Object:To explore the feasibility of using artificial intelligence for quality control of echocardiographic images.Methods:Retrospectively，5 000 two-dimensional echocardiographic video images within the period from 2021 to 2023 were randomly retrieved from the echocardiography database of Nanjing Drum Tower Hospital，Affiliated Hospital of Medical School，Nanjing University. Among these selected images，1 559 of them were apical views. The physician team formulated the scoring rules，which specifically included four scoring criteria：gain，scaling ratio，cardiac axis angle，and structure. Subsequently，the data were labeled with view classification and image quality scores. The labeled data were further partitioned into the training set（ n = 643），the validation set（ n = 276），and the test set（ n = 640）. The training and validation sets were utilized for constructing the models for view classification and quality assessment，while the test set was employed to verify the models' effectiveness. The view classification module was implemented using the SlowFast model，and the quality assessment module involved algorithms such as ResNet，Video Swin Transformer，SSD，and U-Net. Results:The average accuracy，precision，recall rate and F1 score of the classification model in identifying each apical view were 0.987 1，0.983 0，0.987 1 and 0.984 9 respectively，and the inference time was（333.4 ± 105.4）ms. The average accuracies of the quality assessment module in terms of gain，scaling ratio，cardiac axis angle and display of main structures were 0.915 1，0.928 2，0.938 7 and 0.965 6 respectively，and the overall scoring accuracy was 0.912 7.Conclusions:The echocardiogram quality control system developed in this research can effectively classify and evaluate the quality of two-dimensional images of the apical views in echocardiograms. Moreover，it guarantees the objectivity，timeliness and high-efficiency of quality control，which has reference value for the establishment of the echocardiogram quality control system.

Autism spectrum disorder(ASD),characterized by unknown etiology and high heterogeneity,ne-cessitates precise diagnostic and intervention strategies.Neuroimaging techniques have shown great promise in un-covering the neural mechanisms of ASD,providing a foundation for aided diagnosis and transcranial magnetic stim-ulation(TMS)interventions.This review highlights that integrating multimodal neuroimaging and developing indi-vidualized indices with developmental specificity can significantly improve the accuracy of ASD diagnosis and clas-sification.Furthermore,TMS interventions guided by functional connectivity derived from functional magnetic reso-nance imaging(fMRI)offer a personalized approach to ASD treatment.

Autism spectrum disorder(ASD),characterized by unknown etiology and high heterogeneity,ne-cessitates precise diagnostic and intervention strategies.Neuroimaging techniques have shown great promise in un-covering the neural mechanisms of ASD,providing a foundation for aided diagnosis and transcranial magnetic stim-ulation(TMS)interventions.This review highlights that integrating multimodal neuroimaging and developing indi-vidualized indices with developmental specificity can significantly improve the accuracy of ASD diagnosis and clas-sification.Furthermore,TMS interventions guided by functional connectivity derived from functional magnetic reso-nance imaging(fMRI)offer a personalized approach to ASD treatment.

Object:To explore the feasibility of using artificial intelligence for quality control of echocardiographic images.Methods:Retrospectively，5 000 two-dimensional echocardiographic video images within the period from 2021 to 2023 were randomly retrieved from the echocardiography database of Nanjing Drum Tower Hospital，Affiliated Hospital of Medical School，Nanjing University. Among these selected images，1 559 of them were apical views. The physician team formulated the scoring rules，which specifically included four scoring criteria：gain，scaling ratio，cardiac axis angle，and structure. Subsequently，the data were labeled with view classification and image quality scores. The labeled data were further partitioned into the training set（ n = 643），the validation set（ n = 276），and the test set（ n = 640）. The training and validation sets were utilized for constructing the models for view classification and quality assessment，while the test set was employed to verify the models' effectiveness. The view classification module was implemented using the SlowFast model，and the quality assessment module involved algorithms such as ResNet，Video Swin Transformer，SSD，and U-Net. Results:The average accuracy，precision，recall rate and F1 score of the classification model in identifying each apical view were 0.987 1，0.983 0，0.987 1 and 0.984 9 respectively，and the inference time was（333.4 ± 105.4）ms. The average accuracies of the quality assessment module in terms of gain，scaling ratio，cardiac axis angle and display of main structures were 0.915 1，0.928 2，0.938 7 and 0.965 6 respectively，and the overall scoring accuracy was 0.912 7.Conclusions:The echocardiogram quality control system developed in this research can effectively classify and evaluate the quality of two-dimensional images of the apical views in echocardiograms. Moreover，it guarantees the objectivity，timeliness and high-efficiency of quality control，which has reference value for the establishment of the echocardiogram quality control system.

