ObjectiveTo investigate the migration and distribution characteristics of chemical constituents in blood and hippocampal tissues before and after vinegar processing of Citri Reticulatae Pericarpium Viride（CRPV）， and to explore the potential material basis and mechanisms underlying their regulatory effects on emotional disorders by comparing the effects of raw and vinegar-processed products of CRPV. MethodsUltra-performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS/MS） was employed to characterize and identify the chemical constituents of raw and vinegar-processed products of CRPV extracts， as well as their migrating components in blood and hippocampal tissues after oral administration. Reference standards， databases， and relevant literature were utilized for compound annotation， with data processing performed using PeakView 1.2 software. Seventy male C57BL/6 mice were randomly divided into seven groups， including the blank group， model group， diazepam group（2.5 mg·kg^-1）， raw CRPV low/high dose groups（0.6， 1.2 g·kg^-1）， and vinegar-processed CRPV low/high dose groups（0.6， 1.2 g·kg^-1）， with 10 mice per group. Except for the blank group， all other groups underwent chronic restraint stress（2 h·d^-1） for 20 d. Each drug-treated group received oral administration at the predetermined dose starting 10 d after modeling， with a total treatment duration of 10 d. Following model-based drug administration， mice underwent open-field， forced swimming， and elevated plus maze tests. After anesthesia with isoflurane， whole brains were collected from each group of mice， and hippocampi were dissected. Reactive oxygen species（ROS） level in hippocampal tissues was quantified by enzyme-linked immunosorbent assay（ELISA）. Hematoxylin-eosin（HE） staining was used to observe hippocampal tissue morphology. Immunofluorescence was performed to detect neuronal nuclei（NeuN） and peroxisome proliferator-activated receptor alpha（PPARα） expressions in hippocampal tissue. Then， pharmacodynamic evaluations were conducted to assess the effects of raw and vinegar-processed CRPV on mood disorders， exploring the potential mechanisms. ResultsVinegar processing caused significant changes in the chemical composition of CRPV， with 18 components showing increased relative content and 35 components showing decreased relative content. The primary changes occurred in flavonoid compounds， including 20 flavonoids， 20 flavonoid glycosides， 3 triterpenes， 3 phenolic acids， 1 alkaloid， and 6 other compounds. Twenty-one components were detected in blood（15 methoxyflavones， 4 flavonoid glycosides， and 2 phenolic acids）， with 17 shared between raw and vinegar-processed CRPV. Seven components reached hippocampal tissues（all common to both forms）. In regulating emotional disorders， Vinegar-processed CRPV exhibited superior antidepressant-like effects compared to raw products. HE staining revealed that both treatments improved hippocampal neuronal morphology， particularly in the damaged CA1 and CA3 regions. Immunofluorescence and ELISA analyses demonstrated that both raw and vinegar-processed CRPV significantly modulated NeuN and PPARα expressions in hippocampal tissue while alleviating oxidative stress induced by excessive ROS（P<0.05）. ConclusionThe chemical composition of CRPV undergoes changes after vinegar processing， but the migrating components in blood and hippocampus are primarily methoxyflavonoids. These components may serve as the potential material basis for activating the PPARα pathway， thereby negatively regulating ROS generation in the hippocampus， reducing oxidative stress， and promoting the development of NeuN-positive neurons. These findings provide experimental evidence for enhancing quality standards， pharmacodynamic material research， and active drug development of raw and vinegar-processed CRPV.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

