ObjectiveTo explore the potential mechanism of Xianglian Huazhuo prescription （XLHZ） in treating chronic atrophic gastritis （CAG） by regulating cell cycle and inhibiting proliferation， using bioinformatics technology and animal experiments. MethodsDifferential expressed genes （DEGs） related to CAG were screened using GEO database and GEO2R tool. Weighted gene co-expression network analysis （WGCNA） was employed to search for hub genes of CAG. These hub genes were intersected with cell cycle proliferation based on GeneCards database. Eenrichment analysis of the intersecting genes was performed to obtain signaling pathways and biological processes related to CAG. Protein protein interaction （PPI） analysis of genes was conducted using the Protein Interaction Platform （STRING） database to search the super hub gene （hub 2.0）， and animal experiments were conducted for further validation. Fourteen of 70 male Wistar rats were randomly selected as the normal group， and the remaining 56 rats were prepared by the combined modeling method of "starvation disorder+N-methyl-N-nitro-N-nitrosoguanidine （MNNG） + sodium salicylate". The successfully modeled rats were randomly divided into the model group， XLHZ-H， XLHZ-M， and XLHZ-L groups （36， 18， 9 g·kg^-1， respectively）， and Morodan group （1.4 g·kg^-1）. Each group was given corresponding intervention for 60 days. Hematoxylin-eosin （HE） staining was used to observe the histopathological changes of gastric mucosa in rats. The ultrastructure of gastric mucosal tissue cells was observed by transmission electron microscopy. The relative expression levels of TGF-β₁， Smad2 and Smad3 proteins， S/G₂/M phase marker geminin and proliferation marker MCM2 were detected by Western blot in gastric mucosal tissue， and Spearman correlation analysis was performed. ResultsA total of 15 hub 2.0 genes were identified， including TGF-β₁， suggesting the involvement of the TGF-β₁ signaling pathway in the CAG pathogenesis. Compared with the normal group， the expressions of TGF-β₁， Smad2， geminin and MCM2 proteins in the gastric mucosa tissue of the model group were increased （P<0.05）， and the expression of Smad3 protein was decreased （P<0.05）. Compared with the model group， the expressions of TGF-β₁ and geminin in the gastric mucosa were decreased in the drug groups （P<0.05）. The XLHZ-M group， XLHZ-H group and Morodan group had significantly decreased protein expression of Smad2 and MCM2 （P<0.05）. The protein expression of Smad3 was significantly increased in XLHZ-M， XLHZ-H， and Morodan groups （P<0.05）. Spearman correlation analysis showed that Smad3 was negatively correlated with other indicators， and positively correlated with other indicators （P<0.01）. ConclusionXLHZ may inhibit TGF-β₁/Smads signaling pathway， regulate cell cycle， and inhibit proliferation in the treatment of CAG.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

To interpret the evidence-to-decision （EtD） framework and to illustrate its application in traditional Chinese medicine （TCM） guideline development using the example of the Pharmacovigilance Guideline of Chinese Patent Medicine， thereby providing methodological references for TCM guideline standardization. Based on the core three stages of the EtD framework （formulating the question， making an assessment of the evidence， and drawing conclusions）， critical decision points and evaluation evidence within the evidence-translation process were systematically addressed， aligning with the purpose， scope， and key questions of the guideline. Qualitative research methods， such as the nominal group technique， were employed to formulate recommendations. The analysis was conducted based on the EtD framework. During question formulation， the specific characteristics and practical needs of pharmacovigilance for Chinese patent medicines were clarified， focusing on the core objective of safety assurance throughout the product lifecycle. In the evidence assessment， multi-source evidence was integrated， including policy documents， literature research， and expert consensus， completing the evidence evaluation. Finally， in recommendation-forming， dispersed research evidence and expert experience were synthesized into consensus， culminating in the guideline's completion through solicitation of opinions and peer review. The EtD framework provides a structured tool for evidence-to-decision translation in TCM guideline development， effectively enhancing the transparency and scientific rigor of the process. Therefore， it is recommended that TCM guideline development adopt the EtD framework to improve the evidence-to-decision process with TCM characteristics.

