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.

ObjectiveTo investigate the role of Ras-GTPase-activating protein SH3 domain-binding protein 2 （G3BP2） in regulating the activation， proliferation， and migration of hepatic stellate cells （HSCs）. MethodsThe mouse HSCs （JS-1 cell line） were treated with 5 μg/L transforming growth factor-beta 1（TGF-β1） for 24 hours to establish an HSC activation and proliferation model. A G3BP2 knockdown system was constructed using siRNA interference technology. The experiment was divided into four groups： Control， TGF-β1 treatment， TGF-β1+si-NC， and TGF-β1+ G3BP2-siRNA. The expression levels of key fibrosis indicators， including type I collagen （Collagen I）， α-smooth muscle actin （α-SMA）， and G3BP2， were detected by Western blot and RT-qPCR. Cell proliferation activity was assessed using the CCK-8 proliferation assay kit and EdU fluorescence labeling technology. Cell migration ability was analyzed by scratch wound healing assay and Transwell migration assay. The formation level of stress granules was quantified by immunofluorescence microscopy to investigate the effects of G3BP2 on stress granule formation in activated HSCs. ResultsStimulation with TGF-β1 upregulated the expression of G3BP2 in JS-1 cells （RT-qPCR： P0.000 1； Western blot： P0.000 1）， while a downward trend in its expression was observed in the G3BP2‑silenced group （RT-qPCR： P0.01； Western blot： P0.000 1）. Compared with the control group， the TGF-β1 group exhibited increased protein expression levels of α-SMA and Collagen I （RT-qPCR： both P0.01； Western blot： P0.01 and P0.05， respectively）， concomitant with an increased number of stress granules and enhanced cell proliferation and migration capacity （all P0.001）. The experimental results demonstrated that G3BP2 knockout effectively reversed the aforementioned phenotypes， with the G3BP2-silenced group showing reduced expression of fibrotic markers （all P0.01）， decreased stress granule formation （P0.01）， and reduced cell proliferation and migration capacity （all P0.05）， compared to the negative control group. ConclusionG3BP2 enhances the activation， proliferation， and migration of HSCs by promoting the formation of stress granules， thereby accelerating the pathological progression of liver fibrosis. This suggests that stress granules may serve as important regulators in controlling the activation， proliferation， and migration of HSCs.

Background Hypertension is one of the chronic diseases with the highest prevalence in China, and a history of hypertension may potentially exacerbate hearing loss. Investigating the association between long-term blood pressure trends and hearing thresholds could contribute to hearing protection efforts for occupationally noise-exposed populations. Objective By investigating hearing thresholds and blood pressure levels among occupationally noise-exposed workers in an urban rail transit enterprise, and conducting a comprehensive analysis of the association between long-term blood pressure changes and hearing thresholds, to provide data references for health management strategies targeting occupationally noise-exposed workers. Methods Workers exposed to occupational noise at a rail transit enterprise were enrolled as study subjects and underwent pure-tone audiometry. Group-based trajectory modeling was employed to identify blood pressure trajectories. Categorical data were compared using chi-square tests, while normally distributed continuous variables were analyzed via t-tests and analysis of variance (ANOVA). Generalized linear mixed models (GLMMs) were subsequently applied toexamine associations between these trajectory groups and high-frequency hearing thresholds. Results Among 2 002 occupationally noise-exposed workers, the median (P₂₅, P₇₅) age was 32 (28, 35) years, with a median (P₂₅, P₇₅) working tenure of 7 (3, 10) years. In 2019, the positive hypertension rate was 9.04%, with a mean systolic blood pressure (SBP) of (122.97±11.60) mmHg and a mean diastolic blood pressure (DBP) of (76.37±9.02) mmHg. The hearing loss prevalence was 10.1%, showing bilateral high-frequency average hearing thresholds of (17.18±8.71) dB and speech-frequency average thresholds of (13.79±3.46) dB. Three distinct trajectory groups were identified for both SBP and DBP. Compared with other trajectory groups, the high-stable DBP group exhibited significantly higher hearing loss prevalence (χ²=6.34, P=0.042) and elevated high-frequency hearing thresholds (all Ps<0.05). Specifically, within the 30-39 age subgroup, the moderate-stable DBP group demonstrated 1.96 dB lower high-frequency thresholds than the high-stable group [β(95%CI): −1.96 (−3.61, −0.32), P=0.020]. Conclusion Among occupationally noise-exposed workers in a municipal rail transit enterprise, DBP trajectories demonstrated a positive association with high-frequency hearing thresholds. Notably, in young and middle-aged occupationally noise-exposed populations, DBP may exert a more critical influence than SBP on the progression of hearing loss.

