Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocar...Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocardiographic data,traditional Chinese medicine(TCM)tongue manifestations,and facial features were collected from patients who underwent coro-nary computed tomography angiography(CTA)in the Cardiac Care Unit(CCU)of Shanghai Tenth People's Hospital between May 1,2023 and May 1,2024.An adaptive weighted multi-modal data fusion(AWMDF)model based on deep learning was constructed to predict the severity of coronary artery stenosis.The model was evaluated using metrics including accura-cy,precision,recall,F1 score,and the area under the receiver operating characteristic(ROC)curve(AUC).Further performance assessment was conducted through comparisons with six ensemble machine learning methods,data ablation,model component ablation,and various decision-level fusion strategies.Results A total of 158 patients were included in the study.The AWMDF model achieved ex-cellent predictive performance(AUC=0.973,accuracy=0.937,precision=0.937,recall=0.929,and F1 score=0.933).Compared with model ablation,data ablation experiments,and various traditional machine learning models,the AWMDF model demonstrated superior per-formance.Moreover,the adaptive weighting strategy outperformed alternative approaches,including simple weighting,averaging,voting,and fixed-weight schemes.Conclusion The AWMDF model demonstrates potential clinical value in the non-invasive prediction of coronary artery disease and could serve as a tool for clinical decision support.展开更多
Convergent journalism constitutes a systematic investigation into emergent journalistic forms,conceptual frameworks,and practices emerging within media convergence context,characterized by its inherent attributes of c...Convergent journalism constitutes a systematic investigation into emergent journalistic forms,conceptual frameworks,and practices emerging within media convergence context,characterized by its inherent attributes of convergence,datacentricity,and interactivity.Grounded in the theoretical discourse of digital narratology,this monograph crystallizes its analytical focus on the triadic conceptual constellation of"convergence""mediaticity"and"narrativity",By positioning""convergence"as the central problematique,it systematically constructs an epistemological framework for convergent journalistic narrative through three dimensions:narrative theory,narrative language,and narrative praxis,thereby elucidates the ontological foundations and operational logics intrinsic to contemporary journalism studies.展开更多
Jātaka story paintings are common narrative subjects in Dunhuang murals.Based on corresponding scriptures,they present all kinds of good deeds that Sakyamuni sacrificed his life to save sentient beings in his previou...Jātaka story paintings are common narrative subjects in Dunhuang murals.Based on corresponding scriptures,they present all kinds of good deeds that Sakyamuni sacrificed his life to save sentient beings in his previous life.Dunhuang Jātaka story paintings are highly consistent with the scriptures in content,but their intuitiveness and expressiveness are more prominent.By comparing the narrative relationship between Jātaka story paintings in the Mogao Grottoes of Dunhuang and their corresponding scriptures,this study finds that the two have unity in reproducing artistic images and restoring key plots of classic Buddhist scripture stories,but there are great differences in the narrative effect.Dunhuang Jātaka story paintings have three prominent features in narrative,including visualization of key elements,concretization of expressions and movements,and contextualization of cause and effect.This study aims to reveal the intertextual narrative relationship between Dunhuang Jātaka story paintings and their corresponding scriptures.展开更多
Multi-modal Named Entity Recognition(MNER)aims to better identify meaningful textual entities by integrating information from images.Previous work has focused on extracting visual semantics at a fine-grained level,or ...Multi-modal Named Entity Recognition(MNER)aims to better identify meaningful textual entities by integrating information from images.Previous work has focused on extracting visual semantics at a fine-grained level,or obtaining entity related external knowledge from knowledge bases or Large Language Models(LLMs).However,these approaches ignore the poor semantic correlation between visual and textual modalities in MNER datasets and do not explore different multi-modal fusion approaches.In this paper,we present MMAVK,a multi-modal named entity recognition model with auxiliary visual knowledge and word-level fusion,which aims to leverage the Multi-modal Large Language Model(MLLM)as an implicit knowledge base.It also extracts vision-based auxiliary knowledge from the image formore accurate and effective recognition.Specifically,we propose vision-based auxiliary knowledge generation,which guides the MLLM to extract external knowledge exclusively derived from images to aid entity recognition by designing target-specific prompts,thus avoiding redundant recognition and cognitive confusion caused by the simultaneous processing of image-text pairs.Furthermore,we employ a word-level multi-modal fusion mechanism to fuse the extracted external knowledge with each word-embedding embedded from the transformerbased encoder.Extensive experimental results demonstrate that MMAVK outperforms or equals the state-of-the-art methods on the two classical MNER datasets,even when the largemodels employed have significantly fewer parameters than other baselines.展开更多
Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and ...Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and knowledge and the limitations of data sources,the visual knowledge within the knowledge graphs is generally of low quality,and some entities suffer from the issue of missing visual modality.Nevertheless,previous studies of MMKGC have primarily focused on how to facilitate modality interaction and fusion while neglecting the problems of low modality quality and modality missing.In this case,mainstream MMKGC models only use pre-trained visual encoders to extract features and transfer the semantic information to the joint embeddings through modal fusion,which inevitably suffers from problems such as error propagation and increased uncertainty.To address these problems,we propose a Multi-modal knowledge graph Completion model based on Super-resolution and Detailed Description Generation(MMCSD).Specifically,we leverage a pre-trained residual network to enhance the resolution and improve the quality of the visual modality.Moreover,we design multi-level visual semantic extraction and entity description generation,thereby further extracting entity semantics from structural triples and visual images.Meanwhile,we train a variational multi-modal auto-encoder and utilize a pre-trained multi-modal language model to complement the missing visual features.We conducted experiments on FB15K-237 and DB13K,and the results showed that MMCSD can effectively perform MMKGC and achieve state-of-the-art performance.展开更多
Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status...Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.展开更多
