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.

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]

The adulteration and counterfeiting of herbal ingredients in medicinal and food homology (MFH) have a serious impact on the quality of herbal materials, thereby endangering human health. Compared to pharmaceutical drugs, health products derived from traditional Chinese medicine (TCM) are more easily accessible and closely integrated into consumers' daily life. However, the authentication of the authenticity of TCM ingredients in MFH has not received sufficient attention. The lack of clear standards emphasizes the necessity of conducting systematic research in this area. This study utilized DNA barcoding technology, combining ITS2, psbA-trnH, matK as universal barcode sequences, and DX, HH, JYH as specific barcode sequences, to identify the authenticity of commercial medicinal and food homology scented herbal teas. The aim was to investigate the authenticity of scented herbal tea products circulating in the market. The research results revealed that among 180 scented herbal tea samples, DNA barcodes were successfully obtained from 164 samples, while the remaining samples either lacked the target components or suffered severe DNA degradation, resulting in failed amplification. Combined with morphological identification studies, it was found that out of the 180 samples, 141 were authentic, accounting for 78.33% of the total samples, while 31 samples showed adulteration and 8 samples lacked the target components, accounting for 21.67% of the total samples. Additionally, water testing revealed that 5 samples of Carthamus tinctorius herbal tea exhibited the phenomenon of weight adulteration. This study exposed the presence of adulteration and weight adulteration in scented herbal tea products, highlighting the need for regulatory authorities to expedite the establishment of quality standards in scented herbal tea industry. These findings provide valuable insights for the rapid development of the scented herbal tea industry.

Objective To observe the inhibitory effect of Guiqi Yiyuan ointment on tumor growth in mice with Lewis lung cancer,and to explore the molecular mechanism of Guiqi Yiyuan ointment combined with cisplatin through phosphoinositide 3-kinase/protein kinase B/mammalian rapamycin target protein(PI3K/Akt/mTOR)signal pathway.Methods Sixty C57BL/6 mice were randomly divided into 6 groups with 10 mice in each group.Except for the blank group(0.9％NaCl),Lewis lung cancer-bearing mice were randomly divided into model group(0.9％NaCl),control group(0.9％NaCl,cisplatin 5 mg·kg-1)and low,medium,high dose experimental groups(Guiqi Yiyuan ointment 1.6,3.3,6.6 g·kg-1,cisplatin 5 mg·kg-1).Flow cytometry was used to detect bone marrow-derived suppressor cells(MDSCs);the expression of related proteins in tumor tissues was detected by Western blot.Results The tumor inhibition rates in control group and low,medium,high dose experimental groups were(39.87±4.45)％,(45.74±14.97)％,(57.78±4.70)％and(69.82±11.05)％.The proportion of MDSCs in bone marrow of in blank group,model group,control group and low,medium,high dose experimental groups were(36.13±1.08)％,(68.63±2.94)％,(58.93±2.02)％,(58.00±1.50)％,(50.93±5.06)％and(43.07±2.41)％.The protein expressions of p-PI3K/PI3K in model group,control group and low,medium and high experimental groups were 0.97±0.03,0.77±0.02,0.72±0.01,0.68±0.03 and 0.53±0.02;PTEN were 0.21±0.07,0.65±0.07,0.74±0.06,0.99±0.13,1.11±0.13;p-Akt/Akt were 1.01±0.02,0.82±0.02,0.77±0.00,0.72±0.03 and 0.52±0.04;p-mTOR/mTOR were 1.01±0.01,0.76±0.05,0.69±0.07,0.59±0.06 and 0.47±0.06.There were significant differences between low,medium,high experimental groups and control group(all P＜0.05).Conclusion Guiqi Yiyuan ointment combined with cisplatin can significantly improve the quality of life and inhibit tumor growth in mice.The mechanism may be the inhibition of PI3K/Akt/mTOR signal pathway and the enhancement of tumor cell apoptosis and autophagy.

Objective To observe the effects of traditional Chinese medicine compound Platycodon grandiflorum Bai powder on the growth of subcutaneously implanted tumor and the expression of B-cell lymphoma-2(Bcl-2),Bcl-2 associated X protein(Bax),cysteinyl aspartate specific proteinase(caspase)-3 and caspase-9 in subcutaneously implanted tumor of Lewis lung cancer mice.Methods The model of transplanted tumor of Lewis lung cancer in mice was established.Seventy SPF male C57BL/6 mice were randomly divided into blank group,model group,low dose experimental group,medium dose experimental group,high dose experimental group,control group and combined group.Blank group and model group were given 0.9％NaCl 0.2 mL by gavage;control group was given 0.9％NaCl by gavage and 25 mg·kg-1cisplatin intraperitoneally;high,medium,low dose experimental groups were given 193,96,48 mg·kg-1·d-1 Platycodon grandiflorum Bai powder 0.2 mL by gavage,respectively;combined group was given 96 mg·kg-1·d-1 Platycodon grandiflorum Bai powder 0.2 mL by gavage,and 25 mg·kg-1 cisplatin intraperitoneally,once every other day.The myelogenous suppressor cells(MDSCs)of mouse bone marrow were detected by flow cytometry,and the expressions of Bel-2,Bax,caspase-3 and caspase-9 in tumor cells were detected by immunofluorescence.Results The percentage of MDSCs in bone marrow of mice in blank group,model group,low dose experimental,medium,high dose experimental group,control group and combination group were(32.50±2.76)％,(63.13±3.14)％,(48.43±2.23)％,(42.53±1.28)％,(32.93±3.56)％,(51.30±4.25)％and(19.90±6.21)％,respectively.The fluorescence intensities of Bax in model group,low dose experimental group,medium dose experimental group,high dose experimental group,control group and combination group were 10.42±0.68,12.40±1.23,15.14±0.65,22.95±1.76,27.18±1.62 and 31.61±1.28;Bel-2 were 36.85±0.80,33.92±4.20,28.88±1.01,20.04±2.21,15.69±2.36 and 6.05±0.73;caspase-3 were 5.28±0.44,7.63±0.55,9.66±0.85,14.73±1.18,17.95±1.29 and 22.92±1.95;caspase-9 were 9.48±0.90,11.57±0.72,13.45±0.93,15.73±1.44,19.20±0.96 and 23.21±1.51.There were significant differences between medium,high dose experimental groups and model group(all P＜0.05),and there were significant differences between combined group and control group(all P＜0.05).Conclusion Platycodon grandiflorum Bai powder can up-regulating the expression of Bax,caspase-3 and caspase-9,down-regulating the expression of Bel-2,inhibiting MDSCs,promoting tumor cell apoptosis and inhibiting tumor growth.