Liver cirrhosis is the final stage of the progression of various chronic liver diseases， often accompanied by serious complications and high mortality rates. Recent studies have shown that the interaction between gut microbiota and bile acid metabolism （the gut microbiota-bile acid axis） is closely associated with liver cirrhosis. This article systematically reviews the mechanism of action of the gut microbiota-bile acid axis in the progression of liver cirrhosis， elaborates on the pathological features of liver cirrhosis and its harm to the body， and summarizes the association of the gut microbiota-bile acid axis with the development and progression of liver cirrhosis. It also analyzes the key regulatory role of this axis in the progression of liver cirrhosis and explores its potential application value as a therapeutic target for liver cirrhosis， in order to provide a theoretical basis for exploring more effective clinical intervention methods.

ObjectiveTo explore the effects of Wumeiwan on liver metastasis and lung metastasis of colorectal cancer and its potential mechanism. MethodsFirstly， mice were randomized into control， low-dose （20 g·kg^-1） Wumeiwan， high-dose （40 g·kg^-1） Wumeiwan， and paclitaxel （10 mg·kg^-1） groups. Secondly， liver metastasis and lung metastasis models of colorectal cancer were established in mice. After 4 weeks of intervention， the body weight of each mouse was recorded， and the lung weight， liver weight， and survival time of mice with metastatic colorectal cancer were determined. Hematoxylin-eosin （HE） staining was employed to detect the effects of Wumeiwan on liver metastasis and lung metastasis. Real-time PCR was employed to determine the mRNA levels of M1 and M2 macrophage markers in the liver tissue. Finally， the content of M1 macrophage markers CD80 and CD86 in the liver tissue was measured by flow cytometry. ResultsCompared with the control group， Wumeiwan and paclitaxel reduced the body weight （P<0.01） and liver weight （P<0.01） and prolonged the survival of the mouse model of liver metastasis of colorectal cancer （P<0.01）. In the mouse model of lung metastasis of colorectal cancer， Wumeiwan and paclitaxel also reduced the body weight （P<0.01） and lung weight （P<0.01） and extended the survival time （P<0.01）. Histopathological results showed that compared with the control group， Wumeiwan inhibited the liver and lung metastases of colorectal cancer. Real-time PCR results showed that compared with the control group， Wumeiwan upregulated the mRNA levels of M1 macrophage markers IL-1β， IL-6， tumor necrosis factor-α （TNF-α）， inducible nitric oxide synthase （iNOS）， and prostaglandin-endoperoxide synthase 2 （PTGS2） in the liver and lung tissue of mice with liver metastasis and lung metastasis of colorectal cancer （P<0.01）. Meanwhile， Wumeiwan downregulated the mRNA levels of M2 macrophage markers Arg1， CD163， and CD206 （P<0.01）. Meanwhile， the flow cytometry results showed that compared with the control group， Wumeiwan increased the content of CD86 and CD80 （P<0.01）. In addition， immunohistochemical results showed that Wumeiwan promoted the expression of CD86 and inhibited the expression of CD206 in the liver and lung tissue of mice with liver metastasis and lung metastasis. ConclusionWumeiwan can inhibit the liver metastasis and lung metastasis of colorectal cancer by promoting the M1 polarization of macrophages in the liver and lung of the model mice.

Hepatocellular carcinoma （HCC） is a malignant tumor with relatively high incidence and mortality rates worldwide， and its therapeutic resistance and recurrence mechanism are closely associated with liver cancer stem cells （LCSC）. This article systematically introduces the biological characteristics of LCSC and their key role in the progression of HCC， reviews the functional characteristics of the specific surface markers （such as EpCAM and CD133） and related signaling pathways （such as Wnt/β-catenin， TGF-β， and STAT3）， elaborates on the interaction between LCSC and tumor microenvironment， and summarizes the latest clinical treatment strategies targeting LCSC and the countermeasure for existing resistance mechanisms. The article points out that LCSC promote tumor development and progression through metabolic reprogramming and immune microenvironment remodeling， and it is proposed to establish a standardized detection system for LCSC specific markers and promote a triple synergistic therapeutic paradigm combining targeted therapy， immune regulation， and traditional chemotherapy， in order to provide new ideas for the clinical intervention of HCC.

