ObjectiveTo investigate the ameliorative effect of Wendantang combined with Danshenyin and Dushentang （WDD） on mice with ischemic heart disease （IHD） presenting phlegm-stasis syndrome based on the inflammatory phenotype and differentiation of circulating monocytes. MethodsA model of IHD with phlegm-stasis syndrome was established using left anterior descending coronary artery ligation supplemented with a high-fat diet. Eighty model mice were randomly assigned to the model group， WDD low-dose group （WDD-L）， WDD medium-dose group （WDD-M）， WDD high-dose group （WDD-H）， and atorvastatin calcium tablet group， with 16 mice in each group. An additional 16 C57BL/6J mice were designated as the sham-operation group. The WDD groups received intragastric administration at doses of 8.91， 17.81， 35.62 g·kg^-1， and the atorvastatin calcium tablet group received the corresponding drug at 1.3 mg·kg^-1， twice daily. The sham-operation and model groups were given the same volume of pure water by gavage each day. After 5 consecutive weeks of administration， the cardiac index was calculated. Cardiac function was assessed by echocardiography. Myocardial histopathology was examined by hematoxylin-eosin （HE） staining. Serum N-terminal pro-B-type natriuretic peptide （pro-BNP） content was measured by enzyme-linked immunosorbent assay （ELISA）. Hemorheological parameters were analyzed using an automated hemorheology analyzer. Serum levels of total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein （LDL）， and high-density lipoprotein （HDL） were determined using an automated biochemical analyzer. Changes in circulating monocytes were detected by flow cytometry. Mouse bone marrow mononuclear cells were isolated in vitro and divided into blank group， model serum group， WDD-L drug-containing serum group， WDD-M drug-containing serum group， and WDD-H drug-containing serum group. CD36 expression and macrophage differentiation in each group were assessed by flow cytometry. The mechanism by which WDD mediates circulating monocyte differentiation was further explored using CD36 knockdown/overexpression RAW264.7 cell lines. ResultsCompared with the sham-operation group， the model group showed a significantly increased cardiac index （P0.01）， significantly decreased fractional shortening （FS） （P0.01）， and significantly increased left ventricular end-diastolic internal diameter （LVDD） and left ventricular end-systolic internal diameter （LVDS） （P0.01）. Cardiomyocytes exhibited marked deformation and necrosis with inflammatory cell infiltration. Serum pro-BNP levels were significantly elevated （P0.01）， and whole-blood viscosity （BV） at high， medium， and low shear rates was significantly increased （P0.01）. Compared with the model group， the WDD groups showed significantly reduced cardiac index （P0.05， P0.01）， significantly increased FS （P0.05， P0.01）， significantly decreased LVDD and LVDS （P0.01）， markedly improved cardiomyocyte morphology， significantly reduced inflammatory infiltration， significantly decreased serum pro-BNP levels （P0.01）， and significantly decreased BV at high， medium， and low shear rates （P0.01）， with the most pronounced improvement observed in the WDD-M group. Compared with the sham-operation group， TC， TG， and LDL levels were significantly increased in the model group （P0.05， P0.01）， while HDL levels were significantly decreased （P0.05）. After WDD-H treatment， TC， TG， and LDL levels were significantly reduced and HDL levels were significantly increased in mice （P0.05， P0.01）. Compared with the sham-operation group， classical monocytes in blood and bone marrow and intermediate monocytes in blood were significantly increased in the model group （P0.01）， whereas intermediate monocytes in bone marrow and non-classical monocytes in blood were significantly decreased （P0.01）. After WDD administration， all circulating monocyte subsets in blood and bone marrow were significantly alleviated （P0.05， P0.01）， with the WDD-M group showing the optimal effect. In vitro， compared with the blank group， CD36 expression on bone marrow monocytes and the proportion of differentiated macrophages were significantly increased in the model serum group （P0.01）， and CD36 expression was significantly upregulated on RAW264.7 cells （P0.01）. Compared with the model serum group， all drug-containing serum groups exhibited significantly reduced CD36 expression on bone marrow monocytes and significantly reduced macrophage differentiation （P0.01）. WDD downregulated CD36 expression in both CD36 knockdown and overexpression RAW264.7 cell lines （P0.05， P0.01）， with the strongest regulatory effect observed in the WDD-M drug-containing serum group. ConclusionWDD can significantly improve the manifestations of phlegm-stasis syndrome in IHD mice and reduce the proportion of classical circulating monocytes. Its mechanism may be related to the inhibition of CD36 expression on classical circulating monocytes.

