Objective To conduct a scoping review of studies related to latent class trajectory analysis of glucose metabolism indicators in diabetes mellitus patients,so as to clarify the method of latent category trajectory analysis,evaluation indicators and the changing trajectories of different glucose metabolism indicators in diabetes mellitus patients,and to establish foundation for diabetes management and research.Methods Based on Arksey's framework for scoping study,a literature search was performed in CNKI,Wanfang,CBM,Web of Science,PubMed,Embase,Cochrane Library,CINAHL database inception to January 28,2024.Totally 2 researchers independently screened the literature and extracted data.Results A total of 22 pieces of the literature were included.Latent class growth model was the most commonly used model in latent class trajectory analysis,and 4 softwares are available for implementation.The current evaluation indicators for trajectory results are various,with the Bayesian information criterion and average posterior probability as the most frequently utilized.Hemoglobin is the most commonly used glucose metabolism indicators for trajectory analysis,and the number of identified trajectories ranged from 2 to 5.Most studies showed that diabetic patients exhibited a"low-stable"pattern in glucose metabolism indicators.Conclusion Latent class trajectory analysis represents a novel longitudinal data analysis approach that can identify potential subgroups of diabetic patients with similar development trajectory.The models and evaluation indicators employed are diverse,and the stability test of the models can be enhanced in the future.Through trajectory identification,it is recommended that clinical staff pay greater attention to the longitudinal trend of the glucose metabolism indicators and incorporate it into the long-term blood glucose management goal of diabetic patients.

Objective To conduct a scoping review of studies related to latent class trajectory analysis of glucose metabolism indicators in diabetes mellitus patients,so as to clarify the method of latent category trajectory analysis,evaluation indicators and the changing trajectories of different glucose metabolism indicators in diabetes mellitus patients,and to establish foundation for diabetes management and research.Methods Based on Arksey's framework for scoping study,a literature search was performed in CNKI,Wanfang,CBM,Web of Science,PubMed,Embase,Cochrane Library,CINAHL database inception to January 28,2024.Totally 2 researchers independently screened the literature and extracted data.Results A total of 22 pieces of the literature were included.Latent class growth model was the most commonly used model in latent class trajectory analysis,and 4 softwares are available for implementation.The current evaluation indicators for trajectory results are various,with the Bayesian information criterion and average posterior probability as the most frequently utilized.Hemoglobin is the most commonly used glucose metabolism indicators for trajectory analysis,and the number of identified trajectories ranged from 2 to 5.Most studies showed that diabetic patients exhibited a"low-stable"pattern in glucose metabolism indicators.Conclusion Latent class trajectory analysis represents a novel longitudinal data analysis approach that can identify potential subgroups of diabetic patients with similar development trajectory.The models and evaluation indicators employed are diverse,and the stability test of the models can be enhanced in the future.Through trajectory identification,it is recommended that clinical staff pay greater attention to the longitudinal trend of the glucose metabolism indicators and incorporate it into the long-term blood glucose management goal of diabetic patients.

OBJECTIVES: The measurement of diabetic foot ulcers is important for the success in diabetic foot ulcer management. At present, it lacks the accurate and convenient measurement tools in clinical. In recent years, artificial intelligence technology has demonstrated the potential application value in the field of image segmentation and recognition. This study aims to construct an intelligent measurement model of diabetic foot ulcers based on the deep learning method, and to conduct preliminary verification. METHODS: The data of 1 042 diabetic foot ulcers clinical samples were collected. The ulcers and color areas were manually labeled, of which 782 were used as the training data set and 260 as the test data set. The Mask RCNN ulcer tissue color semantic segmentation and RetinaNet scale digital scale target detection were used to build a model. The training data set was input into the model and iterated. The test data set was used to verify the intelligent measurement model. RESULTS: This study established an intelligent measurement model of diabetic foot ulcers based on deep learning. The mean average precision@.5 intersection over union (mAP@.5IOU) of the color region segmentation in the training set and the test set were 87.9% and 63.9%, respectively; the mAP@.5IOU of the ruler scale digital detection in the training set and the test set were 96.5% and 83.4%, respectively. Compared with the manual measurement result of the test sample, the average error of the intelligent measurement result was about 3 mm. CONCLUSIONS The intelligent measurement model has good accuracy and robustness in measuring the diabetic foot ulcers. Future research can further optimize the model with larger-scale data samples.
Artificial Intelligence ; Diabetes Mellitus ; Diabetic Foot ; Humans

