providers to assess outcomes for NORSE patients. Through ordinary bootstrap analyses, the results of the original set prediction were confirmed as consistent with those of the test set.ConclusionThe nomogram model of risk of adverse outcomes in NORSE adult patients developed in this study can facilitate clinicians to predict the risk of adverse outcomes in NORSE patients and make timely and reasonable interventions for patients at high risk of adverse outcomes.

Numerous studies have explored the impact of Outward Foreign Direct Investment (OFDI) on upgrading industrial structures in home countries. However, a notable gap exists in the literature regarding the reverse relationship. Based on the cross-border greenfield investment data of Chinese provinces in Association of Southeast Asian Nations (ASEAN) countries from 2003 to 2021, this study employed the Ordinary Least Squares (OLS) model to evaluate the impact of industrial upgrading in each province on OFDI to address this gap. The findings suggest that China’s industrial structure upgrading significantly promotes OFDI toward ASEAN countries, though the effect varies by region within China and by the income levels of host countries. Regionally, industrial upgrading in eastern China notably stimulates OFDI growth, while the effect is not significant in the central and western regions, reflecting inconsistent evolution of industrial structures in various regions. Regarding host country income levels, the promotion effect of industrial structure upgrading on OFDI is influenced by the economic development level of the host country. Furthermore, we find that R&D intensity acts as a moderator that links industrial structural upgrading to OFDI responses. These findings withstand robustness checks, including tests for endogeneity. Ultimately, this study provides policy insights for strengthening the virtuous cycle between industrial upgrading and OFDI.

The rapid advancement of deep learning technologies presents novel opportunities for restoring damaged patterns in ancient silk, which is pivotal for the preservation and propagation of ancient silk culture. This study systematically scrutinizes the evolutionary trajectory of image inpainting algorithms, with a particular emphasis on those firmly rooted in the Context-Encoder structure. To achieve this study’s objectives, a meticulously curated dataset comprising 6996 samples of ancient Chinese silk (256 × 256 pixels) was employed. Context-Encoder-based image inpainting models—LISK, MADF, and MEDFE—were employed to inpaint damaged patterns. The ensuing restoration effects underwent rigorous evaluation, providing a comprehensive analysis of the inherent strengths and limitations of each model. This study not only provides a theoretical foundation for adopting image restoration algorithms grounded in the Context-Encoder structure but also offers ample scope for exploration in achieving more effective restorations of ancient damaged silk.

To ensure the safety and durability of concrete structures, timely detection and classification of concrete cracks using a low-cost and high-efficiency method is necessary. In this study, a concrete surface crack damage detection method based on the ResNet-18 residual network was developed. This method was implemented by training a model with images to extract the cracks, where the image processing algorithms and deep learning were combined. The results show that the computational accuracy can meet the requirements by utilizing the established image dataset and appropriate model hyperparameters. The trained model had high recognition accuracy when the 256 × 256 resolution images were adopted, and the worst accuracy of crack recognition in the test set was over 90%. The average accuracy in the test set was 91.3% when considering environmental interference generated by processing the images with a brightness adjustment, salt-and-pepper noise, and localized interference. Then, it was demonstrated that the present model possesses good robustness for crack identification in different environments. The average recognition accuracy when dealing with images of a real bridge, which are outside the training dataset, was 99.7%. The residual network model developed in this study has the advantages of low cost, high efficiency, and practicality compared to traditional detection methods. Compared to the existing deep learning methods, the model created in this study requires less computational resources and storage space, and shows a faster training speed and higher accuracy.

