Background Biomarkers-based prediction of long-term risk of acute coronary syndrome(ACS)is scarce.We aim to develop a risk score integrating clinical routine information(C)and plasma biomarkers(B)for predicting long-t...Background Biomarkers-based prediction of long-term risk of acute coronary syndrome(ACS)is scarce.We aim to develop a risk score integrating clinical routine information(C)and plasma biomarkers(B)for predicting long-term risk of ACS patients.Methods We included 2729 ACS patients from the OCEA(Observation of cardiovascular events in ACS patients).The earlier admitted 1910 patients were enrolled as development cohort;and the subsequently admitted 819 subjects were treated as valida-tion cohort.We investigated 10-year risk of cardiovascular(CV)death,myocardial infarction(MI)and all cause death in these pa-tients.Potential variables contributing to risk of clinical events were assessed using Cox regression models and a score was de-rived using main part of these variables.Results During 16,110 person-years of follow-up,there were 238 CV death/MI in the development cohort.The 7 most import-ant predictors including in the final model were NT-proBNP,D-dimer,GDF-15,peripheral artery disease(PAD),Fibrinogen,ST-segment elevated MI(STEMI),left ventricular ejection fraction(LVEF),termed as CB-ACS score.C-index of the score for predica-tion of cardiovascular events was 0.79(95%CI:0.76-0.82)in development cohort and 0.77(95%CI:0.76-0.78)in the validation co-hort(5832 person-years of follow-up),which outperformed GRACE 2.0 and ABC-ACS risk score.The CB-ACS score was also well calibrated in development and validation cohort(Greenwood-Nam-D’Agostino:P=0.70 and P=0.07,respectively).Conclusions CB-ACS risk score provides a useful tool for long-term prediction of CV events in patients with ACS.This model outperforms GRACE 2.0 and ABC-ACS ischemic risk score.展开更多
Understanding the origin of natural gas in deep and ultra-deep reservoirs with multiple potential source rocks remains challenging due to the complex thermal evolution of hydrocarbons at high temperatures and multi-st...Understanding the origin of natural gas in deep and ultra-deep reservoirs with multiple potential source rocks remains challenging due to the complex thermal evolution of hydrocarbons at high temperatures and multi-stage accumulation processes.This study investigates the origin of natural gas in deep hydrothermal dolomite reservoirs of the Maokou Formation,eastern Sichuan Basin,using hydrocarbon inclusion analysis,radiometric U-Pb dating of calcite cements,maturity modeling of potential source rocks,and constraints on reactivation periods of the nearby No.15 Fault System.Results indicate an oil charging event at approximately 246.9 Ma,followed by two episodes of gas charging at 222.4 Ma and 175.2 Ma.Furthermore,the oil and gas charging events occurred synchronously with activities of the No.15 Fault System,suggesting that its reactivation induced episodic hydrocarbon migration.Maturity modeling indicates that during the oil charging period,source rocks in the Qiongzhusi,WufengLongmaxi,and first member of the Maokou formations reached the stages of dry gas generation,significant oil generation,and the threshold of oil generation,respectively.During the subsequent twoepisode gas charging periods,the Qiongzhusi and Wufeng-Longmaxi formations progressed to dry and wet gas generation stages,respectively,while the first member of the Maokou Formation attained the oil generation stage.The hydrocarbon charging time and maturity history of potential source rocks indicate that:1)oil in hydrothermal dolomite reservoirs predominantly originated from the Wufeng-Longmaxi Formation at approximately 246.9 Ma;2)during the subsequent gas charging episodes,the WufengLongmaxi Formation could contribute wet gas,while the Qiongzhusi Formation likely supplied cracking gas from kerogen and residual liquid hydrocarbon;3)all oil in the hydrothermal dolomite reservoirs underwent thermal cracking to gas at approximately 110 Ma.This study indicates that gas pools in(ultra-)deep carbonate reservoirs of the Sichuan Basin have mixed genetic origins,with contributions from multiple sources.The multidisciplinary approach,combining direct dating of hydrocarbon charge events and simulation of hydrocarbon generation,proves robust and effective in identifying the origin of natural gas in(ultra-)deep reservoirs.展开更多
The inherent trade-off between ductility and strength in Mg alloys remains a significant challenge,primarily governed by microstructural distribution and texture characteristics.Friction stir processing(FSP),a severe ...The inherent trade-off between ductility and strength in Mg alloys remains a significant challenge,primarily governed by microstructural distribution and texture characteristics.Friction stir processing(FSP),a severe plastic deformation(SPD)technique,refines microstructures by generating fine grains,uniformly dispersed fragmented particles,and a high fraction of high-angle grain boundaries(HAGBs),thereby facilitating superplastic forming at high strain rates and low temperatures.In the present work,a dual eccentric-pin tool(DEPT)FSP was employed to incorporate ZrO_(2) particles into a 6 mm thick AZ91D Mg alloy,leading to the formation of high volume{10-12}twins,dislocations,and β-Mg_(17)Al_(12) precipitates within the stirred zone.The microstructural evolution and mechanical behaviour of the stir zone under various process parameters were analysed using scanning electron microscopy(SEM),X-ray diffraction(XRD),electron backscatter diffraction(EBSD),and transmission electron microscopy(TEM).The DEPT enhanced plastic shearing and dynamic recrystallization,significantly reducing the grain size from 15.6μm to 2.35μm while promoting uniform dislocation distribution within the stir zone(SZ).Grain orientation analysis revealed a transition from basal to prismatic texture dominance(29.3% volume fraction)due to intensified radial-tangential coupling shear deformation,facilitating the activation of non-basal slip systems.The DEPT evidently improved the hardness of the SZ from 58 to 92 HV and increased tensile strength from 234 MPa to 325 MPa while maintaining an elongation of 23.8%,achieving an optimal strengthductility balance.This work presents a one-step approach for tailoring microstructural heterogeneity and enhancing mechanical properties in AZ91D/ZrO_(2) composites using the DEPT FSP technique.The method provides an effective strategy for mitigating the strength-ductility trade-off commonly observed in Mg alloys.展开更多
High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation.To solve the problems of low accuracy of the diagnostic model and unst...High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation.To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection,a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed.The first layer of the wide convolutional kernel deep convolutional neural network(WDCNN)is used to extract the local features of the signal and suppress the highfrequency noise.A Bidirectional Long Short-Term Memory Network(BILSTM)is used to obtain global time series features of the signal.Cross-attention combines the WDCNN layer and the BILSTM layer so that the model can recognize more comprehensive feature information of the signal.Meanwhile,to improve the accuracy,Variable Modal Decomposition(VMD)is used to decompose the signals and filter and reconstruct the signals using envelope entropy and kurtosis,which enables the pre-processing of the signals so that the data input to the neural network contains richer feature information.The feasibility of the model is tested and experimentally validated using publicly available datasets.The experimental results show that the accuracy of themodel proposed in this paper is significantly improved compared to the traditional WDCNN,BILSTM,and WDCNN-BILSTM models.展开更多
