ObjectiveBased on the clinical characteristics of chronic gouty arthritis （CGA） in both traditional Chinese and western medicine， this study aims to systematically evaluate the clinical concordance of existing CGA animal models， providing recommendations for establishing animal models that align with the pathological characteristics of CGA and the manifestations of traditional Chinese medicine syndromes. MethodsBy comprehensively retrieving Chinese and international databases such as China National Knowledge Infrastructure， Wanfang， VIP Chinese Science and Technology Periodical Database （VIP）， and PubMed， all relevant literature on CGA animal models was collected. Based on the guidelines， the diagnostic criteria of both traditional Chinese and western medicine were summarized and organized. The evaluation indicators for the CGA model were constructed with reference to existing evaluation modes， and the CGA animal models were analyzed to systematically evaluate the clinical concordance of existing models. ResultsThe current methods used to construct CGA animal models mainly include monosodium urate crystal induction， high-protein diet induction （poultry lack urate oxidase）， and high-fat diet combined with urate oxidase inhibitors and joint injection. Based on 11 pieces of included literature， the traditional Chinese and western medicine scoring data of each model were extracted， and the average scoring values of all models were ultimately calculated. The results show that the average clinical concordances of existing CGA animal models in both traditional Chinese and western medicine are 43.33% and 64.44%， respectively. Among them， the model with the highest clinical concordance rate is the one with a high-fat diet combined with potassium oxonate to induce hyperuricemia plus joint injection， achieving 83.33% clinical concordance in western medicine and 60% in traditional Chinese medicine. This model aligns well with the pathogenic characteristics and pathological changes of clinical CGA. ConclusionAlthough current CGA animal models can simulate some pathological characteristics of CGA， they struggle to comprehensively reflect the complex pathological processes of CGA and the characteristics of traditional Chinese medicine syndromes. Therefore， in the future， it is necessary to establish the CGA animal models that incorporate the clinical disease and syndrome characteristics of traditional Chinese and western medicine and formulate the uniform model evaluation criteria， providing more precise tools for CGA mechanism research and the development of traditional Chinese medicine.

ObjectiveTo evaluate the therapeutic effect of Huazhuo SanJie Chubi presciption （HSCD） on chronic gouty arthritis （CGA） rats with low-grade inflammation and to explore the underlying mechanism with a focus on macrophage polarization. MethodsThe 41 male 6-week-old SD rats were randomly allocated， using the random number table， to a normal group （n=8） and a model group （n =33）. CGA with low-grade inflammation was induced in the model group by daily gavage of potassium oxonate （250 mg·kg^-1·d^-1） and hypoxanthine （300 mg·kg^-1·d^-1）， combined with intra-articular injection of a monosodium urate （MSU） crystal suspension （50 μL， 25 g·L^-¹） into the left ankle twice weekly. After 4 weeks of modeling， 3 rats were randomly selected from each group for model validation. The remaining successfully modeled rats were randomly divided into a model group， an HSCD group （10.35 g·kg^-1·d^-1， gavage once daily）， an M1 polarization agonist group （L-methionine sulfoximine， 300 mg·kg^-1， subcutaneous injection every other day）， an M1 polarization agonist + HSCD group， an M2 polarization inhibitor group （PD0325901， 10 mg·kg^-1·d^-1， gavage once daily）， and M2 polarization inhibitor + HSCD group. The corresponding drug or drug combination was administered according to group assignment， whereas rats in the normal and model groups received 0.5% carboxymethyl cellulose sodium （CMC-Na） vehicle （10.35 g·kg^-1·d^-1， gavage once daily）. All interventions were continued for four weeks. During the intervention period， except for the normal group， potassium oxonate （250 mg·kg⁻¹） and hypoxanthine （300 mg·kg^-1） were co-administered by gavage every other day to maintain the model. At the end of treatment， serum uric acid （SUA）， ankle joint diameter and joint swelling index were measured. The levels of high-sensitivity C-reactive protein （hs-CRP）， interleukin-1β （IL-1β）， interleukin-6 （IL-6）， tumor necrosis factor-α （TNF-α）， chemokine C-C motif ligand 2 （CCL2）， S100 calcium-binding protein