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.

There are numerous ethical dilemmas in families of children with kidney disease during the treatment stage. From the perspective of Giddens’ structured theory， this paper analyzed the ethical dilemmas， such as individual and family wealth disparity at the micro-level， doctor-patient information asymmetry and the attribution of medical decision-making rights at the meso-level， as well as unequal medical resources and an incomplete medical security system for children at the macro-level. The ethical dilemmas faced by families of children with kidney disease are the result of the structural constraint effect. The coping strategies they adopt in response to these dilemmas are the basis of structural reproduction and the products of the structural effect. As a group with subjective initiative， they are good at self-reflection. Through repeated cognitive evaluation， they can make a series of effective coping strategies to achieve their own goals， such as relying on family support and linking resources to seek social support， establishing online support groups and building an information sharing platform， assessing children’s best-interests judges and safeguarding their reasonable and legitimate rights and interests， planning and allocating high-quality medical resources and promoting the construction of the medical service system， as well as promoting the reform of the basic medical insurance system for children and improve the protection mechanism for major illnesses.

"Weibing" is a fundamental concept in traditional Chinese medicine (TCM), representing a transitional state characterized by diminished self-regulatory abilities without overt physiological or social dysfunction. This perspective delves into the biological foundations and quantifiable markers of Weibing, aiming to establish a research framework for early disease intervention. Here, we propose the "Health Quadrant Classification" system, which divides the state of human body into health, sub-health, disease-susceptible state, and disease. We suggest the disease-susceptible stage emerges as a pivotal point for TCM interventions. To understand the intrinsic dynamics of this state, we propose laboratory and clinical studies utilizing time-series experiments and stress-induced disease susceptibility models. At the molecular level, bio-omics technologies and bioinformatics approaches are highlighted for uncovering intricate changes during disease progression. Furthermore, we discuss the application of mathematical models and artificial intelligence in developing early warning systems to anticipate and avert the transition from health to disease. This approach resonates with TCM's preventive philosophy, emphasizing proactive health maintenance and disease prevention. Ultimately, our perspective underscores the significance of integrating modern scientific methodologies with TCM principles to propel Weibing research and early intervention strategies forward.

Iron is an essential trace element in the human body, crucial in maintaining normal physiological functions. Recent studies have identified iron ions as a significant factor in initiating the ferroptosis process, a novel mode of programmed cell death characterized by iron overload and lipid peroxide accumulation. The iron metabolism pathway is one of the primary mechanisms regulating ferroptosis, as it maintains iron homeostasis within the cell. Numerous studies have demonstrated that abnormalities in iron metabolism can trigger the Fenton reaction, exacerbating oxidative stress, and leading to cell membrane rupture, cellular dysfunction, and damage to tissue structures. Therefore, regulation of iron metabolism represents a key strategy for ameliorating ferroptosis and offers new insights for treating diseases associated with iron metabolism imbalances. This review first summarizes the mechanisms that regulate iron metabolic pathways in ferroptosis and discusses the connections between the pathogenesis of various diseases and iron metabolism. Next, we introduce natural and synthetic small molecule compounds, hormones, proteins, and new nanomaterials that can affect iron metabolism. Finally, we provide an overview of the challenges faced by iron regulators in clinical translation and a summary and outlook on iron metabolism in ferroptosis, aiming to pave the way for future exploration and optimization of iron metabolism regulation strategies.

Objective To investigate the status quo of health management behaviors in elderly patients with type 2 diabetes mellitus(T2DM)complicating chronic complications and the influencing factors based on health ecology theory.Methods The convenience sampling method was used to select elderly patients with T2DM complicating chronic complications in the Shanghai City from January to July 2022 as the respondents.Based on the health ecology theory,the five aspects of personality traits,psychological and behavioral traits,family and community interpersonal networks,living and working conditions and policy environment were in-vestigated.The Pearson correlation coefficient was used to analyze the correlation of various variables.The hi-erarchical linear regression was used to analyze the influencing factors of health management behaviors in eld-erly patients with T2DM complicating chronic complications.Results A total of 272 questionnaires were dis-tributed and 264 valid questionnaires were recovered,with an effective recovery rate of 97.06％(264/272).There were statistically significant differences in DSMB-Oscores among the patients with different chronic complications number,sleep quality,per capita monthly household income and medical payment methods(P＜0.05).The DSMB-Oscore in elderly patients with T2DM complicating chronic complications was positively correlated with HLSCP,DES,PACIC and CIRS scores(P＜0.05),and negatively correlated with the PAID-5 score(P＜0.05).Conclusion The health management behaviors of elderly patients with T2DM complicating chronic complications is jointly affected and acted by many factors,and it is necessary to identify and manage the related influencing factors from different aspects and different levels.

