This article aims to use TAS1R2/TAS1R3-SPGE biosensing combined with UPLC-MS/MS and molecular docking techniques to identify critical quality attributes(CQAs)for the sweetness of TCM.Jinzhen Oral Liquid was selected as the research subject.Its CQAs for the sweetness were systematically screened,and its immunomodulatory activity was verified,providing support for improving the quality control system of Jinzhen Oral Liquid.This study quantitatively analyzed the interaction between Jinzhen Oral Liquid and sweetness receptors TAS1R2/TAS1R3 by using biosensing technology,identified the structures of its eluted components via UPLC-MS/MS technique,and evaluated the binding ability of the eluted components to the receptors through molecular docking.Furthermore,its immunomodulatory effects were validated by using an LPS-induced BEAS-2B cell inflammation model.19 chemical components interacting with the sweetness receptor were screened through the biosensor,among which calycosin,echinatin,oroxylin A-7-O-β-D-glucuronide,and L-tyrosine exhibited strong binding ability to TAS1R2/TAS1R3 and significantly reduced interleukin-6(IL-6)and tumor necrosis factor-α(TNF-α)levels in the cell model,showing good immunomodulatory activity.By jointly applying multiple technical approaches,this study successfully determined the CQAs for the sweetness in Jinzhen Oral Liquid,providing new ideas and methods for identifying CQAs of TCM preparations and comprehensive and in-depth theoretical support for TCM quality control,thereby contributing to the improvement and development of the quality control system of TCM preparations.

Influenza, an acute respiratory infectious disease caused by the influenza virus, remains a significant challenge for prevention and treatment due to rapid viral mutation and high pathogenicity. Traditional Chinese Medicine (TCM), including Shuangyu Granule (SYKL), has demonstrated efficacy in managing influenza. This study aimed to systematically identify the chemical components of SYKL in vitro and its absorbed constituents in vivo, and to preliminarily explore its potential mechanism in regulating influenza-related immune inflammation. UPLC-Orbitrap-MS/MS and GC-MS were used to characterize SYKL's chemical profile, identifying 148 in vitro components and 21 prototype absorbed blood components. Network target analysis, integrated with single-cell RNA sequencing (scRNA-seq) data from influenza patients, predicted that the absorbed components may target multiple immune-inflammatory regulatory genes across various immune cell types. Molecular docking suggested favorable predicted binding potential between these components and target proteins. Experimental validation using poly(I:C)-induced inflammatory models in both RAW264.7 macrophages and mouse bone marrow-derived macrophages (BMDMs) showed that the absorbed components-loganic acid, 8-epiloganic acid, calycosin, atractylodin, eucalyptol, secoxyloganin, and paeoniflorin-significantly reduced mRNA expression of immune-inflammatory genes (DUSP6, MAPKAPK2, NOD2) and inhibited secretion of TNF-α, IL-6, IL-8, and NO. These findings suggest that SYKL may alleviate influenza-associated inflammation through multi-component, multi-cell, and multi-target pathways, highlighting its potential in modulating excessive immune responses in influenza.

