Objective To observe the value of follicular thyroid imaging reporting and data system(F-TIRADS)for differentiating benign and malignant follicular thyroid lesions.Methods Totally 502 patients with follicular thyroid lesions were retrospectively enrolled,including 104 patients with single malignant lesion(malignant group,containing 77 follicular thyroid carcinomas[FTC]and 27 follicular variant of papillary thyroid carcinomas[FVPTC])and 398 patients with 416 benign lesions(benign group,containing 197 follicular thyroid adenomas[FTA]and 219 thyroid adenomatous hyperplasia).Ultrasonic features of lesions were recorded,and F-TIRADS scores were assigned by 1 junior and 1 senior ultrasound physicians.Taken histopathology results as gold standard,receiver operating characteristic curve was drawn,the area under the curve(AUC)was calculated to evaluate the efficacy for differentiating benign and malignant follicular thyroid lesions using F-TIRADS.Results Significant differences of composition,internal echo,boundary,calcification and trabecular structure of lesions were found between groups(all P＜0.001).Taken F-TIRADS score≥ 7 as the optimal cut-off value,the sensitivity,specificity,accuracy,positive predictive value and negative predictive value for differentiating benign and malignant follicular thyroid lesions by the junior physician was 76.92％,77.40％,77.31％,93.06％and 45.98％,while by the senior physician was 78.84％,81.25％,80.76％,93.89％and 51.25％,respectively.The efficacy of the latter was higher than of the former(AUC was 0.827 and 0.859,respectively,P＜0.05).Conclusion F-TIRADS could effectively identifying benign and malignant follicular thyroid lesions.

Objective To explore the value of improved ResNet18 lightweight deep learning(DL)models for automatically detecting gouty arthritis(GA)based on ultrasonogram of the first metatarsophalangeal joint(MTP1).Methods A total of 2 401 ultrasonograms obtained from 260 patients with suspected gout who underwent MTP1 ultrasound examination were included and divided into training set(1 910 ultrasonograms from 209 cases)and test set(491 ultrasonograms from 51 cases)at the ratio of 4∶1.GA lesions on ultrasonograms were manually labeled.After preprocessing,ResNet18 lightweight network was used to construct DL models for identifying the ultrasonogram category was normal or abnormal(with any manifestation of GA).Five-fold cross-validation method was adopted to evaluate the efficacy of the DL models constructed with 2,3,4 or 6 residual blocks,i.e.model 1,2,3 and 4,respectively,and the computational cost and the amount of parameters of each model were recorded.The efficacy of the models were verified using test set,and the best DL model was screened.Results The computational cost of model 1,2,3 and 4 was 7 558.27,2 963.73,4 012.33 and 6 093.39 M,respectively,while the amount of parameters was 4.61,4.91,4.91 and 5.28 M,respectively.Model 2 had the least computational cost with parameters only slightly more than model 1.In test set,no significant difference of accuracy nor the area under the curve was found among 4 models(all P＞0.05).The sensitivity of model 2 was higher than that of model 3,while its specificity was lower only than that of model 3(both P＜0.05),hence model 2 was the best DL model.Conclusion Improved ResNet18 lightweight DL models could be used for automatically detecting GA based on ultrasonogram of MTP1,among which model 2 was the best one.

Objective To observe the value of follicular thyroid imaging reporting and data system(F-TIRADS)for differentiating benign and malignant follicular thyroid lesions.Methods Totally 502 patients with follicular thyroid lesions were retrospectively enrolled,including 104 patients with single malignant lesion(malignant group,containing 77 follicular thyroid carcinomas[FTC]and 27 follicular variant of papillary thyroid carcinomas[FVPTC])and 398 patients with 416 benign lesions(benign group,containing 197 follicular thyroid adenomas[FTA]and 219 thyroid adenomatous hyperplasia).Ultrasonic features of lesions were recorded,and F-TIRADS scores were assigned by 1 junior and 1 senior ultrasound physicians.Taken histopathology results as gold standard,receiver operating characteristic curve was drawn,the area under the curve(AUC)was calculated to evaluate the efficacy for differentiating benign and malignant follicular thyroid lesions using F-TIRADS.Results Significant differences of composition,internal echo,boundary,calcification and trabecular structure of lesions were found between groups(all P＜0.001).Taken F-TIRADS score≥ 7 as the optimal cut-off value,the sensitivity,specificity,accuracy,positive predictive value and negative predictive value for differentiating benign and malignant follicular thyroid lesions by the junior physician was 76.92％,77.40％,77.31％,93.06％and 45.98％,while by the senior physician was 78.84％,81.25％,80.76％,93.89％and 51.25％,respectively.The efficacy of the latter was higher than of the former(AUC was 0.827 and 0.859,respectively,P＜0.05).Conclusion F-TIRADS could effectively identifying benign and malignant follicular thyroid lesions.