A8/A9 （S100A8/A9）， interleukin-10 （IL-10） and arginase-1 （Arg-1） in serum and joint fluid were determined by enzyme-linked immunosorbent assay （ELISA）. High-frequency ultrasound was used to assess MSU deposition in the ankle joint. Hematoxylin-eosin （HE） staining was performed to evaluate synovial histopathological changes. Quantitative Real-time PCR and immunofluorescence were used to detect the mRNA and protein expression of the M1 macrophage polarization markers inducible nitric oxide synthase （iNOS） and the M2 macrophage polarization marker scavenger receptor cysteine-rich type 1 protein M130 （CD163） in synovial tissue. ResultsCompared with the normal group， the model group showed significantly elevated SUA level and joint swelling index， and increased levels of pro-inflammatory cytokines， CCL2， and S100A8/A9 in both serum and joint fluid （P<0.05）， accompanied by MSU deposition and synovial inflammation in the ankle joint. The mRNA and protein expression levels of macrophage polarization M1/M2 markers iNOS and CD163 in synovial tissues were also significantly up-regulated （P<0.05）. Compared with model group， rats in HSCD group had significantly lower SUA levels， attenuated joint swelling， reduced serum levels of pro-inflammatory cytokines， and decreased levels of CCL2 and S100A8/A9 in both serum and joint fluid， accompanied with alleviated MSU deposition and synovial inflammation （P<0.05）. HSCD markedly downregulated the mRNA and protein expression of M1 marker iNOS （P<0.05）， whereas it had no significant effect on the expression of M2 marker CD163. Compared with the M1 polarization agonist group， the M1 polarization agonist + HSCD group showed significantly reduced joint swelling， lower serum levels of pro-inflammatory cytokines， and decreased levels of CCL2 and S100A8/A9 in joint fluid （P<0.05）. In addition， synovial inflammatory cell infiltration and angiogenesis were attenuated， and iNOS mRNA and protein expression levels were significantly reduced （P<0.05）. Compared with the M2 polarization inhibitor group， the M2 polarization inhibitor + HSCD group exhibited reduced joint swelling， decreased levels of CCL2 and S100A8/A9 in joint fluid and ameliorated synovial inflammation （P<0.05）， whereas the levels of anti-inflammatory mediators （IL-10， Arg-1） and CD163 mRNA and protein expression were not significantly increased. ConclusionHSCD alleviates low-grade inflammation in CGA rats， at least in part， by inhibiting macrophage polarization toward the M1 phenotype.

ObjectiveTo systematically evaluate the content differences of 4 components in Leonuri Herba granules， reveal the quality fluctuation patterns of products from the same and different manufacturers， providing scientific basis for the optimization of production process and quality control. MethodsHigh performance liquid chromatography-diode array detector-charged aerosol detector（HPLC-DAD-CAD） was employed to determine the contents of 4 components（syringic acid， leonurine hydrochloride， ferulic acid， and stachydrine hydrochloride） in samples from 19 manufacturers（53 batches， 159 boxes）. Additionally， fingerprint profiles were constructed， and the fingerprint dissimilarity（P_S） and relative standard deviation（RSD） of different samples from the same manufacturer were calculated. A principal component analysis（PCA） model was established with P_S and the RSD values of the 4 components as variables to classify the manufacturers. Finally， samples from 5 manufacturers（M1-M5） covering three consistency groups were selected to calculate three quality consistency parameters， namely intra-batch consistency（P_A）， inter-batch consistency（P_B）， and P_S. Then， PCA was performed with P_A， P_B， and P_S of these 5 manufacturers as variables. ResultsThe average total content of the 4 index components per bag across the 19 manufacturers ranged from 41.10 mg to 97.54 mg. Among them， the content of stachydrine hydrochloride（a pharmacopoeial quality control component） was 32.46-72.70 mg per bag， all meeting the requirements of the 2025 edition of the Pharmacopoeia of the People's Republic of China， with RSD of 1.7%-17.1%. The content ranges of the other 3 components were as follows：syringic acid of 1.43-41.92 mg per bag， leonurine hydrochloride of 0.67-11.85 mg per bag， and ferulic acid of 0.11-3.81 mg per bag. Notably， leonurine hydrochloride exhibited the most significant content fluctuation among samples from the same manufacturer（RSD of 4.8%-59.2%）. PCA results showed that the 19 manufacturers could be classified into 3 categories. Samples from 8 manufacturers（M2， M6， M7， M8， M10， M15， M17， M18） demonstrated