Autoimmune hepatitis (AIH) is a liver disease of unknown cause characterized by hypergammaglobulinemia, typical or compatible liver histology, the absence of viral hepatitis and the production of autoantibodies. Anti-smooth muscle antibodies (SMAs) detected with indirect immunofluorescence (IIF) in rodent tissues are not disease specific, whereas antibodies directed to the filamentous form of actin (F-actin) are specifically involved in AIH-1. As solid-phase immunoassays (SPAs) specifically targeting F-actin, such as enzyme-linked immunosorbent assay (ELISA), have already been included in recent guidelines, in this study, we evaluated the diagnostic performance of an immunoblotting SPA for F-actin. We selected 86 samples positive for SMA by IIF (titre≥1∶40) and/or for anti-F-actin by ELISA (≥20 units); the patients were divided into 3 groups: AIH-1 (n=14), other liver disorders (n=38) and other nonliver-related conditions (n=34). The samples were tested with an immunoblot SPA (European Autoimmune Liver Diseases 9 Ag plus F-actin, Euroimmun, Germany); the qualitative results were converted into numeric intensity values via EurolineScan software. Immunoblotting revealed 16 positive samples (19%), and ELISA revealed 24 (28%); among them, 7/16 (44%) and 11/24 (46%) had AIH-1. The diagnostic performance metrics were as follows: sensitivity (50%); specificity (87.5%); positive predictive value (PPV), 43.8%; negative predictive value (NPV), 90%; and accuracy (81.4%) and equal to those of ELISA. The mean values were greater in the AIH-1 group; the receiver operating characteristic curve (ROC) had an area under curve (AUC) of 0.77 and was not different from that of the ELISA (0.82); the agreement was 81.4%, with a Cohens kappa of 0.49.

Immunoblotting might be a reliable assay for the identification of anti-F-actin antibodies, and given its high specificity, its implementation in a clinical laboratory might confirm the specific diagnosis of AIH-1 in patients with IIF-detected SMA.

Depression is a heterogeneous mental illness with substantial personal and societal burdens, yet its diagnosis still relies heavily on subjective assessments. Recent advances in blood-based metabolomics have opened new avenues for identifying objective biomarkers associated with depressive symptoms. This review highlights key findings from multicenter clinical and translational research that demonstrate reproducible associations between specific plasma metabolites—such as 3-hydroxybutyrate, betaine, citrate, creatinine, and γ-aminobutyric acid (GABA)—and the severity of depressive states. Several metabolites also appear to be linked to distinct symptom domains, including suicidal ideation (SI), a critical risk factor for self-harm. Notably, combinations of citrate and kynurenine have shown potential for SI severity estimation through machine learning models, suggesting a basis for minimally invasive risk stratification. In parallel, rodent models of stress-induced depression reveal consistent alterations in tryptophan and alanine metabolism, providing insight into possible causal mechanisms involving neurotransmitter biosynthesis and intestinal absorption under stress. Personality-based biotyping and artificial intelligence further refine the stratification of depressive phenotypes, offering prospects for more personalized diagnostics. Although methodological standardization and broader validation remain necessary, accumulating evidence supports the clinical utility of blood metabolomics as a complementary tool for early detection, subtype classification, and suicide risk assessment in depression.

Objective

Serum amyloid A (SAA) is a protein involved in the acute phase of inflammation. SAA expression is upregulated in humans during the acute phase of various viral infections; in addition, SAA can be a useful biomarker to predict the severity and prognosis of COVID-19 patients. This study aimed to evaluate a new chemiluminescence test for SAA detection.

Methods

All serum samples were measured for SAA on a Maglumi 800 (Snibe, Shenzhen, China) and compared with a BN ProSpec (Siemens, Munich, Germany) in the routine of the clinical laboratory of the University Hospital of the Tor Vergata University of Rome (Rome, Italy). Analytical precision, the correlation coefficient, and linearity were assessed. Statistical analyses were performed.

Results

The linearity test was performed via serial dilutions and revealed a correlation coefficient equivalent to 0.9998. The results of the Snibe SAA test correlated well with those obtained by the SAA Siemens test, with a correlation coefficient of 0.974 (P ​< ​0.001). The intra- and interrun precision, as well as carryover, were assessed.

Conclusions

The results obtained from this study demonstrated that the new Snibe SAA test has reliable analytical performance and good accuracy and could represent a valid tool for routine hospital laboratory analysis.

A research team presents XFruitSeg, a deep learning model designed to accurately segment three-dimensional computed tomography (CT) images of plant fruits.