ObjectiveTo investigate the effect and mechanism of transplantation of human umbilical cord mesenchymal stem cell （hUC-MSC） with overexpression of the Numb gene in the treatment of cholestatic liver fibrosis （CLF）. MethodsThe technique of lentiviral transfection was used to induce the overexpression of the Numb gene in hUC-MSC （hUC-MSC^Numb-OE）， and hUC-MSC transfected with empty vector （hUC-MSC^OE-EV） was used as negative control. Bile duct ligation （BDL） was performed to establish a rat model of CLF， and then the rats were randomly divided into BDL group， hUC-MSC group， hUC-MSC^OE-EV group， and hUC-MSC^Numb-OE group， while a sham-operation group was also established. The rats in the intervention groups were given a single splenic injection of the corresponding cells after BDL， and samples were collected at the end of week 4. Related indicators were measured， including serum biochemistry， liver histopathology， the content of hydroxyproline （Hyp） in the liver， hepatic stellate cell activation， ductular reaction， liver regeneration， and the expression levels of key molecules in the Numb-p53 signaling axis. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the least significant difference t-test was used for further comparison between two groups. ResultsCompared with the BDL group， the hUC-MSC group and the hUC-MSC^OE-EV group had significant reductions in the levels of serum biochemical parameters （aspartate aminotransferase， gamma-glutamyl transpeptidase， total bile acid， total bilirubin， and direct bilirubin）， liver fibrosis markers （the content of Hyp and the expression levels of alpha-smooth muscle actin， tumor necrosis factor-α， and transforming growth factor-beta 1）， and ductular reaction markers （the expression levels of CK7 and CK19） （all P <0.05）， and compared with the hUC-MSC^OE-EV group， the hUC-MSC^Numb-OE group had significantly greater improvements in the above indicators （all P <0.05）. In addition， compared with the hUC-MSC^OE-EV group， the hUC-MSC^Numb-OE group had significant improvements in the expression levels of liver regeneration-related markers （albumin and hepatocyte nuclear factor 4α） and the molecules associated with the Numb-p53 signaling axis （Numb， pNumb， Mdm2， and p53） （all P <0.05）. ConclusionOverexpression of the Numb gene can enhance the therapeutic effect of hUC-MSC on CLF， possibly by activating the Numb-PTB^L-p53-HNF4α axis， promoting the hepatic differentiation of hUC-MSCs and subsequently enhancing liver regeneration.

To explore the advantages of traditional Chinese medicine （TCM） and integrative TCM-Western medicine approaches in the treatment of diabetic microvascular complications （DMC）， refine key pathophysiological insights and treatment principles， and promote academic innovation and strategic research planning in the prevention and treatment of DMC. The 38th session of the Expert Salon on Diseases Responding Specifically to Traditional Chinese Medicine， hosted by the China Association of Chinese Medicine， was held in Beijing， 2024. Experts in TCM， Western medicine， and interdisciplinary fields convened to conduct a systematic discussion on the pathogenesis， diagnostic and treatment challenges， and mechanism research related to DMC， ultimately forming a consensus on key directions. Four major research recommendations were proposed. The first is addressing clinical bottlenecks in the prevention and control of DMC by optimizing TCM-based evidence evaluation systems. The second is refining TCM core pathogenesis across DMC stages and establishing corresponding "disease-pattern-time" framework. The third is innovating mechanism research strategies to facilitate a shift from holistic regulation to targeted intervention in TCM. The fourth is advancing interdisciplinary collaboration to enhance the role of TCM in new drug development， research prioritization， and guideline formulation. TCM and integrative approaches offer distinct advantages in managing DMC. With a focus on the diseases responding specifically to TCM， strengthening evidence-based support and mechanism interpretation and promoting the integration of clinical care and research innovation will provide strong momentum for the modernization of TCM and the advancement of national health strategies.

ObjectiveTo investigate the effects of jatrorrhizine on endogenous metabolites and metabolic pathways in the mouse model of ulcerative colitis. MethodsThirty male C57BL/6J mice were randomly divided into the normal group， the model group， the low-dose and high-dose jatrorrhizine groups （0.04， 0.16 g·kg^-1）， and the mesalazine group （0.52 g·kg^-1）The mouse model of ulcerative colitis was established with 3% dextran sulfate sodium （DSS） and treated with different doses of jatrorrhizine by gavage. The changes in body weight， colon length， disease activity index （DAI）， and colonic histopathology were analyzed to evaluate the therapeutic effects of jatrorrhizine. UPLC-Q-TOF/MS was employed to determine the serum and fecal levels of metabolites in mice. Metabolomics methods were used to screen the differential metabolites， on the basis of which the potential therapeutic mechanism of jatrorrhizine on DSS-induced ulcerative colitis in mice was investigated. ResultsAfter intervention with jatrorrhizine， the model mice showed significantly decreased DAI（P<0.05，P<0.01）， recovered colon length，（P<0.05，P<0.01） and alleviated histopathology of the colon. The metabolomics study screened out 13 differential metabolites in the serum and 8 differential metabolites in the feces. The pathway enrichment analysis predicted three potential metabolic pathways： Biosynthesis of unsaturated fatty acids， phenylalanine， tyrosine and tryptophan biosynthesis， and phenylalanine metabolism. ConclusionJatrorrhizine may treat ulcerative colitis by regulating the biosynthesis and metabolism of amino acids and the synthesis of unsaturated fatty acids.

