Development of a rapid and sensitive RPA-CRISPR/Cas12a-based assay for the detection of <i>Brucella melitensis</i>
Bibliographic record
Abstract
ABSTRACT Brucellosis, a zoonotic disease caused by Brucella species, presents significant public health challenges due to its complex diagnosis and the limited availability of rapid detection methods. To address these challenges, we developed a novel detection method that integrates recombinase polymerase amplification (RPA) with the CRISPR/Cas12a system, enabling dual readout through fluorescence (FL) and lateral flow strip (LFS) detection. The RPA-CRISPR/Cas12a-FL assay demonstrated an impressive detection limit of 1 copy/μL, which is 10 times more sensitive than quantitative polymerase chain reaction, while the RPA-CRISPR/Cas12a-LFS method achieved a detection limit of 10 copies/μL, comparable to nested PCR. Specificity testing confirmed the robustness of the assay, as it produced strong signals exclusively for Brucella without cross-reactivity with other bacterial species. Clinical validation using serum samples from 24 confirmed brucellosis patients and six healthy controls demonstrated a 100% concordance with serological results, underscoring the reliability of this method for clinical applications. This assay provides a rapid, sensitive, and specific tool for Brucella detection, suitable for both laboratory and field settings, and holds significant potential for enhancing the diagnosis and control of brucellosis. IMPORTANCE Brucellosis is a significant zoonotic disease, and rapid and accurate diagnosis is crucial for its treatment and control. To address this need, we developed a novel detection method that combines recombinant enzyme polymerase amplification with a CRISPR/Cas12a system, achieving dual readout through fluorescence and lateral flow strips. The test demonstrates excellent sensitivity and specificity, with clinical validation confirming complete concordance with serological results. This approach offers a fast, reliable, and field-deployable solution for brucellosis diagnosis, significantly enhancing disease management and public health outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".