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Abstract Purpose This study aims to investigate the potential impact of positive mental well-being on responses of patient-reported outcome measures (PROMs), such as EQ-5D-5L. Methods This study utilized the data collected in a cross-sectional study in a sample consisted of individuals with different health conditions. Spearman's rank correlations were employed to investigate the relationship between the responses to the dimensions of EQ-5D-5L and the Short Warwick-Edinburgh Mental Well-being Scale (SWEMWBS). A binary logistic regression analysis and ordered logistic multivariable regression were utilized to examine how SWEMWBS scores could impact the responses to EQ-5D-5L dimensions, while controlling for variables such as age, gender, education level, health conditions, caring experience, and data collection methods. The effects of SWEMWBS on EQ-VAS and utility values were also examined. Results One thousand nine individuals participated in the survey. Spearman's rank correlation revealed that all dimensions of EQ-5D-5L, except for the anxiety/depression dimension, exhibited weak correlations with all dimensions of SWEMWBS. Binary logistic regression and ordered logistic multivariable regression indicated that age, SWEMWBS scores, gender, health conditions, data collection methods, and caring experience significantly influenced the likelihood of reporting problems in EQ-5D-5L responses. Notably, better SWEMWBS outcomes increased the likelihood of reporting no or fewer problems across all EQ-5D-5L dimensions. Spearman's rank correlation suggested a moderate or strong positive correlation between SWEMWBS scores and EQ-5D-5L utility values and EQ-VAS. The results of multiple linear regression analysis revealed that SWEMWBS scores, health conditions, caring experience, and data collection methods were significantly associated with EQ-5D utility values and EQ-VAS. Conclusions Individuals with better positive mental well-being results are more likely to report better results in PROMs like EQ-5D-5L. Future study is needed to understand the thought process and to explore strategies to cope with the response heterogeneity that led by the status of mental well-being.

Abstract Background Traditional survival analysis is frequently used to assess the prognosis of ependymomas (EPNs); however, it may not provide additional survival insights for patients who have survived for several years. Thus, the conditional survival (CS) pattern of this disease is yet to be further investigated. This study aimed to evaluate the improvement of survival over time using CS analysis and develop a CS-based nomogram model for real-time dynamic survival estimation for EPN patients. Methods Data on patients with EPN were collected from the Surveillance, Epidemiology, and End Results (SEER) database. In order to construct and validate the model effectively, the selected patients were randomly divided at 7:3 ratio. CS is defined as the probability of surviving for a specified time period (y years) after initial diagnosis, given that the patient has survived x years. The CS pattern of EPN patients were explored. Then, the least absolute shrinkage and selection operator (LASSO) regression method with tenfold cross-validation was employed to identify prognostic predictors. Multivariate Cox regression was employed to develop a CS-based nomogram model, and we used this model to quantify EPN patient risk. Finally, the performance of the prediction model was also evaluated and verified. Results In total, 1829 patients diagnosed with EPN were included in the study, with 1280 and 549 patients in the training and validation cohorts, respectively. The CS analysis demonstrated that patients' OS saw gradual improvements over time. With each additional year of survival post-diagnosis, the 10-year survival rate of EPN patients saw an increase, updating from 74% initially to 79%, 82%, 85%, 87%, 89%, 91%, 93%, 96%, and 98% (after surviving for 1–9 years, respectively). The LASSO regression model, which implements tenfold cross-validation, identified 7 significant predictors (age, tumor grade, tumor site, tumor extension, tumor size, surgery and radiotherapy) to develop a CS-based nomogram model. And further risk stratification was conducted based on nomogram model for these patients. Furthermore, this survival prediction model was successfully validated. Conclusion This study described the CS pattern of EPN patients and highlighted the gradual improvement of survival observed over time for long-term survivors. We also developed the first novel CS-nomogram model that enabled individualized and real-time prognosis prediction. But patients must be counselled that individual circumstances may not always accurately reflect the findings of the nomogram.

Abstract Purpose To investigate whether the same health state results in the same distribution of responses on the EQ-5D youth and adult descriptive systems. Methods Adolescents aged 13–18 years with a range of health conditions and from the general school going population were recruited in South Africa (ZA) and Ethiopia (ET). In ZA participants completed the English EQ-5D-3L, EQ-5D-Y-3L and EQ-5D-5L in parallel. Whereas in ET participants completed the Amharic EQ-5D-5L and EQ-5D-Y-5L in parallel. Analysis aimed to describe the transition between youth and adult instruments and not differences between countries. Results Data from 592 adolescents completing the EQ-5D-3L, EQ-5D-Y-3L and EQ-5D-5L (ZA) and 693 completing the EQ-5D-5L and EQ-5D-Y-5L (ET) were analysed. Adolescents reported more problems on the youth versions compared to the adult version for the dimension of mental health. 13% and 4% of adolescents who reported no problems on the EQ-5D-3L and EQ-5D-5L reported some problems on the EQ-5D-Y-3L respectively. This was less notable with transition between the five level versions with 4% of adolescents reporting more problems on the EQ-5D-Y-5L than the EQ-5D-5L. Very few adolescents reported severe problems (level 3 on the EQ-5D-3L or EQ-5D-Y-3L and level 4 and level 5 on the EQ-5D-5L or EQ-5D-5L) thus there was little variation between responses between the versions. In ZA, discriminatory power, measured on the Shannon’s Index, was higher for Y-3L compared to 3L for pain/discomfort (ΔH′=0.11) and anxiety/depression (ΔH′=0.04) and across all dimensions for Y-3L compared to 5L. Similarly, in ET discriminatory power was higher for Y-5L than 5L (ΔH′ range 0.05–0.09). Gwet’s AC showed good to very good agreement across all paired (ZA) 3L and (ET) 5L dimensions. The summary score of all EQ-5D versions were able to differentiate between known disease groups. Conclusion Despite the overall high levels of agreement between EQ-5D instruments for youth and for adults, they do not provide identical results in terms of health state, from the same respondent. The differences were most notable for anxiety/depression. These differences in the way individuals respond to the various descriptive systems need to be taken into consideration for descriptive analysis, when transitioning between instruments, and when comparing preference-weighted scores.

Abstract Gait phase prediction is important in controlling assistive robotic devices such as exoskeletons, where the control unit must differentiate between gait phases to provide the necessary assistance when the user is wearing the exoskeleton. To achieve the objective of precisely identifying the gait phase of users for the accurate control of the exoskeleton, this study proposes Auto-Correlation and Channel Attention enhanced Deep Graph Convolutional Networks (ACCA-DGCN) for gait phase prediction, and a gait phase prediction model based on multiple inertial measurement units (IMUs) and skeleton graph was established, in order to fully utilize the dependency among joints, and enhance accuracy and reliability of gait phase prediction. First, a human lower limb gait data acquisition equipment was developed, and the gait data of human walking were collected. The skeleton graph of the human lower limb was constructed through the natural connection relationship of joints in the human skeleton. After that, the ACCA-DGCN-based gait phase prediction model was constructed by using the gait data of human walking. Auto-Correlation (AC) and Efficient Channel Attention (ECA) were introduced to effectively capture periodic features of gait data and focus on the channels with high contributions to gait phase prediction. Finally, the effect of the window size on the performance of the ACCA-DGCN model was explored, and the proposed algorithm was compared with the other five deep learning algorithms: CNN, RNN, TCN, LSTM, and DGCN. The experimental results show that the average accuracy of gait phase prediction model based on ACCA-DGCN reaches up to 92.26% and 97.21% in user-independent and user-dependent experiments, respectively, which is superior to the other five algorithms. This study provides a new method for gait phase prediction, which is useful for improving the control of exoskeleton robots.

