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.

Objective To propose a ventilator failure probability prediction method based on kernel ridge regression(KRR).Methods Firstly,the failure interval data of ventilators was collected and preprocessed to remove outliers.Secondly,the median rank method was used to estimate the failure probability.Finally,using the time data as the feature variable and the failure probability value as the target variable,a KRR model was established and trained by selecting the optimal kernel function and hyperparameter combination from radial basis kernel function,linear kernel function,polynomial kernel function,and S-type kernel function through grid search and cross-validation methods to predict ventilator failures.To verify the performance of the KRR model in predicting ventilator failure probability,it was compared with Weibull and its extended models.Results KRR achieved a coefficient of determination of 0.993 5,a mean squared error of 5.399 5×10-4,a root mean squared error of 0.023 2 and a mean absolute error of 0.018 3,outperforming Weibull and its extended models in prediction accuracy and error control.Conclusion The failure probability prediction method for ventilators based on KRR demonstrates exceptional performance in prediction accuracy and error control,and thus holds great potential for application.[Chinese Medical Equipment Journal,2025,46(5):73-77]

Non-small cell lung cancer(NSCLC)constitutes the largest portion of lung cancer overall,with high incidence and mortality rates.Apoptosis,is a hot focus in the clinical treatment of NSCLC,its main pathways include the extrinsic death receptor pathway,intrinsic mitochondrial apoptosis pathway and endoplasmic reticulum stress pathway,collectively regulating the cellular apoptosis process.Traditional Chinese medicine(TCM)has significant efficacy in the treatment and prognosis of NSCLC,with advantages such as boosting the body's resistance and less adverse drug reactions.Studies have shown that various individual Chinese herbal medicines and compound formulas can treat NSCLC through the apoptosis pathway,alleviating the adverse drug reaction of radiotherapy and chemotherapy.Based on this,this article summarizes recent domestic and international literature,focusing on apoptosis,to summarize the research progress of TCM in treating NSCLC by regulating apoptosis,aiming to provide reference for clinical treatment for NSCLC.

Objective To propose a ventilator failure probability prediction method based on kernel ridge regression(KRR).Methods Firstly,the failure interval data of ventilators was collected and preprocessed to remove outliers.Secondly,the median rank method was used to estimate the failure probability.Finally,using the time data as the feature variable and the failure probability value as the target variable,a KRR model was established and trained by selecting the optimal kernel function and hyperparameter combination from radial basis kernel function,linear kernel function,polynomial kernel function,and S-type kernel function through grid search and cross-validation methods to predict ventilator failures.To verify the performance of the KRR model in predicting ventilator failure probability,it was compared with Weibull and its extended models.Results KRR achieved a coefficient of determination of 0.993 5,a mean squared error of 5.399 5×10-4,a root mean squared error of 0.023 2 and a mean absolute error of 0.018 3,outperforming Weibull and its extended models in prediction accuracy and error control.Conclusion The failure probability prediction method for ventilators based on KRR demonstrates exceptional performance in prediction accuracy and error control,and thus holds great potential for application.[Chinese Medical Equipment Journal,2025,46(5):73-77]

Non-small cell lung cancer(NSCLC)constitutes the largest portion of lung cancer overall,with high incidence and mortality rates.Apoptosis,is a hot focus in the clinical treatment of NSCLC,its main pathways include the extrinsic death receptor pathway,intrinsic mitochondrial apoptosis pathway and endoplasmic reticulum stress pathway,collectively regulating the cellular apoptosis process.Traditional Chinese medicine(TCM)has significant efficacy in the treatment and prognosis of NSCLC,with advantages such as boosting the body's resistance and less adverse drug reactions.Studies have shown that various individual Chinese herbal medicines and compound formulas can treat NSCLC through the apoptosis pathway,alleviating the adverse drug reaction of radiotherapy and chemotherapy.Based on this,this article summarizes recent domestic and international literature,focusing on apoptosis,to summarize the research progress of TCM in treating NSCLC by regulating apoptosis,aiming to provide reference for clinical treatment for NSCLC.

Objective To investigate the mechanism by which lectin-like oxidized low density lipoprotein receptor-1(LOX-1)regulates hyperglycemic-induced myocardial fibroblast(CFs)fibrosis through the glycogen synthase kinase-3β(GSK-3β)/signal transducer and activator of transcription 3(STAT3)pathway.Methods CFs were isolated,cultured and identified.LOX-1 RNAi lentiviral vector was constructed and infected CFs.The experimental groups were as follows:Normal control(NC)group,High glucose(HG)group,LV-LOX-1,LV-Con group,Hypertonic(HPG)group.After LV-LOX-1 and LV-Con were infected with CFs,adding 25 mmol/L glucose to culture CFs for 24 h,they were denoted as HG+LV-LOX-1 group and HG+LV-Con group.Cells in HG+LV-LOX-1 group and HG+LV-Con group were treated with 10 μ mol/L SB216763 and 10 μ mol/L STATTIC for 24 h,respectively,and then they were recorded as HG+LV-LOX-1+SB216763 group,HG+LV-Con+SB216763 group,HG+LV-LOX-1+STATTIC group and HG+LV-Con+STATTIC group.CCK-8 was used to detect the activity of CFs,and the expression levels of mRAN and protein of LOX-1,collagen type I(COL-I),thioredoxin 5(TXNDC5),GSK-3β,STAT3,p-GSK-3β and p-STAT3 were detected by qRT-PCR and Western blot.Results CFs infected with LOX-1 RNAi lentiviral vector were obtained,which showed green under fluorescence microscopy.Compared with HG and HG+LV-Con groups,the mRNA expressions of LOX-1,COL-I and TXNDC5 were decreased in HG+LV-LOX-1 group(P<0.05).Compared with HG+LV-LOX-1 group,mRNA expressions of COL-I and TXNDC5 were decreased in HG+LV-LOX-1+SB216763 and HG+LV-LOX-1+STATTIC groups(P<0.05).Compared with HG and HG+LV-Con groups,p-GSK-3β protein expression was increased in HG+LV-LOX-1 group(P<0.05),while LOX-1,p-STAT3,COL-I,TXNDC5 protein expression was decreased in HG+LV-LOX-1 group(P<0.05).Compared with HG+LV-LOX-1 group,p-GSK-3β protein expression was increased in HG+LV-LOX-1+SB216763 group(P<0.05),while the protein expressions of p-STAT3,COL-I and TXNDC5 were decreased in HG+LV-LOX-1+SB216763 and HG+LV-LOX-1+STATTIC groups(P<0.05).Conclusion LOX-1,GSK-3β,STAT3,TXNDC5,and COL-I are involved in high glucose induced CFs fibrosis.LOX-1 promotes the expression of TXNDC5 and COL-I through GSK-3β/STAT3 pathway,and inhibition of LOX-1 can inhibit high glucose induced CFs fibrosis.

