ObjectiveTo establish an ultra-performance liquid fingerprint and multi-components determination method for Naomaili granules. To evaluate the quality of different batches by chemometrics， and the anti-neuroinflammatory effects of water extract and main components of Naomaili granules were tested in vitro. MethodsThe similarity and common peaks of 27 batches of Naomaili granules were evaluated by using Ultra performance liquid chromatography （UPLC） fingerprint detection. Ultra-performance liquid chromatography-tandem mass spectrometry （UPLC-MS/MS） technology was used to determine the content of the index components in Naomaili granules and to evaluate the quality of different batches of Naomaili granules by chemometrics. LPS-induced BV-2 cell inflammation model was used to investigate the anti-neuroinflammatory effects of the water extract and main components of Naomaili granules. ResultsThe similarity of fingerprints of 27 batches of samples was > 0.90. A total of 32 common peaks were calibrated， and 23 of them were identified and assigned. In 27 batches of Naomaili granules， the mass fractions of 14 components that were stachydrine hydrochloride， leonurine hydrochloride， calycosin-7-O-glucoside， calycosin，tanshinoneⅠ， cryptotanshinone， tanshinoneⅡ_A， ginsenoside Rb₁， notoginsenoside R₁， ginsenoside Rg₁， paeoniflorin， albiflorin， lactiflorin， and salvianolic acid B were found to be 2.902-3.498， 0.233-0.343， 0.111-0.301， 0.07-0.152， 0.136-0.228， 0.195-0.390， 0.324-0.482， 1.056-1.435， 0.271-0.397， 1.318-1.649， 3.038-4.059， 2.263-3.455， 0.152-0.232， 2.931-3.991 mg∙g^-1， respectively. Multivariate statistical analysis showed that paeoniflorin， ginsenoside Rg₁， ginsenoside Rb₁ and staphylline hydrochloride were quality difference markers to control the stability of the preparation. The results of bioactive experiment showed that the water extract of Naomaili granules and the eight main components with high content in the prescription had a dose-dependent inhibitory effect on the release of NO in the cell supernatant. Among them， salvianolic acid B and ginsenoside Rb₁ had strong anti-inflammatory activity， with IC₅₀ values of （36.11±0.15） mg∙L^-1 and （27.24±0.54） mg∙L^-1， respectively. ConclusionThe quality evaluation method of Naomaili granules established in this study was accurate and reproducible. Four quality difference markers were screened out， and eight key pharmacodynamic substances of Naomaili granules against neuroinflammation were screened out by in vitro cell experiments.

As people’s attention to health continues to increase, the market demand for traditional Chinese medicine (TCM) is growing steadily. The quality and safety of Chinese medicinal materials have attracted unprecedented social attention. In particular, the issue of exogenous harmful residue pollution in TCM has become a hot topic of concern for both regulatory authorities and society. The Chinese Pharmacopoeia 2025 Edition further refines the detection methods and limit standards for exogenous harmful residues in TCM. This not only reflects China’s high-level emphasis on the quality and safety of TCM but also demonstrates the continuous progress made by China in the field of TCM safety supervision. Basis on this study, by systematically reviewing the development history of the detection standards for exogenous harmful residues in TCM and analyzing the revisions and updates of these detection standards in the Chinese Pharmacopoeia 2025 Edition, deeply explores the key points of the changes in the monitoring standards for exogenous harmful residues in TCM in the Chinese Pharmacopoeia 2025 Edition. Moreover, it interprets the future development directions of the detection of exogenous residues in TCM, aiming to provide a reference for the formulation of TCM safety supervision policies.