BACKGROUND Choledochal cysts(CC)and cystic biliary atresia(CBA)present similarly in early infancy but require different treatment approaches.While CC surgery can be delayed until 3-6 months of age in asymptomatic pati...BACKGROUND Choledochal cysts(CC)and cystic biliary atresia(CBA)present similarly in early infancy but require different treatment approaches.While CC surgery can be delayed until 3-6 months of age in asymptomatic patients,CBA requires intervention within 60 days to prevent cirrhosis.AIM To develop a diagnostic model for early differentiation between these conditions.METHODS A total of 319 patients with hepatic hilar cysts(<60 days old at surgery)were retrospectively analyzed;these patients were treated at three hospitals between 2011 and 2022.Clinical features including biochemical markers and ultrasonographic measurements were compared between CC(n=274)and CBA(n=45)groups.Least absolute shrinkage and selection operator regression identified key diagnostic features,and 11 machine learning models were developed and compared.RESULTS The CBA group showed higher levels of total bile acid,total bilirubin,γ-glutamyl transferase,aspartate aminotransferase,and alanine aminotransferase,and direct bilirubin,while longitudinal diameter of the cysts and transverse diameter of the cysts were larger in the CC group.The multilayer perceptron model demonstrated optimal performance with 95.8% accuracy,92.9% sensitivity,96.3% specificity,and an area under the curve of 0.990.Decision curve analysis confirmed its clinical utility.Based on the model,we developed user-friendly diagnostic software for clinical implementation.CONCLUSION Our machine learning approach differentiates CC from CBA in early infancy using routinely available clinical parameters.Early accurate diagnosis facilitates timely surgical intervention for CBA cases,potentially improving patient outcomes.展开更多
Tai'an city,located in Shandong Province,China,is rich in geothermal resources,characterized by shallow burial,high water temperature,and abundant water supply,making them high value for exploitation.However,corro...Tai'an city,located in Shandong Province,China,is rich in geothermal resources,characterized by shallow burial,high water temperature,and abundant water supply,making them high value for exploitation.However,corrosion and scaling are main challenges that hinder the widespread application and effective utilization of geothermal energy.This study focuses on the typical geothermal fields in Tai'an,employing qualitative evaluations of the geochemical saturation index with temperature,combined with the corrosion coefficient,Ryznar index,boiler scale,and hard scale assessment,to predict corrosion and scaling trends in the geothermal water of the study area.The results show that the hydrochemical types of geothermal water in the study area are predominantly Na-Ca-SO^(4)and Ca-Na-SO_(4)-HCO_(3),with the water being weakly alkaline.Simulations of saturation index changes with temperature reveal that calcium carbonate scaling is dominant scaling type in the area,with no evidence of calcium sulfate scaling.In the Daiyue Qiaogou geothermal field,the water exhibited corrosive bubble water properties,moderate calcium carbonate scaling,and abundant boiler scaling.Feicheng Anjiazhuang geothermal field showed non-corrosive bubble water,moderate calcium carbonate scaling,and significant boiler scaling.The Daidao'an geothermal field presented corrosive semi-bubble water,moderate calcium carbonate scaling,and abundant boiler scaling.The findings provide a foundation for the efficient exploitation of geothermal resources in the region.Implementing anti-corrosion and scale prevention measures can significantly enhance the utilization of geothermal energy.展开更多
Large language models(LLMs)represent significant advancements in artificial intelligence.However,their increasing capabilities come with a serious challenge:misalignment,which refers to the deviation of model behavior...Large language models(LLMs)represent significant advancements in artificial intelligence.However,their increasing capabilities come with a serious challenge:misalignment,which refers to the deviation of model behavior from the designers’intentions and human values.This review aims to synthesize the current understanding of the LLM misalignment issue and provide researchers and practitioners with a comprehensive overview.We define the concept of misalignment and elaborate on its various manifestations,including generating harmful content,factual errors(hallucinations),propagating biases,failing to follow instructions,emerging deceptive behaviors,and emergent misalignment.We explore the multifaceted causes of misalignment,systematically analyzing factors from surface-level technical issues(e.g.,training data,objective function design,model scaling)to deeper fundamental challenges(e.g.,difficulties formalizing values,discrepancies between training signals and real intentions).This review covers existing and emerging techniques for detecting and evaluating the degree of misalignment,such as benchmark tests,red-teaming,and formal safety assessments.Subsequently,we examine strategies to mitigate misalignment,focusing on mainstream alignment techniques such as RLHF,Constitutional AI(CAI),instruction fine-tuning,and novel approaches that address scalability and robustness.In particular,we analyze recent advances in misalignment attack research,including system prompt modifications,supervised fine-tuning,self-supervised representation attacks,and model editing,which challenge the robustness of model alignment.We categorize and analyze the surveyed literature,highlighting major findings,persistent limitations,and current contentious points.Finally,we identify key open questions and propose several promising future research directions,including constructing high-quality alignment datasets,exploring novel alignment methods,coordinating diverse values,and delving into the deep philosophical aspects of alignment.This work underscores the complexity and multidimensionality of LLM misalignment issues,calling for interdisciplinary approaches to reliably align LLMs with human values.展开更多
With the development of the country and society,higher requirements for college students’professional quality and moral cultivation have been put forward.The purpose of teaching ideological and political courses in u...With the development of the country and society,higher requirements for college students’professional quality and moral cultivation have been put forward.The purpose of teaching ideological and political courses in universities is to guide college students to solidly grasp ideological and political theoretical knowledge,continuously improve moral literacy,and become qualified successors of socialism.Practical teaching of ideological and political courses plays an essential role in educating and nurturing students.It can transform abstract theoretical knowledge into practical experiences that students can intuitively feel and understand.Through practical teaching,students can not only better understand and digest theoretical knowledge,but also apply this knowledge in real or simulated social environments,thereby gaining a deeper understanding of social phenomena and problems.Based on this,this article focuses on the analysis of the ideological and political practical education model and innovative path in universities in the new era.展开更多
