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.

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.

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 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.

Objective To establish an expert consensus on the management of mini-midline catheters(hereinafter referred to as the'consensus')to guide nurses in standardizing the insertion and maintenance of mini-midline catheters.Methods Evidence was systematically retrieved,scientifically evaluated,and synthesized using evidence-based methods to draft the initial version of the consensus.From December 2023 to July 2024,totally 2 rounds of expert correspondence and 2 rounds of expert panel discussions were conducted to revise the content,resulting in the final version.Results There were 17 experts from tertiary A general hospitals in Beijing,Shanghai,Hunan,Hubei,Sichuan,Jiangsu,Hainan,Guangxi Zhuang Autonomous Region,and Shandong participating in the consultation,with a 100％response rate.In the 2 rounds of expert correspondence,the authority coefficients were 0.947 and 0.962,respectively.The mean importance scores of all items exceeded 4.00 points.The coefficients of variation(CV)were 0-0.32(first round)and 0-0.15(second round).Kendall's concordance coefficients were 0.097 and 0.101(both P＜0.001).The consensus covers 11 sections,including definition,indications,contraindications,qualification training,pre-insertion preparation,catheter insertion,catheter use,catheter maintenance,catheter removal,prevention and management of common complications,and health education.Conclusion The Consensus demonstrates scientific rigor and comprehensively addresses key procedures before,during,and after the insertion of mini-midline catheters,providing actionable guidance for nurses in catheter insertion and maintenance.

Objective:To explore the medication law of national TCM master Lu Fang in the treatment of primary trigeminal neuralgia (PTN) based on data mining.Methods:With the prescription of the outpatient patients of Harbin Traditional Chinese Medicine Hospital of Professor Lu Fang from September 2014 to September 2022 as the data source, the frequency, property and taste, and meridian tropism of the prescribed drugs were analyzed using Excel 2022 software. R 4.2.1 was used for mining analysis on Chinese materia medica, including correlation, relevance, and clustering,and the medication law in the treatment of PTN was discussed.Results:A total of 300 prescriptions were analyzed, involving 177 kinds of Chinese materia medica, with a frequency of 3 120 times, and 34 kinds of of high-frequency Chinese materia medica. The high frequently Chinese materia medica included Chuanxiong Rhizoma, Angelicae Dahuricae Radix, Puerariae Lobatae Radix, Ligustici Rhizoma et Radix, and Viticis Fructus. The main properties were warm, slightly cold, and neutral, while the main tastes were pungent, bitter, and sweet. The meridian tropism analysis ranked the liver, lung, spleen, and stomach meridians in descending order. Analysis yielded 21 strong association rules, and the association analysis formed a core prescription group based on Chuanxiong Rhizoma, Angelicae Dahuricae Radix, and Ligustici Rhizoma et Radix. The analysis obtained 5 types of clustering combinations.Conclusion:Professor Lu Fang's the medication law to treat primary trigeminal neuralgia is mainly dispelling wind and alleviating pain, which is often combined with the methods, such as searching and dredging collaterals, clearing and dispelling the stagnated heat, calming the liver and subduing yang, soothing the liver and invigorating the spleen.

Objective:To explore the consistency of claudin 18.2 immunohistochemistry (IHC) using 4 different clone antibodies in gastric adenocarcinoma.Methods:A total of 226 gastric adenocarcinomas diagnosed and treated at the Drum Tower Hospital Affiliated to Nanjing University Medical College between January 2022 to March 2023 were included in this study. The cohort consisted of 165 males and 61 females, with a mean age of (61.3±12.1) years. Tumor tissues from radical resection specimens were collected for tissue microarrays. IHC detection of claudin 18.2 was performed using the EnVision method, utilizing 4 clones of antibody: OTIR157B5, 43-14A, EPR19202 and D313D22. The results were interpreted based on both the intensity of staining on tumor cell membranes and the percentage of positive tumor cells relative to the total tumor cells.Results:The positive cutoff value was set as moderately to strongly linear membrane staining in ≥75% of all viable invasive tumor cells, and clone OTIR157B5 demonstrated the highest positive expression rate at 52.2% (118/226). Additionally, the clones OTIR157B5, 43-14A, and EPR19202 were consistently and strongly positive, with all agreement rates of Cohen κ exceeding 0.8. In gastric adenocarcinoma and its three Lauren subtypes, OTIR157B5 exhibited clear membranous localization.Conclusions:Clone OTIR157B5 of claudin 18.2 antibody shows the highest rate of moderately to strongly linear membrane-positive staining, accounting for ≥75% of all viable invasive tumor cells, and clones 43-14A and EPR19202 show strong consistency and high sensitivity.

Purpose To explore the application value of magnetic resonance feature tracking to assess early heart failure in different rat models.Materials and Methods Fifty male SD rats were randomly divided into four groups:the control group(8 rats),the isoproterenol high dose(ISO-H)group(14 rats),the isoproterenol low dose(ISO-L)group(14 rats)and the coronary artery ligation surgery(myocardial infarction,MI)group(14 rats).The ISO-H group received intraperitoneal injections of ISO at 100 mg/kg once a week for two consecutive weeks.The ISO-L group received continuous intraperitoneal injections of ISO at 5 mg/(kg·d)for 14 days.The MI group underwent coronary ligation to establish a heart failure rat model.Scans using a 7T cardiac MRI cine sequence were performed after two weeks and four weeks.Heart function parameters and myocardial strain parameters were obtained using CVI 42 software.Serum markers of myocardial injury,including creatine kinase,creatine kinase-MB,lactate dehydrogenase and α-hydroxybutyric dehydrogenase levels,were measured.HE staining and Masson staining were used to detect myocardial pathological changes.Results Compared with the control group,the ISO-H group,ISO-L group and MI group showed significant changes in left ventricular ejection fraction(t=-2.387,P=0.04;t=5.622,P<0.001;t=17.715,P<0.001)and overall radial strain(t=-4.570,P=0.001;t=6.587,P<0.001;t=10.649,P<0.001)after two weeks.Compared with the control group,the ISO-H group,ISO-L group and MI group showed varying degrees of reduction in left ventricular ejection fraction(t=4.834,P=0.001;t=10.325,P<0.001;t=26.286,P<0.001)and overall radial strain(t=3.890,P=0.003;t=9.547,P<0.001;t=13.751,P<0.001)after four weeks.The serum markers of myocardial injury,including creatine kinase,creatine kinase-MB,lactate dehydrogenase and α-hydroxybutyric dehydrogenase levels,were significantly elevated(t=-93.373--11.019,P<0.001),and pathological staining revealed that all model groups exhibited varying degrees of myocardial cell hypertrophy,necrosis and interstitial fibrosis compared with the control group.Conclusion Magnetic resonance feature tracking has guiding significance for the dynamic monitoring,early diagnosis and prognosis assessment of heart failure in different rat models.