ObjectiveThis study aimed to investigate whether Bushen Tongluo prescription inhibits macrophage polarization by regulating the Wnt3a/β-catenin signaling pathway， thereby reducing epithelial-mesenchymal transition and excessive extracellular matrix deposition， in order to elucidate the anti-pulmonary fibrosis mechanisms of Bushen Tongluo prescription and provide a new theoretical basis for the clinical treatment of pulmonary fibrosis. MethodsFifty male Sprague-Dawley （SD） rats were randomly divided into a blank group， model group， pirfenidone group， and high- and low-dose Bushen Tongluo prescription groups. Except for the blank group， the pulmonary fibrosis model was established by intratracheal instillation of bleomycin. Intervention was initiated on day 28 after modeling. The high- and low-dose Bushen Tongluo prescription groups were administered Bushen Tongluo prescription at doses of 30.88， 15.44 g·kg^-1， respectively， by intragastric gavage. The pirfenidone group was administered pirfenidone capsules at 110 mg·kg^-1 by intragastric gavage. The blank and model groups were given an equal volume of normal saline by gavage， once daily for 90 days. After treatment， the level of transforming growth factor-β₁ （TGF-β₁） in bronchoalveolar lavage fluid （BALF） was detected by enzyme-linked immunosorbent assay （ELISA）. Morphological changes in lung tissue and the collagen volume fraction were compared. The protein distribution and expression of E-cadherin， cytokeratin 19， α-smooth muscle actin （α-SMA）， vimentin， collagen type Ⅰ （Col Ⅰ）， and collagen type Ⅲ （Col Ⅲ） in lung tissue were detected by immunohistochemistry. The protein distribution and expression of CD68， arginase-1 （Arg-1）， inducible nitric oxide synthase （iNOS）， Wnt3a， and β-catenin in lung tissue were detected by immunofluorescence. The protein expression of Wnt3a and β-catenin in lung tissue was detected by Western blot， and the mRNA expression of Wnt3a and β-catenin was detected by Real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）. ResultsCompared with the blank group， a large number of inflammatory cells infiltrated the airway walls， alveolar spaces， and interstitial tissue in the model group， with obvious fibrous tissue hyperplasia. The level of TGF-β₁ in BALF was significantly increased. The protein expression of E-cadherin and cytokeratin 19 in lung tissue was decreased， whereas the protein expression of α-SMA， Vimentin， Wnt3a， β-catenin， Col Ⅰ， and Col Ⅲ was increased. The fluorescence-positive area ratios of CD68， Arg-1， iNOS， Wnt3a， and β-catenin in lung tissue were increased. The protein and mRNA expression levels of Wnt3a and β-catenin in lung tissue were significantly increased （P<0.01）. Compared with the model group， all treatment groups showed varying degrees of improvement in inflammatory cell infiltration and fibrous tissue hyperplasia in the airway walls， alveolar spaces， and interstitial tissue， decreased TGF-β₁ levels in BALF， increased protein expression of E-cadherin and cytokeratin 19 in lung tissue， decreased protein expression of α-SMA， Vimentin， Col Ⅰ， and Col Ⅲ， decreased fluorescence-positive area ratios of CD68， Arg-1， iNOS， Wnt3a， and β-catenin in lung tissue， and decreased protein and mRNA expression levels of Wnt3a and β-catenin in lung tissue （P<0.05， P<0.01）. ConclusionBushen Tongluo prescription can improve bleomycin-induced pulmonary fibrosis in rats by inhibiting epithelial-mesenchymal transition and reducing excessive extracellular matrix deposition. The mechanism may be related to inhibition of the Wnt3a/β-catenin signaling pathway and the macrophage polarization mediated by this pathway.

