To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight arc...To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight architecture based on You Only Look Once(YOLO)v5m.Firstly,a lightweight upsampling operator called Content-Aware Reassembly of Features(CARAFE)is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles,reducing the missed detection rate and false detection rate.Secondly,a new prediction layer for tiny targets is added,and the feature fusion network is redesigned to enhance the detection capability for small targets.Finally,this paper applies L1 regularization to train the improved network,followed by pruning and fine-tuning operations to remove redundant channels,reducing computational and parameter complexity and enhancing the detection efficiency of the network.Training is conducted on the VisDrone2019-DET dataset.The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%,respectively.The average detection accuracy improves by 5.15%,and the detection speed reaches 47 images per second,satisfying real-time requirements.Compared with existing approaches,including YOLOv5m and classical vehicle detection algorithms,our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.展开更多
Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water resources.To address the inefficiency of traditi...Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water resources.To address the inefficiency of traditional cleanup methods and the challenges in detecting small targets,an improved YOLOv5 object detection model was proposed in this study.In order to enhance the model’s sensitivity to small targets and mitigate the impact of redundant information on detection performance,a bi-level routing attention mechanism was introduced and embedded into the backbone network.Additionally,a multi-scale detection head was incorporated into the model,allowing for more comprehensive coverage of floating garbage of various sizes through multi-scale feature extraction and detection.The Focal-EIoU loss function was also employed to optimize the model parameters,improving localization accuracy.Experimental results on the publicly available FloW_Img dataset demonstrated that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of precision and recall,achieving a mAP(mean average precision)of 86.12%,with significant improvements and faster convergence.展开更多
Drone or unmanned aerial vehicle(UAV)technology has undergone significant changes.The technology allows UAV to carry out a wide range of tasks with an increasing level of sophistication,since drones can cover a large ...Drone or unmanned aerial vehicle(UAV)technology has undergone significant changes.The technology allows UAV to carry out a wide range of tasks with an increasing level of sophistication,since drones can cover a large area with cameras.Meanwhile,the increasing number of computer vision applications utilizing deep learning provides a unique insight into such applications.The primary target in UAV-based detection applications is humans,yet aerial recordings are not included in the massive datasets used to train object detectors,which makes it necessary to gather the model data from such platforms.You only look once(YOLO)version 4,RetinaNet,faster region-based convolutional neural network(R-CNN),and cascade R-CNN are several well-known detectors that have been studied in the past using a variety of datasets to replicate rescue scenes.Here,we used the search and rescue(SAR)dataset to train the you only look once version 5(YOLOv5)algorithm to validate its speed,accuracy,and low false detection rate.In comparison to YOLOv4 and R-CNN,the highest mean average accuracy of 96.9%is obtained by YOLOv5.For comparison,experimental findings utilizing the SAR and the human rescue imaging database on land(HERIDAL)datasets are presented.The results show that the YOLOv5-based approach is the most successful human detection model for SAR missions.展开更多
Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for...Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for preventing the spread of diseases and minimizing economic losses.To address this issue,an improved algorithm based on the You Only Look Once v5s(YOLOv5s)lightweight model has been proposed.This enhanced model incorporates a faster lightweight structure and a new Convolutional Block Attention Module(CBAM)to achieve high recognition accuracy.Furthermore,the model introduces theα-SIoU loss function,which combines theα-Intersection over Union(α-IoU)and Shape Intersection over Union(SIoU)loss functions,thereby improving the accuracy of bounding box regression and object recognition.The average precision of the improved model reaches 94.2%for detecting unhealthy fish,representing increases of 11.3%,9.9%,9.7%,2.5%,and 2.1%compared to YOLOv3-tiny,YOLOv4,YOLOv5s,GhostNet-YOLOv5,and YOLOv7,respectively.Additionally,the improved model positively impacts hardware efficiency,reducing requirements for memory size by 59.0%,67.0%,63.0%,44.7%,and 55.6%in comparison to the five models mentioned above.The experimental results underscore the effectiveness of these approaches in addressing the challenges associated with fish health detection,and highlighting their significant practical implications and broad application prospects.展开更多
