Objective: To analyze the effect of continuous nursing on self-care ability and quality of life of patients with permanent artificial pacemaker implantation. Methods: A total of 90 patients receiving permanent artific...Objective: To analyze the effect of continuous nursing on self-care ability and quality of life of patients with permanent artificial pacemaker implantation. Methods: A total of 90 patients receiving permanent artificial pacemaker treatment in our hospital during the first 8 months of November 2021 were selected as samples to compare the data differences between the two groups (control group and intervention group were 45 patients /n = 45 patients/group, respectively). Results: The compliance of the intervention group was higher than that of the control group (P;There was no significant difference in EDCA score between groups before intervention (P>0.05). After intervention, EDCA score in the intervention group was higher than that in the control group (P<0.05). The nursing quality score of intervention group was higher than that of control group (P;The compliance score of intervention group was higher than that of control group (P<0.05). After intervention, SRSS and MNA scores in the intervention group were higher than those in the control group (P<0.05). The scores of the four items in the intervention group were higher than those in the control group (P < 0.05). Discussion: Continuous nursing intervention can effectively improve the quality and effect of nursing, and has significant application value.展开更多
The usability assessment of a pacemaker is a complex task where the dedicated programmer for testing programmed algorithms is necessary.This paper provides the outcomes of development and complex testing of the artifi...The usability assessment of a pacemaker is a complex task where the dedicated programmer for testing programmed algorithms is necessary.This paper provides the outcomes of development and complex testing of the artificial cardiac system to evaluate the pacemaker’s functionality.In this work,we used the modular laboratory platform ELVIS II and created graphical user interface in LabVIEW programming environment.The electrical model of the heart allows signals generation(right atrium,right ventricle)and the monitoring of the stimulation pulses.The LabVIEW user interface allows to set the parameters of the generated signals and the simulation of the cardiac rhythm disorders as well as the monitoring and visualization of the pacemaker behavior in real-time.The results demonstrate the capability of proposed system to evaluate the paced and sensed pulses.The proposed solution allows the scientists to test the behavior of any cardiac pacemaker for its pre-programmed settings and pacing mode.In addition,the proposed system can simulate various disorders and test cardiac pacemakers in different working modes.展开更多
This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to...This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.展开更多
Background:Medical artificial intelligence(MAI)is a synthesis of medical science and artificial intelligence development,serving as a crucial field in the current advancement and application of AI.In the process of de...Background:Medical artificial intelligence(MAI)is a synthesis of medical science and artificial intelligence development,serving as a crucial field in the current advancement and application of AI.In the process of developing medical AI,there may arise not only legal risks such as infringement of privacy rights and health rights but also ethical risks stemming from violations of the principles of beneficence and non-maleficence.Methods:To effectively address the damages caused by MAI in the future,it is necessary to establish a hierarchical governance system with MAI.This paper examines the systematic collection of local practices in China and the induction and integration of legal remedies for the damage of MAI.Results:To effectively address the ethical and legal challenges of medical artificial intelligence,a hierarchical regulatory system should be established,which based on the impact of intervention measures on natural rights and differences in intervention timing.This paper finally obtains a legal hierarchical governance system corresponding to the ethical risks and legal risks of MAI in China.Conclusion:The Chinese government has formed a multi-agent governance system based on the impact of risks on rights and the timing of legal intervention,which provides a reference for other countries to follow up on the research on MAI risk management.展开更多
Objective:Accurate detection of PIK3CA mutations is essential for guiding PI3K-targeted therapies in breast cancer,yet sequencing is not universally accessible,and single-modality prediction models have limited perfor...Objective:Accurate detection of PIK3CA mutations is essential for guiding PI3K-targeted therapies in breast cancer,yet sequencing is not universally accessible,and single-modality prediction models have limited performance.This study developed a multimodal deep learning framework integrating whole-slide imaging(WSI)and structured clinical data to improve mutation prediction.Methods:A total of 1,047 patients from TCGA and 166 patients from 3 external centers were included.The histopathology model used a transformer-based pretrained encoder(H-optimus-0)and a clustering-constrained attention multiple instance learning(CLAM-SB MIL)classifier to generate WSI-level representations.The clinical model incorporated engineered clinical variables and an extreme gradient boosting(XGBoost)model.A decision-level late fusion strategy(Multimodal PIK3CA Model,MPM)combined probabilistic outputs from both branches.Performance was evaluated with the area under the curve(AUC)and secondary metrics.Interpretability was assessed via attention heatmaps and shapley additive explanations(SHAP)analysis.Results:MPM outperformed single-modality models.It achieved an AUC of 0.745 on TCGA and maintained stable performance across external cohorts(0.695,0.690,and 0.680).SHAP analysis identified molecular subtype as the most influential clinical feature,whereas attention maps highlighted mutation-associated morphological regions.Conclusions:The developed multimodal framework effectively integrates complementary morphological and clinical information,and provides a robust and generalizable method for predicting PIK3CA mutation status.Strong multicenter adaptability and biological interpretability support its potential use as a clinical decision-support tool and an accessible alternative to molecular testing.展开更多
The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unatt...The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics.展开更多
Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary...Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency.展开更多