Objective To construct an index system for assessment of schistosomiasis transmission risk following natural disasters such as rainstorms, floods, earthquakes, mudslides, and landslides, so as to provide insights into rapid identification of schistosomiasis transmission risk post-disasters and formulation of targeted schistosomiasis control strategies. Methods An initial framework for the index system for assessment of schistosomiasis transmission risk following natural disasters was drafted through literature review, brainstorming, and focus group discussions. Two rounds of expert correspondence consultations were conducted using the Delphi method to refine and finalize the system, and the degrees of expert activeness, authority and endorse ment, and consensus were evaluated. In addition, the weights of each index were calculated using the analytic hierarchy process. Results A total of 18 experts participated in the consultation. The expert positive coefficients were 100.00% and 94.44% for two rounds of consultations, with authority coefficients of 0.92 and 0.94, respectively. The coefficients of coordination on the index importance, rationality and operability were 0.209, 0.185, 0.222 and 0.407, 0.214, 0.257 for two rounds of consultations, respectively, and all consistency tests were statistically significant (χ² = 246.771 to 505.278, all P values < 0.001). Following two rounds of expert consultations, an index system consisting of 6 first-level indicators, 15 second-level indicators, and 49 third-level indicators was ultimately constructed. In terms of first-level indicators, “disaster situation”, “previous epidemics”, “healthcare guarantee”, “response capacity” and “emergency recovery” had the highest weights, each at 18.18%. Regarding second-level indicators, “Schistosoma japonicum infections in animals”, “S. japonicum infections in snails” and “medical treatment” had the highest weights, each at 7.35%. In terms of third-level indicators, ten items had the highest weights, including “identification of schistosomiasis cases”, “detection of S. japonicum infections in wild feces”, “detection of S. japonicum infections in snails”, “reserves of schistosomiasis diagnostic/testing reagents and consumables”, “reserves of chemotherapy agents for human and animal schistosomiasis”, “reserves of cercariacides”, “periodical surveillance on schistosomiasis”, “identification of schistosomiasis transmission risk and timely response”, “normal provision of diagnosis and treatment services” and “post-disaster schistosomiasis surveillance”, each at 2.40%. Conclusion A scientific, systematic, and practical index system has been constructed for assessment of schistosomiasis transmission risk following natural disasters, which may provide insights into rapid post-disaster identification of schistosomiasis transmission risk, formulation of targeted schistosomiasis control strategies and optimization of resource allocation.

Objective To evaluate the efficacy and safety of CT-guided percutaneous radiofrequency ablation (RFA) as an alternative for video-assisted thoracoscopic surgery (VATS) in treating multiple pulmonary nodules. Methods A retrospective analysis was conducted on the clinical data of 113 patients with multiple pulmonary nodules admitted to Jiangsu Provincial Hospital of Traditional Chinese Medicine from October 2020 to October 2022. The patients were divided into the RFA group (n＝50) and the VATS group (n＝63) based on the treatment method. Perioperative indicators (operation time, intraoperative blood loss, postoperative length of hospital stay), oncological outcomes (recurrence-free survival [RFS], overall survival [OS]), and postoperative complication rates were compared between the two groups. Univariate and multivariate Cox regression analysis was performed to identify independent prognostic factors. Results The operation time in the RFA group was significantly shorter than that in the VATS group ([75.2±20.1] min vs [102.3±28.7]) min, P＜0.001). No statistically significant differences were observed in intraoperative blood loss and postoperative length of hospital stay. After follow-up of 24 (12, 30) months, no statistically significant differences were found in RFS (HR＝1.25, P＝0.445) or OS (HR＝1.42, P＝0.402) between the two groups. Mixed ground-glass nodules with high solid component and solid nodule were identified as independent risk factors for RFS (HR＝2.44, P＝0.023; HR＝2.97, P＝0.007) and OS (HR＝2.87, P＝0.022; HR＝3.43, P＝0.005) in patients with multiple pulmonary nodules. The total complication rate in the RFA group was lower than that in the VATS group (12.0% vs 34.9%, P＝0.009). Conclusions The efficacy of CT-guided RFA in treating multiple pulmonary nodules is comparable to that of VATS, with good safety, and it shows promise as an alternative to surgical treatment for multiple pulmonary nodules.