This work evaluates the performances of climate models in simulating the Southern Ocean(SO)sea surface temperature(SST)by a large ensemble from phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMI...This work evaluates the performances of climate models in simulating the Southern Ocean(SO)sea surface temperature(SST)by a large ensemble from phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6).By combining models from the same community sharing highly similar SO SST biases and eliminating the effect of global-mean biases on local SST biases,the results reveal that the ensemble-mean SO SST bias at 70°-30°S decreases from 0.38℃ in CMIP5 to 0.28℃ in CMIP6,together with increased intermodel consistency.The dominant mode of the intermodel variations in the zonal-mean SST biases is characterized as a meridional uniform warm bias pattern,explaining 79.1% of the intermodel variance and exhibiting positive principal values for most models.The ocean mixed layer heat budget further demonstrates that the SST biases at 70°-50°S primarily result from the excessive summertime heating effect from surface net heat flux.The biases in surface net heat flux south of 50°S are largely impacted by surface shortwave radiation from cloud and clear sky components at different latitudes.North of 50°S,the underestimated westerlies reduce the northward Ekman transport and hence northward cold advection in models,leading to warm SST biases year-round.In addition,the westerly biases are primarily traced back to the atmosphere-alone model simulations forced by the observed SST and sea ice.These results disclose the thermal origin at the high latitude and dynamical origin at the low latitude of the SO SST biases and underscore the significance of the deficiencies of atmospheric models in producing the SO SST biases.展开更多
Background:Myocardial infarction(MI)is known worldwide for its important disabling features,including myocarditis and cardiomyocyte apoptosis.It is believed that microRNA(miRNA)has a role in the cellular processes of ...Background:Myocardial infarction(MI)is known worldwide for its important disabling features,including myocarditis and cardiomyocyte apoptosis.It is believed that microRNA(miRNA)has a role in the cellular processes of apoptosis and myocarditis,and miR-219a-5p has been found to suppress the inflammatory response.However,unknown is the precise mechanism by which miR-219a-5p contributes to MI.Methods:We measured the expression of miR-219a-5p and evaluated its effects on target proteins,inflammatory factors,and apoptosis in a mouse model of MI.Echocardiography was utilized to examine the MI clinical index,and triphenyl tetrazolium chloride staining was employed to analyze the infarcted region.Enzyme-linked immunosorbent assay and Western blotting measured serum and molecular markers in heart tissues.To quantify the association with miR-219a-5p and ATPase sarcoplasmic/endoplasmic reticulum Ca^(2+) transporting 2(ATP2A2),the luciferase activity assay and Pearson’s correlation analysis were employed.Results:MiR-219a-5p exhibited low expression in a mouse model of MI,and its amplification prevented both apoptotic and inflammatory reactions.Specifically,miR-219a-5p targeted ATP2A2.Conclusion:In a mouse model of MI,miR-219a-5p exerted a potent protective effect via direct targeting of ATP2A2.展开更多
In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties ...In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).展开更多
Objective:To explore the intervention effect of the Structured Health Education course and 5A nursing model for self-control of elderly patients with coronary heart disease.Methods:Using the random sampling method,124...Objective:To explore the intervention effect of the Structured Health Education course and 5A nursing model for self-control of elderly patients with coronary heart disease.Methods:Using the random sampling method,124 elderly CAD patients admitted to the First Affiliated Hospital of Bengbu Medical University were randomly divided into an experimental group and a control group.The control group line routine health education,experimental group take structured health education combined with 5A nursing before and after the intervention using a coronary heart disease assessment questionnaire,coronary heart disease self-control scale evaluation of two groups of intervention,compare two groups before and after intervention blood pressure,blood sugar,body mass index,lipid index level and complications within 8 months after discharge.Results:After the course intervention,the disease cognition and self-behavior of the experimental group were higher than that of the control group,and the differences were statistically significant(all P<0.1).Conclusion:This course is suitable for elderly patients with coronary heart disease.The 5A model improves the cognitive and management ability of elderly patients to a certain extent,which is worthy of clinical application.展开更多
