The Chinese expert consensus on the prevention and treatment of acute gastrointestinal injury in severe patients with integrated traditional Chinese and Western medicine is a hard-won guiding document in the field of severe acute gastrointestinal injury(AGI)in recent years,for the combination of traditional Chinese and Western medicine.We invited four non-consensus panel experts(associate senior level or above)to score independently using Joanna Briggs Institute(JBI)critical appraisal checklist for expert consensus released by JBI in Australia,and the appraisal of guidelines for research and evaluationⅡ(AGREEⅡ),to evaluate the methodological quality of the consensus,and to provide reference for clinicians.In the evaluation of JBI checklist for consensus,all experts selected"yes"for 7 items,with high consistency,leading to a pretty good conclusion that the consensus is worthy of recommendation.In the AGREEⅡevaluation,the standardized scoring rates of scope and purpose,stakeholder involvement,rigour of development,clarity of presentation,applicability,editoral independence were 77.78%,90.28%,79.17%,81.94%,72.92%,93.75%.Respectively,all of which were greater than 60%,and the recommended level was a level.The intraclass correlation coefficient(ICC)of the first five domains were 0.700,0.066,0.776,0.688,0.532,respectively.The ICC values of independent domain cannot be calculated because the scores was almost identical.The overall assessment score was 6.00±1.15,with a total score rate of 83.33%.Two experts recommended direct application,and the other two recommended revised use.The overall evaluation believes that the Chinese expert consensus on the prevention and treatment of acute gastrointestinal injury in severe patients with integrated traditional Chinese and Western medicine has high overall quality and good application value.

Objective:To explore the incidence of delirium in patients of advanced age hospitalized in internal medicine departments and analyze its influencing factors.Methods:A retrospective analysis was conducted on the medical records of 586 patients of advanced age hospitalized in internal medicine departments at the Beijing University of Chinese Medicine Third Affiliated Hospital from May 2023 to May 2024. Patients were divided into a delirium group and a non-delirium group based on whether delirium occurred. Univariate analysis and binary Logistic regression analysis were used to explore the factors influencing delirium in patients of advanced age hospitalized in internal medicine departments.Results:Among 586 patients of advanced age hospitalized in internal medicine departments, the incidence of delirium was 21.2% (124/586). Binary Logistic regression analysis showed that age, activities of daily living (Barthel Index), folate deficiency, sleep disorders, and indwelling catheters were factors influencing delirium in patients of advanced age hospitalized in internal medicine departments ( P<0.05) . Conclusions:The incidence of delirium is high among patients of advanced age hospitalized in internal medicine departments. Healthcare professionals should pay particular attention to elderly patients with advanced age, limited activities of daily living, folate deficiency, sleep disorders, and indwelling catheters, and should implement targeted preventive strategies as early as possible.

Purpose To investigate the feasibility of a deep learning framework to automatically analyze echocardiographic color Doppler videos in detecting valvular regurgitation.Materials and Methods This study retrospectively collected echocardiographic images of 1 109 patients with valvular regurgitation in the Fourth Medical Center of PLA General Hospital,from June 2015 to September 2019 as the training and validation sets.A prospective continuous collection of 1 562 echocardiography images was used as the test set in the Fourth Medical Center of PLA General Hospital from May 13 to June 13,2023,including 378 cases of mitral regurgitation and 223 cases of aortic regurgitation.This study developed deep learning networks to establish view classification model and valvular regurgitation recognition model,including the efficiency of section classification of deep learning models.Results The deep learning view classification model in this study could automatically identify two views for diagnosing mitral regurgitation and aortic regurgitation.The recognition accuracy for the parasternal long axis color Doppler view and the apical four chamber mitral color Doppler view was 1.00 and 0.93,respectively.The sensitivity,specificity,accuracy and area under the curve of the deep learning model for diagnosing mitral regurgitation were 0.847,0.852,0.849 and 0.930,respectively.The sensitivity,specificity,accuracy and area under the curve of the deep learning model in diagnosing aortic regurgitation were 0.857,0.861,0.859 and 0.940,respectively.Conclusion Deep learning algorithms can automatically identify valvular regurgitation and have the potential to become a screening tool for valvular heart disease.

Objective To compare the effect between unilateral and bilateral upper limb training on motor function of severely hemi-plegic upper limbs in subacute stroke patients after priming robot-assisted training.Methods From September,2023 to December,2024,60 inpatients with hemiplegia after stroke were recruited from Guangdong Work Injury Rehabilitation Hospital,and randomly divided into control group(n=30)and experi-mental group(n=30).Both groups first received 30 minutes of upper limb robot-assisted training.Subsequently,the control group received movement-based unilateral upper limb training for 30 minutes,while the experimental group received activity-based bilateral upper limb training for 30 minutes,for three weeks.They were assessed with Fugl-Meyer Assessment-Upper Extremities(FMA-UE),Hong Kong version of the Functional Test for the Hemiplegic Upper Extremity(FTHUE-HK)and modified Barthel Index(MBI)before and after treatment.Results The scores of FMA-UE,FTHUE-HK and MBI improved in both groups after treatment(t>4.020,P<0.01),and all the scores were better in the experimental group than in the control group(t>2.456,P<0.05).Conclusion Activity-based bilateral upper limb training after priming robot-assisted training is more effective on motor function of severely hemiplegic upper limbs in stroke patients.