To develop the Pharmacovigilance Guidelines for Clinical Application of Chinese Patent Medicines for Mucosal Administration in response to common problems， including insufficient safety information in package inserts， amplified medication risks in special populations， and non-standard clinical practices， thus establishing a risk management system tailored to the characteristics of Chinese patent medicines for mucosal administration. An approach combining qualitative and quantitative methods was adopted. In accordance with the Drug Administration Law of the People's Republic of China （2019 revision） and the GB/T 1.1—2020 standard， a systematic search was performed in the Chinese Pharmacopoeia （2020 edition）， the Catalog of Medicines Covered by Medical Insurance （2022 edition）， Chinese databases ［China Network of Knowledge Infrastructure （CNKI）， Wanfang Data （Wanfang）， and VIP journal resource integration service platform （VIP）］， and international databases （Cochrane Library， PubMed， and EMbase）. Guideline outlines were developed through questionnaire surveys， expert interviews， and the nominal group technique. The content of each item was formulated with full consideration of traditional Chinese medicine （TCM） incompatibility， as well as the conceptual connotations and extensions of pharmacovigilance. The results included 54 Chinese patent medicines for mucosal administration from the Chinese Pharmacopoeia （2020 edition） and 58 from the Catalog of Medicines Covered by Medical Insurance （2022 edition）. Safety-related items in the corresponding package inserts were collected， and 27 relevant publications were retrieved. Thirty experts from 24 institutions were mobilized for the drafting， and opinions from 61 external experts were solicited. A pharmacovigilance framework was established， covering the full chain of "monitoring， identification， assessment， and control". Based on seven anatomical sites， including nasal， ocular， and oral mucosa， a stratified monitoring system was constructed. The guideline proposed key recommendations on improving package insert sections such as "Adverse Reactions"， "Contraindications"， and "Precautions"， clinical procedure standardization in healthcare institutions， risk control， and dynamic pharmacovigilance. The Guideline provides evidence-based support tailored to the risk profile of Chinese patent medicines for mucosal administration， filling the current gap in international pharmacovigilance standards in this field， while offering technical support for safety management across the full life cycle of medicines for mucosal administration.

Background Methicillin-resistant Staphylococcus aureus (MRSA) is an important pathogen for both human bloodstream infections and mastitis in cows. However, little attention has been paid to the cross-host transmission of MRSA from cows to high-risk groups in China. Objective To determine the MRSA colonization rates among dairy cows and dairy farm workers in Xinjiang, identify the antibiotic resistance profiles and molecular characteristics of the isolates, and provide scientific evidence for the formulation of targeted infection control strategies. Method A cross-sectional survey combined with laboratory pathogen analysis was conducted. From June to August 2024, large-scale dairy farms in Xinjiang region were selected as study sites. Nasal swabs (n=96) and skin swabs (n=39) were collected from workers, and bovine nasal swab samples (n=109) were collected simultaneously. All samples were subjected to MRSA isolation, cultivation, and identification, followed by antibiotic susceptibility testing to characterize resistance phenotypes. Staphylococcus aureus protein A (Spa) typing was performed to determine strain genotypes and elucidate MRSA colonization rates and molecular epidemiological patterns. Results A total of 35 MRSA strains was successfully isolated from 244 samples. The MRSA colonization rates among dairy farm workers and dairy cows were 20.83% (20/96) and 12.84% (14/109), respectively, with an overall isolation rate of 14.34% (35/244). Among the workers, the nasal colonization rate was 16.67% (16/96), and the skin colonization rate was 12.82% (5/39). One worker exhibited MRSA colonization at multiple body sites. All MRSA strains were resistant to cefoxitin (100%, 35/35). The resistance rates to erythromycin and clindamycin were 42.86% (15/35) and 34.29% (12/35), respectively. Thirteen strains showed a multidrug-resistant phenotype, whereas all strains were susceptible to vancomycin. The MRSA isolates exhibited high genetic diversity, with 13 Spa types identified, among which t441 was the most prevalent (8 strains). Both t441 and t034 types were detected in samples from both the dairy cows and their handlers. These two Spa types also carried and stably inherited specific resistance combinations, including erythromycin–clindamycin–cefoxitin and ciprofloxacin–erythromycin–clindamycin–gentamicin–cefoxitin–tetracycline, and a statistically significant association was also observed between the two resistance profiles and the bacterial types (P < 0.001). In addition, one novel Spa type strain was identified. Conclusion MRSA colonization rates among dairy cows and dairy farm workers in Xinjiang are relatively high, with evidence of multi-site colonization. The isolates exhibit high levels of multidrug resistance and genetic diversity, indicating a potential risk of cross-host transmission.