The utilization of cellulose nanocrystals(CNCs),a renewable and eco-friendly nanomaterial,has emerged as the favored option for sustainable fillers.This paper presents diverse methods for CNCs preparation,including ac...The utilization of cellulose nanocrystals(CNCs),a renewable and eco-friendly nanomaterial,has emerged as the favored option for sustainable fillers.This paper presents diverse methods for CNCs preparation,including acid hydrolysis,oxidation,mechanical method,enzymatic hydrolysis,solvent method and hybrid approach.The strategies for modifying CNCs can be summarized as encompassing physical adsorption through non-covalent bond interactions and chemical modifications via covalent bonding.Moreover,the applications of CNCs in sensing systems,electronic skin devices,packaging materials,electronics industries,stabilizers and cosmetics are discussed with a particular emphasis on their contribution to enhancing polymer matrix properties.Lastly,future prospects for the advancement of CNCs are explored with a focus on its potential impact on sustainability efforts.展开更多
The China Space Station Telescope(CSST)is a 2 m three-mirror anastigmat equipped with a Fast Steering Mirror(FSM),which is part of its precision image stabilization system.The FSM is used to compensate for residuals f...The China Space Station Telescope(CSST)is a 2 m three-mirror anastigmat equipped with a Fast Steering Mirror(FSM),which is part of its precision image stabilization system.The FSM is used to compensate for residuals from the previous stage of the image stabilization system.However,a new type of image stabilization residual caused by image rotation and projection distortion is introduced when the FSM performs tip-tilt adjustments,reducing both the image stabilization accuracy and the absolute pointing accuracy of the CSST.In this paper,we propose a scheme to compute the image stabilization residuals across the full field of view(FOV)by using a reference star as the target for stabilization control,which can be utilized for subsequent image position correction.To achieve this,we developed a linear optical model for image point displacement by simplifying an existing image point displacement model and incorporating more readily available parameters.The computational accuracy of the new model is equivalent to that of the original,with computational differences of less than 0.03μm.Based on this linear model,we established a calculation model for image stabilization residuals,including those due to image rotation and projection distortion caused by FSM tip-tilt adjustments.This model provides a theoretical foundation for quantifying such residuals during the image stabilization process.Finally,the results of testing using this scheme are provided.Experimental results demonstrate that within the observation FOV of the CSST,when the FSM tilts by(1″,1″),the maximum absolute value of the image stabilization residuals accounts for 20%of the total image stabilization accuracy requirement.This finding underscores the necessity of computing and correcting these residuals to meet performance requirements.展开更多
Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collect...Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.展开更多
Suffering from the inefficient traditional trial-and-error methods and the huge searching space filled by millions of candidates, discovering new perovskite visible photocatalysts with higher hydrogen production rate(...Suffering from the inefficient traditional trial-and-error methods and the huge searching space filled by millions of candidates, discovering new perovskite visible photocatalysts with higher hydrogen production rate(RH_(2)) still remains a challenge in the field of photocatalytic water splitting(PWS). Herein, we established structural-property models targeted to RH_(2) and the proper bandgap(Eg) via machine learning(ML) technology to accelerate the discovery of efficient perovskite photocatalysts for PWS. The Pearson correlation coefficients(R) of leave-one-out cross validation(LOOCV) were adopted to compare the performances of different algorithms including gradient boosting regression(GBR), support vector regression(SVR), backpropagation artificial neural network(BPANN), and random forest(RF). It was found that the BPANN model showed the highest R values from LOOCV and testing data of 0.9897 and 0.9740 for RH_(2),while the GBR model had the best values of 0.9290 and 0.9207 for Eg. Furtherly, 14 potential PWS perovskite candidates were screened out from 30,000 ABO3-type perovskite structures under the criteria of structural stability, Eg, conduction band energy, valence band energy and RH_(2). The average RH_(2) of these14 perovskites is 6.4% higher than the highest value in the training data set. Moreover, the online web servers were developed to share our prediction models, which could be accessible in http://materialsdata-mining.com/ocpmdm/material_api/ahfga3d9puqlknig(E_g prediction) and http://materials-datamining.com/ocpmdm/material_api/i0 ucuyn3 wsd14940(RH_(2) prediction).展开更多
Collapsing gully erosion is a specific form of soil erosion types in the hilly granitic region of tropical and subtropical South China, and can result in extremely rapid water and soil loss. Knowledge of the soil phys...Collapsing gully erosion is a specific form of soil erosion types in the hilly granitic region of tropical and subtropical South China, and can result in extremely rapid water and soil loss. Knowledge of the soil physical and chemical properties of farmland influenced by collapsing gully erosion is important in understanding the development of soil quality. This study was conducted at the Wuli Watershed of the Tongcheng County, south of Hubei Province, China. The aim is to investigate soil physical and chemical properties of three soil layers （0-20, 20-40 and 40-60 cm） for two farmland types （paddy field and upland field） in three regions influenced by collapsing gully erosion. The three regions are described as follows： strongly influenced region （SIR）, weakly influenced region （WIR） and non-influenced region （NIR）. The results show that collapsing gully erosion significantly increased the soil gravel and sand content in paddy and upland fields, especially the surface soil in the SIR and WIR. In the 0-20 cm layer of the paddy field, the highest gravel content （250.94 g kg-1） was in the SIR and the lowest （78.67 g kg-1） was in the NIR, but in the upland filed, the surface soil （0-20 cm） of the SIR and the 40-60 cm soil layer for the NIR had the highest （177.13 g kg-1） and the lowest （59.96 g kg-1） values of gravel content, respectively. The distribution of gravel and sand decreased with depth in the three influenced regions, but silt and clay showed the inverse change. In the paddy field, the average of sand content decreased from 58.6 （in the SIR） to 49.0% （in the NIR）, but the silt content was in a reverse order, increasing from 27.9 to 36.9%, and the average of the clay content of three regions showed no significant variation （P〈0.05）. But in the upland filed, the sand, silt and clay fluctuated in the NIR and the WIR. Soils in the paddy and upland field were highly acidic （pH〈5.2） in the SIR and WIR; moreover lower nutrient contents （soil organic matter （SOM）, total N and available N, P, K） existed in the SIR. In the 0-20 cm soil layer of the paddy field, compared with the NIR and the WIR, collapsing gully erosion caused a very sharp decrease in the SOM and total N of the SIR （5.23 and 0.56 g kg-1, respectively）. But in the surface soil （0-20 cm） of the upland field, the highest SOM, total N, available N, available P and available K occurred in the NIR, and the lowest ones were in the SIR. Compared with the NIR, the cation exchange capacity （CEC） in the SIR and WIR was found to be relatively lower. These results suggest that collapsing gully erosion seriously affect the soil physical and chemical properties of farmland, lead to coarse particles accumulation in the field and decrease pH and nutrient levels.展开更多