This paper presents a novel artificial intelligence(AI)-assisted two-stage method for optimising rock slope stability by integrating advanced 3D modelling with rock support design,aiming at minimising risks,material u...This paper presents a novel artificial intelligence(AI)-assisted two-stage method for optimising rock slope stability by integrating advanced 3D modelling with rock support design,aiming at minimising risks,material usage,and costs.In the first stage,an extended key block analysis identifies key blocks and key block groups,accounting for progressive failure and force interactions.The second stage uses AI algorithms to optimise rockbolting design,balancing stability,cost,and material use.The most efficient algorithms include the multi-objective tree-structured Parzen estimator(MOTPE)and non-dominated sorting genetic algorithms(NSGA-II and NSGA-III).Applied to the Larvik rock slope,the optimised solution uses 18 pre-tensioned cablebolts,providing 13.2 MN of active force and achieving a factor of safety of 1.31 while reducing the average anchorage length by approximately 16%compared to traditional design.The AI-assisted approach also reduces computation time by over 90%compared to Quasi-Monte Carlo(QMC)methods,demonstrating its efficiency for small-scale civil engineering projects and large-scale mining operations.The developed tool is practical,compatible with Building Information Modelling(BIM),and ready for engineering implementation,supporting sustainable and cost-effective rock slope stabilisation.While the method is largely automated,professional judgement remains crucial for verifying ground conditions and selecting the final solution.Future work will focus on integrating data uncertainties,addressing complex block deformation mechanisms,refining optimisation objectives,and improving the performance of multi-objective optimisation for slope rockboling applications to further enhance the method's versatility.展开更多
Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including ...Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.展开更多
Background:Artificial intelligence medical diagnostic devices(AIMDDs)show strong potential but face barriers to clinical use,emphasizing the need for rigorous clinical research.Objective:We assessed current AIMDD rese...Background:Artificial intelligence medical diagnostic devices(AIMDDs)show strong potential but face barriers to clinical use,emphasizing the need for rigorous clinical research.Objective:We assessed current AIMDD research,key challenges,and future directions.Methods:A scoping review followed Arksey and O'Malley's methodological framework and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines.PubMed,Web of Science Core Collection,and the Cochrane Database of Systematic Reviews(January 2020-December 2024)were searched on AIMDD design,implementation,and evaluation.Two independent researchers screened and extracted data from the literature using predefined criteria.Results:Ninety-seven articles met the inclusion criteria.Machine learning and deep learning approaches dominated across diverse disease fields,with oncology being the most frequent(41%).The key challenges identified include insufficient quantity,quality,representativeness,and diversity of data;research designs that do not adequately address clinical needs;poor patient selection;poorly defined gold standards;lack of external and prospective validation;and a disconnect between validation strategies and clinical practice.Additionally,issues such as the“black box”phenomenon,overfitting,and data privacy concerns hinder clinical translation.Completeness and standardization of reporting were also found to be lacking.Conclusions:Significant challenges remain in the development and clinical application of AIMDD.To facilitate their clinical translation,improvements are needed in dataset optimization,clinically driven research design,development of evaluation frameworks,enhanced interpretability,and standardized reporting and validation of algorithms.展开更多
This study addresses the challenges confronting the ideological and political construction of general artificial intelligence curriculum-namely,the dilution of value guidance amid pluralistic intellectual currents,the...This study addresses the challenges confronting the ideological and political construction of general artificial intelligence curriculum-namely,the dilution of value guidance amid pluralistic intellectual currents,the superficial internalization of concepts resulting from didactic pedagogy,and the ineffectiveness of character cultivation stemming from fragmented and decontextualized techno-ethical cases.This paper proposes centering the value proposition on“Serving the Nation through Science and Technology”.Leveraging the deeply integrated industry-academia-research-application synergy,we integrate ideological and political elements into the comprehensive technological practice workflow.To achieve this,we(1)incorporate authentic enterprise project practicums to foster students’sense of responsibility;(2)construct a virtual debate platform on technology ethics dilemmas to develop ethical discernment;and(3)organize solution competitions targeting urgent social problems to incubate technology-for-good initiatives.Collectively,these approaches enhance students’technological mission awareness,ethical sensitivity,and social responsibility.展开更多
Generative artificial intelligence(AI),specifically large language models,such as DeepSeek,has accelerated the digital transformation of healthcare systems in both developing and developed countries.The use of AI in d...Generative artificial intelligence(AI),specifically large language models,such as DeepSeek,has accelerated the digital transformation of healthcare systems in both developing and developed countries.The use of AI in diagnostics,image processing and interpretation,treatment personalization,clinical documentation,and drug discovery is an example of the implementation of AI in Western medicine.The need for evidence-based studies and a standardized approach to scientific medicine aligns well with these applications.AI can leave a lasting impact on the Chinese medicine(CM)landscape by increasing expectations and presenting new challenges.The analogy between the CM-specific diagnostic methods and pattern differentiation,which is holistic,pattern-oriented,patient-centered,and clinical data analysis,is significant at multiple levels.These qualities pose challenges for AI usage in CM,which heavily relies on structured data and pattern recognition.Despite these adversities,AI can still be used in CM through data standardization,prediction formulation,and treatment planning,provided that the integration of this tool considers the primary principles of CM and adheres to ethical and regulatory considerations.This review examines the dichotomous approach to health and medicine in the contexts of AI and CM,highlighting the evolving potential,inherent limitations,and ethical and regulatory issues associated with the application of AI to CM.It provides a foundation for developing technologically progressive yet culturally and philosophically sensitive strategies that are in harmony with traditional clinical values.展开更多
A methodology for the reduction of radar cross section(RCS)of cambered platforms within the target airspace is presented,which utilizes a dual-polarized ultra-wide-angle artificial electromagnetic absorbing surface.By...A methodology for the reduction of radar cross section(RCS)of cambered platforms within the target airspace is presented,which utilizes a dual-polarized ultra-wide-angle artificial electromagnetic absorbing surface.By applying the theory of generalized Brewster complex wave impedance matching,five distinct unit cell designs are developed to attain more than95%absorption rate for dual-polarized incident waves within five angular ranges:0°-30°,30°-50°,50°-60°,60°-70°,and 70°-80°.To optimally reduce the RCS of a cambered platform,the five types of units can be evenly distributed on the surface based on the local incident angles of plane waves originating from the target airspace.As an illustrative example,the leading edge of an airfoil is taken into account,and experimental measurements validate the efficiency of the proposed structure.Specifically,the absorbing surface achieves more than 10 dB of RCS reduction in the frequency ranges from 5-10 GHz(about66.7%relative bandwidth)for dual polarizations.展开更多
