Background: International guidelines recommend the use of high-sensitivity cardiac troponin (hs-cTn) I and T methods for the detection of myocardial injury as a pre-requisite for the diagnosis of acute myocardial infarction (AMI) in patients admitted to the emergency department. Recently, Mindray (Mindray Bio-Medical Electronics Co., Ltd., Shenzhen, China) has introduced a new chemiluminescence immunoassay (CLIA) for the detection of the cTn complex. The present study aims to verify and validate the hs-cTnI Mindray assay on the new automated CL2600i analyzer compared to the routine Alinity-i series instrument by Abbott (Abbott, Chicago, IL, USA). Methods: This study evaluated linearity, precision through the 5 × 5 protocol, methodological comparison on plasma and serum matrices, hs-cTnI 99th percentile imprecision, and the hs-cTnI detection rate in a healthy population. Results: The results obtained proved that the performance of the Mindray hs-cTnI test on the CL2600i platform was closely comparable to the Abbott Alinity-i system (plasma R²: 0.974; serum R²: 0.995). The CVs were consistently low, and no significant differences were reported. Excellent analytical performance, with high sensitivity, was also observed in the healthy population (overall detection rate: 79%), as well as good linearity within the measuring range (R²: 0.994). Conclusions: The Mindray hs-cTnI test confirms its robustness and utility in routine practice as an advanced assay. The new technology, with more sensitive detection methods, may improve the accuracy and reliability of cardiac biomarker testing, ultimately leading to better outcomes in the management of patients with AMI and other cardiac conditions.

Serum immunofixation electrophoresis (IFE) is the reference method for identifying and characterizing monoclonal immunoglobulins in plasma cell dyscrasias; however, its interpretation remains time-consuming, operator-dependent, and subject to inter-observer variability. This study evaluated a deep learning-based decision-support system (ImmunoGUI) for automated IFE interpretation intended to support the laboratory reporting workflow. A dual-stage framework was developed to isolate electrophoretic regions of interest and automatically generate standardized textual reports, paired with visual attention maps for diagnostic explainability. The system was evaluated on real-patient datasets across six canonical intact monoclonal immunoglobulin classes (IgGκ, IgGλ, IgAκ, IgAλ, IgMκ, and IgMλ). Models were trained using combinations of real-patient and synthetic IFE images and evaluated on independent real-patient test cohorts. The first-stage model achieved 89.5% overall binary accuracy, with 91% sensitivity and 88% specificity. The second-stage structured reporting model achieved a Top-1 diagnostic accuracy of 79.4%, reaching 95.5% Top-3 accuracy when evaluated as a decision-support hypothesis ranker. Visual attention maps reliably highlighted target monoclonal bands, providing intuitive spatial explainability to support clinical review. ImmunoGUI offers a practical, computationally lightweight decision-support tool to semi-automated IFE reporting. Although further validation on larger multicentre datasets will be required before routine clinical implementation, the system has the potential to help in reducing manual reporting workload and enhancing standardization in routine IFE screening under expert human oversight.