OBJECTIVE To explore the potential mechanism by which drug-containing serum of Dianxianqing granules （DXQ） inhibits microglial ferroptosis. METHODS Male SD rats were given normal saline and Dianxianqing granules solution via intragastric administration to prepare normal serum and DXQ， respectively. Mice microglia BV2 cells were collected and successfully transfected with a negative control small interfering RNA （si-NC）， and then they were included in the si-NC group and cultured under normal conditions. Cells successfully transfected with small interfering RNA targeting glutathione peroxidase 4 （GPX4） （si-GPX4） were divided into the si-GPX4 group， the CsA group （treated with 1 μmol/L cyclosporine A）， and the DXQ- L， DXQ-M and DXQ-H groups （treated with 5%， 7% and 10% DXQ， respectively）. These groups were subsequently treated with their corresponding drug solutions and ferroptosis inducer Erastin （10 μmol/L）. The intracellular levels of total iron ions， glutathione （GSH）， reactive oxygen species （ROS）， and the expression of mitochondrial superoxide were determined in each group after 48 h of treatment. Additionally， mitochondrial membrane potential， the opening degree of mitochondrial permeability transition pore （MPTP）， and mRNA expressions of GPX4 and cyclophilin D （CypD） were detected. Furthermore， the expressions of ferroptosis-related proteins[GPX4， transferrin receptor 1 （TfR1） and ferritin heavy chain 1 （FTH1）]， as well as MPTP-related proteins [adenine nucleotide translocator （ANT）， cytochrome C （CytC）， mitochondrial calcium uniporter （MCU） and CypD] were assessed. RESULTS Compared with si-NC group， the levels of total iron ions and ROS， the expression level of mitochondrial superoxide， the opening degree of MPTP， protein and its mRNA expressions of CypD as well as protein expressions of TfR1 and MCU were increased or up-regulated significantly （P＜0.01）； however， GSH content， mitochondrial membrane potential， protein and mRNA expressions of GPX4， and protein expressions of FTH1， ANT and CytC were decreased or down-regulated significantly （P＜0.01）. Compared with the si-GPX4 group， the cells in the DXQ-M， DXQ-H groups showed a general improvement in the above quantitative indicators （P＜0.01 or P＜0.05）. CONCLUSIONS DXQ can enhance antioxidant capacity by activating the GSH/GPX4 pathway， regulate the expressions of TfR1 and FTH1 protein to correct iron ion homeostasis， inhibit excessive opening of MPTP to improve mitochondrial function， and ultimately suppress microglial ferroptosis.

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.

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.

The development of intestinal-type gastric cancer follows a progression from non-atrophic gastritis — atrophic gastritis — intestinal metaplasia — epithelial dysplasia — gastric cancer，known as the " inflammation-cancer transformation. " Leveraging the advantages of integrated traditional Chinese and Western medicine，while collaborating to inhibit this transformation，remains a research focus and challenge. The incidence of gastric cancer in China has declined with the continuous elucidation of the mechanisms behind inflammation-cancer transformation，innovations in early screening and diagnosis techniques in Western medicine，the establishment of risk stratification and treatment systems，and effective interventions through traditional Chinese medicine. Leveraging the theoretical and practical advantages of the " preventing disease before it occurs" philosophy of traditional Chinese medicine，particularly the concept of " preventing transformation in existing diseases，" is essential. This approach emphasizes shifting the focus of prevention and treatment to earlier stages of disease progression. Developing more effective and reliable strategies and innovative drugs that block the dynamic progression of the " inflammation-cancer transformation" in gastric cancer remains a key goal. This article reviews the latest progress in both basic and clinical research in this field，the issues related to high-level clinical research in traditional Chinese medicine，the construction of integrated diagnosis and treatment pathways combining Chinese and Western medicine，the establishment of efficacy evaluation standards，and the elucidation of the integration mechanisms of the complex system of traditional Chinese medicine. It also explores research directions and solutions within the context of multidisciplinary collaboration to provide insights and references to block inflammation-cancer transformation and to construct a gastric cancer prevention and control system with Chinese characteristics，thereby further enhancing the level of gastric cancer prevention and control.