BACKGROUND:Fibromyalgia syndrome,as a common rheumatic disease,is related to central sensitization and immune abnormalities.However,the specific mechanism has not been elucidated,and there is a lack of specific diagnostic markers.Exploring the possible pathogenesis of this disease has important clinical significance. OBJECTIVE:To screen the potential diagnostic marker genes of fibromyalgia syndrome and analyze the possible immune infiltration characteristics based on bioinformatics methods,such as weighted gene co-expression network analysis(WGCNA),and machine learning. METHODS:Gene expression profiles in peripheral serum of fibromyalgia syndrome patients and healthy controls were obtained from the gene expression omnibus(GEO)database.The differentially co-expressed genes were screened in the expression profile by differential analysis and WGCNA analysis.Least absolute shrinkage and selection operator(LASSO)and support vector machine-recursive feature elimination(SVM-RFE)machine learning algorithm were further used to identify hub biomarkers,and draw receiver operating characteristic curve(ROC)to evaluate the accuracy of diagnosing fibromyalgia syndrome.Finally,single sample gene set enrichment analysis(ssGSEA)and gene set enrichment analysis(GSEA)were used to evaluate the immune cell infiltration and pathway enrichment in patients with fibromyalgia syndrome. RESULTS AND CONCLUSION:Eight down-regulated differentially expressed genes(DEGs)were obtained after differential analysis of the GSE67311 dataset according to the conditions of log2|(FC)|>0 and P<0.05.After WGCNA analysis,497 genes were included in the module(MEdarkviolet)with the highest positive correlation(r=0.22,P=0.04),and 19 genes were included in the module(MEsalmon2)with the highest negative correlation(r=-0.41,P=6×10-5).After intersecting DEGs and the module genes of WGCNA,seven genes were obtained.Four genes were screened out by LASSO regression algorithm and five genes were screened out by SVM-RFE machine learning algorithm.After the intersection of the two,three core genes were identified,which were germinal center associated signaling and motility like,integrin beta-8,and carboxypeptidase A3.The areas under the ROC curve of the three core genes were 0.744,0.739,and 0.734,respectively,indicating that they have good diagnostic value and can be used as biomarkers for fibromyalgia syndrome.The results of immune infiltration analysis showed that memory B cells,CD56 bright NK cells,and mast cells were significantly down-regulated in patients with fibromyalgia syndrome compared with the control group(P<0.05),and were significantly positively correlated with the above three biomarkers(P<0.05).The enrichment analysis suggested that there were nine fibromyalgia syndrome enrichment pathways,mainly related to olfactory transduction pathway,neuroactive ligand-receptor interaction,and infection pathway.The above results showed that the occurrence and development of fibromyalgia syndrome are related to the involvement of multiple genes,abnormal immune regulation,and multiple pathways imbalance.However,the interactions between these genes and immune cells,as well as their relationships with various pathways need to be further investigated.

Immobilized enzyme-based enzyme electrode biosensors, characterized by high sensitivity and efficiency, strong specificity, and compact size, demonstrate broad application prospects in life science research, disease diagnosis and monitoring, etc. Immobilization of enzyme is a critical step in determining the performance (stability, sensitivity, and reproducibility) of the biosensors. Random immobilization (physical adsorption, covalent cross-linking, etc.) can easily bring about problems, such as decreased enzyme activity and relatively unstable immobilization. Whereas, directional immobilization utilizing amino acid residue mutation, affinity peptide fusion, or nucleotide-specific binding to restrict the orientation of the enzymes provides new possibilities to solve the problems caused by random immobilization. In this paper, the principles, advantages and disadvantages and the application progress of enzyme electrode biosensors of different directional immobilization strategies for enzyme molecular sensing elements by specific amino acids (lysine, histidine, cysteine, unnatural amino acid) with functional groups introduced based on site-specific mutation, affinity peptides (gold binding peptides, carbon binding peptides, carbohydrate binding domains) fused through genetic engineering, and specific binding between nucleotides and target enzymes (proteins) were reviewed, and the application fields, advantages and limitations of various immobilized enzyme interface characterization techniques were discussed, hoping to provide theoretical and technical guidance for the creation of high-performance enzyme sensing elements and the manufacture of enzyme electrode sensors.

