Colorimetric Detection of Bacteria Using Litmus Test
Bibliographic record
Abstract
There are increasing demands for simple but still effective methods that can be used to detect specific pathogens for point-of-care or field applications. Such methods need to be user-friendly and produce reliable results that can be easily interpreted by both specialists and non-professionals. The litmus test for pH is simple, quick, and effective as it reports the pH of a test sample via a simple color change. We have developed an approach to take advantage of the litmus test for bacterial detection. The method exploits a bacterium-specific RNA-cleaving DNAzyme to achieve two functions: recognizing a bacterium of interest and providing a mechanism to control the activity of urease. Through the use of magnetic beads immobilized with a DNAzyme-urease conjugate, the presence of bacteria in a test sample is relayed to the release of urease from beads to solution. The released urease is transferred to a test solution to hydrolyze urea into ammonia, resulting in an increase of pH that can be visualized using the classic litmus test.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".