Purpose To explore the value of tumor intratumoral and peritumoral radiomics models based on dynamic contrast-enhanced MRI in predicting benign and malignant breast tumors.Materials and Methods This retrospective study analyzed clinical data and MRI imaging features from 309 patients with pathologically confirmed solitary breast tumors at the Second Affiliated Hospital of Xiamen Medical College from August 2018 to December 2022.The cohort was randomly divided into training(n=248)and testing(n=61)cohorts in an 8∶2 ratio.Using second-phase dynamic contrast-enhanced MRI images,five distinct peritumoral regions were segmented through 3D-Slicer software:intratumoral region,peritumoral 2 mm region,peritumoral 4 mm region,intratumoral combined with peritumoral 2 mm region and intratumoral combined with peritumoral 4 mm region.Radiomic features were extracted from these regions of interest.Machine learning algorithms integrated with Logistic regression analysis were employed for feature selection,with cross-validation techniques determining the optimal radiomic signature combination.Results Of 309 patients,150 were benign tumors and 159 were malignant tumors.The age,maximum diameter and enhancement type of benign and malignant breast tumors were statistically significant(Z/χ2=-7.695,-5.775,30.248;all P<0.001).For the region of interest of intratumoral combined with peritumoral 4 mm region,the area under curve,sensitivity,specificity,accuracy and F1 scores of the training and validation were 0.893,89.1%,75.0%,82.3%,0.838 and 0.881,74.2%,86.7%,80.3%,0.793,respectively.In the training set,the area under curve of intratumoral combined with peritumoral 4 mm region was significantly higher than that of intratumoral,peritumoral 2 mm,peritumoral 4 mm and intratumoral combined with peritumoral 2 mm regional imaging models(Z=2.506,2.982,3.392,2.157;all P<0.05).The Hosmer-Lemeshow test showed that the calibration curve of the model combined with region of interest of intratumoral combined with peritumoral 4 mm region was highly consistent with the ideal curve(training P=0.381,validation P=0.159).The decision curve indicated that the net benefit of the radiomics model in the region of interest of intratumoral combined with peritumoral 4 mm region was the highest when the risk threshold was between 0 and 1.0.Conclusion The radiomics model has good predictive performance in predicting benign or malignant breast tumors,among them the combined region of interest of intratumoral combined with peritumoral 4 mm region provides the best performance and highest benefit.The technology clinical application will provide a non-invasive predictive method for the differential diagnosis of benign and malignant breast tumors.

Objective:To construct a symptom network for type 2 diabetes mellitus (T2DM) patients, identify bridging symptoms and core symptoms, and explore influencing factors, providing references for precise and efficient symptom management.Methods:A convenience sampling method was used to select 340 T2DM patients hospitalized in the Endocrinology Department of Affiliated Hospital of North Sichuan Medical College from December 2023 to February 2024. A general information survey and the Chinese version of Diabetes Symptom Checklist-Revised were used to collect data. Multiple linear regression analysis was performed to explore the influencing factors of T2DM patients' lack of vitality. Network analysis was used to identify core symptoms and bridging symptoms among symptom clusters. A total of 340 questionnaires were distributed, and 336 valid questionnaires were returned, resulting in a valid response rate of 98.8% (336/340) .Results:The most common symptoms in T2DM patients included thirst and dry mouth, foamy urine, and overall fatigue. The most troublesome symptoms were overall fatigue, lack of vitality, and thirst and dry mouth. In the symptom network, lack of vitality had the highest intensity ( rs=7.980) and closeness centrality ( rc=0.014), followed by overall fatigue ( rs=7.940) and feeling down ( rs=7.200). Overall fatigue ( rbs=7.190, rbc=0.380) was identified as the strongest bridging symptom in the network. Univariate analysis showed that age, education level, diabetes duration, diabetes complications, and regular blood glucose monitoring were significant factors affecting lack of vitality in T2DM patients ( P<0.05). Multiple linear regression analysis indicated that education level, regular blood glucose monitoring, and diabetes complications were significant factors affecting lack of vitality ( P<0.05) . Conclusions:Lack of vitality is the core symptom in T2DM patients, and overall fatigue is the bridging symptom connecting different symptom clusters. The lack of vitality is influenced by factors such as education level, regular blood glucose monitoring, and diabetes complications. Healthcare providers should use symptom network analysis to timely identify core symptoms, bridging symptoms, and their influencing factors, and develop precise and efficient interventions to improve symptom management and reduce the symptom burden in T2DM patients.