IntroductionSmoking is a leading public health risk. Many countries are reducing the demand for tobacco through graphic health warning labels (GHWLs). This study aims to explore smokers’ perceptions of GHWLs and analyze the effect of GHWLs on their behavioral intentions to quit smoking.MethodsA theoretical model is designed by synthesizing protection motivation theory, an extension of the extended parallel process model, and the theory of planned behavior. We collected a cross-sectional sample of 547 anonymous smokers through a stratified random sampling strategy. GHWLs published in 2011 by the US Food and Drug Administration were used in the survey to assess smokers’ responses to them, and then the hypotheses are validated through structural equation models.ResultsThe results suggest that perceived severity, perceived vulnerability, response efficacy, and health anxiety have a significant impact on smokers’ protection motivation. Furthermore, smokers’ protection motivation directly impacts the behavioral intention to quit smoking and indirectly influences intention to quit through attitudes.DiscussionThese findings have practical implications for the implementation and improvement of GHWLs policies. Meanwhile, this study enriches the literature on public health protection measures (i.e., GHWLs) and smokers’ behavioral intention to quit smoking.

Abstract Background Colorectal cancer (CRC) is a common malignancy with the second highest mortality and the third highest morbidity worldwide. However, the overall survival of patients is unsatisfactory, thus requiring more effective clinical strategies. Celastrol (CLT), a natural bioactive compound, has been reported to induce reactive oxygen species (ROS)-mediated apoptosis to exhibit significant antitumor effects against CRC. However, the poor water solubility, low targeting ability, and bioavailability of CLT have limited its application, and CLT-induced protective autophagy weakens its therapeutic efficiency. Results We designed a targeted chemo-phototherapy nanoplatform (HCR NPs) to improve the application of CLT. The codelivery of IR820 and CLT in HCR NPs solved the water-soluble problem of CLT and enhanced apoptosis via IR820-mediated hyperthermia. In addition, hydroxychloroquine (HCQ) conjugated to hyaluronic acid (HA) not only increased the active targeting of HCR NPs but also inhibited CLT-induced protective autophagy to exacerbate apoptosis, thus achieving an amplified antitumor effect. Importantly, the HCR NPs exhibited an excellent therapeutic effect on CRC both in vitro and in vivo. Conclusion The HCR NPs presented in this study may not merely provide a new reference for the clinical application of CLT but also result in an attractive strategy for CRC treatment.

Colorectal cancer (CRC), a malignancy with a relatively high fatality rate, still poses a growing global public health challenge due to the lack of well-established and effective treatment strategies. Brigatinib, an ALK inhibitor approved for the treatment of ALK-positive NSCLC, has been repurposed to suppress the growth of ALK-negative CRC cells via sustained endoplasmic reticulum (ER) stress. Moreover, the photosensitizer Chlorine e6 (Ce6) exhibits a high singlet oxygen quantum yield, which promote the generation of reactive oxygen species (ROS) to exacerbate ER stress and function synergistically with chemotherapeutics. However, the clinical application of both brigatinib and Ce6 is limited by their poor water solubility. Therefore, a repurposed chemo-photodynamic therapy nanoplatform (BCT NPs) utilizing d-α-tocopheryl polyethylene glycol 1000 succinate (TPGS) as a nanocarrier for the codelivery of the chemotherapeutics brigatinib and photosensitizer Ce6 is developed, which is capable of disrupting redox homeostasis and enhancing ER stress against CRC. The developed nanoplatform successfully addressed therapeutic drug solubility, improved brigatinib biocompatibility with a lower hemolysis rate, and demonstrated satisfactory CRC suppression effects in vitro and in vivo. Overall, BCT NPs provide a promising strategy to construct repurposed and versatile nanoplatforms against CRC.

Information retrieval across multiple modes has attracted much attention from academics and practitioners. One key challenge of cross-modal retrieval is to eliminate the heterogeneous gap between different patterns. Most of the existing methods tend to jointly construct a common subspace. However, very little attention has been given to the study of the importance of different fine-grained regions of various modalities. This lack of consideration significantly influences the utilization of the extracted information of multiple modalities. Therefore, this study proposes a novel text-image cross-modal retrieval approach that constructs a dual attention network and an enhanced relation network (DAER). More specifically, the dual attention network tends to precisely extract fine-grained weight information from text and images, while the enhanced relation network is used to expand the differences between different categories of data in order to improve the computational accuracy of similarity. The comprehensive experimental results on three widely-used major datasets (i.e., Wikipedia, Pascal Sentence, and XMediaNet) show that our proposed approach is effective and superior to existing cross-modal retrieval methods.

