Summary： Immune-mediated inflammatory injury is an important feature of the disease aggravation of hepatitis B virus-related acute-on-chronic liver failure （ACLF）. Toll-like receptors （TLRs） have been shown previ...Summary： Immune-mediated inflammatory injury is an important feature of the disease aggravation of hepatitis B virus-related acute-on-chronic liver failure （ACLF）. Toll-like receptors （TLRs） have been shown previously to play a pivotal role in the activation of innate immunity. The purpose of this study was.to characterize the TLR4 expression in peripheral blood mononuclear cells （PBMCs） of ACLF pa- tients and its possible role in the disease aggravation. Twelve healthy subjects, 15 chronic HBV-infected （CHB） patients and 15 ACLF patients were enrolled in this study. The TLR4 expression in PBMCs and T cells of all subjects was examined by real-time PCR and flow cytometry. The correlation of TLR4 ex- pression on T cells with the markers of disease aggravation was evaluated in ACLF patients. The ability of TLR4 ligands stimulation to induce inflammatory cytokine production in ACLF patients was ana- lyzed by flow cytometry. The results showed that TLR4 mRNA level was upregulated in PBMCs of ACLF patients compared to that in the healthy subjects and the CHB patients. Specifically, the expres- sion of TLR4 on CD4＋ and CD8＋ T cells of PBMCs was significantly increased in ACLF patients. The TLR4 levels on CD4＋ and CD8＋T cells were positively correlated with serum total bilirubin （TBIL）, direct bilirubin （DBIL）, international normalized ratio （INR） levels and white blood cells （WBCs）, and negatively correlated with serum albumin （ALB） levels in the HBV-infected patients, indicating TLR4 pathway may play a role in the disease aggravation of ACLF. In vitro TLR4 ligand stimulation on PBMCs of ACLF patients induced a strong TNF-α production by CD4＋ T cells, which was also posi- tively correlated with the serum markers for liver injury severity. It was concluded that TLR4 expression is upregulated on T cells in PBMCs, which is associated with the aggravation of ACLF.展开更多
Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class imbalance.However,most machine learning models are desig...Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class imbalance.However,most machine learning models are designed based on balanced data and lack interpretability.This study aimed to propose a traditional Chinese medicine(TCM)diagnostic model for HBV-ACLF based on the TCM syndrome differentiation and treatment theory,which is clinically interpretable and highly accurate.Methods We collected medical records from 261 patients diagnosed with HBV-ACLF,including three syndromes:Yang jaundice(214 cases),Yang-Yin jaundice(41 cases),and Yin jaundice(6 cases).To avoid overfitting of the machine learning model,we excluded the cases of Yin jaundice.After data standardization and cleaning,we obtained 255 relevant medical records of Yang jaundice and Yang-Yin jaundice.To address the class imbalance issue,we employed the oversampling method and five machine learning methods,including logistic regression(LR),support vector machine(SVM),decision tree(DT),random forest(RF),and extreme gradient boosting(XGBoost)to construct the syndrome diagnosis models.This study used precision,F1 score,the area under the receiver operating characteristic(ROC)curve(AUC),and accuracy as model evaluation metrics.The model with the best classification performance was selected to extract the diagnostic rule,and its clinical significance was thoroughly analyzed.Furthermore,we proposed a novel multiple-round stable rule extraction(MRSRE)method to obtain a stable rule set of features that can exhibit the model’s clinical interpretability.Results The precision of the five machine learning models built using oversampled balanced data exceeded 0.90.Among these models,the accuracy of RF classification of syndrome types was 0.92,and the mean F1 scores of the two categories of Yang jaundice and Yang-Yin jaundice were 0.93 and 0.94,respectively.Additionally,the AUC was 0.98.The extraction rules of the RF syndrome differentiation model based on the MRSRE method revealed that the common features of Yang jaundice and Yang-Yin jaundice were wiry pulse,yellowing of the urine,skin,and eyes,normal tongue body,healthy sublingual vessel,nausea,oil loathing,and poor appetite.The main features of Yang jaundice were a red tongue body and thickened sublingual vessels,whereas those of Yang-Yin jaundice were a dark tongue body,pale white tongue body,white tongue coating,lack of strength,slippery pulse,light red tongue body,slimy tongue coating,and abdominal distension.This is aligned with the classifications made by TCM experts based on TCM syndrome differentiation and treatment theory.Conclusion Our model can be utilized for differentiating HBV-ACLF syndromes,which has the potential to be applied to generate other clinically interpretable models with high accuracy on clinical data characterized by small sample sizes and a class imbalance.展开更多
