Objective: To explore whether digital facial and tongue diagnostic technologies can support the assessment of coronary heart disease (CHD) patients for coronary artery stenosis severity, and examine potential associations between digital tongue diagnosis features and myocardial biomarkers. Methods: The TFDA-1 face and tongue diagnosis instrument and the TDAS analysis system were used to perform intelligent visual examination and analysis of the facial and tongue in CHD patients who attended the Department of Cardiology at Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine between October 2, 2023 and July 31, 2024. Variables were screened using principal component analysis (PCA) and multicollinearity analysis to construct four machine learning models, including random forest, LightGBM, decision tree, and naive Bayes, for the early prediction of coronary artery stenosis severity. Model performance metrics, including sensitivity, specificity, precision, F1 score, accuracy, and the area under the receiver operating characteristic (ROC) curve (AUC), were evaluated. Visual analyses were performed using the SHapley Additive exPlanations (SHAP) interpreter and decision curve analysis. For patients after percutaneous coronary intervention (PCI), a conceptual model linking cardiac biomarkers and tongue diagnosis was constructed using the partial least squares structural equation modeling (PLS-SEM), and its validity was assessed. Results: A total of 459 CHD patients were enrolled and assigned to a PCI group and a non-PCI group (which comprised two subgroups: mild stenosis or less group, moderate stenosis or greater group). For sublingual vein (SV) features, the PCI group had lower SV-a and SV-b than the other groups (P < 0.01 and P < 0.05, respectively). For tongue surface features, the PCI group had significantly higher tongue body (TB)-L, TB-a, and TB-b (P < 0.05, P < 0.01, and P < 0.001, respectively), as well as higher tongue coating (TC)-a and TC-b (P < 0.01 and P < 0.001, respectively). Age, SV-a, SV-b, creatine kinase-myocardial band (CK-MB), CK, TC-a, lip-L, and lip-b were incorporated in the machine learning models. The random forest model performed best, with an AUC of 0.924, an F1 score of 0.839, precision of 0.807, accuracy of 0.864, sensitivity of 0.873, and specificity of 0.839. Decision curve analysis indicated that both LightGBM and random forest had clinical utility. PLS-SEM confirmed the pathway relationships: myocardial biomarkers → TB and myocardial biomarkers → TC (coefficient = – 0.238, t = 2.239, P = 0.025, and coefficient = – 0.270, t = 2.522, P = 0.012, respectively). Conclusion This study developed a noninvasive early warning model for coronary artery stenosis in patients with CHD. It applied PLS-SEM to investigate the association between post-PCI cardiac biomarkers and tongue diagnosis, and validated the proposed association chain. These findings suggest that intelligent traditional Chinese medicine (TCM) visual diagnosis integrated with modern digital technology may support CHD risk assessment and comprehensive health management.

