Progress on clinical prognosis assessment in liver failure

Liver failure is a group of clinical syndromes with a mortality rate of >50%. The accurate evaluation of severity in patients with liver failure has been a meaningful and hot topic in clinical research and an important guide for liver transplantation. Numerous prognosis studies have emerged in recent years with high accuracy and adequate validity. Nonetheless, different models utilize distinct parameters and have unequal efficiencies, leading to a specific value and unique application situations for each model. This review focused on the progress in recent prognostic studies including the model for end-stage liver disease, sequential organ failure assessment and its derivative models, the Chinese Group on the Study of Severe Hepatitis B Acute-on-Chronic Liver Failure, the Tongji prognostic predictor model, and other emerging prognostic models and predictors. This review aims to assist clinicians understand the framework of recent models and choose the appropriate model and treatment

A novel nomogram based on routine clinical indicators for screening for Wilson's disease

Background and aims: There is currently no single model for predicting Wilson's disease (WD). We aimed to create a nomogram using daily clinical parameters to improve the accuracy of WD diagnosis in patients with abnormal liver function. Methods: Between July 2016 and December 2020, we identified 90 WD patients with abnormal liver function who had homozygous or compound heterozygous mutations in the ATP7B gene. The control group included 128 patients with similar liver function but no WD during the same time period. To create a nomogram, we screened potential predictive variables using the least absolute shrinkage and selection operator model and multivariate logistic regression. Results: We developed a nomogram for screening for WD based on six predictive factors: serum copper, direct bilirubin, uric acid, cholinesterase, prealbumin, and reticulocyte percentage. In the training cohort, the area under curve (AUC) of the nomogram reached 0.967 (95% confidence interval (CI) 0.946–0.988), while the area under the precision-recall curve was 0.961. Based on the optimal cutpoint of 213.55, our nomogram performed well, with a sensitivity of 96% and a specificity of 87%. In the validation cohort, the AUC of the nomogram was as high as 0.991 (95% CI 0.970–1.000). Conclusions: We developed a nomogram that can predict the risk of WD prior to the detection of serum ceruloplasmin or urinary copper, greatly increasing screening efficiency for patients with abnormal liver function

MOESM2 of Enhancing full-length antibody production by signal peptide engineering

Additional file 2: Table S2. Strains and plasmids used in this study

MOESM3 of Enhancing full-length antibody production by signal peptide engineering

Additional file 3: Figure S1. Western blot analysis of light chain secretion