Honokiol is a pleiotropic natural compound isolated from Magnolia and has multiple biological and clinically relevant effects,including anticancer and antimicrobial function.However,the antiviral activity of honokiol ...Honokiol is a pleiotropic natural compound isolated from Magnolia and has multiple biological and clinically relevant effects,including anticancer and antimicrobial function.However,the antiviral activity of honokiol has not yet been well studied.Here we showed that honokiol had no effect on herpes simplex virus-1(HSV-1)entry,but inhibited HSV-1 viral DNA replication,gene expression and the production of new progeny viruses.The combination of honokiol and clinical drug acyclovir augmented inhibition of HSV-1 infection.Our results illustrate that honokiol could be a potential new candidate for clinical consideration in the treatment of HSV-1 infection alone or combination with other therapeutics.展开更多
Stimuli-responsive hydrogel is regarded as one of the most promising smart soft materials for the next-generation advanced technologies and intelligence robots,but the limited variety of stimulus has become a non-negl...Stimuli-responsive hydrogel is regarded as one of the most promising smart soft materials for the next-generation advanced technologies and intelligence robots,but the limited variety of stimulus has become a non-negligible issue restricting its further development.Herein,we develop a new stimulus of“touch”(i.e.,spatial contact with foreign object)for smart materials and propose a flytrap-inspired touch-responsive polymeric hydrogel based on supersaturated salt solution,exhibiting multiple responsive behaviors in crystallization,heat releasing,and electric signal under touch stimulation.Furthermore,utilizing flytrap-like cascade response strategy,a soft actuator with touch-responsive actuation is fabricated by employing the touch-responsive hydrogel and the thermo-responsive hydrogel.This investigation provides a facile and versatile strategy to design touch-responsive smart materials,enabling a profound potential application in intelligence areas.展开更多
A diffusive-stochastic-viscoelastic model is proposed for the specific adhesion of viscoelastic solids via stochastically formed molecular bonds. In this model, we assumed that molecular level behaviours, including th...A diffusive-stochastic-viscoelastic model is proposed for the specific adhesion of viscoelastic solids via stochastically formed molecular bonds. In this model, we assumed that molecular level behaviours, including the diffusion of mobile adhesion molecules and stochastic reaction between adhesion molecules and binding sites, obey the Markovian stochastic processes, while mesoscopic deformations of the viscoelastic media are governed by continuum mechanics. Through Monte Carlo simulations of this model, we systematically investigated how the competition between time scales of molecular diffusion, reaction, and deformation creep of the solids may influence the lifetime and dynamic strength of the adhesion. We revealed that there exists an optimal characteristic time of molecule diffusion corresponding to the longest lifetime and largest adhesion strength, which is in good agreement with experimental observed characteristic time scales of molecular diffusion in cell membranes. In addition, we identified that the media viscosity can significantly increase the lifetime and dynamic strength, since the deformation creep and stress relaxation can effectively reduce the concentration of interfacial stress and increases the rebinding probability of molecular bonds.展开更多
Cancer remains a significant global health challenge with limited treatment options beyond systemic therapies,such as chemotherapy,radiotherapy,and molecular targeted therapy.Immunotherapy has emerged as a promising t...Cancer remains a significant global health challenge with limited treatment options beyond systemic therapies,such as chemotherapy,radiotherapy,and molecular targeted therapy.Immunotherapy has emerged as a promising therapeutic modality but the efficacy has plateaued,which therefore provides limited benefits to patients with cancer.Identification of more effective approaches to improve patient outcomes and extend survival are urgently needed.Drug repurposing has emerged as an attractive strategy for drug development and has recently garnered considerable interest.This review comprehensively analyses the efficacy of various repurposed drugs,such as transforming growth factor-beta(TGF-β)inhibitors,metformin,receptor activator of nuclear factor-κB ligand(RANKL)inhibitors,granulocyte macrophage colony-stimulating factor(GM-CSF),thymosinα1(Tα1),aspirin,and bisphosphonate,in tumorigenesis with a specific focus on their impact on tumor immunology and immunotherapy.Additionally,we present a concise overview of the current preclinical and clinical studies investigating the potential therapeutic synergies achieved by combining these agents with immune checkpoint inhibitors.展开更多
基金funded,in part,by the National Natural Science Fund (NSFC,China) under award number 81900382supported,in part,by the Yang talents Program of Beijing (QML20200302)Beijing Municipal Natural Science Foundation (7222072).
文摘Background Biomarkers-based prediction of long-term risk of acute coronary syndrome(ACS)is scarce.We aim to develop a risk score integrating clinical routine information(C)and plasma biomarkers(B)for predicting long-term risk of ACS patients.Methods We included 2729 ACS patients from the OCEA(Observation of cardiovascular events in ACS patients).The earlier admitted 1910 patients were enrolled as development cohort;and the subsequently admitted 819 subjects were treated as valida-tion cohort.We investigated 10-year risk of cardiovascular(CV)death,myocardial infarction(MI)and all cause death in these pa-tients.Potential variables contributing to risk of clinical events were assessed using Cox regression models and a score was de-rived using main part of these variables.Results During 16,110 person-years of follow-up,there were 238 CV death/MI in the development cohort.The 7 most import-ant predictors including in the final model were NT-proBNP,D-dimer,GDF-15,peripheral artery disease(PAD),Fibrinogen,ST-segment elevated MI(STEMI),left ventricular ejection fraction(LVEF),termed as CB-ACS score.C-index of the score for predica-tion of cardiovascular events was 0.79(95%CI:0.76-0.82)in development cohort and 0.77(95%CI:0.76-0.78)in the validation co-hort(5832 person-years of follow-up),which outperformed GRACE 2.0 and ABC-ACS risk score.The CB-ACS score was also well calibrated in development and validation cohort(Greenwood-Nam-D’Agostino:P=0.70 and P=0.07,respectively).Conclusions CB-ACS risk score provides a useful tool for long-term prediction of CV events in patients with ACS.This model outperforms GRACE 2.0 and ABC-ACS ischemic risk score.