Abstract Introduction The EQ Health and Wellbeing (EQ-HWB) is a new questionnaire for measuring quality of life (QoL) from a broad perspective. The items of the EQ-HWB were derived based on a ‘qualitative review’ of literature, which reported primarily on Western studies. It can be argued that the QoL is a cultural-related concept and therefore people from China have a different understanding of the QoL. This study aimed to explore whether Chinese citizens could understand the EQ-HWB’s candidate items and what they thought of those items. In doing so, we wanted to examine the face validity of the candidate items and explore if further cultural adaptation is necessary. Methods This research was part of the E-QALY project, in which 36 candidate items were selected for the EQ-HWB from a 97-item pool. In China, three interviewers investigated the face validity of these EQ-HWB candidate items in semi-structured qualitative face-to-face interviews. Respondents were invited to report ‘problems’ with regard to the interpretation of the items and these problems were grouped into themes. We explored to what extent those themes related to specific cultural aspects in China. We also classified the rates of reported problems for each item into three groups: 1) less than 20%, 2) from 20–50%, and 3) over 50%. Results For 17 items the rate of reported problems was less than 20%, 15 items fell into the second group (with 20 − 50%) and for 4 items the rate of problems reported was more than 50%. The thematic analysis revealed eight themes: ambiguous problems in the interpretation of 16 items; difficult to understand (11); contained a complex negative expression (10); examples used seemed inappropriate (7); misleading connotation in Chinese (2); long and complex (2); complex response options (1); and use of non-colloquial language (1). Discussion Our research shows that EQ-HWB candidate items require careful examination to make them more comprehensible. Most of the reported problem themes were generic problems related to the items, and only a few face validity issues appeared to relate to specific cultural aspects in China, even though most of the items were based on Western studies. Our findings are reassuring for the instrument’s international application, especially in China.

IntroductionFAS has been implicated in the development of various cancers, but its involvement in lung cancer has not been systematically characterized. In this study, we performed data mining in online tumor databases to investigate the expression, methylation, alterations, protein interactions, co-expression and prognostic significance of FAS in lung cancer.MethodThe expression, prognostic significance and molecular interactions of FAS in lung cancer was mined and analyzed using GENT2, GEPIA2, UALCAN, cBioPortal, STRING, GeneMANIA, UCSC Xena, Enrichr, and OSluca databases. FAS expression was subsequently investigated at the protein level in samples from 578 lung cancer patients to understand its protein-level expression. In vitro validation of FAS gene expression was performed on H1299, H1993, A549 and HBE cell lines.ResultWe found that the expression of FAS was significantly downregulated in both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) compared to normal lung tissue. In addition, we observed a higher level of FAS promoter methylation in LUSC tissue than in normal tissue. FAS alterations were rare (1.9%) in lung cancer samples, with deep deletions being more common than missense mutations, which occurred mainly in the TNFR-like cysteine-rich domain and the death domain. We also identified a list of proteins interacting with FAS and genes co-expressed with FAS, with LUAD having 11 co-expressed genes and LUSC having 90 co-expressed genes. Our results also showed that FAS expression has limited prognostic significance (HR=1.302, 95% CI=0.935-1.139, P=0.530). Protein level investigation revealed that FAS expression varied among individuals, with nTPM values ranging from 5.2 to 67.2.ConclusionThis study provides valuable insights into the involvements and characteristics of FAS in lung cancer. Further studies are needed to investigate the clinical significance of FAS alterations in lung cancer and to explore the potential of targeting FAS for therapeutic intervention.

Abstract Small extracellular vesicles (sEVs) are cell-derived, nanometer-sized particles enclosed by a lipid bilayer. All kinds of biological molecules, including proteins, DNA fragments, RNA, lipids, and metabolites, can be selectively loaded into sEVs and transmitted to recipient cells that are near and distant. Growing shreds of evidence show the significant biological function and the clinical significance of sEVs in cancers. Numerous recent studies have validated that sEVs play an important role in tumor progression and can be utilized to diagnose, stage, grading, and monitor early tumors. In addition, sEVs have also served as drug delivery nanocarriers and cancer vaccines. Although it is still infancy, the field of basic and translational research based on sEVs has grown rapidly. In this review, we summarize the latest research on sEVs in gliomas, including their role in the malignant biological function of gliomas, and the potential of sEVs in non-invasive diagnostic and therapeutic approaches, i.e., as nanocarriers for drug or gene delivery and cancer vaccines.

Abstract Background “Disulfide death,” a form of cellular demise, is triggered by the abnormal accumulation of intracellular disulfides under conditions of glucose deprivation. However, its role in the prognosis of glioma remains undetermined. Therefore, the main objective of this study is to establish prognostic signature based on disulfide death-related genes (DDRGs) and to provide new solutions in choosing the effective treatment of glioma. Methods The RNA transcriptome, clinical information, and mutation data of glioma samples were sourced from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), while normal samples were obtained from the Genotype-Tissue Expression (GTEx). DDRGs were compiled from previous studies and selected through differential analysis and univariate Cox regression analysis. The molecular subtypes were determined through consensus clustering analysis. Further, LASSO analysis was employed to select characteristic genes, and subsequently, a risk model comprising seven DDRGs was constructed based on multivariable Cox analysis. Kaplan-Meier survival curves were employed to assess survival differences between high and low-risk groups. Additionally, functional analyses (GO, KEGG, GSEA) were conducted to explore the potential biological functions and signaling pathways of genes associated with the model. The study also explored immune checkpoint (ICP) genes, immune cell infiltration levels, and immune stromal scores. Finally, the effect of Importin-4(IPO4) on glioma has been further confirmed through RT-qPCR, Western blot, and cell functional experiments. Results 7 genes associated with disulfide death were obtained and two subgroups of patients with different prognosis and clinical characteristics were identified. Risk signature was subsequently developed and proved to serve as an prognostic predictor. Notably, the high-risk group exhibited an immunosuppressive microenvironment characterized by a high concentration of M2 macrophages and regulatory T cells (Tregs). In contrast, the low-risk group showed lower half-maximal inhibitory concentration (IC50) values. Therefore, patients in the high-risk group may benefit more from immunotherapy, while patients in the low-risk group may benefit more from chemotherapy. In addition, in vitro experiments have shown that inhibition of the expression of IPO4 leads to a significant reduction in the proliferation, migration, and invasion of glioma cells. Conclusion This study identified two glioma subtypes and constructed a prognostic signature based on DDRGs. The signature has the potential to optimize the selection of patients for immune- and chemotherapy and provided a potential therapeutic target for glioma.

BackgroundConditional survival (CS) considers the duration since the initial diagnosis and can provide supplementary informative insights. Our objective was to evaluate CS among gliosarcoma (GSM) patients and develop a CS-incorporated nomogram to predict the conditional probability of survival.MethodsThis retrospective study using the Surveillance, Epidemiology, and End Results (SEER) database included patients with GSM between 2000 and 2017. The CS was defined as the probability of surviving additional y years after already surviving for x years. The formula utilized for CS was: CS(y|x) = S(y + x)/S(x), where S(x) denotes the overall survival at x years. Univariate Cox regression, best subset regression (BSR) and the least absolute shrinkage and selection operator (LASSO) were used for significant prognostic factors screening. Following this, backward stepwise multivariable Cox regression was utilized to refine predictor selection. Finally, a novel CS-integrated nomogram model was developed and we also employed diverse evaluation methods to assess its performance.ResultsThis study included a total of 1,015 GSM patients, comprising 710 patients in training cohort and 305 patients in validation cohort. CS analysis indicated a gradual increase in the probability of achieving a 5-year survival, ascending from 5% at diagnosis to 13, 31, 56, and 74% with each subsequent year survived after 1, 2, 3, and 4 years post-diagnosis, respectively. Following variable screening through univariate Cox regression, BSR, and LASSO analysis, five factors-age, tumor stage, tumor size, radiotherapy, and chemotherapy-were ultimately identified for constructing the CS-nomogram model. The performance of the nomogram model was validated through discrimination and calibration assessments in both the training and validation cohorts. Furthermore, we confirmed that the effectiveness of the CS-nomogram in stratifying GSM patient risk status.ConclusionThis nationwide study delineated the CS of patients diagnosed with GSM. Utilizing national data, a CS-nomogram could provide valuable guidance for patient counseling during follow-up and risk stratification.

