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.

Atherosclerosis （AS） is a chronic and inflammatory vascular disease. Macrophages are common immune cells and play an important role in the development of AS. In recent years， research has found that the formation of AS plaques is closely related to pathological and physiological processes such as macrophage polarization， energy metabolism， and lipid phagocytosis. This review aims to summarize the mechanism of macrophages in the development of AS， and to explore potential therapeutic methods for delaying AS by regulating macrophages， providing new ideas for the treatment and research of AS.

Objective:To investigate the clinical characteristics of patients with multiple myeloma(MM)complicated by bone lesions and the risk factors associated with bone lesions.Methods:The clinical data of 294 newly diagnosed MM patients in Gansu Provincial Hospital from January 2017 to June 2021 were retrospectively analyzed.The patients were divided into the bone lesion group(154 cases)and the non-bone lesions group(140 cases)based on the presence of absence of bone lesions at diagnosis.The general data and laboratory parameters were compared between the two groups.The risk factors for bone lesions in MM patients were analyzed by logistic regression analysis,and the characteristic(ROC)curves were plotted to assess the predictive value of each risk factor for the occurrence of bone lesions in MM patients.Results:Compared to the non-bone lesion group,the bone lesion group had significantly higher serum calcium levels and significantly greater proportions of patients with Durie-Salmon(DS)stage Ⅲ,and bone pain(all P＜0.05).Logistic regression analysis showed that elevated serum calcium(OR=5.135,95％CI:1.931-13.653,P=0.001),DS stage Ⅲ(OR=1.841,95％CI:1.019-3.328,P=0.043),and bone pain(OR=8.208,95％CI:4.761-14.151,P＜0.001)were independent risk factors for bone lesions in MM patients.ROC curve analysis showed that serum calcium(AUC=0.619,95％CI:0.555-0.683,P＜0.001)and bone pain(AUC=0.743,95％CI:0.692-0.793,P＜0.001)had predictive value for bone lesions in MM patients.Conclusion:MM patients have a high incidence of bone lesions,and active monitoring and management of risk factors may improve treatment outcomes and prognosis.

OBJECTIVE Hospital-acquired infections have emerged as an increasingly prominent and hard-to-com-pletely-avoid problem,with their complexity and challenges continuing to intensify.As a major public health con-cern,these hospital-acquired infections pose a serious threat to the safety of individuals within hospitals.Among the various routes of transmission,airborne transmission is one of the most important pathways leading to hospi-tal-acquired infections.A variety pathogenic viruses can attach to infectious respiratory particles produced by hu-man body and spread through these particles.Patients in hospitals,as the primary group releasing respiratory in-fectious particles,have behaviors(such as breathing,coughing,talking,etc.)that are closely related to the trans-mission,dissemination and spread of pathogenic microorganisms.It is generally believed that pathogens re-leased into the air by patients will propagate and spread with air currents,thereby elevating the risk of infection.In order to comprehensively safeguard the safety of healthcare workers and patients,and effectively curb the occur-rence of hospital-acquired infections,it is essential to investigate the impact of pathogen-releasing behaviors on the transmission of pathogenic microorganisms.This paper aims to review the research progress on the impact of pathogen-releasing behaviors of patients on the transmission of respiratory pathogenic microorganisms within healthcare buildings,as well as to provide an outlook for further research directions.

Objective:To observe the intervention effect of personalized nutrition program combined with rehabilitation training in stroke rehabilitation patients.Methods:86 stroke rehabilitation patients who were admitted to our hospital from January 2023 to June 2024 were prospectively selected,they were divided into control group and study group according to the random number table method,with 43 cases in each group,the control group received rehabilitation training,while the study group received personalized nutrition program combine with rehabilitation training.Simple Fugl Meyer motor function(FMA)score,immune function indicators[immunoglobulin(Ig)A,IgG,complement C3,IgM,complement C4],National Institutes of Health Stroke Scale(NIHSS),nutritional status indicators[albumin(ALB),prealbumin(PA),total protein(TP),hemoglobin(HB)],Stroke Specific Quality of Life Scale(SS-QOL),Barthel Index(BI)score were compared between the two groups.Results:NIHSS score in the study group at 8 weeks after intervention was lower than that in the control group,and SS-QOL score,BI score,FMA score,IgM,IgA,IgG,complement C3,complement C4,ALB,HB,TP and PA were higher than those in the control group(P＜0.05).Conclusion:Personalized nutrition program combined with rehabilitation training in stroke rehabilitation patients,can reduce neurological damage,improve limb motor function,enhance nutritional status,immunity,and quality of life.

Objective To construct a machine learning prediction model for diabetic kidney disease(DKD)in type 2 diabetes mellitus(T2DM)patients in the plain-sand and loess hilly areas of Gansu Province,and analyze the interpretability of the model.Methods A multi-stage stratified random sampling method was used to collect the data of T2DM patients in the two areas.After key feature screening,eight ML prediction models were constructed for the risk of DKD in the two areas.The receiver operating characteristic(ROC)curve,accuracy and F1 index were used to evaluate the model,and Shapley additive explanation(SHAP)algorithm was used for model interpretation.Results A total of 1599 patients with T2DM were enrolled in this study.After feature screening,ten variables were selected for model construction in the plain-sand areas.Among the eight models,the gradient boosting decision tree(GBDT)model had the highest prediction efficiency.The area under the curve(AUC)of the test dataset was 0.972,the accuracy was 0.949,and the F1 index was 0.884.In the loess hilly region,12 variables were included in the model,and the best model was the random forest(RF).The AUC of the test set was 0.966,the accuracy was 0.951,and the F1 index was 0.861.SHAP analysis showed that in addition to serum creatinine,age,LDL-C,HbA1c,DM duration,serum uric acid and urinary microalbumin were also closely related to the high risk of DKD.Conclusions The GBDT and RF models have good predictive efficiency for the occurrence of DKD in the two areas,which can be used for the screening of DKD high-risk populations and the in-depth exploration of potential risk factors in the two areas.

