Objective: To develop a depression recognition model by integrating the spirit-expression diagnostic framework of traditional Chinese medicine (TCM) with machine learning algorithms. The proposed model seeks to establish a TCM-informed tool for early depression screening, thereby bridging traditional diagnostic principles with modern computational approaches. Methods: The study included patients with depression who visited the Shanghai Pudong New Area Mental Health Center from October 1, 2022 to October 1, 2023, as well as students and teachers from Shanghai University of Traditional Chinese Medicine during the same period as the healthy control group. Videos of 3 – 10 s were captured using a Xiaomi Pad 5, and the TCM spirit and expressions were determined by TCM experts (at least 3 out of 5 experts agreed to determine the category of TCM spirit and expressions). Basic information, facial images, and interview information were collected through a portable TCM intelligent analysis and diagnosis device, and facial diagnosis features were extracted using the Open CV computer vision library technology. Statistical analysis methods such as parametric and non-parametric tests were used to analyze the baseline data, TCM spirit and expression features, and facial diagnosis feature parameters of the two groups, to compare the differences in TCM spirit and expression and facial features. Five machine learning algorithms, including extreme gradient boosting (XGBoost), decision tree (DT), Bernoulli naive Bayes (BernoulliNB), support vector machine (SVM), and k-nearest neighbor (KNN) classification, were used to construct a depression recognition model based on the fusion of TCM spirit and expression features. The performance of the model was evaluated using metrics such as accuracy, precision, and the area under the receiver operating characteristic (ROC) curve (AUC). The model results were explained using the Shapley Additive exPlanations (SHAP). Results: A total of 93 depression patients and 87 healthy individuals were ultimately included in this study. There was no statistically significant difference in the baseline characteristics between the two groups (P > 0.05). The differences in the characteristics of the spirit and expressions in TCM and facial features between the two groups were shown as follows. (i) Quantispirit facial analysis revealed that depression patients exhibited significantly reduced facial spirit and luminance compared with healthy controls (P < 0.05), with characteristic features such as sad expressions, facial erythema, and changes in the lip color ranging from erythematous to cyanotic. (ii) Depressed patients exhibited significantly lower values in facial complexion L, lip L, and a values, and gloss index, but higher values in facial complexion a and b, lip b, low gloss index, and matte index (all P < 0.05). (iii) The results of multiple models show that the XGBoost-based depression recognition model, integrating the TCM “spirit-expression” diagnostic framework, achieved an accuracy of 98.61% and significantly outperformed four benchmark algorithms—DT, BernoulliNB, SVM, and KNN (P < 0.01). (iv) The SHAP visualization results show that in the recognition model constructed by the XGBoost algorithm, the complexion b value, categories of facial spirit, high gloss index, low gloss index, categories of facial expression and texture features have significant contribution to the model. Conclusion This study demonstrates that integrating TCM spirit-expression diagnostic features with machine learning enables the construction of a high-precision depression detection model, offering a novel paradigm for objective depression diagnosis.

BackgroundResident physicians represent a high-risk group for sleep disorders， exhibiting a significantly higher prevalence of such conditions compared with the general population， which severely impairs their physical and mental health. It is hypothesized that perceived stress negatively impacts sleep quality through psychological mechanisms， such as depleting self-control resources and triggering anxiety. However， this pathway warrants empirical validation. ObjectiveTo explore the mediating role of self-control and anxiety emotions in the association between perceived stress and sleep quality among resident physicians， and to elucidate the underlying psychological mechanisms， aiming at providing theoretical basis for developing targeted psychological interventions. MethodsA cross-sectional survey was conducted in April 2025. First- to third- year resident physicians at a hospital in Fuyang City were recruited as participants （n=372）. The Chinese Perceived Stress Scales （CPSS）， the Chinese version of the Dual-Mode of Self-Control Scale （DMSC-S）， the Pittsburgh Sleep Quality Index （PSQI）， and the Generalized Anxiety Disorder Scale-7 item （GAD-7） were used for group testing. The model 6 of the Process macro version 4.1 was ultilized to examine the mediating pathway of self-control and anxiety emotions between perceived stress and sleep quality. ResultsA total of 322 valid questionnaires were collected， yielding an effective responsive rate of 86.56%. Among the respondents， 146 （45.34%） reported poor sleep quality. The CPSS score and GAD-7 score of resident physicians were positively correlated with the PSQI score （r=0.727， 0.784， P<0.01）， while the DMSC-S score was negatively correlated with the PSQI score （r=-0.615， P<0.01）. Perceived stress directly and positively predicted poor sleep quality （B=0.124， P<0.01）， with the direct effect accounting for 31.39% of the total effect. Furthermore， perceived stress indirectly affected sleep quality through the independent mediating effects of self-control and anxiety emotions. The indirect effect values of 0.053 （95% CI： 0.019 - 0.091） and 0.192 （95% CI： 0.141 - 0.249）， accounting for 13.42% and 48.61% of the total effect， respectively. Perceived stress also impact sleep quality through the serial mediating effect of self-control and anxiety， with the indirect effect value of 0.026 （95% CI： 0.005 - 0.049）， accounting for 6.58% of the total effect. ConclusionThe perceived stress of resident physicians can influence sleep quality by impairing self-control， exacerbating anxiety， and through the serial mediation of both factors.