Objective:To construct a scientific and feasible teaching mode based on 5C caring theory and evaluate it,so as to provide a reference basis for future study about nursing humanistic quality education.Methods:Based on ...Objective:To construct a scientific and feasible teaching mode based on 5C caring theory and evaluate it,so as to provide a reference basis for future study about nursing humanistic quality education.Methods:Based on the 5C caring theory,the teaching design and teaching content were revising and implementing in the intervention group.Before and after the implementation of the course,a questionnaire survey was conducted using the Caring Ability Inventory(CAI).The results of the surveys were collected,and the data were statistically analysis using SPSS.Results:After the implementation of the course,the cognitive dimension,patience dimension,and humanistic caring ability scores of the nursing students in the intervention group improved compared with those before the implementation of the course(P<0.05).There were no significant difference in the control group(P>0.05).Conclusion:The humanistic caring teaching model based on 5C caring theory has a positive effect on improving nursing students'humanistic caring ability.In the future nursing teaching,the modules with the characteristics of humanistic caring ability can be increased,and carry out the educational reform throughout the humanistic caring ability.Actively guiding nursing students to establish the awareness of humanistic caring,so as to lay a solid foundation for high-quality clinical nursing work.展开更多
Objective:To explore the application and effect evaluation of the integrated“5A and 3+3”management model in ensuring safe medication use for chemotherapy patients.Methods:A total of 100 intravenous chemotherapy pati...Objective:To explore the application and effect evaluation of the integrated“5A and 3+3”management model in ensuring safe medication use for chemotherapy patients.Methods:A total of 100 intravenous chemotherapy patients admitted to the oncology department of Shaanxi Provincial People’s Hospital were randomly divided into two groups using a random number list method.Both groups received conventional nursing management during chemotherapy,while the study group additionally received the integrated“5A and 3+3”safety management model.The nursing intervention effects between the two groups were compared.Results:After the intervention,the study group showed higher levels of self-management ability,compliance,and nursing satisfaction compared to the control group.The overall incidence of adverse events during hospitalization was lower in the study group,with statistically significant differences(P<0.05).The knowledge scores of medical staff in the study group,related to the prevention and treatment of chemotherapy drug side effects,daily symptom management,and daily life management,were higher than those in the control group,with statistically significant differences(P<0.05).Conclusion:Implementing the integrated“5A and 3+3”model in the safe medication management of intravenous chemotherapy patients can effectively enhance patients’self-management abilities and compliance,improve medical staff’s ability to safely administer chemotherapy drugs,reduce adverse events caused by chemotherapy,and increase patient satisfaction.展开更多
目的探讨甲基转移酶5(methyltransferase-like 5,METTL5)在三阴乳腺癌(triple-negative breast cancer,TNBC)中的作用和潜在机制。方法采用免疫组织化学方法和Western blot检测TNBC肿瘤组织和细胞系中METTL5的表达情况。用靶向METTL5的s...目的探讨甲基转移酶5(methyltransferase-like 5,METTL5)在三阴乳腺癌(triple-negative breast cancer,TNBC)中的作用和潜在机制。方法采用免疫组织化学方法和Western blot检测TNBC肿瘤组织和细胞系中METTL5的表达情况。用靶向METTL5的shRNA(shRNA-METTL5)转染TNBC细胞后,用CCK-8、集落形成、伤口愈合以及Transwell实验分别检测细胞增殖活性、迁移与侵袭,Western blot检测Wnt/β-catenin信号关键蛋白的表达。构建异种移植瘤模型,验证敲降METTL5对TNBC细胞在体内生长以及Wnt/β-catenin信号活性的影响。结果METTL5在TNBC肿瘤组织和细胞系中表达上调(P<0.01)。敲降METTL5可抑制TNBC细胞的增殖、迁移和侵袭并降低了Wnt/β-catenin信号分子β-catenin、细胞周期蛋白(Cyclin)D1、基质金属蛋白酶(MMP)-2和MMP-7的表达(均P<0.01)。体内实验显示,敲降METTL5减缓了移植瘤生长和Wnt/β-catenin信号活性。结论敲降METTL5能抑制TNBC细胞的增殖、迁移与侵袭,其作用可能与抑制Wnt/β-catenin信号通路有关。展开更多
基金funded by the General Project of Key Research and Develop-ment Plan of Shaanxi Province(No.2022NY-087).