OBJECTIVE To analyze the impact of the diagnosis-related groups （DRG） payment reform on the length of stay and medical costs in patients with chronic obstructive pulmonary disease （COPD） in Kashgar region， aiming to provide localized empirical evidence for the optimization of regional medical insurance payment methods. METHODS Based on the inpatient settlement database of the Xinjiang Uygur Autonomous Region Healthcare Security Administration， settlement data of COPD inpatients from 17 medical institutions in Kashgar region between January 1， 2022， and December 31， 2024， were extracted. The overall changes in patients’ length of stay and costs were compared before and after the reform. Subsequently， interrupted time series analysis （ITSA） was employed to explore the impact of the DRG payment reform on these variables. RESULTS Following the reform， both the average length of stay and various cost decreased significantly compared to the pre-reform period （ P ＜0.001）. At the overall sample level， the average length of stay， average total cost， average drug cost， average medical service cost， and average examination cost per admission all demonstrated significant long-term downward trends after the reform （ P ＜0.05）. However， the decrease in average out-of-pocket costs and the increase in average consumable costs per admission were not statistically significant （ P ＞0.05）. In tertiary medical institutions， the average length of stay and all categories of costs （except average consumable costs per admission） exhibited significant long-term upward trends after the reform （ P ＜0.05）； conversely， in secondary and lower-level medical institutions， the average length of stay， average total cost， average drug cost， average medical service cost， and average examination cost per admission showed significant long-term downward trends （ P ＜0.05）. CONCLUSIONS The DRG payment reform has achieved an overall effect of reducing the length of stay and controlling costs in COPD patients from Kashgar region. However， the effects vary across different levels of medical institutions： secondary and lower-level institutions show a long-term downward trend in length of stay and costs， whereas tertiary institutions exhibit a long-term upward trend. Furthermore， patients’ out-of-pocket financial burden does not show significant improvement.

AIM: To investigate the effect and potential mechanism of liraglutide(LIR)combined with neural stem cells(NSCs)in alleviating diabetic retinopathy(DR)in rats.METHODS:A DR rat model was established using a high-fat diet combined with a single intraperitoneal injection of streptozotocin(STZ). Rats in the control group were fed a normal diet and received an intraperitoneal injection of citric acid-sodium citrate buffer. After modeling, the rats were divided into the control group, DR group, DR+LIR group, DR+BMSCs group, DR+NSCs group, DR+BMSCs+LIR group, and DR+NSCs+LIR group. According to the grouping instructions, rats were given intraperitoneal injection of LIR at 200 μg/(kg·d)and intravitreal injection of bone marrow mesenchymal stem cells(BMSCs), or NSCs, or the combined therapy accordingly for 8 wk. Fasting blood glucose(FBG)and serum insulin levels, as well as retinal oxidative stress indexes and serum inflammatory indexes, were detected in each group. Retinopathy was evaluated by hematoxylin-eosin(HE)staining, retinal cell apoptosis was detected by TUNEL staining, SIRT1 expression in retinal tissues was detected by immunohistochemistry, and BDNF expression in retinal tissues was detected by immunofluorescence. The transcriptional levels of cZNF532, miR-29a-3p, SIRT1, TGF-β1, NRF2, Keap1, and BDNF in retinal tissues were detected by qRT-PCR, and the protein expression levels of SIRT1, TGF-β1, SMAD2/3, p-SMAD2/3, NRF2, Keap1, p65, and p-p65 in retinal tissues were detected by Western blot. RESULTS:Compared with the other groups of rats, the retinal morphology of rats in the DR+NSCs+LIR group was significantly improved, the levels of FBG, TNF-α, IL-1β, IL-6 and MDA were decreased(all P<0.05), the serum insulin level and the levels of SOD, CAT, and GSH-Px were increased(all P<0.05), the TUNEL-positive rate of retinal cells was reduced(all P<0.05). The expression levels of cZNF532, SIRT1, NRF2, and BDNF in retinal tissue were up-regulated(all P<0.05), whereas the expression levels of miR-29a-3p, TGF-β1, p-SMAD2/3, Keap1, and p-p65 were down-regulated(all P<0.05). CONCLUSION:LIR combined with NSCs alleviates DR by regulating the cZNF532-miR-29a-3p-SIRT1 network and its downstream TGF-β/SMAD, NF-κB, and Keap1/NRF2 signaling pathways.

