Objective:To construct an indicator system for a nursing science and technology innovation curriculum guided by innovation competence, in order to provide a reference for cultivating innovation ability in nursing students.Methods:The overall research period was from March to December 2024. A nursing innovation curriculum indicator framework was initially developed through literature analysis and brainstorming. From October to December 2024, 19 experts from nine hospitals or universities across five provinces and cities were selected via purposive sampling to participate in two rounds of Delphi consultation. Revisions were made based on expert feedback.Results:Both rounds of expert consultation achieved a 100% response rate. The authority coefficient of the experts was 0.92. The final indicator system included four curriculum elements: course content, course objectives, teaching methods, and assessment, encompassing 14 first-level indicators and 40 second-level indicators.Conclusions:The innovation-oriented indicator system for nursing science and technology education demonstrates good scientific validity and reliability. It offers a foundational framework for advancing innovation-focused nursing education and curriculum design.

Objective:To develop a deep learning-based automated analysis system for precise segmentation of the optic cup and disc in fundus images and automatic measurement of the vertical cup-to-disc ratio (CDR) for early risk assessment and screening of chronic glaucoma.Methods:The proposed automated system comprised three modules: a dual coding-attention U-net (DCoAtUNet) segmentation network for optic cup and disc segmentation, a conditional random field (CRF) post-processing module, and a CDR measurement and glaucoma screening module based on the segmentation results.The system was designed to enhance boundary detection accuracy and measurement stability and its performance was evaluated on the publicly available Drishti-GS dataset.The dataset was divided into a training set and a test set in a 1∶1 ratio.Dice coefficient and intersection over union (IoU) were used to quantify segmentation accuracy and regional consistency, while accaracy, precision, recall, and F1-score were employed to assess glaucoma screening performance.Results:The DCoAtUNet combined with CRF post-processing achieved Dice coefficients of 0.976 0 for the optic disc and 0.908 1 for the optic cup, with corresponding IoU values of 0.953 4 and 0.837 9, demonstrating high segmentation precision and stability in boundary identification and region overlap.In glaucoma screening, the system achieved an accuracy of 0.843 1, precision of 0.840 9, recall of 0.973 7, and F1-score of 0.902 4, indicating good diagnostic sensitivity and accuracy.Conclusions:By integrating high-precision segmentation and automated measurement strategies, the DCoAtUNet+ CRF model significantly improves the accuracy and stability of CDR evaluation.It effectively assists in identifying high-risk individuals during early glaucoma screening and shows strong potential for clinical application in computer-aided diagnosis workflows.

Objective To explore the value of support vector machine(SVM)model based on gray matter volume(GMV)for identifying amyotrophic lateral sclerosis(ALS),also to analyze the relevant brain regions.Methods MR 3D T1WI data of 60 ALS patients(ALS group)and 60 healthy volunteers(control group)were retrospectively analyzed.Taken GMV of each brain region obtained by voxel-based morphometry as the input features.F-score analysis was used to select feature with the highest classification accuracy to construct SVM model.Receiver operating characteristic curve was drawn to evaluate the efficacy of SVM model for identifying ALS,and top 10％was used as the weight threshold to obtain gray matter brain regions contributed the most to this model.Results SVM model constructed based on the top 40％GMV features had the highest classification accuracy(82.50％),with sensitivity,specificity and area under the curve(AUG)of 85.05％,80.40％and 0.890,respectively.The left precentral gyrus,left anterior cingulate gyrus and paracingulate gyrus,right middle temporal gyrus,opercular part of left inferior frontal gyrus,right dorsolateral superior frontal gyrus,left temporal pole:middle temporal gyrus,right superior occipital gyrus,orbital part of right middle frontal gyrus,right calcarine fissure and surrounding cortex,right fusiform gyrus were the top 1-10 gray matter brain regions contributed to this model.Conclusion ALS had specific GMV change pattern.SVM model based on GMV could be used to effectively identify ALS,while the left precentral gyrus was the most contributive brain region to this model.