relatively high consistency， five manufacturers（M3， M9， M12， M13， M14） showed moderate consistency， six manufacturers（M1， M4， M5， M11， M16， M19） exhibited low consistency. The two methods yielded consistent classification results for the 5 representative manufacturers， verifying the reliability of the proposed method. Among these， manufacturer M2 showed the best quality consistency and the highest total content of indicator components among M1-M5. ConclusionThe HPLC-DAD-CAD multi-detector hyphenation technology established in this study enables the accurate detection of 4 components in Leonuri Herba granules. Significant differences in the total content of these four components are observed among products from 19 manufacturers. The application of 2 consistency evaluation methods combined with PCA can effectively classify their consistency into 3 categories， and the classification results of the 2 methods are highly consistent. This study provides scientific basis for the process optimization and quality standard improvement of Leonuri Herba granules.

ObjectiveTo systematically evaluate the content differences of 4 components in Leonuri Herba granules， reveal the quality fluctuation patterns of products from the same and different manufacturers， providing scientific basis for the optimization of production process and quality control. MethodsHigh performance liquid chromatography-diode array detector-charged aerosol detector（HPLC-DAD-CAD） was employed to determine the contents of 4 components（syringic acid， leonurine hydrochloride， ferulic acid， and stachydrine hydrochloride） in samples from 19 manufacturers（53 batches， 159 boxes）. Additionally， fingerprint profiles were constructed， and the fingerprint dissimilarity（P_S） and relative standard deviation（RSD） of different samples from the same manufacturer were calculated. A principal component analysis（PCA） model was established with P_S and the RSD values of the 4 components as variables to classify the manufacturers. Finally， samples from 5 manufacturers（M1-M5） covering three consistency groups were selected to calculate three quality consistency parameters， namely intra-batch consistency（P_A）， inter-batch consistency（P_B）， and P_S. Then， PCA was performed with P_A， P_B， and P_S of these 5 manufacturers as variables. ResultsThe average total content of the 4 index components per bag across the 19 manufacturers ranged from 41.10 mg to 97.54 mg. Among them， the content of stachydrine hydrochloride（a pharmacopoeial quality control component） was 32.46-72.70 mg per bag， all meeting the requirements of the 2025 edition of the Pharmacopoeia of the People's Republic of China， with RSD of 1.7%-17.1%. The content ranges of the other 3 components were as follows：syringic acid of 1.43-41.92 mg per bag， leonurine hydrochloride of 0.67-11.85 mg per bag， and ferulic acid of 0.11-3.81 mg per bag. Notably， leonurine hydrochloride exhibited the most significant content fluctuation among samples from the same manufacturer（RSD of 4.8%-59.2%）. PCA results showed that the 19 manufacturers could be classified into 3 categories. Samples from 8 manufacturers（M2， M6， M7， M8， M10， M15， M17， M18） demonstrated relatively high consistency， five manufacturers（M3， M9， M12， M13， M14） showed moderate consistency， six manufacturers（M1， M4， M5， M11， M16， M19） exhibited low consistency. The two methods yielded consistent classification results for the 5 representative manufacturers， verifying the reliability of the proposed method. Among these， manufacturer M2 showed the best quality consistency and the highest total content of indicator components among M1-M5. ConclusionThe HPLC-DAD-CAD multi-detector hyphenation technology established in this study enables the accurate detection of 4 components in Leonuri Herba granules. Significant differences in the total content of these four components are observed among products from 19 manufacturers. The application of 2 consistency evaluation methods combined with PCA can effectively classify their consistency into 3 categories， and the classification results of the 2 methods are highly consistent. This study provides scientific basis for the process optimization and quality standard improvement of Leonuri Herba granules.