In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation ...In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.展开更多
Background:Large language models(LLMs)have shown considerable promise in supporting clinical decision-making.However,their adoption and evaluation in dermatology remains limited.This study aimed to explore the prefere...Background:Large language models(LLMs)have shown considerable promise in supporting clinical decision-making.However,their adoption and evaluation in dermatology remains limited.This study aimed to explore the preferences of Chinese dermatologists regarding LLM-generated responses in clinical psoriasis scenarios and to assess how they prioritize key quality dimensions,including accuracy,traceability,and logicality.Methods:A cross-sectional,web-based survey was conducted between December 25,2024,and January 22,2025,following the Checklist for Reporting Results of Internet E-Surveys guidelines.A total of 1247 valid responses were collected from practicing dermatologists across 33 of China's provincial-level administrative divisions.Participants evaluated responses to five categories of clinical questions(etiology,clinical presentation,differential diagnosis,treatment,and case study)generated by five LLMs:ChatGPT-4o,Kimi.ai,Doubao,ZuoYiGPT,and Lingyi-agent.Statistical associations between participant characteristics and model preferences were examined using chi-square tests.Results:ChatGPT-4o(Model 1)emerged as the most preferred model across all clinical tasks,consistently receiving the highest number of votes in case study(n=740),clinical presentation(n=666),differential diagnosis(n=707),etiology(n=602),and treatment(n=656).Significant variation in model preference by professional title was observed only for the differential diagnosis task(χ^(2)=21.13,df=12,p=0.0485),while no significant differences were found across hospital tiers(p>0.05).In terms of evaluation dimensions,accuracy was most frequently rated as“very important”(n=635).A significant association existed between hospital tier and the most valued dimension(χ^(2)=27.667,df=9,p=0.0011),with dermatologists in primary hospitals prioritizing traceability more than their peers in higher-tier hospitals.No significant associations were found across professional titles(p=0.127).Conclusions:Chinese dermatologists suggest a strong preference for ChatGPT-4o over domestic LLMs in psoriasis-related clinical tasks.While accuracy remains the primary criterion,traceability and logicality are also critical,particularly for clinicians in lower-tier hospitals.These findings suggest that future clinical LLMs should prioritize not only content accuracy but also source transparency and structural clarity to meet the diverse needs of different clinical settings.展开更多
Understanding how genetic variation within forest species influences growth responses under climate change is essential for improving the accuracy of forest models and guiding adaptive management strategies.This study...Understanding how genetic variation within forest species influences growth responses under climate change is essential for improving the accuracy of forest models and guiding adaptive management strategies.This study models the dynamics of Italian silver fir(Abies alba)forests under varying climate change scenarios using the forest gap model FORMIND.Focusing on three distinct silver fir provenances(Western Alps,Northern Apennines,and Southern Apennines),the study simulates forest growth in the Tuscan-Emilian Apennine National Park under different representative concentration pathways(RCPs).The individual-based model FORMIND was parameterized and validated with field data for each of the provenances,demonstrating its ability to accurately reproduce key forest metrics and dynamics.Our results reveal significant differences in expected growth patterns,productivity,metabolism,and carbon storage capacity among the silver fir provenances in pure and mixed stands.In the simulations,the Northern Apennines provenance showed higher biomass production(biomass>10%±1%)and carbon uptake(net primary productivity,NPP>8%±1%)at the end of the century compared to the Western Alps provenance in the pure provenance(PP)and no regeneration scenario.Conversely,the Southern Apennines provenance showed higher biomass(biomass>5%–10%)and NPP(>15%–18%)in mixed provenance(MP)and regeneration scenarios.These results show that genetic diversity strongly affects forest growth and resilience to environmental changes.Hence,it should be included as a predictor variable in forest models.The study also demonstrates the resilience of silver fir to climatic stressors,emphasizing its potential as a robust species in multiple forest contexts.The integration of forest provenance data into the FORMIND model represents a significant advancement in forest modelling,enabling more accurate and reliable predictions under climate change scenarios.The study's findings advocate for a greater understanding and consideration of genetic diversity in forest management and conservation strategies,in support of assisted migration strategies aiming to enhance the resilience of forest ecosystems in a changing climate.展开更多
Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused ...Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.展开更多
Deep rock engineering is affected by coupled thermo-hydro-mechanical(THM)-dynamic fields,necessitating the elucidation of the dynamic mechanical behavior and failure mechanisms.This study utilized a Multi-field Couple...Deep rock engineering is affected by coupled thermo-hydro-mechanical(THM)-dynamic fields,necessitating the elucidation of the dynamic mechanical behavior and failure mechanisms.This study utilized a Multi-field Coupled Controlled Split Hopkinson Pressure Bar(MCC-SHPB)system to elucidate the cross-scale dynamic responses of rocks and the boundaries of failure modes under THM coupling.Impact tests were conducted on green sandstone under coupled conditions of temperature(25℃-80℃),confining pressure(0-15 MPa),and seepage water pressure(0-15 MPa).Scanning electron microscopy(SEM)microstructural characterization and COMSOL Multiphysics numerical simulations were conducted,and a dynamic constitutive theoretical framework and failure-prediction methodology were established.We investigated the impact toughness index(I_(t)),dynamic modulus(E_(d)),dynamic triaxial compressive strength(TCS_(d)),fragmentation degree(W),and failure modes of green sandstone under thermo-confining pressure-seepage-impact loading conditions.The key findings reveal that the(I_(t))reflects different energy regulation mechanisms across different confining pressure regimes.Thermal-microcrack interactions dominate at low pressure,and energy absorption prevails at high pressure.A triphasic dynamic modulus model captures stiffness evolution under energy-driven conditions,revealing cross-scale crack nucleation-propagation and fragment reorganization.The TCSd inflection point signifies energy dissipation shifts,causing nonlinear skeleton bearing-capacity degradation.A critical criterion based on the W was established to distinguish between the two failure modes and predict the unstable failure initiation.Numerical simulations were used to elucidate the effects of inertia-dominated crack propagation and stress wave interference,validating the critical criterion and the predictive accuracy of the theoretical model during cross-scale failure.This study provides a theoretical foundation for assessing the dynamic stability of rock masses subjected to multi-field coupling during deep resource exploitation.展开更多
Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,...Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.展开更多
BACKGROUND Patients harboring gene mutations like KRAS,NRAS,and BRAF demonstrate highly variable responses to chemotherapy,posing challenges for treatment optimization.Multiparametric magnetic resonance imaging(MRI),w...BACKGROUND Patients harboring gene mutations like KRAS,NRAS,and BRAF demonstrate highly variable responses to chemotherapy,posing challenges for treatment optimization.Multiparametric magnetic resonance imaging(MRI),with its noninvasive capability to assess tumor characteristics in detail,has shown promise in evaluating treatment response and predicting therapeutic outcomes.This technology holds potential for guiding personalized treatment strategies tailored to individual patient profiles,enhancing the precision and effectiveness of colorectal cancer care.AIM To create a multiparametric MRI-based predictive model for assessing chemotherapy efficacy in colorectal cancer patients with gene mutations.METHODS This retrospective study was conducted in a tertiary hospital,analyzing 157 colorectal cancer patients with gene mutations treated between August 2022 and December 2023.Based on chemotherapy outcomes,the patients were categorized into favorable(n=60)and unfavorable(n=50)response groups.Univariate and multivariate logistic regression analyses were performed to identify independent predictors of chemotherapy efficacy.A predictive nomogram was constructed using significant variables,and its performance was assessed using the area under the receiver operating characteristic curve(AUC)in both training and validation sets.RESULTS Univariate analysis identified that tumor differentiation,T2 signal intensity ratio,tumor-to-anal margin distance,and MRI-detected lymph node metastasis as significantly associated with chemotherapy response(P<0.05).Multivariate Logistics regression confirmed these four parameters as independent predictors.The predictive model demonstrated strong discrimination,with an AUC of 0.938(sensitivity:86%;specificity:92%)in the training set,and 0.942(sensitivity:100%;specificity:83%)in the validation set.CONCLUSION We established and validated a multiparametric MRI-based model for predicting chemotherapy response in colorectal cancer patients with gene mutations.This model holds promise for guiding individualized treatment strategies.展开更多
Objective:Neoadjuvant therapy(NAT)has become the standard treatment option for patients with locally advanced breast cancer.How to non-invasively screen out patients with pathological complete response(pCR)after NAT h...Objective:Neoadjuvant therapy(NAT)has become the standard treatment option for patients with locally advanced breast cancer.How to non-invasively screen out patients with pathological complete response(pCR)after NAT has become an urgent world-wide clinical problem.Our work aims to the assessment of neoadjuvant treatment response in breast cancer patients for higher accuracy prediction using innovative artificial intelligence system.Methods:In this study,we retrospectively collected longitudinal(pre-NAT and post-NAT)multi-parametric magnetic resonance imaging(MRI)and clinicopathologic data of a total of 1,315 breast cancer patients(clinical stageⅠ-Ⅲ)who had undergone NAT followed by standard surgery and treated across 5 independent medical centers from January 2010 to January 2023.We used radiomics,3D convolutional neural network technology and clinical data statistical analysis methods to extract and screen multimodal features,and then developed and validated a Clinical-Radiomics-Deep-Learning(CRDL)model to predict patients'pCR outcomes based on multimodal fusion features.Results:We use the area under the receiver operating characteristic curve(AUC)in the primary cohort(PC)and3 external validation cohorts(VC_(1-3))to evaluate the model performance.The results showed that the AUC in the PC composed of 2 medical centers was 0.947[95%confidence interval(95%CI):0.931-0.960],and the AUC values in VC_(1-3)were 0.857(95%CI:0.810-0.901),0.883(95%CI:0.841-0.918)and 0.904(95%CI:0.860-0.941),respectively.Conclusions:The CRDL model demonstrated high accuracy and robustness in predicting pCR to NAT using multimodal fusion data.This study provides a strong foundation for non-invasive assessment of pCR status in breast cancer patients following NAT and offers critical insights to guide clinical decision-making in post-NAT treatment planning.展开更多
We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We...We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We systematically analyze the technical pathways for deploying LLMs in disaster scenarios,emphasizing three breakthrough directions:(1)knowledge graph-driven dynamic risk modeling,(2)reinforcement learning-optimized emergency decision systems,and(3)secure local deployment architectures.The DeepSeek model demonstrates unique advantages through its hybrid reasoning mechanism combining semantic analysis with geospatial pattern recognition,enabling cost-effective processing of multi-source data spanning historical disaster records,real-time IoT sensor feeds,and socio-environmental parameters.A modular system architecture is designed to achieve three critical objectives:(a)automated construction of domain-specific knowledge graphs through unsupervised learning of disaster physics relationships,(b)scenario-adaptive resource allocation using risk simulations,and(c)preserving emergency coordination via federated learning across distributed response nodes.The proposed local deployment paradigm addresses critical data security concerns in cross-border disaster management while complying with the FAIR principles(Findable,Accessible,Interoperable,Reusable)for geoscientific data governance.This work establishes a methodological foundation for next-generation AI-earth science convergence in disaster mitigation.展开更多