Objective:To construct an indicator system for a nursing science and technology innovation curriculum guided by innovation competence, in order to provide a reference for cultivating innovation ability in nursing students.Methods:The overall research period was from March to December 2024. A nursing innovation curriculum indicator framework was initially developed through literature analysis and brainstorming. From October to December 2024, 19 experts from nine hospitals or universities across five provinces and cities were selected via purposive sampling to participate in two rounds of Delphi consultation. Revisions were made based on expert feedback.Results:Both rounds of expert consultation achieved a 100% response rate. The authority coefficient of the experts was 0.92. The final indicator system included four curriculum elements: course content, course objectives, teaching methods, and assessment, encompassing 14 first-level indicators and 40 second-level indicators.Conclusions:The innovation-oriented indicator system for nursing science and technology education demonstrates good scientific validity and reliability. It offers a foundational framework for advancing innovation-focused nursing education and curriculum design.

Objective:To develop a deep learning-based automated analysis system for precise segmentation of the optic cup and disc in fundus images and automatic measurement of the vertical cup-to-disc ratio (CDR) for early risk assessment and screening of chronic glaucoma.Methods:The proposed automated system comprised three modules: a dual coding-attention U-net (DCoAtUNet) segmentation network for optic cup and disc segmentation, a conditional random field (CRF) post-processing module, and a CDR measurement and glaucoma screening module based on the segmentation results.The system was designed to enhance boundary detection accuracy and measurement stability and its performance was evaluated on the publicly available Drishti-GS dataset.The dataset was divided into a training set and a test set in a 1∶1 ratio.Dice coefficient and intersection over union (IoU) were used to quantify segmentation accuracy and regional consistency, while accaracy, precision, recall, and F1-score were employed to assess glaucoma screening performance.Results:The DCoAtUNet combined with CRF post-processing achieved Dice coefficients of 0.976 0 for the optic disc and 0.908 1 for the optic cup, with corresponding IoU values of 0.953 4 and 0.837 9, demonstrating high segmentation precision and stability in boundary identification and region overlap.In glaucoma screening, the system achieved an accuracy of 0.843 1, precision of 0.840 9, recall of 0.973 7, and F1-score of 0.902 4, indicating good diagnostic sensitivity and accuracy.Conclusions:By integrating high-precision segmentation and automated measurement strategies, the DCoAtUNet+ CRF model significantly improves the accuracy and stability of CDR evaluation.It effectively assists in identifying high-risk individuals during early glaucoma screening and shows strong potential for clinical application in computer-aided diagnosis workflows.

Objective To explore the value of support vector machine(SVM)model based on gray matter volume(GMV)for identifying amyotrophic lateral sclerosis(ALS),also to analyze the relevant brain regions.Methods MR 3D T1WI data of 60 ALS patients(ALS group)and 60 healthy volunteers(control group)were retrospectively analyzed.Taken GMV of each brain region obtained by voxel-based morphometry as the input features.F-score analysis was used to select feature with the highest classification accuracy to construct SVM model.Receiver operating characteristic curve was drawn to evaluate the efficacy of SVM model for identifying ALS,and top 10％was used as the weight threshold to obtain gray matter brain regions contributed the most to this model.Results SVM model constructed based on the top 40％GMV features had the highest classification accuracy(82.50％),with sensitivity,specificity and area under the curve(AUG)of 85.05％,80.40％and 0.890,respectively.The left precentral gyrus,left anterior cingulate gyrus and paracingulate gyrus,right middle temporal gyrus,opercular part of left inferior frontal gyrus,right dorsolateral superior frontal gyrus,left temporal pole:middle temporal gyrus,right superior occipital gyrus,orbital part of right middle frontal gyrus,right calcarine fissure and surrounding cortex,right fusiform gyrus were the top 1-10 gray matter brain regions contributed to this model.Conclusion ALS had specific GMV change pattern.SVM model based on GMV could be used to effectively identify ALS,while the left precentral gyrus was the most contributive brain region to this model.