Objective To explore the value of deep learning(DL)models semi-automatic training system for automatic optimization of clinical image quality control of transthoracic echocardiography(TTE).Methods Totally 1 250 TTE videos from 402 patients were retrospectively collected,including 490 apical four chamber(A4C),310 parasternal long axis view of left ventricle(PLAX)and 450 parasternal short axis view of great vessel(PSAXGv).The videos were divided into development set(245 A4C,155 PLAX,225 PSAXGV),semi-automated training set(98 A4C,62 PLAX,90 PSAXGV)and test set(147 A4C,93 PLAX,135 PSAXGV)at the ratio of 5:2:3.Based on development set and semi-automatic training set,DL model of quality control was semi-automatically iteratively optimized,and a semi-automatic training system was constructed,then the efficacy of DL models for recognizing TTE views and assessing imaging quality of TTE were verified in test set.Results After optimization,the overall accuracy,precision,recall,and F1 score of DL models for recognizing TTE views in test set improved from 97.33％,97.26％,97.26％and 97.26％to 99.73％,99.65％,99.77％and 99.71％,respectively,while the overall accuracy for assessing A4C,PLAX and PSAXGV TTE as standard views in test set improved from 89.12％,83.87％and 90.37％to 93.20％,90.32％and 93.33％,respectively.Conclusion The developed DL models semi-automatic training system could improve the efficiency of clinical imaging quality control of TTE and increase iteration speed.

Objective:To understand the current situation of nurses in 52 hospitals in China on mastery of knowledge about skin injury in the elderly based on the background of mixed-mode homogenization training.Methods:Using the convenient sampling method, a total of 1 067 nurses from 52 hospitals in China were selected as the research objects in January 2021. A self-designed questionnaire on knowledge of skin injury in the elderly was used to investigate the nurses through the questionnaire star and univariate analysis was used to analyze the influencing factors. A total of 1 067 questionnaires were distributed and 1 067 valid questionnaires were recovered, and the effective recovery rate was 100%.Results:The knowledge scores of pressure injury, incontinence-associated dermatitis, skin tear and xerosis cutis among 1067 nurses were (95.66±7.37) , (95.65±9.15) , (91.37±15.45) and (87.67±15.91) , respectively. The results of univariate analysis showed that hospital grade was the influencing factor of nurses' knowledge score of pressure injury, skin tear and incontinence-associated dermatitis ( P<0.05) , educational background was the influencing factor of nurses' knowledge score of skin tear ( P<0.05) , professional title was the influencing factor of nurses' knowledge scores of pressure injury, incontinence-associated dermatitis and xerosis cutis ( P<0.05) . Conclusions:Hospitals at all levels need to strengthen the theoretical and practical knowledge training for nurses on skin xerosis and skin tear in the elderly, especially for nurses with primary titles and lower education in grassroots hospitals.