Objective To explore the value of improved ResNet18 lightweight deep learning(DL)models for automatically detecting gouty arthritis(GA)based on ultrasonogram of the first metatarsophalangeal joint(MTP1).Methods A total of 2 401 ultrasonograms obtained from 260 patients with suspected gout who underwent MTP1 ultrasound examination were included and divided into training set(1 910 ultrasonograms from 209 cases)and test set(491 ultrasonograms from 51 cases)at the ratio of 4∶1.GA lesions on ultrasonograms were manually labeled.After preprocessing,ResNet18 lightweight network was used to construct DL models for identifying the ultrasonogram category was normal or abnormal(with any manifestation of GA).Five-fold cross-validation method was adopted to evaluate the efficacy of the DL models constructed with 2,3,4 or 6 residual blocks,i.e.model 1,2,3 and 4,respectively,and the computational cost and the amount of parameters of each model were recorded.The efficacy of the models were verified using test set,and the best DL model was screened.Results The computational cost of model 1,2,3 and 4 was 7 558.27,2 963.73,4 012.33 and 6 093.39 M,respectively,while the amount of parameters was 4.61,4.91,4.91 and 5.28 M,respectively.Model 2 had the least computational cost with parameters only slightly more than model 1.In test set,no significant difference of accuracy nor the area under the curve was found among 4 models(all P＞0.05).The sensitivity of model 2 was higher than that of model 3,while its specificity was lower only than that of model 3(both P＜0.05),hence model 2 was the best DL model.Conclusion Improved ResNet18 lightweight DL models could be used for automatically detecting GA based on ultrasonogram of MTP1,among which model 2 was the best one.

ln the context of the high-quality development of public hospitals,research management is transitioning from a results-oriented approach to a full-process management model,necessitating the exploration of a governance system that integrates topic guidance,process support,and outcome transformation.Based on the Knowledge-to-Action and SECl model,it analyzes the construction logic and application effectiveness of the nursing research rounds mechanism at a teritary grade A hospital in Hainan Province.Through a"three-tier stratification+dual-track collaboration+full-process closed-loop"design,the mechanism bridges the chain from"clinical problem identification to research path optimization to outcome transformation",significantly enhancing research participation,research plan standardization,and research output.The findings validate the practical value of the research rounds mechanism in promoting knowledge transformation,capability enhancement,and organizational knowledge retention.

Objective:To evaluate the performance of the neural network model in segmenting gout tophus in the first metatarsophalangeal（MTP1）joint ultrasound images.Methods:A total of 1 218 tophus images from 381 patients who underwent MTP1 ultrasound examinations in the Affiliated Hospital of Qingdao University between May 2023 and December 2024 were prospectively collected. The images were divided into training，validation，and test sets in a ratio of 7∶2∶1. Multiple neural network models were trained to automatically identify and segment tophus in the images，with physician-annotated tophus regions serving as the reference standard. Model performance was evaluated in the test set，and the impact of tophus characteristics（e.g.，echogenicity，size，and presence of bone erosion）on segmentation efficacy was analyzed.Results:In the test set，CMUNeXt demonstrated superior tophus segmentation performance versus Unet，Unet++，TransUnet，and CMU-Net，achieving an accuracy of 99.1%，precision of 79.1%，recall of 84.6%，intersection over union of 68.8%，and Dice similarity coefficient of 80.2%. Logistic regression identified tophus echogenicity，size，and bone erosion as independent efficacy factors OR（95% CI）=7.275（1.598-33.129），21.303（4.282-105.985），13.520（3.617-50.530），0.076（0.007-0.823）（all P<0.05）. Hypoechoic tophus demonstrated significantly superior segmentation performance compared to mixed-echoic and isoechoic tophus（all P<0.05），and lesions with larger maximum diameters（>10 mm）were segmented more effectively than smaller tophus（ P<0.05）. Conclusions:The CMUNeXt model enables accurate identification and segmentation of tophus in MTP1 ultrasound images，particularly excelling for larger and hypoechoic lesions. This approach holds significant promise for AI-assisted diagnosis of MTP1 gouty arthritis.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