Background Ozone (O₃) is a major air pollutant. The existing monitoring system has uneven distribution of sites, insufficient coverage in underdeveloped areas, and low temporal resolution, making it difficult to obtain hourly data. This limits the dynamic identification of pollution and the formulation of prevention and control strategies. Objective To construct an hourly O₃ concentration estimation model based on ensemble machine learning, aiming to improve the accuracy of pollution exposure assessment and explore O₃ health impacts. Methods This study integrated land use regression modeling with modern machine learning techniques, employing random forest and XGBoost algorithms to construct base models, and stacking integration using non-negative least squares. The ensemble model was trained and validated across China using high-resolution, multi-source geographic data (e.g., meteorologicaldata, population density, land cover types, and aerosol optical thickness). It was tested in Taiyuan City, combined with a distributed lag non-linear model to analyze the association between O₃ and emergency admissions. Results The constructed ensemble model performed well in predicting O₃ concentration, with a higher coefficient of determination (R²) and a lower root-mean-square deviation (RMSE) compared to the single models. The R² improved from 0.90 to 0.92, and the RMSE decreased from 11.41 to 10.62, enhancing both prediction accuracy and generalization ability. In the application to Taiyuan City, the model successfully imputed the hourly-level data for the entire year. The distributed lag non-linear model analysis revealed that the relative risk (RR) values for the 6th to 8th days following O₃ exposure were 1.14 (95%CI: 1.01, 1.29), 1.16 (95%CI: 1.02, 1.31), and 1.14 (95%CI: 1.01, 1.29), respectively, which were significantly higher than 1, indicating a significant lagged association (lagged 6-8 d) between O₃ and the number of emergency room visits. Conclusion A high-precision, hourly-level O₃ concentration estimation model is successfully constructed by combining the land use regression model with an ensemble machine learning approach to provide a scientific basis for environmental policy formulation and public health intervention. The application of the model verifies its generalization ability and practical application value, which can provide a new technical framework for subsequent environmental health research.

Purpose: Our previous study showed that circulating tumor cell (CTC) count combined with gene mutation detection might help differentiate benign and malignant pulmonary nodules (PNs). Herein, we aimed to expand the study cohort and conduct further sequencing analysis. Materials and Methods: Patients with PNs were included, and CTCs were identified before operation. Low-coverage whole-genome sequencing (LC-WGS) and lung cancer-related targeted gene sequencing were performed on CTCs. The diagnostic efficacy was evaluated by receiver operating characteristic (ROC) curve. The differences in CTC counts among subgroups classified by demographic–clinical characteristics were analyzed. LC-WGS–based copy number variation (CNV) analysis and targeted gene mutation analysis were conducted. Results: A total of 172 patients were included. CTC count of 2.5 was identified by the ROC curves as the optimal diagnostic cutoff. The sensitivity and specificity of CTC count for differentiating benign and malignant PNs were 54.2% and 78.6%, respectively. The diagnostic sensitivity and specificity of combined CTC count, radiological nodule type, and any malignant imaging features were 84.7% and 71.4%, respectively. The CTC counts were significantly greater in patients with aggressive tumors, later stage, and spread through air spaces. CTCs from malignant cases had more CNVs than those from benign cases. Conclusion CTC count can be used in identifying malignant PNs. The diagnostic efficacy can be improved if combined with computed tomography imaging characteristics. Further CNV analysis might help differential diagnosis. Greater CTC count might suggest more aggressive tumors. CTC detection can provide important information and guidance for subsequent management of PNs.

