Based on the "regulating the pivot and unblocking the stomach" theory， this article proposes that the core pathogenesis of bile reflux gastritis （BRG） is disharmony of the three pivots and imbalance between the gallbladder and the stomach. Specifically， the pivot of zang-fu （脏腑） organs （spleen and stomach） exhibits abnormal ascending and descending functions， while the pivot of opening and closing （shaoyang） shows impaired flow， and the pivot of mind （heart and brain） has disordered regulation. These three pivots interact， ultimately leading to bile reflux attacking the stomach， injuring the gastric mucosa， and causing symptoms such as epigastric distension， pain， acid regurgitation， and heartburn. Clinically， the treatment principle is unblocking and regulating the three pivots， harmonizing the gallbladder and descending stomach qi， which closely follows the different pathomechanisms of three-pivot dysregulation， applying treatment according to syndrome differentiation. The regulation of the pivot of zang-fu organs focuses on fortifying the spleen and harmonzing stomach， raising the clear and directing the turbid downward. The regulation of the opening-and-closing pivot emphasizes harmonizing shaoyang， promoting gallbladder function and bowel movement. The regulation of the mind pivot centers on calming the heart and the mind， and harmonizing the stomach to ensure smooth qi flow. "Unblocking and regulating three pivots" method aims to restore the physiological functions of the gallbladder and stomach， providing a conceptual framework for the clinical management of BRG.

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.

ObjectiveTo evaluate the anti-inflammatory activity of the novel magnolol ester derivative YW and to investigate its effects on chondrocyte senescence and preliminary mechanisms. MethodsMagnolol and p-methylbenzoic acid were used as raw materials to synthesize the magnolol ester derivative YW （Molecular Formula： C₂₆H₂₄O₃， Molecular Weight： 384.17， HPLC Purity >96%） via DCC/DMAP-catalyzed esterification. Cytotoxicity was assessed using the CCK-8 assay. A lipopolysaccharide （LPS）-induced RAW264.7 macrophage activation model and an interleukin-1β （IL-1β）-induced rat primary chondrocyte model were established. The release and mRNA expression of inflammatory factors including nitric oxide （NO）， IL-1β， tumor necrosis factor-alpha （TNF-α）， and IL-6 were detected by enzyme-linked immunosorbent assay （ELISA）， Griess reagent method， and quantitative real-time PCR （RT-qPCR）. The expression of senescence markers such as inducible nitric oxide synthase （iNOS）， pro-interleukin-1β （pro-IL-1β）， lysine acetyltransferase 7 （KAT7）， cyclin-dependent kinase inhibitor 1A （p21）， and cyclin-dependent kinase inhibitor 2A （p16）， as well as proteins related to chondrocyte extracellular matrix synthesis and catabolism， were analyzed by Western blot （WB）. Molecular docking was performed using Discovery Studio 2019 to validate target binding. ResultsYW exhibited no significant cytotoxicity at concentrations ≤20 μmol/L. YW concentration-dependently inhibited LPS-induced macrophage inflammatory cytokine release， significantly downregulated iNOS， Pro-IL-1β protein， and inflammatory cytokine mRNA expression （P<0.01）. YW stably bound to KAT7 protein （binding energy： -94.2 kcal/mol）； YW downregulated KAT7 and aging marker protein expression in naturally aged and IL-1β-induced chondrocyte models （P<0.01）； YW regulated chondrocyte matrix synthesis and catabolic protein expression in IL-1β-induced chondrocytes （P<0.01）. ConclusionYW inhibits macrophage activation and inflammatory cytokine release while downregulating KAT7 and senescence marker protein expression in chondrocytes， thereby blocking chondrocyte senescence.

Yunnan Province borders with Myanmar, Vietnam, and Laos, the China-Myanmar border area is the key area for prevention of re-establishment from imported malaria after the disease was eliminated in China. Since the malaria elimination action plan was launched in Yunnan Province in 2011, 129 counties (cities, districts) were classified into three categories according to malaria incidence and transmission risk, and different technical strategies and measures were implemented with adaptations to local circumstances. A total of 68 malaria consultation service stations were established on the Chinese side of the China-Myanmar border and 80 malaria prevention and control stations were established on the Myanmar side by Yunnan Province in 2014. Then, the “Three Lines of Defense” strategy was implemented for malaria elimination in the China-Myanmar border area in Yunnan Province during the period from 2015 to 2018, and this strategy was further refined and adjusted to the “3 + 1” strategy for prevention of re-establishment from imported malaria in 2019. Through decades of multifaceted efforts, the malaria elimination goal was achieved in Yunnan Province in June 2021. However, the number of imported malaria cases appeared a tendency towards a rise in Yunnan Province in 2023 and 2024, due to changes in the situation in Myanmar and the gradual resumption of international travel and border crossings following the adjustment of the COVID-19 prevention and control policy in China. The joint malaria prevention and control cooperation between China and Myanmar was initiated with the pilot project for joint malaria prevention and control in the China-Myanmar border area in 2005, and this project was progressed into the joint malaria and dengue fever prevention and control project in parts of the Greater Mekong Subregion border areas in 2010. The threat of overseas malaria epidemics to border areas in Yunnan Province was effectively reduced through implementation of coordination meetings with Myanmar health departments, establishment of efficient information exchange mechanisms, establishment of overseas surveillance sentinel sites, technical training, provision of material supports, joint propagation activities and joint responses to malaria epidemics. This project was incorporated into the Five-Year Plan of Action on Lancang-Mekong Cooperation (2018—2022) in China in 2018, with 5 liaison offices and 20 liaison workstations established in Myanmar, Laos, Vietnam, Cambodia, and Thailand, and 21 cross-border malaria surveillance sites assigned in border areas of Myanmar, Laos and Vietnam, and a long-term malaria prevention and control cooperation mechanisms was established through meetings, training, propagation, and joint investigations. Currently, Yunnan Province is poised to engage in more extensive and in-depth cooperation with neighboring countries, including malaria diagnosis and treatment techniques, drug and vaccine research and development, talent cultivation, information sharing, cross-border human health services, and health promotion, under the guidance of the Five-Year Plan of Action on Lancang-Mekong Cooperation (2023—2027).