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
Structural damage detection is hard to conduct in large-scale civil structures due to enormous structural data and insufficient damage features.To improve this situation,a damage detection method based on model reduct...Structural damage detection is hard to conduct in large-scale civil structures due to enormous structural data and insufficient damage features.To improve this situation,a damage detection method based on model reduction and response reconstruction is presented.Based on the framework of two-step model updating including substructure-level localization and element-level detection,the response reconstruction strategy with an improved sensitivity algorithm is presented to conveniently complement modal information and promote the reliability of model updating.In the iteration process,the reconstructed response is involved in the sensitivity algorithm as a reconstruction-related item.Besides,model reduction is applied to reduce computational degrees of freedom(DOFs)in each detection step.A numerical truss bridge is modelled to vindicate the effectiveness and efficiency of the method.The results showed that the presented method reduces the requirement for installed sensors while improving efficiency and ensuring accuracy of damage detection compared to traditional methods.展开更多
Background:It is well recognized that developing new animal models,refining the existing mouse models,and thoroughly characterizing their features are essential for gaining a deeper understanding of rosacea pathogenes...Background:It is well recognized that developing new animal models,refining the existing mouse models,and thoroughly characterizing their features are essential for gaining a deeper understanding of rosacea pathogenesis and for advancing therapeutic strategies in this direction.Accordingly,we aimed to characterize the pathological features of a long-term LL-37-induced mouse model of rosacea and to compare the disease manifestations and pathophysiological characteristics between short-term and long-term LL-37-induced models.A key focus was to investigate differential gene expression and the underlying mechanisms of immune system dysregulation in these models.Methods:We comparatively assessed skin lesion manifestations,the extent of inflammatory infiltration,sebaceous gland alterations,fibrosis,and angiogenesis in both models.Assessments were performed using photographic documentation,hematoxylin-eosin(HE)staining,Van Gieson's(VG)staining,immunohistochemistry,and Western blotting.Furthermore,we employed RNA sequencing to analyze differential gene expression in mouse skin.The RNA sequencing data were validated using immunofluorescence staining and Western blotting,with a specific focus on gene variations and mechanisms related to immune system dysregulation.Results:Mice subjected to long-term LL-37 induction developed rosacea-like pathological features,including angiogenesis,thickened skin tissue,and sebaceous gland hypertrophy.In the short-term LL-37-induced model,immune dysregulation primarily involved the innate immune response.However,long-term LL-37 induction resulted in significant activation of both innate and adaptive immune responses.Conclusion:The long-term LL-37-induced mouse model offers a valuable animal model for the detailed investigation of the pathological mechanisms driving moderate-to-severe rosacea with prolonged disease duration.Importantly,this model provides a significant experimental foundation for exploring the potential role of immune system dysregulation in rosacea pathogenesis.展开更多
Background:The efficacy of balloon angioplasty for treating peripheral artery disease is influenced by various factors,some of them not yet totally understood.This study aimed to evaluate the role of elastin content i...Background:The efficacy of balloon angioplasty for treating peripheral artery disease is influenced by various factors,some of them not yet totally understood.This study aimed to evaluate the role of elastin content in vascular responses 28 days postangioplasty using uncoated and paclitaxel-coated balloons with the same platform in femoral arteries of a healthy porcine model.Methods:Eight animals underwent balloon angioplasty on the external and internal branches of femoral arteries.Histopathologic evaluation was conducted at follow-up to assess the elastin content,vascular damage,morphological features,and neointimal formation.Results:The elastin content was significantly higher in the external than in the internal femoral artery(p=0.0014).After balloon angioplasty,it was inversely correlated with vascular injury score(ρ=−0.4510,p=0.0096),neointimal inflammation(ρ=−0.3352,p=0.0607),transmural(ρ=−0.4474,p=0.0103)and circumferential(ρ=−0.4591,p=0.0082)smooth muscle cell loss,presence of proteoglycans(ρ=−0.5172,p=0.0024),fibrin deposition(ρ=−0.3496,p=0.0499),and adventitial fibrosis(ρ=−0.6229,p=0.0002).Neointimal formation inhibition with paclitaxel was evident only in arteries with disruption of the internal elastic lamina,with a significant smaller neointimal area in arteries treated with paclitaxel-coated balloons compared to uncoated balloons(median[Q1–Q3]:10.25[7.49–15.64]vs.24.44[18.96–30.52],p=0.0434).Conclusions:Elastin content varies between branches of the femoral artery and significantly influences the integrity of the internal elastic lamina,the vessel's adaptive response,and paclitaxel efficacy after balloon angioplasty.展开更多
Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making sys...Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making.展开更多
This study decouples the material microstructure into matrix and void phases.The undamaged constitutive is derived from the matrix phase,while the void phase contributes to damage evolution.A constitutive model is est...This study decouples the material microstructure into matrix and void phases.The undamaged constitutive is derived from the matrix phase,while the void phase contributes to damage evolution.A constitutive model is established by coupling the two.According to the void-phase evolution during damage,a damage sequence interaction model is proposed.Tests on new vehicles and vehicles in service materials yield stress-strain curves of materials without and with fatigue damage and measure the apparent elastic modulus.The damage sequence interaction model accurately predicts the residual mechanical properties of undamaged materials.A trolley collision test validates the constitutive model.Collision simulations at 25,36,and 48 km/h reveal that compared with undamaged models,the maximum vertical lift heights of moving vehicles with fatigue damage are 4.54%,3.74%,and 9.17%lower,respectively,and the maximum longitudinal compressions of stationary vehicles are 4.76%,14.53%,and 33.15%higher respectively.This research emphasizes the importance of considering fatigue damage in vehicle design and maintenance.The damage sequence interaction model has high engineering application value,applicable to vehicle safety checks and design,and provides a reference for improving relevant standards.展开更多