文摘To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight architecture based on You Only Look Once(YOLO)v5m.Firstly,a lightweight upsampling operator called Content-Aware Reassembly of Features(CARAFE)is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles,reducing the missed detection rate and false detection rate.Secondly,a new prediction layer for tiny targets is added,and the feature fusion network is redesigned to enhance the detection capability for small targets.Finally,this paper applies L1 regularization to train the improved network,followed by pruning and fine-tuning operations to remove redundant channels,reducing computational and parameter complexity and enhancing the detection efficiency of the network.Training is conducted on the VisDrone2019-DET dataset.The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%,respectively.The average detection accuracy improves by 5.15%,and the detection speed reaches 47 images per second,satisfying real-time requirements.Compared with existing approaches,including YOLOv5m and classical vehicle detection algorithms,our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.
文摘Detection of floating garbage in inland rivers is crucial for water environmental protection,as it effectively reduces ecological damage and ensures the safety of water resources.To address the inefficiency of traditional cleanup methods and the challenges in detecting small targets,an improved YOLOv5 object detection model was proposed in this study.In order to enhance the model’s sensitivity to small targets and mitigate the impact of redundant information on detection performance,a bi-level routing attention mechanism was introduced and embedded into the backbone network.Additionally,a multi-scale detection head was incorporated into the model,allowing for more comprehensive coverage of floating garbage of various sizes through multi-scale feature extraction and detection.The Focal-EIoU loss function was also employed to optimize the model parameters,improving localization accuracy.Experimental results on the publicly available FloW_Img dataset demonstrated that the improved YOLOv5 model outperforms the original YOLOv5 model in terms of precision and recall,achieving a mAP(mean average precision)of 86.12%,with significant improvements and faster convergence.
文摘Drone or unmanned aerial vehicle(UAV)technology has undergone significant changes.The technology allows UAV to carry out a wide range of tasks with an increasing level of sophistication,since drones can cover a large area with cameras.Meanwhile,the increasing number of computer vision applications utilizing deep learning provides a unique insight into such applications.The primary target in UAV-based detection applications is humans,yet aerial recordings are not included in the massive datasets used to train object detectors,which makes it necessary to gather the model data from such platforms.You only look once(YOLO)version 4,RetinaNet,faster region-based convolutional neural network(R-CNN),and cascade R-CNN are several well-known detectors that have been studied in the past using a variety of datasets to replicate rescue scenes.Here,we used the search and rescue(SAR)dataset to train the you only look once version 5(YOLOv5)algorithm to validate its speed,accuracy,and low false detection rate.In comparison to YOLOv4 and R-CNN,the highest mean average accuracy of 96.9%is obtained by YOLOv5.For comparison,experimental findings utilizing the SAR and the human rescue imaging database on land(HERIDAL)datasets are presented.The results show that the YOLOv5-based approach is the most successful human detection model for SAR missions.
基金supported by The Agricultural Science and Technology Independent Innovation Fund Project of Jiangsu Province(CX(22)3111)the National Natural Science Foundation of China Project(62173162)partly by the Changzhou Science and Technology Support Project(CE20225016).