Objective:To explore the incidence of delirium in patients of advanced age hospitalized in internal medicine departments and analyze its influencing factors.Methods:A retrospective analysis was conducted on the medical records of 586 patients of advanced age hospitalized in internal medicine departments at the Beijing University of Chinese Medicine Third Affiliated Hospital from May 2023 to May 2024. Patients were divided into a delirium group and a non-delirium group based on whether delirium occurred. Univariate analysis and binary Logistic regression analysis were used to explore the factors influencing delirium in patients of advanced age hospitalized in internal medicine departments.Results:Among 586 patients of advanced age hospitalized in internal medicine departments, the incidence of delirium was 21.2% (124/586). Binary Logistic regression analysis showed that age, activities of daily living (Barthel Index), folate deficiency, sleep disorders, and indwelling catheters were factors influencing delirium in patients of advanced age hospitalized in internal medicine departments ( P<0.05) . Conclusions:The incidence of delirium is high among patients of advanced age hospitalized in internal medicine departments. Healthcare professionals should pay particular attention to elderly patients with advanced age, limited activities of daily living, folate deficiency, sleep disorders, and indwelling catheters, and should implement targeted preventive strategies as early as possible.

In recent years, with the exploration and development of machine learning algorithms in the medical scenario, their potential for application in clinical diagnosis and treatment has been gradually explored. Image-based machine learning algorithms have made a lot of progress in urological cancer-related research, and have been proven to have a positive effect on the diagnosis, treatment and prognosis of urological cancer. Despite the great impact of machine learning algorithms clinically, they are still in the exploratory stage, with many deficiencies and limitations.

In recent years, with the exploration and development of machine learning algorithms in the medical scenario, their potential for application in clinical diagnosis and treatment has been gradually explored. Image-based machine learning algorithms have made a lot of progress in urological cancer-related research, and have been proven to have a positive effect on the diagnosis, treatment and prognosis of urological cancer. Despite the great impact of machine learning algorithms clinically, they are still in the exploratory stage, with many deficiencies and limitations.

Objective To compare the effect between unilateral and bilateral upper limb training on motor function of severely hemi-plegic upper limbs in subacute stroke patients after priming robot-assisted training.Methods From September,2023 to December,2024,60 inpatients with hemiplegia after stroke were recruited from Guangdong Work Injury Rehabilitation Hospital,and randomly divided into control group(n=30)and experi-mental group(n=30).Both groups first received 30 minutes of upper limb robot-assisted training.Subsequently,the control group received movement-based unilateral upper limb training for 30 minutes,while the experimental group received activity-based bilateral upper limb training for 30 minutes,for three weeks.They were assessed with Fugl-Meyer Assessment-Upper Extremities(FMA-UE),Hong Kong version of the Functional Test for the Hemiplegic Upper Extremity(FTHUE-HK)and modified Barthel Index(MBI)before and after treatment.Results The scores of FMA-UE,FTHUE-HK and MBI improved in both groups after treatment(t>4.020,P<0.01),and all the scores were better in the experimental group than in the control group(t>2.456,P<0.05).Conclusion Activity-based bilateral upper limb training after priming robot-assisted training is more effective on motor function of severely hemiplegic upper limbs in stroke patients.

Purpose To investigate the feasibility of a deep learning framework to automatically analyze echocardiographic color Doppler videos in detecting valvular regurgitation.Materials and Methods This study retrospectively collected echocardiographic images of 1 109 patients with valvular regurgitation in the Fourth Medical Center of PLA General Hospital,from June 2015 to September 2019 as the training and validation sets.A prospective continuous collection of 1 562 echocardiography images was used as the test set in the Fourth Medical Center of PLA General Hospital from May 13 to June 13,2023,including 378 cases of mitral regurgitation and 223 cases of aortic regurgitation.This study developed deep learning networks to establish view classification model and valvular regurgitation recognition model,including the efficiency of section classification of deep learning models.Results The deep learning view classification model in this study could automatically identify two views for diagnosing mitral regurgitation and aortic regurgitation.The recognition accuracy for the parasternal long axis color Doppler view and the apical four chamber mitral color Doppler view was 1.00 and 0.93,respectively.The sensitivity,specificity,accuracy and area under the curve of the deep learning model for diagnosing mitral regurgitation were 0.847,0.852,0.849 and 0.930,respectively.The sensitivity,specificity,accuracy and area under the curve of the deep learning model in diagnosing aortic regurgitation were 0.857,0.861,0.859 and 0.940,respectively.Conclusion Deep learning algorithms can automatically identify valvular regurgitation and have the potential to become a screening tool for valvular heart disease.

The Chinese expert consensus on the prevention and treatment of acute gastrointestinal injury in severe patients with integrated traditional Chinese and Western medicine is a hard-won guiding document in the field of severe acute gastrointestinal injury(AGI)in recent years,for the combination of traditional Chinese and Western medicine.We invited four non-consensus panel experts(associate senior level or above)to score independently using Joanna Briggs Institute(JBI)critical appraisal checklist for expert consensus released by JBI in Australia,and the appraisal of guidelines for research and evaluationⅡ(AGREEⅡ),to evaluate the methodological quality of the consensus,and to provide reference for clinicians.In the evaluation of JBI checklist for consensus,all experts selected"yes"for 7 items,with high consistency,leading to a pretty good conclusion that the consensus is worthy of recommendation.In the AGREEⅡevaluation,the standardized scoring rates of scope and purpose,stakeholder involvement,rigour of development,clarity of presentation,applicability,editoral independence were 77.78%,90.28%,79.17%,81.94%,72.92%,93.75%.Respectively,all of which were greater than 60%,and the recommended level was a level.The intraclass correlation coefficient(ICC)of the first five domains were 0.700,0.066,0.776,0.688,0.532,respectively.The ICC values of independent domain cannot be calculated because the scores was almost identical.The overall assessment score was 6.00±1.15,with a total score rate of 83.33%.Two experts recommended direct application,and the other two recommended revised use.The overall evaluation believes that the Chinese expert consensus on the prevention and treatment of acute gastrointestinal injury in severe patients with integrated traditional Chinese and Western medicine has high overall quality and good application value.