Objective: o develop an onset risk prediction nomogram for patients with homocysteine-type (H-type) hypertension (HTH) based on pulse diagram parameters to assist early clinical prediction and diagnosis of HTH. Methods: Patients diagnosed with essential hypertension and admitted to Shanghai Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai Hospital of Traditional Chinese Medicine, and Shanghai Hospital of Integrated Traditional Chinese and Western Medicine from July 6th 2020 to June 16th 2021, and from August 11th 2023 to January 22nd 2024, were enrolled in this retrospective research. The baselines and clinical biochemical indicators of patients were collected. The SMART-I TCM pulse instrument was applied to gather pulse diagram parameters. Multivariate logistic regression was adopted to analyze the risk factors for HTH. RStudio was employed to construct the nomogram model, receiver operating characteristic (ROC) curve, and calibration curve (bootstrap self-sampling 200 times), and clinical decision curve were drawn to evaluate the model’s discrimination and clinical effectiveness. Results: A total of 168 hospitalized patients with essential hypertension were selected and divided into non-HTH group (n = 29) and HTH group (n = 139). Compared with non-HTH group, HTH group had a lower body mass index (BMI), and higher proportions of male patients and drinkers (P < 0.05). The ventricular wall thickening (VWT) could not be determined. The proportions of left common carotid intima-media wall thickness (LCCIMWT) and serum creatinine (SCR) were higher in HTH group (P < 0.05). The pulse diagram parameter As was significantly higher, and H4/H1 and T1/T were lower in HTH group (P < 0.05). Gender, alcohol consumption, serum creatinine, and the pulse diagram parameter H4/H1 were identified as independent risk factors for HTH (P < 0.05). The nomogram’s area under the ROC curve (AUC) was 0.795 [95% confidence interval (CI): (0.706 6, 0.882 8)], with a specificity of 0.724 and sensitivity of 0.799. After 200 times repeated bootstrap self-samplings, the calibration curve showed that the simulated curve fits well with the actual curve (x2 = 9.5002, P = 0.301 9). The clinical decision curve indicated that the nomogram’s applicability was optimal when the threshold for predicting HTH was between 0.38 and 1.00. Conclusion The nomogram model could be valuable for predicting the onset risk of HTH and pulse diagram parameters can facilitate early screening and prevention of HTH.

Objective To construct a model for predicting the risk of essential hypertension accompanied by left ventricular hypertrophy using machine learning algorithms based on pulse diagram parameters;To explore its clinical application value.Methods A total of 295 patients with essential hypertension who were hospitalized in Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shanghai Hospital of Traditional Chinese Medicine and Shanghai Hospital of Integrated Traditional Chinese and Western Medicine were selected from July 2020 to May 2021 and July 2023 to July 2024.According to the echocardiographic results,the selected research subjects were divided into the essential hypertension with left ventricular hypertrophy group(referred to as the"LVH group")and the essential hypertension without left ventricular hypertrophy group(referred to as the"non-LVH group").The general data and clinical biochemical indicators were collected,and the pulse diagram parameters of the patients were detected using the SMART-I type TCM digital pulse analyzer.A clinical prediction model was constructed based on decision tree,support vector machine and extreme gradient boosting model algorithms.The predictive performance of the model was evaluated in terms of discrimination,calibration and clinical prediction ability by using the receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis respectively.The influence of each predictive factor on the risk of LVH in essential hypertension was explained based on the SHAP algorithm.Results Compared with the non-LVH group,the BMI,the proportion of males,drinkers and smokers was lower in the LVH group,with statistical significance(P<0.05);the thickened ventricular wall,left ventricular internal dimension enlargement,left common carotid artery intima-media thickness and high density lipoprotein cholesterol were higher in the LVH group than in the non-LVH group(P<0.05);the left common carotid peak systolic velocity,left common carotid resistance index,serum uric acid and serum creatinine were lower in the LVH group than in the non-LVH group(P<0.05).The pulse diagram parameters T4,T,W1,W2,H3/H1 and H4/H1 were higher in the LVH group than in the non-LVH group(P<0.05).The areas of the ROC curves of the models constructed by the three types of machine learning algorithms were 0.887,0.962 and 0.873 respectively,indicating that the model had good discrimination and certain diagnostic efficacy.The calibration curve suggested that the prediction accuracy of the model was average;the clinical decision curve showed that XGBoost model has a higher net benefit.Conclusion The interpretable model constructed based on pulse diagram parameters and machine learning algorithms can be used as a reliable tool for predicting the risk of essential hypertension with LVH.