ln the context of the high-quality development of public hospitals,research management is transitioning from a results-oriented approach to a full-process management model,necessitating the exploration of a governance system that integrates topic guidance,process support,and outcome transformation.Based on the Knowledge-to-Action and SECl model,it analyzes the construction logic and application effectiveness of the nursing research rounds mechanism at a teritary grade A hospital in Hainan Province.Through a"three-tier stratification+dual-track collaboration+full-process closed-loop"design,the mechanism bridges the chain from"clinical problem identification to research path optimization to outcome transformation",significantly enhancing research participation,research plan standardization,and research output.The findings validate the practical value of the research rounds mechanism in promoting knowledge transformation,capability enhancement,and organizational knowledge retention.

Body weight abnormalities, including overweight, obesity, and underweight, have become a dual public health challenge in Chinese adults: overweight and obesity lead to a variety of chronic complications, while underweight increases the risks of malnutrition, sarcopenia, and organ dysfunction. To systematically address these issues, multidisciplinary experts in endocrinology, sports science, nutrition, and psychiatry from various regions have held multiple weight management seminars. Based on the latest epidemiological data and clinical evidence, they expanded the guideline to include assessment and intervention strategies for underweight, in addition to the core content of obesity management. This guideline outlines the etiological mechanisms, evaluation methods, and multidimensional management strategies for overweight and obesity, covering key areas such as diagnosis and assessment, medical nutrition therapy, exercise prescription, pharmacological intervention, and psychological support. It is intended to provide a scientific and standardized approach to weight management across the adult population, aiming to curb the rising prevalence of obesity, mitigate complications associated with abnormal body weight, and improve nutritional status and overall quality of life.

Objective:To evaluate the performance of the neural network model in segmenting gout tophus in the first metatarsophalangeal（MTP1）joint ultrasound images.Methods:A total of 1 218 tophus images from 381 patients who underwent MTP1 ultrasound examinations in the Affiliated Hospital of Qingdao University between May 2023 and December 2024 were prospectively collected. The images were divided into training，validation，and test sets in a ratio of 7∶2∶1. Multiple neural network models were trained to automatically identify and segment tophus in the images，with physician-annotated tophus regions serving as the reference standard. Model performance was evaluated in the test set，and the impact of tophus characteristics（e.g.，echogenicity，size，and presence of bone erosion）on segmentation efficacy was analyzed.Results:In the test set，CMUNeXt demonstrated superior tophus segmentation performance versus Unet，Unet++，TransUnet，and CMU-Net，achieving an accuracy of 99.1%，precision of 79.1%，recall of 84.6%，intersection over union of 68.8%，and Dice similarity coefficient of 80.2%. Logistic regression identified tophus echogenicity，size，and bone erosion as independent efficacy factors OR（95% CI）=7.275（1.598-33.129），21.303（4.282-105.985），13.520（3.617-50.530），0.076（0.007-0.823）（all P<0.05）. Hypoechoic tophus demonstrated significantly superior segmentation performance compared to mixed-echoic and isoechoic tophus（all P<0.05），and lesions with larger maximum diameters（>10 mm）were segmented more effectively than smaller tophus（ P<0.05）. Conclusions:The CMUNeXt model enables accurate identification and segmentation of tophus in MTP1 ultrasound images，particularly excelling for larger and hypoechoic lesions. This approach holds significant promise for AI-assisted diagnosis of MTP1 gouty arthritis.