Background Protein tyrosine phosphatase non-receptor type II (PTPN2) is essential for the regulation of inflammation and immunity, but the specific mechanism of action of Ptpn2 in silicosis is unknown. Objective To investigate the regulatory role of overexpression of Ptpn2 in SiO₂-mediated inflammatory response in alveolar type II epithelial cells based on transcriptome sequencing. Methods This study was an in vitro study. A negative control group (vector transferred) and an overexpression of Ptpn2 group of mouse lung epithelial cell line MLE-12 cells were firstly constructed. Transcriptome sequencing was performed to detect differentially expressed genes (DEGs), differentially expressed mRNAs, and differentially expressed ncRNAs in the two groups of MLE-12 cells, and then the DEGs were analyzed by the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). Constructed MLE-12 cells and A549 cells were stimulated using SiO₂ suspension, and divided into a negative control group (vector transferred), an overexpression of Ptpn2 group, a negative control + SiO₂ group, and an overexpression of Ptpn2 + SiO₂ group, respectively. Protein expressions of tumor necrosis factor-α (TNF-α) and interleukin (IL)-17A, IL-2, IL-1β were detected by Western blot. Positive TNF-α expression was detected by immunofluorescence staining. Results The results of Western blot showed that the protein expression level of PTPN2 was up-regulated in the overexpressed Ptpn2 group compared with the negative control group (P < 0.05). The volcano plot and clustering heat map showed that there were 1122 DEGs, 3681 differentially expressed mRNAs, and 474 differentially expressed ncRNAs in the overexpressed Ptpn2 group compared with the negative control group. The results of GO enrichment showed that, in terms of the biological process, the DEGs were mainly related to the regulation of metal ion transport, extracellular matrix organization, and extracellular structural organization; in terms of cellular composition, the DEGs were mainly related to collagen-containing extracellular matrix, receptor complexes, and basement membranes; in terms of molecular function, the DEGs were mainly related to the extracellular matrix structural constituents, glycosaminoglycan binding, and semaphorin receptor binding. The results of KEGG enrichment showed that the DEGs were closely related to cytokin-cytokine receptor interaction, IL-17 signaling pathway, TNF signaling pathway, and chemokine signaling pathway. In MLE-12 and A549 cells, the results of Western blot showed that the protein expression levels of TNF-α , IL-17A, IL-2, and IL-1β were up-regulated in the negative control + SiO₂ group compared with the negative control group (P < 0.05), and the protein expression levels of TNF-α, IL-17A, IL-2, and IL-1β were down-regulated in the overexpression of Ptpn2 + SiO₂ group compared with the negative control + SiO₂ group (P < 0.05). The immunofluorescence staining showed that the expression of TNF-α was increased in the negative control + SiO₂ group compared with the negative control group, and decreased in the overexpression of Ptpn2 + SiO₂ group compared with the negative control + SiO₂ group. Conclusion Overexpression of Ptpn2 inhibits SiO₂-mediated inflammatory response in MLE-12 cells and A549 cells.

"Stagnation" is an important pathological state in the development and progression of malignant tumors. However， its intrinsic connection with different stages of tumor evolution has not been clearly elucidated in previous studies. Drawing on clinical practice， this paper proposes the theory of stagnant toxin， emphasizing stage-specific pathogenesis and differentiated treatment strategies for tumors based on the varying manifestations of stagnation at each phase. The theory interprets the pathogenesis of stagnant toxin across the stages of tumor development through the five elements "wood， fire， earth， metal， and water" corresponding respectively to wood stagnation in the precancerous stage， metal stagnation in the postoperative phase， fire stagnation during adjuvant therapy， earth stagnation in the progressive stage， and water stagnation in the advanced stage. Each type of stagnation reflects a distinct pathogenic mechanism， such as wood stagnation giving rise to disease， metal stagnation inducing residual symptoms， fire stagnation resulting in ulceration， earth stagnation spreading toxin transmission， and water stagnation leading to critical deterioration. Accordingly， the treatment principles include guiding wood stagnation with counterflow， dispersing metal stagnation to harmonize symptoms， venting fire stagnation to regress ulcers， depleting earth stagnation to block progression， and controlling water stagnation to preserve vitality. This theoretical framework offers a traditional Chinese medicine perspective for understanding and treating malignant tumors based on the concept of stagnant toxin.

