Rapid Enumeration of Phage in Monodisperse Emulsions
Bibliographic record
Abstract
Phage-based detection assays have been developed for the detection of viable bacteria for applications in clinical diagnosis, monitoring of water quality, and food safety. The majority of these assays deliver a positive readout in the form of newly generated progeny phages by the bacterial host of interest. Progeny phages are often visualized as plaques, or holes, in a lawn of bacteria on an agar-filled Petri dish; however, this rate-limiting step requires up to 12 h of incubation time. We have previously described an amplification of bacteriophages M13 inside droplets of media suspended in perfluorinated oil; a single phage M13 in a droplet yields 10(7) copies in 3-4 h. Here, we describe that encapsulation of reporter phages, both lytic T4-LacZ and nonlytic M13, in monodisperse droplets can also be used for rapid enumeration of phage. Compartmentalization in droplets accelerated the development of the signal from the reporter enzyme; counting of "positive" droplets yields accurate enumeration of phage particles ranging from 10(2) to 10(6) pfu/mL. For enumeration of T4-LacZ phage, the fluorescent signal appeared in as little as 90 min. Unlike bulk assays, quantification in emulsion is robust and insensitive to fluctuations in environmental conditions (e.g., temperature). Power-free emulsification using gravity-driven flow in the absence of syringe pumps and portable fluorescence imaging solutions makes this technology promising for use at the point of care in low-resource environments. This droplet-based phage enumeration method could accelerate and simplify point-of-care detection of the pathogens for which reporter bacteriophages have been developed.
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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".