Objective To detect the characteristics of whole-brain functional changes in patients with subjective tinnitus(ST)after"frontal-temporal"dual-site repetitive transcranial magnetic stimulation(rTMS)by resting-state functional magnetic resonance imaging(rs-fMRI).Methods A total of 45 ST patients were enrolled,and assessments of tinnitus severity and rs-fMRI scans were performed before and 2 weeks after treatment with"frontal-temporal"dual-site rTMS.Regional homogeneity(ReHo),fractional amplitude of low-frequency fluctuations(fALFF),degree centrality(DC)and seed-based functional connectivity(FC)were analyzed before and after treatment in ST patients.Results Tinnitus handicap inventory(THI)score of ST patients 2 weeks after treatment was significantly decreased compared with that before treatment(P<0.001).ReHo values of the right inferior parietal lobule decreased,fALFF values of the right temporal pole increased,fALFF values of the right superior temporal gyrus decreased,and DC(weighted)and DC(Binarized)values of the right medial temporal gyrus all decreased in ST patients 2 weeks after treatment compared with those before treatment(P<0.05,GRF correction).Using the above differential brain regions as seed points for FC analysis,FC values between right superior temporal gyrus(fALFF)and right middle temporal gyrus reduced,FC values between right middle temporal gyrus[(DC(weighted)]and right superior occipital gyrus reduced,and FC values between right middle temporal gyrus[DC(Binarized)]and right superior occipital gyrus reduced 2 weeks after treatment compared with those before treatment(P<0.05,GRF correction).Conclusion"Frontal-temporal"dual-site rTMS is initially effective for ST patients,and the auditory and non-auditory brain regions of ST patients showed different degrees of regional and interbrain function changes,mainly involving default mode network and visual-auditory network.

AIM To establish a rapid quantitative analysis model for saponins in Paris polyphylla var.yunnanensis(PPY)by near infrared spectroscopy.METHODS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ and there total content in PPY were determined by HPLC,while spectral data within the range of 10 000 to 4 000 cm-1 were collected.A quantitative analysis model was established by combining these data with partial least squares regression(PLSR).Multivariate scatter correction(MSC)and vector normalization(SNV)were applied prior to further preprocessing the spectra with original,first-order derivative(1stD),or second-order derivative(2ndD)treatments.Lastly,the model was optimized through non-smoothing(NS),Norris Derivative filtering(Nd),and Savitzky-Golay filtering(S-G)method.Model stability was evaluated based on correlation coefficients and variance.The predicted contents of each saponin component in the validation set samples were calculated.RESULTS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ were 0.42-17.98,0.46-10.44,0.23-3.86 mg/g,respectively.The total content ranged from 2.91 to 22.1 mg/g.The optimal parameters of three saponins were achieved when selecting the MSC+2ndD+S-G pretreatment method.The corresponding ratio of line segment length to segment gap was 13∶5,15∶5,11∶5,with correlation coefficients of 0.982,0.930,0.958,respectively.The root mean square errors of calibration(RMSEC)were 0.702,0.797,0.238,and the root mean square errors of prediction(RMSEP)were 1.120,0.835,0.304,respectively.The optimal parameters for the total content were obtained when selecting the MSC+2ndD+NS pretreatment method,with a correlation coefficient of 0.970,a RMSEC of 1.090,and a RMSEP of 1.740.CONCLUSION This accurate and rapid method can be used for detection of saponin contents in P.Polyphylla.

Objective:To explore the predictive value of epicardial adipose thickness(EAT)for pre-eclampsia(PE)evaluated by echocardiography.Methods:The clinical data of 242 early pregnant women admitted to Wuhu First People's Hospital from September 2020 to November 2023 were retrospectively analyzed.According to whether PE occurred,they were divided into PE group(n=31)and non-PE group(n=211).The echocardio-graphic data of pregnant women at 11+0-13+6 weeks of gestation were collected,and the influencing factors of PE in pregnant women were analyzed by multivariate logistic regression.Receiver operating characteristic(ROC)curve was drawn to evaluate the predictive value of EAT for PE in pregnant women.Results:There were statisti-cally significant differences in age,pregnancy mode,body mass index(BMI),history of hypertension,systolic and diastolic blood pressure,serum soluble vascular endothelial growth factor receptor-1(sFlt-1)/placental growth fac-tor(PLGF)ratio and EAT between PE group and non-PE group(P<0.05).Multivariate Logistic regression analy-sis showed that increased BMI(OR 1.492,95%CI 1.161-2.724),history of hypertension(OR 3.684,95%CI 2.074-6.542),increased serum sFlt-1/PLGF value(OR 1.982,95%CI 1.268-3.099),and increased EAT(OR 2.246,95%CI 1.292-3.903)were independent risk factors for PE in pregnant women(P<0.05).The results of ROC curve analysis showed that the area under the curve(AUC)of ultrasound evaluation of EAT in predicting PE in pregnant women was0.848(95%CI0.785-0.910,P<0.001).When the optimal cutoff value was5.63 mm,the sensitivity was 85.71%and the specificity was 67.14%.Conclusions:Ultrasound evaluation of EAT for PE pre-diction has good application value.