ObjectiveTo explore the potential categories and characteristics of the public hospice care demand in Hainan Province， and analyze different potential types of influencing factors， so as to provide reference for relevant departments to improve the public awareness and demand of hospice care. MethodsUsing convenience sampling method， select 6484 cities of the public as the survey object， using the general data questionnaire， the hospice care demand questionnaire of the potential profile analysis， and analyze the influencing factors of the public hospice care demand category. ResultsThe characteristics of the hospice care demand in Hainan Province were divided into three potential categories： low demand group （14.19%）， medium demand group （49.99%） and high demand group （35.82%）. Multivariate analysis showed that gender， age， education level， cultural belief， and life-death education experience were the main influencing factors of public hospice care demand （p<0.05）. Males， those aged 41-60 years， and those with high school education or below had relatively lower hospice care demand， while those with life-death education experience had relatively higher demand. ConclusionRelevant departments should focus on hospice care knowledge popularization and demand enhancement for males， middle-aged groups， and people with low education levels， while strengthening universal life-death education through stratified and classified publicity strategies and educational interventions to improve different populations’ awareness and acceptance of hospice care.

With the continuous rise in the incidence of colorectal cancer and the trend towards younger patient population,the existing treatment options,while able to prolong survival,are difficult to avoid significant side effects.It is imperative to develop new treatment strategies.Luteolin(LUT),as a natural herbal active ingredient,has been proved to have broad-spectrum anti-tumor effects in studies of multiple cancer types.However,the mechanism of LUT action in colorectal cancer has not been systematically elucidated.In this study,for the first time,the molecular mechanism of LUT on colorectal cancer SW620 cells from the perspective of proteomics-glycoproteomics co-regulation was revealed.Proteomic analysis identified 472 differentially expressed proteins.Functional enrichment analysis showed that down-regulated proteins were mainly involved in oxidative stress response,mRNA processing,RNA splicing,and actin filament organization among key biological processes,involving oxidative phosphorylation and peroxisome pathways.Up-regulated proteins were mainly involved in DNA replication,protein folding,and rRNA metabolism,closely related to DNA replication and protein processing pathways in the endoplasmic reticulum.At the level of glycoproteomics,231 differentially expressed intact N-glycopeptides were identified.Functional enrichment analysis of corresponding glycoproteins indicateed that LUT might exert biological effects by regulating biological processes such as nuclear organization,nuclear membrane organization,and Fc receptor-mediated signaling pathways,as well as endoplasmic reticulum protein processing and N-glycan biosynthesis pathways.Analysis of key interaction networks revealed 5 core target proteins namely RPS15A,WDR43,FBL,UTP18,and UTP11.The loss of these proteins had been confirmed to inhibit the proliferation and migration of various tumor cells.Notably,altered glycosylation modifications of the lysosome-associated membrane proteins LAMP1 and LAMP2 suggested that LUT might affect tumor metastatic potential by regulating organelle dynamics.It was found that LUT could inhibit the malignant phenotype of colon cancer cells through a dual mechanism of specifically regulating protein expression networks and glycosylation modification patterns,providing new molecular targets and theoretical basis for precise treatment of colorectal cancer based on natural products.

Accurate identification of endogenous and exogenous substances in food,particularly in competition supplies,is crucial for ensuring food safety and fair competition,as well as for protecting the legitimate rights and professional reputations of athletes.Testosterone(T)and dehydroepiandrosterone(DHEA)are important steroid hormones that can stimulate protein synthesis,increase the number and volume of muscle cells,and promote muscle growth and recovery.Both are often illegally used in the animal husbandry industry to promote animal growth and improve meat quality.However,current research in this area remains limited,and identification technologies require further investigation.This study focused on the techniques for identifying endogenous and exogenous hormones including T and DHEA in beef.A Soxhlet extraction method was established,reducing the pretreatment cycle to 110 min while achieving high extraction efficiency,with recovery rates of 102.5%for T and 91.9%for DHEA,respectively.Based on this,a gas chromatography-combustion-isotope ratio mass spectrometry(GC-C-IRMS)method was developed for analyzing carbon isotopes in T and DHEA,eliminating the need for derivatization.By adding reference materials to the extract,simultaneous measurement of reference materials and target analytes was achieved.The measurement of caffeine reference material,T and DHEA was completed within 40 min,with a measurement repeatability of 0.02‰.Theδ13C values of T and DHEA in standard substances,which may serve as exogenous additives,were determined using elemental analysis-isotope ratio mass spectrometry(EA-IRMS).The results indicated an average δ13C value of-29.44‰±0.81‰(k=1)for 10 T standards and-30.86‰±0.87‰(k=1)for 14 kinds of DHEA standards.This approach effectively distinguished between endogenous sources and exogenous addition of these two hormones in beef,thereby providing vital technical support for the assurance and supervision of food safety.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.