Background Sepsis-induced systemic inflammation, characterized by immune dysregulation and cytokine storms, presents significant therapeutic challenges. Reduning injection (RDN), a Traditional Chinese Medicine formulation, demonstrates clinical efficacy in sepsis management, yet its molecular mechanisms remain elusive. Purpose This study aimed to unravel RDN’s immunomodulatory mechanisms and identify its core effective components targeting key inflammatory signaling networks in sepsis. Methods Initially, six complementary in vitro hyperinflammation models (macrophages, endothelial, epithelial, and intestinal barrier cells) were established, with their transcriptomes integrated with patient-derived septic data to prioritize canonical pathways. Transcriptome profiling of RDN, its 14 phytochemicals, and dexamethasone (DEX) was then performed to analyze pathway enrichment and identify key components. In vitro mechanistic validation included enzyme-linked immunosorbent assay (ELISA) for IL-6 inhibition screening; ADP-Glo™ Kinase Assay for IKKβ/TBK1 inhibition; Western blot to assess phosphorylation dynamics of tank-binding kinase 1 (TBK1), inhibitor of nuclear factor-kappa B kinase subunit beta (IKKβ), and nuclear factor-kappa B (NF-κB); quantitative real-time polymerase chain reaction (qRT-PCR) to confirm downregulation of canonical NF-κB targets; and molecular dynamics (MD) simulations to explore binding mechanisms. For in vivo studies, an LPS-rat sepsis model was used, with preliminary studies defining optimal LPS dose, induction timepoint, and RDN dose. Assessments on septic rats included histopathology (hematoxylin-eosin staining), cytokine profiling (ELISA), complete blood counts, transcriptomic analysis of peripheral blood mononuclear cells (PBMCs) to uncover NF-κB pathway modulation, and qRT-PCR validation of transcriptomic changes. Results The clinically anchored approach effectively prioritized canonical pathways (NF-κB, TNF, and cytokine-cytokine receptor interactions) with strong translational relevance. Transcriptome profiling revealed RDN’s broad pathway enrichment and identified cynaroside (CYN) as the principal effective component exerting multi-pathway anti-inflammatory effects. Mechanistically, CYN dual-inhibited TBK1 (IC₅₀: 8.9 μM) and IKKβ (IC₅₀: 23.3 μM), suppressing NF-κB signaling and cytokine production in macrophages. MD simulations showed stable IKKβ-CYN and TBK1-CYN complexes, with CYN occupying catalytic pockets via hydrogen bonds with key residues and minimal binding site fluctuations. In LPS-induced septic rats, RDN and CYN mitigated multi-organ injury, reduced systemic inflammation (decreased IL-6 and TNF-α), restored complete blood counts, and inhibited NF-κB activation. Conclusion This study advances understanding of TCM’s multi-target immunomodulation in sepsis via a framework integrating high-dimensional data and experimental validation. CYN’s dual-kinase inhibition highlights its potential as a precision therapeutic for sepsis and hyperinflammatory disorders, while reinforcing TCM’s value in novel immunomodulatory drug discovery within network pharmacology and systems medicine paradigms.

Background: Jingu Zhitong Gel (JGZTG), a topical formulation of 12 Chinese herbs, is therapeutically effective against knee osteoarthritis (KOA).Purpose: Characterizing its multi-component profile and the pharmacokinetics of its key effective components is critical for quality control and mechanistic elucidation.Methods: UPLC-Orbitrap-MS was employed to identify the chemical constituents in JGZTG and to detect prototype components and metabolites in rat tissues. A validated UPLC-TQ-MS method was established for the simultaneous quantification of nine core components and for evaluating their pharmacokinetics in both normal and KOA model rats. Compound-target-disease and protein–protein interaction network analyses were performed to investigate component-target associations.Results:A total of 168 chemical constituents were identified in JGZTG. Of these, 73 prototype components and 29 phase I/II metabolites (generated via oxidation, dehydration, methylation) were detected in rat tissues. Following transdermal administration, the nine core components were rapidly absorbed (Tmax < 1.5 h). Notably, the absorption of tetrahydropalmatine and hydroxy-β-sanshool was significantly modulated by the KOA pathological state. Network analysis revealed that the components exhibit strong binding affinities for core proteins, including MMP9, JAK2, and PIK3CA, potentially regulating the AGE-RAGE and HIF-1 signaling pathways.Conclusion: This study systematically elucidates the in vivo material basis and pharmacokinetic characteristics of JGZTG, and integrates network pharmacology to identify component-target associations, providing a scientific foundation for understanding its therapeutic mechanisms.

