Skip to main content
Supplement Research and Comparison WebsiteBest Price Guarantee
Supplement Research and Comparison Website

Study Design

Methods
Developed novel monoclonal antibodies via co-expression technology and established a single-integrated chemiluminescence immunoassay.
Funding
Unclear
This study aimed to develop novel, highly specific monoclonal antibodies for the rapid and precise detection of the plasmin-α2-antiplasmin complex (PIC), an early biomarker indicative of fibrinolytic activation. This approach addresses the limitations of existing methods, which are often time-consuming and susceptible to interference from abundant free plasminogen and α2-antiplasmin in plasma, thereby improving the diagnosis and management of venous thromboembolism (VTE) and related disorders. We successfully generated self-assembled PIC complexes via co-expression technology to immunize mice and subsequently screened a pair of monoclonal antibodies (4C11 and 3 × 105) that specifically target novel epitopes of the PIC, exhibiting high affinity and specificity, with affinity constants of 1.0 × 10-12 M and 8.9 × 10-10 M, respectively. Utilizing this antibody pair, we established a single-integrated chemiluminescence immunoassay capable of completing detection within 15 minutes. The developed assay demonstrated excellent analytical performance, characterized by a detection limit as low as 0.06 µg mL-1, a broad linear range (0.08-40 µg mL-1), and a total coefficient of variation below 10%. Multicenter clinical evaluations further confirmed strong concordance with the reference method (R2 = 0.99), alongside enhanced sensitivity and specificity, as evidenced by an area under the curve (AUC) of 0.95 compared to 0.90. In summary, we have developed a rapid, high-performance PIC detection platform based on novel monoclonal antibodies. This platform is compatible with point-of-care testing (POCT) and represents a highly promising and reliable tool for the clinical assessment of hyperfibrinolysis.

Research Insights

SupplementDoseHealth OutcomeEffect TypeEffect SizeSource
Back to top