Optimization of qPCR diagnosis for Entamoeba histolytica by ddPCR
Accurate diagnosis of Entamoeba histolytica infection is critical for both clinical management and epidemiological monitoring. However, current diagnostic methods on TaqMan-qPCR for detecting this pathogen is hampered by inconsistent methodologies and ambiguous results, particularly in low-titer stool samples with high cycle threshold (Ct) values. To address this issue, this study introduces a novel approach using droplet digital PCR (ddPCR) to evaluate qPCR amplification efficacy and establish a robust theoretical cut-off Ct value for improved diagnostic accuracy. By evaluating different primer-probe sets, we identified optimal candidates and determined a Ct cut-off of 36 cycles, allowing for more reliable differentiation between true infections and false positives. Our findings enhance the diagnostic specificity and reliability, providing a practical and scalable framework for improving pathogen detection protocols, particularly in resource-limited settings. The methodology represents a significant advancement in molecular diagnostics, addressing a critical gap in accurate detection of E. histolytica in clinical stool samples and potentially other clinical specimens in clinical and research settings.