RNA detection with high specificity and sensitivity using nested fluorogenic Mango NASBA
Bibliographic record
Abstract
There is a pressing need for nucleic acid-based assays that are capable of rapidly and reliably detecting pathogenic organisms. Many of the techniques available for the detection of pathogenic RNA possess one or more limiting factors that make the detection of low-copy RNA challenging. Although RT-PCR is the most commonly used method for detecting pathogen-related RNA, it requires expensive thermocycling equipment and is comparatively slow. Isothermal methods promise procedural simplicity but have traditionally suffered from amplification artifacts that tend to preclude easy identification of target nucleic acids. Recently, the isothermal SHERLOCK system overcame this problem by using CRISPR to distinguish amplified target sequences from artifactual background signal. However, this system comes at the cost of introducing considerable enzymatic complexity and a corresponding increase in total assay time. Therefore, simpler and less expensive strategies are highly desirable. Here, we demonstrate that by nesting NASBA primers and modifying the NASBA inner primers to encode an RNA Mango aptamer sequence we can dramatically increase the sensitivity of NASBA to 1.5 RNA molecules per microliter. As this isothermal nucleic acid detection scheme directly produces a fluorescent reporter, real-time detection is intrinsic to the assay. Nested Mango NASBA is highly specific and, in contrast to existing RNA detection systems, offers a cheap, simple, and specific way to rapidly detect single-molecule amounts of pathogenic RNA.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".