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BackgroundAutoimmune hepatitis (AIH) is an immune-mediated chronic liver disease that can lead to severe fibrosis and cirrhosis. Transient elastography (TE, FibroScan) can assess the fibrotic stages of chronic liver diseases by liver stiffness measurement (LSM). Studies on the diagnostic accuracy of FibroScan for the detection of fibrosis in AIH patients are still limited.Material/MethodsThis study enrolled 108 AIH patients who underwent liver biopsies. Using the METAVIR scoring system as the reference, Spearman’s rank correlation was performed to explore the relationship between the markers and stages of fibrosis. The area under the receiver operating characteristic curve (AUROC) was used to evaluate the diagnostic accuracy. The optimal LSM cut-off values for predicting the stages of fibrosis were calculated.ResultsLSM was superior to other non-invasive markers in differentiating the stages of fibrosis in AIH patients. AUROC value of LSM was 0.885 for stage F2, 0.897 for stage F3, and 0.878 for stage F4. The optimal LSM cut-off value was 6.27 kPa for stage F2, 8.18 kPa for F3, and 12.67 kPa for F4.ConclusionsFibroScan is a valuable non-invasive method for the evaluation of liver fibrosis of AIH patients.

The translation of rare codons relies on their corresponding rare tRNAs, which could not be fully charged under amino acid starvation. Theoretically, disrupted or retarded translation caused by the lack of charged rare tRNAs can be partially restored by feeding or intracellular synthesis of the corresponding amino acids. Inspired by this assumption, we develop a screening or selection system for obtaining overproducers of a target amino acid by replacing its common codons with the corresponding synonymous rare alternative in the coding sequence of selected reporter proteins or antibiotic-resistant markers. Results show that integration of rare codons can inhibit gene translations in a frequency-dependent manner. As a proof-of-concept, Escherichia coli strains overproducing l-leucine, l-arginine or l-serine are successfully selected from random mutation libraries. The system is also applied to Corynebacterium glutamicum to screen out l-arginine overproducers. This strategy sheds new light on obtaining and understanding amino acid overproduction strains.