Objective To construct a model for predicting the risk of essential hypertension accompanied by left ventricular hypertrophy using machine learning algorithms based on pulse diagram parameters;To explore its clinical application value.Methods A total of 295 patients with essential hypertension who were hospitalized in Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine,Shanghai Hospital of Traditional Chinese Medicine and Shanghai Hospital of Integrated Traditional Chinese and Western Medicine were selected from July 2020 to May 2021 and July 2023 to July 2024.According to the echocardiographic results,the selected research subjects were divided into the essential hypertension with left ventricular hypertrophy group(referred to as the"LVH group")and the essential hypertension without left ventricular hypertrophy group(referred to as the"non-LVH group").The general data and clinical biochemical indicators were collected,and the pulse diagram parameters of the patients were detected using the SMART-I type TCM digital pulse analyzer.A clinical prediction model was constructed based on decision tree,support vector machine and extreme gradient boosting model algorithms.The predictive performance of the model was evaluated in terms of discrimination,calibration and clinical prediction ability by using the receiver operating characteristic(ROC)curve,calibration curve and decision curve analysis respectively.The influence of each predictive factor on the risk of LVH in essential hypertension was explained based on the SHAP algorithm.Results Compared with the non-LVH group,the BMI,the proportion of males,drinkers and smokers was lower in the LVH group,with statistical significance(P<0.05);the thickened ventricular wall,left ventricular internal dimension enlargement,left common carotid artery intima-media thickness and high density lipoprotein cholesterol were higher in the LVH group than in the non-LVH group(P<0.05);the left common carotid peak systolic velocity,left common carotid resistance index,serum uric acid and serum creatinine were lower in the LVH group than in the non-LVH group(P<0.05).The pulse diagram parameters T4,T,W1,W2,H3/H1 and H4/H1 were higher in the LVH group than in the non-LVH group(P<0.05).The areas of the ROC curves of the models constructed by the three types of machine learning algorithms were 0.887,0.962 and 0.873 respectively,indicating that the model had good discrimination and certain diagnostic efficacy.The calibration curve suggested that the prediction accuracy of the model was average;the clinical decision curve showed that XGBoost model has a higher net benefit.Conclusion The interpretable model constructed based on pulse diagram parameters and machine learning algorithms can be used as a reliable tool for predicting the risk of essential hypertension with LVH.

