CRISPR-induced DNA reorganization for multiplexed nucleic acid detection
Bibliographic record
Abstract
Nucleic acid sensing powered by the sequence recognition of CRIPSR technologies has enabled major advancement toward rapid, accurate and deployable diagnostics. While exciting, there are still many challenges facing their practical implementation, such as the widespread need for a PAM sequence in the targeted nucleic acid, labile RNA inputs, and limited multiplexing. Here we report FACT (Functionalized Amplification CRISPR Tracing), a CRISPR-based nucleic acid barcoding technology compatible with Cas12a and Cas13a, enabling diagnostic outputs based on cis- and trans-cleavage from any sequence. Furthermore, we link the activation of CRISPR-Cas12a to the expression of proteins through a Reprogrammable PAIRing system (RePAIR). We then combine FACT and RePAIR to create FACTOR (FACT on RePAIR), a CRISPR-based diagnostic, that we use to detect infectious disease in an agricultural use case: honey bee viral infection. With high specificity and accuracy, we demonstrate the potential of FACTOR to be applied to the sensing of any nucleic acid of interest.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".