ObjectiveThe objective of this study is to explore the risk factors associated with new-onset postoperative atrial fibrillation (POAF) following Sun's surgery(total arch replacement using a tetrafurcate graft with stented elephant trunk implantation) for acute type A aortic dissection(AAAD) and to develop a predictive model for assessing the likelihood of new-onset POAF in patients undergoing Sun's surgery for AAAD.MethodsWe reviewed the clinical parameters of patients diagnosed with AAAD who underwent Sun's surgery at Qilu Hospital between December 1, 2017 and December 31, 2022. The data was analyzed through univariable and multivariable logistic regression analysis. Variance inflation factor was used to investigate for variable collinearity. A nomogram for predicting new-onset POAF was developed and verified by bootstrap resampling. In addition, the calibration of our model was evaluated by the calibration curve and Hosmer-Lemeshow test. Furthermore, the clinical utility of our model was evaluated using the net benefit curve.ResultsThis study focused on a cohort of 242 patients with AAAD, among whom 42 experienced new-onset POAF, indicating an incidence rate of 17.36%. Age, left atrial diameter (LA), right atrial diameter (RA), preoperative red blood cells (RBC), and previous acute coronary syndrome (preACS) emerged as independent influences on new-onset POAF following Sun's surgery, as identified by univariable and multivariable logistic regression analysis. Collinearity analysis with demonstrated no collinearity among the variables. A user-friendly prediction nomogram for new onset POAF following Sun's surgery was formulated. The model demonstrated commendable diagnostic accuracy with an area under the curve (AUC) of 0.7852. Validation of the model through bootstrapping (1,000 repetitions) yielded an AUC of 0.8080 (95% CI: 0.8056–0.8104). affirming its robustness. Additionally, the model exhibited favorable fit, calibration, and positive net benefits in decision curve analysis.ConclusionsDrawing upon these findings, we have developed a predictive model for the occurrence of new-onset POAF. These results suggest the potential efficacy of this prediction model for identifying patients at risk of developing POAF. The visualization of this model empowers healthcare professionals to conveniently and promptly assess the risk of AF in patients, thereby facilitating the timely intervention implementation.

To date, the aging softening of Mg-Li-Zn based alloys has been a critical issue limiting their industrial application. Here, we design a new Mg-Li alloy that exhibits exceptional mechanical stability after solution treatment and then water quenching by means of a hitherto unrecognized mechanism. The results indicate that precipitation of a semi-coherent MgLi2(ZnAg) phase leads to rapid age-hardening of the Mg-Li alloy after water quenching, as evidenced by the Ag atoms in the precipitate. A massive solid solution of Ag atoms in the β-Li phase strengthens the stability of the alloy and retards the diffusion of Li atoms from the β phase into the α matrix during aging. Zn atoms in the MgLi2Zn precipitate are replaced by Ag atoms which explain the origin of age-hardening and inhibit the evolution of the substable strengthened phase MgLi2Zn to the equilibrium softened phase MgLiZn. This research may contribute to the theoretical basis for solving the age-softening problem of Mg-Li alloys.

Abstract Background The Core Outcome Measures in Effectiveness Trials (COMET) working group proposed core outcome sets (COS) to address the heterogeneity in outcome measures in clinical studies. According to the recommendations of COMET, performing systematic reviews (SRs) usually was the first step for COS development. However, the SRs that serve as a basis for COS are not specifically appraised by organizations such as COMET regarding their quality. Here, we investigated the status of SRs related to development of COS and evaluated their methodological quality. Methods We conducted a search on PubMed to identify SRs related to COS development published from inception to May 2022. We qualitatively summarized the disease included in SR topics, and the studies included in the SRs. We evaluated the methodological quality of the SRs using AMSTAR 2.0 and compared the overall quality of SRs with and without protocols using the Mann-Whitney U test. Results We included 175 SRs from 23 different countries or regions, and they mainly focused on five diseases: musculoskeletal system or connective tissue disease (n = 19, 10.86%), injury, poisoning, or certain other consequences of external causes (n = 18, 10.29%), digestive system disease (n = 16, 9.14%), nervous system disease (n = 15, 8.57%), and genitourinary system disease (n = 15, 8.57%). Although 88.00% of SRs included randomized controlled trials (RCTs), only a few SRs (23.38%) employed appropriate tools to assess the risk of bias in RCTs. The assessment results on the basis of AMSTAR 2.0 indicated that most SRs (93.71%) were rated as ‘’critically low’’ to ‘’low’’ in terms of overall confidence. The overall confidence of SRs with protocols was significantly higher than that without protocols (P

Damage to semi-rigid base asphalt pavement is related to improper matching of the pavement structure moduli. This study mainly focused on the modulus matching of structural layers and the development of a pavement structure optimization method. First, the modulus loss of existing pavement structures was analysed, and a three-dimensional finite element model was established based on the existing pavement. Second, the influence of the modulus of each structural layer on the mechanical response indicators and fatigue life was analysed. Based on the results, a pavement structure design method using the smoothness of the stress-strain curve as the modulus matching criterion of the structural layers was proposed. And it was found that a strain convex point was present and that the stress mutation between the structural layers was significant when the modulus matching of the pavement structure was reasonable. Further, the evaluation indicators were divided into two groups, namely, mechanical indicators and fatigue life indicators. And it was proposed an optimized pavement structure design method based on modulus matching and multi-indicator range analysis. Finally, the optimal modulus combination of pavement structure was determined by this method. The research systematically studied the influence of the modulus of each structural layer on the mechanical response and fatigue life of the pavement, and proposed the concept and specific executive criteria of modulus matching for the first time. Meanwhile, it also provided an effective optimization method for pavement structure design.

Radiological science and nuclear technology have made great strides in the twenty-first century, with wide-ranging applications in various fields, including energy, medicine, and industry. However, those developments have been accompanied by the inherent risks of exposure to nuclear radiation, which is a source of concern owing to its potentially adverse effects on human health and safety and which is of particular relevance to medical personnel who may be exposed to certain cancers associated with low-dose radiation in their working environment. While medical radiation workers have seen a decrease in their occupational exposure since the 1950s thanks to improved measures for radiation protection, a concerning lack of understanding and awareness persists among medical professionals regarding these potential hazards and the required safety precautions. This issue is further compounded by insufficient capabilities in emergency response. This highlights the urgent need to strengthen radiation safety education and training to ensure the well-being of medical staff who play a critical role in radiological and nuclear emergencies. This review examines the health hazards of nuclear radiation to healthcare workers and the awareness and willingness and education of healthcare workers on radiation protection, calling for improved training programs and emergency response skills to mitigate the risks of radiation exposure in the occupational environment, providing a catalyst for future enhancement of radiation safety protocols and fostering of a culture of safety in the medical community.

Summary: The prediction of drug-target interactions (DTIs) is a critical phase in the sustainable drug development process, especially when the research focus is to capitalize on the repositioning of existing drugs. Computational approaches to predicting DTIs can provide important insights into drug mechanisms of action. However, current methods for predicting DTIs based on the structural information of the knowledge graph may suffer from the sparseness and incompleteness of the knowledge graph and neglect the latent type information of the knowledge graph. In this paper, we propose TTModel, a knowledge graph embedding model for DTI prediction. By exploiting biomedical text and type information, TTModel can learn latent text semantics and type information to improve the performance of representation learning. Comprehensive experiments on two public datasets demonstrate that our model outperforms the state-of-the-art methods significantly on the task of DTI prediction.

BackgroundThe current survival prediction methodologies for primary bone lymphoma (PBL) of the spine are deficient. This study represents the inaugural utilization of conditional survival (CS) to assess the outcome of this disease. Moreover, our objective was to devise a CS-based nomogram for predicting overall survival (OS) in real-time for spinal PBL.MethodsPatients with PBL of the spine diagnosed between January 2000 and December 2015 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The OS was determined through the Kaplan–Meier method. The CS characteristic of patients with spinal PBL was delineated, with the CS being estimated utilizing the formula: CS(α|β) = OS(α+β)/OS(β). CS(α|β) denotes the probability of additional α-year survivorship, assuming the patient has already survived β years after the time of observation. Three methods including univariate Cox regression, best subset regression (BSR) and the least absolute shrinkage and selection operator (LASSO) regression were used to identify predictors for CS-based nomogram construction.ResultsKaplan-Meier analysis was executed to determine the OS rate for these patients, revealing a survival rate of 68% and subsequently 63% at the 3-year and 5-year mark respectively. We then investigated the CS patterning exhibited by these patients and discovered the survival of PBL in the spine progressively improved with time. Meanwhile, through three different prognostic factor selection methods, we identified the best predicter subset including age, tumor histology, tumor stage, chemotherapy and marital status, for survival prediction model construction. Finally, we successfully established and validated a novel CS-based nomogram model for real-time and dynamic survival estimation. Moreover, we further designed a risk stratification system to facilitate the identification of high-risk patients.ConclusionsThis is the first study to analyze the CS pattern of PBL of the spine. And we have also developed a CS-based nomogram that provide dynamic prognostic data in real-time, thereby aiding in the formulation of personalized treatment strategies in clinical practice.

