Virulence-associated factors as targets for phage infection
Bibliographic record
Abstract
Bacterial pathogens can infect a wide range of hosts and pose a threat to public and animal health as well as to agriculture. The emergence of antibiotic-resistant strains has increased this risk by making the treatment of bacterial infections even more challenging. Pathogenic bacteria thrive in various ecological niches, but they can also be specifically targeted and killed by bacteriophages (phages). Lytic phages are now investigated and even used, in some cases, as alternatives or complements to antibiotics for preventing or treating bacterial infections (phage therapy). As such, it is key to identify factors responsible for phage specificity and efficiency. Here, we review recent advances in virulence-associated factors that are targeted by phages. We highlight components of the bacterial cell surface, effector systems, and motility structures exploited by phages and the effects of phages on cell aggregation and communication. We also look at the fitness trade-off of phage resistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".