This study systematically analyzed the global research landscape, technological composition, and core patents in the field of networks target and network pharmacology, and proposes further suggestions based on the IncoPat patent citation database and VOSviewer bibliometric network visualization tool. Using patent literature metrics and scientific knowledge mapping method, technological innovation pathways, research hotspots, and future directions in this field were further revealed. In particular, this field is moving towards data-driven, intelligent, and systematic approaches. Patent analysis indicated that most patent applications in this domain focused on traditional Chinese medicine(TCM), which have provided key engineering technical approaches to explore and solve complex problems of TCM. By integrating big data and artificial intelligence technologies, network targets and network pharmacology have conferred high-precision screening and quality control of key components and targets in herbal formulations and prescriptions, accelerating the clinical translation and industrialization of TCM-based new drugs and health products with medicine-food homology. Therefore, it is essential to optimize the patent protection system and establish integrated technology platforms in this field for ensuring the competitiveness of technological achievements in research and clinical application. These efforts will advance the widespread application and high-quality development of TCM modernization, precision medicine, and innovative drug discovery.

To validate the feasibility of using near-infrared (NIR) spectroscopy for real-time monitoring of multiple active pharmaceutical ingredients dissolution, this study focused on Guizhi Fuling capsules and tablets. The NIR spectroscopy fiber probe was inserted into the dissolution apparatus and connected to a Fourier transform near-infrared spectrometer (FT-NIR) to capture spectral data. During the dissolution tests, dissolution behavior curves for seven components, gallic acid (GA), alibiflorin (ALI), paeoniflorin (PF), paeonol (PAE), amygdalin (AMY), cinnamaldehyde (CL), and cinnamic acid (CA) in the capsules, were obtained by sampling from the dissolution cups at specific time intervals. Linear regression was applied to models corrected using various pre-process techniques with the partial least squares (PLS) algorithm. Additionally, an artificial neural network (ANN), a nonlinear regression algorithm, was utilized to explore the complex relationship between spectra and multicomponent dissolution. Ultimately, the ANN model achieved a lower prediction mean square error (RMSEP) and relative error compared to the PLS model, with significantly higher correlation coefficient (Rp) for the validation set. The highest Rp value reached 0.8825. The paired t-test results also indicated no significant difference between predicted and measured values. Furthermore, the ANN model demonstrated the best predictive performance in the tablet experiments, achieving an Rp of 0.8134. The findings indicate that real-time monitoring of multicomponent drug dissolution using NIR spectroscopy combined with chemometric methods is feasible, offering a promising new direction to replace traditional dissolution testing.

Ischemic stroke (IS) is a globally life-threatening disease. Presently, few therapeutic medicines are available for treating IS, and rt-PA is the only drug approved by the US Food and Drug Administration (FDA) in the US. In fact, many agents showing excellent neuroprotection but no blood flow-improving activity in animals have not achieved ideal clinical efficacy, while thrombolytic drugs only improving blood flow without neuroprotection have limited their wider application. To address these challenges and meet the huge unmet clinical need, we have designed and identified a novel compound AAPB with dual effects of neuroprotection and cerebral blood flow improvement. AAPB significantly reduced cerebral infarction and neural function deficit in tMCAO rats, pMCAO rats, and IS rhesus monkeys, as well as displayed exceptional safety profiles and excellent pharmacokinetic properties in rats and dogs. AAPB has now entered phase I of clinical trials fighting IS in China.

Artificial intelligence (AI) refers to a system that can simulate and execute the processes of human thinking and learning, and make informed decisions. Fueled by the development of AI, the quality and effectiveness of medical work have gained momentum. AI technology plays an increasingly important role in healthcare, exhibiting substantial potential in clinical practice and decision-making processes. In Alzheimer's disease (AD), where early diagnosis and treatment remain challenging due to clinical heterogeneity and insidious progression, AI could offer excellent solutions. AI models can integrate multi-modal data to identify pre-symptomatic biomarkers and stratify high-risk cohorts, improving diagnostic accuracy, assisting with personalizing treatment and care. Furthermore, AI can accelerate drug discovery and development through drug-target identification and predictive modeling of compound efficacy. However, data quality, supervision, transparency, privacy, and ethical concerns need to be addressed. By identifying and retrieving studies for the systematic review, this article provides a comprehensive overview of current progress and related AI applications in AD.