文摘Real-time detection of unhealthy fish remains a significant challenge in intensive recirculating aquaculture.Early recognition of unhealthy fish and the implementation of appropriate treatment measures are crucial for preventing the spread of diseases and minimizing economic losses.To address this issue,an improved algorithm based on the You Only Look Once v5s(YOLOv5s)lightweight model has been proposed.This enhanced model incorporates a faster lightweight structure and a new Convolutional Block Attention Module(CBAM)to achieve high recognition accuracy.Furthermore,the model introduces theα-SIoU loss function,which combines theα-Intersection over Union(α-IoU)and Shape Intersection over Union(SIoU)loss functions,thereby improving the accuracy of bounding box regression and object recognition.The average precision of the improved model reaches 94.2%for detecting unhealthy fish,representing increases of 11.3%,9.9%,9.7%,2.5%,and 2.1%compared to YOLOv3-tiny,YOLOv4,YOLOv5s,GhostNet-YOLOv5,and YOLOv7,respectively.Additionally,the improved model positively impacts hardware efficiency,reducing requirements for memory size by 59.0%,67.0%,63.0%,44.7%,and 55.6%in comparison to the five models mentioned above.The experimental results underscore the effectiveness of these approaches in addressing the challenges associated with fish health detection,and highlighting their significant practical implications and broad application prospects.
基金supported by the National Natural Science Foundation of China(Nos.42076208,42141019,41831175 and 41706026)the National Key Research and Development Program of China(No.2017YFA0604600)+1 种基金the Natural Science Foundation of Jiangsu Province(No.BK20211209)the Fundamental Research Funds for the Central Universities(Nos.B210202135 and B210201015).
文摘This work evaluates the performances of climate models in simulating the Southern Ocean(SO)sea surface temperature(SST)by a large ensemble from phases 5 and 6 of the Coupled Model Intercomparison Project(CMIP5 and CMIP6).By combining models from the same community sharing highly similar SO SST biases and eliminating the effect of global-mean biases on local SST biases,the results reveal that the ensemble-mean SO SST bias at 70°-30°S decreases from 0.38℃ in CMIP5 to 0.28℃ in CMIP6,together with increased intermodel consistency.The dominant mode of the intermodel variations in the zonal-mean SST biases is characterized as a meridional uniform warm bias pattern,explaining 79.1% of the intermodel variance and exhibiting positive principal values for most models.The ocean mixed layer heat budget further demonstrates that the SST biases at 70°-50°S primarily result from the excessive summertime heating effect from surface net heat flux.The biases in surface net heat flux south of 50°S are largely impacted by surface shortwave radiation from cloud and clear sky components at different latitudes.North of 50°S,the underestimated westerlies reduce the northward Ekman transport and hence northward cold advection in models,leading to warm SST biases year-round.In addition,the westerly biases are primarily traced back to the atmosphere-alone model simulations forced by the observed SST and sea ice.These results disclose the thermal origin at the high latitude and dynamical origin at the low latitude of the SO SST biases and underscore the significance of the deficiencies of atmospheric models in producing the SO SST biases.
基金supported by the National Nature Science Foundation of the People’s Republic of China(No.81400225 for Zulong Sheng and No.82000382 for Yanru He)the Jiangsu Provincial Medical Youth Talent(No.QNRC2016815).
文摘Background:Myocardial infarction(MI)is known worldwide for its important disabling features,including myocarditis and cardiomyocyte apoptosis.It is believed that microRNA(miRNA)has a role in the cellular processes of apoptosis and myocarditis,and miR-219a-5p has been found to suppress the inflammatory response.However,unknown is the precise mechanism by which miR-219a-5p contributes to MI.Methods:We measured the expression of miR-219a-5p and evaluated its effects on target proteins,inflammatory factors,and apoptosis in a mouse model of MI.Echocardiography was utilized to examine the MI clinical index,and triphenyl tetrazolium chloride staining was employed to analyze the infarcted region.Enzyme-linked immunosorbent assay and Western blotting measured serum and molecular markers in heart tissues.To quantify the association with miR-219a-5p and ATPase sarcoplasmic/endoplasmic reticulum Ca^(2+) transporting 2(ATP2A2),the luciferase activity assay and Pearson’s correlation analysis were employed.Results:MiR-219a-5p exhibited low expression in a mouse model of MI,and its amplification prevented both apoptotic and inflammatory reactions.Specifically,miR-219a-5p targeted ATP2A2.Conclusion:In a mouse model of MI,miR-219a-5p exerted a potent protective effect via direct targeting of ATP2A2.