Objective The aim of this study was to analyze the incidence and mortality of esophageal cancer in Yunnan province in 2020,as well as the changing trends from 2012 to 2020,in order to provide the data basis for the prevention and control strategies of esophageal cancer in Yunnan province.Methods The incidence and mortality data of esophageal cancer in tumor regis-tration areas of Yunnan province from 2012 to 2020 were collected and analyzed.The crude incidence,crude mortality,age-standard-ized incidence rate by Chinese standard population(ASIRC),age-standardized mortality rate by Chinese standard population(ASMRC),age-standardized incidence rate by World standard population(ASIRW)and age-standardized mortality rate by World standard population(ASMRW),0-74 years old cumulative rate and other indicators of esophageal cancer in Yunnan province in 2020 were calculated by gender and age,and the annual incidence and mortality trends of esophageal cancer in Yunnan province from 2012 to 2020 were analyzed by using the Joinpoint regression model.Results In 2020,the crude incidence of esophageal cancer in Yunnan province was 5.84/100,000,including 10.19/100,000 men and 1.28/100,000 women.ASIRC was 3.85/100,000,and ASIRW was 3.88/100,000.The incidence of esophageal cancer was at a low level before the age of 45,rising rapidly after the age of 45,and reached the peak in the 75-79 age group.The crude mortality of esophageal cancer was 5.08/100,000,with a male mortality of 9.02/100,000 and a female mortality of 0.95/100,000.ASMRC was 3.31/100,000,and ASMRW was 3.35/100,000.The mortality of e-sophageal cancer was at a low level before the age of 50,but rapidly increased after the age of 50,reaching its peak in the 75-79 age group.The incidence and mortality of men in all age groups were higher than those of women.From 2012 to 2020,the crude incidence(APC=8.14%,P<0.05),ASIRW(APC=7.65%,P<0.05),crude mortality(APC=8.99%,P<0.05),and ASMRW(APC=9.20%,P<0.05)of esophageal cancer all showed an upward trend.Conclusion The incidence and mortality of esophageal cancer in Yunnan province are on the rise.The incidence and mortality of men are higher than those of women.Age is an important factor affect-ing the occurrence and development of esophageal cancer.Men and the elderly should be the focus of daily intervention.

Objective The aim of this study was to analyze the incidence and mortality of gastric cancer in tumor registration areas of Yunnan province in 2020,as well as the changing trends from 2012 to 2020,and provide suggestion for the prevention and treatment of gastric cancer in Yunnan province.Methods The incidence and death cases of gastric cancer in tumor registration areas of Yunnan province from 2012 to 2020 were collected and complied.After the quality control,the data was included in 89 monitoring points in 2020.Excel 2016 and SPSS 18.0 software were used to calculate the crude incidence,crude mortality,age-standardized inci-dence rate by World standard population(ASIRW),age-standardized mortality rate by World standard population(ASMRW),cumula-tive rate and other indicators of gastric cancer in Yunnan province in 2020.Joinpoint 4.8.0.1 software was used to calculate the annu-al percentage change(APC)and 95%CI of the ASIRW and ASMRW of gastric cancer from 2012 to 2020,and analyze the trend of change.Results In 2020,the crude incidence and ASIRW of gastric cancer in Yunnan province were 11.59/100,000 and 7.60/100,000,respectively.Males(14.90/100,000 and 10.25/100,000)were higher than those in females(8.10/100,000 and 5.04/100,000).In 2020,the crude mortality and ASMRW of gastric cancer in the Yunnan in 2020 were 9.06/100,000 and 5.82/100,000,respectively.Males(11.51/100,000 and 7.89/100,000)were higher than those in females(6.48/100,000 and 3.82/100,000).The crude incidence and mortality of gastric cancer in Yunnan province increased with age.They were at a low level before the age of 45 years old,and then increased rapidly.The 80-84 age group reached the peak(64.12/100,000 and 72.67/100,000),respectively.The APC for ASIRW and ASMRW of gastric cancer in Yunnan province from 2012 to 2020 were-0.35%and 0.22%,re-spectively,there were no significant difference in the trend of change(P>0.05).Conclusion ASIRW and ASMRW of gastric cancer of Yunnan province in 2020 are higher for men than women.The trend of ASIRW and ASMRW maintained stable from 2012 to 2020,and the males and middle-aged elderly people over 45 years old in Yunnan province are the key population for gastric cancer preven-tion and control.