Objective:To construct a symptom network for type 2 diabetes mellitus (T2DM) patients, identify bridging symptoms and core symptoms, and explore influencing factors, providing references for precise and efficient symptom management.Methods:A convenience sampling method was used to select 340 T2DM patients hospitalized in the Endocrinology Department of Affiliated Hospital of North Sichuan Medical College from December 2023 to February 2024. A general information survey and the Chinese version of Diabetes Symptom Checklist-Revised were used to collect data. Multiple linear regression analysis was performed to explore the influencing factors of T2DM patients' lack of vitality. Network analysis was used to identify core symptoms and bridging symptoms among symptom clusters. A total of 340 questionnaires were distributed, and 336 valid questionnaires were returned, resulting in a valid response rate of 98.8% (336/340) .Results:The most common symptoms in T2DM patients included thirst and dry mouth, foamy urine, and overall fatigue. The most troublesome symptoms were overall fatigue, lack of vitality, and thirst and dry mouth. In the symptom network, lack of vitality had the highest intensity ( rs=7.980) and closeness centrality ( rc=0.014), followed by overall fatigue ( rs=7.940) and feeling down ( rs=7.200). Overall fatigue ( rbs=7.190, rbc=0.380) was identified as the strongest bridging symptom in the network. Univariate analysis showed that age, education level, diabetes duration, diabetes complications, and regular blood glucose monitoring were significant factors affecting lack of vitality in T2DM patients ( P<0.05). Multiple linear regression analysis indicated that education level, regular blood glucose monitoring, and diabetes complications were significant factors affecting lack of vitality ( P<0.05) . Conclusions:Lack of vitality is the core symptom in T2DM patients, and overall fatigue is the bridging symptom connecting different symptom clusters. The lack of vitality is influenced by factors such as education level, regular blood glucose monitoring, and diabetes complications. Healthcare providers should use symptom network analysis to timely identify core symptoms, bridging symptoms, and their influencing factors, and develop precise and efficient interventions to improve symptom management and reduce the symptom burden in T2DM patients.

Purpose To explore the value of tumor intratumoral and peritumoral radiomics models based on dynamic contrast-enhanced MRI in predicting benign and malignant breast tumors.Materials and Methods This retrospective study analyzed clinical data and MRI imaging features from 309 patients with pathologically confirmed solitary breast tumors at the Second Affiliated Hospital of Xiamen Medical College from August 2018 to December 2022.The cohort was randomly divided into training(n=248)and testing(n=61)cohorts in an 8∶2 ratio.Using second-phase dynamic contrast-enhanced MRI images,five distinct peritumoral regions were segmented through 3D-Slicer software:intratumoral region,peritumoral 2 mm region,peritumoral 4 mm region,intratumoral combined with peritumoral 2 mm region and intratumoral combined with peritumoral 4 mm region.Radiomic features were extracted from these regions of interest.Machine learning algorithms integrated with Logistic regression analysis were employed for feature selection,with cross-validation techniques determining the optimal radiomic signature combination.Results Of 309 patients,150 were benign tumors and 159 were malignant tumors.The age,maximum diameter and enhancement type of benign and malignant breast tumors were statistically significant(Z/χ2=-7.695,-5.775,30.248;all P<0.001).For the region of interest of intratumoral combined with peritumoral 4 mm region,the area under curve,sensitivity,specificity,accuracy and F1 scores of the training and validation were 0.893,89.1%,75.0%,82.3%,0.838 and 0.881,74.2%,86.7%,80.3%,0.793,respectively.In the training set,the area under curve of intratumoral combined with peritumoral 4 mm region was significantly higher than that of intratumoral,peritumoral 2 mm,peritumoral 4 mm and intratumoral combined with peritumoral 2 mm regional imaging models(Z=2.506,2.982,3.392,2.157;all P<0.05).The Hosmer-Lemeshow test showed that the calibration curve of the model combined with region of interest of intratumoral combined with peritumoral 4 mm region was highly consistent with the ideal curve(training P=0.381,validation P=0.159).The decision curve indicated that the net benefit of the radiomics model in the region of interest of intratumoral combined with peritumoral 4 mm region was the highest when the risk threshold was between 0 and 1.0.Conclusion The radiomics model has good predictive performance in predicting benign or malignant breast tumors,among them the combined region of interest of intratumoral combined with peritumoral 4 mm region provides the best performance and highest benefit.The technology clinical application will provide a non-invasive predictive method for the differential diagnosis of benign and malignant breast tumors.