Narrative nursing has emerged as a vital approach in patient-centered care,and emphasize the importance of understanding patients’emotional experiences in addition to their physical health needs.In this article,we co...Narrative nursing has emerged as a vital approach in patient-centered care,and emphasize the importance of understanding patients’emotional experiences in addition to their physical health needs.In this article,we comment on the article by Zhou et al.Diseases such as acute pancreatitis can cause significant suffering and severely impact patients’quality of life.During treatment,routine nursing procedures such as gastric tube placement,oxygen therapy,monitoring,and nasogastric feeding often lack effective communication,which can adversely affect patients’recovery.This article highlights how narrative nursing can provide deeper insights into patients’emotional experiences,ultimately resulting in improved care outcomes.We also emphasize the role of narrative nursing in understanding and addressing these emotional needs to achieve personalized care,which can strengthen the therapeutic relationship between healthcare providers and patients.By recognizing the critical role of emotional well-being in patient care,we can develop comprehensive strategies that facilitate recovery and enhance overall quality of life.展开更多
To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities...To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.展开更多
At present,strengthening China’s international communication capabilities and enhancing China’s global influence have become important tasks.This study selects 60 pieces of soft news from China Daily from March 2023...At present,strengthening China’s international communication capabilities and enhancing China’s global influence have become important tasks.This study selects 60 pieces of soft news from China Daily from March 2023 to February 2024 as research objects and explores China’s national image from the source texts.Then,based on Mona Baker’s narrative theory,it analyzes the translation strategies to reconstruct the image of China,further revealing the regular characteristics of their application.Through translation,the reconstructed national image of China becomes more positive and more acceptable to foreign readers,effectively promoting the dissemination of Chinese stories in the international community.It is significant for promoting international understanding and cooperation,as well as effectively utilizing translation as a tool to enhance China’s national image.展开更多
As the number and complexity of sensors in autonomous vehicles continue to rise,multimodal fusionbased object detection algorithms are increasingly being used to detect 3D environmental information,significantly advan...As the number and complexity of sensors in autonomous vehicles continue to rise,multimodal fusionbased object detection algorithms are increasingly being used to detect 3D environmental information,significantly advancing the development of perception technology in autonomous driving.To further promote the development of fusion algorithms and improve detection performance,this paper discusses the advantages and recent advancements of multimodal fusion-based object detection algorithms.Starting fromsingle-modal sensor detection,the paper provides a detailed overview of typical sensors used in autonomous driving and introduces object detection methods based on images and point clouds.For image-based detection methods,they are categorized into monocular detection and binocular detection based on different input types.For point cloud-based detection methods,they are classified into projection-based,voxel-based,point cluster-based,pillar-based,and graph structure-based approaches based on the technical pathways for processing point cloud features.Additionally,multimodal fusion algorithms are divided into Camera-LiDAR fusion,Camera-Radar fusion,Camera-LiDAR-Radar fusion,and other sensor fusion methods based on the types of sensors involved.Furthermore,the paper identifies five key future research directions in this field,aiming to provide insights for researchers engaged in multimodal fusion-based object detection algorithms and to encourage broader attention to the research and application of multimodal fusion-based object detection.展开更多
BACKGROUND Narrative nursing uses narrative methods to establish an interaction between nursing staff and patients,in which the experience of the patient’s illness is understood and comprehended.By listening,the pati...BACKGROUND Narrative nursing uses narrative methods to establish an interaction between nursing staff and patients,in which the experience of the patient’s illness is understood and comprehended.By listening,the patient’s understanding,comprehension,and acceptance of their own disease symptoms,quality of life,and living conditions are understood,thereby providing a basis for formulating corresponding nursing plans for the patient,further promoting the psychological and physical rehabilitation of the patient.AIM To explore the impact of the new narrative nursing model on postoperative recovery,psychological status,and satisfaction of patients.METHODS A total of 108 patients with resectable gastric cancer who were treated from January 2024 to December 2024 were selected as the study subjects.They were divided into a routine nursing group and a narrative nursing group using a random number table method.Postoperative recovery indicators were compared between the two groups,and questionnaires and position and postoperative nausea and vomiting were conducted on the day of discharge.RESULTS There were statistically significant differences in visual analogue scale pain scores at 12-96 hours postoperatively,the time of first ambulation postoperatively,and the length of postoperative hospital stay between the two groups(P<0.05).There were statistically significant differences in postoperative self-rating anxiety scale,self-rating depression scale,and satisfaction scores between the two groups(P<0.05).Further analysis using a binary logistic regression model found that the new narrative nursing model adopted postoperatively could improve patients’satisfaction with the work of nursing staff during their hospitalization.CONCLUSION The new narrative nursing model not only eliminated the negative emotions of patients,but also further promoted their postoperative recovery,and gained patients’trust and satisfaction with the nursing staff.展开更多
BACKGROUND Stress ulcers are common complications in critically ill patients,with a higher incidence observed in older patients following gastrointestinal surgery.This study aimed to develop and evaluate the effective...BACKGROUND Stress ulcers are common complications in critically ill patients,with a higher incidence observed in older patients following gastrointestinal surgery.This study aimed to develop and evaluate the effectiveness of a multi-modal intervention protocol to prevent stress ulcers in this high-risk population.AIM To assess the impact of a multi-modal intervention on preventing stress ulcers in older intensive care unit(ICU)patients postoperatively.METHODS A randomized controlled trial involving critically ill patients(aged≥65 years)admitted to the ICU after gastrointestinal surgery was conducted.Patients were randomly assigned to either the intervention group,which received a multimodal stress ulcer prevention protocol,or the control group,which received standard care.The primary outcome measure was the incidence of stress ulcers.The secondary outcomes included ulcer healing time,complication rates,and length of hospital stay.RESULTS A total of 200 patients(100 in each group)were included in this study.The intervention group exhibited a significantly lower incidence of stress ulcers than the control group(15%vs 30%,P<0.01).Additionally,the intervention group demonstrated shorter ulcer healing times(mean 5.2 vs 7.8 days,P<0.05),lower complication rates(10%vs 22%,P<0.05),and reduced length of hospital stay(mean 12.3 vs 15.7 days,P<0.05).CONCLUSION This multi-modal intervention protocol significantly reduced the incidence of stress ulcers and improved clinical outcomes in critically ill older patients after gastrointestinal surgery.This comprehensive approach may provide a valuable strategy for managing high-risk populations in intensive care settings.展开更多