AIM:To analyze the characteristics and correlation of phacoemulsification parameters and anterior segment parameters in cataract patients with different blood glucose levels.METHODS:A total of 45 type 2 diabetic cataract patients(45 eyes)treated in our hospital from March 2023 to April 2024 were stratified into two groups based on glycosylated hemoglobin(HbA1c)levels: group A: HbA1c <7%(n=18)and group B: 7%≤HbA1c<8.5%(n=27); a total of 94 age-matched age-related cataract patients(94 eyes)were enrolled as the control group(group C). All underwent phacoemulsification with intraocular lens implantation. Anterior segment parameters, including corneal, lens and anterior chamber measurements, were recorded. Correlations between phacoemulsification parameters and anterior segment parameters were analyzed, and differences among groups were compared.RESULTS: In groups A and B, effective phacoemulsification time(EPT)negatively correlated with corneal endothelial cell density(CECD)(r=-0.315, P=0.035). Average phacoemulsification time(APT)positively correlated with the anterior corneal surface radius of curvature(Rm; r=0.402, P=0.006)and negatively correlated with the flat axis meridian curvature(K1), steep axis meridian curvature(K2), mean curvature(Km)of the anterior corneal surface, and lens density at 6 mm zones(PDZ3; all P<0.05). Average phacoemulsification energy(AVE)positively correlated with mean lens density(LD-mean), lens density at 2 mm zones(PDZ1), lens density at 4 mm zones(PDZ2), and PDZ3(all P<0.05), and negatively with pupil diameter(r=-0.385, P=0.009). In the group C, EPT showed a positive correlation with Pentacam nucleus staging(PNS)density grade, PDZ1, PDZ2, and PDZ3(all P<0.05). A positive correlation was observed between AVE and PNS classification(r=0.246, P=0.018). Conversely, AVE exhibited a negative correlation with CECD(r=-0.245, P=0.018). EPT in groups A and B was higher than that in the group C(P<0.05). Both EPT and APT in the group B were higher than those in the group A(P<0.05). In diabetic cataract patients, CECD, corneal density(CD), and posterior corneal surface height positively correlated with diabetes duration(P<0.05). Posterior corneal surface K1 and Rm positively correlated with 7%≤HbA1c<8.5%(P<0.05). Total corneal astigmatism negatively correlated with HbA1c, 2-hour post-breakfast blood glucose(2hPBG), and fasting insulin(FINS; P<0.05). CD and lens thickness(LT)positively correlated with FINS(P<0.05).CONCLUSION: Phacoemulsification parameters and blood glucose-related indices exhibited varying degrees of correlation with anterior segment parameters in cataract patients with different blood glucose levels. EPT in diabetic cataract patients was higher than that in age-related cataract patients, while EPT and APT in diabetic cataract patients with poor glycemic control were higher than those with good glycemic control.

ObjectiveTo study the mechanism of compound Xishu granules （CXG） in inhibiting the proliferation of hepatocellular carcinoma cells by regulating ferroptosis. MethodsThe transplanted tumor model of human Huh7 was established with nude mice and the successfully modeled mice were randomized into model， Fufang Banmao （0.21 g·kg^-1）， low-dose （1.87 g·kg^-1） CXG， medium-dose （3.74 g·kg^-1） CXG， and high-dose （7.49 g·kg^-1） CXG groups. Mice were administrated with drinking water or CXG for 28 days， and the body weight and tumor volume were measured every 4 days. Hematoxylin-eosin staining was employed to observe the histopathological changes of tumors. The cell-counting kit-8 （CCK-8） was used to examine the survival rate of Huh7 cells treated with different concentrations （0， 31.25， 62.5， 125， 250， 500， 1 000 mg·L^-1） of CXG for 24 h and 48 h. CA-AM， DCFH-DA， and C11-BODIPY^581/591 fluorescent probes were used to determine the intracellular levels of ferrous ion （Fe²⁺）， reactive oxygen species （ROS）， and lipid peroxide （LPO）， respectively. The colorimetric method was employed to measure the levels of glutathione （GSH） and superoxide dismutase （SOD）. Western blot was employed to determine the protein levels of glutathione peroxidase 4 （GPX4）， transferrin receptor 1 （TFR1）， and ferritin heavy chain 1 （FTH1）， respectively. ResultsIn the animal experiment， compared with the model group， the drug treatment groups showed reductions in the tumor volume from day 12 （P<0.01）. After treatment， the Fufang Banmao and low-， medium-， and high-dose CXG groups had lower tumor volume， relative tumor volume， and tumor weight than the model group （P<0.05）， with tumor inhibition rates of 48.99%， 79.93%， 91.38%， and 97.36%， respectively. Moreover， the CXG