基金financially supported by National Natural Science Foundation of China(No.92255302)the Joint Funds of the National Natural Science Foundation of China(No.U20B6001)。
文摘Understanding the origin of natural gas in deep and ultra-deep reservoirs with multiple potential source rocks remains challenging due to the complex thermal evolution of hydrocarbons at high temperatures and multi-stage accumulation processes.This study investigates the origin of natural gas in deep hydrothermal dolomite reservoirs of the Maokou Formation,eastern Sichuan Basin,using hydrocarbon inclusion analysis,radiometric U-Pb dating of calcite cements,maturity modeling of potential source rocks,and constraints on reactivation periods of the nearby No.15 Fault System.Results indicate an oil charging event at approximately 246.9 Ma,followed by two episodes of gas charging at 222.4 Ma and 175.2 Ma.Furthermore,the oil and gas charging events occurred synchronously with activities of the No.15 Fault System,suggesting that its reactivation induced episodic hydrocarbon migration.Maturity modeling indicates that during the oil charging period,source rocks in the Qiongzhusi,WufengLongmaxi,and first member of the Maokou formations reached the stages of dry gas generation,significant oil generation,and the threshold of oil generation,respectively.During the subsequent twoepisode gas charging periods,the Qiongzhusi and Wufeng-Longmaxi formations progressed to dry and wet gas generation stages,respectively,while the first member of the Maokou Formation attained the oil generation stage.The hydrocarbon charging time and maturity history of potential source rocks indicate that:1)oil in hydrothermal dolomite reservoirs predominantly originated from the Wufeng-Longmaxi Formation at approximately 246.9 Ma;2)during the subsequent gas charging episodes,the WufengLongmaxi Formation could contribute wet gas,while the Qiongzhusi Formation likely supplied cracking gas from kerogen and residual liquid hydrocarbon;3)all oil in the hydrothermal dolomite reservoirs underwent thermal cracking to gas at approximately 110 Ma.This study indicates that gas pools in(ultra-)deep carbonate reservoirs of the Sichuan Basin have mixed genetic origins,with contributions from multiple sources.The multidisciplinary approach,combining direct dating of hydrocarbon charge events and simulation of hydrocarbon generation,proves robust and effective in identifying the origin of natural gas in(ultra-)deep reservoirs.
基金the financial support from the Shandong Provincial Science Foundation for Outstanding Young Scholars(Grant No ZR2024YQ020)the National Natural Science Foundation of China(Grant Nos.52275349 and 52035005)+3 种基金the National Key Research and Development Program of China(Grant No 2022YFB4600902)the Excellent Young Team Project of Central Universities(No.2023QNTD002)Key Research and Development Program of Shandong Province(Grant No 2021ZLGX01)sponsored by the China/Shandong University International Postdoctoral Exchange Program.
文摘The inherent trade-off between ductility and strength in Mg alloys remains a significant challenge,primarily governed by microstructural distribution and texture characteristics.Friction stir processing(FSP),a severe plastic deformation(SPD)technique,refines microstructures by generating fine grains,uniformly dispersed fragmented particles,and a high fraction of high-angle grain boundaries(HAGBs),thereby facilitating superplastic forming at high strain rates and low temperatures.In the present work,a dual eccentric-pin tool(DEPT)FSP was employed to incorporate ZrO_(2) particles into a 6 mm thick AZ91D Mg alloy,leading to the formation of high volume{10-12}twins,dislocations,and β-Mg_(17)Al_(12) precipitates within the stirred zone.The microstructural evolution and mechanical behaviour of the stir zone under various process parameters were analysed using scanning electron microscopy(SEM),X-ray diffraction(XRD),electron backscatter diffraction(EBSD),and transmission electron microscopy(TEM).The DEPT enhanced plastic shearing and dynamic recrystallization,significantly reducing the grain size from 15.6μm to 2.35μm while promoting uniform dislocation distribution within the stir zone(SZ).Grain orientation analysis revealed a transition from basal to prismatic texture dominance(29.3% volume fraction)due to intensified radial-tangential coupling shear deformation,facilitating the activation of non-basal slip systems.The DEPT evidently improved the hardness of the SZ from 58 to 92 HV and increased tensile strength from 234 MPa to 325 MPa while maintaining an elongation of 23.8%,achieving an optimal strengthductility balance.This work presents a one-step approach for tailoring microstructural heterogeneity and enhancing mechanical properties in AZ91D/ZrO_(2) composites using the DEPT FSP technique.The method provides an effective strategy for mitigating the strength-ductility trade-off commonly observed in Mg alloys.
基金funded by the Jilin Provincial Department of Science and Technology,grant number 20230101208JC。
文摘High-speed train engine rolling bearings play a crucial role in maintaining engine health and minimizing operational losses during train operation.To solve the problems of low accuracy of the diagnostic model and unstable model due to the influence of noise during fault detection,a rolling bearing fault diagnosis model based on cross-attention fusion of WDCNN and BILSTM is proposed.The first layer of the wide convolutional kernel deep convolutional neural network(WDCNN)is used to extract the local features of the signal and suppress the highfrequency noise.A Bidirectional Long Short-Term Memory Network(BILSTM)is used to obtain global time series features of the signal.Cross-attention combines the WDCNN layer and the BILSTM layer so that the model can recognize more comprehensive feature information of the signal.Meanwhile,to improve the accuracy,Variable Modal Decomposition(VMD)is used to decompose the signals and filter and reconstruct the signals using envelope entropy and kurtosis,which enables the pre-processing of the signals so that the data input to the neural network contains richer feature information.The feasibility of the model is tested and experimentally validated using publicly available datasets.The experimental results show that the accuracy of themodel proposed in this paper is significantly improved compared to the traditional WDCNN,BILSTM,and WDCNN-BILSTM models.