BACKGROUND Gastrointestinal stromal tumors(GISTs)are rare mesenchymal neoplasms primarily originating in the stomach or small intestine.Duodenal GISTs are particularly uncommon,accounting for only a small fraction of ...BACKGROUND Gastrointestinal stromal tumors(GISTs)are rare mesenchymal neoplasms primarily originating in the stomach or small intestine.Duodenal GISTs are particularly uncommon,accounting for only a small fraction of GIST cases.These tumors often present with nonspecific symptoms,making early detection challenging.This case discusses a duodenal GIST misdiagnosed as pancreatic cancer due to obstructive jaundice.CASE SUMMARY A 40-year-old male with jaundice and abdominal symptoms underwent imaging,which suggested a malignant periampullary tumor.Preoperative misdiagnosis of pancreatic cancer was made,and surgery was performed.Postoperative histopathology confirmed a duodenal GIST.The role of artificial intelligence in the diagnostic pathway is explored,emphasizing its potential to differentiate between duodenal GISTs and other similar conditions using advanced imaging analysis.CONCLUSION Artificial intelligence in radiomic imaging holds significant promise in enhancing the diagnostic process for rare cancers like duodenal GISTs,ensuring timely and accurate treatment.展开更多
The regulation of signal transmission speed is one of the most important capabilities of the biological nervous system.This study explores the mechanisms and methods for regulating signal transmission speed among nonm...The regulation of signal transmission speed is one of the most important capabilities of the biological nervous system.This study explores the mechanisms and methods for regulating signal transmission speed among nonmyelinated neurons within the same brain region,starting from spike-timing-dependent plasticity(STDP)of synapses.Building upon the Hodgkin-Huxley model,the dynamic behavior of synapses is incorporated,and the adaptive growth neuron(AGN)model is proposed.Artificial synaptic structures and neuronal physical nodes are also designed.The artificial synaptic structure exhibits unidirectionality,memory capacity,and STDP,enabling it to connect neuronal physical nodes through branching and merging structures.Furthermore,the artificial synapse can adjust signal transmission speed,regulate functional competition between different regions of the neuromorphic network,and promote information interaction.The findings of this study endow neuromorphic networks with the ability to regulate signal transmission speed over the long term,providing new insights into the development of neuromorphic networks.展开更多
Aqueous zinc metal batteries(AZMBs)are promising candidates for next-generation energy storage,but their commercialization is hindered by zinc anode challenges,notably parasitic reactions and dendrite growth.Herein,we...Aqueous zinc metal batteries(AZMBs)are promising candidates for next-generation energy storage,but their commercialization is hindered by zinc anode challenges,notably parasitic reactions and dendrite growth.Herein,we present a biodegradable biomass-derived protective layer,primarily composed of curcumin,as a zincophilic interface for AZMBs.The curcumin-based layer,fabricated via a homogeneous solution process,exhibits strong adhesion,uniform coverage,and robust mechanical integrity.Rich polar functional groups in curcumin facilitate homogeneous Zn~(2+)flux and suppress side reactions.The curcumin-based layer shows a favorable affinity for zinc trifluoromethanesulfonate(Zn(OTf)_(2))electrolyte,which is the representative of organic zinc salts,enabling optimal thickness for both protection and ion transport.The protected Zn anodes demonstrate an extended lifespan of 2500 h in symmetrical cells and a high Coulombic efficiency of 99.15%.Furthermore,Zn(OTf)_(2)-based system typically exhibits poor stability at high current densities.Fortunately,the lifespan of symmetrical cells was extended by 40-fold at the high current density.When paired with an Na V_(3)O_(8)·1.5H_(2)O(NVO)cathode,the system achieves 86.5%capacity retention after 3000 cycles at a large specific current density of 10 A g^(-1).These results underscore the efficacy of the curcumin-based protective layer in enhancing the reversibility and stability of metal electrodes,specifically relieving the instability of Zn(OTf)_(2)-based systems at high current densities,advancing its commercial viability.展开更多
The integration of multi-omic liquid biopsies with artificial intelligence(AI)represents a rapidly evolving frontier in early cancer detection,offering the potential to enhance personalized medicine and improve patien...The integration of multi-omic liquid biopsies with artificial intelligence(AI)represents a rapidly evolving frontier in early cancer detection,offering the potential to enhance personalized medicine and improve patient outcomes.This review explores the current state and emerging directions of this approach,focusing on the synergistic value of combining genomics,epigenomics,transcriptomics,proteomics,and metabolomics with AIdriven analytics.We discuss advances in multi-analyte blood tests such as CancerSEEK,which have demonstrated promising multi-cancer detection capabilities in early studies,as well as efforts to integrate liquid biopsy data with imaging modalities to improve diagnostic performance.The review also highlights ongoing challenges,including the need for greater analytical sensitivity,improved specificity for early-stage disease,standardization of workflows,and harmonization with existing screening modalities.We outline the prospective—but still largely investigational—impact of these technologies on cancer management,including early detection,treatment monitoring,and minimal residual disease assessment,along with their potential economic implications.Ultimately,we envision a future in which multi-omic liquid biopsies integrated with AI may contribute to more effective,noninvasive cancer detection strategies,while recognizing that substantial validation,regulatory approval,and health-system integration are required before widespread clinical adoption can occur.展开更多
Nursing education is undergoing a paradigm shift from skill training to clinical thinking cultivation.The integration of artificial intelligence technology offers technical possibilities for this transformation,but it...Nursing education is undergoing a paradigm shift from skill training to clinical thinking cultivation.The integration of artificial intelligence technology offers technical possibilities for this transformation,but it also brings about a deep tension between the cultivation of humanistic qualities and a standardized training.Based on the analysis of the practical forms of nursing smart education,this paper examines the cognitive gap between the deterministic feedback of virtual simulation systems and the complexity of real clinical scenarios,reveals the potential narrowing effect of data-driven ability profiling on the all-round development of nursing students,and then proposes the design logic of intelligent teaching resources centered on real clinical problems,a hierarchical teaching model with clear human-machine division of labor,and a dynamic assessment mechanism for technology application led by professional nursing teachers,in an attempt to find a balance between technological empowerment and humanistic commitment in smart nursing education.展开更多
Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for me...Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards,rapid technological evolution,and insufficient localized ethical norms.Objective:To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students.Methods:A multidisciplinary(including medical education,clinical medicine,medical AI,public health,and medical ethics)expert group(n=32)developed an initial competency list based on the“Knowledge-Skills-Attitude”Medical Competency Model.Two Delphi rounds(100%response rate;consensus threshold:mean≥4.0,CV≤0.25)refined the framework.Core competencies were prioritized via Analytic Hierarchy Process(AHP).The final consensus document was established after multiple expert group meetings.Results:The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into profes-sional knowledge,clinical practice,research,and health management.It comprises a 21-item Competencies of AI Proficiency(CAIP)list across knowledge(eight indicators),skills(seven indicators),and attitude(six indicators)dimensions.Key com-petencies prioritized include understanding AI's role in multidisciplinary knowledge integration(CAIP3),identifying AI output biases(CAIP4),understanding health data governance(CAIP2),maintaining physician-led AI-assisted diagnosis(CAIP16),and identifying AI diagnostic biases(CAIP12).A multi-modal assessment framework is recommended,including paper-based/computerized tests for knowledge,situational judgment tests(SJTs)for attitudes,and objective structured clinical examinations(OSCEs)with a specific“AI Clinical Decision Conflict Scoring Scale”for skills.A multi-stage dynamic assessment system(“Pre-enrollment-Pre-clinical-Post-clinical”)is proposed for longitudinal tracking.Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years,constructing an integrated curriculum covering fundamental principles,advanced large model applications(e.g.,prompt engineering,agent development),and ethical considerations,supported by a"digital twin hospital platform."Conclusion:This consensus provides authoritative,China-specific guidance for defining and assessing medical students'AI literacy,adhering to national policies and regulations.It offers a core action framework for optimizing AI integration into medical education,fostering future healthcare professionals proficient in both AI technology and medical humanism,with a commitment to dynamic updating to adapt to evolving AI advancements.展开更多
Background:High-mobility group box 1(HMGB1)is a critical damage-associated molecular pattern protein that participates in diverse physiological and pathological processes.However,its relevance to the prognosis of arti...Background:High-mobility group box 1(HMGB1)is a critical damage-associated molecular pattern protein that participates in diverse physiological and pathological processes.However,its relevance to the prognosis of artificial liver support therapy in patients with acute liver injury(ALF)remains unclear.Methods:Bioinformatics analyses were performed to identify HMGB1-interacting proteins and associated inflammatory signaling pathways.Peripheral blood samples were collected from ALF patients before and after artificial liver support therapy,and serum HMGB1 concentrations were quantified using ELISA.Primary mouse hepatocytes were stimulated with lipopolysaccharide(LPS)in vitro and HMGB1 expression was verified by western blot.Results:Single-cell transcriptomic profiling showed that HMGB1 is widely expressed across tissues and predominantly localized in the nucleus.In the liver,HMGB1 was primarily expressed in hepatocytes and hepatic stellate cells.STRING database analysis revealed that human HMGB1 interacts with multiple proteins,including TLR4,TP53,and BECN1.The constructed interaction network comprised 11 nodes with an average local clustering coefficient of 0.888,and the protein–protein interaction enrichment P-value was 1.42×10^(-5),indicating significant enrichment.Gene Ontology and KEGG pathway enrichment analyses demonstrated that HMGB1 is closely linked to inflammatory and injury-related signaling pathways,including the TLR and NLR pathways.Metabolomic profiling revealed significant metabolic alterations between patients with ALF and healthy controls under both positive and negative ion modes and functional analysis showed necroptosis was activated.The cell viability gradually decreased with time and dose under LPS treatment and extracellular HMGB1 was upregulated in LPS induced ALF model and patients(P<0.05).Serum HMGB1/RIPK3/MLKL levels were markedly elevated in ALF patients compared with controls(P<0.05)and progressively declined following artificial liver support therapy.Furthermore,elevated HMGB1 concentrations were positively correlated with unfavorable clinical outcomes.Conclusion:Peripheral blood HMGB1 levels are significantly increased in patients with acute liver failure,decrease following artificial liver support therapy,and are positively associated with poor clinical prognosis.展开更多
文摘Objective: To analyze the effect of continuous nursing on self-care ability and quality of life of patients with permanent artificial pacemaker implantation. Methods: A total of 90 patients receiving permanent artificial pacemaker treatment in our hospital during the first 8 months of November 2021 were selected as samples to compare the data differences between the two groups (control group and intervention group were 45 patients /n = 45 patients/group, respectively). Results: The compliance of the intervention group was higher than that of the control group (P;There was no significant difference in EDCA score between groups before intervention (P>0.05). After intervention, EDCA score in the intervention group was higher than that in the control group (P<0.05). The nursing quality score of intervention group was higher than that of control group (P;The compliance score of intervention group was higher than that of control group (P<0.05). After intervention, SRSS and MNA scores in the intervention group were higher than those in the control group (P<0.05). The scores of the four items in the intervention group were higher than those in the control group (P < 0.05). Discussion: Continuous nursing intervention can effectively improve the quality and effect of nursing, and has significant application value.
基金Thework and the contributions were supported by the project SV4502261/SP2022/98‘Biomedical Engineering systems XVIII’.
文摘The usability assessment of a pacemaker is a complex task where the dedicated programmer for testing programmed algorithms is necessary.This paper provides the outcomes of development and complex testing of the artificial cardiac system to evaluate the pacemaker’s functionality.In this work,we used the modular laboratory platform ELVIS II and created graphical user interface in LabVIEW programming environment.The electrical model of the heart allows signals generation(right atrium,right ventricle)and the monitoring of the stimulation pulses.The LabVIEW user interface allows to set the parameters of the generated signals and the simulation of the cardiac rhythm disorders as well as the monitoring and visualization of the pacemaker behavior in real-time.The results demonstrate the capability of proposed system to evaluate the paced and sensed pulses.The proposed solution allows the scientists to test the behavior of any cardiac pacemaker for its pre-programmed settings and pacing mode.In addition,the proposed system can simulate various disorders and test cardiac pacemakers in different working modes.
