The Age of Phage: Friend or Foe in the New Dawn of Therapeutic and Biocontrol Applications?
Bibliographic record
Abstract
Extended overuse and misuse of antibiotics and other antibacterial agents has resulted in an antimicrobial resistance crisis. Bacteriophages, viruses that infect bacteria, have emerged as a legitimate alternative antibacterial agent with a wide scope of applications which continue to be discovered and refined. However, the potential of some bacteriophages to aid in the acquisition, maintenance, and dissemination of negatively associated bacterial genes, including resistance and virulence genes, through transduction is of concern and requires deeper understanding in order to be properly addressed. In particular, their ability to interact with mobile genetic elements such as plasmids, genomic islands, and integrative conjugative elements (ICEs) enables bacteriophages to contribute greatly to bacterial evolution. Nonetheless, bacteriophages have the potential to be used as therapeutic and biocontrol agents within medical, agricultural, and food processing settings, against bacteria in both planktonic and biofilm environments. Additionally, bacteriophages have been deployed in developing rapid, sensitive, and specific biosensors for various bacterial targets. Intriguingly, their bioengineering capabilities show great promise in improving their adaptability and effectiveness as biocontrol and detection tools. This review aims to provide a balanced perspective on bacteriophages by outlining advantages, challenges, and future steps needed in order to boost their therapeutic and biocontrol potential, while also providing insight on their potential role in contributing to bacterial evolution and survival.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".