The relationship between the neighborhood environment and well-being is attracting increasingly attention from researchers and policymakers,as the goal of development has shift from economy to well-being.However,exist...The relationship between the neighborhood environment and well-being is attracting increasingly attention from researchers and policymakers,as the goal of development has shift from economy to well-being.However,existing literature predominantly adopts the utilitarian approach,understanding well-being as people’s feelings about their lives and viewing the neighborhood environment as resources that benefit well-being.The Capability Approach,a novel approach that conceptualize well-being as the freedoms to do or to be and regard environment as conversion factors that influence well-being,can offer new lens by incorporating human development in-to these topics.This paper proposes an alternative theoretical framework:well-being is conceptualized and measured by capability;neighborhood environment affects well-being by providing spatial services,functioning as environmental conversion factors,and serving as social conversion factors.We conducted a case study of Changshu City located in eastern China,utilizing multiple resource data,applying explainable artificial intelligence(XAI),namely eXtreme Gradient Boosting(XGBoost)and SHapley Additive exPlana-tions(SHAP).Our findings highlight the significance of viewing the neighborhood environment as a set of conversion factors,as it provides more explanatory power than providing spatial services.Compared to conventional research based on linear relationship as-sumption,our results demonstrate that the effects of neighborhood environment on well-being are non-linear,characterized by threshold effects and interaction effects.These insights are crucial for informing urban planning and public policy.This research enriches our un-derstanding of well-being,neighborhood environment,and their relationship as well as provides empirical evidence for the core concept of conversion factors in the capability approach.展开更多
In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with l...In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with low molecular weight and amorphous state.X-ray diffraction results revealed that the natural starch crystalline region was largely disrupted by ionic liquid owing to the broken intermolecular and intramolecular hydrogen bonds.After hydrolysis,the morphology of starch changed from particles of native corn starch into little pieces,and their molecular weight could be effectively regulated during the hydrolysis process,and also the hydrolyzed starch samples exhibited decreased thermal stability with the extension of hydrolysis time.This work would counsel as a powerful tool for the development of native starch in realistic applications.展开更多
Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and...Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.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: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
With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has prog...With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has progressed from the fabrication of simple models(1.0)to the fabrication of permanent implants(2.0),tissue engineering scaffolds(3.0),and complex biostructures utilizing living cells(4.0).Nevertheless,significant challenges remain,particularly in accurately replicating the structure and function of host tissues,selecting appropriate materials,and optimizing printing parameters.The integration of artificial intelligence(AI),especially machine learning,provides promising novel opportunities in bioprinting(5.0).This review systematically summarizes the current applications of AI in bioprinting,discussing both construction strategies and application scenarios.It also explores the potential of AI to improve bioprinting in the preparation of complex functional tissues and in situ tissue repair.Overall,the synergy between AI and bioprinting is poised to drive the development of personalized medicine,facilitate high-throughput preparation of in vitro models,and provide robust tools for regenerative medicine and precision healthcare.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
Dermatological diagnosis is inherently visual,greatly relying on clinicians′interpretation of images and accumulated experience.Traditional teaching models have long been constrained by limited case diversity,lack of...Dermatological diagnosis is inherently visual,greatly relying on clinicians′interpretation of images and accumulated experience.Traditional teaching models have long been constrained by limited case diversity,lack of personalization,and inadequate assessment of competency development.Recent advances in artificial intelligence(AI)offer new technological support for dermatology education.To address the risk of fragmented adoption,a process-oriented approach,conceptualizing AI-assisted dermatology education as an integrated system embedded throughout the learning process is adopted.Within this framework,AI is not only examined as an isolated tool but also as a component aligned with educational workflows.AI′s primary applications in dermatology education are analyzed,focusing on its potential to improve standardization so as to expand access to high-quality resources and support competency-based teaching,and facilitate lifelong learning.Meaningful educational benefits emerge when AI is systematically integrated into structured teaching processes.However,associated risks-including data bias,learner overreliance,implementation constraints,and potential impacts on medical humanities education must also be considered.Based on these findings,the strategic principles centered on educational objectives are proposed,emphasizing human-AI collaboration,transparency,and continuous governance to support the sustainable development of dermatology talent.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
The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a n...The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.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