With the advent of the next-generation Air Traffic Control(ATC)system,there is growing interest in using Artificial Intelligence(AI)techniques to enhance Situation Awareness(SA)for ATC Controllers(ATCOs),i.e.,Intellig...With the advent of the next-generation Air Traffic Control(ATC)system,there is growing interest in using Artificial Intelligence(AI)techniques to enhance Situation Awareness(SA)for ATC Controllers(ATCOs),i.e.,Intelligent SA(ISA).However,the existing AI-based SA approaches often rely on unimodal data and lack a comprehensive description and benchmark of the ISA tasks utilizing multi-modal data for real-time ATC environments.To address this gap,by analyzing the situation awareness procedure of the ATCOs,the ISA task is refined to the processing of the two primary elements,i.e.,spoken instructions and flight trajectories.Subsequently,the ISA is further formulated into Controlling Intent Understanding(CIU)and Flight Trajectory Prediction(FTP)tasks.For the CIU task,an innovative automatic speech recognition and understanding framework is designed to extract the controlling intent from unstructured and continuous ATC communications.For the FTP task,the single-and multi-horizon FTP approaches are investigated to support the high-precision prediction of the situation evolution.A total of 32 unimodal/multi-modal advanced methods with extensive evaluation metrics are introduced to conduct the benchmarks on the real-world multi-modal ATC situation dataset.Experimental results demonstrate the effectiveness of AI-based techniques in enhancing ISA for the ATC environment.展开更多
The multi-modal characteristics of mineral particles play a pivotal role in enhancing the classification accuracy,which is critical for obtaining a profound understanding of the Earth's composition and ensuring ef...The multi-modal characteristics of mineral particles play a pivotal role in enhancing the classification accuracy,which is critical for obtaining a profound understanding of the Earth's composition and ensuring effective exploitation utilization of its resources.However,the existing methods for classifying mineral particles do not fully utilize these multi-modal features,thereby limiting the classification accuracy.Furthermore,when conventional multi-modal image classification methods are applied to planepolarized and cross-polarized sequence images of mineral particles,they encounter issues such as information loss,misaligned features,and challenges in spatiotemporal feature extraction.To address these challenges,we propose a multi-modal mineral particle polarization image classification network(MMGC-Net)for precise mineral particle classification.Initially,MMGC-Net employs a two-dimensional(2D)backbone network with shared parameters to extract features from two types of polarized images to ensure feature alignment.Subsequently,a cross-polarized intra-modal feature fusion module is designed to refine the spatiotemporal features from the extracted features of the cross-polarized sequence images.Ultimately,the inter-modal feature fusion module integrates the two types of modal features to enhance the classification precision.Quantitative and qualitative experimental results indicate that when compared with the current state-of-the-art multi-modal image classification methods,MMGC-Net demonstrates marked superiority in terms of mineral particle multi-modal feature learning and four classification evaluation metrics.It also demonstrates better stability than the existing models.展开更多
This study,conducted during an internship at the Tumor Hospital of S Province,employs a qualitative research paradigm.The primary research subjects were 11 hospitalized breast cancer patients,with data collected throu...This study,conducted during an internship at the Tumor Hospital of S Province,employs a qualitative research paradigm.The primary research subjects were 11 hospitalized breast cancer patients,with data collected through semi-structured interviews and observation,followed by content analysis.The study aims to explore the disease experiences and life experiences of breast cancer patients,investigating what their illness means to them and whether it has led to a different understanding of the meaning of their lives.The findings reveal that the reconstruction of life meaning among breast cancer patients manifests as“new perceptions of existence,new attitudes toward life,and new life goals.”展开更多
Objective:This study aimed to explore the effect of standardized patient(SP)-narrative nursing in the experimental teaching of surgical nursing.Methods:A quasi-experimental study design was adopted.A total of 200 unde...Objective:This study aimed to explore the effect of standardized patient(SP)-narrative nursing in the experimental teaching of surgical nursing.Methods:A quasi-experimental study design was adopted.A total of 200 undergraduate nursing students were recruited from the Nursing College of Guilin Medical University in China from March 2023 to December 2024.The intervention group recruited students from the Class of 2022(n=100),and the control group recruited students from the Class of 2021(n=100).The intervention group adopted a teaching model combining standardized patients with narrative nursing based on traditional scenariobased simulation teaching,which was applied to the nursing of perioperative patients(4 class hours)and scenario-based case drills(4 class hours)in the experimental teaching of surgical nursing.The control group used traditional scenario-based simulation teaching.The Nurse Humanistic Care Quality Evaluation Scale,Clinical Thinking Ability Evaluation Index System Scale for Medical Students,and Nurse-Patient Communication Ability Evaluation Scale for Nursing Students were used to investigate and compare the teaching effects between the two groups of students.Results:The total scores of the intervention group on humanistic care(91.39±3.97),clinical thinking(79.64±6.33),and nurse-patient communication(157.22±7.95)abilities were significantly higher than those of the control group(82.29±3.62,65.11±7.24,and 147.05±7.84,respectively),with statistically significant differences(P<0.01).Conclusion:This study confirms that integrating the dual teaching model of standardized patients and narrative nursing in experimental teaching of surgical nursing has significantly optimized the theoretical and practical structure of teaching strategies.This innovative teaching method provides a promotable paradigm for nursing humanities education and is of positive significance for improving the effectiveness of cultivating the core literacy of nursing talents.展开更多
