Inexpensive and colorimetric RNA detection at ambient temperature with a cell-free protein synthesis platform
Bibliographic record
Abstract
Abstract While many proposals of paper-based diagnostics utilize cell-free gene expression systems, these assays oftentimes suffer from the need for temperature cycling and high operational costs, particularly for developing countries. Here, we explore and report the experimental conditions for the colorimetric detection of viral RNA with an in vitro transcription/translation assay that uses crude E. coli extracts at room temperature where the signal amplification is aided by body heat. Clinically-relevant concentrations of RNA (ca. 600 copies/test) were detected from synthetic RNA samples. The activation of cell-free gene expression was achieved using toehold-switch-mediated riboregulatory elements that are specific to RNA sequences. The colorimetric output was generated by the α-complementation of β-galactosidase ω-fragment (LacZω) with cell-free expressed LacZα, using an X-gal analogue as a substrate. The estimated cost of a single reaction is as low as ~ 0.26 euro/test, which may help to facilitate the accessibility of the diagnostic kit in developing countries. With future optimizations and bacterial strain engineering, production costs can be even further brought down, and the test times can be shortened. Graphical Abstract
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".