Sand-bentonite(SB)cutoff walls are commonly used as barriers in polluted areas.The embedded part of an SB wall in an aquitard is crucial for its performance.In this study,a centrifuge modeling test was carried out to ...Sand-bentonite(SB)cutoff walls are commonly used as barriers in polluted areas.The embedded part of an SB wall in an aquitard is crucial for its performance.In this study,a centrifuge modeling test was carried out to investigate the effect of contact between the key and the aquitard on the migration behavior of contaminants within an SB cutoff wall.The centrifuge was accelerated to 100g(gravitational acceleration)and maintained in-flight for 36 h,equivalent to 41 years of transport time in the prototype.Results showed that the contaminant concentration within the SB wall was higher downstream than in the middle in the thickness direction,and deeper regions exhibited a greater concentration than shallower ones.This concentration distribution indicated that contaminants were transported along the interface between the SB wall and the aquitard,bypassing the base of the SB wall to reach the downstream aquifer rapidly.An improved numerical simulation considering preferential interface migration was performed,which agreed with the centrifuge test results.The simulation results indicated that preferential interface migration,as a defect,significantly accelerated the speed of contaminant migration,reducing the breakthrough time of the SB wall to 1/9 of that without preferential interface migration.展开更多
Background:Dengue fever,an acute insect-borne infectious disease caused by the dengue virus(DENV),poses a great challenge to global public health.Hepatic involve-ment is the most common complication of severe dengue a...Background:Dengue fever,an acute insect-borne infectious disease caused by the dengue virus(DENV),poses a great challenge to global public health.Hepatic involve-ment is the most common complication of severe dengue and is closely related to the occurrence and development of disease.However,the features of adaptive immune responses associated with liver injury in severe dengue are not clear.Methods:We used single-cell sequencing to examine the liver tissues of mild or se-vere dengue mice model to analyze the changes in immune response of T cells in the liver after dengue virus infection,and the immune interaction between macrophages and T cells.Flow cytometry was used to detect T cells and macrophages in mouse liver and blood to verify the single-cell sequencing results.Results:Our result showed CTLs were significantly activated in the severe liver injury group but the immune function-related signal pathway was down-regulated.The rea-son may be that the excessive immune response in the severe group at the late stage of DENV infection induces the polarization of macrophages into M2 type,and the macrophages then inhibit T cell immunity through the TGF-βsignaling pathway.In ad-dition,the increased proportion of Treg cells suggested that Th17/Treg homeostasis was disrupted in the livers of severe liver injury mice.Conclusions:In this study,single-cell sequencing and flow cytometry revealed the characteristic changes of T cell immune response and the role of macrophages in the liver of severe dengue fever mice.Our study provides a better understanding of the pathogenesis of liver injury in dengue fever patients.展开更多
文摘In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade.
基金National Key Research and Development Program of China,Grant/Award Number:2024YFF0507404Special Clinical Business Fund for High-Level Hospitals of China-Japan Friendship Hospital,Grant/Award Number:2024-NHLHCRF-TS-01。
文摘Background:Large language models(LLMs)have shown considerable promise in supporting clinical decision-making.However,their adoption and evaluation in dermatology remains limited.This study aimed to explore the preferences of Chinese dermatologists regarding LLM-generated responses in clinical psoriasis scenarios and to assess how they prioritize key quality dimensions,including accuracy,traceability,and logicality.Methods:A cross-sectional,web-based survey was conducted between December 25,2024,and January 22,2025,following the Checklist for Reporting Results of Internet E-Surveys guidelines.A total of 1247 valid responses were collected from practicing dermatologists across 33 of China's provincial-level administrative divisions.Participants evaluated responses to five categories of clinical questions(etiology,clinical presentation,differential diagnosis,treatment,and case study)generated by five LLMs:ChatGPT-4o,Kimi.ai,Doubao,ZuoYiGPT,and Lingyi-agent.Statistical associations between participant characteristics and model preferences were examined using chi-square tests.Results:ChatGPT-4o(Model 1)emerged as the most preferred model across all clinical tasks,consistently receiving the highest number of votes in case study(n=740),clinical presentation(n=666),differential diagnosis(n=707),etiology(n=602),and treatment(n=656).Significant variation in model preference by professional title was observed only for the differential diagnosis task(χ^(2)=21.13,df=12,p=0.0485),while no significant differences were found across hospital tiers(p>0.05).In terms of evaluation dimensions,accuracy was most frequently rated as“very important”(n=635).A significant association existed between hospital tier and the most valued dimension(χ^(2)=27.667,df=9,p=0.0011),with dermatologists in primary hospitals prioritizing traceability more than their peers in higher-tier hospitals.No significant associations were found across professional titles(p=0.127).Conclusions:Chinese dermatologists suggest a strong preference for ChatGPT-4o over domestic LLMs in psoriasis-related clinical tasks.While accuracy remains the primary criterion,traceability and logicality are also critical,particularly for clinicians in lower-tier hospitals.These findings suggest that future clinical LLMs should prioritize not only content accuracy but also source transparency and structural clarity to meet the diverse needs of different clinical settings.