Objective:To investigate how age and nail-tract volumetric bone mineral density (vBMD) are associated with postoperative mechanical performance of proximal femoral nail anti-rotation (PFNA-Ⅱ) fixation for geriatric intertrochanteric fractures using a finite-element analysis.Methods:Fifteen elderly patients with intertrochanteric fracture of the femur were selected for this study. They were 11 females and 4 males and divided into 5 groups based on their ages ( n=3): 55-year-old, 65-year-old, 75-year-old, 85-year-old, and 95-year-old groups. After three-dimensional models of the proximal femur were constructed using the preoperative CT data of their healthy contralateral hip, 31-A1.2 fractures of the AO/OTA type were created and PFNA-Ⅱ fixations simulated. Two loading schemes were created: graded quasi-static axial loads (700 N, 1,400 N, 2,100 N, and 2,800 N) were applied to compute equivalent plastic strain volumes in the femoral head region; displacement-controlled loading was applied to failure to derive load-displacement curves for stiffness and the maximum failure load. Nail-tract vBMD and regional hip vBMDs were measured by quantitative CT. Pearson correlation analysis was conducted to investigate the associations of age and nail-tract vBMD with the aforementioned mechanical indicators. Results:Under the same load, compared with the 55-year-old, 65-year-old, and 75-year-old groups, the plastic strain unit volumes of the fracture models in the 85-year-old and 95-year-old groups increased significantly. Under a load of 700 N, no plastic strain was observed in the fracture models in the 55-year-old, 65-year-old, and 75-year-old groups, while an average plastic strain of approximately 50 mm 3 was observed in the fracture models in the 85-year-old group. Under a load of 2,800 N, the high strain areas in the fracture models in the 85-year-old and 95-year-old groups were mainly concentrated at the tip of the head nail and the junction between the head nail and the main nail. Load-displacement curves showed a marked reduction in the failure load in patients aged ≥85 years. Under loads of 1,400 N, 2,100 N, and 2,800 N, there was a strong association between the nail-tract vBMD and the volume of the plastic strain unit ( r=-0.82, -0.88, -0.89, respectively), which was stronger than those for total-hip vBMD. Conclusions:Finite-element analysis indicates that age and nail-tract vBMD are closely related to local plastic strain and overall stiffness of the proximal femur after PFNA-Ⅱ fixation for the geriatric intertrochanteric fractures. Patients aged ≥85 years old are more prone to plastic yielding, which compromises fixation stability.

Objective:To analyze the liver histological characteristics and clinically related factors in inactive hepatitis B surface antigen (HBsAg) carriers (IHC), and also explore whether antiviral treatment is necessary for IHC, as defined in the 2022 version of the hepatitis B prevention and treatment guidelines.Methods:A multicenter, retrospective cohort study was conducted. Two hundred and thirty-one IHC cases who underwent liver biopsy histopathological examination in nine medical institutions, including Beijing Youan Hospital affiliated with Capital Medical University, from January 2018 to December 2023 were included. General informative data, clinical serological markers, and transient elastography (TE) examination results were collected. Patients were divided into a positive (148 cases) and a negative group (83 cases) according to the results of hepatitis B virus (HBV) DNA detection. The differences in liver pathological inflammatory activity (G) and liver fibrosis stage (S) were analyzed between the two groups to explore the correlation between liver tissue conditions and clinically related factors. Comparsions of normally distributed continwous data, skeukd continuous data, and categorical data between groups are performed using t tests, Mann-Whitney U tests and χ2 tests, respectively. Results:The age of 231 IHC cases was 43 (38, 51) years old, with 95.2% (220/231) aged ≥30 years, and males accounted for 64.9% (150/231). HBsAg and HBV DNA levels were 131.9 (20.8, 400.9) IU/mL and 94.0 (0, 448.5) IU/mL, respectively, of which 35.9% (83/231) were HBV DNA negative (<20 IU/mL). The remarkable proportions of G≥2, S≥2, and liver injury (G≥2 and/or S≥2) in liver tissue were 16.5% (38/231), 29% (67/231), and 35.9% (83/231), respectively. The S≥2 proportion was significantly higher in the HBV DNA-negative group than the positive group (42.2% vs. 21.6%, P<0.001), and it mainly occurred in the population cohort over 30 years old (44.9% vs. 31.0%, P=0.04). The liver stiffness measurement (LSM), aspartate transaminase to platelet ratio index (APRI), and platelet (PLT) were significantly higher in the S≥2 group than the S<2 group ( P<0.05). Conclusion:Clinicians can comprehensively evaluate the degree of liver fibrosis in IHC based on clinical factors such as age, PLT, APRI, and LSM, even if the liver histological results are lacking. The China 2022 version guidelines define that nearly half of IHC has histological indications for antiviral therapy, and liver biopsy and prompt treatment can be recommended.