Hyperglycemia is correlated with worse in-hospital outcomes in acute infectious diseases such as coronavirus disease 2019 (COVID-19) and severe fever with thrombocytopenia syndrome (SFTS). This study assessed the relationship between fasting plasma glucose (FPG) levels and in-hospital mortality, disease type, and secondary infections among individuals with SFTS without preexisting diabetes. The clinical data and laboratory results upon admission of 560 patients with SFTS without preexisting diabetes meeting the inclusion criteria at Wuhan Union Hospital were collected. FPG levels in surviving patients with SFTS subjects were significantly lower than those in patients with SFTS who had died (P

Stereolithographic three-dimensional (3D) printing of metals is a new additive manufacturing technology. The metal parts prepared by this technology have the characteristics of complex shape and precise structure, and no post-processing is required. It is an advanced manufacturing technology that has enormous potential in fabricating complex parts owing to the considerable progress of stereolithography, digital light processing, liquid crystal displays, continuous liquid interface production, and two-photon polymerization in recent years. This paper summarizes the current research progress on stereolithographic 3D printing of metals and defines four major methodologies, namely, curing sintering, curing coating, mixed curing, and curing mold. Of these, the curing sintering method is the primary technique to prepare metal parts by mixing metal powder with photosensitive resin and forming parts through degreasing and sintering at high temperatures. In curing coating, a metal film is coated on the surface of a solidified resin matrix, which is often used to prepare precision electromagnetic equipment. The mixed curing process is used to directly cure metal slurry without sintering to complete the one-step formation of parts. The curing mold technique is used to realize the multistep formation of parts by using the resin matrix or the ceramic matrix as the mold, followed by subsequent treatments such as casting. In this review, the composition of metal slurry and the resulting properties of the stereolithographic 3D printed metal parts are summarized. Key scientific questions on the property difference between metals and polymer in the slurry, immature processing parameters, lack of reliable compositions for photosensitive resin are still unresolved. The direction of future development is forecasted by studying on the influence of process parameters on the performance of parts, on the new formulas of photosensitive resin, and on more suitable equipment for the stereolithographic 3D printing of metals.

In order to improve the recognition accuracy of coal vitrinite macerals and performance of recognition model, reduce the manual intervention in the training of recognition model, a lightweight deep learning network model for coal vitrinite submaceral recognition, which is embedded with an improved attention mechanism, is proposed. Firstly, aiming at the problem of small amount of vitrinite sample data, a data enhancement method of random clipping, rotating, mirroring and noise adding is employed to enhance the original sample data and improve the generalization ability of the recognition model. Then, a backbone network is constructed based on the ShuffleNet V2 model pre-trained on a large data set ImageNet. A transfer learning method, which freezes some layers near the input and adjust the weight parameters of layers near the output of the pre-trained model according the enhanced training sample set of vitrinite microscopic image, is employed to generate some deep feature extraction layers. Finally, a channel attention mechanism ECA, which is improved according to the characteristics of distinct texture feature of vitrinite microscopic image, is embedded before the output of the ShuffleNet V2 model, and a new lightweight end-to-end recognition network model is constructed to implement the automatic recognition of seven submacerals of coal vitrinite. The experimental results show that the average recognition accuracy of submacerals with this method can reach 97.85%, gone beyond 5.71% of the original ShuffleNet V2 model. Compared with the classical neural network, the proposed model has higher accuracy, lesser number of parameters and calculation, and rapid convergence velocity. Compared with other lightweight networks and other attention embedding mechanisms, the proposed network not only maintains a less amount of parameters, but also significantly improves the recognition accuracy.

It is well known that the corrosion resistance of ultra-light Mg–Li alloys is inferior to traditional Mg alloys due to the rapid oxidation of reactive Li on the surface. In this study, microalloying Sn has been found to improve the corrosion resistance of Mg–Li alloys due to the formation of Mg2Sn on the corrosion pits and reduction of electro-potential difference. Indeed, the addition of Sn not only reduced the corrosion rate to the minimum value of 4.27 mmpy but also promote the formation of dense corrosion oxide films. The volt potential difference between the matrix (α-Mg and β-Li) and Al-X (Gd, Y, Mn) phases has also been measured to reduce from 260 mV (α-Mg) and 343 mV (β-Li) to 220 mV (α-Mg) and 220 mV (β-Li). After homogenization, pitting was significantly reduced, as shown by the in-situ observation of the corrosion process using X-ray tomography. By quantifying the secondary phases, it has been found that the corrosion expansion was restricted by the Mg2Sn and Al-X phases. The corrosion products are primarily MgO, SnO, and Li2O oxides and the corrosion mechanism follows three stages: I. Breakthrough of MgO protective layer by corrosive fluid; II. Hydrogen evolution reaction (HER) between matrix phase and water immersion; III. Segregation of secondary phases and corrosion products. It has been found that all three processes are highly dependent on the Sn solutes and Mg2Sn precipitates, showing the bright future development of corrosion-resistant Mg–Li alloys by Sn microalloying.

Abstract Mg alloy suffers from its poor corrosion resistance as a result of anodic dissolution of Mg and hydrogen evolution reaction (HER) in humid environments. In this study, the effects of alloying elements (Al, Zn, Y, Ce, and Mn) on both processes in Mg alloys have been quantitatively predicted. Using first‐principle calculations, we first obtained the substitution energies of alloying elements to compare their segregation preference, and then analyzed the influence of solutes at different layers on the stability and hydrogen adsorption properties of Mg(0001) surface by calculating the formation enthalpy, surface energy, vacancy formation energy, work function, Bader charge, deformation charge density, and adsorption free energy of H atom. It has been found that, on the one hand, the interior Mn solute atoms reduce the dissolution of Mg atoms and the transfer of electrons, consequently slowing down the anodic dissolution process. On another hand, the Mn, Y, and Ce elements on the surface inhibit the cathodic HER process by elevating the absolute value of hydrogen adsorption free energy, as a result of those solutes effectively controlling H adsorption behavior on Mg(0001) surface. In contrast, all five elements dissolved inside the Mg grain do not show significant effects on the H adsorption behavior.

The luminescent properties of some materials undergo significant changes under compression. High pressure generated by a diamond anvil cell (DAC) is often used as an external stimulus to explore the relationship between the structures and luminescent properties of materials, provide traceable color and structural changes, and quantify the environment in which the materials are located. Under high pressure, the luminous intensity or color of materials changes, which has important potential applications in fields such as safety detection, information storage, optoelectronic devices, and mechanical sensing. Recently, many phenomena of pressure-induced luminescence enhancement have been discovered in DAC, commonly referred to as pressure-induced emission enhancement. In this review, recent pressure-induced emission enhancement phenomena have been collected, and the role of pressure in promoting the luminescent enhancement of materials in DAC was revealed and discussed, which helps to design some materials with specific emission characteristics and provides a perspective for in-depth research on the photophysical behavior of materials.