基金the University of Milan for funding the“ProForesta”project through the 2020 Research Support Planthe“Ente Parco Nazionale dell'Appennino Tosco-Emiliano”for having financed the project“First urgent measures to promote the adaptation of the silver fir forests of the Tuscan-Emilian Apennine National Park to the effects of climate change”。
文摘Understanding how genetic variation within forest species influences growth responses under climate change is essential for improving the accuracy of forest models and guiding adaptive management strategies.This study models the dynamics of Italian silver fir(Abies alba)forests under varying climate change scenarios using the forest gap model FORMIND.Focusing on three distinct silver fir provenances(Western Alps,Northern Apennines,and Southern Apennines),the study simulates forest growth in the Tuscan-Emilian Apennine National Park under different representative concentration pathways(RCPs).The individual-based model FORMIND was parameterized and validated with field data for each of the provenances,demonstrating its ability to accurately reproduce key forest metrics and dynamics.Our results reveal significant differences in expected growth patterns,productivity,metabolism,and carbon storage capacity among the silver fir provenances in pure and mixed stands.In the simulations,the Northern Apennines provenance showed higher biomass production(biomass>10%±1%)and carbon uptake(net primary productivity,NPP>8%±1%)at the end of the century compared to the Western Alps provenance in the pure provenance(PP)and no regeneration scenario.Conversely,the Southern Apennines provenance showed higher biomass(biomass>5%–10%)and NPP(>15%–18%)in mixed provenance(MP)and regeneration scenarios.These results show that genetic diversity strongly affects forest growth and resilience to environmental changes.Hence,it should be included as a predictor variable in forest models.The study also demonstrates the resilience of silver fir to climatic stressors,emphasizing its potential as a robust species in multiple forest contexts.The integration of forest provenance data into the FORMIND model represents a significant advancement in forest modelling,enabling more accurate and reliable predictions under climate change scenarios.The study's findings advocate for a greater understanding and consideration of genetic diversity in forest management and conservation strategies,in support of assisted migration strategies aiming to enhance the resilience of forest ecosystems in a changing climate.
基金financially supported by the National Natural Science Foundation of China(Nos.42577209 and U22A20239)the Key R&D Program of Hunan Province(No.2024WK2004)the Key Technologies for Accurate Diagnosis and Intelligent Prevention and Control of Slope Hazards in Open pit Mines,181 Major R&D projects of Metallurgical Corporation of China Ltd。
文摘Excessive blasting-induced vibration during drilling-and-blasting excavation of deep tunnels can trigger geological hazards and compromise the stability of both the rock mass and support structures.This study focused on the deep double-line Sejila Mountain tunnel to systematically analyze the spatial response of blasting-induced vibration and to develop a prediction model through field tests and numerical simulations.The results revealed that the presence of a cross passage significantly altered propagation paths and the spatial distribution of blasting-induced vibration velocity.The peak particle velocity(PPV)at the cross-passage corner was amplified by approximately 1.92 times due to wave reflection and geometric focusing.Blasting-induced vibration waves attenuated non-uniformly across the tunnel cross-section,where PPV on the blast-face side was 1.54–6.56 times higher than that on the opposite side.We propose an improved PPV attenuation model that accounts for the propagation path effect.This model significantly improved fitting accuracy and resolved anomalous parameter(k and a)estimates in traditional equations,thereby improving prediction reliability.Furthermore,based on the observed spatial distribution of blasting-induced vibration,optimal monitoring point placement and targeted vibration control measures for tunnel blasting were discussed.These findings provide a scientific basis for designing blasting schemes and vibration mitigation strategies in deep tunnels.
基金supported by the National Natural Science Foundation of China(Grant Nos.12272411 and 42007259).
文摘Deep rock engineering is affected by coupled thermo-hydro-mechanical(THM)-dynamic fields,necessitating the elucidation of the dynamic mechanical behavior and failure mechanisms.This study utilized a Multi-field Coupled Controlled Split Hopkinson Pressure Bar(MCC-SHPB)system to elucidate the cross-scale dynamic responses of rocks and the boundaries of failure modes under THM coupling.Impact tests were conducted on green sandstone under coupled conditions of temperature(25℃-80℃),confining pressure(0-15 MPa),and seepage water pressure(0-15 MPa).Scanning electron microscopy(SEM)microstructural characterization and COMSOL Multiphysics numerical simulations were conducted,and a dynamic constitutive theoretical framework and failure-prediction methodology were established.We investigated the impact toughness index(I_(t)),dynamic modulus(E_(d)),dynamic triaxial compressive strength(TCS_(d)),fragmentation degree(W),and failure modes of green sandstone under thermo-confining pressure-seepage-impact loading conditions.The key findings reveal that the(I_(t))reflects different energy regulation mechanisms across different confining pressure regimes.Thermal-microcrack interactions dominate at low pressure,and energy absorption prevails at high pressure.A triphasic dynamic modulus model captures stiffness evolution under energy-driven conditions,revealing cross-scale crack nucleation-propagation and fragment reorganization.The TCSd inflection point signifies energy dissipation shifts,causing nonlinear skeleton bearing-capacity degradation.A critical criterion based on the W was established to distinguish between the two failure modes and predict the unstable failure initiation.Numerical simulations were used to elucidate the effects of inertia-dominated crack propagation and stress wave interference,validating the critical criterion and the predictive accuracy of the theoretical model during cross-scale failure.This study provides a theoretical foundation for assessing the dynamic stability of rock masses subjected to multi-field coupling during deep resource exploitation.
基金supported by the Project of Stable Support for Youth Team in Basic Research Field,CAS(grant No.YSBR-018)the National Natural Science Foundation of China(grant Nos.42188101,42130204)+4 种基金the B-type Strategic Priority Program of CAS(grant no.XDB41000000)the National Natural Science Foundation of China(NSFC)Distinguished Overseas Young Talents Program,Innovation Program for Quantum Science and Technology(2021ZD0300301)the Open Research Project of Large Research Infrastructures of CAS-“Study on the interaction between low/mid-latitude atmosphere and ionosphere based on the Chinese Meridian Project”.The project was supported also by the National Key Laboratory of Deep Space Exploration(Grant No.NKLDSE2023A002)the Open Fund of Anhui Provincial Key Laboratory of Intelligent Underground Detection(Grant No.APKLIUD23KF01)the China National Space Administration(CNSA)pre-research Project on Civil Aerospace Technologies No.D010305,D010301.
