Quorum sensing and antibiotic resistance in polymicrobial infections
Bibliographic record
Abstract
Quorum sensing (QS) is a critical bacterial communication system regulating behaviors like biofilm formation, virulence, and antibiotic resistance. This review highlights QS's role in polymicrobial infections, where bacterial species interactions enhance antibiotic resistance. We examine QS mechanisms, such as acyl-homoserine lactones (AHLs) in Gram-negative bacteria and autoinducing peptides (AIPs) in Gram-positive bacteria, and their impact on biofilm-associated antibiotic resistance. The challenges uniquely associated with polymicrobial infections, such as those found in cystic fibrosis lung infections, chronic wound infections, and medical device infections, are also summarized. Furthermore, we explore various laboratory models, including flow cells and dual-species culture models, used to study QS interactions in polymicrobial environments. The review also discusses promising quorum sensing inhibitors (QSIs), such as furanones and AHL analogs, which have demonstrated efficacy in reducing biofilm formation and virulence in laboratory and clinical studies. By addressing the interplay between QS and antibiotic resistance, this paper aims to advance therapeutic strategies that disrupt bacterial communication and improve antibiotic efficacy, ultimately mitigating the global challenge of antibiotic resistance in polymicrobial infections.
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".