Polygonatum sibiricum， as a traditional Chinese medicine with both medicinal and edible properties， has attracted considerable attention due to its functions of nourishing Yin and moistening the lungs， tonifying the spleen and benefiting Qi， and nourishing the kidneys and filling essence. Recent studies have demonstrated that Polygonatum sibiricum plays a significant role in regulating the immune system， effectively enhancing and improving the morphology and function of immune organs， stimulating the proliferation and activation of immune cells， and regulating the secretion and release of immune factors， thereby enhancing the immune function of the body and improving various immune-related diseases. Although a large number of studies have explored the pharmacological effects and mechanisms of P. sibiricum， there has been no systematic review and summary of its immune regulatory activity and mechanisms. Therefore， this article comprehensively reviews the research achievements of P. sibiricum polysaccharides and saponins in the field of immune regulation in recent years， and further sorts out the immune regulatory mechanisms of P. sibiricum in multiple aspects： including increasing the organ index of the spleen and thymus， increasing the number and activity of tumor-suppressive bone marrow hematopoietic stem cells， improving intestinal flora imbalance， regulating the quantity and proportion of T lymphocyte subsets， increasing the level of immunoglobulin， promoting the proliferation of macrophages， enhancing the activity of natural killer cells， increasing the number of white blood cells， and promoting the maturation of dendritic cells， providing a solid theoretical basis and scientific evidence for the research and application of P. sibiricum， and promoting its development and application in traditional Chinese medicine immune enhancers and various functional products.

Objective:To create nodal integrated topological attributes (NITA) index and explore its application value in obsessive-compulsive disorder (OCD) identification by combining with machine learning model.Methods:Sixty-nine patients with OCD and 69 healthy volunteers matched with gender, age and years of education from the Second Affiliated Hospital of Xinxiang Medical University who met the enrollment criteria from January 2022 to September 2023 were included in the study.Their whole-brain functional magnetic resonance imaging (MRI) data were collected and preprocessed to construct the brain functional network, and the global and nodal topological attributes were extracted as the two sets of training features for the support vector machine (SVM), random forest and gradient boosting tree, and the better features were selected by comparing the classification results of the three machine learning models. The selected features were downgraded using principal component analysis algorithm, and the above models were trained again to filter out the models that were compatible with the new dimensional features. Finally, the new dimensional features with statistically significant differences in brain regions were screened and used to train the adapted model. SPSS 20.0 software was used to process relevant data, and independent sample t-test was used for inter group comparison. Results:Each machine learning model trained based on node topological attribute metrics was higher than the global metrics in terms of accuracy, recall, F1 value and AUC, and the average accuracy of the former was higher than that of the latter by about 10.00%. The node topology attribute metrics were downscaled and named NITA, which can synthesize about 95.00% of the feature information of node topology attribute metrics on average. SVM was finally chosen as the fitness model for NITA (accuracy of 86.00%, recall of 87.00%, F1 value of 0.86, AUC of 0.92). Compared with healthy controls, the differences in NITA in the medial superior frontal gyrus, middle frontal gyrus, ventral inferotemporal gyrus, caudal inferior parietal lobule, medial precuneus, insula hypergranular cellular area, caudal cuneus gyrus, inferior occipital gyrus, caudal hippocampus, dorsal caudate nucleus, and several subregions of the superior temporal gyrus and the thalamus were statistically significant in the OCD group (all P<0.05, FDR-corrected). Training the NITA of the above brain regions as features yielded the optimal model FDR-NITA-SVM, which had an accuracy of 91.38% in the training group and 90.00% in the test group. Conclusion:NITA can be used as a potential imaging marker for recognizing OCD.NITA abnormal brain regions are key nodes for information exchange and integration among brain networks in OCD patients.

Objective:To create nodal integrated topological attributes (NITA) index and explore its application value in obsessive-compulsive disorder (OCD) identification by combining with machine learning model.Methods:Sixty-nine patients with OCD and 69 healthy volunteers matched with gender, age and years of education from the Second Affiliated Hospital of Xinxiang Medical University who met the enrollment criteria from January 2022 to September 2023 were included in the study.Their whole-brain functional magnetic resonance imaging (MRI) data were collected and preprocessed to construct the brain functional network, and the global and nodal topological attributes were extracted as the two sets of training features for the support vector machine (SVM), random forest and gradient boosting tree, and the better features were selected by comparing the classification results of the three machine learning models. The selected features were downgraded using principal component analysis algorithm, and the above models were trained again to filter out the models that were compatible with the new dimensional features. Finally, the new dimensional features with statistically significant differences in brain regions were screened and used to train the adapted model. SPSS 20.0 software was used to process relevant data, and independent sample t-test was used for inter group comparison. Results:Each machine learning model trained based on node topological attribute metrics was higher than the global metrics in terms of accuracy, recall, F1 value and AUC, and the average accuracy of the former was higher than that of the latter by about 10.00%. The node topology attribute metrics were downscaled and named NITA, which can synthesize about 95.00% of the feature information of node topology attribute metrics on average. SVM was finally chosen as the fitness model for NITA (accuracy of 86.00%, recall of 87.00%, F1 value of 0.86, AUC of 0.92). Compared with healthy controls, the differences in NITA in the medial superior frontal gyrus, middle frontal gyrus, ventral inferotemporal gyrus, caudal inferior parietal lobule, medial precuneus, insula hypergranular cellular area, caudal cuneus gyrus, inferior occipital gyrus, caudal hippocampus, dorsal caudate nucleus, and several subregions of the superior temporal gyrus and the thalamus were statistically significant in the OCD group (all P<0.05, FDR-corrected). Training the NITA of the above brain regions as features yielded the optimal model FDR-NITA-SVM, which had an accuracy of 91.38% in the training group and 90.00% in the test group. Conclusion:NITA can be used as a potential imaging marker for recognizing OCD.NITA abnormal brain regions are key nodes for information exchange and integration among brain networks in OCD patients.