ObjectiveThis study utilized computed tomography angiography (CTA) to assess the risk of acute type A aortic dissection (ATAAD) by analyzing the imaging morphology indicators of the ascending aorta, along with the relevant risk factors associated with aortic dissection.MethodsThe study utilized a retrospective observational research design. The population consisted of 172 patients who received treatment in the Department of Cardiothoracic Surgery at Qilu Hospital, Shandong University, from January 2018 to December 2022. The patients were divided into two groups: the ATAAD group (n = 97) and the thoracic aortic aneurysm group (TAA, n = 75). Demographic data and ascending aorta CTA measurements were collected from all patients. Single factor and multivariate logistic regression were employed to analyze the statistical differences in clinical data and ascending aorta CTA imaging morphology indicators between the two groups.ResultsThe variables were included in logistic multivariate analysis for further screening, indicating that the length of the ascending aorta (LAA) before ATAAD (OR = 3.365; 95% CI :1.742–6.500, P

Introduction EQ-5D is one of the most frequently used health-related quality of life (HRQoL) measures but has been found to be insensitive in detecting differences in health status in some general populations and disease groups. For example, the appropriateness of applying EQ-5D in the Chinese cultural setting has been widely discussed. Adding additional HRQoL dimensions (bolt-on items) can be a solution to both retain the original descriptive system of EQ-5D, while enhancing its sensitivity to the local context. To date, no studies have proposed culturally relevant bolt-ons for China or examined the psychometric properties of such bolt-on items. This protocol documents the identification, development, selection and psychometric testing of culturally relevant bolt-on items for EQ-5D-5L in China.Methods and analysis We will identify and develop candidate bolt-on items that are most relevant in the Chinese culture, through former literature reviews on health concepts important for the Chinese population, conducting expert consultations and qualitative interviews. We will quantitatively test the acceptability and measurement properties (including distributional characteristics and construct validity) of the candidate items in both general and disease populations in a cross-sectional setting. The patient group will be followed up to collect two-time-point data to assess test–retest reliability of the candidate items. Bolt-on item selection will consider both the qualitative and quantitative evidence gathered. This protocol outlines a comprehensive mixed-methods process for identifying, developing, selecting and testing bolt-on items that are relevant and culturally appropriate in China. This study may serve as a guide for similar initiatives in other cultural contexts.Ethics and dissemination This study received ethics approval from the Institutional Review Board of School of Public Health, Fudan University (IRB number: 2022-TYSQ-03-154). Study findings will be disseminated through international peer-reviewed journal articles as well as public, academic presentations at national and international conferences.

Abstract Introduction Cost-utility analysis (CUA) is the preferred form of economic evaluation in many countries. As one of the key data inputs in cost-utility models, health state utility (HSU) has a crucial impact on CUA results. In the past decades, health technology assessment has been expanding rapidly in Asia, yet research examining the methodology and process used to generate cost-effectiveness evidence is scarce. The aim of this study was to examine the reporting of the characteristics of HSU data used in CUAs in Asia and how the characteristics have changed over time. Methods A systematic literature search was performed to identify published CUA studies targeting Asian populations. Information was extracted for both the general characteristics of selected studies and the characteristics of reported HSU data. For each HSU value identified, we extracted data for four key characteristics, including 1) estimation method; 2) source of health-related quality of life (HRQoL) data; 3) source of preference data; and 4) sample size. The percentage of nonreporting was calculated and compared over two time periods (1990–2010 vs 2011–2020). Results A total of 789 studies were included and 4,052 HSUs were identified. Of these HSUs, 3,351 (82.7%) were from published literature and 656 (16.2%) were from unpublished empirical data. Overall, the characteristics of HSU data were not reported in more than 80% of the studies. Of HSUs whose characteristics were reported, most of them were estimated using the EQ-5D (55.7%), Asian HRQoL data (91.9%), and Asian health preferences (87.7%); 45.7% of the HSUs was estimated with a sample of 100 or more individuals. All four characteristics showed improvements after 2010. Conclusion Over the past two decades, there has been a significant increase in CUA studies targeting Asian populations. However, HSU’s characteristics were not reported in most of the CUA studies, making it difficult to evaluate the quality and appropriateness of the HSUs used in those cost-effectiveness studies.

Abstract Recently, well-being of older people measure (WOOP) was developed and validated in a Dutch population. Although WOOP was developed targeting the older people, it has the potential for use in a wider population. In this study, we aimed to examine the relationship between WOOP and EQ-5D-5L and compared their psychometric properties in a sample of patients, carers and healthy general public covering a wider age group. We conducted a cross-sectional study in Guizhou Province, China between July and August 2022. Data was collected using paper and pencil. We analysed and reported the acceptability, item response distribution, the Spearman correlation coefficients of all items, the Exploratory Factor Analysis (EFA) of all items, the known-group validity and the convergent validity of EQ-5D-5L utility and WOOP utility. A total of 322 participants completed the survey with 105 patients, 101 carers and 116 healthy general public. 9% of participants had at least one missing response. Three items of WOOP did not have any level 5 responses and EQ-5D-5L had more level 1 responses. The correlations were low between EQ-5D-5L and WOOP items and the three-factor EFA showed these two instruments had only one shared factor and the other two factors were only related to WOOP items. Younger people had lower missing response rate and a different response distribution for three items. WOOP measures a broader construct beyond health while EQ-5D-5L is a more sensitive instrument when health is considered alone. There is a potential of using WOOP in a wider population.

Fine particle content is an important physical state parameter of graded macadam materials used in heavy-haul railway subgrade beds. Understanding the micro-influence mechanism of the fine particle content on the filler’s dynamical characteristics is of great significance. In this study, the granular system generation logic in PFC3D software was modified to enhance efficiency and quality, and the three-dimensional discrete element numerical model of graded macadam materials dynamic triaxial specimen was established. The servo program was used to simulate the dynamic triaxial numerical test confining pressure and dynamic loading conditions. A series of laboratory tests were performed using a GCTS large-scale cyclic triaxial test system to calibrate and verify the numerical model. Finally, dynamic triaxial numerical tests were conducted for graded macadam materials with different fine particle contents. These tests had the aim of determining the variation law for the contact force chain distribution, mechanical coordination number, average rotational angular velocity, and energy dissipation of the specimens under cyclic loading with the increase of fine particles and then revealing the influence mechanism of fine particles on the dynamic characteristics of graded macadam materials used in heavy-haul railway subgrade beds from a microscopic perspective. The study’s findings serve as a framework for describing the mechanism of subgrade bed disease occurrence and proposing effective remediation measures.

Single-nucleotide polymorphism (SNP) plays a key role in the carcinogenesis of the human genome, and understanding the intrinsic relationship between individual genetic variations and carcinogenesis lies heavily in the establishment of a precise and sensitive SNP detection platform. Given this, a powerful and reliable SNP detection platform is proposed by a flap endonuclease 1 (FEN 1)-driven DNA walker-like reaction coupling with a magnetic bead (MB)-based separation. A carboxyfluorescein (FAM)-labeled downstream probe (DP) was decorated on a streptavidin magnetic bead (SMB). The target DNA, as a walker strand, was captured by hybridization with DP and an upstream probe (UP) to form a three-base overlapping structure and execute the walking function on the surface of SMB. FEN 1 was employed to specifically recognize the three-base overlapping structure and cut the 5′flap at the SNP site to report the walking event and signal amplification. Considering the fact that the fluorescence was labeled on the cleavage and uncleavage sequences of DP and the target DNA-triggered walking event was undistinguishable from the mixtures, magnetic separation came in handy for cleavage probe (CP) isolation and discrimination of the amplified signal from the background signal. In comparison with the conventional DNA walker reaction, this strategy was coupling with SMB-based separation, thus promising a powerful and reliable method for SNP detection and signal amplification.

Abstract Background Super-enhancers (SEs), driving high-level expression of genes with tumor-promoting functions, have been investigated recently. However, the roles of super-enhancer-associated lncRNAs (SE-lncRNAs) in tumors remain undetermined, especially in gliomas. We here established a SE-lncRNAs expression-based prognostic signature to choose the effective treatment of glioma and identify a novel therapeutic target. Methods Combined analysis of RNA sequencing (RNA-seq) data and ChIP sequencing (ChIP-seq) data of glioma patient-derived glioma stem cells (GSCs) screened SE-lncRNAs. Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) datasets served to construct and validate SE-lncRNA prognostic signature. The immune profiles and potential immuno- and chemotherapies response prediction value of the signature were also explored. Moreover, we verified the epigenetic activation mechanism of LINC00945 via the ChIP assay, and its effect on glioma was determined by performing the functional assay and a mouse xenograft model. Results 6 SE-lncRNAs were obtained and identified three subgroups of glioma patients with different prognostic and clinical features. A risk signature was further constructed and demonstrated to be an independent prognostic factor. The high-risk group exhibited an immunosuppressive microenvironment and was higher enrichment of M2 macrophage, regulatory T cells (Tregs), and Cancer-associated fibroblasts (CAFs). Patients in the high-risk group were better candidates for immunotherapy and chemotherapeutics. The SE of LINC00945 was further verified via ChIP assay. Mechanistically, BRD4 may mediate epigenetic activation of LINC00945. Additionally, overexpression of LINC00945 promoted glioma cell proliferation, EMT, migration, and invasion in vitro and xenograft tumor formation in vivo. Conclusion Our study constructed the first prognostic SE-lncRNA signature with the ability to optimize the choice of patients receiving immuno- and chemotherapies and provided a potential therapeutic target for glioma.