This study examines the integration of narrative medicine(NM)into primary healthcare(PHC)settings,evaluating its role in enhancing medical humanities education within grassroots healthcare institutions.Through a compr...This study examines the integration of narrative medicine(NM)into primary healthcare(PHC)settings,evaluating its role in enhancing medical humanities education within grassroots healthcare institutions.Through a comprehensive literature review and case analysis,the research investigates the current state,challenges,and practical barriers to embedding NM into PHC systems,while proposing targeted strategies for improvement.The findings suggest that NM fosters stronger doctor-patient trust,enhances healthcare quality,and promotes humanistic care.However,primary hospitals face numerous challenges in advancing medical humanities,including a lack of trust between doctors and patients,tensions arising from the commercialization of healthcare,institutional limitations,unequal distribution of resources,and issues related to physicians'professional competencies and stress management.These interrelated obstacles detract from the quality of PHC services and the overall patient experience.Drawing on successful case studies from primary hospitals,the paper outlines effective strategies for overcoming these challenges.The study provides both theoretical and practical insights for advancing medical humanities in PHC,contributing to improvements in healthcare service quality and supporting the development of high standards in the healthcare sector.Ultimately,the findings aim to promote the broader adoption and ongoing refinement of NM within PHC institutions.展开更多
This study examines the narrative interplay between Chinese and Australian cinemas,focusing on how Chinese cinematic forms-such as cyclical storytelling,mythological motifs,and magical realism-are influencing and resh...This study examines the narrative interplay between Chinese and Australian cinemas,focusing on how Chinese cinematic forms-such as cyclical storytelling,mythological motifs,and magical realism-are influencing and reshaping contemporary Australian storytelling traditions.Through a comparative qualitative methodology,the research analyzes selected films,screenwriting practices,critical essays,and industry reports to identify key narrative structures,thematic patterns,and cultural dynamics across the two national cinemas.Findings reveal significant contrasts between the cyclical,mythologically rooted,and symbolically layered narratives characteristic of Chinese cinema and the traditionally linear,realist frameworks dominant in Australian filmmaking.However,increasing cultural exchange,co-productions,and multicultural influences have prompted Australian filmmakers to experiment with non-linear structures,mythic elements,and more complex representations of identity.The study contributes to cross-cultural film scholarship by proposing integrated analytical frameworks that highlight hybrid narrative forms and by offering practical implications for future Sino-Australian collaboration.These findings underscore the evolving global landscape of film narratives and the growing relevance of culturally adaptive storytelling strategies.展开更多
Objective:To explore the effect of narrative nursing combined with exercise training on health beliefs and cardiac rehabilitation in patients with acute myocardial infarction(AMI)after interventional treatment.Methods...Objective:To explore the effect of narrative nursing combined with exercise training on health beliefs and cardiac rehabilitation in patients with acute myocardial infarction(AMI)after interventional treatment.Methods:A total of 111 patients with acute myocardial infarction(AMI)who received interventional therapy in Department of Cardiology of Hefei First People’s Hospital from January 2022 to September 2023 were selected as the study subjects.They were randomly divided into a control group(n=55)and a study group(n=56)using a random table method.Both groups received routine nursing care,while the control group received exercise training on top of it.The study group received narrative nursing care on top of the control group.Both groups were intervened until the patients were discharged.The health beliefs,heart function,psychological status,and quality of life after intervention were compared between two groups using a 6-minute walk test(6MWT).Results:After intervention,the scores of the TSK-SV Heart in all dimensions(fear of movement,functional disorders,risk perception,and avoidance of movement)decreased in both groups,and the study group was lower than the control group,with a statistical significant difference(P<0.05).After intervention,the scores of the Self Rating Anxiety Scale(SAS)and Self Rating Depression Scale(SDS)in both groups decreased,and the study group was lower than the control group,with a statistical significant difference(P<0.05).After intervention,both groups showed an increase in left ventricular ejection fraction(LVEF)and left ventricular early diastolic peak flow velocity/left ventricular late diastolic peak flow velocity(E/A),with the study group being higher than the control group.The left ventricular end diastolic diameter(LVEDD)and left atrial volume index(LAVI)decreased,and the study group was lower than the control group,with statistical significant differences(P<0.05).The walking distance of the study group at 6MWT(488.8±31.4)m was greater than that of the control group(425.54±30.7)m,with statistical significant differences(P<0.05).The quality of life measurement scale(CROQ-PTCA-Post)of the study group after coronary intervention treatment had higher scores in all dimensions(physical function,satisfaction,psychosocial function,adverse reactions,symptoms,cognitive function)than the control group,with statistical significant differences(P<0.05).Conclusion:Narrative nursing combined with exercise training can enhance the health beliefs of AMI patients towards exercise training after intervention therapy,which is beneficial for cardiac rehabilitation and can improve psychological status and quality of life.展开更多
基金Construction Program of the Key Discipline of State Administration of Traditional Chinese Medicine of China(ZYYZDXK-2023069)Research Project of Shanghai Municipal Health Commission (2024QN018)Shanghai University of Traditional Chinese Medicine Science and Technology Development Program (23KFL005)。
文摘Objective To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data.Methods Clinical indicators,echocardiographic data,traditional Chinese medicine(TCM)tongue manifestations,and facial features were collected from patients who underwent coro-nary computed tomography angiography(CTA)in the Cardiac Care Unit(CCU)of Shanghai Tenth People's Hospital between May 1,2023 and May 1,2024.An adaptive weighted multi-modal data fusion(AWMDF)model based on deep learning was constructed to predict the severity of coronary artery stenosis.The model was evaluated using metrics including accura-cy,precision,recall,F1 score,and the area under the receiver operating characteristic(ROC)curve(AUC).Further performance assessment was conducted through comparisons with six ensemble machine learning methods,data ablation,model component ablation,and various decision-level fusion strategies.Results A total of 158 patients were included in the study.The AWMDF model achieved ex-cellent predictive performance(AUC=0.973,accuracy=0.937,precision=0.937,recall=0.929,and F1 score=0.933).Compared with model ablation,data ablation experiments,and various traditional machine learning models,the AWMDF model demonstrated superior per-formance.Moreover,the adaptive weighting strategy outperformed alternative approaches,including simple weighting,averaging,voting,and fixed-weight schemes.Conclusion The AWMDF model demonstrates potential clinical value in the non-invasive prediction of coronary artery disease and could serve as a tool for clinical decision support.
