OBJECTIVE To study the protective effect and potential mechanism of chikusetsu saponin Ⅳ a （chsⅣ） on renal function in diabetic nephropathy （DN） model rats. METHODS DN rat model was established by high-fat diet combined with streptozotocin injection. Thirty-six model rats were randomly divided into model group （i.g. administration of normal saline， high-fat diet）， chsⅣ low-dose and high-dose groups （i.g. administration of 90， 180 mg/kg chsⅣ， high-fat diet）， with 12 rats in each group. Additionally， 10 normal rats were set as the control group （i.g. administration of normal saline， regular diet）. From the 5th to the 12th week after streptozotocin injection， they were given intragastric administration of relevant drug or normal saline， once a day. After the last medication， the levels of fasting blood glucose， fasting insulin， blood urea nitrogen， serum creatinine and urine protein as well as the levels of reduced glutathione （GSH）， superoxide dismutase （SOD） and malondialdehyde （MDA） in renal tissues were measured. Additionally， the insulin resistance index was calculated. Hematoxylin-eosin， periodic acid-Schiff， and Masson staining techniques were employed to examine the histopathological alterations in the renal tissue. The expressions of Notch signaling pathway-related proteins in renal tissue were detected by immunohistochemical staining and Western blot methods. RESULTS Compared with model group， the histomorphological of renal tissues in the chsⅣ low- and high-dose groups were significantly improved， with significant decreases in renal histological scores， mesangial expansion index， and glomerulosclerosis scores （ P ＜0.05）； the levels of fasting blood glucose， fasting insulin， blood urea nitrogen， serum creatinine， urine protein and homeostasis model assessment for insulin resistance， as well as MDA content， the expression levels of Notch1， Notch intracellular domain， hairy and enhancer of Split 1 and Delta-like protein 1 in renal tissue were all significantly decreased （ P ＜0.05）. The levels of GSH and SOD in renal tissue were significantly elevated （ P ＜0.05）. Moreover， the improvement in these indicators was significantly more pronounced in the chsⅣ high-dose group compared to the chsⅣ low-dose group （ P ＜0.05）. CONCLUSIONS ChsⅣ can ameliorate renal pathological damage and functional impairment in DN rats. Its underlying mechanisms include restoration of glucose homeostasis and insulin sensitivity， attenuation of renal oxidative stress， and suppression of aberrant Notch signaling pathway activation.

BackgroundCurrent studies on the relationship between depressive symptoms and the risks of stroke and all-cause mortality mostly focus on the elderly population or high-risk groups with chronic diseases. The long-term impact of depressive symptoms in young and middle-aged people on the risks of stroke and all-cause mortality still lacks systematic assessment， and the relevant evidence is insufficient. ObjectiveTo explore the association between depressive symptoms and the risks of stroke and all-cause mortality in young and middle-aged adults， thereby expanding the risk evidence in this age group and providing epidemiological basis for early identification of high-risk individuals and the development of targeted intervention strategies. MethodsBased on data from the National Health and Nutrition Examination Survey （NHANES） database （2009–2018）， a total of 14 947 young and middle-aged respondents aged 18 to 64 were included as the research subjects. Logistic regression and restricted cubic spline （RCS） models were employed to examine the association between depressive symptoms and stroke. Cox proportional hazards model， RCS model， and Kaplan-Meier （K-M） survival curves were used to clarify the relationship between depressive symptoms and all-cause mortality risk. To verify the robustness of main findings， sensitivity analysis was conducted stratified by gender， hypertension， diabetes， hyperlipidemia， body mass index （BMI）， and age. ResultsLogistic regression analysis revealed that compared with the non-depressed group， the risk of stroke in the mild depression group increased by 53.50% （OR=1.535， 95% CI： 1.147–2.053）， while the moderate-to-severe depression group showed the highest risk with a 132.90% increase （OR=2.329， 95% CI： 1.733–3.129）. RCS analysis indicated an approximately linear dose-response relationship between PHQ-9 score and the risk of stroke （P_{for non-linearity}=0.089）， with stroke risk increasing as PHQ-9 scores rose. As the PHQ-9 score increased， the risk of stroke also showed an upward trend. Cox regression analysis demonstrated that compared with the non-depressed group， the risk of all-cause mortality in the mild depression group increased by 48.80% （HR=1.488， 95% CI： 1.164–1.902）， and the moderate-to-severe depression group had a 70.60% increased risk （HR=1.706， 95% CI： 1.310–2.221）. RCS analysis showed a linear dose-response relationship between PHQ-9 scores and all-cause mortality risk （P_{for non-linearity}=0.440）. K-M curves indicated that as the severity of depressive symptoms increases， the risk of all-cause mortality gradually rises （P<0.05）. Sensitivity analysis confirmed that the associations between depression symptoms and risks of stroke and all-cause mortality were not modified by gender， hypertension， diabetes， hyperlipidemia， BMI， or age， demonstrating robust findings. ConclusionAmong the young and middle-aged population， both mild and moderate-to-severe depression were associated with increased risks of stroke and all-cause mortality， with the moderate-to-severe depression group showed more pronounced risk elevations. A positive dose-response relationship was observed between depressive symptoms and these risks.