Abstract Background The purpose of this study is to examine the relationship between the Short Warwick-Edinburgh Mental Well-being Scale (SWEMWBS) and EQ-5D-5L and compare their psychometric properties in 4 chronic conditions in China. Methods Participants were invited to complete the online survey. Spearman’s rank correlation was used to evaluate the correlation between SWEMWBS and EQ-5D-5L; exploratory factor analysis was used to ascertain the number of unique underlying latent factors measured by SWEMWBS and EQ-5D-5L. Next, we assessed the psychometric properties of SWEMWBS and EQ-5D-5L by reporting distributions and examining their known-group validity and convergent validity. Results In total, 500 individuals participated the online survey. Spearman’s rank correlation showed that EQ-5D-5L dimensions, except for the anxiety/depression dimension, were weakly correlated with all dimensions of SWEMWBS. The two-factor solution for exploratory factor analysis found that all of SWEMWBS dimensions loaded onto one factor, four EQ-5D-5L dimensions (mobility, self-care, usual activities and pain/discomfort) onto another, and the EQ-5D-5L item of anxiety/depression item loaded moderately onto both factors. Patients of four disease groups had different distributions of responses for both SWEMWBS and EQ-5D-5L. In terms of known-group validity, both the F statistic and AUROC value of EQ-5D-5L utility scores were significantly higher than SWEMWBS scores in all four pair-wised comparisons. The Pearson correlation coefficient between EQ-5D-5L utility scores, SWEMWBS scores and EQ-VAS was 0.44 (P

This paper proposes a distribution locational marginal pricing (DLMP) based bi-level Stackelberg game framework between the internet service company (ISC) and distribution system operator (DSO) in the data center park. To minimize electricity costs, the ISC at the upper level dispatches the interactive workloads (IWs) across different data center buildings spatially and schedules the battery energy storage system temporally in response to DLMP. Photovoltaic generation and static var generation provide extra active and reactive power. At the lower level, DSO calculates the DLMP by minimizing the total electricity cost under the two-part tariff policy and ensures that the distribution network is uncongested and bus voltage is within the limit. The equilibrium solution is obtained by converting the bi-level optimization into a single-level mixed-integer second-order cone programming optimization using the strong duality theorem and the binary expansion method. Case studies verify that the proposed method benefits both the DSO and ISC while preserving the privacy of the ISC. By taking into account the uncertainties in IWs and photovoltaic generation, the flexibility of distribution networks is enhanced, which further facilitates the accommodation of more demand-side resources.

The present work, which describes the development of a novel, portable, low-cost, effective, hybrid-actuator rehabilitation exoskeleton, aims to present a solution for the rehabilitation of functional finger injuries. In this robotic system, a simple and ingenious actuator is designed on the synchronizing wheel of each finger joint, which enables the independent passive training of each finger joint with the actuation of the motor. In addition, three damping shafts with leaf springs as another type of actuator, corresponding to PIP, MIP and DIP joints, are used as damping devices to supply the damping force for active training. Moreover, a gesture-based signal recognition algorithm, including a preprocessing algorithm, a feature vector extraction algorithm, and a clustering algorithm, is designed and integrated to serve the system for further automatic controllability. By utilizing this hybrid actuator mode, the robotic exoskeleton is able to train each finger joint independently in a passive training mode and maintain the damping force output within acceptable ranges for different levels of muscle strength. Importantly, with further optimization and upgrades, we deduce that this system has excellent potential applications for finger rehabilitation.

Abstract Genetic testing plays an important role in human health management and disease prevention. However, traditional genetic testing methods have been unable to fulfill the current diagnostic needs owing to several limitations, including high cost, complexity, and difficulty in performing. Smartphones have multiple inherent advantages, such as easy portability, ubiquity, fast processing speed, and excellent imaging capabilities, and have enormous potential in realizing rapid on‐site genetic testing. The present review documents the research progress of smartphone‐based optical imaging biosensors in the fields of colorimetry, fluorescence, and microscopic imaging for genetic testing. Furthermore, the review describes their potential applications in diagnostics, which range from infectious diseases to hereditary diseases and cancers. Finally, the challenges and perspectives of smartphone‐based optical imaging biosensors regarding genetic testing are discussed.

The interlayer adhesive material is important to ensure the integrity of a pavement structure, but its influence on pavement performance as an actual material layer is usually ignored in pavement structure design and analysis. At present, the relationship between the shear stress and slip is usually expressed in the form of a coefficient. The interlayer adhesive material is a kind of heterogeneous composite material with rheological performance. Therefore, the relationship between a force and the displacement cannot be characterized by a simple linear relationship. In this paper, we develop a method for characterizing the properties of interlayer bonding materials using an interfacial cohesive zone model. First, we perform experimental tests, that is, a pull-out test and an oblique-shear test, to determine the optimal dosage of adhesive material, and then obtain the normal stress-displacement characteristic curve and shear stress-displacement characteristic curve for the optimal dosage of the adhesive material. Then, we divide the shear stress-displacement characteristic curve into four stages. Next, based on the characteristics of normal stress displacement curve and shear stress displacement curve, we develop a cohesive zone model based pavement interlayer simulation approach that can be used to characterize the stress displacement characteristics of interlayer adhesive materials. Finally, we present the performance of the interfacial cohesive zone model using finite element software to simulate the shear test and pull-out test, and find that the simulation results are basically consistent with the experimental results. Further, we apply the model to a special case, and the results are also consistent with the actual monitoring. It shows that the proposed method is reasonable.

This article aims to conduct an in-depth investigation into the environmental impact of Hall thruster plumes on spacecraft surface charging. The non-uniform plasma plume generated by Hall thrusters may trigger charging and discharging effects, making the assessment of surface charging risks crucial. Through numerical simulations using SPIS system, this study evaluates the surface charging characteristics of a complex spacecraft in orbit, simulating the effects of turning on and off the thrusters, as well as varying distances between the thrusters and the spacecraft. The simulation demonstrates that turning on the thrusters significantly affects spacecraft charging, reducing the potential difference between spacecraft surfaces from 3740 V to 19.2 V, effectively alleviating electrostatic discharge on the spacecraft surface. The closer the thruster is to the spacecraft, the more CEX ions are collected on the surface, influenced by the beam ions, resulting in a surface potential change of 1.3 V, with minor effects on surface potential but contributing to increased deposition contamination on the spacecraft surface.

Sea buckthorn pomace (SBP) is a by-product of sea buckthorn processing that is rich in bioactive compounds. In this study, different active ingredients were extracted by using different solvents (water, methanol, ethanol, glycerol, ethyl acetate, and petroleum ether) combined with an ultrasonic assisted method. The correlation between the active ingredients and antioxidant properties of the extract was studied, which provided a research basis for the comprehensive utilization of SBP. This study revealed that the 75% ethanol extract had the highest total phenolic content (TPC) of 42.86 ± 0.73 mg GAE/g, while the 75% glycerol extract had the highest total flavonoid content (TFC) of 25.52 ± 1.35 mg RTE/g. The ethanol extract exhibited the strongest antioxidant activity at the same concentration compared with other solvents. The antioxidant activity of the ethanol, methanol, and glycerol extracts increased in a concentration-dependent manner. Thirteen phenolic compounds were detected in the SBP extracts using UPLC-MS/MS analysis. Notably, the 75% glycerol extract contained the highest concentration of all identified phenolic compounds, with rutin (192.21 ± 8.19 μg/g), epigallocatechin (105.49 ± 0.69 μg/g), and protocatechuic acid (27.9 ± 2.38 μg/g) being the most abundant. Flavonols were found to be the main phenolic substances in SBP. A strong correlation was observed between TPC and the antioxidant activities of SBP extracts. In conclusion, the choice of solvent significantly influences the active compounds and antioxidant activities of SBP extracts. SBP extracts are a valuable source of natural phenolics and antioxidants.