基金Supported by Japan Society for the Promotion of Science,No.24K11935.
文摘This review comprehensively summarized the potential of artificial intelligence(AI)in the management of esophageal cancer.It highlighted the significance of AI-assisted endoscopy in Japan where endoscopy is central to both screening and diagnosis.For the clinical adaptation of AI,several challenges remain for its effective translation.The establishment of high-quality clinical databases,such as the National Clinical Database and Japan Endoscopy Database in Japan,which covers almost all cases of esophageal cancer,is essential for validating multimodal AI models.This requires rigorous external validation using diverse datasets,including those from different endoscope manufacturers and image qualities.Furthermore,endoscopists’skills significantly affect diagnostic accuracy,suggesting that AI should serve as a supportive tool rather than a replacement.Addressing these challenges,along with country-specific legal and ethical considerations,will facilitate the successful integration of multimodal AI into the management of esophageal cancer,particularly in endoscopic diagnosis,and contribute to improved patient outcomes.Although this review focused on Japan as a case study,the challenges and solutions described are broadly applicable to other high-incidence regions.
基金funded by China Law Society 2025 Annual Legal Research,Project grant number:CLS(2025)Y04.
文摘Background:Medical artificial intelligence(MAI)is a synthesis of medical science and artificial intelligence development,serving as a crucial field in the current advancement and application of AI.In the process of developing medical AI,there may arise not only legal risks such as infringement of privacy rights and health rights but also ethical risks stemming from violations of the principles of beneficence and non-maleficence.Methods:To effectively address the damages caused by MAI in the future,it is necessary to establish a hierarchical governance system with MAI.This paper examines the systematic collection of local practices in China and the induction and integration of legal remedies for the damage of MAI.Results:To effectively address the ethical and legal challenges of medical artificial intelligence,a hierarchical regulatory system should be established,which based on the impact of intervention measures on natural rights and differences in intervention timing.This paper finally obtains a legal hierarchical governance system corresponding to the ethical risks and legal risks of MAI in China.Conclusion:The Chinese government has formed a multi-agent governance system based on the impact of risks on rights and the timing of legal intervention,which provides a reference for other countries to follow up on the research on MAI risk management.
基金financially supported by the Hebei Natural Science Foundation(Grant No.H2024206504)the Medical Science Research Project of Hebei(Grant No.20260484,20260530)the Fundamental Research Funds for the Central Universities(Grant No.20822041J4123).
文摘Objective:Accurate detection of PIK3CA mutations is essential for guiding PI3K-targeted therapies in breast cancer,yet sequencing is not universally accessible,and single-modality prediction models have limited performance.This study developed a multimodal deep learning framework integrating whole-slide imaging(WSI)and structured clinical data to improve mutation prediction.Methods:A total of 1,047 patients from TCGA and 166 patients from 3 external centers were included.The histopathology model used a transformer-based pretrained encoder(H-optimus-0)and a clustering-constrained attention multiple instance learning(CLAM-SB MIL)classifier to generate WSI-level representations.The clinical model incorporated engineered clinical variables and an extreme gradient boosting(XGBoost)model.A decision-level late fusion strategy(Multimodal PIK3CA Model,MPM)combined probabilistic outputs from both branches.Performance was evaluated with the area under the curve(AUC)and secondary metrics.Interpretability was assessed via attention heatmaps and shapley additive explanations(SHAP)analysis.Results:MPM outperformed single-modality models.It achieved an AUC of 0.745 on TCGA and maintained stable performance across external cohorts(0.695,0.690,and 0.680).SHAP analysis identified molecular subtype as the most influential clinical feature,whereas attention maps highlighted mutation-associated morphological regions.Conclusions:The developed multimodal framework effectively integrates complementary morphological and clinical information,and provides a robust and generalizable method for predicting PIK3CA mutation status.Strong multicenter adaptability and biological interpretability support its potential use as a clinical decision-support tool and an accessible alternative to molecular testing.
文摘The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics.
基金the Natural Science Foundation of China(Project for Young Scientists:Grant No.52105010,Regular Project:Grant No.62173096)Natural Science Foundationof Guangdong Province(Regular Project:Grant No.2025A1515012124,Grant No.2022A1515010327)Guangdong-Hong Kong-Macao Key Laboratory of Multi-scaleInformation Fusion and Collaborative Optimization Control Manufacturing Process.
文摘Legged robots have considerable potential for traversing unstructured situations;nonetheless,their inflexible frameworks often constrain adaptability and obstacle negotiation.The study article presents a revolutionary Soft Tri-Legged Robot(STLR)that improves movement and obstacle-avoidance skills by using a bio-inspired pneumatic artificial muscle(Bubble Artificial Muscles)and a bio-inspired tactile sensor(TacTip).The STLR is activated by BAMs,which are flexible,pneu-matic-driven actuators that provide fine control over forward,backward,and steering movements.Obstacle identification and avoidance are facilitated by the TacTip sensor,which delivers tactile input for traversing unstructured terrains.We delineate the mechanical features of the BAMs,assess the functionality of the robot's legs,and elaborate on the incorpora-tion of the tactile sensing system.Experimental results demonstrate that the STLR can effectively achieve multi-directional flexible movement and obstacle avoidance through a cross-modal perception-actuation mechanism.This study highlights the promise of soft robotics for search and rescue,medical aid,and autonomous exploration,while delineating difficulties and opportunities for future improvements in functionality and efficiency.
基金support from Research Council of Norway via STIPINST PhD grant(Grant No.323307),Bever Control AS,and Bane NOR.