Objective: To investigate factors influencing perioperative blood transfusion in patients with scarred uterus during pregnancy undergoing cesarean section, construct and validate a transfusion risk prediction model, and provide evidence for preoperative assessment and blood management. Methods: Clinical data of 405 patients undergoing cesarean section for scarred uterus during pregnancy at the First Affiliated Hospital of Xi'an Jiaotong University from January 2020 to December 2024 were retrospectively collected. The dataset was randomly divided into a training set (n=284) and a validation set (n=121) at a 7∶3 ratio. Within the training set, Firth-penalized logistic regression was employed for multivariate analysis to identify independent factors influencing perioperative blood transfusion and construct a predictive model. Model performance was evaluated in the validation set. Results: Multivariate Firth regression analysis showed that severe placenta previa (OR=75.566, 95%CI: 8.603-9979.174) and placenta accreta (OR=4.591, 95%CI: 1.120-19.416) were independent risk factors for perioperative blood transfusion, while preoperative red blood cell count (OR=0.189, 95%CI: 0.083-0.405) and fibrinogen levels (OR=0.588, 95%CI: 0.395-0.855) were protective factors. The predictive model constructed based on these four variables demonstrated good discriminatory performance, with areas under the receiver operating characteristic curves of 0.803 (95%CI: 0.740-0.867) and 0.753 (95%CI: 0.644-0.862) in the training and validation sets, respectively. Conclusion: For patients with scarred uterus during pregnancy undergoing cesarean section, severe placenta previa and placenta accreta significantly increase the risk of transfusion, while higher preoperative red blood cell count and fibrinogen levels exert a protective effect. The predictive model established in this study facilitates the identification of patients requiring transfusion, thereby enabling preoperative blood preparation and optimized blood management.

Objective To analyze the influencing factors of respiratory failure in patients with sepsis, and to construct and validate a risk prediction model. Methods A total of 308 patients with sepsis in Ganzi Tibetan Autonomous Prefecture People's Hospital from August 2021 to September 2024 were enrolled and divided into a modeling group and a validation group. According to whether respiratory failure occurred during hospitalization, the patients were divided into a respiratory failure group and a non-respiratory failure group. Single-factor and binary logistic regression analysis were used to construct a risk prediction model, and the ROC, calibration, and DCA curves were used to assess the model's predictive performance. Results The incidence of respiratory failure was 20.83% (45/216) in the modeling group and 20.65% (19/92) in the validation group. Albumin, D-dimer, serum calcium, procalcitonin at admission, and sequential organ failure assessment were independent influencing factors of respiratory failure in patients with sepsis. The ROC curve analysis nomogram model predicted that the AUC of respiratory failure in the modeling group was 0.994 (95%CI: 0.985-1.000), and the AUC of the validation group was 0.897 (95%CI: 0.826-0.968). The calibration curve analysis showed that the calibration curve fit well with the ideal curve. DCA curve analysis showed that the net benefit was greater than 0 across all threshold probability ranges. Conclusion The risk prediction model constructed in this study is highly effective and can provide reference for medical staff to evaluate respiratory failure in patients with sepsis.

In recent years, there has been a growing interest in the research on NOD-like receptor thermal protein domain associated protein 3(NLRP3) inflammasome inhibitors in the treatment of inflammatory diseases. The NLRP3 inflammasome is integral to the innate immune response, and its abnormal activation can lead to the release of pro-inflammatory cytokine, consequently facilitating the progression of various pathological conditions. Therefore, investigating the pharmacological inhibition pathway of the NLRP3 inflammasome represents a promising strategy for the treatment of inflammation-related diseases. Currently, the Food and Drug Administration(FDA) has not approved drugs targeting the NLRP3 inflammasome for clinical use due to concerns regarding liver toxicity and gastrointestinal side effects associated with chemical small molecule inhibitors in clinical trials. Natural small molecule compounds such as polyphenols, flavonoids, and alkaloids are ubiquitously found in animals, plants, and other natural substances exhibiting pharmacological activities. Their abundant sources, intricate and diverse structures, high biocompatibility, minimal adverse reactions, and superior biochemical potency in comparison to synthetic compounds have attracted the attention of extensive scholars. Currently, certain natural small molecule compounds have been demonstrated to impede the activation of the NLRP3 inflammasome via various action mechanisms, so they are viewed as the innovative, feasible, and minimally toxic therapeutic agents for inhibiting NLRP3 inflammasome activation in the treatment of both acute and chronic inflammatory diseases. Hence, this study systematically examined the effects and potential mechanisms of natural small molecule compounds derived from traditional Chinese medicine on the activation of NLRP3 inflammasomes at their initiation, assembly, and activation stages. The objection is to furnish theoretical support and practical guidance for the effective clinical application of these natural small molecule inhibitors.
NLR Family, Pyrin Domain-Containing 3 Protein/metabolism* ; Inflammasomes/metabolism* ; Inflammation/drug therapy* ; Anti-Inflammatory Agents/therapeutic use* ; Humans ; Animals ; Disease Models, Animal ; Biological Products/therapeutic use* ; Drug Discovery ; Medicine, Chinese Traditional/methods*