Objective:To analyze pathogenic characteristics of viral diarrhea in children aged <5 years in Hebei Province and provide reference for the prevention and control of viral diarrhea in children.Methods:Stool samples were collected from in-patients with diarrhea under five years old from sentinel hospitals in Lulong County of Hebei between 2010 and 2020. ELISA detected rotavirus antigen, and then positive samples were genotyped by semi nested reverse transcription PCR of two rounds. Calicivirus, genotyping astrovirus, and adenovirus were detected by real-time fluorescence quantification PCR. The data were analyzed by using software SPSS 20.0.Results:In 2 925 detected stool samples, 1 919 (65.61%) were positive. The positive rates of rotavirus, calicivirus, adenovirus, and astrovirus were 42.80% (1 252/2 925), 22.12% (647/2 925), 6.19% (181/2 925), 3.56% (104/2 925). Viral diarrhea was mainly caused by rotavirus infection, accounting for 59.30% (1 017/1 715) between 2010 and 2017, and by calicivirus infection accounting for 53.43% (109/204) between 2018 and 2020. The peak positive rate of rotavirus occurred in winter, with the highest rate in infants aged 12 to 17 months (52.96%,483/912). In the rotavirus positive samples, G9P[8] was mainly detected strains (58.31%,730/1 252), followed by G3P[8] (8.15%,102/1 252). The calicivirus-positive samples were mainly infected with norovirus GⅡ. Sequence analysis indicated that the main type was GⅡ.4 [P31] between 2011 and 2016 and GⅡ.3 [P12] in 2018.Conclusions:Rotavirus and calicivirus were the main pathogens causing infant diarrhea in children under five years old in Hebei from 2010 to 2020. Winter was the main epidemic season.

Objective The aim of this study was to analyze the incidence and mortality of esophageal cancer in Yunnan province in 2020,as well as the changing trends from 2012 to 2020,in order to provide the data basis for the prevention and control strategies of esophageal cancer in Yunnan province.Methods The incidence and mortality data of esophageal cancer in tumor regis-tration areas of Yunnan province from 2012 to 2020 were collected and analyzed.The crude incidence,crude mortality,age-standard-ized incidence rate by Chinese standard population(ASIRC),age-standardized mortality rate by Chinese standard population(ASMRC),age-standardized incidence rate by World standard population(ASIRW)and age-standardized mortality rate by World standard population(ASMRW),0-74 years old cumulative rate and other indicators of esophageal cancer in Yunnan province in 2020 were calculated by gender and age,and the annual incidence and mortality trends of esophageal cancer in Yunnan province from 2012 to 2020 were analyzed by using the Joinpoint regression model.Results In 2020,the crude incidence of esophageal cancer in Yunnan province was 5.84/100,000,including 10.19/100,000 men and 1.28/100,000 women.ASIRC was 3.85/100,000,and ASIRW was 3.88/100,000.The incidence of esophageal cancer was at a low level before the age of 45,rising rapidly after the age of 45,and reached the peak in the 75-79 age group.The crude mortality of esophageal cancer was 5.08/100,000,with a male mortality of 9.02/100,000 and a female mortality of 0.95/100,000.ASMRC was 3.31/100,000,and ASMRW was 3.35/100,000.The mortality of e-sophageal cancer was at a low level before the age of 50,but rapidly increased after the age of 50,reaching its peak in the 75-79 age group.The incidence and mortality of men in all age groups were higher than those of women.From 2012 to 2020,the crude incidence(APC=8.14%,P<0.05),ASIRW(APC=7.65%,P<0.05),crude mortality(APC=8.99%,P<0.05),and ASMRW(APC=9.20%,P<0.05)of esophageal cancer all showed an upward trend.Conclusion The incidence and mortality of esophageal cancer in Yunnan province are on the rise.The incidence and mortality of men are higher than those of women.Age is an important factor affect-ing the occurrence and development of esophageal cancer.Men and the elderly should be the focus of daily intervention.