Phage Therapy Approaches to Reducing Pathogen Persistence and Transmission in Animal Production Environments: Opportunities and Challenges
Bibliographic record
Abstract
The era of genomics has allowed for characterization of phages for use as antimicrobials to treat animal infections with a level of precision never before realized. As more research in phage therapy has been conducted, several advantages of phage therapy have been realized, including the ubiquitous nature, specificity, prevalence in the biosphere, and low inherent toxicity of phages, which makes them a safe and sustainable technology for control of animal diseases. These unique qualities of phages have led to several opportunities with respect to emerging trends in infectious disease treatment. However, the opportunities are tempered by several challenges to the successful implementation of phage therapy, such as the fact that an individual phage can only infect one or a few bacterial strains, meaning that large numbers of different phages will likely be needed to treat infections caused by multiple species of bacteria. In addition, phages are only effective if enough of them can reach the site of bacterial colonization, but clearance by the immune system upon introduction to the animal is a reality that must be overcome. Finally, bacterial resistance to the phages may develop, resulting in treatment failure. Even a successful phage infection and lysis of its host has consequences, because large amounts of endotoxin are released upon lysis of Gram-negative bacteria, which can lead to local and systemic complications. Overcoming these challenges will require careful design and development of phage cocktails, including comprehensive characterization of phage host range and assessment of immunological risks associated with phage treatment.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".