groups had lower tumor volume and relative tumor volume （P<0.05 in all the three dose groups） and lower tumor weight （P<0.05 in medium-dose and high-dose groups） than the Fufang Banmao group. Compared with the model group， the drug treatment groups showed reduced number of tumor cells， necrotic foci with karyopyknosis， nuclear fragmentation， and nucleolysis， and the high-dose CXG group showed an increase in the proportion of interstitial fibroblasts. In the cell experiment， compared with the blank group， CXG reduced the survival rate of Huh7 cells in a dose-dependent manner after incubation for 24 h and 48 h （P<0.05）. Compared with the blank group， the RSL3 group and the low-， medium-， and high-dose CXG groups showed a decrease in the relative fluorescence intensity of CA-AM and increases in the fluorescence intensity of DCFH-DA and fluorescence ratio of C11-BODIPY^581/591， which indicated elevations in the levels of Fe²⁺ （P<0.01）， ROS （P<0.05）， and LPO （P<0.01）， respectively. Compared with the blank group， the RSL3 and low-， medium-， and high-dose CXG groups showed lowered levels of GSH and SOD （P<0.05）. In addition， the RSL3 group and the medium- and high-dose CXG groups showed down-regulated expression of GPX4 and FTH1 （P<0.05）， and the low- and high-dose CXG groups presented up-regulated expression of TFR1 （P<0.05）. ConclusionCXG suppresses the proliferation of hepatocellular carcinoma cells by inducing ferroptosis via downregulating the GSH-GPX4 signaling axis and increasing intracellular Fe²⁺and LPO levels.

BACKGROUND:Post and core restoration is a common choice for tooth defects,but the repair effects of various post and core materials are different. OBJECTIVE:To evaluate the stress distribution at the post and core,tooth root,and bonding agent site of post and core models made of different elastic modulus post and core materials using finite element method. METHODS:A three-dimensional root canal treated maxillary central incisor model was built using three-dimensional modeling software,which was restored with a full ceramic crown.The post and core materials in the restoration used nanoceramic resin(elastic modulus=12.8 GPa),composite resin(elastic modulus=16 GPa),hybrid ceramic(elastic modulus=34.7 GPa),glass ceramic(elastic modulus=95 GPa),titanium alloy(elastic modulus=112 GPa),and zirconia(elastic modulus=209.3 GPa).The model was fixed in cortical bone.A 100 N concentrated force of 45° from the long axis of the tooth was applied to 1/3 of the crown and tongue side of the central incisor.The stress distribution of the post and core,dentin,and tooth-root bonding agent in the model was repaired by the maximum principal stress criterion. RESULTS AND CONCLUSION:(1)When the post and core materials with higher elastic modulus was used,the post-core stress in the repair model was more concentrated.When the elastic modulus of the post and core materials(nanoceramic resin and composite resin)was close to dentin,the stress distribution of the post and core was more uniform.The stress distribution of dentin in all restoration models was similar regardless of post and core materials.When the post and core with higher elastic modulus was used,more stress concentration was shown at the post and root bonding agent in the repair model.(2)The maximum stress values at the post and core,tooth root,and the bonding agent site of post and tooth root in the nanoceramic resin model were 31.00,33.21,and 0.51 MPa,respectively.The maximum stress values at the post and core,tooth root,and the bonding agent between the post and tooth root in the composite resin model were 36.84,33.14,and 0.59 MPa,respectively.In the mixed ceramic model,the maximum stress values at the post and core,tooth root,and the bonding agent between the post and tooth root were 64.05,32.83,and 1.00 MPa,respectively.In the glass ceramic model,the maximum stress values at the post and core,tooth root,and the bonding agent between the post and tooth root were 112.30,32.69,and 1.73 MPa,respectively.In the titanium alloy model,the maximum stress values of the post and core,tooth root,and the bonding agent between the post and tooth root were 120.00,32.17,and 1.86 MPa,respectively.In the zirconia model,the maximum stress values of the post and core,tooth root,and the bonding agent between the post and tooth root were 148.80,31.85,and 2.28 MPa,respectively.(3)The higher the elastic modulus of the post and core material,the higher the maximum stress at the post and core during restoration.The elastic modulus of the post and core material had no significant effect on the maximum stress of the dental bonding agent and dentin.