ReviewFood chemistry. Molecular sciences2026
CRISPR-based electrochemiluminescence biosensors: Principles, optimization strategies, and translational challenges - A review of recent progress.
Review in Food chemistry. Molecular sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Improving the Precision of Etiological Diagnosis in Bacterial Infections Using Molecular Technologies: A Comparative Analysis of Platforms, AI Integration, and Point-of-Care Deployment.International journal of molecular sciences · 2026Review
- An RCA-Coupled CRISPR/Cas12a Cascade-Enhanced SERS Platform for Ultrasensitive Detection of Exosomal miRNA-21 in Colorectal Cancer Serum.International journal of nanomedicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of CRISPR/Cas systems with electrochemiluminescence (ECL) has emerged as a promising strategy for constructing high-performance biosensing platforms. CRISPR systems, particularly Cas12a and Cas13a, offer programmable recognition of nucleic acid targets and activatable trans-cleavage activity. ECL provides sensitive signal readout with low background and wide dynamic range. As a narrative review, this article provides a comprehensive overview of recent advances in CRISPR-ECL biosensors, with an emphasis on optimization strategies and practical applications. We first discuss the working principles of Cas12a and Cas13a relevant to biosensing, highlighting their distinct kinetic properties, crRNA design considerations, and reaction condition requirements. We then examine optimization approaches at three interconnected levels: nucleic acid probe design (signal-on, signal-off, and auxiliary probes), sensing interface engineering (probe structures, luminophores, electrode materials, and magnetic nanomaterials), and cascade signal amplification (PCR, CHA, RCA, SDA, EDA, and RPA). Through cross-study comparison, we evaluate the strengths and limitations of different approaches and identify critical knowledge gaps. Their applications in detecting disease biomarkers, pathogen nucleic acids, environmental contaminants, and enzyme activities are summarized. Despite remarkable sensitivity achieved, challenges remain in assay time, reproducibility in complex matrices, and clinical validation. From industrialization and global health perspectives, regulatory approval, manufacturing scalability, cost control, and deployment in low-resource settings are also discussed. Finally, future directions toward simplified workflows, enhanced matrix robustness, standardized validation, multiplexed detection, and point-of-care compatible platforms are proposed. This review provides a structured reference and critical perspective for researchers working on CRISPR-ECL biosensing and related fields.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.