Objective:To explore the current situation of hypoglycemia fear of elderly patients with type 2 diabetes mellitus (T2DM) in the community, identify different types of hypoglycemia fear among elderly T2DM patients in the community by latent profile analysis, and analyze its influencing factors, so as to provide reference for formulating intervention measures.Methods:From February to April 2023, convenience sampling was used to select 300 elderly T2DM patients from three community health service centers in Shunqing District, Nanchong City, Sichuan Province as the research subject. The self-designed General Information Questionnaire, the Chinese Version Hypoglycemia Fear Survey-Ⅱ (CHFS-Ⅱ), and the Summary Diabetes Self Care Activities (SDSCA) were used to investigate the patients, and the latent profile of hypoglycemia fear was analyzed. Binary Logistic regression was used to analyze the influencing factors of different latent categories of hypoglycemic fear in elderly T2DM patients in the community.Results:A total of 300 questionnaires were distributed, and 290 valid questionnaires were collected, with an effective response rate of 96.7% (290/300). Among 290 elderly T2DM patients in the community, the hypoglycemia fear could be divided into two latent categories, including the "moderate worry-normal coping" group ( n=134) and the "high fear-cautious behavior" group ( n=156). The binary Logistic regression showed that marital status, whether there were complications of diabetes, treatment plan and self-management were the factors influencing the latent categories of elderly T2DM patients in the community, and the difference was statistically significant ( P<0.05) . Conclusions:There are classification characteristics of hypoglycemic fear levels in elderly T2DM patients in the community. Community medical and nursing staff should early identify patients in the "high fear-cautious behavior" group, regularly carry out targeted hypoglycemic health education and psychological intervention, reduce their hypoglycemic fear, and improve their quality of life.

Objective:To investigate the current status of multi-drug use in elderly inpatients with multiple chronic conditions in internal medicine and its correlation with nursing risk events, and to put forward targeted suggestions to help manage clinical multiple drug use in elderly patients.Methods:Using the convenient sampling method, a total of 2 254 elderly inpatients in Department of Internal Medicine in 4 Class Ⅲ Grade A general hospitals in Sichuan province were selected to conduct a questionnaire survey from March 2020 to January 2021, and their general conditions, multiple chronic conditions and multiple drug use were analyzed. Single factor and binary Logistic regression were used to analyze the current situation of multiple drug use in elderly patients with chronic disease and its influence on nursing risk events.Results:There were statistically significant differences in patients with different ages, weights, education levels, personal income, medical payment methods, whether they were weak or not, and whether patients with multiple chronic conditions took multiple drugs ( P<0.05) . Logistic regression analysis showed that education level and the number of chronic diseases were independent risk factors for multiple drug use ( P<0.05) . There were statistically significant differences in the total number of nursing risk events and the occurrence of pressure injuries, deep vein thrombosis and cardiac arrest in patients with multiple medications ( P<0.05) . In the same state of multimorbidity, the nursing risk events of patients with multiple drug use increased. In patients with the same multiple medications, the incidence of patient nursing risk events with an increase in the number of multimorbidities showed a downward trend. Conclusions:Elderly patients with a high education level, a large number of chronic diseases and aged 65-74 years old should pay special attention to whether they have multiple drug use behaviors. In the future, it is necessary to actively popularize the knowledge of rational drug use, actively prevent the occurrence of chronic diseases, strengthen the cooperation of drug management teams and comprehensively consider treatment options in multiple specialties, so as to fundamentally reduce multiple drug use.

