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 102to 108cells/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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".