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 investigate cardiovascular disease (CVD) prevention status among residents with intra-urban migration in Central China, so as to provide insights into targeted prevention and control of CVD. Methods: Basic data of residents aged 35 to 75 years who participated in Early Screening and Comprehensive Intervention Project for CVD high-risk populations in Central China from September 2015 to August 2020 were collected. According to birth place, type of registered residence and current residence, residents were divided into four groups: local residents in old urban area, local residents in new urban area, other urban migrants and other rural migrants. The status of CVD primary and secondary prevention, were analysed by using a robust Poisson regression model. Results: A total of 76 513 residents were recruited, including 29 420 males (38.45%) and 47 093 females (61.55%), and had a mean age of (56.36±9.84) years. There were 45 087 (58.93%) local residents in old urban area, 23 868 (31.19%) local residents in new urban area, 5 668 (7.41%) other urban migrants and 1 890 (2.47%) other rural migrants. After adjusting for variables such as age, gender and educational level, the results of robust Poisson regression analysis showed that compared with local residents in old urban area, local residents in new urban area had lower compliance rates of non- or moderate-drinking (RR=0.987, 95%CI: 0.975-1.000) and healthy diet (RR=0.535, 95%CI: 0.365-0.782), lower proportion of using aspirin as primary prevention in CVD high-risk population (RR=0.616, 95%CI: 0.511-0.741), lower awareness (RR=0.873, 95%CI: 0.782-0.974) and control rates (RR=0.730, 95%CI: 0.627-0.849) of hypertension; other urban migrants had higher compliance rate of non-smoking (RR=1.045, 95%CI: 1.017-1.075); other rural migrants had lower proportion of using aspirin as primary prevention in CVD high-risk population (RR=0.826, 95%CI: 0.707-0.966). Conclusion The CVD primaryprevention among local residents in new urban area is relatively poor among four groups of residents in Central China, and key interventions are needed.

ObjectiveBased on non-targeted metabolomics， to analyze the regulation of endogenous differential metabolites in serum of type 2 diabetes mellitus（T2DM） rats by Fuling Yunhua granules， and to clarify the metabolic pathways through which this granules exerted its effect on improving T2DM. MethodSeventy SD rats， half male and half female， were randomly divided into the control group， model group， and high， medium， low dose groups of Fuling Yunhua granules（20.70， 10.35， 5.18 g·kg^-1 in raw drug amount） and the positive drug group（pioglitazone hydrochloride tablets， 8.1 mg·kg^-1）. Except for the control group， other groups were fed with high-sugar and high-fat diet combined with intraperitoneal injection of streptozotocin（STZ） to establish a T2DM rat model. After successful modeling， the treatment groups were administered the corresponding drugs by gavage， and the control group and model group were treated with an equal volume of saline by gavage， once/d， for 28 d. Fasting blood glucose（FBG） and glycosylated hemoglobin A1c（GHbA1c） levels were measured in all groups of rats during the administration period， and hematoxylin-eosin（HE） staining was used to observe the pathomorphological changes in the pancreatic tissues of rats at the end of the administration period. The endogenous metabolite levels in rat serum were detected by ultra-performance liquid chromatography-linear ion trap-electrostatic field orbitrap high-resolution mass spectrometry（UPLC-LTQ-Orbitrap MS）， and the data were processed using principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA）. Differential metabolites were identified by the Human Metabolome Database（HMDB） and the Kyoto Encyclopedia of Genes and Genomes（KEGG）， and screened for differential metabolites with variable importance in the projection（VIP） value>1， P<0.05， and fold change（FC）<0.6 or FC>1. And the metabolic pathway enrichment analysis of the screened differential metabolites was performed by MetaboAnalyst 5.0， then the screened differential metabolites were diagnosed and evaluated by the receiver operating characteristic（ROC） curves. ResultCompared with the control group， the FBG level of rats in the model group increased significantly（P<0.01）， the GHbA1c content tended to increase， but the difference was not statistically significant， and the pancreatic tissue of rats was obviously damaged， the number of pancreatic islets decreased， and the pancreatic β-cells were obviously reduced， atrophied and enlarged. Compared with the model group， the FBG levels of rats in the high dose group of Fuling Yunhua granules and the positive drug group were significantly reduced after 2 weeks of administration（P<0.05， P<0.01）， the GHbA1c content of rats in the high dose group of Fuling Yunhua granules was significantly reduced（P<0.05）， and the pancreatic tissue lesions of rats in the different dose groups of Fuling Yunhua granules were reduced. The results of non-targeted metabolomics showed that 46 differential metabolites were significantly changed in the model group compared with the blank group. Pathway enrichment analysis found that T2DM mainly affected biological processes including biosynthesis of primary bile acid， D-amino acid metabolism， steroid hormone biosynthesis， and glycerophospholipid metabolism in rats. Compared with the model group， the levels of 8 differential metabolites in the high dose group of Fuling Yunhua granules were significantly adjusted， and the pathway enrichment analysis found that D-amino acid metabolism， retinol metabolism， glycine， serine and threonine metabolism， tryptophan metabolism and other metabolic pathways were mainly involved. ROC curves further analysis revealed that the four characteristic differential markers of 11-cis-retinol， D-piperidinic acid， D-serine， and p-cresol sulfate had high diagnostic value for the treatment of T2DM with Fuling Yunhua granules. ConclusionFuling Yunhua granules can improve the symptoms of T2DM rats by regulating the amino acid metabolic and retinol metabolic pathways through the modulation of endogenous differential metabolites.

Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with unclear etiology and limited treatment options. The median survival time for IPF patients is approximately 2-3 years and there is no effective intervention to treat IPF other than lung transplantation. As important components of lung tissue, endothelial cells (ECs) are associated with pulmonary diseases. However, the role of endothelial dysfunction in pulmonary fibrosis (PF) is incompletely understood. Sphingosine-1-phosphate receptor 1 (S1PR1) is a G protein-coupled receptor highly expressed in lung ECs. Its expression is markedly reduced in patients with IPF. Herein, we generated an endothelial-conditional S1pr1 knockout mouse model which exhibited inflammation and fibrosis with or without bleomycin (BLM) challenge. Selective activation of S1PR1 with an S1PR1 agonist, IMMH002, exerted a potent therapeutic effect in mice with bleomycin-induced fibrosis by protecting the integrity of the endothelial barrier. These results suggest that S1PR1 might be a promising drug target for IPF therapy.

Humans ; Blood Pressure/physiology* ; Cohort Studies ; Patient Discharge ; Ischemic Stroke ; Stroke ; Brain Ischemia ; Diabetes Mellitus ; Hypertension

Objective:To improve the prognosis stratification, especially early mortality(EM), of elderly patients with newly diagnosed multiple myeloma(NDMM).Methods:In this retrospective study, univariate and multivariate Cox regression analysis were conducted to identify the independent prognostic factors associated with overall survival(OS)and the chi-square test and multivariate Logistic analysis were used to identify the prognostic factors associated with EM in 223 elderly patients(age≥65 years)with NDMM from three centers in the country.Results:Increased NT-pro-BNP(≥300 pg/ml), ECOG-PS≥2 and stage Ⅲ R-ISS were identified as three independent adverse prognostic factors of OS.The rates of EM3, EM6, EM12 and EM24 were 12.1%, 20.1%, 32.2% and 60%, respectively.The most common cause for EM6(particularly EM3)was disease-related complications resulting from ineligibility for treatment due to poor physical performance, severe organ dysfunction or treatment discontinuation due to treatment intolerance, while the most common cause for EM12(particularly EM24)was disease progression or relapse mainly as a result of inadequate treatment.R-ISS staging failed to predict EM, while decreased eGFR, ECOG-PS≥2, and increased NT-pro-BNP were able to estimate the risk of EM, with increased NT-pro-BNP as a common independent factor for EM12( P=0.03)and EM24( P=0.015). Conclusions:R-ISS staging, which primarily reflects MM biology, cannot predict EM.However, factors such as NT-pro-BNP, eGFR and ECOG-PS associated with frailty and impairment of organ functions can be used to estimate the risk of EM, among which NT-pro-BNP may be the most important independent factor for EM.Therefore, incorporation of these frailty-related biomarkers into R-ISS staging may be able to more precisely estimate the prognosis and particularly early death of elderly patients with NDMM.

Objective:To investigate risk factors for early mortality (EM) in patients with newly diagnosed multiple myeloma (NDMM) and to build an EM-predictive model.Methods:In a cohort of 275 patients with NDMM, risk factors for EM at 6, 12, and 24 months after diagnosis (EM6, EM12, and EM24, respectively) were determined to establish a model to predict EM.Results:The rates of EM6, EM12, and EM24 were 5.5% , 12.7% , and 30.2% , respectively. The most common cause for EM was disease progression/relapse, accounting for 60.0% , 77.1% , and 84.3% of EM6, EM12, and EM24, respectively. EM6 was associated with corrected serum calcium >2.75 mmol/L and platelet count <100×10 9/L, whereas risk factors for EM12 included age >75 years, ISS Ⅲ, R-ISS Ⅲ, corrected serum calcium >2.75 mmol/L, serum creatinine >177 μmol/L, platelet count <100×10 9/L, and bone marrow plasma cell ratio ≥ 60% . In addition to the risk factors for EM12, EM24 was also associated with male sex and 1q21 gain. By multivariate analysis, age >75 years, platelet count <100×10 9/L, and 1q21 gain were independent risk factors for EM24 but there were no independent risk factors significantly associated with EM6 and EM12. Using a scoring system including these three risk factors, a Cox model for EM24 was generated to distinguish patients with low (score<3) and high (score ≥ 3) risk. The sensitivity and specificity of the model were 20.7% and 99.2% , respectively. Further, an internal validation performed in a cohort of 183 patients with NDMM revealed that the probability of EM24 in high-risk patients was 26 times higher than that in low-risk patients. Moreover, this model was also able to predict overall survival. The median overall survival of patients with scores of 0, 1, 2, 3, 4, and 5 were 59, 41, 22, 17.5, and 16 months, respectively. Conclusion:In the study cohort, the EM6, EM12, and EM24 rates were 5.5% , 12.7% , and 30.2% , respectively, and disease progression or relapse were main causes of EM. An EM24-predictive model built on three independent risk factors for EM24 (age>75 years, platelet count<100×10 9/L, and 1q21 gain) might predict EM risk and overall survival.