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.

Background/Aims: Treatment indications for patients with chronic hepatitis B (CHB) remain contentious, particularly for patients with mild alanine aminotransferase (ALT) elevation. We aimed to evaluate treatment effects in this patient population. Methods: This rollover study extended a placebo-controlled trial that enrolled non-cirrhotic patients with CHB and ALT levels below two times the upper limit of normal. Following 3 years of randomized intervention with either tenofovir disoproxil fumarate (TDF) or placebo, participants were rolled over to open-label TDF for 3 years. Liver biopsies were performed before and after the treatment to evaluate histopathological changes. Virological, biochemical, and serological outcomes were also assessed (NCT02463019). Results: Of 146 enrolled patients (median age 47 years, 80.8% male), 123 completed the study with paired biopsies. Overall, the Ishak fibrosis score decreased in 74 (60.2%), remained unchanged in 32 (26.0%), and increased in 17 (13.8%) patients (p<0.0001). The Knodell necroinflammation score decreased in 58 (47.2%), remained unchanged in 29 (23.6%), and increased in 36 (29.3%) patients (p=0.0038). The proportion of patients with an Ishak score ≥ 3 significantly decreased from 26.8% (n=33) to 9.8% (n=12) (p=0.0002). Histological improvements were more pronounced in patients switching from placebo. Virological and biochemical outcomes also improved in placebo switchers and remained stable in patients who continued TDF. However, serum HBsAg levels did not change and no patient cleared HBsAg. Conclusions In CHB patients with minimally raised ALT, favorable histopathological, biochemical, and virological outcomes were observed following 3-year TDF treatment, for both treatment-naïve patients and those already on therapy.

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 the risk factors for esophageal varices in patients with liver cirrhosis,to establish a predictive model,and to provide reasonable guidance for the prevention of early esophageal varices in patients with liver cirrhosis.Methods:A retrospective analysis was performed for 1 113 patients with liver cirrhosis who attended the hospitals in Chongqing,China from Decem-ber 2006 to May 2021.Recursive feature elimination(RFE)and four machine learning methods were used for the screening of features,and five machine learning predictive models were established by logistic regression,random forest,support vector machine(SVM),de-cision tree,and eXtreme Gradient Boosting(XGBoost).The receiver operating characteristic(ROC)curve was used to evaluate the per-formance of each model,and the model with the best performance was used to investigate the risk factors for esophageal varices in pa-tients with liver cirrhosis.SHAP plots were used to explain the impact of each risk factor on patients.Results:The XGBoost model showed the best performance in predicting the risk of esophageal varices in patients with liver cirrhosis,with an area under the ROC curve of 0.872(95%CI=0.813-0.918).SHAP plots showed that platelet count,diameter of the portal vein,cholinesterase,albumin,ala-nine aminotransferase,hemoglobin,prothrombin ratio,prothrombin time,and serum total protein were risk factors for esophageal vari-ces in patients with liver cirrhosis.Conclusion:This study shows that the XGBoost predictive model has a relatively high predictive value,and the risk factors obtained by this model have a certain guiding significance for the clinical prevention and treatment of early esophageal varices in patients with liver cirrhosis.

Peroxynitrite anion(ONOO-),which is originated from the reaction of nitric oxide(NO)and superoxide anion(O2-),plays a pivotal role in immune defense and signal regulation.Excessive levels of ONOO-may induce the occurrence of various diseases such as Alzheimer's disease,inflammation,cardiovascular disease and cancer,etc.Existing detection methods for ONOO-have limitations such as complexity,time consumption,and low cell biocompatibility.In this study,a novel fluorescent probe QFPD was developed using quinoline and diphenylphosphinamide as fluorescent keleton and recognition group,respectively.The strong oxidizing property of ONOO-triggerd the cleavage of phosphoramide bond,leading to fluorescence quenching and a visible color change of the solution from yellow to purple,thereby enabling"ON-OFF"type recognition of ONOO-.QFPD exhibited excellent characteristics including good selectivity and high sensitivity in fluorescence detection(Limit of detection of 1.37 μmol/L),large Stokes shift(130 nm),rapid response(10 min),and strong anti-interference ability.Additionally,QFPD demonstrated good biocompatibility and could realize real-time dynamic monitoring of endogenous ONOO-.Furthermore,QFPD was successfully applied to environmental toxicology research by visualizing the oxidative stress effects induced by microplastics polymethyl methacrylate(PMMA)and Hg2+exposure,which might be a novel tool for the mechanism research on oxidative stress associated with microplastic pollution and heavy metal exposure.