ObjectiveTo systematicly review the researches of gesture action intent recognition based on surface electromyography (sEMG). MethodsExperimental researches on gesture action intention recognition based on sEMG were retrieved from CNKI, Wanfang Data, PubMed and Web of Science. The literatures were screened, and the classification methods and other related factors were summarized. ResultsA total of 735 researches were returned, and 25 researches were finally included. The publication time was mainly from 2012 to 2021. The subjects were healthy people or amputees. The classification model included traditional machine learning models and deep learning models. Other related factors included acquisition, noise interference and sliding window size. ConclusionTraditional machine learning models based on sEMG signals have been maturely applied, and gesture recognition with deep learning models are of great potential. The individual differences of subjects, the real-time requirements of gesture classification and the stability requirements of sEMG devices still need to be addressed.

Objective To optimize the treatment process of patients in Ophthalmology Outpatient, so as to enhance patient′s satisfaction and quality of care service. Methods The nurse manager of Department of Ophthalmology experienced treatment process in other hospital with team and summarized the feeling of other hospital′s treatment process, and improve the treatment process according to the existed problems including reconstruction of Outpatient work process, the implementation of registration system in Outpatient, building up Office of Returning Visit, doing extended nursing, developing “I am patient” activity, training communication ability and skills for constructing doctor-patient relationship, strengthening health education and other nursing services. Results 2014 and 2015 in Ophthalmology Outpatient of our hospital, the patients′ waiting time for treatment became short after carrying out the experience of nurses′transposition experience ( P<0.05) , and the patients′satisfaction were significantly improved after exchanging ( P<0. 05 ) , as well as the reduction of effective complaint rate (P<0.05).Conclusions To optimize the treatment process of patients in Department of Ophthalmology can shorten the time of treatment waiting time, improve the satisfaction of outpatients, so as to improve the quality of nursing service.

Objective To evaluate the validity, reliability, and acceptability of the scale of knowledge, attitude, and behavior of lifestyle intervention in a diabetes high-risk population (HILKAB), and provide scientific evidence for its usage. Methods By convenient sampling, we selected 406 individuals at high risk for diabetes for survey using the HILKAB. Pearson correlation coefficient, factor analysis, independent sampling, and t-test for high-and low-score groups were used to evaluate the content validity, construct validity, and discriminant validity of the scale. Reliability of the scale was evaluated by internal consistency, which included Cronbach'sαcoefficient,θcoefficient,Ωcoefficient, and split-half reliability. Scale acceptability was evaluated by acceptance rate and completion time of the survey. Results In this study, 366 questionnaires (90.1%) was qnalified and the completion time was (8.62 ± 2.79) minutes. Scores for knowledge, attitude, and behavior were 10.60±3.73, 26.56±3.58, 17.09±9.74, respectively. The scale had good face validity and content validity. The correlation coefficient of items and the dimension to which they belong was between 0.25 and 0.97, and the correlation coefficient of three dimensions and the entire scale was between 0.64 and 0.91, all with P<0.001. Factor analysis of the scale extracted eight common factors. The cumulative variance contribution rate was 65.23%, thereby reaching the 50% approved standard. Of 30 items there were 29 items with factor loadings ≥0.40, indicating the scale had good construct validity. For the high-score group, scores for knowledge, attitude, and behavior dimensions were 13.89±2.55, 29.56± 2.46, 28.05 ± 2.93, respectively, which were higher than those for the low-score group (7.67 ± 2.78, 23.89 ± 3.35, 6.25 ± 3.13); t-values were 55.14, 119.40, 95.29, respectively, with P<0.001. The scale consisted of three dimensions: knowledge, attitude, and behavior. The Cronbach's α coefficient was between 0.84 and 0.92, the θ coefficient was between 0.85 and 0.96, the Ω coefficient was between 0.90 and 0.94, and the split-half reliability was between 0.77 and 0.95, reaching the 0.70 standard letter. Conclusion The validity, reliability, and acceptability of the HILKAB scale were satisfactory for use in a population at high risk of diabetes.

Objective To evaluate the validity, reliability, and acceptability of the scale of knowledge, attitude, and behavior of lifestyle intervention in a diabetes high-risk population (HILKAB), and provide scientific evidence for its usage. Methods By convenient sampling, we selected 406 individuals at high risk for diabetes for survey using the HILKAB. Pearson correlation coefficient, factor analysis, independent sampling, and t-test for high-and low-score groups were used to evaluate the content validity, construct validity, and discriminant validity of the scale. Reliability of the scale was evaluated by internal consistency, which included Cronbach'sαcoefficient,θcoefficient,Ωcoefficient, and split-half reliability. Scale acceptability was evaluated by acceptance rate and completion time of the survey. Results In this study, 366 questionnaires (90.1%) was qnalified and the completion time was (8.62 ± 2.79) minutes. Scores for knowledge, attitude, and behavior were 10.60±3.73, 26.56±3.58, 17.09±9.74, respectively. The scale had good face validity and content validity. The correlation coefficient of items and the dimension to which they belong was between 0.25 and 0.97, and the correlation coefficient of three dimensions and the entire scale was between 0.64 and 0.91, all with P<0.001. Factor analysis of the scale extracted eight common factors. The cumulative variance contribution rate was 65.23%, thereby reaching the 50% approved standard. Of 30 items there were 29 items with factor loadings ≥0.40, indicating the scale had good construct validity. For the high-score group, scores for knowledge, attitude, and behavior dimensions were 13.89±2.55, 29.56± 2.46, 28.05 ± 2.93, respectively, which were higher than those for the low-score group (7.67 ± 2.78, 23.89 ± 3.35, 6.25 ± 3.13); t-values were 55.14, 119.40, 95.29, respectively, with P<0.001. The scale consisted of three dimensions: knowledge, attitude, and behavior. The Cronbach's α coefficient was between 0.84 and 0.92, the θ coefficient was between 0.85 and 0.96, the Ω coefficient was between 0.90 and 0.94, and the split-half reliability was between 0.77 and 0.95, reaching the 0.70 standard letter. Conclusion The validity, reliability, and acceptability of the HILKAB scale were satisfactory for use in a population at high risk of diabetes.

Two patients with coexistence of thyroid disease and suspected Gitelman's syndrome underwent SLC12A3 gene analysis. The results confirmed that both patients were compound heterozygotes of SLC12A3 gene mutation. Three novel variants of SLC12A3 were found in this study. This report suggests that Gitelman's syndrome may coexist with other disorders associated with hypokalemia, such as Graves' disease.