Objective: To explore the influencing factors of anxiety symptoms and to construct a predictive model based on machine learning algorithms, so as to provide support for the prevention and management of anxiety symptoms among junior high school students. Methods: From April to May 2023, a stratified random cluster sampling method was adopted to select 8 176 junior high school students from Zhengzhou and Shangqiu citys. All participants completed the Adolescent Self rating Life Events Checklist, the 10item Connor-Davidson Resilience Scale, the School Connectedness Scale, the Parent-Child Cohesion Questionnaire, and the 7 item Generalized Anxiety Disorder Scale. Logistic regression analysis identified the associated factors of anxiety symptoms among junior high school students. Predictive models were constructed using Logistic regression, Random Forest, and eXtreme Gradient Boosting (XGBoost) algorithms, with SHapley Additive exPlanations analysis explaining the optimal model. Results: The detection rate of anxiety symptoms among junior high school students was 16.3%. Logistic regression analysis showed that junior high school students who were female ( OR =1.22), in the ninth grade ( OR =1.27), living in urban areas ( OR =1.37), having a father with a college education or above ( OR =1.26), having a mother with a senior high school education ( OR =1.26), and experiencing higher levels of negative life events ( OR =1.05) reported a higher risk of anxiety symptoms(all P <0.05). In contrast, those with moderate family economic status ( OR =0.71), moderate academic burden ( OR =0.59), low academic burden ( OR =0.54), moderate sleep quality ( OR =0.46), good sleep quality ( OR =0.26), excellent sleep quality ( OR =0.15), higher levels of psychological resilience ( OR =0.96), higher levels of school connectedness ( OR =0.96), and higher levels of parent-child cohesion ( OR =0.98) reported a lower risk of anxiety symptoms (all P <0.05). Three machine learning models demonstrated good predictive performance for anxiety symptoms among junior high school students (all AUC>0.8), with the XGBoost model achieving the best predictive performance. SHAP analysis revealed that negative life events, sleep quality, school connectedness, psychological resilience and parent-child cohesion were the top five relevant factors for predicting anxiety symptoms. Conclusions The detection rate of anxiety symptoms among junior high school students is relatively high. The XGBoost model is the optimal predictive model for anxiety symptoms in the population. Negative life events, sleep quality, school connectedness, psychological resilience, and parent-child cohesion are significant correlates of anxiety symptoms among junior high school students.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

The initiation of adaptive immune responses relies on the precise recognition and interpretation of antigenic information. In this process, the specific binding of T cell receptors (TCRs) to peptide-major histocompatibility complex (pMHC) molecules represents one of the key molecular events in the initiation of adaptive immune responses. Accordingly, the structural features of TCR-pMHC complexes provide a fundamental basis for dissecting antigen recognition mechanisms and support rational vaccine design, therapeutic target discovery in TCR-based immunotherapy, and TCR identification and optimization. However, experimental determination of TCR-pMHC structures remains costly, time-consuming, and limited in coverage, making computational approaches essential for rapidly obtaining reliable structural information. Computational methods for predicting the structures of TCR-pMHC complexes have advanced rapidly in recent years, driven by progress in deep learning-based modeling frameworks and the increasing availability of structural and sequence resources. Despite these developments, most existing tools do not adequately distinguish the key structural and biophysical differences between MHC class I (MHC-I) and MHC class II (MHC-II) complexes during model construction. As a consequence, their predictive performance differs substantially between class I and class II complexes. In general, structural predictions for class I complexes outperform those for class II complexes. This discrepancy may be related to several fundamental differences between the two systems, including the architecture of the peptide-binding groove, the distribution of peptide lengths, and the properties of peptide flanking residues (PFRs). Compared with MHC-I molecules, MHC-II molecules usually bind longer antigenic peptides, which typically range from 13 to 25 amino acids in length. PFRs at both termini of these peptides participate in regulating the overall conformation of TCR-pMHC class II complexes and exert a pronounced effect on the geometric and physicochemical characteristics of the TCR-pMHC binding interface. Furthermore, within the TCR recognition interface, the complementarity-determining