文摘Convergent journalism constitutes a systematic investigation into emergent journalistic forms,conceptual frameworks,and practices emerging within media convergence context,characterized by its inherent attributes of convergence,datacentricity,and interactivity.Grounded in the theoretical discourse of digital narratology,this monograph crystallizes its analytical focus on the triadic conceptual constellation of"convergence""mediaticity"and"narrativity",By positioning""convergence"as the central problematique,it systematically constructs an epistemological framework for convergent journalistic narrative through three dimensions:narrative theory,narrative language,and narrative praxis,thereby elucidates the ontological foundations and operational logics intrinsic to contemporary journalism studies.
文摘Jātaka story paintings are common narrative subjects in Dunhuang murals.Based on corresponding scriptures,they present all kinds of good deeds that Sakyamuni sacrificed his life to save sentient beings in his previous life.Dunhuang Jātaka story paintings are highly consistent with the scriptures in content,but their intuitiveness and expressiveness are more prominent.By comparing the narrative relationship between Jātaka story paintings in the Mogao Grottoes of Dunhuang and their corresponding scriptures,this study finds that the two have unity in reproducing artistic images and restoring key plots of classic Buddhist scripture stories,but there are great differences in the narrative effect.Dunhuang Jātaka story paintings have three prominent features in narrative,including visualization of key elements,concretization of expressions and movements,and contextualization of cause and effect.This study aims to reveal the intertextual narrative relationship between Dunhuang Jātaka story paintings and their corresponding scriptures.
基金funded by Research Project,grant number BHQ090003000X03.
文摘Multi-modal Named Entity Recognition(MNER)aims to better identify meaningful textual entities by integrating information from images.Previous work has focused on extracting visual semantics at a fine-grained level,or obtaining entity related external knowledge from knowledge bases or Large Language Models(LLMs).However,these approaches ignore the poor semantic correlation between visual and textual modalities in MNER datasets and do not explore different multi-modal fusion approaches.In this paper,we present MMAVK,a multi-modal named entity recognition model with auxiliary visual knowledge and word-level fusion,which aims to leverage the Multi-modal Large Language Model(MLLM)as an implicit knowledge base.It also extracts vision-based auxiliary knowledge from the image formore accurate and effective recognition.Specifically,we propose vision-based auxiliary knowledge generation,which guides the MLLM to extract external knowledge exclusively derived from images to aid entity recognition by designing target-specific prompts,thus avoiding redundant recognition and cognitive confusion caused by the simultaneous processing of image-text pairs.Furthermore,we employ a word-level multi-modal fusion mechanism to fuse the extracted external knowledge with each word-embedding embedded from the transformerbased encoder.Extensive experimental results demonstrate that MMAVK outperforms or equals the state-of-the-art methods on the two classical MNER datasets,even when the largemodels employed have significantly fewer parameters than other baselines.
基金funded by Research Project,grant number BHQ090003000X03。
文摘Multi-modal knowledge graph completion(MMKGC)aims to complete missing entities or relations in multi-modal knowledge graphs,thereby discovering more previously unknown triples.Due to the continuous growth of data and knowledge and the limitations of data sources,the visual knowledge within the knowledge graphs is generally of low quality,and some entities suffer from the issue of missing visual modality.Nevertheless,previous studies of MMKGC have primarily focused on how to facilitate modality interaction and fusion while neglecting the problems of low modality quality and modality missing.In this case,mainstream MMKGC models only use pre-trained visual encoders to extract features and transfer the semantic information to the joint embeddings through modal fusion,which inevitably suffers from problems such as error propagation and increased uncertainty.To address these problems,we propose a Multi-modal knowledge graph Completion model based on Super-resolution and Detailed Description Generation(MMCSD).Specifically,we leverage a pre-trained residual network to enhance the resolution and improve the quality of the visual modality.Moreover,we design multi-level visual semantic extraction and entity description generation,thereby further extracting entity semantics from structural triples and visual images.Meanwhile,we train a variational multi-modal auto-encoder and utilize a pre-trained multi-modal language model to complement the missing visual features.We conducted experiments on FB15K-237 and DB13K,and the results showed that MMCSD can effectively perform MMKGC and achieve state-of-the-art performance.
基金supported by the Deanship of Research and Graduate Studies at King Khalid University under Small Research Project grant number RGP1/139/45.
文摘Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.
基金Supported by National Natural Science Foundation of China,No.82100123。
文摘Narrative nursing has emerged as a vital approach in patient-centered care,and emphasize the importance of understanding patients’emotional experiences in addition to their physical health needs.In this article,we comment on the article by Zhou et al.Diseases such as acute pancreatitis can cause significant suffering and severely impact patients’quality of life.During treatment,routine nursing procedures such as gastric tube placement,oxygen therapy,monitoring,and nasogastric feeding often lack effective communication,which can adversely affect patients’recovery.This article highlights how narrative nursing can provide deeper insights into patients’emotional experiences,ultimately resulting in improved care outcomes.We also emphasize the role of narrative nursing in understanding and addressing these emotional needs to achieve personalized care,which can strengthen the therapeutic relationship between healthcare providers and patients.By recognizing the critical role of emotional well-being in patient care,we can develop comprehensive strategies that facilitate recovery and enhance overall quality of life.
