Hepatotoxicity remains one of the most frequent and clinically important safety concerns in adeno-associated virus (AAV) gene therapy. Yet the laboratory patterns observed after vector administration are biologically complex and often difficult to interpret. Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) elevations are non-specific, as they may arise from AAV-induced innate and adaptive immune recognition of vector capsid, endoplasmic reticulum stress, mitochondrial strain, cholestasis, or background muscle turnover, especially in neuromuscular disorders where ALT and AST frequently exceed upper limits of normal at baseline. Traditional drug-induced liver injury frameworks, which rely on fixed multiples of population-derived reference ranges, are poorly suited to these settings and risk both overcalling benign changes and overlooking evolving toxicity. This clinical practice review outlines a mechanism-anchored strategy for interpreting hepatic biomarkers in AAV trials. We describe how vector tropism, intracellular capsid burden, and immune kinetics shape post-dose laboratory trajectories across systemic, liver-directed, and neuromuscular programs. The tissue specificity and kinetic behavior of hepatic biomarkers are examined to show why no single analyte is reliable in isolation. A personalized, baseline-anchored interpretive approach is proposed, with quantitative criteria for AAV-associated liver injury and phenotype-based classifications tailored to pediatric, neuromuscular, and liver disease populations. Finally, we translate these biological principles into a practical clinical framework emphasizing enzyme trajectory, corroboration with hepatic markers such as gamma glutamyl transferase (GGT), recognition of delayed adaptive immune responses, and early attention to bilirubin or international normalized ratio (INR) drift. Together, these concepts provide a structured and biologically grounded approach, proposed here, for interpreting liver tests after AAV gene therapy, strengthening safety monitoring while preserving transgene expression and trial integrity.

Automation has become essential in clinical biochemistry laboratories to meet increasing demands for efficiency, standardization, and reliable turnaround times. Large automated analyzer (LAA) systems are now central to modern laboratory operations and are frequently integrated within total laboratory automation solutions encompassing pre-analytical, analytical, and post-analytical workflows. However, the implementation of LAA systems is complex and requires coordinated oversight across scientific, operational, informatics, and vendor stakeholders. This article presents a practical framework for the validation and implementation of LAA systems from the perspective of a clinical biochemist. Key aspects discussed include pre-implementation planning, management of unanticipated challenges, and implementation strategy with real world examples from our institutional experience. The overall goal of this article is to demonstrate that approaching LAA implementations with structured planning, effective collaboration, and risk-based decision-making can ensure implementation success in terms of analytical quality, operational continuity, and patient safety.

The clinical laboratory is undergoing a paradigm shift toward the integration of liquid chromatography-tandem mass spectrometry(MS) into Total Laboratory Automation workflows. While historically relegated to specialized satellite units due to manual pre-analytical requirements, recent technological advancements have enabled the development of fully automated, 'sample-to-result' MS modules. This review provides an authoritative analysis of the technical integration of systems such as the Roche cobas® i 601 and Shimadzu CLAM-2040 in core laboratories. We examine the role of intelligent middleware and artificial intelligence-assisted algorithms in ensuring standardized data validation and quality control tracking and real-time operational monitoring. Furthermore, we evaluate the clinical impact of this integration on diagnostic accuracy for low-concentration analytes in endocrinology and therapeutic drug monitoring. By transitioning from batch-based processing to continuous automated streams, MS establishes itself as a robust, high-throughput tool for routine clinical diagnostics.

Analytical performance specifications (APS) represent a crucial part of analytical quality. Laboratories use APS in the forms of total error (TE), imprecision, bias and measurement uncertainty (MU) to accurately assess the quality of the analytical phase. This narrative review summarizes the history and evolution of APS. It also reviews the current consensus on APS models as represented by the 2014 Milan models. Moreover, it illustrates the similarities and differences of TE and MU approaches and their use in the clinical laboratories. Finally, it provides a decision and practical framework for laboratories to choose APS in real practice and points to the future hot topics related to APS.