AIM: To investigate the changes in ocular surface-related indicators and tear matrix metalloproteinase-9(MMP-9)levels in diabetic patients complicated with meibomian gland dysfunction(MGD), analyze the effects of diabetes mellitus on ocular surface status and tear MMP-9 levels in patients with MGD, and provide evidence for clinical diagnosis and treatment.METHODS: Prospective observational study.Patients with type 2 diabetes mellitus complicated with MGD who visited the Department of Ophthalmology of the hospital from June 2024 to December 2024 were enrolled(diabetes mellitus+MGD group), and patients diagnosed solely with MGD without a history of diabetes mellitus during the same period were recruited as the non-diabetic MGD group. Patients in the diabetes mellitus+MGD group were further subdivided into aglycosylated hemoglobin(HbA1c)<7.5% subgroup and an HbA1c≥7.5% subgroup based on HbA1c levels, and into a diabetes duration<10 y subgroup and a diabetes duration ≥10 y subgroup according to the duration of diabetes mellitus. Multiple ocular surface parameters, including the Ocular Surface Disease Index(OSDI)score and eyelid margin alteration score, as well as tear MMP-9 concentration, were measured in all participants, followed by between-group comparative analyses.RESULTS:Total of 101 patients(202 eyes)with type 2 diabetes mellitus combined with MGD were enrolled, including 56 males and 45 females with a mean age of 60.2±8.5 y. The non-diabetic MGD group consisted of 151 patients(302 eyes), with 82 males and 69 females and a mean age of 57.5±7.8 y.The HbA1c <7.5% group comprised 55 patients(110 eyes)aged 59.6±8.2 y; 31 males and 24 females. The HbA1c ≥7.5% group included 46 patients(92 eyes)aged 60.9±8.7 y; 25 males and 21 females.The subgroup with diabetes duration <10 y had 50 patients(100 eyes)aged 58.7±8.1 y; 28 males and 22 females, and the subgroup with duration ≥10 y consisted of 51 patients(102 eyes)aged 61.6±8.4 y; 27 males and 24 females. Patients in diabetes mellitus+MGD group exhibited higher OSDI scores, meibomian gland orifice scores, meibum expressibility scores, meibum quality scores, corneal fluorescein staining scores, and tear MMP-9 levels compared with non-diabetic MGD patients(all P<0.01), while tear film breakup time(BUT)and Schirmer test values were lower(all P<0.01).Among diabetic patients with MGD, the HbA1c≥7.5% subgroup had significantly higher OSDI score, meibomian gland orifice score, meibum expressibility scores, meibum quality scores, corneal fluorescein staining scores, and tear MMP-9 concentration than the HbA1c<7.5% subgroup(all P<0.05), accompanied by significantly shorter BUT and lower Schirmer I test results(all P<0.05). Similarly, the diabetes duration≥10 y subgroup showed significantly higher OSDI score, meibomian gland orifice score, meibum expressibility scores, meibum quality scores, corneal fluorescein staining scores, and tear MMP-9 concentration compared with the diabetes duration<10 y subgroup(all P<0.05), whereas BUT and Schirmer I values were significantly lower(all P<0.05).CONCLUSION: Diabetes mellitus may be associated with aggravated ocular surface damage in patients with MGD, accompanied by elevated tear MMP-9 levels. Poor glycemic control and longer disease duration are related to more severe ocular surface injury and higher tear MMP-9 levels. Tear MMP-9 may serve as a potential indicator for evaluating ocular surface inflammation and damage in diabetic patients with MGD, providing certain evidence for clinical treatment decision-making.

OBJECTIVE To study the mechanism of action of Rehmannia glutinosa-medicated serum （RG） in regulating lipopolysaccharide （LPS）-induced polarization of BV2 microglia to interfere with neuroinflammation. METHODS An in vitro neuroinflammation model of BV2 cells induced by LPS was established. Cells were divided into control group， LPS group， 10%RG group， 20%RG group， BAY 11-7082 [nuclear factor kappa B （NF-κB） inhibitor] group， and 20%RG+BAY 11-7082 group. Except for the control group， cells in all other groups were pretreated with corresponding drugs for 12 h and then stimulated with LPS for 24 h. The morphology of cells in each group was observed， and the fluorescence intensity of ionized calcium binding adaptor molecule 1 （Iba-1）， inducible nitric oxide synthase （iNOS）， CD206， the mRNA expression of tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， IL-1β， iNOS， IL-4， IL-10， transforming growth factor-β （TGF-β）， arginase-1 （Arg-1）， and the Toll-like receptor 4 （TLR4）/NF-κB/NOD-like receptor thermal protein domain associated protein 3 （NLRP3） signaling pathway-related protein expression were detected. RESULTS Compared with the control group， the LPS group showed significant changes in cell morphology， exhibiting a typical M1 activated morphology. The fluorescence intensity of Iba-1 and iNOS， the relative expression levels of TNF-α， IL-6， IL-1β， iNOS mRNA， the relative expression levels of TLR4， NLRP3， caspase-1 p20， IL-1β p17 protein， and the phosphorylation levels of NF-κB p65 and inhibitor of nuclear factor kappa B α were significantly increased （P＜0.05）. The fluorescence intensity of CD206 and the relative expression levels of IL-4， IL-10， TGF-β， and Arg-1 mRNA were significantly reduced （P＜0.05）. Compared with the LPS group， the changes in the above indicators in the cells of the 10%RG group， 20%RG group， BAY 11-7082 group， and 20%RG+BAY 11-7082 group were significantly reversed （P＜0.05）， and there was no statistically significant difference in the comparison of the last three groups （P＞0.05）. CONCLUSIONS RG can effectively inhibit the overall activation of microglia and correct their M1/M2 polarization imbalance， and its mechanism may be closely related to the inhibition of excessive activation of the TLR4/NF-κB/NLRP3 signaling pathway.