ObjectiveTo establish a model that can quickly identify the aroma components in different parts of Nardostachys jatamansi， so as to provide a quality control basis for the market circulation and clinical use of N. jatamansi. MethodsHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） combined with Smart aroma database and National Institute of Standards and Technology（NIST） database were used to characterize the aroma components in different parts of N. jatamansi， and the aroma components were quantified according to relative response factor（RRF） and three internal standards， and the markers of aroma differences in different parts of N. jatamansi were identified by orthogonal partial least squares-discriminant analysis（OPLS-DA） and cluster thermal analysis based on variable importance in the projection（VIP） value >1 and P<0.01. The odor data of different parts of N. jatamansi were collected by Heracles Ⅱ Neo ultra-fast gas phase electronic nose， and the correlation between compound types of aroma components collected by the ultra-fast gas phase electronic nose and the detection results of HS-SPME-GC-MS was investigated by drawing odor fingerprints and odor response radargrams. Chromatographic peak information with distinguishing ability≥0.700 and peak area≥200 was selected as sensor data， and the rapid identification model of different parts of N. jatamansi was established by principal component analysis（PCA）， discriminant factor alysis（DFA）， soft independent modeling of class analogies（SIMCA） and statistical quality control analysis（SQCA）. ResultsThe HS-SPME-GC-MS results showed that there were 28 common components in the underground and aboveground parts of N. jatamansi， of which 22 could be quantified and 12 significantly different components were screened out. Among these 12 components， the contents of five components（ethyl isovalerate， 2-pentylfuran， benzyl alcohol， nonanal and glacial acetic acid，） in the aboveground part of N. jatamansi were significantly higher than those in the underground part（P<0.01）， the contents of β-ionone， patchouli alcohol， α-caryophyllene， linalyl butyrate， valencene， 1，8-cineole and p-cymene in the underground part of N. jatamansi were significantly higher than those in the aboveground part（P<0.01）. Heracles Ⅱ Neo electronic nose results showed that the PCA discrimination index of the underground and aboveground parts of N. jatamansi was 82， and the contribution rates of the principal component factors were 99.94% and 99.89% when 2 and 3 principal components were extracted， respectively. The contribution rate of the discriminant factor 1 of the DFA model constructed on the basis of PCA was 100%， the validation score of the SIMCA model for discrimination of the two parts was 99， and SQCA could clearly distinguish different parts of N. jatamansi. ConclusionHS-SPME-GC-MS can clarify the differential markers of underground and aboveground parts of N. jatamansi. The four analytical models provided by Heracles Ⅱ Neo electronic nose（PCA， DFA， SIMCA and SQCA） can realize the rapid identification of different parts of N. jatamansi. Combining the two results， it is speculated that terpenes and carboxylic acids may be the main factors contributing to the difference in aroma between the underground and aboveground parts of N. jatamansi.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Atherosclerosis is a complex pathological condition resulting from lipid deposition， chronic inflammatory responses， and fibrosis， with a prolonged disease course and multifactorial etiology. Based on the traditional Chinese medicine （TCM） theory of accumulation syndrome， atherosclerosis can be classified under this category， with its pathogenesis involving phlegm， blood stasis， deficiency， and accumulation. This paper proposed a stage-based intervention strategy using the four therapeutic principles of "attacking， supplementing， dispersing， dissipating"， and divided into six stages based on the pathological progression， including the stage of accumulation before formation， the stage of accumulation already formed， the stage of nucleus accumulation， the stage of nucleus accumulation decay， the stage of nucleus accumulation consolidation， and the stage of severe stenosis of nucleus. At different stages， the intervention focuses on reinforcing healthy qi and consolidating the root， tonifying the kidneys and spleen， dispersing and removing turbidity， removing phlegm stagnation， promoting qi circulation， dispersing accumulations and removing stasis， attacking accumulation and expelling stasis， directing the turbid downward and dispersing accumulation， and treatment would be adjusted based on specific symptoms， which provides a theoretical framework for the prevention and treatment of atherosclerosis with TCM.