基金partially supported by the National Natural Science Foundation of China under Grants 62471493 and 62402257(for conceptualization and investigation)partially supported by the Natural Science Foundation of Shandong Province,China under Grants ZR2023LZH017,ZR2024MF066,and 2023QF025(for formal analysis and validation)+1 种基金partially supported by the Open Foundation of Key Laboratory of Computing Power Network and Information Security,Ministry of Education,Qilu University of Technology(Shandong Academy of Sciences)under Grant 2023ZD010(for methodology and model design)partially supported by the Russian Science Foundation(RSF)Project under Grant 22-71-10095-P(for validation and results verification).
文摘To address the challenge of missing modal information in entity alignment and to mitigate information loss or bias arising frommodal heterogeneity during fusion,while also capturing shared information acrossmodalities,this paper proposes a Multi-modal Pre-synergistic Entity Alignmentmodel based on Cross-modalMutual Information Strategy Optimization(MPSEA).The model first employs independent encoders to process multi-modal features,including text,images,and numerical values.Next,a multi-modal pre-synergistic fusion mechanism integrates graph structural and visual modal features into the textual modality as preparatory information.This pre-fusion strategy enables unified perception of heterogeneous modalities at the model’s initial stage,reducing discrepancies during the fusion process.Finally,using cross-modal deep perception reinforcement learning,the model achieves adaptive multilevel feature fusion between modalities,supporting learningmore effective alignment strategies.Extensive experiments on multiple public datasets show that the MPSEA method achieves gains of up to 7% in Hits@1 and 8.2% in MRR on the FBDB15K dataset,and up to 9.1% in Hits@1 and 7.7% in MRR on the FBYG15K dataset,compared to existing state-of-the-art methods.These results confirm the effectiveness of the proposed model.
文摘At present,strengthening China’s international communication capabilities and enhancing China’s global influence have become important tasks.This study selects 60 pieces of soft news from China Daily from March 2023 to February 2024 as research objects and explores China’s national image from the source texts.Then,based on Mona Baker’s narrative theory,it analyzes the translation strategies to reconstruct the image of China,further revealing the regular characteristics of their application.Through translation,the reconstructed national image of China becomes more positive and more acceptable to foreign readers,effectively promoting the dissemination of Chinese stories in the international community.It is significant for promoting international understanding and cooperation,as well as effectively utilizing translation as a tool to enhance China’s national image.
基金funded by the Yangtze River Delta Science and Technology Innovation Community Joint Research Project(2023CSJGG1600)the Natural Science Foundation of Anhui Province(2208085MF173)Wuhu“ChiZhu Light”Major Science and Technology Project(2023ZD01,2023ZD03).
文摘As the number and complexity of sensors in autonomous vehicles continue to rise,multimodal fusionbased object detection algorithms are increasingly being used to detect 3D environmental information,significantly advancing the development of perception technology in autonomous driving.To further promote the development of fusion algorithms and improve detection performance,this paper discusses the advantages and recent advancements of multimodal fusion-based object detection algorithms.Starting fromsingle-modal sensor detection,the paper provides a detailed overview of typical sensors used in autonomous driving and introduces object detection methods based on images and point clouds.For image-based detection methods,they are categorized into monocular detection and binocular detection based on different input types.For point cloud-based detection methods,they are classified into projection-based,voxel-based,point cluster-based,pillar-based,and graph structure-based approaches based on the technical pathways for processing point cloud features.Additionally,multimodal fusion algorithms are divided into Camera-LiDAR fusion,Camera-Radar fusion,Camera-LiDAR-Radar fusion,and other sensor fusion methods based on the types of sensors involved.Furthermore,the paper identifies five key future research directions in this field,aiming to provide insights for researchers engaged in multimodal fusion-based object detection algorithms and to encourage broader attention to the research and application of multimodal fusion-based object detection.
文摘BACKGROUND Narrative nursing uses narrative methods to establish an interaction between nursing staff and patients,in which the experience of the patient’s illness is understood and comprehended.By listening,the patient’s understanding,comprehension,and acceptance of their own disease symptoms,quality of life,and living conditions are understood,thereby providing a basis for formulating corresponding nursing plans for the patient,further promoting the psychological and physical rehabilitation of the patient.AIM To explore the impact of the new narrative nursing model on postoperative recovery,psychological status,and satisfaction of patients.METHODS A total of 108 patients with resectable gastric cancer who were treated from January 2024 to December 2024 were selected as the study subjects.They were divided into a routine nursing group and a narrative nursing group using a random number table method.Postoperative recovery indicators were compared between the two groups,and questionnaires and position and postoperative nausea and vomiting were conducted on the day of discharge.RESULTS There were statistically significant differences in visual analogue scale pain scores at 12-96 hours postoperatively,the time of first ambulation postoperatively,and the length of postoperative hospital stay between the two groups(P<0.05).There were statistically significant differences in postoperative self-rating anxiety scale,self-rating depression scale,and satisfaction scores between the two groups(P<0.05).Further analysis using a binary logistic regression model found that the new narrative nursing model adopted postoperatively could improve patients’satisfaction with the work of nursing staff during their hospitalization.CONCLUSION The new narrative nursing model not only eliminated the negative emotions of patients,but also further promoted their postoperative recovery,and gained patients’trust and satisfaction with the nursing staff.