基金Supported by the Beijing Municipal Science and Technology Commission,No.Z191100006619002Haiyou Health High-Caliber Talent Project,No.202412the Research Unit of Minimally Invasive Pediatric Surgery on Diagnosis and Treatment,Chinese Academy of Medical Sciences,No.2021RU015.
文摘BACKGROUND Choledochal cysts(CC)and cystic biliary atresia(CBA)present similarly in early infancy but require different treatment approaches.While CC surgery can be delayed until 3-6 months of age in asymptomatic patients,CBA requires intervention within 60 days to prevent cirrhosis.AIM To develop a diagnostic model for early differentiation between these conditions.METHODS A total of 319 patients with hepatic hilar cysts(<60 days old at surgery)were retrospectively analyzed;these patients were treated at three hospitals between 2011 and 2022.Clinical features including biochemical markers and ultrasonographic measurements were compared between CC(n=274)and CBA(n=45)groups.Least absolute shrinkage and selection operator regression identified key diagnostic features,and 11 machine learning models were developed and compared.RESULTS The CBA group showed higher levels of total bile acid,total bilirubin,γ-glutamyl transferase,aspartate aminotransferase,and alanine aminotransferase,and direct bilirubin,while longitudinal diameter of the cysts and transverse diameter of the cysts were larger in the CC group.The multilayer perceptron model demonstrated optimal performance with 95.8% accuracy,92.9% sensitivity,96.3% specificity,and an area under the curve of 0.990.Decision curve analysis confirmed its clinical utility.Based on the model,we developed user-friendly diagnostic software for clinical implementation.CONCLUSION Our machine learning approach differentiates CC from CBA in early infancy using routinely available clinical parameters.Early accurate diagnosis facilitates timely surgical intervention for CBA cases,potentially improving patient outcomes.
基金funded by the Key R&D Program of Henan,China(No.241111321000)China Geological Survey Program(DD20221676).
文摘Tai'an city,located in Shandong Province,China,is rich in geothermal resources,characterized by shallow burial,high water temperature,and abundant water supply,making them high value for exploitation.However,corrosion and scaling are main challenges that hinder the widespread application and effective utilization of geothermal energy.This study focuses on the typical geothermal fields in Tai'an,employing qualitative evaluations of the geochemical saturation index with temperature,combined with the corrosion coefficient,Ryznar index,boiler scale,and hard scale assessment,to predict corrosion and scaling trends in the geothermal water of the study area.The results show that the hydrochemical types of geothermal water in the study area are predominantly Na-Ca-SO^(4)and Ca-Na-SO_(4)-HCO_(3),with the water being weakly alkaline.Simulations of saturation index changes with temperature reveal that calcium carbonate scaling is dominant scaling type in the area,with no evidence of calcium sulfate scaling.In the Daiyue Qiaogou geothermal field,the water exhibited corrosive bubble water properties,moderate calcium carbonate scaling,and abundant boiler scaling.Feicheng Anjiazhuang geothermal field showed non-corrosive bubble water,moderate calcium carbonate scaling,and significant boiler scaling.The Daidao'an geothermal field presented corrosive semi-bubble water,moderate calcium carbonate scaling,and abundant boiler scaling.The findings provide a foundation for the efficient exploitation of geothermal resources in the region.Implementing anti-corrosion and scale prevention measures can significantly enhance the utilization of geothermal energy.
基金supported by National Natural Science Foundation of China(62462019,62172350)Guangdong Basic and Applied Basic Research Foundation(2023A1515012846)+6 种基金Guangxi Science and Technology Major Program(AA24263010)The Key Research and Development Program of Guangxi(AB24010085)Key Laboratory of Equipment Data Security and Guarantee Technology,Ministry of Education(GDZB2024060500)2024 Higher Education Scientific Research Planning Project(No.24NL0419)Nantong Science and Technology Project(No.JC2023070)the Open Fund of Advanced Cryptography and System Security Key Laboratory of Sichuan Province(GrantNo.SKLACSS-202407)sponsored by the Cultivation of Young andMiddle-aged Academic Leaders in the“Qing Lan Project”of Jiangsu Province and the 2025 Outstanding Teaching Team in the“Qing Lan Project”of Jiangsu Province.
文摘Large language models(LLMs)represent significant advancements in artificial intelligence.However,their increasing capabilities come with a serious challenge:misalignment,which refers to the deviation of model behavior from the designers’intentions and human values.This review aims to synthesize the current understanding of the LLM misalignment issue and provide researchers and practitioners with a comprehensive overview.We define the concept of misalignment and elaborate on its various manifestations,including generating harmful content,factual errors(hallucinations),propagating biases,failing to follow instructions,emerging deceptive behaviors,and emergent misalignment.We explore the multifaceted causes of misalignment,systematically analyzing factors from surface-level technical issues(e.g.,training data,objective function design,model scaling)to deeper fundamental challenges(e.g.,difficulties formalizing values,discrepancies between training signals and real intentions).This review covers existing and emerging techniques for detecting and evaluating the degree of misalignment,such as benchmark tests,red-teaming,and formal safety assessments.Subsequently,we examine strategies to mitigate misalignment,focusing on mainstream alignment techniques such as RLHF,Constitutional AI(CAI),instruction fine-tuning,and novel approaches that address scalability and robustness.In particular,we analyze recent advances in misalignment attack research,including system prompt modifications,supervised fine-tuning,self-supervised representation attacks,and model editing,which challenge the robustness of model alignment.We categorize and analyze the surveyed literature,highlighting major findings,persistent limitations,and current contentious points.Finally,we identify key open questions and propose several promising future research directions,including constructing high-quality alignment datasets,exploring novel alignment methods,coordinating diverse values,and delving into the deep philosophical aspects of alignment.This work underscores the complexity and multidimensionality of LLM misalignment issues,calling for interdisciplinary approaches to reliably align LLMs with human values.