ObjectiveTo investigate the ameliorative effect of Wendantang combined with Danshenyin and Dushentang （WDD） on mice with ischemic heart disease （IHD） presenting phlegm-stasis syndrome based on the inflammatory phenotype and differentiation of circulating monocytes. MethodsA model of IHD with phlegm-stasis syndrome was established using left anterior descending coronary artery ligation supplemented with a high-fat diet. Eighty model mice were randomly assigned to the model group， WDD low-dose group （WDD-L）， WDD medium-dose group （WDD-M）， WDD high-dose group （WDD-H）， and atorvastatin calcium tablet group， with 16 mice in each group. An additional 16 C57BL/6J mice were designated as the sham-operation group. The WDD groups received intragastric administration at doses of 8.91， 17.81， 35.62 g·kg^-1， and the atorvastatin calcium tablet group received the corresponding drug at 1.3 mg·kg^-1， twice daily. The sham-operation and model groups were given the same volume of pure water by gavage each day. After 5 consecutive weeks of administration， the cardiac index was calculated. Cardiac function was assessed by echocardiography. Myocardial histopathology was examined by hematoxylin-eosin （HE） staining. Serum N-terminal pro-B-type natriuretic peptide （pro-BNP） content was measured by enzyme-linked immunosorbent assay （ELISA）. Hemorheological parameters were analyzed using an automated hemorheology analyzer. Serum levels of total cholesterol （TC）， triglycerides （TG）， low-density lipoprotein （LDL）， and high-density lipoprotein （HDL） were determined using an automated biochemical analyzer. Changes in circulating monocytes were detected by flow cytometry. Mouse bone marrow mononuclear cells were isolated in vitro and divided into blank group， model serum group， WDD-L drug-containing serum group， WDD-M drug-containing serum group， and WDD-H drug-containing serum group. CD36 expression and macrophage differentiation in each group were assessed by flow cytometry. The mechanism by which WDD mediates circulating monocyte differentiation was further explored using CD36 knockdown/overexpression RAW264.7 cell lines. ResultsCompared with the sham-operation group， the model group showed a significantly increased cardiac index （P<0.01）， significantly decreased fractional shortening （FS） （P<0.01）， and significantly increased left ventricular end-diastolic internal diameter （LVDD） and left ventricular end-systolic internal diameter （LVDS） （P<0.01）. Cardiomyocytes exhibited marked deformation and necrosis with inflammatory cell infiltration. Serum pro-BNP levels were significantly elevated （P<0.01）， and whole-blood viscosity （BV） at high， medium， and low shear rates was significantly increased （P<0.01）. Compared with the model group， the WDD groups showed significantly reduced cardiac index （P<0.05， P<0.01）， significantly increased FS （P<0.05， P<0.01）， significantly decreased LVDD and LVDS （P<0.01）， markedly improved cardiomyocyte morphology， significantly reduced inflammatory infiltration， significantly decreased serum pro-BNP levels （P<0.01）， and significantly decreased BV at high， medium， and low shear rates （P<0.01）， with the most pronounced improvement observed in the WDD-M group. Compared with the sham-operation group， TC， TG， and LDL levels were significantly increased in the model group （P<0.05， P<0.01）， while HDL levels were significantly decreased （P<0.05）. After WDD-H treatment， TC， TG， and LDL levels were significantly reduced and HDL levels were significantly increased in mice （P<0.05， P<0.01）. Compared with the sham-operation group， classical monocytes in blood and bone marrow and intermediate monocytes in blood were significantly increased in the model group （P<0.01）， whereas intermediate monocytes in bone marrow and non-classical monocytes in blood were significantly decreased （P<0.01）. After WDD administration， all circulating monocyte subsets in blood and bone marrow were significantly alleviated （P<0.05， P<0.01）， with the WDD-M group showing the optimal effect. In vitro， compared with the blank group， CD36 expression on bone marrow monocytes and the proportion of differentiated macrophages were significantly increased in the model serum group （P<0.01）， and CD36 expression was significantly upregulated on RAW264.7 cells （P<0.01）. Compared with the model serum group， all drug-containing serum groups exhibited significantly reduced CD36 expression on bone marrow monocytes and significantly reduced macrophage differentiation （P<0.01）. WDD downregulated CD36 expression in both CD36 knockdown and overexpression RAW264.7 cell lines （P<0.05， P<0.01）， with the strongest regulatory effect observed in the WDD-M drug-containing serum group. ConclusionWDD can significantly improve the manifestations of phlegm-stasis syndrome in IHD mice and reduce the proportion of classical circulating monocytes. Its mechanism may be related to the inhibition of CD36 expression on classical circulating monocytes.