文摘BACKGROUND Stress ulcers are common complications in critically ill patients,with a higher incidence observed in older patients following gastrointestinal surgery.This study aimed to develop and evaluate the effectiveness of a multi-modal intervention protocol to prevent stress ulcers in this high-risk population.AIM To assess the impact of a multi-modal intervention on preventing stress ulcers in older intensive care unit(ICU)patients postoperatively.METHODS A randomized controlled trial involving critically ill patients(aged≥65 years)admitted to the ICU after gastrointestinal surgery was conducted.Patients were randomly assigned to either the intervention group,which received a multimodal stress ulcer prevention protocol,or the control group,which received standard care.The primary outcome measure was the incidence of stress ulcers.The secondary outcomes included ulcer healing time,complication rates,and length of hospital stay.RESULTS A total of 200 patients(100 in each group)were included in this study.The intervention group exhibited a significantly lower incidence of stress ulcers than the control group(15%vs 30%,P<0.01).Additionally,the intervention group demonstrated shorter ulcer healing times(mean 5.2 vs 7.8 days,P<0.05),lower complication rates(10%vs 22%,P<0.05),and reduced length of hospital stay(mean 12.3 vs 15.7 days,P<0.05).CONCLUSION This multi-modal intervention protocol significantly reduced the incidence of stress ulcers and improved clinical outcomes in critically ill older patients after gastrointestinal surgery.This comprehensive approach may provide a valuable strategy for managing high-risk populations in intensive care settings.
基金supported by the National Natural Science Foundation of China(Nos.62371323,62401380,U2433217,U2333209,and U20A20161)Natural Science Foundation of Sichuan Province,China(Nos.2025ZNSFSC1476)+2 种基金Sichuan Science and Technology Program,China(Nos.2024YFG0010 and 2024ZDZX0046)the Institutional Research Fund from Sichuan University(Nos.2024SCUQJTX030)the Open Fund of Key Laboratory of Flight Techniques and Flight Safety,CAAC(Nos.GY2024-01A).
文摘With the advent of the next-generation Air Traffic Control(ATC)system,there is growing interest in using Artificial Intelligence(AI)techniques to enhance Situation Awareness(SA)for ATC Controllers(ATCOs),i.e.,Intelligent SA(ISA).However,the existing AI-based SA approaches often rely on unimodal data and lack a comprehensive description and benchmark of the ISA tasks utilizing multi-modal data for real-time ATC environments.To address this gap,by analyzing the situation awareness procedure of the ATCOs,the ISA task is refined to the processing of the two primary elements,i.e.,spoken instructions and flight trajectories.Subsequently,the ISA is further formulated into Controlling Intent Understanding(CIU)and Flight Trajectory Prediction(FTP)tasks.For the CIU task,an innovative automatic speech recognition and understanding framework is designed to extract the controlling intent from unstructured and continuous ATC communications.For the FTP task,the single-and multi-horizon FTP approaches are investigated to support the high-precision prediction of the situation evolution.A total of 32 unimodal/multi-modal advanced methods with extensive evaluation metrics are introduced to conduct the benchmarks on the real-world multi-modal ATC situation dataset.Experimental results demonstrate the effectiveness of AI-based techniques in enhancing ISA for the ATC environment.
基金supported by the National Natural Science Foundation of China(Grant Nos.62071315 and 62271336).
文摘The multi-modal characteristics of mineral particles play a pivotal role in enhancing the classification accuracy,which is critical for obtaining a profound understanding of the Earth's composition and ensuring effective exploitation utilization of its resources.However,the existing methods for classifying mineral particles do not fully utilize these multi-modal features,thereby limiting the classification accuracy.Furthermore,when conventional multi-modal image classification methods are applied to planepolarized and cross-polarized sequence images of mineral particles,they encounter issues such as information loss,misaligned features,and challenges in spatiotemporal feature extraction.To address these challenges,we propose a multi-modal mineral particle polarization image classification network(MMGC-Net)for precise mineral particle classification.Initially,MMGC-Net employs a two-dimensional(2D)backbone network with shared parameters to extract features from two types of polarized images to ensure feature alignment.Subsequently,a cross-polarized intra-modal feature fusion module is designed to refine the spatiotemporal features from the extracted features of the cross-polarized sequence images.Ultimately,the inter-modal feature fusion module integrates the two types of modal features to enhance the classification precision.Quantitative and qualitative experimental results indicate that when compared with the current state-of-the-art multi-modal image classification methods,MMGC-Net demonstrates marked superiority in terms of mineral particle multi-modal feature learning and four classification evaluation metrics.It also demonstrates better stability than the existing models.
基金Lingnan Normal University and College of Education’s university-level course-based ideological and political education demonstration project,“Course-Based Ideological and Political Education Demonstration Course for‘Women’s Social Work’”(Project No.:LSSZ202507)Undergraduate Innovation and Entrepreneurship Training Program project,“Sharing a Room,Healing Together:Leading the Way in‘Three-Shared,One-Community’Services for Patients’Family Members”(Project No.:X202410579028)。
文摘This study,conducted during an internship at the Tumor Hospital of S Province,employs a qualitative research paradigm.The primary research subjects were 11 hospitalized breast cancer patients,with data collected through semi-structured interviews and observation,followed by content analysis.The study aims to explore the disease experiences and life experiences of breast cancer patients,investigating what their illness means to them and whether it has led to a different understanding of the meaning of their lives.The findings reveal that the reconstruction of life meaning among breast cancer patients manifests as“new perceptions of existence,new attitudes toward life,and new life goals.”
基金supported by the Undergraduate Teaching Innovation Project of Guangxi Higher Education(grant number:2023JGB307,Department of Education of Guangxi Zhuang Autonomous Region,China)the Guangxi Autonomous Regionlevel Research and Practice Project on New Engineering,New Medicine,New Agriculture and New Liberal Arts"Research on the Reform of the Integration of Undergraduate Courses in Nursing Major under the Background of Grand Health"(grant number:XYK202414).