Objective: To investigate the biological effects of carvacrol on rats with stomach-heat and stomach-cold and its regulation on transient receptor potential(TRP) channels in rats with stomach-heat, and to study the cold and heat properties of carvacrol and its possible mechanism. Methods: According to the random number method, 100 SD rats were divided into stomach-heat blank group, stomach-heat model group, Coptidis Rhizoma group, stomach-heat low-dose and high-dose carvacrol group, stomach-cold blank group, stomach-cold model group, Baked ginger group, stomach-cold low-dose group and high-dose carvacrol group, 10 rats in each group. The rat model of stomach-heat was established by intragastric administration of pepper aqueous solution (0.80 g/kg) and anhydrous ethanol, and the rat model of stomach-cold was established by intragastric administration of water extract of Anemarrhena asphodeloides and sodium hydroxide (10.40 g/kg). On the day of modeling, the rats in the Baked ginger group were given Baked ginger decoction (0.78 g/kg), and the rats in the Coptidis Rhizoma group were given Coptidis Rhizoma decoction (0.43 g/kg).The stomach-cold and stomach-heat low-dose group of carvacrol was given carvacrol emulsion (40 mg/kg), high-dose group was given carvacrol emulsion (80 mg/kg).All rats of the blank and model groups were given the equal volume of emulsion prepared by 5% dimethyl sulfoxide, 1% Tween 80, 1% polyethylene glycol 400, and 93% normal saline, once a day, for 7 days. The general condition of rats was observed and the body mass was recorded. The pathological morphology of gastric tissue was observed by hematoxylin-eosin staining. The changes of material and energy metabolism, cyclic nucleotide (cAMP), thyroid hormone and gastrointestinal hormone in each group were determined by enzyme-linked immunosorbent assay. The expression levels of transient receptor potential vanilloid subtype 1 (TRPV1), transient receptor potential channel M8 (TRPM8) and uncoupling protein-1 (UCP1) in rats with gastric fever were detected by Western blotting. Results: Compared with the stomach-heat blank group, the body mass of rats in the stomach-heat model group decreased at the fifth and seventh day (P<0.05). The contents (or ratio) of hepatic glycogen (HGlyc), total cholesterol (TC), triglyceride (TG), and vasoactive intestinal peptide (VIP) were decreased (P<0.05), and Na+ -K+ -ATPase, Ca2+ -Mg2+ -ATPase, cytochrome C oxidase (COX), NADH dehydrogenase (ND), cyclic adenosine phosphate (cAMP), cAMP/cyclic guanosine phosphate (cGMP), triiodothyronine (T3), thyroxine (T4), gastrin (GAS), motilin (MTL), and α-amylase (α-AMS) all increased (P<0.05). Compared with the stomach-heat model group, the body mass of rats in the Coptidis Rhizoma group decreased at the third, fifth, and seventh day, the contents (or ratio) of HGlyc, TC, TG, VIP and α-AMS were increased, and Na+ -K+ -ATPase, COX, ND, cAMP, cAMP/cGMP, T3, T4, and GAS all decreased (P<0.05). The body mass of rats in the stomach-heat low-dose carvacrol group decreased at the seventh day. The contents (or ratio) of HGlyc, TC, and VIP were increased, Na+ -K+ -ATPase, COX, ND, cAMP, cAMP/cGMP, T3, T4, and MTL all decreased, the expression of TRPV1 and UCP1 in gastric tissue decreased, while TRPM8 increased (P<0.05) in rats of the stomach-heat low-dose and high-dose carvacrol groups. Compared with the stomach-cold blank group, the body mass of rats in the stomach-cold model group decreased at the third, fifth, and seventh day, the contents (or ratio) of HGlyc, TC, TG, α-AMS, and VIP all increased, while Na+ -K+ -ATPase, Ca2+ -Mg2+ -ATPase, COX, ND, cAMP, cAMP/cGMP, T3, T4, GAS, and MTL all decreased (P<0.05). Compared with the stomach-cold model group, the body mass of rats in the Baked ginger group was increased at the seventh day, and the contents (or ratio) of HGlyc, VIP, and α-AMS all decreased, while Na+ -K+ -ATPase, COX, ND, cAMP/cGMP, T3, T4, GAS, and MTL all increased (P<0.05). The contents of HGlyc, cAMP, α-AMS, and VIP of rats in the stomach-cold low and high-dose carvacrol group all decreased (P<0.05). TG in the stomach-cold low-dose carvacrol group was increased. TC, Ca2+ -Mg2+ -ATPase, and cGMP all increased, while cAMP/cGMP decreased (P<0.05) in the high-dose carvacrol group. Conclusion In this study, the rat model of stomach-cold and stomach-heat were successfully established by using cold and heat factors. The result showed that carvacrol had a certain inhibitory effect on body mass, material energy metabolism, cyclic nucleotide level, thyroid hormone and gastrointestinal function in rats with stomach-heat, indicating that the drug was cold. Carvacrol′s cold medicinal property could be biologically explained by TRPV1 activation, UCP1 induction, and TRPM8 suppression.

