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 explore the correlation of different HBV DNA loads with peripheral blood lymphocyte subsets and interleukin-6 (IL-6) in patients with chronic hepatitis B.Methods:A cross-sectional study was conducted. A total of 519 patients with chronic hepatitis B admitted to the Fifth Medical Center of the General Hospital of the People′s Liberation Army from April 2019 to June 2024 were included. The patients were divided into high, medium, and low viral load groups and a negative group based on HBV DNA load. Another 100 healthy individuals who underwent physical examinations during the same period were recruited as the control group. The quantities of peripheral blood lymphocyte subsets and IL-6 levels were compared among the groups. Meanwhile, alanine aminotransferase (ALT) levels were recorded and compared among the groups. The correlation of HBV DNA levels with lymphocyte subsets and IL-6 was analyzed using Pearson correlation analysis.Results:HBV DNA loads were negatively correlated with the counts of CD3 +, CD4 +, CD8 +, CD19 +, and CD56 + lymphocyte subsets (correlation coefficients r were -0.483, -0.508, -0.524, -0.573, and -0.561, respectively; all P<0.001) and positively correlated with IL-6 levels ( r=0.862, P<0.05). Compared with the control group, the counts of each lymphocyte subset were higher in the high, medium, and low viral load groups ( P<0.05). In the HBV DNA-negative chronic hepatitis B group, the counts of CD8 + and CD19 + lymphocyte subsets were significantly higher [712.32(526.00,898.64) and 495.62(345.74,645.50) cells/μl] than those in the control group [612.10(479.89,744.31) and 470.32 (396.00,544.64) cells/μl] ( P<0.05). Conclusion:The degree of HBV replication activity in patients with chronic hepatitis B is associated with the immune status of the body, and negatively correlated with the quantities of lymphocyte subsets and positively correlated with IL-6 levels.

Objective:To develop a preoperative CT-based radiomics model integrating multidimensional features for the accurate prediction of TFE3-rearranged renal cell carcinoma（TFE3-rRCC）.Methods:This study retrospectively enrolled 865 pathologically confirmed renal cell carcinoma（RCC）patients in The First Affiliated Hospital of Naval Medical University from June 2013 to June 2023，including 60 cases of TFE3-rRCC and 805 cases of non-TFE3 RCC（comprising clear cell RCC，papillary RCC，and chromophobe RCC）. Among them，627 were male and 238 were female，with a mean age of（54.1 ± 12.7）years（range：14?82 years）. The median maximum tumor diameter was 4.0（2.6，6.0）cm. Based on the chronological order of CT examinations，the patients were divided into training（ n=478），validation（ n=206），and test（ n=181）sets in an approximate 6∶2∶2 ratio. Using precontrast and corticomedullary phase CT images，we extracted peritumoral imaging features，habitat features，3D radiomic features，and 2.5D deep learning radiomic features. A deep learning radiomics score（DLR-SCORE）prediction model was constructed using least absolute shrinkage and selection operator（LASSO）regression. The diagnostic performance of the model was evaluated by receiver operating characteristic（ROC）curve analysis，with the area under the curve（AUC）as the primary metric. Additionally，sensitivity，specificity，and accuracy were calculated based on the confusion matrix. Results:A total of 12 442 features were extracted from non-contrast and corticomedullary phase CT images，from which eight key features were selected to construct the DLR-SCORE model. The model demonstrated diagnostic accuracies for TFE3-rRCC of 98.5%（471/478）in the training set，81.6%（168/206）in the validation set，and 86.2%（156/181）in the test set. The AUC of ROC curve was 0.98（95% CI 0.96?1.00）in the training set，0.83（95% CI 0.71?0.94）in the validation set，and 0.88（95% CI 0.76?1.00）in the test set. In the test set，the DLR-SCORE model achieved a sensitivity of 88.9%（16/18）and a specificity of 85.9%（140/163）for detecting TFE3-rRCC. Conclusions:The DLR-SCORE model integrating multidimensional CT radiomics features demonstrated favorable predictive performance for TFE3-rRCC，offering a promising noninvasive tool to assist preoperative diagnosis.

Objective To analyze chemical constituents of compound Maxing Shigan decoction by ultra-high perfor-mance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS). Methods The separation was performed on a UPLC BEH C₁₈ column (2.1 mm×100 mm, 2.5 µm)，with a gradient elution applying 0.1% aqueous formic acid solution and 0.1% formic acid acetonitrile as a mobile phase. The column temperature was 40 °C. The flow rate was 0.4 ml/min and the analysis time was 15 min. Mass spectrometry (MS) data were collected in both positive and negative ESI ion modes. Results Through UPLC-QTOF/MS analysis and reference validation, a total of 59 chemical components in Maxing Shigan decoction were identified. Conclusion An ultra-high performance liquid chromatography quadrupole time-of-flight mass spectrometry (UPLC-QTOF/MS) method was established to identify the chemical components of Maxing Shigan decoction. This method is simple, efficient, sensitive and accurate, and provides a basis for the elucidation of the pharmacodynamic material basis and mechanism of Maxing Shigan decoction. It can provide data reference for the optimization of the compatibility of traditional Chinese medicine in the treatment of COVID-19.