Review The Application of Artificial Intelligence in the Research and Development of Traditional Chinese Medicine Zhipeng Ke 1,2, Minxuan Liu 1,2,3, Jing Liu 1,2, Zhenzhen Su 1,2, Lu Li 1,2, Mengyu Qian 1,2, Xinzhuang Zhang 1,2, Tuanjie Wang 1,2, Liang Cao 1,2, Zhenzhong Wang 1,2, and Wei Xiao 1,2, * 1 National Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture, Lianyungang 222106, China 2 Jiangsu Kanion Pharmaceutical Co., Ltd, Lianyungang 222104, China 3 ‍School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210009, China * Correspondence: xw_kanion@163.com Received: 4 September 2023 Accepted: 4 November 2023 Published: 6 March 2024 Abstract: With the accumulation of data in the pharmaceutical industry and the development of artificial intelligence technology, various artificial intelligence methods have been successfully employed in the drug discovery process. The integration of artificial intelligence in Traditional Chinese medicine has also gained momentum, encompassing quality control of Chinese patent medicines, prescriptions optimization, discovery of effective substances, and prediction of side effects. However, artificial intelligence also faces challenges and limitations in Traditional Chinese medicine development, such as data scarcity and complexity, lack of interdisciplinary professionals, black-box models, etc. Therefore, more research and collaboration are needed to address these issues and explore the best ways to integrate artificial intelligence and Traditional Chinese medicine to improve human health.

目的 探究水-醇双提工艺下中药复方粉末影响颗粒流动性的关键物性参数.方法 以水-醇双提工艺下杏贝止咳颗粒(Xingbei Zhike Keli,XZK)、桂枝茯苓胶囊(Guizhi Fuling Jiaonang,GFJ)以及参乌益肾片(Shenwu Yishen Pian,SYP)3个中药复方品种的制粒前粉末与制粒后颗粒为研究对象,采用多元统计分析方法,绘制粉末物理指纹图谱,结合Pearson相关系数评价粉末质量一致性;采用主成分分析(principal component analysis,PCA)结合因子分析评价颗粒流动性;并构建以松装密度(Da)、振实密度(Dc)、休止角(a)、豪斯纳比(IH)、粒径＜50 μm百分比(Pf)、均匀性(HG)、均齐度(UN)、粒径(D10、D50、D60、D90)、分布宽度(span)、分布范围(width)、比表面积(SSA)、孔隙率(Ie)、卡尔指数(IC)、含水量(HR)、吸湿率(H)为自变量、以颗粒流动性总因子得分(TFS)为因变量的正交偏最小二乘法-判别分析(orthogonal partial least-squares discrimination analysis,OPLS-DA)模型,用以辨识粉末关键物性参数.结果 分别构建了 3个品种各15批粉末的物理指纹图谱,结合Pearson相关系数结果显示3个品种粉末质量一致性良好,其中SYP颗粒优于XZK颗粒再优于GFJ颗粒;计算得颗粒流动性TFS,整体来看流动性XZK颗粒优于SYP颗粒再优于GFJ颗粒;OPLS-DA模型优化后辨识出H、UN、SSA、HR为影响颗粒流动性的关键物性参数,置换检验结果表明模型有效可靠.结论 基于OPLS-DA模型辨识出影响水-醇双提工艺下中药复方粉末影响颗粒流动性的关键物性参数为H、UN、SSA、HR.

目的 探究水-醇双提物的中药物料粉体性质对颗粒吸湿性的影响,筛选潜在关键影响因素.方法 以5个品种共计175批中间体粉末的18个粉体参数为自变量,颗粒吸湿性为因变量,进行原始数值描述、变量相关性分析、主成分分析(principal component analysis,PCA)和偏最小二乘法(partial least squares,PLS)预测模型分析,研究水-醇双提物的中药物料粉体性质对颗粒吸湿性的影响.结果 原始数值描述发现中间体粉末及颗粒均存在强吸湿性,但相对于粉末,颗粒吸湿性较低,表明制粒工艺一定程度上可改善粉末吸湿性;相关性分析结果显示中间体粉末的粒度分布宽度、均匀性、吸湿性、含水量与颗粒吸湿性呈强相关;PCA确定2个关键主成分,其方差贡献率分别为57.13和19.82;PLS预测模型在2个关键主成分前提下,以变量投影重要性(variable importance in the projection,VIP)、回归系数、方差膨胀因子(variance inflation factor,VIF)及平均相对预测误差为判定指标,确定粉末含水量、吸湿性、振实密度和D50是影响颗粒吸湿性的潜在关键物性指标.结论 采用相关性分析、PCA和PLS预测模型探究中药粉体物料性质对颗粒吸湿性的影响,为中药粉体物料性质影响颗粒吸湿性的共性技术研究提供数据依据和理论参考.

