Bacteriophage‐Activated DNAzyme Hydrogels Combined with Machine Learning Enable Point‐of‐Use Colorimetric Detection of <i>Escherichia coli</i>
Bibliographic record
Abstract
Abstract Developing cost‐effective, consumer‐accessible platforms for point‐of‐use environmental and clinical pathogen testing is a priority, to reduce reliance on laborious, time‐consuming culturing approaches. Unfortunately, a system offering ultrasensitive detection capabilities in a form that requires little auxiliary equipment or training has remained elusive. Here, a colorimetric DNAzyme‐crosslinked hydrogel sensor is presented. In the presence of a target pathogen, DNAzyme cleavage results in hydrogel dissolution, yielding the release of entrapped gold nanoparticles in a manner visible to the naked eye. Recognizing that Escherichia coli holds high relevance within both environmental and clinical environments, an E. coli ‐responsive DNAzyme is incorporated into this platform. Through the optimization of the hydrogel polymerization process and the discovery of bacteriophage‐induced DNAzyme signal amplification, 10 1 CFU mL −1 E. coli is detected within real‐world lake water samples. Subsequent pairing with an artificial intelligence model removed ambiguity in sensor readout, offering 96% true positive and 100% true negative accuracy. Finally, high sensor specificity and stability results supported clinical use, where 100% of urine samples collected from patients with E. coli urinary tract infections are accurately identified. No false positives are observed when testing healthy samples. Ultimately, this platform stands to significantly improve population health by substantially increasing pathogen testing accessibility.
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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 | 0.001 |
| 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".