Objective:To develop a preoperative CT-based radiomics model integrating multidimensional features for the accurate prediction of TFE3-rearranged renal cell carcinoma（TFE3-rRCC）.Methods:This study retrospectively enrolled 865 pathologically confirmed renal cell carcinoma（RCC）patients in The First Affiliated Hospital of Naval Medical University from June 2013 to June 2023，including 60 cases of TFE3-rRCC and 805 cases of non-TFE3 RCC（comprising clear cell RCC，papillary RCC，and chromophobe RCC）. Among them，627 were male and 238 were female，with a mean age of（54.1 ± 12.7）years（range：14?82 years）. The median maximum tumor diameter was 4.0（2.6，6.0）cm. Based on the chronological order of CT examinations，the patients were divided into training（ n=478），validation（ n=206），and test（ n=181）sets in an approximate 6∶2∶2 ratio. Using precontrast and corticomedullary phase CT images，we extracted peritumoral imaging features，habitat features，3D radiomic features，and 2.5D deep learning radiomic features. A deep learning radiomics score（DLR-SCORE）prediction model was constructed using least absolute shrinkage and selection operator（LASSO）regression. The diagnostic performance of the model was evaluated by receiver operating characteristic（ROC）curve analysis，with the area under the curve（AUC）as the primary metric. Additionally，sensitivity，specificity，and accuracy were calculated based on the confusion matrix. Results:A total of 12 442 features were extracted from non-contrast and corticomedullary phase CT images，from which eight key features were selected to construct the DLR-SCORE model. The model demonstrated diagnostic accuracies for TFE3-rRCC of 98.5%（471/478）in the training set，81.6%（168/206）in the validation set，and 86.2%（156/181）in the test set. The AUC of ROC curve was 0.98（95% CI 0.96?1.00）in the training set，0.83（95% CI 0.71?0.94）in the validation set，and 0.88（95% CI 0.76?1.00）in the test set. In the test set，the DLR-SCORE model achieved a sensitivity of 88.9%（16/18）and a specificity of 85.9%（140/163）for detecting TFE3-rRCC. Conclusions:The DLR-SCORE model integrating multidimensional CT radiomics features demonstrated favorable predictive performance for TFE3-rRCC，offering a promising noninvasive tool to assist preoperative diagnosis.

Objective:To explore the correlation of different HBV DNA loads with peripheral blood lymphocyte subsets and interleukin-6 (IL-6) in patients with chronic hepatitis B.Methods:A cross-sectional study was conducted. A total of 519 patients with chronic hepatitis B admitted to the Fifth Medical Center of the General Hospital of the People′s Liberation Army from April 2019 to June 2024 were included. The patients were divided into high, medium, and low viral load groups and a negative group based on HBV DNA load. Another 100 healthy individuals who underwent physical examinations during the same period were recruited as the control group. The quantities of peripheral blood lymphocyte subsets and IL-6 levels were compared among the groups. Meanwhile, alanine aminotransferase (ALT) levels were recorded and compared among the groups. The correlation of HBV DNA levels with lymphocyte subsets and IL-6 was analyzed using Pearson correlation analysis.Results:HBV DNA loads were negatively correlated with the counts of CD3 +, CD4 +, CD8 +, CD19 +, and CD56 + lymphocyte subsets (correlation coefficients r were -0.483, -0.508, -0.524, -0.573, and -0.561, respectively; all P<0.001) and positively correlated with IL-6 levels ( r=0.862, P<0.05). Compared with the control group, the counts of each lymphocyte subset were higher in the high, medium, and low viral load groups ( P<0.05). In the HBV DNA-negative chronic hepatitis B group, the counts of CD8 + and CD19 + lymphocyte subsets were significantly higher [712.32(526.00,898.64) and 495.62(345.74,645.50) cells/μl] than those in the control group [612.10(479.89,744.31) and 470.32 (396.00,544.64) cells/μl] ( P<0.05). Conclusion:The degree of HBV replication activity in patients with chronic hepatitis B is associated with the immune status of the body, and negatively correlated with the quantities of lymphocyte subsets and positively correlated with IL-6 levels.

