Bacteriophages for prophylaxis and therapy in cattle, poultry and pigs
Bibliographic record
Abstract
The successful use of virulent (lytic) bacteriophages (phages) in preventing and treating neonatal enterotoxigenic Escherichia coli infections in calves, lambs and pigs has prompted investigation of other applications of phage therapy in food animals. While results have been very variable, some indicate that phage therapy is potentially useful in virulent Salmonella and E. coli infections in chickens, calves and pigs, and in control of the food-borne pathogens Salmonella and Campylobacter jejuni in chickens and E. coli O157:H7 in cattle. However, more rigorous and comprehensive research is required to determine the true potential of phage therapy. Particular challenges include the selection and characterization of phages, practical modes of administration, and development of formulations that maintain the viability of phages for administration. Also, meaningful evaluation of phage therapy will require animal studies that closely represent the intended use, and will include thorough investigation of the emergence and characteristics of phage resistant bacteria. As well, effective use will require understanding the ecology and dynamics of the endemic and therapeutic phages and their interactions with target bacteria in the farm environment. In the event that the potential of phage therapy is realized, adoption will depend on its efficacy and complementarity relative to other interventions. Another potential challenge will be regulatory approval.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".