In recent years, suppliers’ sustainability has been the primary source of conflict in multinational supply chains. How suppliers from developing nations may accomplish sustainable development and competitive advantage goals has become a tough issue inside the multinational supply chain, particularly in the agri-food cold chain. Using an integrated theory-based perspective, this paper analyzes how sustainable logistics service providers’ supply chain cooperation (MSCC) might drive the sustainable transformation of suppliers in developing nations. Between 1 June 2020 and 15 January 2021, we gathered feedback from 215 Chinese vendors and analyzed and discussed possible models using structural equation modeling. This study demonstrates that the sustainable practices of logistics service providers are the foundation for influencing the collaboration of suppliers in developing nations with regard to sustainability. In addition, the five-stage transnational supply chain cooperation based on the theory of innovation diffusion is an efficient strategy for ensuring the sustainable development of suppliers in developing nations. This paper examines the theoretical framework at the forefront of multinational supply chain sustainability development. This presents the most recent experience and policy application for suppliers from emerging nations to acquire a competitive advantage.

The digital archive of cultural heritage provides new opportunities for the protection of the cultural heritage and the development of online museums. One of the essential requirements for the digitization is to achieve accurate color reproduction. Taking the Imperial Chinese robes in the Qing Dynasty as an example, this study aims to develop a digital achieve system to digitize the robes using a high-end imaging system and accurately reproduce their color properties on a display. Currently, there has been very limited study focused on the color reproduction of silk fabrics or other textile materials. The conventional color management process using a traditional color chart, however, may not be suitable for the reproduction of silk fabrics because they have very high gloss. To address this difficulty, a unique “Qianlong Palette” color chart, consisting of 210 silk fabric samples, has been specifically produced for optimizing the color reproduction of silk fabrics and a color image reproduction system has been developed for the digitization and archiving of the clothing fabric for the royal court. Color characterization models using both the “Qianlong Palette” color chart and the traditional color chart, and different mapping methods, are compared and the model with highest accuracy used in a self-programmed interface for automatically processing textile images in the future. Finally, the digital archive system has been validated using six garments of silk fabric relics. The color differences after the color image reproduction are all less than 3.00ΔE*ab, indicating acceptable color reproduction of the system. The images after color reproduction have also been evaluated subjectively by experts from the museum and the results are considered satisfactory. Our results show that the newly designed “Qianlong Palette” color chart exhibits superior performance over the conventional color chart in effectively predicting the color of the silk fabrics. The self-programmed graphical user interface for image color management can serve as a powerful tool to truly reproduce the color of various silk fabric relics in museums in the future and digitally archive those valuable cultural relics for different uses.

Predicting and optimizing ship fuel use is a crucial technology for lowering greenhouse gas emissions. Unfortunately, existing research is rarely capable of developing fuel consumption forecasts and optimization models for a particular transport system. This study develops a fuel consumption prediction model based on machine learning and a fuel consumption optimization model based on particle swarm optimization for ships. We studied nearly ten years of big data from a large Korean pure car and truck shipping company (PCTC), which contained 16,189 observations from 2012 to 2021. Results indicate that the XGBoost deep learning model outperforms conventional prediction models at the stage of fuel consumption prediction, with an R2 of 0.97. Furthermore, in the fuel consumption optimization stage, the particle swarm optimization method can effectively reduce fuel consumption. This study helps PCTC companies control shipping costs and save energy. Insights for shipping businesses to meet environmental demands are provided as well.

This study aims to understand the influence of COVID-19 on consumers' fears and self-protection motivations. Furthermore, the study seeks to understand the effects of these fears and motivations on consumers' intentions to use omnichannel retailing. A modified theoretical model is proposed by integrating protection motivation theory (PMT) and extending the extended parallel process model (E-EPPM). A total of 398 valid questionnaires are collected and used for further structural equation modeling analysis. The results suggest that the perceived severity, perceived vulnerability, and health anxiety positively impact perceived fears surrounding COVID-19. Furthermore, it is found that perceived fear, self-efficacy, and response efficacy will affect the protection motivation of consumers and ultimately contribute to their behavioral intention to use omnichannel retailing. The findings theoretically enrich the research on COVID-19, PMT, and E-EPPM and empirically provide managerial implications for omnichannel retail service providers.