Periodontitis is a chronic inflammatory disease triggered by periodontal pathogens and mediated by immune responses. Traditionally, gingival fibroblasts (GFs) were considered to be primarily responsible for maintaining periodontal matrix homeostasis. However, recent studies reveal that GFs play a significant immunoregulatory role in periodontitis. Through signaling pathways, such as the Toll-like receptor 4 (TLR4) pathway, GFs recognize virulence factors from pathogens, such as Porphyromonas gingivalis, and secrete various inflammatory mediators, thus driving extracellular matrix degradation and osteoclast differentiation. Simultaneously, GFs modulate immune cells, including neutrophils and macrophages, amplifying inflammatory responses and fostering a chronic inflammatory microenvironment. Risk factors, such as hyperglycemia and smoking, exacerbate GFs dysfunction via oxidative stress-mediated activation of the nuclear factor kappa B (NF-κB) pathway and other mechanisms, while inflammation and cellular senescence form a vicious cycle. Senescent GFs further aggravate alveolar bone destruction by activating the mechanistic target of the rapamycin (mTOR) pathway. Therapeutic strategies targeting GFs, such as suppressing NF-κB signaling or modulating mTOR-mediated senescence, may disrupt the link between inflammation and tissue destruction, showing promising therapeutic potential. Future studies should employ advanced technologies such as spatial multi-omics and single-cell proteomics to elucidate the spatial distribution, functional interactomes, and heterogeneity of GFs subsets, in order to deepen our understanding of their roles in periodontitis progression. This review summarizes the multifaceted mechanisms of GFs in periodontitis and explores potential therapeutic strategies targeting GFs, offering novel insights for periodontitis prevention and treatment.

ObjectiveCerebral ischemic injury triggers a complex pathological cascade characterized by excessive reactive oxygen species (ROS) accumulation, persistent oxidative stress, and sustained neuroinflammation in the injured brain microenvironment. These events collectively drive mitochondrial dysfunction, microglial overactivation, pro-inflammatory cytokine release, and progressive neuronal apoptosis, ultimately leading to severe and irreversible neurological deficits. However, conventional therapeutic strategies face critical limitations, including poor blood-brain barrier penetration, insufficient local drug concentration, uncontrolled drug release, and off-target systemic side effects. To address this pathological process, we rationally designed and fabricated an injectable ROS-responsive hydrogel loaded with polydopamine nanoparticles (PDA NPs) for spatiotemporally controlled antioxidation, anti-inflammation, and neuroprotection in the ischemic injury microenvironment. The present study aimed to systematically characterize the physicochemical properties, ROS-responsive drug release behavior, biocompatibility, and neuroprotective efficacy of this composite hydrogel system in vitro. MethodsPDA NPs were fabricated via oxidative self-polymerization. The ROS-responsive hydrogel was cross-linked using N1-(4-boronobenzyl)-N3-(4-boronophenyl)-N1,N1,N3,N3-tetramethylpropane-1, 3-diaminium (TSPBA) and polyvinyl alcohol (PVA). Morphology, particle size, Zeta potential, and structure of PDA NPs were characterized by dynamic light scattering (DLS), Zeta potential analysis, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Microstructure, rheological properties, shear-thinning behavior, and ROS-triggered release profiles of the hydrogel were examined by SEM and rheometry. Biocompatibility was evaluated using HT22 mouse hippocampal neurons with CCK-8 and live/dead staining. An oxygen-glucose deprivation/reoxygenation (OGD/R) model was established to simulate ischemic injury in vitro. ROS levels and neuronal apoptosis were detected by DHE staining and TUNEL assay. Microglial polarization and pro-inflammatory cytokine expression were analyzed using immunofluorescence and RT-qPCR in BV-2 microglia. Transwell co-culture was used to verify the indirect neuroprotection mediated by modulated microglia. ResultsCharacterization results confirmed that the as-prepared PDA NPs were monodispersed spherical nanoparticles with uniform diameter and negative surface potential, demonstrating favorable dispersibility and robust ROS-scavenging activity. The TSPBA-PVA hydrogel exhibited a highly porous interconnected network, suitable mechanical strength, and obvious shear-thinning behavior, supporting its application as an injectable implant. More importantly, the hydrogel displayed typical ROS-responsive degradation and on-demand PDA NP release in a ROS-concentration-dependent manner. In vitro cellular experiments demonstrated that the PDA NP-loaded hydrogel possessed excellent biocompatibility with HT22 cells. In the OGD/R model, the hydrogel significantly reduced intracellular ROS accumulation and markedly suppressed neuronal apoptosis. Furthermore, the composite hydrogel effectively redirected BV-2 microglia from the pro-inflammatory M1 toward the anti-inflammatory M2 phenotypes, downregulated the expression of pro-inflammatory cytokines including TNF-α, IL-1β, and IL-6, and reduced inflammatory damage. Transwell co-culture assays further validated that M2-polarized microglia mediated by the hydrogel significantly enhanced the survival of OGD/R-injured HT22 neurons and attenuated apoptosis. ConclusionIn this study, we successfully developed a novel injectable ROS-responsive hydrogel loaded with PDA NPs for synergistic antioxidative and anti-inflammatory neuroprotection. This intelligent hydrogel system enables ROS-triggered on-demand release of PDA NPs, efficiently scavenges excessive ROS, inhibits oxidative stress injury, modulates microglial polarization, and suppresses neuroinflammation, thereby exerting robust neuroprotective effects in vitro. This biomaterial platform provides a promising strategy for the targeted and controlled delivery of bioactive nanomaterials in the central nervous system diseases and establishes a solid experimental foundation for the development of in situ injectable therapies for ischemic brain injury.