Objective To explore job satisfaction degrees and the influencing factors among community nurses. Method General information questionnaire and the revised job satisfaction questionnaire were adopted in the investigation among 124 nurses from 28 community health service centers in Nanchong City between February to October 2016. Results The total score was (119.62 ± 15.37), at the moderate to high level. Of the eight dimensions, relationship with colleagues was scored the highest, and personal growth was the lowest. The influencing factors of the nurse's job satisfaction were found to be employment type, work positions, service duration in the community and whether they have been trained on community nursing position (all P<0.05). Conclusions The job satisfaction degree of community nurses is generally at middle level. Therefore, measures should be taken to improve the job satisfaction for the development of community nursing services.

Objective To explore the application effect of broaden-and-build theory of gratitude on continued nursing of COPD patients with anxiety disorders, and to conduct corresponding positive psychological intervention. Methods Totals of 182 COPD patients with anxiety disorders were selected as research objects, with 91 from one hospital as observation group and the other 91 from another hospital as control group. COPD continued nursing program was conducted to patients in the control group, while besides that, positive behavior intervention was conducted to patients in the observation group, basing on the broaden-and-build theory of gratitude. The patients were surveyed by Self-rating Anxiety Scale (SAS) before intervention, at 3 months, 6 months and 12 months after intervention, with anxiety improvement compared and analyzed. Results The SAS scores in the observation group decreased significantly (P<0.01), while the scores in the control group were without any significant change (P>0.05); there was obvious interaction between time and intervention (P<0.01); it was found by further pairwise comparison that at 3, 6 and 12 months after the intervention, SAS scores were (45.85±3.88), (41.14±3.29) and (37.94±2.64) in the observation group, and (47.27±4.27), (45.22±3.85) and (43.47±3.44) in the control group (FInter-group=39.91, FTime=660.61, FInter-group×Time=23.14;P<0.01). The difference between the two groups increased as time went by. Conclusions Applying the broaden-and-build theory of gratitude in continued nursing of COPD patients with anxiety disorders can effectively improve their symptoms of anxiety.

Objective To explore the influence of continuity care model on how to deal with medical waste of insulin injections among domestic patients Methods Consecutive diabetes patients using the home insulin injections were recruited as the the members of our Diabetes Education Classfrom September 2014 to March 2015. The home medical waste disposal was brought into the content of diabetes education;problems were intervened through the continuity care model. Using the questionnaire survey to investigate the cognitive and disposal of patients on medical waste, to compare the difference of results of questionnaire before and after the intervention. Results The cognitive rate of medical waste after intervention was 97.70%(212/217),while the cognitive rate before intervention was 3.69%(8/217), the difference was significant (χ2=383.63,P<0.01). Disposal of diabetes patients using the home insulin injections had improved after the continuity care, the rate of dealing with medical waste according to specification after the intervention was 91.24% (198/217),while the rate before the intervention was 3.23%(7/217), the difference was significant (χ2=337.26,P<0.01). Conclusions The continuity care model standardized the behavior of patients for disposal of medical waste, reduced the environment pollution of medical waste and decreased the spread of disease.

Objective To analyze the training effect for junior nurses from internal medical departments of our hospital.Methods Data gathering tool,based on the Kirkpatrick Four Levels,was formulated by means of theoretical research and expert consultation.Before training,investigation of needs was conducted and then training contents were organized.Objective structured clinical examination (OSCE) was implemented before and after the training to obtain information about nurses' comprehensive abilities and to compare with their self-assessed data.Twelve months after the training,the training effect was kept track on from those subjects' head nurses.Results After the training,80 subjects' comprehensive abilities obtained from OSCE scored higher than before the training,(71.51±0.49) vs.(61.92±0.55),t=4.961,P ＜ 0.01.Their selfassessed comprehensive abilities also scored higher than that before,(83.49±0.75) vs.(72.73 ±0.43),t=7.020,P ＜ 0.01,the differences were both statistically significant.By questionnaire training,26 head nurses (26/32,88.46％) in general medical department showed positive assessment over training effect.Nurse's behaviors had improved significantly.In 80 training nurses,60 (75.00％) had become the examples of their colleagues.In 80 patients and their family members,73 cases showed their satisfaction and the satisfaction degree was higher than 90％.Conclusions The Kirkpatrick Four Levels can be applied to assess the training effect for junior nurses and the training mode can effectively improve comprehensive abilities of junior nurses.