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective To analyze the prevalence status of cardiometabolic risk factor (CMRF) and its aggregation among workers engaged in new forms of employment. Methods A total of 5 429 new employment workers (including couriers, online food delivery workers, and ride hailing drivers) who underwent health medical examinations at a tertiary hospital in Beijing City were selected as the research subjects using the judgment sampling method. Data on waist circumference, blood pressure, blood glucose, and blood lipid levels were collected to analyze their CMRF [central obesity, elevated blood pressure, elevated blood glucose, elevated triglycerides, and reduced high-density lipoprotein cholesterol (HDL-C)] and their aggregation (with ≥ 2 of the above 5 risk factors) status. Results The detection rates of central obesity, elevated blood pressure, elevated blood glucose, elevated triglycerides, and reduced HDL-C were 61.2%, 38.2%, 29.5%, 40.9% and 22.6%, respectively. The detection rates of CMRF aggregation was 57.8%. The result of multivariable logistic regression analysis showed that male, age ≥45 years, smoking, overweight, and obesity were risk factors for CMRF aggregation (all P<0.05). Conclusion The detection rate of CMRF and its aggregation among workers with new forms of employment in Beijing City is relatively high. Targeted prevention and control efforts should be strengthened for high-risk populations, especially males, workers aged ≥45 years, smokers, and those who are overweight or obese.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Post-stroke aphasia is associated with a significantly elevated risk of depression, yet the underlying mechanisms remain unclear. This study recorded 64-channel electroencephalogram data and depression scale scores from 12 aphasic patients with depression, 8 aphasic patients without depression, and 12 healthy controls during resting state and an emotional Stroop task. Spectral and microstate analyses were conducted to examine brain activity patterns across conditions. Results showed that depression scores significantly negatively explained the occurrence of microstate class C and positively explained the transition probability from microstate class A to B. Furthermore, aphasic patients with depression exhibited increased alpha-band activation in the frontal region. These findings suggest distinct neural features in aphasic patients with depression and offer new insights into the mechanisms contributing to their heightened vulnerability to depression.
Humans ; Electroencephalography ; Aphasia/etiology* ; Stroop Test ; Emotions/physiology* ; Depression/etiology* ; Male ; Female ; Middle Aged ; Stroke/complications* ; Brain/physiopathology* ; Aged ; Adult ; Rest/physiology*

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.

Objective: This study investigates the potential of radiomics to predict postoperative progression of ossification of the posterior longitudinal ligament (OPLL) after posterior cervical spine surgery. Methods: This retrospective study included 473 patients diagnosed with OPLL at Peking University Third Hospital between October 2006 and September 2022. Patients underwent posterior spinal surgery and had at least 2 computed tomography (CT) examinations spaced at least 1 year apart. OPLL progression was defined as an annual growth rate exceeding 7.5%. Radiomic features were extracted from preoperative CT images of the OPLL lesions, followed by feature selection using correlation coefficient analysis and least absolute shrinkage and selection operator, and dimensionality reduction using principal component analysis. Univariable analysis identified significant clinical variables for constructing the clinical model. Logistic regression models, including the Rad-score model, clinical model, and combined model, were developed to predict OPLL progression. Results: Of the 473 patients, 191 (40.4%) experienced OPLL progression. On the testing set, the combined model, which incorporated the Rad-score and clinical variables (area under the receiver operating characteristic curve [AUC] = 0.751), outperformed both the radiomics-only model (AUC = 0.693) and the clinical model (AUC = 0.620). Calibration curves demonstrated good agreement between predicted probabilities and observed outcomes, and decision curve analysis confirmed the clinical utility of the combined model. SHAP (SHapley Additive exPlanations) analysis indicated that the Rad-score and age were key contributors to the model’s predictions, enhancing clinical interpretability. Conclusion Radiomics, combined with clinical variables, provides a valuable predictive tool for assessing the risk of postoperative progression in cervical OPLL, supporting more personalized treatment strategies. Prospective, multicenter validation is needed to confirm the utility of the model in broader clinical settings.