A simple cassette as point-of-care diagnostic device for naked-eye colorimetric bacteria detection
Bibliographic record
Abstract
Effective pathogen detection is necessary for treatment of infectious diseases. Point of care (POC) devices have tremendously improved the global human heath. However, design criteria for sample processing POC devices for pathogen detection in limited infrastructure are challenging and can make a significant contribution to global health by providing rapid and sensitive detection of bacteria in food, water, and patient samples. In this paper, we demonstrate a novel portable POC diagnostic device that is simple to assemble for genetic detection of bacterial pathogens by isothermal DNA amplification. The device is fabricated with very low production cost, using simple methods and easy-to-access materials on a flexible ribbon polyethylene substrate. We showed that the device is capable of detection of 30 CFU mL(-1) of E. coli and 200 CFU mL(-1) of S. aureus in less than 1 hour. Through numerical simulations, we estimated that the device can be extended to high-throughput detection simultaneously performing a minimum of 36 analyses. This robust and sensitive detection device can be assembled and operated by non-specialist personnel, particularly for multiple bacterial pathogen detections in low-resource settings.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".