目的 为加快实现中药生产过程中对中药配方颗粒的质量控制,从粉体物料属性出发,探究其对水提工艺下干法制粒的中药配方颗粒溶化性影响机制.方法 以60种中药配方颗粒及中间体混合粉为研究对象,通过对各品种混合粉粉体属性进行测定,建立粉体物性参数与颗粒溶化性关联模型.采用二维矩阵热点图对各品种间及物性指标间相似性进行分析,结合系统聚类分析对其进行归类,并运用多元统计方法初步筛选影响水提工艺下中药配方颗粒溶化性的关键因素.结果 各品种间粉体物性参数相关系数在-0.951～1.000,并按照各品种物理属性大致可以分为5类,结合变量投影响应值(variable importance for the projection,VIP)、自变量回归系数与方差膨胀因子(variance inflation factor,VIF)分析,最终筛选出其休止角(a)、含水量(HR)、吸湿性(H)与比表面积(SSA)4个指标为影响中药配方颗粒溶化性的关键物料属性(critical material attributes,CMAs).结论 基于中药粉体物料属性与数据分析初步探寻影响水提条件下干法制粒的中药配方颗粒的溶化性机制,为后期工艺改进提供参考.

This study aims to develop a rapid and non-destructive method to identify counterfeit and substandard drugs, addressing the critical need for better quality control in drug production. According to the reasons for counterfeit products in actual production, the commonly used solid preparation excipients such as HPMC, MCC, Mg-St and Pregelatinized Starch, as well as three chemical drugs with similar efficacy to Guizhi-Fuling (GZFL) Capsule as adulterants, including Aspirin, Ibuprofen and Sinomenine Hydrochloride were selected and designed as adulteration samples with different levels of adulteration. NIR spectra were collected in a non-invasive mode and analyzed by one-class classification methods. The feasibility of using Near-infrared (NIR) spectroscopy as a detection method to qualitatively identify adulterated samples was explored at three packaging levels of powder, intact capsules and capsules in PVC. The differences between the samples were analyzed by NIR spectra comparison, cluster analysis and principal component analysis. The performance of SVM, OCPLS and DD-SIMCA models in dealing with the authentication of genuine and counterfeit products was established and compared. The results show that the spectra contain sample information and the adulterated samples could be discriminated correctly by established models. Moreover, applying appropriate spectral preprocessing methods can further improve the model's performance. In addition, a PLS regression model was developed to predict the adulteration levels of the three packing level samples, which yielded satisfactory results. This study highlights the potential of NIR spectroscopy combined with Chemometrics as a rapid and non-destructive testing analysis method to accurately identify counterfeit and substandard drugs, thereby ensuring drug quality.

In the new stage for intelligent manufacturing of traditional Chinese medicine(TCM) from pilot demonstration to in-depth application and comprehensive promotion, how to raise the degree of intelligence for the process quality control system has become the bottleneck of the development of TCM production process control technology. This article has sorted out 226 TCM intelligent manufacturing projects that have been approved by the national and provincial governments since the implementation of the "Made in China 2025" plan and 145 related pharmaceutical enterprises. Then, the patents applied by these pharmaceutical enterprises were thoroughly retrieved, and 135 patents in terms of intelligent quality control technology in the production process were found. The technical details about intelligent quality control at both the unit levels such as cultivation, processing of crude herbs, preparation pretreatment, pharmaceutical preparations, and the production workshop level were reviewed from three aspects, i.e., intelligent quality sensing, intelligent process cognition, and intelligent process control. The results showed that intelligent quality control technologies have been preliminarily applied to the whole process of TCM production. The intelligence control of the extraction and concentration processes and the intelligent sensing of critical quality attributes are currently the focus of pharmaceutical enterprises. However, there is a lack of process cognitive patent technology for the TCM manufacturing process, which fails to meet the requirements of closed-loop integration of intelligent sensing and intelligent control technologies. It is suggested that in the future, with the help of artificial intelligence and machine learning methods, the process cognitive bottleneck of TCM production can be overcome, and the holistic quality formation mechanisms of TCM products can be elucidated. Moreover, key technologies for system integration and intelligent equipment are expected to be innovated and accelerated to enhance the quality uniformity and manufacturing reliability of TCM.