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.

Objective:To investigate effect of hospital-family chain rehabilitation management mode on self-man-agement and quality of life in elderly patients with coronary heart disease(CHD).Methods:This randomized con-trolled study enrolled 220 elderly CHD patients admitted in Hai'an Hospital of Traditional Chinese Medicine between December 2020 and August 2022.They were divided into control group(n=110,routine nursing)and intervention group(n=110,hospital-family chain rehabilitation management mode nursing).After 6-month intervention,scores of Coronary Self-Management Scale(CSMS),China Questionnaire of Quality of life in patients with Cardio-vascular diseases(CQQC)and Perceived Social Support Scale(PSSS)were compared between two groups as well as incidence of adverse cardiovascular events.Pearson method was employed to analyze the association among total scores of CSMS,CQQC and PSSS.Results:After 6-month nursing,compared with patients in control group,those in intervention group had significant higher CSMS total score[(116.03±5.41)points vs.(89.97±4.21)points],CQQC total score[(107.30±6.99)points vs.(80.86±6.38)points],PSSS total score[(69.05±7.42)points vs.(57.05±5.19)points]and each dimensional score(P＜0.001 all).Pearson correlation analysis indica-ted that total scores of CSMS,CQQC and PSSS were positively correlated with each other(r=0.437～0.562,P＜0.001 all).Incidence of adverse cardiovascular events in intervention group was significantly lower comparing to that of control group(2.73％vs.13.64％,P=0.003).Conclusion:Hospital-family chain rehabilitation manage-ment mode could improve the self-management ability and quality of life,increase the patient's social support,and reduce incidence of adverse cardiovascular events in elderly patients with coronary heart disease.

Objective To investigate the predictive value of cerebroplacental ratio(CPR),cerebro-uterine ratio(CUR)combined with cystatin C(CysC)for adverse pregnancy outcomes in preeclampsia(PE)fetuses.Methods A retrospective analysis was conducted on the clinical data of 150 PE patients admitted to the department of obstetrics and gynecology of Nuclear Industry 416 Hospital from January 2019 to December 2024,and patients were divided into the adverse-outcome group and the favorable-outcome group according to pregnancy outcomes.The clinical data,CPR,CUR and CysC level were compared between the two groups.Multivariate Logistic regression was used to analyze the independent influencing factors of adverse pregnancy outcome in PE patients;then,receiver operating characteristic(ROC)curve analysis was performed to evaluate the predictive efficiency of each index on adverse pregnancy outcome.Results The adverse-outcome group had shorter/lower gestational age at diagnosis,estimated fetal body weight,CUR,and CPR,but higher body mass index and CysC level compared to those in the favorable-outcome group,with significant differences(P<0.05).Multivariate Logistic regression identified that the elevated CysC level,and decreased CUR and CPR were related influencing factors for adverse pregnancy outcome(P<0.05).ROC curve analysis demonstrated that CUR,CPR and CysC had strong predictive value for adverse pregnancy outcome,with the area under the curve(AUC)of 0.802(95%CI:0.729 to 0.863),0.834(95%CI:0.764 to 0.890),and 0.791(95%CI:0.717 to 0.853),respectively;the combined prediction of CUR,CPR and CysC had grater AUC of 0.909(95%CI:0.851 to 0.950)than the individual prediction of the above three indicators(P<0.05).Conclusion CUR,CPR and CysC are influencing factors of adverse pregnancy outcome in PE patients,and their combined detection demonstrates good predictive value for adverse pregnancy outcome in PE patients.