Phages for Africa: The Potential Benefit and Challenges of Phage Therapy for the Livestock Sector in Sub-Saharan Africa
Bibliographic record
Abstract
One of the world's fastest-growing human populations is in Sub-Saharan Africa (SSA), accounting for more than 950 million people, which is approximately 13% of the global population. Livestock farming is vital to SSA as a source of food supply, employment, and income. With this population increase, meeting this demand and the choice for a greater income and dietary options come at a cost and lead to the spread of zoonotic diseases to humans. To control these diseases, farmers have opted to rely heavily on antibiotics more often to prevent disease than for treatment. The constant use of antibiotics causes a selective pressure to build resistant bacteria resulting in the emergence and spread of multi-drug resistant (MDR) organisms in the environment. This necessitates the use of alternatives such as bacteriophages in curbing zoonotic pathogens. This review covers the underlying problems of antibiotic use and resistance associated with livestock farming in SSA, bacteriophages as a suitable alternative, what attributes contribute to making bacteriophages potentially valuable for SSA and recent research on bacteriophages in Africa. Furthermore, other topics discussed include the creation of phage biobanks and the challenges facing this kind of advancement, and the regulatory aspects of phage development in SSA with a focus on Kenya.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".