Fast and inexpensive detection of bacterial viability and drug effectiveness through metabolic monitoring
Bibliographic record
Abstract
Abstract Conventional methods for the detection of bacterial infection such as DNA or immunoassays are either expensive, time consuming, or not definitive; thus may not provide all the information sought by the medical professionals. In particular, it is difficult to obtain information about viability or drug effectiveness, which are crucial to formulate a treatment. Bacterial culture test is the “gold standard” because it is inexpensive and does not require extensive sample preparation, and most importantly, provides all the necessary information sought by healthcare professionals, such as bacterial presence, viability and drug effectiveness. These conventional culture methods, however, have a long turnaround time: anywhere between 1 day to 4 weeks. Here, we solve this problem by monitoring the growth of bacteria in thousands of nanowells simultaneously to identify its presence in the sample and its viability, faster. The segmentation of a sample with low bacterial concentration into thousands of nanoliter wells digitizes the samples and increases the effective concentration in those wells that contain bacteria. We monitor the metabolism of aerobic bacteria by using an oxygen sensitive fluorophore, ruthenium tris (2,2’-diprydl) dichloride hexahydrate (RTDP) that allows us to monitor the dissolved oxygen concentration in the nanowells. Using E.Coli K12 as a model pathogen, we demonstrate that the detection time of E.coli can be as fast as 35-60 minutes with sample concentrations varying from 10 4 (62 minutes for detection), 10 6 (42 minutes) and 10 8 cells/mL (38 minutes). More importantly, we also demonstrate that reducing the well size can reduce the time of detection. Finally we show that drug effectiveness information can be obtained in this format by loading the wells with the drug and monitoring the metabolism of the bacteria. The method that we have developed is low cost, simple, requires minimal sample preparation and can potentially be used with a wide variety of samples in resource poor setting to detect bacterial infections such as Tuberculosis.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".