Two undescribed sesquiterpenoids, including one nor-eudesmane type (1) and one guaiane type (2), together with two known analogues (3-4) have been isolated and identified from the fruits of Alpinia oxyphylla. The structures of these new compounds were elucidated by extensive spectroscopic analyses (1D-, 2D-NMR, HRESIMS, IR, UV) and NMR calculations with DP4+ analysis. The anti-inflammatory activities of all isolates were evaluated by measuring their inhibitory effects on PGE2 production in LPS stimulated RAW 264.7 macrophages.

Smart manufacturing still remains critical challenges for pharmaceutical manufacturing. Here, an original data-driven engineering framework was proposed to tackle the challenges. Firstly, from sporadic indicators to five kinds of systematic quality characteristics, nearly 2,000,000 real-world data points were successively characterized from Ginkgo Folium tablet manufacturing. Then, from simplex to the multivariate system, the digital process capability diagnosis strategy was proposed by multivariate Cpk integrated Bootstrap-t. The Cpk of Ginkgo Folium extracts, granules, and tablets were discovered, which was 0.59, 0.42, and 0.78, respectively, indicating a relatively weak process capability, especially in granulating. Furthermore, the quality traceability was discovered from unit to end-to-end analysis, which decreased from 2.17 to 1.73. This further proved that attention should be paid to granulating to improve the quality characteristic. In conclusion, this paper provided a data-driven engineering strategy empowering industrial innovation to face the challenge of smart pharmaceutical manufacturing.

Abstract Background The limited therapeutic outcomes of atherosclerosis (AS) have allowed, traditional Chinese medicine has been well established as an alternative approach in ameliorating AS and associated clinical syndromes. Clinically, Tongsaimai tablet (TSMT), a commercial Chinese patent medicine approved by CFDA, shows an obvious therapeutic effect on AS treatment. However, its effective mechanism and quality control still need thorough and urgent exploration. Methods The mice were orally administered with TSMT and their serum was investigated for the absorbed compounds using serum pharmacochemistry via the UPLC-Q-Exactive Orbitrap/MS analysis was employed to investigate these absorbed compounds in serum of mice orally administrated with TSMT. Based on these absorbed prototype compounds in serum derived from TSMT, a component-target-disease network was constructed using network pharmacology strategy, which elucidated the potential bioactive components, effective targets, and molecular mechanisms of TSMT against AS. Further, the screened compounds from the component-target network were utilized as the quality control (QC) markers, determining multi-component content determination and HPLC fingerprint to assess quality of nine batches of TSMT samples. Results A total of 164 individual components were identified in TSMT. Among them, 29 prototype compounds were found in serum of mice administrated with TSMT. Based on these candidate prototype components, 34 protein targets and 151 pathways related to AS were predicted, and they might significantly exhibit potential anti-AS mechanisms via synergistic regulations of lipid regulation, shear stress, and anti-inflammation, etc. Five potentially bioactive ingredients in TSMT, including Ferulic acid, Liquiritin, Senkyunolide I, Luteolin and Glycyrrhizic acid in quantity not less than 1.2798, 0.4716, 0.5419, 0.1349, 4.0386 mg/g, respectively, screened from the component-target-pathway network. Thereby, these indicated that these five compounds of TMST which played vital roles in the attenuation of AS could serve as crucial marker compounds for quality control. Conclusions Overall, based on the combination of serum pharmacochemistry and network pharmacology, the present study firstly provided a useful strategy to establish a quality assessment approach for TSMT by screening out the potential anti-AS mechanisms and chemical quality markers. Graphical Abstract