Objective:To evaluate the applicability of a new anti-G capability assessment trainer (AG-CAT) in high-performance (HP) anti-G maneuver training and positive pressure breathing for high-G (PHP) training for pilots.Methods:A total of 142 fighter pilots who were subjected to anti-G maneuver training at Dujiangyan Special Crew Sanatorium of PLA Air Force between January and November 2023 were enrolled. According to the Guidelines for Aviation Physiological Training, 123 pilots underwent both HP anti-G maneuver training and PHP positive pressure breathing training, 15 received only HP training, and 4 received only PHP training. Based on the training devices used, these pilots were divided into AG-CAT group and an anti-G and anti-hypoxia capability detection instrument (GHyCDI) group. The 2 groups were compared regarding the pedal force of lower limbs, blood pressure, and improvement of +G z tolerance during training. Results:Of the 138 pilots undergoing HP training, 88 used AG-CAT and 50 used GHyCDI. One hundred and twenty-seven pilots participated in PHP training, with 73 in the AG-CAT group and 54 in the GHyCDI group. During HP training, the pedal force of left lower limbs in the AG-CAT group was greater than that of the right limbs and of the GHyCDI group ( t=4.38, 2.64, P<0.001, =0.009). In PHP training, the AG-CAT group exhibited greater pedal force in left limbs than in right ones, while the GHyCDI group showed an opposite trend ( t=2.25, 3.37, P=0.029, 0.002). Systolic and diastolic blood pressures during HP training (with or without anti-G suits) were higher in the AG-CAT group than in the GHyCDI group ( t=3.50, 3.72, 2.55, 4.21, P=0.001,<0.001,=0.012,<0.001). Similarly, during PHP training, both systolic and diastolic pressures were higher in the AG-CAT group ( t=2.03, 3.81, P=0.045,<0.001). The AG-CAT group demonstrated superior improvements in +G z tolerance during HP training (without/with anti-G suits: Z=2.14, 3.21, P=0.049, 0.001) and PHP training ( Z=2.56, P=0.010) compared with the GHyCDI group. Conclusions:AG-CAT shows excellent applicability in aviation physiological training of pilots. Its ergonomic design, practical functionalities and enhanced compatibility with personnel protective equipment can better meet training requirements compared to conventional devices.

As a common syndrome type in TCM,blood stasis syndrome is diagnosed based on the four diagnostic methods of TCM,with inspection providing especially intuitive information.In recent years,with the advancement of objectification studies on the four diagnostic methods,inspection techniques for blood stasis syndrome have gradually transitioned from qualitative to quantitative analysis.This article reviewed recent progress in modern research on multi-region inspection for blood stasis syndrome,including facial complexion,tongue characteristics,sublingual collateral vessels,and microcirculation signs.Multi-region inspection technology has progressively established standardized acquisition protocols integrated with artificial intelligence technology,achieving a transition from qualitative to quantitative analysis.These region-specific data demonstrate clear associations with cardiovascular and metabolic diseases,supporting diagnostic objectification of blood stasis syndrome.However,further efforts remain necessary to expand clinical samples,integrate macro-micro data,standardize quantitative criteria,and establish collaborative diagnostic criteria for precise syndrome differentiation.

Objective:To evaluate the applicability of a new anti-G capability assessment trainer (AG-CAT) in high-performance (HP) anti-G maneuver training and positive pressure breathing for high-G (PHP) training for pilots.Methods:A total of 142 fighter pilots who were subjected to anti-G maneuver training at Dujiangyan Special Crew Sanatorium of PLA Air Force between January and November 2023 were enrolled. According to the Guidelines for Aviation Physiological Training, 123 pilots underwent both HP anti-G maneuver training and PHP positive pressure breathing training, 15 received only HP training, and 4 received only PHP training. Based on the training devices used, these pilots were divided into AG-CAT group and an anti-G and anti-hypoxia capability detection instrument (GHyCDI) group. The 2 groups were compared regarding the pedal force of lower limbs, blood pressure, and improvement of +G z tolerance during training. Results:Of the 138 pilots undergoing HP training, 88 used AG-CAT and 50 used GHyCDI. One hundred and twenty-seven pilots participated in PHP training, with 73 in the AG-CAT group and 54 in the GHyCDI group. During HP training, the pedal force of left lower limbs in the AG-CAT group was greater than that of the right limbs and of the GHyCDI group ( t=4.38, 2.64, P<0.001, =0.009). In PHP training, the AG-CAT group exhibited greater pedal force in left limbs than in right ones, while the GHyCDI group showed an opposite trend ( t=2.25, 3.37, P=0.029, 0.002). Systolic and diastolic blood pressures during HP training (with or without anti-G suits) were higher in the AG-CAT group than in the GHyCDI group ( t=3.50, 3.72, 2.55, 4.21, P=0.001,<0.001,=0.012,<0.001). Similarly, during PHP training, both systolic and diastolic pressures were higher in the AG-CAT group ( t=2.03, 3.81, P=0.045,<0.001). The AG-CAT group demonstrated superior improvements in +G z tolerance during HP training (without/with anti-G suits: Z=2.14, 3.21, P=0.049, 0.001) and PHP training ( Z=2.56, P=0.010) compared with the GHyCDI group. Conclusions:AG-CAT shows excellent applicability in aviation physiological training of pilots. Its ergonomic design, practical functionalities and enhanced compatibility with personnel protective equipment can better meet training requirements compared to conventional devices.

