Next-generation Biosensors for Small RNA Detection: From Mechanistic Design to Biomedical Implementation

Introduction:: The small non-coding RNAs (sRNAs), such as microRNAs and piRNAs, have been discovered as key regulators of gene expression and potential biomarkers of cancer, infectious, cardiovascular, and neurological diseases. These conventional methods of detection (RT-qPCR, microarrays, next-generation sequencing) are highly sensitive for analysis but are clinically translationable only due to high cost, extended turnaround times, complex workflows, and their inability to be used in decentralized diagnostics. Biosensor-based systems have also been in the limelight as an alternative technology for detecting sRNA in a rapid, sensitive, and point-of-care manner. Method:: The present narrative review is a systematic review of peer-reviewed articles published between 2019 and 2025, sourced from databases such as PubMed, Scopus, Web of Science, and Google Scholar. Articles were sampled based on their relevance to sRNA biosensor design, biorecognition, signal transduction, and analytical and clinical/translational applicability. Data synthesis was conducted qualitatively due to heterogeneity in experimental design and reporting standards. Results:: Recent developments indicate that biosensors based on electrochemical, optical, piezoelectric, and nanomaterials can achieve high sensitivity, low detection limits, and improved specificity in the detection of sRNA. The use of aptamers, Cas9/CRISPR systems, nanostructured surfaces, and microfluidic systems has increased signal amplification, assay speed, and portability. In spite of these achievements, issues of reproducibility, long-term stability, massscale production, and clinical validation remain. Discussion:: Sensors based on biosensor sRNA detection platforms demonstrate strong prospects for closing the gap between analysis and clinical applications. Nevertheless, to become popular, they should have standardized performance measures, rigorous validation in clinical samples, and direct comparison with the gold-standard diagnostic tools. It will be necessary to overcome these limitations to translate biosensor technologies into routine clinical and point-of-care applications. Conclusion:: Biosensor-based sRNA detection represents a transformative approach for nextgeneration diagnostics, bridging molecular biology with precision medicine. Continued integration of nanotechnology, bioinformatics, and artificial intelligence will further advance biosensor performance, enabling early disease detection, personalized therapy, and global health monitoring.