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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".