regions (CDRs) consist of segments that differ markedly in conformational behavior. They commonly include regions that are relatively rigid and structurally stable, together with highly flexible segments exhibiting substantial conformational plasticity. These rigidity-flexibility features constitute an essential structural basis enabling TCRs to recognize diverse peptide-MHC ligands and to accommodate conformational heterogeneity at the interface. However, many current modeling tools, in an effort to enforce global conformational stability or reduce structural noise, tend to over-constrain intrinsically flexible regions. Such oversimplification may lead to inappropriate rigidification of flexible CDR loops, resulting in local structural distortions, compromised interface geometry, or even complete modeling failure for specific complexes. Against this background, the review approaches the field from the perspective of computational differences between MHC-I and MHC-II complexes. We first systematically organize and summarize available resources related to TCRs and pMHCs, including structural datasets, sequence databases, prediction tools, and benchmarking studies. We then focus on five representative tools capable of predicting both class I and class II complexes—AlphaFold2, AlphaFold3, TCRmodel2, tFold-TCR, and TCR-pHLA_ModellerS. After excluding structures present in the training sets of these tools, we constructed a benchmark dataset comprising 25 class I and 10 class II TCR-pMHC complexes in the bound state and conducted a systematic evaluation using this dataset. We first employ widely used general evaluation metrics, including All-Atom Root Mean Square Deviation (All-Atom RMSD), Backbone RMSD, Template Modeling score (TM-score), and DockQ, to assess the global conformational accuracy and interface modeling quality of class I and class II complexes. For class II complexes, we propose for the first time a peptide flanking residue deviation index, including the PFRs-Deviation Index (PFRs-DI), N-PFR-Deviation Index (N-PFR-DI), and C-PFR-Deviation Index (C-PFR-DI), to quantitatively characterize conformational deviations in PFRs. In addition, we propose the CDR conformational consistency index (CCC) designed to qualitatively evaluate the ability of prediction tools to capture TCR CDR conformational flexibility. These metrics collectively assess a tool’s ability to model both overall conformation and critical functional regions, thereby addressing the limitations of existing evaluation criteria that overemphasize global structure while inadequately capturing modeling quality in key functional areas. This establishes a unified analytical framework for MHC-I and MHC-II complexes to guide data resource selection, modeling strategy formulation, and evaluation system development. The framework further advances computational modeling and provides crucial support for multi-scale analysis of TCR-pMHC recognition mechanisms and their biological functions.

Multidrug-resistant (MDR) bacterial infections have emerged as a serious challenge of global public health crisis. The overuse and misuse of conventional antibiotics have dramatically accelerated the emergence, evolution and worldwide spread of drug-resistant bacterial strains, necessitating urgent exploration of novel antibacterial strategies. Bacteriophages serve as natural bacterial predators offering distinct advantages including high host specificity, autonomous self-replication capabilities and cost-effective large-scale production. However, wild-type phages present significant clinical limitations due to their narrow host ranges, susceptibility to rapid immune clearance and poor penetration of bacterial biofilms, which severely restrict their therapeutic applications. The convergence of synthetic biology, nanotechnology and advanced gene editing technologies has accelerated the development of engineered bacteriophage platforms, providing programmable, scalable and clinically translatable pathways to overcome these inherent biological constraints. Here, we systematically delineate four fundamental strategies for engineered bacteriophage development. Chemical modification utilizes reactive functional groups such as amino, carboxyl and thiol moieties on capsid proteins through esterification, amidation or click chemistry reactions to achieve precise drug conjugation and surface functionalization. In vivo editing encompasses ultraviolet or chemical mutagenesis for random mutation induction, homologous recombination for targeted genetic alterations, recombineering methodologies including electroporation-mediated bacteriophage recombination engineering, and CRISPR-Cas systems for precise genome editing to enable exact genetic reconstruction and host range reprogramming. In vitro synthesis leverages genome engineering platforms where intact phage genomes are transferred into yeast or host bacteria to facilitate highly efficient homologous recombination, enabling