From Bits and Pieces to Whole Phage to Nanomachines: Pathogen Detection Using Bacteriophages
Bibliographic record
Abstract
The innate specificity of bacteriophages toward their hosts makes them excellent candidates for the development of detection assays. They can be used in many ways to detect pathogens, and each has its own advantages and disadvantages. Whole bacteriophages can carry reporter genes to alter the phenotype of the target. Bacteriophages can act as staining agents or the progeny of the infection process can be detected, which further increases the sensitivity of the detection assay. Compared with whole-phage particles, use of phage components as probes offers other advantages: for example, smaller probe size to enhance binding activity, phage structures that can be engineered for better affinity, as well as specificity, binding properties, and robustness. When no natural binding with the target exists, phages can be used as vehicles to identify new protein-ligand interactions necessary for diagnostics. This review comprehensively summarizes many uses of phages as detection tools and points the way toward how phage-based technologies may be improved.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".