基金supported by the National Science and Technology Major Project for Infectious Diseases of China(No.2012ZX10004503)
文摘Summary： Immune-mediated inflammatory injury is an important feature of the disease aggravation of hepatitis B virus-related acute-on-chronic liver failure （ACLF）. Toll-like receptors （TLRs） have been shown previously to play a pivotal role in the activation of innate immunity. The purpose of this study was.to characterize the TLR4 expression in peripheral blood mononuclear cells （PBMCs） of ACLF pa- tients and its possible role in the disease aggravation. Twelve healthy subjects, 15 chronic HBV-infected （CHB） patients and 15 ACLF patients were enrolled in this study. The TLR4 expression in PBMCs and T cells of all subjects was examined by real-time PCR and flow cytometry. The correlation of TLR4 ex- pression on T cells with the markers of disease aggravation was evaluated in ACLF patients. The ability of TLR4 ligands stimulation to induce inflammatory cytokine production in ACLF patients was ana- lyzed by flow cytometry. The results showed that TLR4 mRNA level was upregulated in PBMCs of ACLF patients compared to that in the healthy subjects and the CHB patients. Specifically, the expres- sion of TLR4 on CD4＋ and CD8＋ T cells of PBMCs was significantly increased in ACLF patients. The TLR4 levels on CD4＋ and CD8＋T cells were positively correlated with serum total bilirubin （TBIL）, direct bilirubin （DBIL）, international normalized ratio （INR） levels and white blood cells （WBCs）, and negatively correlated with serum albumin （ALB） levels in the HBV-infected patients, indicating TLR4 pathway may play a role in the disease aggravation of ACLF. In vitro TLR4 ligand stimulation on PBMCs of ACLF patients induced a strong TNF-α production by CD4＋ T cells, which was also posi- tively correlated with the serum markers for liver injury severity. It was concluded that TLR4 expression is upregulated on T cells in PBMCs, which is associated with the aggravation of ACLF.
基金Key research project of Hunan Provincial Administration of Traditional Chinese Medicine(A2023048)Key Research Foundation of Education Bureau of Hunan Province,China(23A0273).
文摘Objective Clinical medical record data associated with hepatitis B-related acute-on-chronic liver failure(HBV-ACLF)generally have small sample sizes and a class imbalance.However,most machine learning models are designed based on balanced data and lack interpretability.This study aimed to propose a traditional Chinese medicine(TCM)diagnostic model for HBV-ACLF based on the TCM syndrome differentiation and treatment theory,which is clinically interpretable and highly accurate.Methods We collected medical records from 261 patients diagnosed with HBV-ACLF,including three syndromes:Yang jaundice(214 cases),Yang-Yin jaundice(41 cases),and Yin jaundice(6 cases).To avoid overfitting of the machine learning model,we excluded the cases of Yin jaundice.After data standardization and cleaning,we obtained 255 relevant medical records of Yang jaundice and Yang-Yin jaundice.To address the class imbalance issue,we employed the oversampling method and five machine learning methods,including logistic regression(LR),support vector machine(SVM),decision tree(DT),random forest(RF),and extreme gradient boosting(XGBoost)to construct the syndrome diagnosis models.This study used precision,F1 score,the area under the receiver operating characteristic(ROC)curve(AUC),and accuracy as model evaluation metrics.The model with the best classification performance was selected to extract the diagnostic rule,and its clinical significance was thoroughly analyzed.Furthermore,we proposed a novel multiple-round stable rule extraction(MRSRE)method to obtain a stable rule set of features that can exhibit the model’s clinical interpretability.Results The precision of the five machine learning models built using oversampled balanced data exceeded 0.90.Among these models,the accuracy of RF classification of syndrome types was 0.92,and the mean F1 scores of the two categories of Yang jaundice and Yang-Yin jaundice were 0.93 and 0.94,respectively.Additionally,the AUC was 0.98.The extraction rules of the RF syndrome differentiation model based on the MRSRE method revealed that the common features of Yang jaundice and Yang-Yin jaundice were wiry pulse,yellowing of the urine,skin,and eyes,normal tongue body,healthy sublingual vessel,nausea,oil loathing,and poor appetite.The main features of Yang jaundice were a red tongue body and thickened sublingual vessels,whereas those of Yang-Yin jaundice were a dark tongue body,pale white tongue body,white tongue coating,lack of strength,slippery pulse,light red tongue body,slimy tongue coating,and abdominal distension.This is aligned with the classifications made by TCM experts based on TCM syndrome differentiation and treatment theory.Conclusion Our model can be utilized for differentiating HBV-ACLF syndromes,which has the potential to be applied to generate other clinically interpretable models with high accuracy on clinical data characterized by small sample sizes and a class imbalance.