large DNA fragment assembly and cross-gene host range expansion without bacterial toxicity constraints. Directed evolution combines artificial selection through mutation library screening with rational design approaches involving chimeric receptor binding protein construction or site-specific mutagenesis, effectively balancing the discovery of unknown adaptive pathways with targeted host specificity modification. Moreover, we comprehensively discuss therapeutic applications across diverse clinical scenarios. Engineered bacteriophage effectively disrupt bacterial biofilms through sophisticated functionalized delivery platforms including nanozyme-conjugated phages, phage-liposome nanoconjugates and bio-responsive hydrogels, demonstrating significantly enhanced bactericidal efficiency compared to unmodified free phages. These bioengineered vectors attenuate bacterial virulence and resensitize pathogens to antibiotics by delivering CRISPR-Cas systems or base editors to disrupt critical virulence factors such as pili, capsule synthesis machineries and quorum sensing systems, or by inactivating antibiotic resistance determinants including beta-lactamase genes. As an intelligent nanomedicine delivery platform, engineered bacteriophage enable precise pathogen elimination an through photocatalytic reactive oxygen species generation, immunomodulatory interventions, or controlled release of antibacterial drugs. Furthermore, oral administration of engineered bacteriophage facilitates microbiota modulation, which selectively eliminate intestinal pathogens while preserve beneficial commensal microbiota, thereby restoring microbial community balance and preventing complications associated with dysbiosis. Finally, we critically analyze persistent challenges including host strain matching complexity, evolution of bacterial resistance mechanisms, pharmacokinetic optimization requirements, optimal administration route selection, large-scale production quality control standards and clinical dosing determination protocols. Through multidisciplinary integration of synthetic biology, infectious disease medicine and immunology, future translational medicine studies of bacteriophage should establish comprehensive technical platforms encompassing rapid phage screening, intelligent rational design, rigorous in vivo evaluation and standardized clinical validation processes, ultimately advancing engineered bacteriophage from laboratory innovations to clinically approved therapeutics for effectively combating MDR bacterial infections.

ObjectiveTo study the effect and mechanism of Shentong Zhuyutang in treating intervertebral disc degeneration （IDD） in rats. MethodsIn the cell experiment， male rats were administrated with normal saline or low-， medium-， and high-dose （3.38， 6.75，13.5 g·kg^-1， respectively） Shentong Zhuyutang by gavage， respectively， and serum samples were collected after 7 days of continuous administration. Another 10 male rats were selected for the isolation of nucleus pulposus cells. The cell model of IDD was established by treatment with interleukin （IL）-1β. The modeled cells were then treated with Shentong Zhuyutang-containing serum and the ferroptosis inhibitor ferrostatin-1 （Fer-1）， respectively， to investigate the effects of Shentong Zhuyutang-containing serum on the proliferation and ferroptosis of nucleus pulposus cells. To study the role of silent information regulator 1 （SIRT1）/nuclear factor erythroid 2-related factor 2 （Nrf2） in the regulation of ferroptosis in nucleus pulposus cells by Shentong Zhuyutang-containing serum， this study treated the cells with the SIRT1 inhibitor Ex 527 and the Nrf2 inhibitor ML385， respectively， in addition to the treatment with IL-1β and high-dose Shentong Zhuyutang-containing serum. The cell-counting kit-8 （CCK-8） assay and EdU staining were employed to measure the cell viability and proliferation， respectively. The Fe²⁺， glutathione （GSH）， and malondiadehyde （MDA） levels were measured by colorimetric assay. Western blot was employed to determine the protein levels of glutathione peroxidase 4 （GPX4）， acyl-CoA synthetase long-chain family 4 （ACSL4）， Collagen Ⅱ， Aggrecan， SIRT1， and Nrf2. Immunofluorescence was used detect SIRT1 expression. In the animal experiment， male rats were treated with anulus puncture for the modeling of IDD. Rats were randomly assigned into sham operation， model， Shentong Zhuyutang-containing serum （13.5 g·kg^-1）， and positive control （nimesulide dispersible tablets， 0.18 mg·kg^-1） groups. Rats in the drug intervention groups were administrated with corresponding agents at 1 mL·kg^-1， and those in the sham operation and model groups were administrated with equal volumes of normal saline， once daily for 28 consecutive days. At the end of the last administration， the histopathological changes in the intervertebral discs of rats were observed by hematoxylin-eosin staining and scored by the Masuda method. Western blot was employed to determine the protein levels