Abstract Lung adenocarcinoma (LUAD) is the most frequent subtype of lung cancer globally. However, the survival rate of lung adenocarcinoma patients remains low. Immune checkpoints and long noncoding RNAs are emerging as vital tools for predicting the immunotherapeutic response and outcomes of patients with lung adenocarcinoma. It is critical to identify lncRNAs associated with immune checkpoints in lung adenocarcinoma patients. In this study, immune checkpoint-related lncRNAs (IClncRNAs) were analysed and identified by coexpression. Based on the immune checkpoint-related lncRNAs, we divided patients with lung adenocarcinoma into two clusters and constructed a risk model. Kaplan–Meier analysis, Gene Set Enrichment Analysis, and nomogram analysis of the 2 clusters and the risk model were performed. Finally, the potential immunotherapeutic prediction value of this model was discussed. The risk model consisting of 6 immune checkpoint-related lncRNAs was an independent predictor of survival. Through regrouping the patients with this model, we can distinguish between them more effectively in terms of their immunotherapeutic response, tumour microenvironment, and chemotherapy response. This risk model based on immune checkpoint-based lncRNAs may have an excellent clinical value for predicting the immunotherapeutic response and outcomes of patients with LUAD.

Immature function and fragility hinder application of hESC-derived β cells (SC-β cell) for diabetes cell therapy. Here, the authors identify ZnT8 as a gene editing target to enhance the insulin secretion and cell survival under metabolic stress by abolishing zinc transport in SC-β cells.

Transplantation of neural stem cells (NSCs) has been proved to promote functional rehabilitation of brain lesions including ischemic stroke. However, the therapeutic effects of NSC transplantation are limited by the low survival and differentiation rates of NSCs due to the harsh environment in the brain after ischemic stroke. Here, we employed NSCs derived from human induced pluripotent stem cells together with exosomes extracted from NSCs to treat cerebral ischemia induced by middle cerebral artery occlusion/reperfusion in mice. The results showed that NSC-derived exosomes significantly reduced the inflammatory response, alleviated oxidative stress after NSC transplantation, and facilitated NSCs differentiation in vivo. The combination of NSCs with exosomes ameliorated the injury of brain tissue including cerebral infarction, neuronal death, and glial scarring, and promoted the recovery of motor function. To explore the underlying mechanisms, we analyzed the miRNA profiles of NSC-derived exosomes and the potential downstream genes. Our study provided the rationale for the clinical application of NSC-derived exosomes as a supportive adjuvant for NSC transplantation after stroke.

This study aimed to investigate the role and mechanism of CXC chemokine receptor 4 (CXCR4) in cadmium (Cd)-induced renal injury. CXCR4 and TGF-β1/Smad pathway protein levels were detected by western blotting. Indicators related to renal function and oxidative stress factors were assessed and reactive oxygen species (ROS) level was evaluated by staining. TUNEL was used to measure apoptosis rate. PAS and Masson's trichrome staining were used to detect the level of renal fibrosis. The expression of Bcl-2, Bax, Cleaved-caspase 3, fibronectin, and collagen I proteins were detected by western blotting, immunohistochemistry, or immunofluorescence. The expression of CXCR4 was increased in a Cd-induced chronic renal injury model in rats. Si-CXCR4 decreased levels of TGF-β1, TGF-βR1, p-Smad2/Smad2, p-Smad3/Smad3, the renal weight index, urine protein, blood urea nitrogen, blood creatinine, and levels of MDA but raised the levels of SOD and GSH-Px. In addition, si-CXCR4 inhibited apoptosis in Cd-treated rats. CXCR4 inhibition alleviated fibrosis levels in Cd-treated rats. In Cd-treated cells, TGF-β attenuated the suppressive effect of CXCR4 inhibition on the TGF-β1/Smad pathway. TGF-β intervention increased MDA and ROS, and downregulated SOD and GSH-Px. TGF-β attenuated the inhibitory effect of CXCR4 on apoptosis and fibrosis. CXCR4 inhibition decreased levels of Cd-induced renal oxidative stress, apoptosis, and fibrosis by inhibiting the TGF-β1/Smad pathway.

Abstract Background Glioma is the most common brain tumor in adults and is characterized by a short survival time and high resistance to chemotherapy. It is imperative to determine the prognosis and therapy-related targets for glioma. Endoplasmic reticulum stress (ERS), as an adaptive protective mechanism, indicates the unfolded protein response (UPR) to determine cell survival and affects chemotherapy sensitivity, which is related to the prognosis of glioma. Methods Our research used the TCGA database as the training group and the CGGA database as the testing group. Lasso regression and Cox analysis were performed to construct an ERS signature-based risk score model in glioma. Three methods (time-dependent receiver operating characteristic analysis and multivariate and univariate Cox regression analysis) were applied to assess the independent prognostic effect of texture parameters. Consensus clustering was used to classify the two clusters. In addition, functional and immune analyses were performed to assess the malignant process and immune microenvironment. Immunotherapy and anticancer drug response prediction were adopted to evaluate immune checkpoint and chemotherapy sensitivity. Results The results revealed that the 7-gene signature strongly predicts glioma prognosis. The two clusters have markedly distinct molecular and prognostic features. The validation group result revealed that the signature has exceptional repeatability and certainty. Functional analysis showed that the ERS-related gene signature was closely associated with the malignant process and prognosis of tumors. Immune analysis indicated that the ERS-related gene signature is strongly related to immune infiltration. Immunotherapy and anticancer drug response prediction indicated that the ERS-related gene signature is positively correlated with immune checkpoint and chemotherapy sensitivity. Conclusions Collectively, the ERS-related risk model can provide a novel signature to predict glioma prognosis and treatment.

Abstract Integrated, timely data about pavement structures, materials and performance information are crucial for the continuous improvement and optimization of pavement design by the engineering research community. However, at present, pavement structures, materials and performance information in China are relatively isolated and cannot be integrated and managed. This results in a waste of a large amount of effective information. One of the significant development trends of pavement engineering is to collect, analyze, and manage the knowledge assets of pavement information to realize intelligent decision-making. To address these challenges, a knowledge graph (KG) is adopted, which is a novel and effective knowledge management technology and provides an ideal technical method to realize the integration of information in pavement engineering. First, a neural network model is used based on the principle of deep learning to obtain knowledge. On this basis, the relationship between knowledge is built from siloed databases, data in textual format and networks, and the knowledge base. Second, KG-Pavement is presented, which is a flexible framework that can integrate and ingest heterogeneous pavement engineering data to generate knowledge graphs. Furthermore, the index and unique constraints on attributes for knowledge entities are proposed in KG-Pavement, which can improve the efficiency of internal retrieval in the system. Finally, a pavement information search engine based on a knowledge graph is constructed to realize information interaction and target information matching between a webpage server and graph database. This is the first successful application of knowledge graphs in pavement engineering. This will greatly promote knowledge integration and intelligent decision-making in the domain of pavement engineering.

Abstract Background Electronic medical records (EMR) contain detailed information about patient health. Developing an effective representation model is of great significance for the downstream applications of EMR. However, processing data directly is difficult because EMR data has such characteristics as incompleteness, unstructure and redundancy. Therefore, preprocess of the original data is the key step of EMR data mining. The classic distributed word representations ignore the geometric feature of the word vectors for the representation of EMR data, which often underestimate the similarities between similar words and overestimate the similarities between distant words. This results in word similarity obtained from embedding models being inconsistent with human judgment and much valuable medical information being lost. Results In this study, we propose a biomedical word embedding framework based on manifold subspace. Our proposed model first obtains the word vector representations of the EMR data, and then re-embeds the word vector in the manifold subspace. We develop an efficient optimization algorithm with neighborhood preserving embedding based on manifold optimization. To verify the algorithm presented in this study, we perform experiments on intrinsic evaluation and external classification tasks, and the experimental results demonstrate its advantages over other baseline methods. Conclusions Manifold learning subspace embedding can enhance the representation of distributed word representations in electronic medical record texts. Reduce the difficulty for researchers to process unstructured electronic medical record text data, which has certain biomedical research value.