文摘This paper presents a novel artificial intelligence(AI)-assisted two-stage method for optimising rock slope stability by integrating advanced 3D modelling with rock support design,aiming at minimising risks,material usage,and costs.In the first stage,an extended key block analysis identifies key blocks and key block groups,accounting for progressive failure and force interactions.The second stage uses AI algorithms to optimise rockbolting design,balancing stability,cost,and material use.The most efficient algorithms include the multi-objective tree-structured Parzen estimator(MOTPE)and non-dominated sorting genetic algorithms(NSGA-II and NSGA-III).Applied to the Larvik rock slope,the optimised solution uses 18 pre-tensioned cablebolts,providing 13.2 MN of active force and achieving a factor of safety of 1.31 while reducing the average anchorage length by approximately 16%compared to traditional design.The AI-assisted approach also reduces computation time by over 90%compared to Quasi-Monte Carlo(QMC)methods,demonstrating its efficiency for small-scale civil engineering projects and large-scale mining operations.The developed tool is practical,compatible with Building Information Modelling(BIM),and ready for engineering implementation,supporting sustainable and cost-effective rock slope stabilisation.While the method is largely automated,professional judgement remains crucial for verifying ground conditions and selecting the final solution.Future work will focus on integrating data uncertainties,addressing complex block deformation mechanisms,refining optimisation objectives,and improving the performance of multi-objective optimisation for slope rockboling applications to further enhance the method's versatility.
基金Supported by Xuhui District Health Commission,No.SHXH202214.
文摘Gastrointestinal tumors require personalized treatment strategies due to their heterogeneity and complexity.Multimodal artificial intelligence(AI)addresses this challenge by integrating diverse data sources-including computed tomography(CT),magnetic resonance imaging(MRI),endoscopic imaging,and genomic profiles-to enable intelligent decision-making for individualized therapy.This approach leverages AI algorithms to fuse imaging,endoscopic,and omics data,facilitating comprehensive characterization of tumor biology,prediction of treatment response,and optimization of therapeutic strategies.By combining CT and MRI for structural assessment,endoscopic data for real-time visual inspection,and genomic information for molecular profiling,multimodal AI enhances the accuracy of patient stratification and treatment personalization.The clinical implementation of this technology demonstrates potential for improving patient outcomes,advancing precision oncology,and supporting individualized care in gastrointestinal cancers.Ultimately,multimodal AI serves as a transformative tool in oncology,bridging data integration with clinical application to effectively tailor therapies.
基金supported by the National Key R&D Program of China(2022YFC3501000,2022YFC3502300)National Natural Science Foundation of China(82374627)+2 种基金Fundamental Research Funds for the Central public welfare research institutes(Z0876)Fundamental Research Funds for the Central Universities(2024-JYB-KYPT-01)Beijing Municipal Science and Technology Commission(Z241100007724010).
文摘Background:Artificial intelligence medical diagnostic devices(AIMDDs)show strong potential but face barriers to clinical use,emphasizing the need for rigorous clinical research.Objective:We assessed current AIMDD research,key challenges,and future directions.Methods:A scoping review followed Arksey and O'Malley's methodological framework and the Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews guidelines.PubMed,Web of Science Core Collection,and the Cochrane Database of Systematic Reviews(January 2020-December 2024)were searched on AIMDD design,implementation,and evaluation.Two independent researchers screened and extracted data from the literature using predefined criteria.Results:Ninety-seven articles met the inclusion criteria.Machine learning and deep learning approaches dominated across diverse disease fields,with oncology being the most frequent(41%).The key challenges identified include insufficient quantity,quality,representativeness,and diversity of data;research designs that do not adequately address clinical needs;poor patient selection;poorly defined gold standards;lack of external and prospective validation;and a disconnect between validation strategies and clinical practice.Additionally,issues such as the“black box”phenomenon,overfitting,and data privacy concerns hinder clinical translation.Completeness and standardization of reporting were also found to be lacking.Conclusions:Significant challenges remain in the development and clinical application of AIMDD.To facilitate their clinical translation,improvements are needed in dataset optimization,clinically driven research design,development of evaluation frameworks,enhanced interpretability,and standardized reporting and validation of algorithms.
基金supported by 2024 General Program from the Beijing Association of Higher Education(MS2024232).
文摘This study addresses the challenges confronting the ideological and political construction of general artificial intelligence curriculum-namely,the dilution of value guidance amid pluralistic intellectual currents,the superficial internalization of concepts resulting from didactic pedagogy,and the ineffectiveness of character cultivation stemming from fragmented and decontextualized techno-ethical cases.This paper proposes centering the value proposition on“Serving the Nation through Science and Technology”.Leveraging the deeply integrated industry-academia-research-application synergy,we integrate ideological and political elements into the comprehensive technological practice workflow.To achieve this,we(1)incorporate authentic enterprise project practicums to foster students’sense of responsibility;(2)construct a virtual debate platform on technology ethics dilemmas to develop ethical discernment;and(3)organize solution competitions targeting urgent social problems to incubate technology-for-good initiatives.Collectively,these approaches enhance students’technological mission awareness,ethical sensitivity,and social responsibility.
文摘Generative artificial intelligence(AI),specifically large language models,such as DeepSeek,has accelerated the digital transformation of healthcare systems in both developing and developed countries.The use of AI in diagnostics,image processing and interpretation,treatment personalization,clinical documentation,and drug discovery is an example of the implementation of AI in Western medicine.The need for evidence-based studies and a standardized approach to scientific medicine aligns well with these applications.AI can leave a lasting impact on the Chinese medicine(CM)landscape by increasing expectations and presenting new challenges.The analogy between the CM-specific diagnostic methods and pattern differentiation,which is holistic,pattern-oriented,patient-centered,and clinical data analysis,is significant at multiple levels.These qualities pose challenges for AI usage in CM,which heavily relies on structured data and pattern recognition.Despite these adversities,AI can still be used in CM through data standardization,prediction formulation,and treatment planning,provided that the integration of this tool considers the primary principles of CM and adheres to ethical and regulatory considerations.This review examines the dichotomous approach to health and medicine in the contexts of AI and CM,highlighting the evolving potential,inherent limitations,and ethical and regulatory issues associated with the application of AI to CM.It provides a foundation for developing technologically progressive yet culturally and philosophically sensitive strategies that are in harmony with traditional clinical values.
