Translating Bacterial Detection by DNAzymes into a Litmus Test
Bibliographic record
Abstract
Abstract Microbial pathogens pose serious threats to public health and safety, and results in millions of illnesses and deaths as well as huge economic losses annually. Laborious and expensive pathogen tests often represent a significant hindrance to implementing effective front‐line preventative care, particularly in resource‐limited regions. Thus, there is a significant need to develop low‐cost and easy‐to‐use methods for pathogen detection. Herein, we present a simple and inexpensive litmus test for bacterial detection. The method takes advantage of a bacteria‐specific RNA‐cleaving DNAzyme probe as the molecular recognition element and the ability of urease to hydrolyze urea and elevate the pH value of the test solution. By coupling urease to the DNAzyme on magnetic beads, the detection of bacteria is translated into a pH increase, which can be readily detected using a litmus dye or pH paper. The simplicity, low cost, and broad adaptability make this litmus test attractive for field applications, particularly in the developing world.
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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".