文摘Sporadic E(Es)layers in the ionosphere are characterized by intense plasma irregularities in the E region at altitudes of 90-130 km.Because they can significantly influence radio communications and navigation systems,accurate forecasting of Es layers is crucial for ensuring the precision and dependability of navigation satellite systems.In this study,we present Es predictions made by an empirical model and by a deep learning model,and analyze their differences comprehensively by comparing the model predictions to satellite RO measurements and ground-based ionosonde observations.The deep learning model exhibited significantly better performance,as indicated by its high coefficient of correlation(r=0.87)with RO observations and predictions,than did the empirical model(r=0.53).This study highlights the importance of integrating artificial intelligence technology into ionosphere modelling generally,and into predicting Es layer occurrences and characteristics,in particular.
基金Supported by Shenzhen High-level Hospital Construction Fund.
文摘BACKGROUND Patients harboring gene mutations like KRAS,NRAS,and BRAF demonstrate highly variable responses to chemotherapy,posing challenges for treatment optimization.Multiparametric magnetic resonance imaging(MRI),with its noninvasive capability to assess tumor characteristics in detail,has shown promise in evaluating treatment response and predicting therapeutic outcomes.This technology holds potential for guiding personalized treatment strategies tailored to individual patient profiles,enhancing the precision and effectiveness of colorectal cancer care.AIM To create a multiparametric MRI-based predictive model for assessing chemotherapy efficacy in colorectal cancer patients with gene mutations.METHODS This retrospective study was conducted in a tertiary hospital,analyzing 157 colorectal cancer patients with gene mutations treated between August 2022 and December 2023.Based on chemotherapy outcomes,the patients were categorized into favorable(n=60)and unfavorable(n=50)response groups.Univariate and multivariate logistic regression analyses were performed to identify independent predictors of chemotherapy efficacy.A predictive nomogram was constructed using significant variables,and its performance was assessed using the area under the receiver operating characteristic curve(AUC)in both training and validation sets.RESULTS Univariate analysis identified that tumor differentiation,T2 signal intensity ratio,tumor-to-anal margin distance,and MRI-detected lymph node metastasis as significantly associated with chemotherapy response(P<0.05).Multivariate Logistics regression confirmed these four parameters as independent predictors.The predictive model demonstrated strong discrimination,with an AUC of 0.938(sensitivity:86%;specificity:92%)in the training set,and 0.942(sensitivity:100%;specificity:83%)in the validation set.CONCLUSION We established and validated a multiparametric MRI-based model for predicting chemotherapy response in colorectal cancer patients with gene mutations.This model holds promise for guiding individualized treatment strategies.
基金supported by the Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences(No.2023-JKCS-23)the Special Research Fund for Central Universities,Peking Union Medical College[No.2022-I2M-C&T-A-014,CAMS Innovation Fund for Medical Sciences(CIFMS)]。
文摘Objective:Neoadjuvant therapy(NAT)has become the standard treatment option for patients with locally advanced breast cancer.How to non-invasively screen out patients with pathological complete response(pCR)after NAT has become an urgent world-wide clinical problem.Our work aims to the assessment of neoadjuvant treatment response in breast cancer patients for higher accuracy prediction using innovative artificial intelligence system.Methods:In this study,we retrospectively collected longitudinal(pre-NAT and post-NAT)multi-parametric magnetic resonance imaging(MRI)and clinicopathologic data of a total of 1,315 breast cancer patients(clinical stageⅠ-Ⅲ)who had undergone NAT followed by standard surgery and treated across 5 independent medical centers from January 2010 to January 2023.We used radiomics,3D convolutional neural network technology and clinical data statistical analysis methods to extract and screen multimodal features,and then developed and validated a Clinical-Radiomics-Deep-Learning(CRDL)model to predict patients'pCR outcomes based on multimodal fusion features.Results:We use the area under the receiver operating characteristic curve(AUC)in the primary cohort(PC)and3 external validation cohorts(VC_(1-3))to evaluate the model performance.The results showed that the AUC in the PC composed of 2 medical centers was 0.947[95%confidence interval(95%CI):0.931-0.960],and the AUC values in VC_(1-3)were 0.857(95%CI:0.810-0.901),0.883(95%CI:0.841-0.918)and 0.904(95%CI:0.860-0.941),respectively.Conclusions:The CRDL model demonstrated high accuracy and robustness in predicting pCR to NAT using multimodal fusion data.This study provides a strong foundation for non-invasive assessment of pCR status in breast cancer patients following NAT and offers critical insights to guide clinical decision-making in post-NAT treatment planning.
基金funded by the Chongqing Water Resources Bureau,China(Project No.CQS24C00836).