基金supported by the National Key Research and Development Program of China(2023YFB3907304-3)the National Natural Science Foundation of China(NSFC)(62271050)。
文摘A methodology for the reduction of radar cross section(RCS)of cambered platforms within the target airspace is presented,which utilizes a dual-polarized ultra-wide-angle artificial electromagnetic absorbing surface.By applying the theory of generalized Brewster complex wave impedance matching,five distinct unit cell designs are developed to attain more than95%absorption rate for dual-polarized incident waves within five angular ranges:0°-30°,30°-50°,50°-60°,60°-70°,and 70°-80°.To optimally reduce the RCS of a cambered platform,the five types of units can be evenly distributed on the surface based on the local incident angles of plane waves originating from the target airspace.As an illustrative example,the leading edge of an airfoil is taken into account,and experimental measurements validate the efficiency of the proposed structure.Specifically,the absorbing surface achieves more than 10 dB of RCS reduction in the frequency ranges from 5-10 GHz(about66.7%relative bandwidth)for dual polarizations.
文摘BACKGROUND Gastrointestinal stromal tumors(GISTs)are rare mesenchymal neoplasms primarily originating in the stomach or small intestine.Duodenal GISTs are particularly uncommon,accounting for only a small fraction of GIST cases.These tumors often present with nonspecific symptoms,making early detection challenging.This case discusses a duodenal GIST misdiagnosed as pancreatic cancer due to obstructive jaundice.CASE SUMMARY A 40-year-old male with jaundice and abdominal symptoms underwent imaging,which suggested a malignant periampullary tumor.Preoperative misdiagnosis of pancreatic cancer was made,and surgery was performed.Postoperative histopathology confirmed a duodenal GIST.The role of artificial intelligence in the diagnostic pathway is explored,emphasizing its potential to differentiate between duodenal GISTs and other similar conditions using advanced imaging analysis.CONCLUSION Artificial intelligence in radiomic imaging holds significant promise in enhancing the diagnostic process for rare cancers like duodenal GISTs,ensuring timely and accurate treatment.
基金supported by the National Natural Science Foundation of China(Grant No.62171182)the Natural Scienceof Hunan Province(Grant No.2025JJ50345)the Postgraduate Scientific Research Innovation Project of Hunan Province(Grant No.CX20240452)。
文摘The regulation of signal transmission speed is one of the most important capabilities of the biological nervous system.This study explores the mechanisms and methods for regulating signal transmission speed among nonmyelinated neurons within the same brain region,starting from spike-timing-dependent plasticity(STDP)of synapses.Building upon the Hodgkin-Huxley model,the dynamic behavior of synapses is incorporated,and the adaptive growth neuron(AGN)model is proposed.Artificial synaptic structures and neuronal physical nodes are also designed.The artificial synaptic structure exhibits unidirectionality,memory capacity,and STDP,enabling it to connect neuronal physical nodes through branching and merging structures.Furthermore,the artificial synapse can adjust signal transmission speed,regulate functional competition between different regions of the neuromorphic network,and promote information interaction.The findings of this study endow neuromorphic networks with the ability to regulate signal transmission speed over the long term,providing new insights into the development of neuromorphic networks.
基金the financial support from Research Institute for Smart Energy at the Hong Kong Polytechnic University(Grant No.CDB2)the support of the Hong Kong PhD Fellowship Scheme(Grant No.PF21-65328)。
文摘Aqueous zinc metal batteries(AZMBs)are promising candidates for next-generation energy storage,but their commercialization is hindered by zinc anode challenges,notably parasitic reactions and dendrite growth.Herein,we present a biodegradable biomass-derived protective layer,primarily composed of curcumin,as a zincophilic interface for AZMBs.The curcumin-based layer,fabricated via a homogeneous solution process,exhibits strong adhesion,uniform coverage,and robust mechanical integrity.Rich polar functional groups in curcumin facilitate homogeneous Zn~(2+)flux and suppress side reactions.The curcumin-based layer shows a favorable affinity for zinc trifluoromethanesulfonate(Zn(OTf)_(2))electrolyte,which is the representative of organic zinc salts,enabling optimal thickness for both protection and ion transport.The protected Zn anodes demonstrate an extended lifespan of 2500 h in symmetrical cells and a high Coulombic efficiency of 99.15%.Furthermore,Zn(OTf)_(2)-based system typically exhibits poor stability at high current densities.Fortunately,the lifespan of symmetrical cells was extended by 40-fold at the high current density.When paired with an Na V_(3)O_(8)·1.5H_(2)O(NVO)cathode,the system achieves 86.5%capacity retention after 3000 cycles at a large specific current density of 10 A g^(-1).These results underscore the efficacy of the curcumin-based protective layer in enhancing the reversibility and stability of metal electrodes,specifically relieving the instability of Zn(OTf)_(2)-based systems at high current densities,advancing its commercial viability.
文摘The integration of multi-omic liquid biopsies with artificial intelligence(AI)represents a rapidly evolving frontier in early cancer detection,offering the potential to enhance personalized medicine and improve patient outcomes.This review explores the current state and emerging directions of this approach,focusing on the synergistic value of combining genomics,epigenomics,transcriptomics,proteomics,and metabolomics with AIdriven analytics.We discuss advances in multi-analyte blood tests such as CancerSEEK,which have demonstrated promising multi-cancer detection capabilities in early studies,as well as efforts to integrate liquid biopsy data with imaging modalities to improve diagnostic performance.The review also highlights ongoing challenges,including the need for greater analytical sensitivity,improved specificity for early-stage disease,standardization of workflows,and harmonization with existing screening modalities.We outline the prospective—but still largely investigational—impact of these technologies on cancer management,including early detection,treatment monitoring,and minimal residual disease assessment,along with their potential economic implications.Ultimately,we envision a future in which multi-omic liquid biopsies integrated with AI may contribute to more effective,noninvasive cancer detection strategies,while recognizing that substantial validation,regulatory approval,and health-system integration are required before widespread clinical adoption can occur.