This study explored the potential therapeutic targets and mechanisms of Danggui Shaoyao San(DSS) in the prevention and treatment of Alzheimer's disease(AD) through transcriptomics and metabolomics, combined with animal experiments. Fifty male C57BL/6J mice, aged seven weeks, were randomly divided into the following five groups: control, model, positive drug, low-dose DSS, and high-dose DSS groups. After the intervention, the Morris water maze was used to assess learning and memory abilities of mice, and Nissl staining and hematoxylin-eosin(HE) staining were performed to observe pathological changes in the hippocampal tissue. Transcriptomics and metabolomics were employed to sequence brain tissue and identify differential metabolites, analyzing key genes and metabolites related to disease progression. Reverse transcription-quantitative polymerase chain reaction(RT-qPCR) was employed to validate the expression of key genes. The Morris water maze results indicated that DSS significantly improved learning and cognitive function in scopolamine(SCOP)-induced model mice, with the high-dose DSS group showing the best results. Pathological staining showed that DSS effectively reduced hippocampal neuronal damage, increased Nissl body numbers, and reduced nuclear pyknosis and neuronal loss. Transcriptomics identified seven key genes, including neurexin 1(Nrxn1) and sodium voltage-gated channel α subunit 1(Scn1a), and metabolomics revealed 113 differential metabolites, all of which were closely associated with synaptic function, oxidative stress, and metabolic regulation. RT-qPCR experiments confirmed that the expression of these seven key genes was consistent with the transcriptomics results. This study suggests that DSS significantly improves learning and memory in SCOP model mice and alleviates hippocampal neuronal pathological damage. The mechanisms likely involve the modulation of synaptic function, reduction of oxidative stress, and metabolic balance, with these seven key genes serving as important targets for DSS in the treatment of AD.
Animals ; Alzheimer Disease/genetics* ; Male ; Drugs, Chinese Herbal/administration & dosage* ; Mice ; Mice, Inbred C57BL ; Metabolomics ; Transcriptome/drug effects* ; Maze Learning/drug effects* ; Hippocampus/metabolism* ; Humans ; Disease Models, Animal ; Memory/drug effects*

Medical institutions, with their clinical practice foundation and abundant human use experience data, have become important carriers for the inheritance and innovation of traditional Chinese medicine(TCM) and the "cradles" of the preparation of new TCM. To effectively promote the transformation of new TCM originating from the TCM clinical practice in medical institutions and establish an effective evaluation index system for the transformation of new TCM conforming to the characteristics of TCM, consensus experts adopted the literature research, questionnaire survey, Delphi method, etc. By focusing on the policy and technical evaluation of new TCM originating from the TCM clinical practice in medical institutions, a comprehensive evaluation from the dimensions of drug safety, efficacy, feasibility, and characteristic advantages was conducted, thus forming a comprehensive evaluation system with four primary indicators and 37 secondary indicators. The expert consensus reached aims to encourage medical institutions at all levels to continuously improve the high-quality research and development and transformation of new TCM originating from the TCM clinical practice in medical institutions and targeted at clinical needs, so as to provide a decision-making basis for the preparation, selection, cultivation, and transformation of new TCM for medical institutions, improve the development efficiency of new TCM, and precisely respond to the public medication needs.
Medicine, Chinese Traditional/standards* ; Humans ; Consensus ; Drugs, Chinese Herbal/therapeutic use* ; Surveys and Questionnaires

Sishen Pills and its separated prescriptions are classic prescriptions of traditional Chinese medicine to treat intestinal diseases. In this study, a high-performance liquid chromatography-electrospray ionization tandem mass spectrometry(HPLC-ESI-MS/MS) technology was used to identify the components of Sishen Pills, Ershen Pills, and Wuweizi Powder. The positive and negative ion sources of electrospray ionization were simultaneously collected by mass spectrometry. A total of 11 effective components were detected in Sishen Pills, with four effective components detected in Ershen Pills and eight effective components detected in Wuweizi Powder, respectively. To explore the effects of Sishen Pills and its separated prescriptions on the human intestinal flora, an in vitro anaerobic fermentation model was established, and the human intestinal flora was incubated with Sishen Pills, Ershen Pills, and Wuweizi Powder in vitro. The 16S rDNA sequencing technology was used to analyze the changes in the intestinal flora. The results showed that compared with the control group, Sishen Pills, and its separated prescriptions could decrease the intestinal flora abundance and increase the Shannon index after fermentation. The abundance of Bifidobacterium was significantly increased in the Sishen Pills and Ershen Pills groups. However, the abundance of Lactobacillus, Weissella, and Pediococcus was significantly increased in the Wuweizi Powder group. After fermentation for 12 h, the pH of the fermentation solution of three kinds of liquids with feces gradually decreased and was lower than that of the control group. The decreasing amplitude in the Wuweizi Powder group was the most obvious. The single-bacteria fermentation experiments further confirmed that Sishen Pills and Wuweizi Powder had inhibitory effects on Escherichia coli, Staphylococcus aureus, and Enterococcus faecalis, and the antibacterial activity of Wuweizi Powder was stronger than that of Sishen Pills. Both Sishen Pills and Ershen Pills could promote the growth of Lactobacillus brevis, and Ershen Pills could promote the growth of Bifidobacterium adolescentis. This study provided a more sufficient theoretical basis for the clinical application of Sishen Pills and its separated prescriptions.
Humans ; Gastrointestinal Microbiome/drug effects* ; Drugs, Chinese Herbal/chemistry* ; Fermentation/drug effects* ; Bacteria/drug effects* ; Chromatography, High Pressure Liquid ; Tandem Mass Spectrometry ; Intestines/microbiology*