文摘We proposes an AI-assisted framework for integrated natural disaster prevention and emergency response,leveraging the DeepSeek large language model(LLM)to advance intelligent decision-making in geohazard management.We systematically analyze the technical pathways for deploying LLMs in disaster scenarios,emphasizing three breakthrough directions:(1)knowledge graph-driven dynamic risk modeling,(2)reinforcement learning-optimized emergency decision systems,and(3)secure local deployment architectures.The DeepSeek model demonstrates unique advantages through its hybrid reasoning mechanism combining semantic analysis with geospatial pattern recognition,enabling cost-effective processing of multi-source data spanning historical disaster records,real-time IoT sensor feeds,and socio-environmental parameters.A modular system architecture is designed to achieve three critical objectives:(a)automated construction of domain-specific knowledge graphs through unsupervised learning of disaster physics relationships,(b)scenario-adaptive resource allocation using risk simulations,and(c)preserving emergency coordination via federated learning across distributed response nodes.The proposed local deployment paradigm addresses critical data security concerns in cross-border disaster management while complying with the FAIR principles(Findable,Accessible,Interoperable,Reusable)for geoscientific data governance.This work establishes a methodological foundation for next-generation AI-earth science convergence in disaster mitigation.
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
基金Projects(51925808,52078504)supported by the National Natural Science Foundation of ChinaProject(2022JJ10082)supported by the Natural Science Fund for Distinguished Young Scholar of Hunan Province,ChinaProject(2021RC3016)supported by the Science and Technology Innovation Program of Hunan Province,China。
文摘Structural damage detection is hard to conduct in large-scale civil structures due to enormous structural data and insufficient damage features.To improve this situation,a damage detection method based on model reduction and response reconstruction is presented.Based on the framework of two-step model updating including substructure-level localization and element-level detection,the response reconstruction strategy with an improved sensitivity algorithm is presented to conveniently complement modal information and promote the reliability of model updating.In the iteration process,the reconstructed response is involved in the sensitivity algorithm as a reconstruction-related item.Besides,model reduction is applied to reduce computational degrees of freedom(DOFs)in each detection step.A numerical truss bridge is modelled to vindicate the effectiveness and efficiency of the method.The results showed that the presented method reduces the requirement for installed sensors while improving efficiency and ensuring accuracy of damage detection compared to traditional methods.
基金The National Natural Science Foundation of China,Grant/Award Number:82204006Science and Technology Project of Hebei Education Department,Grant/Award Number:QN2022009+1 种基金Medical Science Research Project of Hebei,Grant/Award Number:20221534National Natural Science Foundation of Hebei Province,Grant/Award Number:H2024209038。
文摘Background:It is well recognized that developing new animal models,refining the existing mouse models,and thoroughly characterizing their features are essential for gaining a deeper understanding of rosacea pathogenesis and for advancing therapeutic strategies in this direction.Accordingly,we aimed to characterize the pathological features of a long-term LL-37-induced mouse model of rosacea and to compare the disease manifestations and pathophysiological characteristics between short-term and long-term LL-37-induced models.A key focus was to investigate differential gene expression and the underlying mechanisms of immune system dysregulation in these models.Methods:We comparatively assessed skin lesion manifestations,the extent of inflammatory infiltration,sebaceous gland alterations,fibrosis,and angiogenesis in both models.Assessments were performed using photographic documentation,hematoxylin-eosin(HE)staining,Van Gieson's(VG)staining,immunohistochemistry,and Western blotting.Furthermore,we employed RNA sequencing to analyze differential gene expression in mouse skin.The RNA sequencing data were validated using immunofluorescence staining and Western blotting,with a specific focus on gene variations and mechanisms related to immune system dysregulation.Results:Mice subjected to long-term LL-37 induction developed rosacea-like pathological features,including angiogenesis,thickened skin tissue,and sebaceous gland hypertrophy.In the short-term LL-37-induced model,immune dysregulation primarily involved the innate immune response.However,long-term LL-37 induction resulted in significant activation of both innate and adaptive immune responses.Conclusion:The long-term LL-37-induced mouse model offers a valuable animal model for the detailed investigation of the pathological mechanisms driving moderate-to-severe rosacea with prolonged disease duration.Importantly,this model provides a significant experimental foundation for exploring the potential role of immune system dysregulation in rosacea pathogenesis.
基金iVascular,S.L.U.,Camíde Can Ubach,11–Nave 3,08620 Sant Vicençdels Horts,Barcelona,Spain。
文摘Background:The efficacy of balloon angioplasty for treating peripheral artery disease is influenced by various factors,some of them not yet totally understood.This study aimed to evaluate the role of elastin content in vascular responses 28 days postangioplasty using uncoated and paclitaxel-coated balloons with the same platform in femoral arteries of a healthy porcine model.Methods:Eight animals underwent balloon angioplasty on the external and internal branches of femoral arteries.Histopathologic evaluation was conducted at follow-up to assess the elastin content,vascular damage,morphological features,and neointimal formation.Results:The elastin content was significantly higher in the external than in the internal femoral artery(p=0.0014).After balloon angioplasty,it was inversely correlated with vascular injury score(ρ=−0.4510,p=0.0096),neointimal inflammation(ρ=−0.3352,p=0.0607),transmural(ρ=−0.4474,p=0.0103)and circumferential(ρ=−0.4591,p=0.0082)smooth muscle cell loss,presence of proteoglycans(ρ=−0.5172,p=0.0024),fibrin deposition(ρ=−0.3496,p=0.0499),and adventitial fibrosis(ρ=−0.6229,p=0.0002).Neointimal formation inhibition with paclitaxel was evident only in arteries with disruption of the internal elastic lamina,with a significant smaller neointimal area in arteries treated with paclitaxel-coated balloons compared to uncoated balloons(median[Q1–Q3]:10.25[7.49–15.64]vs.24.44[18.96–30.52],p=0.0434).Conclusions:Elastin content varies between branches of the femoral artery and significantly influences the integrity of the internal elastic lamina,the vessel's adaptive response,and paclitaxel efficacy after balloon angioplasty.