Objective:To explore the status and influencing factors of incontinence-associated dermatitis among elderly inpatients in 52 hospitals nationwide, and to analyze the nursing of elderly inpatients with incontinence, so as to provide a reference for clinical intervention.Methods:On March 31, 2021, convenience sampling was used to select 14 675 elderly inpatients from 52 hospitals across the country as the research object. The self-designed Incontinence-associated Dermatitis Questionnaire for Elderly Inpatients was used to collect general demographic data, health status, incontinence, and skin nursing. Binomial Logistic regression was used to investigate the influencing factors of incontinence-associated dermatitis in elderly inpatients.Results:Among 14 675 elderly inpatients, the prevalence rates of xerosis cutis, incontinence and incontinence-associated dermatitis were 38.78% (5 691/14 675) , 11.06% (1 623/14 675) and 1.91% (280/14 675) , respectively. The prevalence of mild, moderate and severe incontinence-associated dermatitis were 1.27% (186/14 675) , 0.55% (81/14 675) , and 0.09% (13/14 675) , respectively. Among the nursing of 1 623 elderly inpatients with incontinence, the items with low implementation rate were the use neutral lotion to clean skin (14.17%, 230/1 623) , use of skin protectant after moisturizing (17.68%, 287/1 623) , moisturizing after cleansing the skin (28.90%, 469/1 623) . The results of binomial Logistic regression analysis showed that xeroderma, fecal incontinence, urinary and fecal incontinence, ≥2 kinds of combined medication, and hospital stay >30 days were risk factors for incontinence-associated dermatitis in elderly inpatients.Conclusions:The risk factors of incontinence-associated dermatitis in elderly inpatients mainly include xerosis cutis, type of incontinence, ≥2 kinds of combined medication, and hospital stay >30 days.

Pneumonia caused by SARS-CoV-2 has seriously threatened human life and health worldwide and caused a large number of deaths. Viral infection and acute inflammation are important causes of death, so it is particularly important to combine antiviral therapy with anti-inflammatory therapy. Glycyrrhizic acid, the main component of the glycyrrhizic root extract, has a wide range of pharmacological effects as well as high efficiency and low toxicity, its preparation has been widely used in the treatment of chronic hepatitis and other diseases. Glycyrrhizic acid can regulate the expression and release of a variety of cytokines and play a significant anti-inflammatory effect. At the same time, glycyrrhizic acid also showed significant inhibition towards a variety types of viruses. Therefore, the potential application of glycyrrhizic acid as COVID-19 treatment should be explored.

Objective The aim of this study is to analyze the factors influencing the outcomes of the traditional Chinese medicine (TCM) percutaneous release treatment for the stenosal tendosynovitis. Methods A total of 119 eligible participants, from outpatient of Dongzhimen Hospital during June, 2014 to April, 2017, were included into the study. The participants received TCM percutaneous release treatment, and were followed-up and assessed outcomes at 27 w. Responders were defined as participants with normal movement in week 27 compared with the baseline period. The NRS assessment in both groups was described, and the baseline characteristics of participants potentially related to cure response were mainly analyzed using Logistic regression analysis. Results Cure group and non-cure group were determined according to the cure response. And the outcomes of pain relief along the timeframe showed the feasibility of criteria of cure response. The uni-factor Logistic regression analysis showed that the factors age,course of disease,interventions and pain severity were significantly different between the cure and non-cure groups,and the multi-factor Logistic regression confirmed the four factors influenced the cure response of the TCM percutaneous release treatment for the stenosal tendosynovitis. The cutting knife was 5.85 fold than the traditional needling knife at increasing the cure response (OR=5.853,95% CI 1.853-18.485;P=0.003).All the factors that age equal to or older than 60 years(OR=6.170, 95% CI 1.890-20.141; P=0.003), course of disease more than six months (OR=4.696, 95% CI 1.371-16.085;P=0.014)and pain severity from 6 to 7(OR=5.184,95% CI 1.416-18.975;P=0.013)were negatively associated with clinical response. Conclusions The patients with increasing age, long course of disease and distinct pain severity may be less likely to respond to the TCM percutaneous release treatment. These findings contribute to guiding clinical practice in terms of pretreatment patient selection. Further research is needed to confirm the association.