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 To investigate the effect and mechanism of Jingangteng capsules in the treatment of non-alcoholic fatty liver disease （NAFLD）. METHODS Thirty-two SD rats were randomly divided into normal group and modeling group. The modeling group was fed a high-fat diet to establish a NAFLD model. The successfully modeled rats were then randomly divided into model group， atorvastatin group[positive control， 2 mg/（kg·d）]， and Jingangteng capsules low- and high-dose groups [0.63 and 2.52 mg/（kg·d）]， with 6 rats in each group. The pathological changes of the liver were observed by hematoxylin-eosin staining and oil red O staining. Enzyme-linked immunosorbent assay was performed to determine the serum levels of triglycerides （TG）， total cholesterol （TC）， high-density lipoprotein cholesterol （HDL-C）， low-density lipoprotein cholesterol （LDL-C）， alanine transaminase （ALT）， aspartate transaminase （AST）， tumour necrosis factor-α （TNF-α）， interleukin-1β （IL-1β）， IL-6， IL-18. 16S rDNA amplicon sequencing and metabolomics techniques were applied to explore the effects of Jingangteng capsules on gut microbiota and metabolisms in NAFLD rats. Based on the E-mail：591146765@qq.com metabolomics results， Western blot analysis was performed to detect proteins related to the nuclear factor kappa-B （NF-κB）/NOD-like receptor family protein 3 （NLRP3） signaling pathway in the livers of NAFLD rats. RESULTS The experimental results showed that Jingangteng capsules could significantly reduce the serum levels of TG， TC， LDL-C， AST， ALT， TNF-α， IL-1β， IL-6， IL-18， while increased the level of HDL-C， and alleviated the hepatic cellular steatosis and inflammatory infiltration in NAFLD rats. They could regulate the gut microbiota disorders in NAFLD rats， significantly increased the relative abundance of Romboutsia and Oscillospira， and significantly decreased the relative abundance of Blautia （P＜0.05）. They also regulated metabolic disorders primarily by affecting secondary bile acid biosynthesis， fatty acid degradation， O-antigen nucleotide sugar biosynthesis， etc. Results of Western blot assay showed that they significantly reduced the phosphorylation levels of NF-κB p65 and NF-κB inhibitor α， and the protein expression levels of NLRP3， caspase-1 and ASC （P＜0.05 or P＜0.01）. CONCLUSIONS Jingangteng capsules could improve inflammation， lipid accumulation and liver injury in NAFLD rats， regulate the disorders of gut microbiota and metabolisms， and inhibit NF-κB/NLRP3 signaling pathway. Their therapeutic effects against NAFLD are mediated through the inhibition of the NF-κB/NLRP3 signaling pathway.

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.

This study aims to establish BHK-21 stable cell lines expressing APN from four species(human,pig,dog,and cat),the APN fragments were amplified from pEGFP-C1-APN plasmids of the four species stored in the laboratory to generate the recombinant plasmids pcDNA4.0-APN.Af-ter the recombinant plasmids were transfected into BHK-21 cells,the stable BHK-21 cell lines ex-pressing the APNs were selected by two rounds of limited dilution.The constructed BHK-21 cell lines were identified by indirect immunofluorescence assay(IFA),and their susceptibility to PD-CoV and TGEV was tested for these four cell lines.Virus infection experiments revealed that PD-CoV infected cells expressing human,pig,and dog APNs,while it did not infect cells expressing cat APN.Simultaneously,TGEV infected cells expressing pig,dog,and cat APNs,but did not infect cells expressing human APN.The results suggest that the risk of cross-species infection for different coronaviruses and the established cell line can be used effectively to evaluate the virus in-fection.The findings also revealed that PDCoV has the potential risk of cross-species infection of human and dog,and TGEV has the potential risk of cross-species infection of dog and cat.These results provide a basis for the prevention and control strategy of coronaviruses.