Liquid NanoBiosensors Enable One‐Pot Electrochemical Detection of Bacteria in Complex Matrices
Bibliographic record
Abstract
Abstract There is a need for point‐of‐care bacterial sensing and identification technologies that are rapid and simple to operate. Technologies that do not rely on growth cultures, nucleic acid amplification, step‐wise reagent addition, and complex sample processing are the key for meeting this need. Herein, multiple materials technologies are integrated for overcoming the obstacles in creating rapid and one‐pot bacterial sensing platforms. Liquid‐infused nanoelectrodes are developed for reducing nonspecific binding on the transducer surface; bacterium‐specific RNA‐cleaving DNAzymes are used for bacterial identification; and redox DNA barcodes embedded into DNAzymes are used for binding‐induced electrochemical signal transduction. The resultant single‐step and one‐pot assay demonstrates a limit‐of‐detection of 10 2 CFU mL −1 , with high specificity in identifying Escherichia coli amongst other Gram positive and negative bacteria including Klebsiella pneumoniae , Staphylococcus aureus , and Bacillus subtilis . Additionally, this assay is evaluated for analyzing 31 clinically obtained urine samples, demonstrating a clinical sensitivity of 100% and specify of 100%. When challenging this assay with nine clinical blood cultures, E. coli ‐positive and E. coli ‐negative samples can be distinguished with a probability of p < 0.001.
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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 | 0.002 |
| 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".