Currently, therapies for ischemic stroke are limited. Ginkgolides, unique Folium Ginkgo components, have potential benefits for ischemic stroke patients, but there is little evidence that ginkgolides improve neurological function in these patients. Clinical studies have confirmed the neurological improvement efficacy of diterpene ginkgolides meglumine injection (DGMI), an extract of Ginkgo biloba containing ginkgolides A (GA), B (GB), and K (GK), in ischemic stroke patients. In the present study, we performed transcriptome analyses using RNA-seq and explored the potential mechanism of ginkgolides in seven in vitro cell models that mimic pathological stroke processes. Transcriptome analyses revealed that the ginkgolides had potential antiplatelet properties and neuroprotective activities in the nervous system. Specifically, human umbilical vein endothelial cells (HUVEC-T1 cells) showed the strongest response to DGMI and U251 human glioma cells ranked next. The results of pathway enrichment analysis via gene set enrichment analysis (GSEA) showed that the neuroprotective activities of DGMI and its monomers in the U251 cell model were related to their regulation of the sphingolipid and neurotrophin signaling pathways. We next verified these in vitro findings in an in vivo cuprizone (CPZ, bis(cyclohexanone)oxaldihydrazone)-induced model. GB and GK protected against demyelination in the corpus callosum (CC) and promoted oligodendrocyte regeneration in CPZ-fed mice. Moreover, GB and GK antagonized platelet-activating factor (PAF) receptor (PAFR) expression in astrocytes, inhibited PAF-induced inflammatory responses, and promoted brain-derived neurotrophic factor (BDNF) and ciliary neurotrophic factor (CNTF) secretion, supporting remyelination. These findings are critical for developing therapies that promote remyelination and prevent stroke progression.

黄芩清热除痹胶囊为安徽中医药大学第一附属医院院内制剂,具有清热利湿、通络除痹之效,临床用于治疗湿热痹阻型类风湿关节炎[1],疗效确切.本文通过对其进行指纹图谱[2]初步研究,并建立黄芩苷、黄芩素、栀子苷 3 个指标成分 HPLC 含量测定方法[3-4],为其制剂工艺及质量控制研究提供参考[5],提高制剂质量控制水平.

Because of the complex components, simple content determination can hardly reflect the overall quality of Guizhi Fuling Capsules. Therefore, it is necessary to carry out a multi-component dissolution test. The variability of quality among different batches of products from different manufacturers is a common problem of Chinese medicine solid preparations. To comprehensively control the quality of Guizhi Fuling Capsules, we studied the dissolution behaviors of 7 index components in the capsules under different conditions, and investigated the consistency of dissolution behaviors among different batches of products from the same manufacturer. The basket method of general rule 0931 in Chinese Pharmacopoeia was adopted, and the rotating speeds were set at 50, 75, and 100 r·min~(-1), respectively. The hydrochloric acid solution(pH 1.2), acetate buffer solution(pH 4.0), pure water, and phosphate buffer solution(pH 6.8) were used as the dissolution media. Automatic sampling was carried out at the time points of 5, 10, 20, 30, 45, and 60 min, respectively. The cumulative dissolution of 7 index components was measured through ultra-performance liquid chromatography(UPLC). The difference factor f_1 and similarity factor f_2 were calculated to comprehensively evaluate the similarity of the dissolution curves among 8 batches of Guizhi Fuling Capsules, and a variety of dissolution and release equations were fitted. The results showed that multiple components had faster dissolution rates at higher rotating speed and in hydrochloric acid medium. The 8 batches of Guizhi Fuling capsules showed the average f_1 value lower than 15 and the average f_2 value higher than 50, which indicated that different batches of products had similar dissolution behaviors. Most components had synchronous dissolution behaviors and similar release cha-racteristics. This study provides a reference for the quality consistency evaluation among batches, processing optimization, and dosage form improvement of Guizhi Fuling Capsules.