Taking the main bearing-main journal of a six-cylinder diesel engine as the research object，based on the elastic- -fluid lubrication theory and the micro-bump contact theory and considering factors such as journal misalignment and main bearing surface roughness，the main bearing lubrication characteristics model under the flexible whole body is established，and the influence of the main bearing surface profile on the main bearing lubrication characteristics and main journal vibration characteristics is analyzed. The results show that，compared with the result that does not take into account the main bearing surface profile，when the main bearing surface profile is included，the minimum oil film thickness of the main bearing is increased by 75.53%，the maximum oil film pressure is reduced by 6.15%，the average friction loss is reduced by 2%，the horizontal flexural vibration amplitudes of the main journal is reduced by 6%，the vertical flexural vibration amplitudes is reduced by 5%，and both the axial vibration and torsional vibration amplitudes are reduced by 2%. With the increase of the main bearing surface profile height，the minimum oil film thickness of the main bearing increases，the maximum oil film pressure decreases，the average friction loss decreases，and both the maximum roughness contact force amplitude and the number of occurrences are reduced; the amplitudes of flexural vibration，axial vibration and torsional vibration of the main journal are all reduced.

With the continuous development of social economy and the rapid progress in science and technology, the three-dimensional multilayer usufruct of sea areas has become an effective way to fulfil the increasing marine usage activities in China. However, this pattern has put forward higher demands for the spatial scope definition and ownership management of sea areas. This paper analyzed the connotation, basic principles and consideration factors of multilayer usufruct of sea areas based on the nature of stratification in three-dimensional space of sea areas, and the difficulties in the development and utilization of three-dimensional space of sea areas and the design of the associated management system were comprehensively discussed. Research results showed that the sea area space could be divided into five parts: above the water surface, water surface, water body, seabed and subsoil. The three-dimensional multilayer usufruct of sea areas was the process of confirming the right to use the specific sea space occupied by specific functional purposes in the multi-level use of the same sea area. A legal system of three-dimensional property rights in sea space should be constructed and improved to ensure the implementation of three-dimensional multilayer usufruct of sea areas.

Abstract Memory device is an important part of electronic equipment. 2D transition metal dichalcogenides that exhibit novel electrical characteristics hold promise in developing new types of memory device. Here, the memory effect in 2D mica/WSe2 heterostructure is investigated. Under applying constant bias voltage and gate voltage, the K+ ions in mica will migrate in the direction of the electric field and be trapped to enable the memory function. The gradient K+ ions work as long‐range scatters to electrostatically dope the WSe2 channel. The shift of the threshold voltage indicates an electrostatic doping concentration up to 2.11 × 1012 cm−2. The operating voltage is as low as 10 V and the on/off ratio is estimated to be 104. To determine the mechanism, the dynamic behavior of the device is discussed and a model is proposed which reveals the correlation between the programming/erasing process and the device performance. Moreover, through defining and studying the effective charge trapping rate, θ, it is found that the trapped charge is proportional to the charge flowing through the channel which means that the device performance can be finely tuned by the programming process.

Recent discoveries have suggested that the F-actin capping protein α1 subunit (CAPZA1) in various human tumors could play a significantly important role in regulating cell proliferation, metastasis, and epithelial–mesenchymal transition. However, the immune-regulating role of CAPZA1 in the initiation and development of lung adenocarcinoma (LUAD) remains unclear. In our research, we first found that CAPZA1 serves as an oncogene in pan-cancers from the TCGA data and higher CAPZA1 expression process unfavorably prognostic value in LUAD based on starBase database, PrognoScan, and LOGpc database. Then, in our analyses, lncRNAs AC026356.1 in LUAD acted as a competitive endogenous RNA (ceRNA) of miR-30d-5p, which might be the possible regulatory miRNA of CAPZA1 based on the starBase database. Finally, we confirmed that CAPZA1 expression had a tightly positive correlation with immune infiltration cells, immune infiltration markers, TMB, MSI, immune score, stromal score, and immune checkpoints, indicating that CAPZA1 was a markedly reliable therapeutic target for immunological antitumor strategies. In conclusion, our investigations revealed that CAPZA1 might function as an immune-associated biomarker in the development and treatment of LUAD, thereby acting as a promising prognostic and therapeutic target against LUAD.

Abstract Background The recognition of pharmacological substances, compounds and proteins is essential for biomedical relation extraction, knowledge graph construction, drug discovery, as well as medical question answering. Although considerable efforts have been made to recognize biomedical entities in English texts, to date, only few limited attempts were made to recognize them from biomedical texts in other languages. PharmaCoNER is a named entity recognition challenge to recognize pharmacological entities from Spanish texts. Because there are currently abundant resources in the field of natural language processing, how to leverage these resources to the PharmaCoNER challenge is a meaningful study. Methods Inspired by the success of deep learning with language models, we compare and explore various representative BERT models to promote the development of the PharmaCoNER task. Results The experimental results show that deep learning with language models can effectively improve model performance on the PharmaCoNER dataset. Our method achieves state-of-the-art performance on the PharmaCoNER dataset, with a max F1-score of 92.01%. Conclusion For the BERT models on the PharmaCoNER dataset, biomedical domain knowledge has a greater impact on model performance than the native language (i.e., Spanish). The BERT models can obtain competitive performance by using WordPiece to alleviate the out of vocabulary limitation. The performance on the BERT model can be further improved by constructing a specific vocabulary based on domain knowledge. Moreover, the character case also has a certain impact on model performance.

Abstract Accumulating evidences revealed that long noncoding RNAs (lncRNAs) have been participated in cancer malignant progression, including glioblastoma multiforme (GBM). Despite much studies have found the precise biological role in the regulatory mechanisms of GBM, however the molecular mechanisms, particularly upstream mechanisms still need further elucidated. RT-QPCR, cell transfection, western blotting and bioinformatic analysis were executed to detect the expression of EGR1, HNF1A-AS1, miR-22-3p and ENO1 in GBM. Cell proliferation assay, colony formation assay, wound healing, migration and invasion assays were performed to detect the malignant characters of GBM cells. The molecular regulation mechanism was confirmed by luciferase reporter assay, ChIP and RIP. Finally, orthotopic mouse models were established to examine the effect of HNF1A-AS1 in vivo. In the current study, we analyzed clinical samples to show that the HNF1A-AS1 expression is upregulated and associated with poor patient survival in GBM. Functional studies revealed that HNF1A-AS1 knockdown markedly inhibits malignant phenotypes of GBM cells, whereas overexpression of HNF1A-AS1 exerts opposite effect. Mechanistically, the transcription factor EGR1 forced the HNF1A-AS1 expression by directly binding the promoter region of HNF1A-AS1. Furthermore, combined bioinformatics analysis with our mechanistic work, using luciferase reporter assays and RIP, we first demonstrated that HNF1A-AS1 functions as a competing endogenous RNA (ceRNA) with miR-22-3p to regulate ENO1 expression in GBM cells. HNF1A-AS1 directly binds to miR-22-3p and significantly inhibits miR-22-3p expression, while ENO1 expression was increased. miR-22-3p inhibitor offsets the HNF1A-AS1 silencing induced suppression in malignant behaviors of GBM cells. ENO1 was verified as a direct target of miR-22-3p and its expression levels was negatively with the prognosis in GBM patients. Taken together, our study illuminated the definite mechanism of HNF1A-AS1 in promoting GBM malignancy, and provided a novel therapeutic target for further clinical application.

In this work, effect of martensite pre-quenching on the subsequent bainite transformation kinetics of GCr15 bearing steel has been investigated by means of dilatometry. The results show that the pre-quenched martensite is significantly accelerate nucleation of subsequent bainite transformation in comparison with direct bainite austempering. Meanwhile, specimen of martensite pre-quenching at 200 °C has the shortest incubation period of bainite transformation at 240 °C. Moreover, the transformation rate of bainite transformation after martensite pre-quenching is faster than direct bainite austempering in the initial growth stage, and decrease with the decreasing of martensite pre-quenching temperature. Compared with the conventional quenched and tempered (QT) specimen, the tensile strength is slightly decrease from 2170 MPa to 2143 MPa, but the impact toughness is obviously increase from 43 J to 71 J in specimen heat-treated by martensite pre-quenching at 200 °C and subsequent bainite transformation at 240 °C. In addition, the mechanical stability of retained austenite in all martensite pre-quenching specimens are higher than that of QT specimen.