Resistance to antimicrobial peptides in Gram-negative bacteria
Bibliographic record
Abstract
Antimicrobial peptides (AMPs) are present in virtually all organisms and are an ancient and critical component of innate immunity. In mammals, AMPs are present in phagocytic cells, on body surfaces such as skin and mucosa, and in secretions and body fluids such as sweat, saliva, urine, and breast milk, consistent with their role as part of the first line of defense against a wide range of pathogenic microorganisms including bacteria, viruses, and fungi. AMPs are microbicidal and have also been shown to act as immunomodulators with chemoattractant and signaling activities. During the co-evolution of hosts and bacterial pathogens, bacteria have developed the ability to sense and initiate an adaptive response to AMPs to resist their bactericidal activity. Here, we review the various mechanisms used by Gram-negative bacteria to sense and resist AMP-mediated killing. These mechanisms play an important role in bacterial resistance to host-derived AMPs that are encountered during the course of infection. Bacterial resistance to AMPs should also be taken into consideration in the development and use of AMPs as anti-infective agents, for which there is currently a great deal of academic and commercial interest.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".