Sustainable aluminum alloys,renowned for their lower energy consumption and carbon emissions,present a critical path towards a circular materials economy.However,their design is fraught with challenges,including compl...Sustainable aluminum alloys,renowned for their lower energy consumption and carbon emissions,present a critical path towards a circular materials economy.However,their design is fraught with challenges,including complex performance variability due to impurity elements and the time-consuming,cost-prohibitive nature of traditional trial-and-error methods.The high-dimensional parameter space in processing optimization and the reliance on human expertise for quality control further complicate their development.This paper provides a comprehensive review of Artificial Intelligence(AI)techniques applied to sustainable aluminum alloy design,analyzing their methodologies and identifying key challenges and optimization strategies.We review how AI methods such as knowledge graphs,evolutionary algorithms,and machine learning transformconventional processes into efficient,data-driven workflows,thereby enhancing development speed and precision.The review explicitly highlights existing bottlenecks,including insufficient data quality and standardization,the complexity of cross-scale modeling,and the need for industrial coordination.We conclude that AI holds immense potential to drive the recycled aluminum industry toward a more sustainable and intelligent future.Future research is poised to leverage generative AI,autonomous experimental platforms,and blockchain for improved life-cycle management,while also focusing on developing physics-informed models and establishing standardized data ecosystems.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
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.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
基金Under the auspices of National Natural Science Foundation of China(No.42271230,42330510)。
文摘The relationship between the neighborhood environment and well-being is attracting increasingly attention from researchers and policymakers,as the goal of development has shift from economy to well-being.However,existing literature predominantly adopts the utilitarian approach,understanding well-being as people’s feelings about their lives and viewing the neighborhood environment as resources that benefit well-being.The Capability Approach,a novel approach that conceptualize well-being as the freedoms to do or to be and regard environment as conversion factors that influence well-being,can offer new lens by incorporating human development in-to these topics.This paper proposes an alternative theoretical framework:well-being is conceptualized and measured by capability;neighborhood environment affects well-being by providing spatial services,functioning as environmental conversion factors,and serving as social conversion factors.We conducted a case study of Changshu City located in eastern China,utilizing multiple resource data,applying explainable artificial intelligence(XAI),namely eXtreme Gradient Boosting(XGBoost)and SHapley Additive exPlana-tions(SHAP).Our findings highlight the significance of viewing the neighborhood environment as a set of conversion factors,as it provides more explanatory power than providing spatial services.Compared to conventional research based on linear relationship as-sumption,our results demonstrate that the effects of neighborhood environment on well-being are non-linear,characterized by threshold effects and interaction effects.These insights are crucial for informing urban planning and public policy.This research enriches our un-derstanding of well-being,neighborhood environment,and their relationship as well as provides empirical evidence for the core concept of conversion factors in the capability approach.
文摘In this work,we proposed a strategy for the hydrolysis of native corn starch after the treatment of corn starch in an ionic liquid aqueous solution,and it is an awfully“green”and simple means to obtain starch with low molecular weight and amorphous state.X-ray diffraction results revealed that the natural starch crystalline region was largely disrupted by ionic liquid owing to the broken intermolecular and intramolecular hydrogen bonds.After hydrolysis,the morphology of starch changed from particles of native corn starch into little pieces,and their molecular weight could be effectively regulated during the hydrolysis process,and also the hydrolyzed starch samples exhibited decreased thermal stability with the extension of hydrolysis time.This work would counsel as a powerful tool for the development of native starch in realistic applications.
文摘Standard bacterial suspensions play a crucial role in microbiological diagnosis.Traditional prepar-ation methods,which rely heavily on manual operations,face challenges such as poor reproducibility,low ef-ficiency,and biosafety concerns.In this study,we propose a high-precision automated colony extraction and separation system that combines large-field imaging and artificial intelligence(AI)to facilitate intelligent screening and localization of colonies.Firstly,a large-field imaging system was developed to capture high-resolution images of 90 mm Petri dishes,achieving a physical resolution of 13.2μm and an imaging speed of 13 frames per second.Subsequently,AI technology was employed for the automatic recognition and localiza-tion of colonies,enabling the selection of target colonies with diameters ranging from 1.9 to 2.3 mm.Next,a three-axis motion control platform was designed,accompanied by a path planning algorithm for the efficient extraction of colonies.An electronic pipette was employed for accurate colony collection.Additionally,a bacterial suspension concentration measurement module was developed,incorporating a 650 nm laser diode as the light source,achieving a measurement accuracy of 0.01 McFarland concentration(MCF).Finally,the system’s performance was validated through the preparation of an Esckerichia coli(E.coli)suspension.After 17 hours of cultivation,E.coli was extracted four times,achieving the target concentration set by the system.This work is expected to enable rapid and accurate microbial sample preparation,significantly reducing de-tection cycles and alleviating the workload of healthcare personnel.