Objective To explore the efficacy and safety of ticagrelor de-escalation and nicorandil therapy in elderly patients with acute coronary syndrome(ACS)after percutaneous coronary intervention(PCI).Methods A total of 300 elderly patients with ACS were selected from the Sixth and Seventh Medical Center of Chinese PLA General Hospital and Beijing Chaoyang Integrative Medicine Emergency Rescue and First Aid Hospital from November 2016 to June 2019,including 153 males and 147 females,aged>65 years old.All the patients received PCI,and all had double antiplatelet therapy(DAPT)scores≥2 and a new DAPT(PRECISE-DAPT)score of≥25.All patients were divided into two groups by random number table method before operation:ticagrelor group(n=146,ticagrelor 180 mg load dose followed by PCI,and ticagrelor 90 mg bid after surgery)and ticagrelor de-escalation + nicorandil group(n=154,ticagrelor 180 mg load dose followed by PCI,ticagrelor 90 mg bid+nicorandil 5 mg tid after surgery,changed to ticagrelor 60 mg bid+ nicorandil 5 mg tid 6 months later).Follow-up was 12 months.The composite end points of cardiovascular death,myocardial infarction and stroke,the composite end points of mild hemorrhage,minor hemorrhage,other major hemorrhage and major fatal/life-threatening hemorrhage as defined by the PLATO study,and the composite end points of cardiovascular death,myocardial infarction,stroke and bleeding within 12 months in the two groups were observed.Results The comparison of general baseline data between the two groups showed no statistically significant difference(P>0.05).There was also no significant difference in the composite end points of cardiovascular death,myocardial infarction and stroke between the two groups(P>0.05).The cumulative incidence of bleeding events in ticagrelor de-escalation + nicorandil group was significantly lower than that in ticagrelor group(P<0.05),while the composite end points of cardiovascular death,myocardial infarction,stroke and bleeding were also significantly lower than those in tecagrelor group(P<0.05).Conclusion In elderly patients with ACS,the treatment of ticagrelor de-escalation + nicorandil after PCI may not increase the incidence of ischemic events such as cardiovascular death,myocardial infarction or stroke,and it may reduce the incidence of hemorrhagic events.

Objective To investigate the mechanism of matrine hydrochloride injection on acetaminophen(APAP)induced liver injury in mice.Methods Sixty male Kunming mice were randomly divided into blank group,model group and experimental-L,-M,-H groups.The mice were abdominal injected 300 mg·kg-1 APAP to build the acute liver injury modeling.The experimental-L,-M,-H groups were intravenous injected matrine of 0.7,1.4,and 2.8 mg·kg-1,respectively.Blank and model groups were injected with 10％glucose solution 10 mL·kg-1·d-1 via tail vein.Enzyme-linked immunosorbent assay were used to measure the levels of glutamic oxaloacetic transaminase(GOT),glutamic oxaloacetic transaminase(GPT),interleukin-6(IL-6)and tumor necrosis factor-α(TNF-α)in mice serum.The kit were employed to detect the levels of superoxide dismutase(SOD)and malondialdehyde(MDA)in liver tissue.Results The serum GOT levels in the experimental-M,-H groups,model group and blank group were(5 593.45±128.46),(2 316.24±125.37),(8 302.16±267.15)and(40.13±7.69)U·L-1;the GPT levels were(6 159.37±129.64),(2 597.10±120.37),(8 1699.54±259.87)and(36.47±9.46)U·L-1;the IL-6 levels were(159.43±26.42),(96.37±25.84),(215.34±47.68)and(83.72±26.37)pg·mL-1;the TNF-α levels were(327.68±38.37),(249.69±44.97),(477.68±58.59)and(254.35±50.67)pg·mL-1;the SOD levels were(329.46±37.49),(371.16±33.76),(246.84±38.79)and(429.67±23.76)U·mg-1;the MDA levels were(5.34±0.76),(4.09±0.54),(12.46±3.76)and(3.12±0.42)nmol·mg-1.There were significant differences between the experimental-M,-H groups and the model group(all P＜0.05).Conclusion Matrine has a significant protective effect on APAP induced drug-induced liver injury,and its protective mechanism is related to inhibiting inflammatory response and alleviating oxidative stress response.

Purpose To analyze the relationship between different molecular types and imaging manifestations of simultaneous bilateral breast cancer.Materials and Methods Eighty-one patients with simultaneous bilateral breast cancer confirmed by surgery and pathology in the Affiliated Hospital of Qingdao University from January 2016 to May 2022 were retrospectively analyzed,80 patients received mammography and 38 patients received MRI.Imaging features referred to the 5th edition of the breast imaging reporting and data system standards.Molecular typing was evaluated and classified according to the 2013 revised St.Gallen international expert consensus recommended the determination.The clinicopathological and imaging features of the index and contralateral breast cancer were compared,and the imaging features of different molecular types of the index and contralateral breast cancer were also compared.Results There were statistically significant differences in histological types and molecular typing between the index and contralateral cancers(x2=39.72,12.23,P＜0.05).Mammograph showed that the index cancer was mostly a mass(51.9％,40/77),while the contralateral cancer was mostly calcification(38.4％,28/73);the index cancer was mostly polymorphic calcification(68.8％,22/32),while the contralateral cancer was mostly amorphous calcification(45.2％,19/42)(x2=33.15,10.47,P＜0.05).There was a statistically significant difference in the MRI enhancement between the index cancer and the contralateral cancer(x2=6.79,P＜0.05).For contralateral cancer,mammograms showed statistically significant differences in the four molecular subtypes on tumor density,as well as MRI enhancement patterns,early enhancement degree,and time-signal intensity curve(x2=26.72,7.49,8.95,13.44,12.85,P＜0.05).There was a significant difference in the distribution of calcification among the four molecular subtypes on the X-ray of the first cancer(x2=20.15,P＜0.05).Conclusion The molecular typing and some imaging characteristics of simultaneous bilateral breast cancer are different,and some imaging characteristics can provide reference for predicting the molecular typing of simultaneous bilateral breast cancer.