LEVERAGING CRISPR-BASED DIAGNOSTICS FOR RAPID AND ACCURATE TUBERCULOSIS DETECTION IN RESOURCE-LIMITED SETTINGS
Keywords:
CRISPR-Based Diagnostic, Tuberculosis, Detection, Leveraging, RapidAbstract
Tuberculosis (TB) remains a major global health challenge, particularly in low- and middle-income countries where
access to rapid and reliable diagnostic tools is limited. Delays in diagnosis continue to drive transmission, worsen
patient outcomes, and increase mortality. Although existing diagnostic methods such as smear microscopy, culture,
and GeneXpert have improved TB detection, they are often constrained by limited sensitivity, long turnaround
times, high costs, or infrastructure requirements. This highlights the urgent need for innovative, accessible, and
efficient diagnostic solutions. This study aimed to develop and evaluate a CRISPR-based diagnostic kit for the rapid
detection of Mycobacterium tuberculosis, specifically designed for use in resource-limited settings. A mixed
methods experimental design was employed, involving laboratory development, clinical validation using sputum
samples, and comparative analysis against conventional diagnostic methods. The performance of the CRISPR assay
was assessed in terms of sensitivity, specificity, and turnaround time. The results demonstrated that the CRISPR
based assay achieved high diagnostic accuracy, with sensitivity of 96.4% and specificity of 97.8%, outperforming
smear microscopy and slightly exceeding the performance of GeneXpert. In addition, the assay delivered results
within 90 minutes, making it significantly faster than traditional culture methods and slightly quicker than
GeneXpert. Its minimal equipment requirements and adaptability further highlight its suitability for decentralized
healthcare settings. In conclusion, CRISPR-based diagnostics represent a promising and practical solution for
improving TB detection in resource-constrained environments. By combining speed, accuracy, and affordability,
this technology has the potential to strengthen TB control efforts, reduce diagnostic delays, and ultimately improve
patient outcomes.