OBJECTIVES: Depression is a common negative emotion that can significantly impact physical and mental health. Due to their occupational characteristics, medical staff are more susceptible to depression compared to the general population. This study aims to explore the influence of future-oriented coping on depression among medical staff and the mediating roles of psychological resilience and perceived stress, providing theoretical guidance for depression intervention strategies in this group. METHODS: A cross-sectional survey was conducted among medical staff at a tertiary hospital using convenience sampling. Data were collected via the "Wenjuanxing" platform. A total of 754 questionnaires were distributed; after excluding invalid responses (e.g., duplicate IPs or insufficient completion time), 655 valid questionnaires were retained (valid response rate: 86.87%). Instruments included a demographic questionnaire, the Future-Oriented Coping Scale, the Connor-Davidson Resilience Scale, the Perceived Stress Scale, and the Self-Rating Depression Scale. All scales demonstrated high internal consistency (Cronbach's α>0.88) and validity. SPSS 27.0 was used for descriptive analysis, and PROCESS macro (Model 6) was used to test the chain mediation model. Harman's one-factor test was applied to control for common method bias. RESULTS: Descriptive analyses showed that future-oriented coping was positively correlated with psychological resilience and negatively correlated with perceived stress and depression. Mediation analysis revealed that future-oriented coping significantly predicted lower depression levels among medical staff (β=-0.283, P<0.001). Psychological resilience partially mediated the relationship (effect size=-0.329, accounting for 34.13% of the total effect), as did perceived stress (effect size=-0.099, 10.27%). A significant chain mediation path was identified: "future-oriented coping → psychological resilience → perceived stress → depression" (effect size=-0.253, 26.24%). The total indirect effect accounted for 70.64% of the overall effect, highlighting the substantial role of the mediating pathways. CONCLUSIONS Future-oriented coping can reduce depressive symptoms in medical staff, with psychological resilience and perceived stress serving as key mediators in a chain structure. These findings suggest that enhancing future-oriented coping strategies and psychological resilience may improve stress adaptation and reduce depression levels in this population.
Humans ; Adaptation, Psychological ; Resilience, Psychological ; Cross-Sectional Studies ; Depression/psychology* ; Surveys and Questionnaires ; Stress, Psychological/psychology* ; Male ; Female ; Adult ; Middle Aged ; Medical Staff/psychology* ; Occupational Stress/psychology*

Hepatitis B virus（HBV）infection remains a major global public health challenge. Over the past three decades，significant progress has been made in HBV prevention and control，including the widespread implementation of hepatitis B vaccination and mother-to-child transmission（MTCT）blockade strategies，advancements in antiviral therapies，and improvements in public health awareness. The extensive administration of hepatitis B vaccines has markedly reduced the incidence of new infections. The development of antiviral treatments，such as nucleos（t）ide analogues，has effectively suppressed viral replication，thereby decreased the incidence of liver cirrhosis and hepatocellular carcinoma. Furthermore，enhanced public health awareness and the implementation of screening programs have enabled earlier diagnosis and treatment for more patients. Nevertheless，the complete elimination of hepatitis B continues to face challenges. Firstly，inadequate vaccine coverage，particularly in low- and middle-income countries，sustains the risk of HBV transmission. Secondly，suboptimal diagnosis and treatment rates persist among individuals with chronic HBV infection. Additionally，the long-term management of chronic HBV carriers remains complex，necessitating continuous monitoring and personalized therapeutic strategies. This article summarizes the major achievements in the prevention and control of hepatitis B over the past 30 years，analyzes current challenges，and explores future directions. Novel vaccines，including therapeutic vaccines and mRNA-based vaccines，are demonstrating therapeutic potential in clinical trials and may offer new hope for chronic hepatitis B patients. Emerging antiviral agents，such as capsid assembly modulators and RNA interference-based therapeutics，which target distinct stages of the HBV life cycle，hold promise for achieving functional cure of hepatitis B. In conclusion，despite ongoing challenges，the complete elimination of hepatitis B remains an attainable goal through sustained innovation and global collaboration.

Chronic hepatitis B（CHB）is one of the major challenges in the global public health field. As of 2022，approximately 254 million people worldwide were infected with the hepatitis B virus（HBV）. CHB is one of the main causes of liver cirrhosis and hepatocellular carcinoma（HCC）. Nucleos（t）ide analogs（NAs）and interferon therapy can delay the progression of liver fibrosis by inhibiting viral replication，but they cannot completely avoid the problem of heterogeneous treatment responses. Some patients are in a state of low-level viremia（LLV）during treatment. The persistent LLV state can induce chronic inflammation and the progression of liver fibrosis，ultimately increase the risk of HCC. In patients with poor treatment responses，the continuous active viral replication can induce immune disorders，accelerate the evolution of fibrosis to the decompensated stage of liver cirrhosis，and increase the risk of patient death. This article aims to review the definition，mechanisms，and impact on treatment outcomes of LLV and suboptimal response based on the latest research，provide a basis for optimizing antiviral therapy for CHB.