RESUMEN
OBJECTIVE: The objective of this study is to detect the liver stiffness of hepatitis B virus (HBV)-infected patients with an alanine aminotransferase (ALT) level of <2 upper limit of normal (2ULN) by FibroScan and compare histological changes to assess the progression of liver lesions and its test results. METHODS: There were 36 patients who had a liver FibroScan degree of >7.3 KD (F1), and a liver biopsy was conducted. Along with serology of liver fibrosis, indexes and hierarchical processing were used for evaluation. The correlation between these factors was analyzed. RESULTS: The histopathological results of the liver were closely correlated with liver hardness. In the pathological diagnosis of chronic hepatitis, G represents the grade of inflammation and S represents the stage of hepatic fibrosis. Pathological examination results of H&E staining of liver tissue sections revealed that the area under the work characteristic curve of the subjects in G2S1, G2S2, G3S2, and G3S3 stages was 0.923, 0.916, 0.955, and 0.971, respectively, with diagnostic cut-off values of 9.03, 9.85, 15.14, and 30.67, respectively. Furthermore, hydroxyapatite, type III procollagen, laminin, and type IV collagen of serum fibrosis indexes are associated with liver stiffness values (P < 0.05). CONCLUSION: FibroScan can be used as an alternative to liver biopsy. It is meaningful in determining whether HBV infected patients with an ALT level of <2 ULN should receive antiviral therapy.
RESUMEN
OBJECTIVE: Early recognition of children with severe Hand-Foot-and-Mouth disease (HFMD) is especially important, as severe cases are associated with poor prognosis. To accomplish this, authors designed a quantitative assessment tool to build a nomogram to assist in clinical diagnosis. METHODS: A total of 2332 HFMD patients were enrolled in this study; 1750 cases in the mild group and 582 cases in the severe group. Analysis of all of the data was performed using R software version 3.4.3. Multivariate logistic regression was utilized to screen predictors to construct a nomogram model. Finally, predictive performance of the model was evaluated using a receiver operating characteristic (ROC) curve and classifier calibration plot. RESULTS: A nomogram was constructed with five variables: age, peak temperature, fever duration, pathogen, and vomiting. For the nomogram, the area under the curve was 0.87, and the model prediction accuracy rate was 85.2%. Depending upon the comparison of the area under the ROC curve, the nomogram model was superior to the traditional pediatric clinical illness score (PCIS). With the help of the Hosmer-Lemeshow test and resampling model calibration curve, the fitting performance of the nomogram was stable. CONCLUSIONS: With advantages such as simplicity, intuitiveness, and practicality, the nomogram (including age, peak temperature, fever duration, pathogen, and vomiting) is capable of predicting severe HFMD and has certain auxiliary value in clinical applications.