Due to the spike in online-retail demand during the pandemic, couriers confront increased workload and safety concerns, posing significant social-sustainability challenges for courier companies. This study explores the impact of social-sustainability practices on couriers’ job satisfaction in the context of the COVID-19 pandemic. We designed the research model from the theoretical lens of Maslow’s hierarchy of needs, equity, and psychological-safety theories. We collected the views of 428 couriers from the Chinese market, where there is a developed e-commerce industry. The structural-equation-model analysis results found that social-sustainability practices such as working environment, working conditions, health and safety, education, and training positively affected the job satisfaction of couriers during the pandemic through the mediators (psychological safety and perceived fairness). These findings provide empirical recommendations for improving employees’ job satisfaction in courier companies during COVID-19 and addressing the social-sustainability issues of courier companies.

Long non-coding RNAs (lncRNAs) and their N6-methyladenosine (m6A) modifications play an essential role in tumorigenesis and cancer progression. This study was designed to explore the value of m6A-related lncRNAs in prognosis and therapeutic applications of immune infiltration of colon adenocarcinoma (COAD). We downloaded the COAD gene expression and clinical data from The Cancer Genome Atlas project. By co-expression analysis, Lasso Cox regression analysis, and univariate and multivariate Cox regression, we constructed an independent prognostic signature of seven m6A-related lncRNAs. The prognostic lncRNAs were divided into two clusters by consistent clustering analysis, as well as into two groups of low–high risk based on the signature. Then we identified the relationship between the different groups with clinical features and immune cell infiltration. Cluster 2 had a higher risk score with a lower survival rate. The risk score was higher in groups with advanced clinical features, such as stage III–IV, N1-3, and M1. The expression of AC156455.1 was increased in tumor tissues and cluster 2, and the lncRNA ZEB1−AS1 was notably higher in the high-risk group. Five types of immune cells showed differences in two clusters, and most were upregulated in type 2. The expression of memory B cells was positively correlated with the risk score. The prognostic model was verified by the Gene Expression Omnibus (GEO) dataset. Besides, we found that the expression of these seven lncRNAs in tumor tissues was significantly higher than that in normal tissues, which verified the feasibility of the model. Thus, the signature of seven m6A-related lncRNAs can independently predict the prognosis of COAD. This signature is also closely associated with immune cell infiltration, and new therapeutic targets can be explored from this field.

This paper presents exact solutions for the mechanical behavior of the interface during the normal collision between a rigid sphere and an elastic half-space based on kinematics and particle dynamics theory. The interfacial contact stress is significantly different from the static solution obtained from the Hertz contact theory. Firstly, according to the kinematics theory, the elastic half-space interface deformation of the sphere and the half-space interface in the collision process is divided into the deformation of the contact area and the non-contact area. The curve of the non-contract area is strictly antisymmetric to the deformation curve of the contact area, and the lateral deformation for each particle at the interface can be neglected when the collision depth is relatively small. Then, the vertical deformation equation of the contact area of the half-space interface, the dynamic equation of the rigid sphere, and the dynamic equation of the interface particle in the contact region are established. The proportional relationship between the stress or strain of any particle in the contact area, the stress or strain of the collision center point, and the method for determining the maximum collision depth are obtained. The equal deformation depth in the contact and non-contact regions, and the proportional relationship between the stress or strain in the contact area and the center point of the collision at any moment are consistent with the Hertz contact theory, which verifies the reliability of the current study. Taking the sphere of no initial velocity collides with the half-space under pure gravity as an example, when the elastic half-space Poisson’s ratio is taken as 0.2–0.4, the ratio of the maximum contact stress determined by the Hertz theory and the current solution when the sphere reaches the maximum collision depth is 0.58–0.54. Based on this study, the contact stress and its distribution in the interfacial contact region can be obtained when the motion state of the sphere and the interface are determined.

