Polymerase Chain Reaction-Free, Sample-to-Answer Bacterial Detection in 30 Minutes with Integrated Cell Lysis
Bibliographic record
Abstract
An important goal for improved diagnosis and management of infectious disease is the development of rapid and accurate technologies for the decentralized detection of bacterial pathogens. Most current clinical methods that identify bacterial strains require time-consuming culture of the sample or procedures involving the polymerase chain reaction. Neither of these approaches has enabled testing at the point-of-need because of the requirement for skilled technicians and laboratory facilities. Here, we demonstrate the performance of an effective, integrated platform for the rapid detection of bacteria that combines a universal bacterial lysis approach and a sensitive nanostructured electrochemical biosensor. The lysis is rapid, is effective at releasing intercellular RNA from bacterial samples, and can be performed in a simple, cost-effective device integrated with an analysis chip. The platform was directly challenged with these unpurified lysates in buffer and urine. We successfully detected the presence of bacteria with high sensitivity and specificity and achieved a sample-to-answer turnaround time of 30 min. We have met the clinically relevant detection limit of 1 cfu/μL, indicating that uncultured samples can be analyzed. This advance will greatly reduce time to successful detection from days to minutes.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".