Background The Inner Mongolia Autonomous Region is a vast area with a wide array of ecological environments, resulting in considerable regional variations in air pollution characteristics. Current research is limited by a scarcity of systematic, region-wide studies and risk assessments. Objective To assess the health risks associated with inhalation exposure to nine heavy metal and metalloid elements in atmospheric fine particulate matter (PM_2.5) for the population of the Inner Mongolia Autonomous Region. Methods From the 10th to the 16th of each month throughout 2023, atmospheric PM_2.5 samples were collected at designated monitoring sites in 12 leagues (cities) across the Inner Mongolia Autonomous Region to analyze the characteristics and trends in concentration. The health risk assessment model developed by the United States Environmental Protection Agency was employed to evaluate both the non-carcinogenic and carcinogenic risks associated with the heavy metal elements beryllium (Be), cadmium (Cd), chromium (Cr), hydrargyrum (Hg), plumbum (Pb), manganese (Mn), and nickel (Ni) and the metalloid elements stibium (Sb) and arsenic (As). Results In 2023, a total of 1206 valid PM_2.5 samples were analyzed from the Inner Mongolia Autonomous Region. The annual average PM_2.5 concentrations in Inner Mongolia, as well as in the cities of Ordos, Wuhai, and the Alxa League, all exceeded the national limit of 35 μg·m⁻³. The PM_2.5 concentrations across the leagues (cities) exhibited a clear seasonal variation, peaking in winter, followed by spring, and reaching a minimum in summer and autumn. The median PM_2.5 concentration in Ordos throughout the year was higher than that in the other leagues (cities) of the region. Concerning health risks, the non-carcinogenic risks associated with the 9 elements detected in each league (city) were all below 1, indicating acceptable levels. However, the non-carcinogenic risk values for Mn, Cr, and As were relatively high; specifically, Ordos City displayed the highest non-carcinogenic risk values for Mn (7.74×10⁻¹) and Cr (2.97×10⁻²), while Chifeng City recorded the highest value for As (2.34×10⁻³). The carcinogenic risk values for As, Cd, Cr, and Ni fell within the range of 1×10⁻⁹ to 1×10⁻⁵, representing an acceptable level. The health risks varied with age, and the elderly residents faced the highest non-carcinogenic and carcinogenic health risks. Conclusion While the carcinogenic and non-carcinogenic risks from heavy metals and metalloids within atmospheric PM_2.5 in the Inner Mongolia Autonomous Region are generally within acceptable limits, the potential risks that Mn, As, and Cr pose to the health of elderly people warrants particular attention, and Ordos and Chifeng cities exhibit notably higher health risks among other areas. It is recommended that regular, long-term monitoring be conducted, taking into account the specific conditions at monitoring sites across each league (city), and that targeted air quality management strategies be implemented to protect public health.

To formulate an expert consensus on the principles of preoperative hair removal and provide scientific guidance for standardized removal of hair before surgical procedures so as to reduce the incidence of surgical site infections.METHODS Led by the Hospital Management Institute of National Health Commission of the People's Republic of China,this consensus was reached with the joint efforts from the expects of relevant fields such as surgeries,interventional therapies,nursing,and infection prevention and control.The consensus facilitates the classification and evaluation of literatures by following the evidence grade formulated by Oxford Evidence-based Medicine Center and focuses on the association of preoperative hair removal with surgical site infection,it reaches the evidence grade of expert consensus and recommendation intensity by integrating with discussions on meetings and clinical experience of the expects from relevant fields.RESULTS A total of 6 items of consensus were reached by summarizing the latest evidence on the aspects including the indications for preoperative hair removal,tools,range,timing and places.CONCLUSION The consensus,to some extent,make supplements to and complete the exiting regulations and standards.It provides guidance for the medical institutions to carry out the preoperative hair removal.