OBJECTIVES: To evaluate residents' knowledge, attitude and behavior towards coronavirus disease 2019 (COVID-19) in Hunan Province, and to explore the factors influencing behaviors. METHODS: A self-designed questionnaire was used to conduct an online survey for 4 139 Hunan residents. The contents included general population information, residents' knowledge, attitude and practice to COVID-19. RESULTS: Mean scores of knowledge, attitude, and behavior were 29.82±3.16, 6.71±1.12, and 14.93±1.45, respectively. Residents had the highest score of major symptoms of COVID-19 (3.96±0.39), but the lowest was the main transmission routes (3.47±0.89). A total of 22.68% of the residents were very or relatively afraid of the outbreak, but 95.22% of the residents had confidence in defeating COVID-19. In behavior dimension, "handling of suspicious symptoms" had the lowest score (3.58±0.75). The behavior implementation rate of "keep the surfaces of household items clean" (80.50%), "doing more exercise, reasonable diet, working and resting regularly" (84.59%), and "avoid hand contacting with eyes, mouth or nose" (89.51%) were relatively low. Pearson correlation coefficient showed that the knowledge, attitude, and practices score were correlated with each other (knowledge vs behavior: =0.366; knowledge vs attitude: =0.041; attitude vs behavior: =0.100; all <0.05). Multiple linear regression analysis showed that the knowledge, attitude and behavior on COVID-19 were mostly influenced by education background (all <0.05), and the independent factors affecting behavior included knowledge and attitude, gender, permanent residence, education background (all <0.05). CONCLUSIONS Residents in Hunan Province have a good knowledge, attitude, and behavior to COVID-19. Nevertheless there are still weak links to be improved in all dimensions. It is necessary to strengthen knowledge and behavior of family protection, and care for residents' psychological health, especially persons with low education degree, male and rural residents.
Betacoronavirus ; China ; Coronavirus Infections ; psychology ; Health Knowledge, Attitudes, Practice ; Humans ; Pandemics ; Pneumonia, Viral ; psychology ; Surveys and Questionnaires

To explore the clinical characteristics, risk factors and factors affecting the severity of the disease in patients with diabetic foot at the current stage through a multi-center cross-sectional survey.
 Methods: Clinical data of 326 patients with diabetic foot (205 males and 121 females) from 13 general hospitals nationwide were collected from October to November 2017 using a unified clinical data collection table. The clinical characteristics were analyzed, and the influential factors for severe diabetic foot were analyzed by logistic regression analysis.
 Results: Among 326 patients with diabetic foot, 68.4% of the patients were more than 60 years old, and 60.1% of the patients received primary or junior high school education; 96.3% of the patients developed Type 2 diabetes; 80.1% of patients had glycated hemoglobin (HbA1c)≥7%; 60.1% of patients suffered dyslipidemia. Improper wearing of footwear (38.5%) is the main cause of diabetic foot. Diabetic neuropathy (76.7%), diabetic retinopathy (62.3%) and lower limb vascular disease (57.4%) were the most common complications. Logistic regression analysis showed that diabetic nephropathy, diabetic lower extremity vascular disease, and HbA1c levels were independent risk factors for severe diabetic foot, and receiving foot care education can be regarded as a protective factor.
 Conclusion: The diabetic foot occurs mostly in male patients, and Type 2 diabetes with older age, lower education level, poor glycemic control and dyslipidemia are the risk factors. Diabetic nephropathy, diabetic lower extremity vascular disease, HbA1c, and receiving foot care education are independent influential factors for the severity of diabetic foot.
China ; Cross-Sectional Studies ; Diabetes Mellitus, Type 2 ; Diabetic Foot ; Diabetic Neuropathies ; Female ; Humans ; Male ; Middle Aged