Fast and inexpensive detection of bacterial viability and drug resistance through metabolic monitoring
Bibliographic record
Abstract
Conventional methods for the detection of bacterial viability and drug resistance are either expensive, time consuming, or not definitive, and thus do not provide all the information sought by the medical professionals. Here, we solve these problems by introducing an innovative detection method to produce rapid and accurate diagnosis of bacterial infection through miniaturization and parallelization. This method is demonstrated with wells of several shapes (square, circle), diameters (100 – 1000µm) and depths (≤100 µm). In the development of proof of concept, we use laboratory strain of E.coli as the model pathogen. The integration of the fluorescent oxygen sensor, ruthenium tris (2,2′-diprydl) dichloride hexahydrate (RTDP), allows us to monitor the dissolved oxygen concentration as a measure of bacterial metabolism. Detection time of the bacteria within the microwells can be as fast as a few of hours (4–5hrs), with concentrations that vary between 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sup> cells/mL. Adding the appropriate drug to the broth and measuring growth through fluorescence also probed drug resistance. This reported method for microfabrication of the wells, is rapid, economical, versatile and simple.
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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 | 0.000 |
| 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".