Objective: To develop a non-invasive predictive model for coronary artery stenosis severity based on adaptive multi-modal integration of traditional Chinese and western medicine data. Methods: Clinical indicators, echocardiographic data, traditional Chinese medicine (TCM) tongue manifestations, and facial features were collected from patients who underwent coronary computed tomography angiography (CTA) in the Cardiac Care Unit (CCU) of Shanghai Tenth People's Hospital between May 1, 2023 and May 1, 2024. An adaptive weighted multi-modal data fusion (AWMDF) model based on deep learning was constructed to predict the severity of coronary artery stenosis. The model was evaluated using metrics including accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic (ROC) curve (AUC). Further performance assessment was conducted through comparisons with six ensemble machine learning methods, data ablation, model component ablation, and various decision-level fusion strategies. Results: A total of 158 patients were included in the study. The AWMDF model achieved excellent predictive performance (AUC = 0.973, accuracy = 0.937, precision = 0.937, recall = 0.929, and F1 score = 0.933). Compared with model ablation, data ablation experiments, and various traditional machine learning models, the AWMDF model demonstrated superior performance. Moreover, the adaptive weighting strategy outperformed alternative approaches, including simple weighting, averaging, voting, and fixed-weight schemes. Conclusion The AWMDF model demonstrates potential clinical value in the non-invasive prediction of coronary artery disease and could serve as a tool for clinical decision support.

Objective:To explore the dilemmas in the discharge preparation process of postoperative breast cancer patients within the framework of Meleis' Transition Theory, and to provide evidence for the development of nursing interventions to improve discharge readiness.Methods:This was a qualitative study. Using purposive sampling, postoperative breast cancer patients hospitalized in the Department of Breast Surgery of Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University between September and October 2024 were selected for the study. Semi-structured, in-depth interviews were conducted. Data were analyzed and themes extracted using Colaizzi's seven-step analysis method.Results:A total of four themes were extracted: personal status-patients experienced complex emotions at discharge; disease knowledge preparation-patients had insufficient mastery of relevant knowledge after discharge; coping ability-patients lacked confidence in home self-care after returning home; social support-patients desired support from peers and medical staff.Conclusions:Postoperative breast cancer patients face multiple dilemmas during discharge preparation. Medical staff are advised to pay attention to the psychological status of discharged patients and develop individualized emotional coping strategies; provide professional information and meet patients' fertility and sexual health knowledge needs; optimize health education models to enhance patients' home coping ability; and strengthen the integration of mobile health with nursing practice to build a multidimensional support system.

As a common syndrome type in TCM,blood stasis syndrome is diagnosed based on the four diagnostic methods of TCM,with inspection providing especially intuitive information.In recent years,with the advancement of objectification studies on the four diagnostic methods,inspection techniques for blood stasis syndrome have gradually transitioned from qualitative to quantitative analysis.This article reviewed recent progress in modern research on multi-region inspection for blood stasis syndrome,including facial complexion,tongue characteristics,sublingual collateral vessels,and microcirculation signs.Multi-region inspection technology has progressively established standardized acquisition protocols integrated with artificial intelligence technology,achieving a transition from qualitative to quantitative analysis.These region-specific data demonstrate clear associations with cardiovascular and metabolic diseases,supporting diagnostic objectification of blood stasis syndrome.However,further efforts remain necessary to expand clinical samples,integrate macro-micro data,standardize quantitative criteria,and establish collaborative diagnostic criteria for precise syndrome differentiation.