文摘With the development of the country and society,higher requirements for college students’professional quality and moral cultivation have been put forward.The purpose of teaching ideological and political courses in universities is to guide college students to solidly grasp ideological and political theoretical knowledge,continuously improve moral literacy,and become qualified successors of socialism.Practical teaching of ideological and political courses plays an essential role in educating and nurturing students.It can transform abstract theoretical knowledge into practical experiences that students can intuitively feel and understand.Through practical teaching,students can not only better understand and digest theoretical knowledge,but also apply this knowledge in real or simulated social environments,thereby gaining a deeper understanding of social phenomena and problems.Based on this,this article focuses on the analysis of the ideological and political practical education model and innovative path in universities in the new era.
基金National Natural Science Foundation of China(grant number 52341301)the Basic Scientific Research Project of Liaoning Provincial Department of Education,China(grant number LJKMZ20220767)Outstanding Young Talent Projects of Shenyang University of Chemical Technology,China(grant number 2019YQ003).
文摘The utilization of cellulose nanocrystals(CNCs),a renewable and eco-friendly nanomaterial,has emerged as the favored option for sustainable fillers.This paper presents diverse methods for CNCs preparation,including acid hydrolysis,oxidation,mechanical method,enzymatic hydrolysis,solvent method and hybrid approach.The strategies for modifying CNCs can be summarized as encompassing physical adsorption through non-covalent bond interactions and chemical modifications via covalent bonding.Moreover,the applications of CNCs in sensing systems,electronic skin devices,packaging materials,electronics industries,stabilizers and cosmetics are discussed with a particular emphasis on their contribution to enhancing polymer matrix properties.Lastly,future prospects for the advancement of CNCs are explored with a focus on its potential impact on sustainability efforts.
基金financially supported by the National Key R&D Program of China(2022YFB3806300)。
文摘The China Space Station Telescope(CSST)is a 2 m three-mirror anastigmat equipped with a Fast Steering Mirror(FSM),which is part of its precision image stabilization system.The FSM is used to compensate for residuals from the previous stage of the image stabilization system.However,a new type of image stabilization residual caused by image rotation and projection distortion is introduced when the FSM performs tip-tilt adjustments,reducing both the image stabilization accuracy and the absolute pointing accuracy of the CSST.In this paper,we propose a scheme to compute the image stabilization residuals across the full field of view(FOV)by using a reference star as the target for stabilization control,which can be utilized for subsequent image position correction.To achieve this,we developed a linear optical model for image point displacement by simplifying an existing image point displacement model and incorporating more readily available parameters.The computational accuracy of the new model is equivalent to that of the original,with computational differences of less than 0.03μm.Based on this linear model,we established a calculation model for image stabilization residuals,including those due to image rotation and projection distortion caused by FSM tip-tilt adjustments.This model provides a theoretical foundation for quantifying such residuals during the image stabilization process.Finally,the results of testing using this scheme are provided.Experimental results demonstrate that within the observation FOV of the CSST,when the FSM tilts by(1″,1″),the maximum absolute value of the image stabilization residuals accounts for 20%of the total image stabilization accuracy requirement.This finding underscores the necessity of computing and correcting these residuals to meet performance requirements.
基金funded by the Jilin Provincial Department of Science and Technology,grant number 20230101208JC.
文摘Fault diagnosis of rolling bearings is crucial for ensuring the stable operation of mechanical equipment and production safety in industrial environments.However,due to the nonlinearity and non-stationarity of collected vibration signals,single-modal methods struggle to capture fault features fully.This paper proposes a rolling bearing fault diagnosis method based on multi-modal information fusion.The method first employs the Hippopotamus Optimization Algorithm(HO)to optimize the number of modes in Variational Mode Decomposition(VMD)to achieve optimal modal decomposition performance.It combines Convolutional Neural Networks(CNN)and Gated Recurrent Units(GRU)to extract temporal features from one-dimensional time-series signals.Meanwhile,the Markovian Transition Field(MTF)is used to transform one-dimensional signals into two-dimensional images for spatial feature mining.Through visualization techniques,the effectiveness of generated images from different parameter combinations is compared to determine the optimal parameter configuration.A multi-modal network(GSTCN)is constructed by integrating Swin-Transformer and the Convolutional Block Attention Module(CBAM),where the attention module is utilized to enhance fault features.Finally,the fault features extracted from different modalities are deeply fused and fed into a fully connected layer to complete fault classification.Experimental results show that the GSTCN model achieves an average diagnostic accuracy of 99.5%across three datasets,significantly outperforming existing comparison methods.This demonstrates that the proposed model has high diagnostic precision and good generalization ability,providing an efficient and reliable solution for rolling bearing fault diagnosis.
基金Financial support to this work from the National Key Research and Development Program of China (No. 2016YFB0700504)the Science and Technology Commission of Shanghai Municipality (18520723500) is gratefully acknowledged。
文摘Suffering from the inefficient traditional trial-and-error methods and the huge searching space filled by millions of candidates, discovering new perovskite visible photocatalysts with higher hydrogen production rate(RH_(2)) still remains a challenge in the field of photocatalytic water splitting(PWS). Herein, we established structural-property models targeted to RH_(2) and the proper bandgap(Eg) via machine learning(ML) technology to accelerate the discovery of efficient perovskite photocatalysts for PWS. The Pearson correlation coefficients(R) of leave-one-out cross validation(LOOCV) were adopted to compare the performances of different algorithms including gradient boosting regression(GBR), support vector regression(SVR), backpropagation artificial neural network(BPANN), and random forest(RF). It was found that the BPANN model showed the highest R values from LOOCV and testing data of 0.9897 and 0.9740 for RH_(2),while the GBR model had the best values of 0.9290 and 0.9207 for Eg. Furtherly, 14 potential PWS perovskite candidates were screened out from 30,000 ABO3-type perovskite structures under the criteria of structural stability, Eg, conduction band energy, valence band energy and RH_(2). The average RH_(2) of these14 perovskites is 6.4% higher than the highest value in the training data set. Moreover, the online web servers were developed to share our prediction models, which could be accessible in http://materialsdata-mining.com/ocpmdm/material_api/ahfga3d9puqlknig(E_g prediction) and http://materials-datamining.com/ocpmdm/material_api/i0 ucuyn3 wsd14940(RH_(2) prediction).