ObjectiveTo investigate the effect and potential mechanism of caffeic acid （CA） on severe acute pancreatitis （SAP） induced by caerulein combined with lipopolysaccharide （LPS）， and to provide a basis for the research on novel drugs for the treatment of SAP. MethodsC57BL/6J mice， aged 6 weeks， were divided into control group， model group， CA group， and octreotide acetate （OA） group， with 6 mice in each group. The mice in the control group were given injection of normal saline， and those in the other groups were given intraperitoneal injection of caerulein combined with LPS to establish a mouse model of SAP. At 1 hour after the first injection of caerulein， the mice in the CA group and the OA group were given intraperitoneal injection of CA or subcutaneous injection of OA at an interval of 8 hours. The general status of the mice was observed after 24 hours of modeling， and serum， pancreas， lung， and colon samples were collected. HE staining was used to observe the histopathological changes of the pancreas and lungs， and the serum levels of α-amylase， lipase， tumor necrosis factor-α （TNF-α）， interleukin-6 （IL-6）， alanine aminotransferase， aspartate aminotransferase， and creatinine were measured. RT-PCR was used to measure the expression of proinflammatory factors in the pancreas and lungs； myeloperoxidase （MPO） immunohistochemistry was used to observe the degree of neutrophil infiltration； Western blot was used to measure the activation of nuclear factor-kappa B （NF-κB） and the level of citrullinated histone H3 （CitH3）， a marker for the formation of neutrophil extracellular traps （NETs）， in the pancreas and lungs， as well as the expression level of ZO-1 in colon tissue. A one-way analysis of variance was used for comparison of continuous data between multiple groups， and the Dunnett’s t-test was used for further comparison between two groups. ResultsCompared with the control group， the model group had severe injury in the pancreas and lungs and significant increases in the activity of serum α- amylase and lipase and the levels of the proinflammatory cytokines IL-6， interleukin-1β （IL-1β）， and TNF-α in serum and lung tissue （all P<0.05）， as well as significant increases in NF-κB activation， neutrophil infiltration， and the formation of NETs in the pancreas and lungs （all P<0.05）. Compared with the model group， the CA group had alleviated pathological injury of the pancreas and lungs and significant reductions in the activity of serum α-amylase and the levels of the proinflammatory cytokines IL-6， IL-1β， and TNF-α in serum and lung tissue （all P<0.05）， as well as significant reductions in NF-κB activation， neutrophil infiltration， and the formation of NETs in the pancreas and lungs （all P<0.05）. ConclusionCA can alleviate SAP induced by caerulein combined with LPS in mice， possibly by inhibiting neutrophil recruitment and the formation of NETs.

ObjectiveTo explore the potential mechanism of action of the combination of Sanguisorbae Radix-Sophorae Flos （DH） in the treatment of ulcerative colitis （UC） using network pharmacology methods and molecular docking technology. MethodsNetwork pharmacology analysis was utilized to predict the potential targets of DH for the treatment of UC. The therapeutic effects were experimentally validated by inducing a UC model in mice with 3% dextran sulfate sodium （DSS）. The experimental groups were the normal group， the model group， the salazosulfapyridine group （100 mg·kg^-1）， and the low， medium， and high dose groups of DH （1.2， 2.4， and 4.8 g·kg^-1）. The efficacy of the treatment was assessed through the general condition of the mice， histopathological examination， and the expression levels of inflammatory markers in the colon. The effect of DH on angiogenesis was explored by messenger RNA （mRNA） detection of colonic angiogenesis-related mediators， vascular endothelial growth factor （VEGF） immunohistochemistry， microvessel density （MVD） detection， and transmission electron microscopy. The phosphatidylinositol-3-kinase （PI3K）-protein kinase B （Akt） signaling pathway proteins were quantitatively analyzed through Western blot to assess whether the suppression of pathological angiogenesis by DH is associated with this pathway. ResultsNetwork pharmacological analysis yielded 112 potential core therapeutic targets for the treatment of UC with DH， of which the core targets were tumor protein 53 （TP53）， JUN， interleukin （IL）-6， Akt1， and tumor necrosis factor （TNF）. Compared with the normal group， mice in the model group showed significant weight loss， colon shortening， and high DAI score， increased expression of inflammatory factors IL-6， IL-1β， and TNF-α， as well as increased mRNA expression levels of angiogenesis-related mediators VEGF， vascular cell adhesion molecule 1 （VCAM1）， angiotensin 1 （Ang1）， matrix metalloproteinase （MMP）-1， MMP-2， and MMP-9. The positive expression of CD31 and VEGF in colonic tissue increased， and the protein expression of the PI3K/Akt pathway was increased （P<0.05）. The endothelial cells of the colonic mucosa and the colonic vasculature were severely damaged. Compared with the model group， mice in the DH groups had significantly reduced weight loss and colon shortening， lower DAI scores， and a significant decrease in mRNA expression of inflammatory factors and angiogenesis-related mediators. In addition， there was decreased positive expression of CD31 and VEGF in colonic tissue and decreased protein expression of the PI3K/Akt pathway （P<0.05）. ConclusionNetwork pharmacology， molecular docking， and experimental validation are applied to explore the mechanism of action of DH in the treatment of UC， and it is found that DH is able to improve the symptoms of colitis and inhibit the pathological angiogenesis in UC mice. Its action might be related to affecting the PI3K/Akt pathway.