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financially supported by the National Natural Science Foundation of China(Nos.32471396,82230071,82172098,82201716,and 61973206)the National Key R&D Program of China(No.2023YFC2411303)+4 种基金the Integrated Project of Major Research Plan of the National Natural Science Foundation of China(No.92249303)the Shanghai Committee of Science and Technology(No.23141900600,Laboratory Animal Research Project)the Shanghai Clinical Research Plan of SHDC2023CRT01the Young Elite Scientist Sponsorship Program by the China Association for Science and Technology(No.YESS20230049)the Baoshan District Health Commission Talents(Excellent Academic Leaders)Program(No.BSWSYX-2024-05)。
文摘With the rapid advancements in biomedical engineering,bioprinting has emerged as a pivotal solution to address the shortage of organ transplants and advance disease model research.The evolution of bioprinting has progressed from the fabrication of simple models(1.0)to the fabrication of permanent implants(2.0),tissue engineering scaffolds(3.0),and complex biostructures utilizing living cells(4.0).Nevertheless,significant challenges remain,particularly in accurately replicating the structure and function of host tissues,selecting appropriate materials,and optimizing printing parameters.The integration of artificial intelligence(AI),especially machine learning,provides promising novel opportunities in bioprinting(5.0).This review systematically summarizes the current applications of AI in bioprinting,discussing both construction strategies and application scenarios.It also explores the potential of AI to improve bioprinting in the preparation of complex functional tissues and in situ tissue repair.Overall,the synergy between AI and bioprinting is poised to drive the development of personalized medicine,facilitate high-throughput preparation of in vitro models,and provide robust tools for regenerative medicine and precision healthcare.
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Dermatological diagnosis is inherently visual,greatly relying on clinicians′interpretation of images and accumulated experience.Traditional teaching models have long been constrained by limited case diversity,lack of personalization,and inadequate assessment of competency development.Recent advances in artificial intelligence(AI)offer new technological support for dermatology education.To address the risk of fragmented adoption,a process-oriented approach,conceptualizing AI-assisted dermatology education as an integrated system embedded throughout the learning process is adopted.Within this framework,AI is not only examined as an isolated tool but also as a component aligned with educational workflows.AI′s primary applications in dermatology education are analyzed,focusing on its potential to improve standardization so as to expand access to high-quality resources and support competency-based teaching,and facilitate lifelong learning.Meaningful educational benefits emerge when AI is systematically integrated into structured teaching processes.However,associated risks-including data bias,learner overreliance,implementation constraints,and potential impacts on medical humanities education must also be considered.Based on these findings,the strategic principles centered on educational objectives are proposed,emphasizing human-AI collaboration,transparency,and continuous governance to support the sustainable development of dermatology talent.
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 Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1A6A1A10044950).
文摘The thermal conductivity of nanofluids is an important property that influences the heat transfer capabilities of nanofluids.Researchers rely on experimental investigations to explore nanofluid properties,as it is a necessary step before their practical application.As these investigations are time and resource-consuming undertakings,an effective prediction model can significantly improve the efficiency of research operations.In this work,an Artificial Neural Network(ANN)model is developed to predict the thermal conductivity of metal oxide water-based nanofluid.For this,a comprehensive set of 691 data points was collected from the literature.This dataset is split into training(70%),validation(15%),and testing(15%)and used to train the ANN model.The developed model is a backpropagation artificial neural network with a 4–12–1 architecture.The performance of the developed model shows high accuracy with R values above 0.90 and rapid convergence.It shows that the developed ANN model accurately predicts the thermal conductivity of nanofluids.
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.
基金the financial support of Shanghai Natural Science Foundation(25ZR1401430)Science and Technology Cooperation Program of Shanghai Jiao Tong University in Inner Mongolia Autonomous Region-Action Plan of Shanghai Jiao Tong University for“Revitalizing Inner Mongolia through Science and Technology”(2023XYJG0001-01-01).
文摘Sustainable aluminum alloys,renowned for their lower energy consumption and carbon emissions,present a critical path towards a circular materials economy.However,their design is fraught with challenges,including complex performance variability due to impurity elements and the time-consuming,cost-prohibitive nature of traditional trial-and-error methods.The high-dimensional parameter space in processing optimization and the reliance on human expertise for quality control further complicate their development.This paper provides a comprehensive review of Artificial Intelligence(AI)techniques applied to sustainable aluminum alloy design,analyzing their methodologies and identifying key challenges and optimization strategies.We review how AI methods such as knowledge graphs,evolutionary algorithms,and machine learning transformconventional processes into efficient,data-driven workflows,thereby enhancing development speed and precision.The review explicitly highlights existing bottlenecks,including insufficient data quality and standardization,the complexity of cross-scale modeling,and the need for industrial coordination.We conclude that AI holds immense potential to drive the recycled aluminum industry toward a more sustainable and intelligent future.Future research is poised to leverage generative AI,autonomous experimental platforms,and blockchain for improved life-cycle management,while also focusing on developing physics-informed models and establishing standardized data ecosystems.
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Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
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.
