The Virulence Index: A Metric for Quantitative Analysis of Phage Virulence
Bibliographic record
Abstract
Background: One of the main challenges in developing phage therapy and manufacturing phage products is the reliable evaluation of their efficacy, performance, and quality. Since phage virulence is intrinsically difficult to fully capture, researchers have turned to rapid but partially inadequate methods for its evaluation. Materials and Methods: This study demonstrates a standardized quantitative method to assess phage virulence based on three parameters: the virulence index (VP)—quantifying the virulence of a phage against a host, the local virulence (vi)—assessing killing potential at given multiplicities of infection (MOIs), and MV50—the MOI at which the phage achieves 50% of its maximum theoretical virulence. This was shown through comparative analysis of the virulence of phages T4, T5, and T7. Results: Under the conditions tested, phage T7 displayed the highest virulence, followed by phage T4 and, finally, by phage T5. The impact of parameters such as temperature and medium composition on virulence was shown for each phage. The use of the method to evaluate the virulence of combinations of phages—for example, for cocktail formulation—is also shown with phages T5 and T7. Conclusions: The method presented provides a platform for high-throughput quantitative assessment of phage virulence and quality control of phage products. It can also be applied to phage screening, evaluation of phage strains, phage mutants, infection conditions and/or the susceptibility of host strains, and the formulation of phage cocktails.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".