文摘In this study, the mechanical properties of aluminum-5%magnesium doped with rare earth metal neodymium were evaluated. Fuzzy logic (FL) and artificial neural network (ANN) were used to model the mechanical properties of aluminum-5%magnesium (0-0.9 wt%) neodymium. The single input (SI) to the fuzzy logic and artificial neural network models was the percentage weight of neodymium, while the multiple outputs (MO) were average grain size, ultimate tensile strength, yield strength elongation and hardness. The fuzzy logic-based model showed more accurate prediction than the artificial neutral network-based model in terms of the correlation coefficient values (R).
基金2022 Campus-level Scientific and Technological Project of Qilu Institute of Technology"Exploring the Material Basis and Mechanism of Action of Erjing Pill in Preventing and Treating Kidney Yin Deficiency AD Based on Network Pharmacology and Molecular Biology"(Project No.:QIT22NN009)。
文摘Objective:To explore the intervention effect of the Structured Health Education course and 5A nursing model for self-control of elderly patients with coronary heart disease.Methods:Using the random sampling method,124 elderly CAD patients admitted to the First Affiliated Hospital of Bengbu Medical University were randomly divided into an experimental group and a control group.The control group line routine health education,experimental group take structured health education combined with 5A nursing before and after the intervention using a coronary heart disease assessment questionnaire,coronary heart disease self-control scale evaluation of two groups of intervention,compare two groups before and after intervention blood pressure,blood sugar,body mass index,lipid index level and complications within 8 months after discharge.Results:After the course intervention,the disease cognition and self-behavior of the experimental group were higher than that of the control group,and the differences were statistically significant(all P<0.1).Conclusion:This course is suitable for elderly patients with coronary heart disease.The 5A model improves the cognitive and management ability of elderly patients to a certain extent,which is worthy of clinical application.
基金supported by the Research Program on Educational Teaching Reform of Tianjin University of Traditional Chinese Medicine(2020JY041)。
文摘Objective:To construct a scientific and feasible teaching mode based on 5C caring theory and evaluate it,so as to provide a reference basis for future study about nursing humanistic quality education.Methods:Based on the 5C caring theory,the teaching design and teaching content were revising and implementing in the intervention group.Before and after the implementation of the course,a questionnaire survey was conducted using the Caring Ability Inventory(CAI).The results of the surveys were collected,and the data were statistically analysis using SPSS.Results:After the implementation of the course,the cognitive dimension,patience dimension,and humanistic caring ability scores of the nursing students in the intervention group improved compared with those before the implementation of the course(P<0.05).There were no significant difference in the control group(P>0.05).Conclusion:The humanistic caring teaching model based on 5C caring theory has a positive effect on improving nursing students'humanistic caring ability.In the future nursing teaching,the modules with the characteristics of humanistic caring ability can be increased,and carry out the educational reform throughout the humanistic caring ability.Actively guiding nursing students to establish the awareness of humanistic caring,so as to lay a solid foundation for high-quality clinical nursing work.
文摘Objective:To explore the application and effect evaluation of the integrated“5A and 3+3”management model in ensuring safe medication use for chemotherapy patients.Methods:A total of 100 intravenous chemotherapy patients admitted to the oncology department of Shaanxi Provincial People’s Hospital were randomly divided into two groups using a random number list method.Both groups received conventional nursing management during chemotherapy,while the study group additionally received the integrated“5A and 3+3”safety management model.The nursing intervention effects between the two groups were compared.Results:After the intervention,the study group showed higher levels of self-management ability,compliance,and nursing satisfaction compared to the control group.The overall incidence of adverse events during hospitalization was lower in the study group,with statistically significant differences(P<0.05).The knowledge scores of medical staff in the study group,related to the prevention and treatment of chemotherapy drug side effects,daily symptom management,and daily life management,were higher than those in the control group,with statistically significant differences(P<0.05).Conclusion:Implementing the integrated“5A and 3+3”model in the safe medication management of intravenous chemotherapy patients can effectively enhance patients’self-management abilities and compliance,improve medical staff’s ability to safely administer chemotherapy drugs,reduce adverse events caused by chemotherapy,and increase patient satisfaction.