of SIRT1， Nrf2， GPX4， and Collagen Ⅱ in the nucleus pulposus tissue. ResultsCompared with the control group， the IL-1β group of nucleus pulposus cells showed elevated levels of Fe²⁺， MDA， and ACSL4 （P<0.05）， decreased cell viability， lowered GSH level， and down-regulated protein levels of GPX4， Collagen Ⅱ， and Aggrecan （P<0.05）. Shentong Zhuyutang-containing serum and Fer-1 reversed the effects of IL-1β on the viability and ferroptosis of nucleus pulposus cells and up-regulated the protein levels of Collagen Ⅱ and Aggrecan in nucleus pulposus cells （P<0.05）. Compared with the control group， the IL-1β group showcased down-regulated expression of Sirt1 and Nrf2 in nucleus pulposus cells （P<0.05）. Compared with the IL-1β group， the high-dose Shentong Zhuyutang-containing serum+IL-1β group showed up-regulated expression of SIRT1 and Nrf2 in nucleus pulposus cells （P<0.05）. Compared with the high-dose Shentong Zhuyutang-containing serum+IL-1β group， the ML385 group showed down-regulated protein levels of Nrf2 and GPX4， lowered GSH level， and elevated Fe²⁺ and MDA levels （P<0.05）. In addition， the Ex 527 group showed down-regulated protein levels of SIRT1， Nrf2， and GPX4 （P<0.05）. The results of the animal experiment showed that compared with the sham operation group， the model group had severe degeneration of the intervertebral disc tissue with increased pathological score， up-regulated protein level of ACSL4 （P<0.05）， and down-regulated protein levels of SIRT1， Nrf2， GPX4， and Collagen Ⅱ （P<0.05）. Compared with the model group， the Shentong Zhuyutang group showed alleviated IDD with declined pathological score， down-regulated protein level of ACSL4 （P<0.05）， and up-regulated protein levels of SIRT1， Nrf2， GPX4， and Collagen Ⅱ （P<0.05）. ConclusionShentong Zhuyutang may activate the SIRT1/Nrf2 signaling pathway to inhibit the ferroptosis of nucleus pulposus cells， thereby delaying the process of IDD in rats.

ObjectiveTo explore the mechanism of Shouhui Tongbian capsules in treating constipation based on the research foundation of its active components combined with network pharmacology and animal experiments. MethodsThe drug components were imported into SwissTargetPrediction to predict the targets of Shouhui Tongbian capsules， and constipation-related targets were collected from disease databases. A protein-protein interaction （PPI） network was constructed for the common targets shared by Shouhui Tongbian capsules and constipation to screen key targets， which was followed by gene ontology （GO） function and Kyoto encyclopedia of genes and genomes （KEGG） pathway enrichment analyses. A "bioactive component-target-pathway" network was constructed， and the core components of Shouhui Tongbian capsules in treating constipation were screened based on the topological parameters of this network. Molecular docking was employed to predict the binding affinity of core components to key targets. A mouse model of constipation was constructed to screen the key pathways and targets of the drug intervention in constipation. ResultsThe PPI network revealed six key constipation-related targets： protein kinase B （Akt1）， B-cell lymphoma-2 （Bcl-2）， glycogen synthase kinase-3β （GSK-3β）， cyclooxygenase-2 （PTGS2）， estrogen receptor 1 （ESR1）， and epidermal growth factor receptor （EGFR）. The KEGG pathway analysis showed that the phosphatidylinositol 3-kinase （PI3K）/Akt signaling pathway was the most enriched. The topological parameter analysis of the "bioactive component-target-pathway" network screened out the top 10 core components： auranetin， isosinensetin， naringin， diosmetin， quercetin， apigenin， luteolin， hesperidin， isorhapontigenin， and chrysophanol. Molecular docking results showed that the 10 core components had strong binding affinity with the 6 key targets. Animal experiments showed that after intervention with different doses of Shouhui Tongbian capsules， the time to the first black stool excretion was reduced and the fecal water content and small intestine charcoal propulsion rate of mice were improved. After treatment with Shouhui Tongbian capsules， the colonic mucosal injury and glandular arrangement were alleviated， and the muscle layer thickness was increased. Western blot results showed that Shouhui Tongbian capsules recovered the expression of apoptosis-related molecules mediated by the PI3K/Akt pathway in the colonic tissue of constipated mice. Terminal-deoxynucleotidyl transferase-mediated nick end labeling （TUNEL） results showed that the cell apoptosis rate of the colon significantly reduced after intervention with Shouhui Tongbian capsules. ConclusionThe results of network pharmacology and animal experiments confirmed that Shouhui Tongbian capsules can treat constipation through multiple targets and pathways. The capsules can effectively intervene in loperamide-induced constipation in mice by regulating the constipation indicators and reducing cell apoptosis in the colon tissue via activating the PI3K/Akt signaling pathway.