Objective:To explore the dilemmas in the discharge preparation process of postoperative breast cancer patients within the framework of Meleis' Transition Theory, and to provide evidence for the development of nursing interventions to improve discharge readiness.Methods:This was a qualitative study. Using purposive sampling, postoperative breast cancer patients hospitalized in the Department of Breast Surgery of Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University between September and October 2024 were selected for the study. Semi-structured, in-depth interviews were conducted. Data were analyzed and themes extracted using Colaizzi's seven-step analysis method.Results:A total of four themes were extracted: personal status-patients experienced complex emotions at discharge; disease knowledge preparation-patients had insufficient mastery of relevant knowledge after discharge; coping ability-patients lacked confidence in home self-care after returning home; social support-patients desired support from peers and medical staff.Conclusions:Postoperative breast cancer patients face multiple dilemmas during discharge preparation. Medical staff are advised to pay attention to the psychological status of discharged patients and develop individualized emotional coping strategies; provide professional information and meet patients' fertility and sexual health knowledge needs; optimize health education models to enhance patients' home coping ability; and strengthen the integration of mobile health with nursing practice to build a multidimensional support system.

It is found that acupuncture can significantly improve the clinical symptoms of rheumatoid arthritis (RA) such as pain and joint stiffness, and improve rheumatoid factor, high-sensitivity CRP, ESR and other clinical indicators. It can inhibit the proliferation of synovial cells, the apoptosis of chondrocytes, and regulate polarization balabce of mononuclear macrophages, T cells, as well as inhibit the inflammatory function of multiple immune cells, in order to improve inflammation state of RA joints. In clinical treatment of RA, bladder meridian, stomach meridian, spleen meridian , and Governor Vessel are mostly selected. Acupoints with the efficacy of warming meridian, dispelling coldness and dredging collaterals were commonly selected such as Zusanli (ST36), Yanglinquan (GB34), Dazhui (GV14), Quchi (LI11). Several researches have proved that combined therapy of acupuncture and medicine is worthy promotion in clinic.

Objective To develop an integrated risk model for coronary artery occlusion based on data of both traditional Chinese medicine(TCM)and western medicine data,and to evaluate the contribution of TCM-specific indicators to conventional coronary heart disease(CHD)risk prediction.Methods Data of TCM indicators(tongue,facial,and pulse diagnostics)and clinical parame-ters from patients diagnosed with CHD at the Cardiology Department of Shanghai Baoshan Hospital of Integrated Traditional Chinese and Western Medicine,from October 3,2023 to March 15,2024,were collected.Important variables were identified using importance screen-ing and correlation analysis with CHD risk factors and laboratory markers.Six machine learn-ing models including logistic regression(LR),decision tree(DT),support vector machine(SVM),k-nearest neighbors(KNN),random forest(RF),and gradient boosting(GB),were ap-plied to evaluate the risk of coronary artery obstruction by combining clinical and TCM data of CHD.Model performance was assessed using metrics such as accuracy,precision,and re-call,with reliability validated through ten-fold cross-validation.Results A total of 288 patients were included in the study.Fifteen clinical risk factors,includ-ing body mass index(BMI),myoglobin,and alcohol consumption history,were incorporated into the diagnostic models.The KNN model showed good performance when combining clin-ical data with tongue and facial data.The SVM model performed well when clinical data was combined with pulse data.Among all the models,the KNN model with 10-fold cross-valida-tion,which integrates the three types of TCM diagnostic data(tongue,face,and pulse)with clinical data,performs the best(accuracy:0.837,precision:0.814,and recall:0.809).Conclusion Incorporating TCM diagnostic data can enhance the accuracy of coronary artery obstruction risk assessment.The KNN prediction model that integrate tongue,facial,and pulse data performs the best and can be recommended as a clinical decision support tool.