ObjectiveTo explore the potential mechanism of Xianglian Huazhuo prescription （XLHZ） in treating chronic atrophic gastritis （CAG） by regulating cell cycle and inhibiting proliferation， using bioinformatics technology and animal experiments. MethodsDifferential expressed genes （DEGs） related to CAG were screened using GEO database and GEO2R tool. Weighted gene co-expression network analysis （WGCNA） was employed to search for hub genes of CAG. These hub genes were intersected with cell cycle proliferation based on GeneCards database. Eenrichment analysis of the intersecting genes was performed to obtain signaling pathways and biological processes related to CAG. Protein protein interaction （PPI） analysis of genes was conducted using the Protein Interaction Platform （STRING） database to search the super hub gene （hub 2.0）， and animal experiments were conducted for further validation. Fourteen of 70 male Wistar rats were randomly selected as the normal group， and the remaining 56 rats were prepared by the combined modeling method of "starvation disorder+N-methyl-N-nitro-N-nitrosoguanidine （MNNG） + sodium salicylate". The successfully modeled rats were randomly divided into the model group， XLHZ-H， XLHZ-M， and XLHZ-L groups （36， 18， 9 g·kg^-1， respectively）， and Morodan group （1.4 g·kg^-1）. Each group was given corresponding intervention for 60 days. Hematoxylin-eosin （HE） staining was used to observe the histopathological changes of gastric mucosa in rats. The ultrastructure of gastric mucosal tissue cells was observed by transmission electron microscopy. The relative expression levels of TGF-β₁， Smad2 and Smad3 proteins， S/G₂/M phase marker geminin and proliferation marker MCM2 were detected by Western blot in gastric mucosal tissue， and Spearman correlation analysis was performed. ResultsA total of 15 hub 2.0 genes were identified， including TGF-β₁， suggesting the involvement of the TGF-β₁ signaling pathway in the CAG pathogenesis. Compared with the normal group， the expressions of TGF-β₁， Smad2， geminin and MCM2 proteins in the gastric mucosa tissue of the model group were increased （P<0.05）， and the expression of Smad3 protein was decreased （P<0.05）. Compared with the model group， the expressions of TGF-β₁ and geminin in the gastric mucosa were decreased in the drug groups （P<0.05）. The XLHZ-M group， XLHZ-H group and Morodan group had significantly decreased protein expression of Smad2 and MCM2 （P<0.05）. The protein expression of Smad3 was significantly increased in XLHZ-M， XLHZ-H， and Morodan groups （P<0.05）. Spearman correlation analysis showed that Smad3 was negatively correlated with other indicators， and positively correlated with other indicators （P<0.01）. ConclusionXLHZ may inhibit TGF-β₁/Smads signaling pathway， regulate cell cycle， and inhibit proliferation in the treatment of CAG.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

To interpret the evidence-to-decision （EtD） framework and to illustrate its application in traditional Chinese medicine （TCM） guideline development using the example of the Pharmacovigilance Guideline of Chinese Patent Medicine， thereby providing methodological references for TCM guideline standardization. Based on the core three stages of the EtD framework （formulating the question， making an assessment of the evidence， and drawing conclusions）， critical decision points and evaluation evidence within the evidence-translation process were systematically addressed， aligning with the purpose， scope， and key questions of the guideline. Qualitative research methods， such as the nominal group technique， were employed to formulate recommendations. The analysis was conducted based on the EtD framework. During question formulation， the specific characteristics and practical needs of pharmacovigilance for Chinese patent medicines were clarified， focusing on the core objective of safety assurance throughout the product lifecycle. In the evidence assessment， multi-source evidence was integrated， including policy documents， literature research， and expert consensus， completing the evidence evaluation. Finally， in recommendation-forming， dispersed research evidence and expert experience were synthesized into consensus， culminating in the guideline's completion through solicitation of opinions and peer review. The EtD framework provides a structured tool for evidence-to-decision translation in TCM guideline development， effectively enhancing the transparency and scientific rigor of the process. Therefore， it is recommended that TCM guideline development adopt the EtD framework to improve the evidence-to-decision process with TCM characteristics.