基金financially supported by the National Natural Science Foundation of China (41630858)
文摘Collapsing gully erosion is a specific form of soil erosion types in the hilly granitic region of tropical and subtropical South China, and can result in extremely rapid water and soil loss. Knowledge of the soil physical and chemical properties of farmland influenced by collapsing gully erosion is important in understanding the development of soil quality. This study was conducted at the Wuli Watershed of the Tongcheng County, south of Hubei Province, China. The aim is to investigate soil physical and chemical properties of three soil layers （0-20, 20-40 and 40-60 cm） for two farmland types （paddy field and upland field） in three regions influenced by collapsing gully erosion. The three regions are described as follows： strongly influenced region （SIR）, weakly influenced region （WIR） and non-influenced region （NIR）. The results show that collapsing gully erosion significantly increased the soil gravel and sand content in paddy and upland fields, especially the surface soil in the SIR and WIR. In the 0-20 cm layer of the paddy field, the highest gravel content （250.94 g kg-1） was in the SIR and the lowest （78.67 g kg-1） was in the NIR, but in the upland filed, the surface soil （0-20 cm） of the SIR and the 40-60 cm soil layer for the NIR had the highest （177.13 g kg-1） and the lowest （59.96 g kg-1） values of gravel content, respectively. The distribution of gravel and sand decreased with depth in the three influenced regions, but silt and clay showed the inverse change. In the paddy field, the average of sand content decreased from 58.6 （in the SIR） to 49.0% （in the NIR）, but the silt content was in a reverse order, increasing from 27.9 to 36.9%, and the average of the clay content of three regions showed no significant variation （P〈0.05）. But in the upland filed, the sand, silt and clay fluctuated in the NIR and the WIR. Soils in the paddy and upland field were highly acidic （pH〈5.2） in the SIR and WIR; moreover lower nutrient contents （soil organic matter （SOM）, total N and available N, P, K） existed in the SIR. In the 0-20 cm soil layer of the paddy field, compared with the NIR and the WIR, collapsing gully erosion caused a very sharp decrease in the SOM and total N of the SIR （5.23 and 0.56 g kg-1, respectively）. But in the surface soil （0-20 cm） of the upland field, the highest SOM, total N, available N, available P and available K occurred in the NIR, and the lowest ones were in the SIR. Compared with the NIR, the cation exchange capacity （CEC） in the SIR and WIR was found to be relatively lower. These results suggest that collapsing gully erosion seriously affect the soil physical and chemical properties of farmland, lead to coarse particles accumulation in the field and decrease pH and nutrient levels.
基金supported by the grant from National Key Research and Development Program (2016YFA0502100)
文摘Honokiol is a pleiotropic natural compound isolated from Magnolia and has multiple biological and clinically relevant effects,including anticancer and antimicrobial function.However,the antiviral activity of honokiol has not yet been well studied.Here we showed that honokiol had no effect on herpes simplex virus-1(HSV-1)entry,but inhibited HSV-1 viral DNA replication,gene expression and the production of new progeny viruses.The combination of honokiol and clinical drug acyclovir augmented inhibition of HSV-1 infection.Our results illustrate that honokiol could be a potential new candidate for clinical consideration in the treatment of HSV-1 infection alone or combination with other therapeutics.
基金supported by the National Natural Science Foundation of China(52103152)China Postdoctoral Science Foundation(2021M690157)Ningbo Natural Science Foundation(2121J206).
文摘Stimuli-responsive hydrogel is regarded as one of the most promising smart soft materials for the next-generation advanced technologies and intelligence robots,but the limited variety of stimulus has become a non-negligible issue restricting its further development.Herein,we develop a new stimulus of“touch”(i.e.,spatial contact with foreign object)for smart materials and propose a flytrap-inspired touch-responsive polymeric hydrogel based on supersaturated salt solution,exhibiting multiple responsive behaviors in crystallization,heat releasing,and electric signal under touch stimulation.Furthermore,utilizing flytrap-like cascade response strategy,a soft actuator with touch-responsive actuation is fabricated by employing the touch-responsive hydrogel and the thermo-responsive hydrogel.This investigation provides a facile and versatile strategy to design touch-responsive smart materials,enabling a profound potential application in intelligence areas.
基金the National Natural Science Foundation of China (Grants 11472119 and 11602099)the Fundamental Research Funds for the Central Universities (Grant lzujbky-2017-ot11)the 111 Project (Grant B14044).
文摘A diffusive-stochastic-viscoelastic model is proposed for the specific adhesion of viscoelastic solids via stochastically formed molecular bonds. In this model, we assumed that molecular level behaviours, including the diffusion of mobile adhesion molecules and stochastic reaction between adhesion molecules and binding sites, obey the Markovian stochastic processes, while mesoscopic deformations of the viscoelastic media are governed by continuum mechanics. Through Monte Carlo simulations of this model, we systematically investigated how the competition between time scales of molecular diffusion, reaction, and deformation creep of the solids may influence the lifetime and dynamic strength of the adhesion. We revealed that there exists an optimal characteristic time of molecule diffusion corresponding to the longest lifetime and largest adhesion strength, which is in good agreement with experimental observed characteristic time scales of molecular diffusion in cell membranes. In addition, we identified that the media viscosity can significantly increase the lifetime and dynamic strength, since the deformation creep and stress relaxation can effectively reduce the concentration of interfacial stress and increases the rebinding probability of molecular bonds.
基金supported by grants from the National Natural Science Foundation of China(Grant No.81772830)。
文摘Cancer remains a significant global health challenge with limited treatment options beyond systemic therapies,such as chemotherapy,radiotherapy,and molecular targeted therapy.Immunotherapy has emerged as a promising therapeutic modality but the efficacy has plateaued,which therefore provides limited benefits to patients with cancer.Identification of more effective approaches to improve patient outcomes and extend survival are urgently needed.Drug repurposing has emerged as an attractive strategy for drug development and has recently garnered considerable interest.This review comprehensively analyses the efficacy of various repurposed drugs,such as transforming growth factor-beta(TGF-β)inhibitors,metformin,receptor activator of nuclear factor-κB ligand(RANKL)inhibitors,granulocyte macrophage colony-stimulating factor(GM-CSF),thymosinα1(Tα1),aspirin,and bisphosphonate,in tumorigenesis with a specific focus on their impact on tumor immunology and immunotherapy.Additionally,we present a concise overview of the current preclinical and clinical studies investigating the potential therapeutic synergies achieved by combining these agents with immune checkpoint inhibitors.
