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.

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.

【Objective】 To genetically analyze the D^el sample from a blood donor in Jiangyin and make clear the molecular basis of the serological phenotype. 【Methods】 The EDTA anticoagulant blood were collected: buffy coat were used for nucleic acid extract and cDNA analysis; red blood cells for serological test. Tube method and microcolumn gel were used for serological test. Genotyping kit were used for exon analysis. Gene mutation was analyzed using the sequence analyzer. 【Results】 Serological analysis demonstrated the sample′s RhD phenotype was D^el. The phenotype of RhCE was CCEe. Real-time fluorescence quota PCR result demonstrated the existence of all exones. Weak D15 and RHD^* DEL1 [RHD(1227G>A)], which had a high frequency of occurrence in China, were excluded according to real-time fluorescence quota PCR result. Sequence analyzing result verified RHD(28C>T) SNP mutation in cDNA. The genotype of this sample was RHD^*01 W. 61[RHD(28C>T)]. 【Conclusion】 A weak D61 was found among blood donors in our city, Jiangyin.

Lung cancer is the most common incident cancer and the leading cause of cancer death. In recent years, the development of tumor immunotherapy especially chimeric antigen receptor T (CAR-T) cell has shown a promising future. Epidermal growth factor receptor variant III (EGFRvIII) is a tumor-specific mutation expressed in various types of tumors and has been detected in non-small cell lung cancer with a mutation rate of 10%. Thus, EGFRvIII is a potential antigen for targeted lung cancer therapy. In this study, CAR vectors were constructed and transfected into virus-packaging cells. Then, activated T cells were infected with retrovirus harvested from stable virus-producing single clone cell lines. CAR expression on the surfaces of the T cells was detected by flow cytometry and Western blot. The function of CAR-T targeting EGFRvIII was then evaluated. The EGFRvIII-CAR vector was successfully constructed and confirmed by DNA sequencing. A stable virus-producing cell line was produced from a single clone by limited dilution. The culture conditions for the cell line, including cell density, temperature, and culture medium were optimized. After infection with retrovirus, CAR was expressed on more than 90% of the T cells. The proliferation of CAR-T cells were induced by cytokine and specific antigen in vitro. More importantly, EGFRvIII-CART specifically and efficiently recognized and killed A549-EGFRvIII cells with an effector/target ratio of 10:1 by expressing and releasing cytokines, including perforin, granzyme B, IFN-γ, and TNF-α. The in vivo study indicated that the metastasis of A549-EGFRvIII cells in mice were inhibited by EGFRvIII-CART cells, and the survival of the mice was significantly prolonged with no serious side effects. EGFRvIII-CART showed significantly efficient antitumor activity against lung cancer cells expressing EGFRvIII in vivo and in vitro. Therefore, CAR-T targeting EGFRvIII is a potential therapeutic strategy in preventing recurrence and metastasis of lung cancer after surgery.
Animals ; Carcinoma, Non-Small-Cell Lung ; immunology ; therapy ; Cell Line, Tumor ; ErbB Receptors ; immunology ; metabolism ; Female ; Humans ; Immunotherapy, Adoptive ; methods ; Lung Neoplasms ; immunology ; therapy ; Mice ; Mice, Inbred NOD ; Receptors, Chimeric Antigen ; immunology ; T-Lymphocytes ; immunology ; Xenograft Model Antitumor Assays