Ultrasensitive Electrochemical Dnazyme Based Sensing Platform for Clinical Detection of Uropathogenic Escherichia coli
Bibliographic record
Abstract
Urinary tract infections (UTIs) are one of the most ubiquitous bacterial infection diseases affecting half of the global population at least once in their lifetime. Given the occurrence of the false positive result in Dipstics and the time-consuming identification of bacteria using culturing method to detect UroPathogenic Escherichia coli (UPEC), there is the need for a point-of-care diagnostic platform that is reliable, sensitive, specific, cost-effective, fast and easy to use. With the advent electrochemical methods coupled with the specific biorecognition elements, the detection of any biomarker has become possible with one touch. Among many biorecognition molecules, DNAzymes are gaining attention due to their robustness, stability, specificity, and the ease of selection without prior knowledge of the biomarker. Their structure switching and functionality (RNA cleaving) can be utilized with redox active species (like methylene blue) to design highly sensitive and selective electrochemical assays for pathogen detection. In this work, we have developed a dual signaling electrochemical DNAzyme based platform for specific and sensitive detection of Escherichia coli (E.coli) in UTI patient urine samples. The two on-chip working electrodes named as release channel and capture channel are integrated with DNAzyme-redox barcode and DNA probe respectively. In the presence of the bacterial target the DNAzyme catalyzes the cleavage of the redox DNA barcode in the release channel, which is subsequently translated into a redox signal upon hybridization with the probe on the capture channel. The differential signal generated by the two channels in the sensor demonstrated the sensitive detection of 10 CFU for E.coli in buffer and urine. Additionally, the sensor specifically detected E. coli in the presence of a panel of other gram positive and negative bacteria. Clinical validation of the assay was performed using 40 E.coli+ and E.coli- patient urine delineating a clinical sensitivity of 100% and specificity of 78% in a 30 minute testing time. This cost effective, reagent-free, time-efficient, and amplification-free electrochemical assay is suitable for detecting a wide range of pathogens, also alleviates the additional knowledge requirement of the biomarker as compared to other protein and aptamer-based assay. With the advancement in the chip fabrication and microfluidics, this assay can also be multiplexed with other pathogen for simultaneous detection of multiple pathogens in a single urine sample.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".