The liver plays an important regulatory role in maintaining the dynamic balance of coagulation and anticoagulation in the body. Such dynamic balance is fragile in patients with liver cirrhosis， and the risk of bleeding can be increased due to reductions in coagulation factors and platelet count and excessive fibrinolysis； meanwhile， thrombus can be formed due to the increases in von Willebrand factor and coagulation factor Ⅷ， the reductions in anticoagulant protein C and anticoagulant protein S， the increase in thrombin-generating potential， and alterations in antifibrinolytic components. This article reviews the mechanisms of coagulation disorder in liver cirrhosis， so as to help clinicians with the prevention and treatment of bleeding or thrombotic disorders in patients with liver cirrhosis.

Objective:To compare the prognostic values of different classification by using transpulmonary pressure gradient (TPG), diastolic pressure gradient (DPG) and pulmonary vascular resistance (PVR) in patients with pulmonary hypertension due to left heart disease (PH-LHD), and investigated hemodynamic and clinical factors associated with mortality in patients with PH-LHD.Methods:This was a single-center prospective cohort study. In-hospital patients diagnosed with PH-LHD via right heart catheterization at the Department of Cardiology, the First Affiliated Hospital of Nanjing Medical University, from September 2013 to December 2019 were enrolled. Patients were divided according to TPG (cutoff value 12 mmHg; 1 mmHg=0.133 kPa), DPG (cutoff value 7 mmHg), PVR (cutoff value 3 Wood Units), and the combination of TPG and PVR. Baseline characteristic was recorded. All patients were followed up until the occurrence of endpoint event, defined as all-cause death that occurred during the follow-up period, or until April 18, 2022. Receiver operating characteristic curves were used to compare the predictive value of 3 classification methods for all-cause death in PH-LHD patients. The optimal cutoff values were calculated using Jorden index. Survival analysis was performed using Kaplan-Meier analysis, and log-rank test was used to compare the predictive efficacy of classification methods based on optimal cutoff values or guidance-recommended thresholds for the survival of PH-LHD patients. Variables showing statistical significance in the univariate analysis were incorporated into multivariate Cox regression model to analyze the independent risk factors for all-cause mortality.Results:A total of 243 patients were enrolled, aged (54.9±12.7) years old, including 169 (69.5%) males. During a median follow-up of 57 months, there were 101 (41.6%) deaths occurred. Grouping results were as follows: (1) TPG: TPG≤12 mmHg group 115 patients, TPG>12 mmHg group 128 patients; (2) DPG: DPG<7 mmHg group 193 patients, DPG≥7 mmHg group 50 patients; (3) PVR: PVR≤3 Wood Units group 108 patients, PVR>3 Wood Units group 135 patients; (4) TPG and PVR: TPG≤12 mmHg and PVR≤3 Wood Units group 89 patients, TPG>12 mmHg and PVR>3 Wood Units group 109 patients. PVR ( AUC=0. 698,95% CI:0.631-0.766) had better predictive value for all-cause mortality than TPG ( AUC=0.596, 95% CI: 0.523-0.669) and DPG ( AUC=0.526, 95% CI: 0.452-0.601) (all P<0.05). The optimal cutoff values for TPG, DPG, and PVR were13.9 mmHg, 2.8 mmHg, and 3.8 Wood Units, respectively. Kaplan-Meier analysis based on the optimal cutoff values or guidance-recommended thresholds showed that PVR and TPG were the predictors of survival ( P<0.05), while DPG did not showed significance ( P>0.05). Multivariate Cox regression analysis showed that age, PVR and log 2N-terminal pro-B-type natriuretic peptide were independent risk factors for all-cause mortality in PH-LHD patients (all P<0.05). Conclusion:Classification according to PVR was most valuable in predicting all-cause death in PH-LHD patients, while TPG showed moderate predictive ability and DPG had no predictive value.

