RESUMO
BACKGROUND: Testing for the presence of liver cirrhosis (LC) is one of the most critical diagnostic and prognostic assessments for patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). More non-invasive tools are needed to diagnose LC but the predictive abilities of current models are still inconclusive. This study aimed to develop and validate a novel and non-invasive artificial neural network (ANN) model for diagnosing LC in patients with HBV-related HCC using routine laboratory serological indicators. METHODS: A total of 1152 HBV-related HCC patients who underwent hepatectomy were included and randomly divided into the training set (n = 864, 75%) and validation set (n = 288, 25%). The ANN model was constructed from the training set using multivariate Logistic regression analysis and then verified in the validation set. RESULTS: The morbidity of LC in the training and validation sets was 41.2% and 46.8%, respectively. Multivariate analysis showed that age, platelet count, prothrombin time and total bilirubin were independent risk factors for LC (P < 0.05). The area under the ROC curve (AUC) analyses revealed that the ANN model had higher predictive accuracy than the Logistic model (ANN: 0.757 vs Logistic: 0.721; P < 0.001), and other scoring systems (ANN: 0.757 vs CP: 0.532, MELD: 0.594, ALBI: 0.575, APRI: 0.621, FIB-4: 0.644, AAR: 0.491, and GPR: 0.604; P < 0.05 for all) in diagnosing LC. Similar results were obtained in the validation set. CONCLUSION: The ANN model has better diagnostic capabilities than other commonly used models and scoring systems in assessing LC risk in patients with HBV-related HCC.
RESUMO
This study is to establish an UPLC fingerprint of Resina Draconis from different manufacturers, which can provide a comprehensive evaluation for its quality control. The analysis was performed on a Phenomenex Kinetex 2.6 µ C18 100A column by agradientelution program with acetonitrile-water as mobile phase at a flow rate of 1.7 mL x min(-1). The column temperature was 40 degrees C and the detection wavelengthwas 280 nm. The fingerprints of 18 batches of Draconis Resina were further evaluated by chemometrics methods including similarity analysis (SA), hierarchical clustering analysis (HCA) and principal component analysis (PCA). As a result, there were 15 common peaks, 13 of which had been identified by LC-Q-TOF MS, and the similarity degrees of 15 batches of the samples was more than 0.9, and the samples were divided into 4 clusters by their quality difference. The method is reproducible, simple and reliablethat it can be used for quality control and evaluation of Resina Draconis from different manufacturers.