Objective:Using resting-state functional magnetic resonance imaging (rs-fMRI), we explored the changes in dynamic functional network connections (dFNC) in the brains of patients with first-episode schizophrenia (SZ) and evaluated the potential clinical value of dFNC changes in combination with a machine learning model.Methods:Clinical data of 50 patients with schizophrenia (schizophrenia group), 29 males and 21 females, aged 18-47 (28.3±7.2) years, who attended the psychiatric department of the Second Affiliated Hospital of Xinxiang Medical College from January 2022 to August 2023, were retrospectively included. In the same period, 50 healthy controls matched for age and education (healthy control group) were recruited, of which 24 were male and 26 were female, aged 18-48 (28.0±6.9) years. The rs-fMRI imaging data were acquired for each subject. The dFNC cluster analysis was performed based on independent component analysis, and the differences between groups with different state FNC matrices were statistically analyzed. The dataset samples were divided into a training set (35 SZ patients and 35 healthy controls) and a validation set (15 SZ patients and 15 healthy controls) in a 7∶3 ratio. A machine learning classification model was constructed based on the dFNC matri. The performance of the model for distinguishing between schizophrenia and healthy controls was assessed by five-fold cross-validation using accuracy (ACC), recall (REC), F1 score, and area under curve (AUC) metrics of the working characteristics of the subjects.Results:Five network functional connectivity states were obtained by dFNC cluster analysis. Patients with first SZ showed a wide range of high connectivity and low connectivity changes on the neural dynamic functional networks, as shown by increased dynamic connectivity within the visual network (VIS) in state 1 (weak connectivity); The dynamic connectivity between executive control network (ECN) and VIS, frontal parietal network (FPN) and VIS decreases at state 3 (strong connectivity); The dynamic connectivity between default mode network (DMN) and FPN, DMN and ventral attention network (VAN) decreases at state 4 (weak connectivity). The machine learning results show that the classification model constructed by the dFNC matrix combined with SVM in state 3 (strongly connected) in the validation set obtains the best classification results (ACC=0.938; REC=0.938; F1=0.937; AUC=0.984), and the overall average classification ACC of the five states reaches 0.751, and AUC reaches 0.784.Conclusion:Patients with first-episode SZ have some brain functional network connectivity abnormalities, and a machine learning model based on dFNC features has high classification performance in distinguishing first-episode SZ from HC.

Objective:To explore the effects of Baduanjin and brisk walking on the sleep quality among fe-male college students.Methods:Ninety female college students with poor sleep quality[Pittsburgh Sleep Quality Index(PSQI)≥ 8]were recruited randomly assigned to Baduanjin,brisk walking,and control groups,with 30 par-ticipants in each.The Baduanjin and brisk walking groups participated in 10-week intervention(five 45-minute ses-sions per week),while the control group did not receive any intervention.Baseline and post-intervention assessments were conducted using the PSQI,a lung capacity test,echocardiography,and the Fatigue Scale(FS-14).Results:Af-ter 10 weeks,participants in both the Baduanjin and brisk walking groups got significantly lower PSQI and FS-14 total scores compared to baseline(Ps＜0.001).Cardiopulmonary function indicators,including stroke volume(SV),forced expiratory volume in one second(FEV1.0),the vital capacity-to-body mass index(VC/W),and maximum voluntary ventilation per minute(MVV),also significantly improved(Ps＜0.001).Furthermore,the Baduanjin group had significantly lower PSQI and FS-14 scores than both the brisk walking and control groups(P＜0.001),along with superior improvements in cardiopulmonary function(P＜0.001).Conclusion:This study in-dicates that Baduanjin is particularly effective in improving sleep quality,cardiopulmonary function,and reducing fatigue among female college students,showing advantages over brisk walking.