Abstract Currently, there is limited evidence regarding the association between prenatal exposure to environmental fine particulate matter (PM2.5) and the occurrence of cryptorchidism. The objective of this study was to evaluate the potential correlation between prenatal exposure to PM2.5 and the likelihood of cryptorchidism developing in offspring. We performed a 1:1 case–control study, defining the cases as children diagnosed with cryptorchidism at the Children’s Hospital Affiliated to Chongqing Medical University from 2013 to 2017, while the control group comprised children born in the corresponding years who did not have any birth defects, chromosomal abnormalities, and had only trauma-related treatments. Between 2012 and 2017, monthly averages of PM2.5, other pollutants (O3, PM10) and temperature were gathered based on the geographical coordinates of patients’ residences. The study assessed the correlation between the two using multivariate logistic regression model, and sensitivity analysis was conducted to assess the stability of the model. We included a total of 2137 cases and 2137 matched controls from 2013 to 2017. Our findings revealed that there was a positive association between exposure to PM2.5 during the first 2 months of pregnancy and the occurrence of cryptorchidism. According to this study, the development of cryptorchidism appears to be associated with maternal exposure to PM2.5 during early pregnancy.

Abstract. Background:. Renal ischemia–reperfusion (R-I/R) injury is the most prevalent cause of acute kidney injury, with high mortality and poor prognosis. However, the underlying pathological mechanisms are not yet fully understood. Therefore, this study aimed to investigate the role of N‐myc downstream‐regulated gene 2 (Ndrg2) in R-I/R injury. Methods:. We examined the expression of Ndrg2 in the kidney under normal physiological conditions and after R-I/R injury by immunofluorescence staining, real-time polymerase chain reaction, and western blotting. We then detected R-I/R injury in Ndrg2-deficient (Ndrg2–/–) mice and wild type (Ndrg2+/+) littermates in vivo, and detected oxygen and glucose deprivation and reperfusion (OGD-R) injury in HK-2 cells. We further conducted transcriptomic sequencing to investigate the role of Ndrg2 in R-I/R injury and detected levels of oxidative stress and mitochondrial damage by dihydroethidium staining, biochemical assays, and western blot. Finally, we measured the levels of mitophagy in Ndrg2+/+ and Ndrg2–/– mice after R-I/R injury or HK-2 cells in OGD-R injury. Results:. Ndrg2 was primarily expressed in renal proximal tubules and its expression was significantly decreased 24 h after R-I/R injury. Ndrg2–/– mice exhibited significantly attenuated R-I/R injury compared to Ndrg2+/+ mice. Transcriptomics profiling showed that Ndrg2 deficiency induced perturbations of multiple signaling pathways, downregulated inflammatory responses and oxidative stress, and increased autophagy following R-I/R injury. Further studies revealed that Ndrg2 deficiency reduced oxidative stress and mitochondrial damage. Notably, Ndrg2 deficiency significantly activated phosphatase and tensin homologue on chromosome ten-induced putative kinase 1 (PINK1)/Parkin-mediated mitophagy. The downregulation of NDRG2 expression significantly increased cell viability after OGD-R injury, increased the expression of heme oxygenase-1, decreased the expression of nicotinamide adenine dinucleotide phosphate oxidase 4, and increased the expression of the PINK1/Parkin pathway. Conclusion:. Ndrg2 deficiency might become a therapy target for R-I/R injury by decreasing oxidative stress, maintaining mitochondrial homeostasis, and activating PINK1/Parkin-mediated mitophagy.