基金supported by the Central Government Guiding Local Science and Technology Development Fund Project(No.2024SZY0343)the Joint Research Program for Ecological Conservation and High Quality Development of the Yellow River Basin(No.2022-YRUC-01-050205)+2 种基金the Higher Education Scientific Research Project of Inner Mongolia Autonomous Region(No.NJZZ23078)the project of Inner Mongolia"Prairie Talents"Engineering Innovation Entrepreneurship Talent Team,the Major Projects of Erdos Science and Technology(No.2022EEDSKJZDZX015)the Innovation Team of the Inner Mongolia Academy of Science and Technology(No.CXTD2023-01-016).
文摘Rural domestic sewage treatment is critical for environmental protection.This study defines the spatial pattern of villages from the perspective of rural sewage treatment and develops an integrated decision-making system to propose a sewage treatment mode and scheme suitable for local conditions.By considering the village spatial layout and terrain factors,a decision tree model of residential density and terrain type was constructed with accuracies of 76.47%and 96.00%,respectively.Combined with binary classification probability unit regression,an appropriate sewage treatment mode for the village was determined with 87.00%accuracy.The Analytic Hierarchy Process(AHP),combined with the Technique for Order Preference(TOPSIS)by Similarity to an Ideal Solution model,formed the basis for optimal treatment process selection under different emission standards.Verification was conducted in 542 villages across three counties of the Inner Mongolia Autonomous Region,focusing on the standard effluent effect(0.3773),low investment cost(0.3196),and high standard effluent effect(0.5115)to determine the best treatment process for the same emission standard under different needs.The annual environmental and carbon emission benefits of sewage treatment in these villages were estimated.This model matches village density,geographic feature,and social development level,and provides scientific support and a theoretical basis for rural sewage treatment decision-making.
基金Supported by National Natural Science Foundation of China(Grant No.52175123)Sichuan Provincial Natural Science Foundation Innovation Research Group Project(Grant No.2025NSFTD0014)Independent Research Project of RVL(Grant No.2024RVL-T03).
文摘This study decouples the material microstructure into matrix and void phases.The undamaged constitutive is derived from the matrix phase,while the void phase contributes to damage evolution.A constitutive model is established by coupling the two.According to the void-phase evolution during damage,a damage sequence interaction model is proposed.Tests on new vehicles and vehicles in service materials yield stress-strain curves of materials without and with fatigue damage and measure the apparent elastic modulus.The damage sequence interaction model accurately predicts the residual mechanical properties of undamaged materials.A trolley collision test validates the constitutive model.Collision simulations at 25,36,and 48 km/h reveal that compared with undamaged models,the maximum vertical lift heights of moving vehicles with fatigue damage are 4.54%,3.74%,and 9.17%lower,respectively,and the maximum longitudinal compressions of stationary vehicles are 4.76%,14.53%,and 33.15%higher respectively.This research emphasizes the importance of considering fatigue damage in vehicle design and maintenance.The damage sequence interaction model has high engineering application value,applicable to vehicle safety checks and design,and provides a reference for improving relevant standards.
基金supported by the National Key Research and Development Program of China(No.2018YFC1802304)the National Natural Science Foundation of China(Nos.51988101 and 42077241)the Natural Science Foundation of Zhejiang Province(No.LCZ19E080002),China.
文摘Sand-bentonite(SB)cutoff walls are commonly used as barriers in polluted areas.The embedded part of an SB wall in an aquitard is crucial for its performance.In this study,a centrifuge modeling test was carried out to investigate the effect of contact between the key and the aquitard on the migration behavior of contaminants within an SB cutoff wall.The centrifuge was accelerated to 100g(gravitational acceleration)and maintained in-flight for 36 h,equivalent to 41 years of transport time in the prototype.Results showed that the contaminant concentration within the SB wall was higher downstream than in the middle in the thickness direction,and deeper regions exhibited a greater concentration than shallower ones.This concentration distribution indicated that contaminants were transported along the interface between the SB wall and the aquitard,bypassing the base of the SB wall to reach the downstream aquifer rapidly.An improved numerical simulation considering preferential interface migration was performed,which agreed with the centrifuge test results.The simulation results indicated that preferential interface migration,as a defect,significantly accelerated the speed of contaminant migration,reducing the breakthrough time of the SB wall to 1/9 of that without preferential interface migration.
基金Chinese Academy of Medical Sciences Initiative for Innovative Medicine,Grant/Award Number:2021-I2M-1-035 and 2022-I2M-1-011。
文摘Background:Dengue fever,an acute insect-borne infectious disease caused by the dengue virus(DENV),poses a great challenge to global public health.Hepatic involve-ment is the most common complication of severe dengue and is closely related to the occurrence and development of disease.However,the features of adaptive immune responses associated with liver injury in severe dengue are not clear.Methods:We used single-cell sequencing to examine the liver tissues of mild or se-vere dengue mice model to analyze the changes in immune response of T cells in the liver after dengue virus infection,and the immune interaction between macrophages and T cells.Flow cytometry was used to detect T cells and macrophages in mouse liver and blood to verify the single-cell sequencing results.Results:Our result showed CTLs were significantly activated in the severe liver injury group but the immune function-related signal pathway was down-regulated.The rea-son may be that the excessive immune response in the severe group at the late stage of DENV infection induces the polarization of macrophages into M2 type,and the macrophages then inhibit T cell immunity through the TGF-βsignaling pathway.In ad-dition,the increased proportion of Treg cells suggested that Th17/Treg homeostasis was disrupted in the livers of severe liver injury mice.Conclusions:In this study,single-cell sequencing and flow cytometry revealed the characteristic changes of T cell immune response and the role of macrophages in the liver of severe dengue fever mice.Our study provides a better understanding of the pathogenesis of liver injury in dengue fever patients.