ObjectiveTo investigate the effect and therapeutic role of quercetin on ferroptosis in lipopolysaccharide （LPS）-induced acute kidney injury （AKI） rats based on the Kelch-like epichlorohydrin-related protein-1 （Keap1）/nuclear factor erythroid-2-related factor 2 （Nrf2）/antioxidant response element （ARE） pathway. MethodsSixty male SD rats were randomly divided into normal group， model group， quercetin high-dose （100 mg·kg^-1） and low-dose （10 mg·kg^-1） groups， ferroptosis inhibitor Ferrostatin 1 （FER1） group （5 mg·kg^-1）， and quercetin high-dose + Nrf2 inhibitor group （ML385， 30 mg·kg^-1）. Except for the normal group， the AKI rat model was established in each group by intraperitoneal injection of LPS （10 mg·kg^-1）. Following successful modeling， each treatment group received the corresponding dose of drug intervention， while the normal and model groups were administered an equal volume of normal saline. The intervention lasted for 3 weeks. Serum creatinine （SCr） and blood urea nitrogen （BUN） levels were measured biochemically to assess renal function. Serum tumor necrosis factor-α （TNF-α） and interleukin （IL）-1β and IL-6 levels were detected by enzyme-linked immunosorbent assay （ELISA）. The levels of Fe²⁺， malondialdehyde （MDA）， superoxide dismutase （SOD）， and glutathione （GSH） in renal tissue were detected. Hematoxylin-eosin （HE）， Masson， and periodic acid-Schiff （PAS） staining were employed to observe pathological morphological changes in renal tissue. Mitochondrial morphological changes were observed using transmission electron microscopy. Reactive oxygen species （ROS） levels in renal tissue were detected by immunofluorescence （IF）. The protein and mRNA expression levels of Keap1， Nrf2， heme oxygenase-1 （HO-1）， glutathione peroxidase 4 （GPX4）， transferrin receptor （TFR1）， and kidney injury molecule-1 （KIM-1） were assessed by immunohistochemistry （IHC） and real-time fluorescence quantitative polymerase chain reaction （Real-time PCR）. ResultsCompared with the normal group， the model group exhibited significantly elevated serum levels of SCr， BUN， TNF-α， IL-1β， IL-6， Fe²⁺ and MDA in renal tissue， and significantly reduced SOD and GSH levels （P<0.01）. Pathological injury in renal tissue was severe， with evident mitochondrial damage characteristic of ferroptosis and a reduced mitochondrial count. ROS levels in renal tissue were significantly increased. The protein and mRNA expression levels of Keap1， TFR1， and KIM-1 in renal tissue were significantly elevated， while those of Nrf2， HO-1， and GPX4 were significantly decreased （P<0.01）. Compared with the model group， serum levels of SCr， BUN， TNF-α， IL-1β， IL-6， Fe²⁺ and MDA in renal tissue in the quercetin dosage groups and FER1 group showed varying degrees of reduction， while SOD and GSH levels were significantly increased （P<0.05）. Pathological injury in renal tissue was markedly alleviated， mitochondrial damage improved， and mitochondrial counts increased. ROS levels in renal tissue were significantly reduced. The protein and mRNA levels of Keap1， TFR1， and KIM-1 in renal tissue were significantly decreased， while those of Nrf2， HO-1， and GPX4 were significantly increased， with the most notable improvement in the high-dose quercetin group （P<0.05）. In comparison to the high-dose quercetin group， the ML385 group significantly weakened the protective effect of quercetin on AKI rats （P<0.05）. ConclusionQuercetin effectively inhibits ferroptosis， improves renal tissue injury， and repairs renal function in AKI rats， and its mechanism may be related to the activation of the Keap1/Nrf2/ARE pathway.