基金Funding Project for Ideological and Political Model Courses of“Epidemic Fighting”Courses in Henan Province(Project No.:531,2020)University-level Curriculum Ideological and Political Demonstration Course Support Project of Zhengzhou Sias University(Project No.:34,2024)+2 种基金University-level Key Discipline Support Project of Zhengzhou Sias University(Project No.:1,2022)2025 Key Scientific Research Projects of Henan Universities(Project No.:25B360003)Henan Province Private Brand Professional Support Project(Project No.:527,2019)。
文摘Nursing education is undergoing a paradigm shift from skill training to clinical thinking cultivation.The integration of artificial intelligence technology offers technical possibilities for this transformation,but it also brings about a deep tension between the cultivation of humanistic qualities and a standardized training.Based on the analysis of the practical forms of nursing smart education,this paper examines the cognitive gap between the deterministic feedback of virtual simulation systems and the complexity of real clinical scenarios,reveals the potential narrowing effect of data-driven ability profiling on the all-round development of nursing students,and then proposes the design logic of intelligent teaching resources centered on real clinical problems,a hierarchical teaching model with clear human-machine division of labor,and a dynamic assessment mechanism for technology application led by professional nursing teachers,in an attempt to find a balance between technological empowerment and humanistic commitment in smart nursing education.
基金Science and Technology Innovation 2030 Major Project,Grant/Award Number:2023ZD0508506。
文摘Background:Artificial intelligence(AI)is transforming healthcare,demanding reevaluation of medical education.China's“New Medical Education”initiative urgently requires a standardized AI literacy framework for medical students to address fragmented standards,rapid technological evolution,and insufficient localized ethical norms.Objective:To establish a Chinese expert consensus defining core AI competencies and a multi-modal assessment framework for medical students.Methods:A multidisciplinary(including medical education,clinical medicine,medical AI,public health,and medical ethics)expert group(n=32)developed an initial competency list based on the“Knowledge-Skills-Attitude”Medical Competency Model.Two Delphi rounds(100%response rate;consensus threshold:mean≥4.0,CV≤0.25)refined the framework.Core competencies were prioritized via Analytic Hierarchy Process(AHP).The final consensus document was established after multiple expert group meetings.Results:The consensus defines AI literacy for medical students as a comprehensive attribute for integrating AI into profes-sional knowledge,clinical practice,research,and health management.It comprises a 21-item Competencies of AI Proficiency(CAIP)list across knowledge(eight indicators),skills(seven indicators),and attitude(six indicators)dimensions.Key com-petencies prioritized include understanding AI's role in multidisciplinary knowledge integration(CAIP3),identifying AI output biases(CAIP4),understanding health data governance(CAIP2),maintaining physician-led AI-assisted diagnosis(CAIP16),and identifying AI diagnostic biases(CAIP12).A multi-modal assessment framework is recommended,including paper-based/computerized tests for knowledge,situational judgment tests(SJTs)for attitudes,and objective structured clinical examinations(OSCEs)with a specific“AI Clinical Decision Conflict Scoring Scale”for skills.A multi-stage dynamic assessment system(“Pre-enrollment-Pre-clinical-Post-clinical”)is proposed for longitudinal tracking.Educational integration pathways emphasize embedding AI literacy modularly from early undergraduate years,constructing an integrated curriculum covering fundamental principles,advanced large model applications(e.g.,prompt engineering,agent development),and ethical considerations,supported by a"digital twin hospital platform."Conclusion:This consensus provides authoritative,China-specific guidance for defining and assessing medical students'AI literacy,adhering to national policies and regulations.It offers a core action framework for optimizing AI integration into medical education,fostering future healthcare professionals proficient in both AI technology and medical humanism,with a commitment to dynamic updating to adapt to evolving AI advancements.
基金supported by the National Natural Science Foundation of China(No.82360120)the Kunming Medical University Joint Special Project on Applied Basic Research(202401AY070001-134),and project iGandanF-1082022-RGG049+2 种基金the Open Project of Yunnan Provincial Clinical Medical Center for Digestive System Diseases(2022LCZXXF-XH07/17)the 14th Undergraduate Scientific Research Project of Mudanjiang Medical University(2024057)Yunnan Provincial Key Laboratory of Clinical Virology(No.2023A4010403-04).
文摘Background:High-mobility group box 1(HMGB1)is a critical damage-associated molecular pattern protein that participates in diverse physiological and pathological processes.However,its relevance to the prognosis of artificial liver support therapy in patients with acute liver injury(ALF)remains unclear.Methods:Bioinformatics analyses were performed to identify HMGB1-interacting proteins and associated inflammatory signaling pathways.Peripheral blood samples were collected from ALF patients before and after artificial liver support therapy,and serum HMGB1 concentrations were quantified using ELISA.Primary mouse hepatocytes were stimulated with lipopolysaccharide(LPS)in vitro and HMGB1 expression was verified by western blot.Results:Single-cell transcriptomic profiling showed that HMGB1 is widely expressed across tissues and predominantly localized in the nucleus.In the liver,HMGB1 was primarily expressed in hepatocytes and hepatic stellate cells.STRING database analysis revealed that human HMGB1 interacts with multiple proteins,including TLR4,TP53,and BECN1.The constructed interaction network comprised 11 nodes with an average local clustering coefficient of 0.888,and the protein–protein interaction enrichment P-value was 1.42×10^(-5),indicating significant enrichment.Gene Ontology and KEGG pathway enrichment analyses demonstrated that HMGB1 is closely linked to inflammatory and injury-related signaling pathways,including the TLR and NLR pathways.Metabolomic profiling revealed significant metabolic alterations between patients with ALF and healthy controls under both positive and negative ion modes and functional analysis showed necroptosis was activated.The cell viability gradually decreased with time and dose under LPS treatment and extracellular HMGB1 was upregulated in LPS induced ALF model and patients(P<0.05).Serum HMGB1/RIPK3/MLKL levels were markedly elevated in ALF patients compared with controls(P<0.05)and progressively declined following artificial liver support therapy.Furthermore,elevated HMGB1 concentrations were positively correlated with unfavorable clinical outcomes.Conclusion:Peripheral blood HMGB1 levels are significantly increased in patients with acute liver failure,decrease following artificial liver support therapy,and are positively associated with poor clinical prognosis.
