A Rapid Assay for Hepatitis C Virus RNA Detection Using Reverse-Transcription Loop-Mediated Isothermal Amplification–Coupled CRISPR-Cas12b–Based Strategy
Bibliographic record
Abstract
BACKGROUND: Hepatitis C virus (HCV) diagnosis usually requires detection of antibody followed by HCV RNA. The requirement for 2 tests leads to major drop-offs in the cascade of care. Existing near-care HCV RNA tests have slow turnaround time and are expensive with limited availability. We aim to develop a cost-effective, rapid, and sensitive test for detection of HCV RNA to enhance screening, particularly in marginalized and remote populations. METHODS: After RNA extraction from plasma, HCV RNA is reverse-transcribed and amplified using loop-mediated isothermal amplification with HCV-specific primers. The amplified HCV DNA is then detected via CRISPR-Cas12b with a fluorescence readout. RESULTS: HCV RNA from patient samples with genotypes 1a, 1b, 2, 3a, and 4 was detected with high sensitivity and specificity. The lower limit of detection (LLOD) with HCV JFH1 plasmid (genotype 2) is 250 plasmid copies/mL (approximately 100 IU/mL). For clinical samples, we determined the LLOD for genotypes 1 and 3, the most common in North America. Using 500 μL of plasma, genotype 1 RNA ≥100 IU/mL was detected within 40-45 minutes, while genotype 3 had an LLOD of 5000 IU/mL. The clinical sensitivity was 100% in 72 HCV patient samples, including acute HCV and HCV/hepatitis B virus (HBV) coinfection. The specificity was 100%, with no false-positives in 33 HCV-negative samples, including those with HBV or human immunodeficiency virus/HBV coinfection. CONCLUSIONS: Our assay shows high specificity and sensitivity to detect HCV RNA directly from plasma within 45 minutes and hence could be used for efficient screening and diagnosis of HCV infection globally.
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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 | 0.001 | 0.001 |
| 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".