To develop the Pharmacovigilance Guidelines for Clinical Application of Chinese Patent Medicines for Mucosal Administration in response to common problems， including insufficient safety information in package inserts， amplified medication risks in special populations， and non-standard clinical practices， thus establishing a risk management system tailored to the characteristics of Chinese patent medicines for mucosal administration. An approach combining qualitative and quantitative methods was adopted. In accordance with the Drug Administration Law of the People's Republic of China （2019 revision） and the GB/T 1.1—2020 standard， a systematic search was performed in the Chinese Pharmacopoeia （2020 edition）， the Catalog of Medicines Covered by Medical Insurance （2022 edition）， Chinese databases ［China Network of Knowledge Infrastructure （CNKI）， Wanfang Data （Wanfang）， and VIP journal resource integration service platform （VIP）］， and international databases （Cochrane Library， PubMed， and EMbase）. Guideline outlines were developed through questionnaire surveys， expert interviews， and the nominal group technique. The content of each item was formulated with full consideration of traditional Chinese medicine （TCM） incompatibility， as well as the conceptual connotations and extensions of pharmacovigilance. The results included 54 Chinese patent medicines for mucosal administration from the Chinese Pharmacopoeia （2020 edition） and 58 from the Catalog of Medicines Covered by Medical Insurance （2022 edition）. Safety-related items in the corresponding package inserts were collected， and 27 relevant publications were retrieved. Thirty experts from 24 institutions were mobilized for the drafting， and opinions from 61 external experts were solicited. A pharmacovigilance framework was established， covering the full chain of "monitoring， identification， assessment， and control". Based on seven anatomical sites， including nasal， ocular， and oral mucosa， a stratified monitoring system was constructed. The guideline proposed key recommendations on improving package insert sections such as "Adverse Reactions"， "Contraindications"， and "Precautions"， clinical procedure standardization in healthcare institutions， risk control， and dynamic pharmacovigilance. The Guideline provides evidence-based support tailored to the risk profile of Chinese patent medicines for mucosal administration， filling the current gap in international pharmacovigilance standards in this field， while offering technical support for safety management across the full life cycle of medicines for mucosal administration.

Objective To explore the potential molecular mechanism of Sangma Xingbei Tang （SMXBT） in the treatment of acute exacerbation of chronic obstructive pulmonary disease （AECOPD）. Methods TCMSP and TCMID databases were searched for the active ingredients of SMXBT, the targets of the active ingredients were predicted by SwissTargetPrediction database, and the AECOPD-related targets were searched by GeneCards and OMIM databases; the drug-active ingredient-target network and protein interaction network were constructed, and the GO enrichment and KEGG pathway enrichment were analyzed by the DAVID database. The drug-active ingredient-target network and protein interaction network were constructed, and the GO enrichment and KEGG pathway enrichment were analyzed by DAVID database, and molecular docking was performed by AutoDock Tools software. Animal experiments were conducted for validation. Results 192 active ingredients were obtained and 1066 targets were predicted in SMXBT. Network analysis showed that the top 3 active ingredients in SMXBT were quercetin, kaempferol, and glycyrrhizin, and the top 3 therapeutic targets were TNF, ALB, and IL-1β, etc. The GO analysis and KEGG showed that the main pathways were the cancer pathway, the MAPK signaling pathway, and the PI3K-Akt signaling pathway. Molecular docking showed that the binding energies of glycyrrhizin and AKT1, IL-1β, GFR, SRC, and quercetin and kaempferol and IL-1β were all <−5 kcal·mol. Animal experiments showed that Sangma Xingbei Decoction could treat AECOPD by anti-inflammatory effects and reduce changes in lung tissue structure. Additionally, it could lower the serum inflammatory levels of TNF-α, IL-6, and IL-8. Conclusion SMXBT may act on the targets of AKT1, IL-1β, EGFR, SRC, etc., reduce the serum inflammation levels of TNF-α, IL-6, IL-8, and improve the lung tissue structure and inflammatory response to achieve the therapeutic effect on AECOPD through the modulation of the PI3K/Akt signaling pathway, the MAPK signaling pathway, the cancer pathway, and other pathways of action.