文摘Objective:This study aimed to explore the effect of standardized patient(SP)-narrative nursing in the experimental teaching of surgical nursing.Methods:A quasi-experimental study design was adopted.A total of 200 undergraduate nursing students were recruited from the Nursing College of Guilin Medical University in China from March 2023 to December 2024.The intervention group recruited students from the Class of 2022(n=100),and the control group recruited students from the Class of 2021(n=100).The intervention group adopted a teaching model combining standardized patients with narrative nursing based on traditional scenariobased simulation teaching,which was applied to the nursing of perioperative patients(4 class hours)and scenario-based case drills(4 class hours)in the experimental teaching of surgical nursing.The control group used traditional scenario-based simulation teaching.The Nurse Humanistic Care Quality Evaluation Scale,Clinical Thinking Ability Evaluation Index System Scale for Medical Students,and Nurse-Patient Communication Ability Evaluation Scale for Nursing Students were used to investigate and compare the teaching effects between the two groups of students.Results:The total scores of the intervention group on humanistic care(91.39±3.97),clinical thinking(79.64±6.33),and nurse-patient communication(157.22±7.95)abilities were significantly higher than those of the control group(82.29±3.62,65.11±7.24,and 147.05±7.84,respectively),with statistically significant differences(P<0.01).Conclusion:This study confirms that integrating the dual teaching model of standardized patients and narrative nursing in experimental teaching of surgical nursing has significantly optimized the theoretical and practical structure of teaching strategies.This innovative teaching method provides a promotable paradigm for nursing humanities education and is of positive significance for improving the effectiveness of cultivating the core literacy of nursing talents.
基金Supported by National Natural Science Foundation of China,No.82272204 and No.824721882022 Key Clinical Specialty of Zhejiang Province(Critical Care Medicine)+4 种基金“Pioneer”and“Leading Goose”R&D Program of Zhejiang,No.2023C03084Wenzhou Major Science and Technology Innovation Project,No.ZY2023005Central Guiding Local Technology Development,No.2024ZY01012Zhejiang Provincial College Students'Science and Technology Innovation Activity Program,No.2024R413A037National Innovation and Entrepreneurship Training Program for College Students,No.202410343030.
文摘This study examines the integration of narrative medicine(NM)into primary healthcare(PHC)settings,evaluating its role in enhancing medical humanities education within grassroots healthcare institutions.Through a comprehensive literature review and case analysis,the research investigates the current state,challenges,and practical barriers to embedding NM into PHC systems,while proposing targeted strategies for improvement.The findings suggest that NM fosters stronger doctor-patient trust,enhances healthcare quality,and promotes humanistic care.However,primary hospitals face numerous challenges in advancing medical humanities,including a lack of trust between doctors and patients,tensions arising from the commercialization of healthcare,institutional limitations,unequal distribution of resources,and issues related to physicians'professional competencies and stress management.These interrelated obstacles detract from the quality of PHC services and the overall patient experience.Drawing on successful case studies from primary hospitals,the paper outlines effective strategies for overcoming these challenges.The study provides both theoretical and practical insights for advancing medical humanities in PHC,contributing to improvements in healthcare service quality and supporting the development of high standards in the healthcare sector.Ultimately,the findings aim to promote the broader adoption and ongoing refinement of NM within PHC institutions.
文摘This study examines the narrative interplay between Chinese and Australian cinemas,focusing on how Chinese cinematic forms-such as cyclical storytelling,mythological motifs,and magical realism-are influencing and reshaping contemporary Australian storytelling traditions.Through a comparative qualitative methodology,the research analyzes selected films,screenwriting practices,critical essays,and industry reports to identify key narrative structures,thematic patterns,and cultural dynamics across the two national cinemas.Findings reveal significant contrasts between the cyclical,mythologically rooted,and symbolically layered narratives characteristic of Chinese cinema and the traditionally linear,realist frameworks dominant in Australian filmmaking.However,increasing cultural exchange,co-productions,and multicultural influences have prompted Australian filmmakers to experiment with non-linear structures,mythic elements,and more complex representations of identity.The study contributes to cross-cultural film scholarship by proposing integrated analytical frameworks that highlight hybrid narrative forms and by offering practical implications for future Sino-Australian collaboration.These findings underscore the evolving global landscape of film narratives and the growing relevance of culturally adaptive storytelling strategies.
文摘Objective:To explore the effect of narrative nursing combined with exercise training on health beliefs and cardiac rehabilitation in patients with acute myocardial infarction(AMI)after interventional treatment.Methods:A total of 111 patients with acute myocardial infarction(AMI)who received interventional therapy in Department of Cardiology of Hefei First People’s Hospital from January 2022 to September 2023 were selected as the study subjects.They were randomly divided into a control group(n=55)and a study group(n=56)using a random table method.Both groups received routine nursing care,while the control group received exercise training on top of it.The study group received narrative nursing care on top of the control group.Both groups were intervened until the patients were discharged.The health beliefs,heart function,psychological status,and quality of life after intervention were compared between two groups using a 6-minute walk test(6MWT).Results:After intervention,the scores of the TSK-SV Heart in all dimensions(fear of movement,functional disorders,risk perception,and avoidance of movement)decreased in both groups,and the study group was lower than the control group,with a statistical significant difference(P<0.05).After intervention,the scores of the Self Rating Anxiety Scale(SAS)and Self Rating Depression Scale(SDS)in both groups decreased,and the study group was lower than the control group,with a statistical significant difference(P<0.05).After intervention,both groups showed an increase in left ventricular ejection fraction(LVEF)and left ventricular early diastolic peak flow velocity/left ventricular late diastolic peak flow velocity(E/A),with the study group being higher than the control group.The left ventricular end diastolic diameter(LVEDD)and left atrial volume index(LAVI)decreased,and the study group was lower than the control group,with statistical significant differences(P<0.05).The walking distance of the study group at 6MWT(488.8±31.4)m was greater than that of the control group(425.54±30.7)m,with statistical significant differences(P<0.05).The quality of life measurement scale(CROQ-PTCA-Post)of the study group after coronary intervention treatment had higher scores in all dimensions(physical function,satisfaction,psychosocial function,adverse reactions,symptoms,cognitive function)than the control group,with statistical significant differences(P<0.05).Conclusion:Narrative nursing combined with exercise training can enhance the health beliefs of AMI patients towards exercise training after intervention therapy,which is beneficial for cardiac rehabilitation and can improve psychological status and quality of life.