Abstract Rapid, accurate and non-destructive acquisition of soil total nitrogen (TN) content in the black soil zone is significant for achieving precise fertilization. In this study, the soil types of corn and soybean fields in Jilin Agricultural University, China, were selected as the study area. A total of 162 soil samples were collected using a five-point mixed sampling method. Then, spectral data were obtained and the noisy edge were initially eliminated. Subsequently, the denoised spectral data underwent smoothing by using the Savitzky–Golay (SG) method. After performing the first-order difference (FD) and second-order difference (SD) transformations on the data, it was input to the model. In this study, a hybrid deep learning model, CBiResNet-BiLSTM, was designed for precise prediction of soil TN content. This model was optimized based on ResNet34, and its capabilities were enhanced by incorporating CBAM in the residual module to facilitate additional eigenvalue extraction. Also, Bidirectional Long Short-Term Memory (BiLSTM) was integrated to enhance model accuracy. Besides, partial least squares regression (PLSR), random forest regression (RFR), support vector machine regression (SVR), and back propagation neural network (BP), as well as ResNet(18, 34, 50, 101, 152) models were taken for comparative experiments. The results indicated that the traditional machine learning model PLSR achieved good performance, with R 2 of 0.883, and the hybrid deep learning model CBiResNet-BiLSTM had the best inversion capability with R 2 of 0.937, with the R 2 being improved by 5.4%, compared with the PLSR model. On this basis, we present the LUCAS dataset to demonstrate the generalisability of the model. Therefore, the CBiResNet-BiLSTM model is a fast and feasible hyperspectral estimation method for soil TN content. Graphical abstract

Abstract Background The deformation of oral and maxillofacial region leads to not only the damage of morphology and function, but also a series of aesthetic and psychological problems, severely affecting the quality of life of patients. Oral tissue engineering refers to developing biomaterials for repair or regeneration, with the application of tissue engineering technologies. This has become an area of increasing prominence. Current biologically inert materials are insufficient to fulfill clinical requirements. Therefore, tissue-engineered biomaterials with bioactive, even bionic properties are desperately needed. Main body The complexity of the anatomy and the diversity of tissue types of oral and maxillofacial region pose great challenges to the regeneration, in the aspects of both biomaterials and manufacturing technologies. Biomaterials in clinical practice or research have evolved from natural materials to synthetic materials, from homogeneous materials to multiple composite materials. And now composite materials have increasingly demonstrated their advantages in terms of physicochemical and biological properties over conventional materials. In terms of manufacturing, traditional coating, sintering, and milling technologies can no longer satisfy the requirements for high-precision bionic structures of oral-tissue-engineering biomaterials. Scientists have turned to biofabrication technologies such as microfluidics and additive manufacturing. Short conclusion This review aims to summarize the noteworthy advancements made in biomaterials of oral tissue engineering. We outlined the current biomaterials and manufacturing technologies and focused on various applications of these materials that may be connected to clinical treatment and research. We also suggested the future direction of development for biomaterials in oral tissue engineering. In future, biomaterials characterized by precision, functionalization, and individualization will be manufactured through digital, microfluidic, and 3D printing technologies. Graphical abstract

The tight oil formation develops with microfractures and matrix pores, it is important to study the influence of matrix physical properties on flow characteristics. At first, the representative fracture and matrix samples are selected respectively in the dual media, the fracture and matrix digital rocks are constructed with micro-CT scanning at different resolutions, and the corresponding fracture and matrix pore networks are extracted, respectively. Then, the modified integration method is proposed to build the dual network model containing both fracture and matrix pore-throat elements, while the geometrictopological structure equivalent matrix pores are generated to fill in the skeleton domain of fracture network, the constructed dual network could describe the geometric-topological structure characteristics of fracture and matrix pore-throat simultaneously. At last, by adjusting the matrix pore density and the matrix filling domain factor, a series of dual network models are obtained to analyze the influence of matrix physical properties on flow characteristics in dual-media. It can be seen that the matrix system contributes more to the porosity of the dual media and less to the permeability. With the decrease in matrix pore density, the porosity/permeability contributions of matrix system to dual media keep decreasing, but the decrease is not significant, the oilewater co-flow zone decreases and the irreducible water saturation increases, and the saturation interval dominated by the fluid flow in the fracture keeps increasing. With the decrease in matrix filling domain factor, the porosity/permeability contributions of matrix system to dual media decreases, the oilewater co-flow zone increases and the irreducible water saturation decreases, and the saturation interval dominated by the fluid flow in the fracture keeps increasing. The results can be used to explain the dual-media flow pattern under different matrix types and different fracture control volumes during tight oil production. [ABSTRACT FROM AUTHOR]