Objective:To investigate whether dual-energy computed tomography(DECT) measurement of monosodium urate(MSU) crystal loading can predict the risk of gout flares.Methods:A single-center, prospective, observational study included 229 gout patients initially diagnosed at the Gout Clinic of Qingdao University from August 2021 to February 2022. The patients underwent MSU assessment of the bilateral feet using DECT. Following enrolment, all patients commenced uric acid-lowering therapy(ULT) and were followed up at 3 and 6 months. Patients who experienced at least one flare within 6 months were compared with those who did not, and the odds ratio( OR) for the risk of gout flares was calculated. Results:Patients who experienced gout flare had a significantly longer disease duration[(6.69±5.42) vs(4.14±4.86) years, P<0.01], a higher number of flares in the past year(4.80±1.73 vs 2.02±1. 23, P<0.01), a higher proportion of fatty livers(11.0% vs 1.4%, P<0.05), and a greater volume of MSU crystals in the feet[(3.52±9.74) vs(0.29±0.98)cm 3,P<0.05] compared to patients without gout flare. The results of the multifactorial logistic regression analysis indicated that the number of flares in the past year( OR=1.295, 95% CI 1.032-1.613, P<0.05) and feet MSU crystal volume( OR=3.245, 95% CI 1.164-9.064, P<0.05) were independent risk factors for gout flares. The receiver operating characteristic(ROC) curve indicated the integration of the MSU prediction model into the clinical prediction model resulted in a comprehensive prediction model with an area under curve(AUC) value of 0.780(95% CI 0.710-0.840), sensitivity of 0.83, and specificity of 0.62. Internal validation of the comprehensive prediction model using the Bootstrap method yielded a C-index of 0.770(95% CI 0.701-0.833) for predicting flares. The calibration curve of the model demonstrated a good fit between the predicted probability of flares and the actual probability, indicating high calibration accuracy. Conclusion:The volume of MSU crystals in the feet is an independent risk factor for flares following ULT. A larger volume of MSU crystals in the foot increases the likelihood of a flare. This study provides a basis for early prediction of flare and a reference for early preventive treatment.

Objective:To develop the Fecal Microbiota Transplantation Knowledge, Attitude, and Practice Scale for Patients with Inflammatory Bowel Disease (IBD), and test its reliability and validity.Methods:Guided by the theory of knowledge, attitude, and practice, a preliminary draft of the scale was formed through literature review, Delphi expert consultation, and pre-survey. From May to August 2022, convenience sampling was used to select 200 IBD patients who visited the Gastroenterology Clinic of three ClassⅢ Grade A comprehensive hospitals in Jiangsu Province as the research subject for a questionnaire survey. The critical ratio method, correlation analysis method, internal consistency method, commonality and factor loadings were used for item analysis of the scale. Exploratory factor analysis, content validity index, and internal consistency reliability were applied to test the reliability and validity of the scale.Results:A total of 200 questionnaires were distributed, and 181 valid questionnaires were collected, with an effective response rate of 90.50% (181/200). The Fecal Microbiota Transplantation Knowledge, Attitude, and Practice Scale for Patients with IBD included three dimensions of knowledge, attitude and practice, with a total of 21 items. The content validity index at the scale level was 0.917, and the content validity index at the item level ranged from 0.833 to 1.000. Exploratory factor analysis extracted three common factors, with a cumulative variance contribution rate of 74.197%. The Cronbach's α coefficient of the total scale was 0.951, and the coefficients of each dimension were 0.914 to 0.942. The test-retest reliability coefficient of the total scale was 0.918, and the test-retest reliability coefficients of each dimension ranged from 0.737 to 0.833.Conclusions:The Fecal Microbiota Transplantation Knowledge, Attitude, and Practice Scale for Patients with IBD has good reliability and validity, which can help medical and nursing staff evaluate patients' understanding and acceptance of microbial transplantation, so as to provide a basis for personalized communication in shared decision making between doctors and patients.

Objective:To systematically search and evaluate risk prediction models for ventilator-associated pneumonia (VAP) of ICU in order to provide references for developing higher-quality VAP risk prediction models.Methods:Relevant literature was retrieved from databases including China Biology Medicine disc, WanFang data, China National Knowledge Infrastructure, Embase, PubMed, CINAHL, Web of Science, and Cochrane Library. The search timeframe was from the establishment of the databases to September 30, 2023, limited to English and Chinese languages. Two researchers independently screened the literature and extracted data, and the PROBAST tool was used to evaluate the risk of bias and applicability of the included studies.Results:A total of 15 studies on VAP risk prediction models were included. The area under the receiver operating characteristic curve for the 15 models ranged from 0.722 to 0.982. The most frequently involved predictors were age, duration of mechanical ventilation, ICU length of stay, and comorbid chronic obstructive pulmonary disease. The overall adaptability was good, but the risk of bias was high. The main sources of bias included insufficient sample size, inappropriate data sources, lack of model performance evaluation, and inadequate attention to missing data.Conclusions:The risk of bias in studies on VAP risk prediction models is high, indicating that the field is still developing. Future research should focus on the effectiveness of different risk assessment methods to construct models with low bias, excellent predictive performance, and suitability for clinical practice in China.