Objective To investigate the molecular mechanism of lectin-like oxidized low-density lipoprotein receptor 1(LOX-1)in the regulation of high glucose induced phagocytosis dysfunction of mouse microglia(BV2 microglia).Methods BV2 cells were cultured in vitro,lentivirus LOX-1RNAi vector(LV-LOX-1)and lentivirusempty vector(LV-Con)were constructed and divided into normal control(NC)group,HG group,LV-LOX-1 group and LV-Con group.After infecting BV2 cells with LV-LOX-1 and LV-Con,the cells were cultured with 25 mmol/L glucose for 24 h,and then divided into HG+LV-LOX-1 group and HG+LV-Con group.After treatment of HG+LV-LOX-1 and HG+LV-Con infected BV2 microglia with 15 μmol/L FH535(β-catenin inhibitor)and AEBSF(ATF6α inhibitor)for 24 h,respectively,they were denoted as HG+LV-LOX-1+FH535 group,HG+LV-Con+FH535 group,HG+LV-LOX-1+AEBSF group,and HG+LV-Con+AEBSF group.Transfection efficiency was determined by fluorescence microscopy,RT-PCR and Western blot.Cell viability was detected b CCK-8.RT-PCR and Western blot were used to detect the mRNA and protein expression of LOX-1,β-catenin,ATF6α and milk fat globular-surface growth factor Ⅷ(MFG-E8)in each group.Results After 72 h of LV-LOX-1 infection,the cells in LV-LOX-1 and LV-Con groups showed a lot of green fluorescence,but not in NC group.Compared with NC group,the mRNA and protein expression of LOX-1 and ATF6α were increased(P<0.05),while the mRNA and protein expression of MFG-E8 and β-catenin decreased in HG group(P<0.05).Compared with HG+LV-Con group,the mRNA and protein expression of LOX-1 and ATF6α were decreased(P<0.05),while the mRNA and protein expression of MFG-E8 and β-catenin increasedin HG+LV-LOX-1 group(P<0.05).Compared with HG+LV-LOX-1 group,the mRNA and protein expressions of MFG-E8 and β-catenin were decreased(P<0.05),and the mRNA and protein expressions of ATF6α and p-β-catenin and p-ATF6α were increased in HG+LV-LOX-1+FH535 group(P<0.05).Compared with HG+LV-LOX-1 group,the mRNA and protein expression were increased(P<0.05),ATF6α mRNA and protein expression and p-ATF6α protein expression were decreased MFG-E8 in HG+LV-LOX-1+AEBSF group(P<0.05).Conclusions LOX-1,MFG-E8,β-catenin and ATF6α are involved in the regulation of phagocytosis of BV2 cells.LOX-1 promotes the phagocytosis dysfunction of BV2 microglia induced by high glucose through β-catenin/ATF6α signaling pathway.

Objective:To investigate the effect of GRIM-19 on the resistance of carcinoma cells to the chemotherapeutic agent docetaxel in the treatment of PCa.Methods:Using siRNA technology to interfere with the gene expression in PCa cells,we estab-lished a model of GRIM-19 overexpression/knockdown in PCa cells.We investigated the effect of different expression levels of GRIM-19 on docetaxel-induced death of the PCa cells by qPCR,Western blot and flow cytometry,and assessed the value of GRIM-19 in re-ducing the chemotherapy-resistance of PCa cells.Results:GRIM-19 was down-regulated in PCa tissues and cells.Knockout of GRIM-19 significantly decreased the expression of siGRIM19 in the PC-3 and LNCaP cells,and reduced their death rate when treated with docetaxel compared with the control group.The expressions of GRIM-19 mRNA and protein were remarkably upregulated after transfection with GRIM-19,and the overexpressed GRIM-19 promoted the death of the PC-3 and LNCaP cells treated with docetaxel in a dose-dependent manner.Flow cytometry analysis showed a lower apoptosis rate of PC-3-R cells than that of PC-3 cells at different time points of docetaxel-induction at different doses.Conclusion:GRIM-19 is a PCa suppressor gene with a significant facilitating effect on the apoptosis of PCa cells,and the overexpression of GRIM-19 promotes docetaxel-induced PCa cell death and improves the sensitivity of chemotherapy.