The durability of cement-based materials is related to water transport and storage in their pore network under different humidity conditions. To understand the mechanism and characteristics of water adsorption and desorption processes from the microscopic scale, this study introduces different points of view for the pore space model generation and numerical simulation of water transport by considering the "ink-bottle" effect. On the basis of the pore structure parameters (i.e., pore size distribution and porosity) of cement paste and mortar with water-binder ratios of 0.3, 0.4 and 0.5 obtained via mercury intrusion porosimetry, randomly formed 3D pore space models are generated using two-phase transformation on Gaussian random fields and verified via image analysis method of mathematical morphology. Considering the Kelvin-Laplace equation and the influence of "ink-bottle" pores, two numerical calculation scenarios based on mathematical morphology are proposed and applied to the generated model to simulate the adsorption-desorption process. The simulated adsorption and desorption curves are close to those of the experiment, verifying the effectiveness of the developed model and methods. The obtained results characterize water transport in cement-based materials during the variation of relative humidity and further explain the hysteresis effect due to "ink-bottle" pores from the microscopic scale. [ABSTRACT FROM AUTHOR]

Abstract Gestational hypertension (PIH), especially pre-eclampsia (PE), is a common complication of pregnancy. This condition poses significant risks to the health of both the mother and the fetus. Emerging evidence suggests that epigenetic modifications, particularly DNA methylation, may play a role in initiating the earliest pathophysiology of PIH. This article describes the relationship between DNA methylation and placental trophoblast function, genes associated with the placental microenvironment, the placental vascular system, and maternal blood and vascular function, abnormalities of umbilical cord blood and vascular function in the onset and progression of PIH, as well as changes in DNA methylation in the progeny of PIH, in terms of maternal, fetal, and offspring. We also explore the latest research on DNA methylation-based early detection, diagnosis and potential therapeutic strategies for PIH. This will enable the field of DNA methylation research to continue to enhance our understanding of the epigenetic regulation of PIH genes and identify potential therapeutic targets.

PurposeIt is crucial to identify biomarkers that influence the aging process and associated health risks, given the growing severity of the global population aging issue. The objectives of our research were to evaluate cardiac metabolic index (CMI) as a novel biomarker for identifying individuals at increased risk of accelerated biological aging and to assess its use in guiding preventive strategies for aging-related health risks.MethodsThe National Health and Nutrition Examination Survey (NHANES) provided cross-sectional data on participants with complete information on CMI, phenotypic age (PA), and other variables. Analyses of variance and weighted χ2 tests were conducted to assess differences between groups. The relationship between CMI and biological aging was investigated using a weighted multivariate logistic regression model, restricted cubic spline (RCS) regression analysis, subgroup analysis, and interaction testing.ResultsA positive correlation between CMI and biological aging was observed in 6,272 participants. RCS regression analysis confirmed the non-linear relationship, identifying significant inflection point at 1.10. In the crude or adjusted models, the OR (95% CI), for the highest group versus the reference were 3.608 (3.108, 4.188), 3.397 (2.920, 3.952), and 1.550 (1.299, 1.850), respectively, when categorizing CMI into different groups. Subgroup analyses and interaction tests indicate that the association between CMI and biological aging remained consistent across different subgroups. Gender, race, education level, marital status, poverty income ratio (PIR), drinking status and diabetes had an interaction with CMI in relation to biological aging.ConclusionAn elevated CMI is linked to increased risk for biological aging. This relationship may inform more effective prevention and treatment strategies for biological aging in the future. CMI be integrated into routine health screenings or aging assessments by healthcare professionals.