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.

BACKGROUND:Cervical instability is a common orthopedic disease in young and middle-aged people,and is the early manifestation of cervical spondylosis,which has a great impact on the quality of life of patients.Therefore,early diagnosis of cervical instability to implement early intervention has positive clinical and social significance.OBJECTIVE:The clinical prediction model of cervical instability in young and middle-aged people was constructed based on machine learning to realize early screening of cervical instability in young and middle-aged people before X-ray examination.METHODS:From September 2022 to October 2023,155 young and middle-aged adults with cervical instability and 88 with non-cervical instability recruited through recruitment advertisements and spinal department outpatient of Wangjing Hospital,China Academy of Chinese Medical Sciences were selected as research subjects.The research subjects'general information,living and working habits,discomfort symptoms,visual analog scale score,Neck Disability Index,and 36-ltem Short Form Health Survey were collected on site based on questionnaires.The above information was used as predictive factors.After screening,six machine learning algorithms of Support Vector Machine,LightGBM,RandomForest,Logistic,AdaBoost,and XGBClassifier were used to train the model by ten-fold cross-validation method,and the clinical prediction model of cervical instability was constructed.Area under the curve was used as the main evaluation index.Univariate analysis was performed on the predictors,and SHAP method was used to rank the importance of the predictors.Correlation heat maps were used to show the degree of linear correlation between the predictors and the cervical instability.RESULTS AND CONCLUSION:(1)Among the six machine learning models,RandomForest model was chosen as the final prediction model,including nine predictors,such as age,body mass index,neck circumference/neck length,visual analog scale score,Neck Disability Index,bodily pain,general health,vitality,and mental health,area under the curve=0.725 4,and the calibration degree was good.It could be used as a reference tool for early screening of cervical instability in young and middle-aged people.(2)There were significant differences in age,visual analog scale score,Neck Disability Index,bodily pain,general health,and vitality between the two groups(P＜0.05).(3)The order of importance of predictors was age,Neck Disability Index,visual analog scale score,general health,body mass index,vitality,bodily pain,neck circumference/neck length,mental health,among which age,visual analog scale score,Neck Disability Index were positively correlated with cervical instability,while general health,body mass index,vitality,bodily pain,neck circumference/neck length,and mental health were negatively correlated with cervical instability.

Chronic pain causes physical suffering and can have major psychological impacts in patients.Chronic pain can induce depressive disorder,and clinical studies have consistently shown that chronic pain and depression frequently co-occur,suggesting the possibility of shared pathogenic mechanisms underlying these conditions.Acupuncture,as an alternative therapy,has been widely used for analgesia and to treat depression,with demonstrated clinical efficacy.The therapeutic mechanism of acupuncture is related to neural and endocrine regulation.This review considers the mechanism of chronic pain accompanied by depression,in relation to the brain regions and neural circuits affected by acupuncture treatment.This review provides a new approach for the treatment of depression caused by chronic pain.

Anxiety is a major emotional disorder manifested in the individual's expectation of future threats.The incidence rate of anxiety is about 7.3％,with the highest lifetime prevalence rate among mental health conditions.The mechanism of anxiety overlaps with depression,and anxiety is a typical symptom of various mental diseases or emotional disorders in traditional Chinese medicine.The high rates of comorbidity and disability pose serious threats to people's health.Animal models are important tools for studying anxiety and are of great use for deciphering the pathogenesis of anxiety and for developing drugs.The traditional paradigm of stress-induced anxiety,however,is relatively limited.Based on traditional theory combined with clinical and animal experimental data,we propose a new hypothesis of"insufficiency of ZhiYi'causing anxiety,defined as"an anxiety state induced by the inability of an individual to meet their own needs,limited or lacking after multiple attempts,rendered hindered and powerless by an inability to meet their desires".This hypothesis is more in line with the typical manifestations of despair,lack of pleasure,and social withdrawal in clinical patients,and is supported by traditional theory and experimental data showing"hunger but unable to eat,food but unable to obtain,and gain but not full".Based on this,the established modeling paradigm is easy to apply,with good repeatability and low cost,and can be used to establish anxiety models in rats and mice,to provide a theoretical and model basis for the development and pharmacological evaluation of anti-anxiety drugs.