Expand and Sensitize: Guanidine‐Functionalized Exopolysaccharide Nanoparticles Cause Bacterial Cell Expansion and Antibiotic Sensitization
Bibliographic record
Abstract
Abstract Conventional antibacterial agents and mechanisms are frequently observed to be ineffective due to the evolution of bacteria to the strains with stronger antibiotic resistance, and hence developing alternative antibacterial materials and mechanisms is urgently needed. Here, guanidine‐functionalized exopolysaccharide (EPS) nanoparticles (termed EPGNs) with durable antibacterial and antibiofilm activities are developed. Very interestingly, the EPGNs obtained by the reaction of EPS, epichlorohydrin, and polyhexamethylene guanidine hydrochloride exhibit an unconventional antibacterial mechanism, i.e., they can induce substantial bacterial cell expansion by upregulating the SulA and DicB proteins that are responsible for cell division inhibition, along with the increase of reactive oxygen species production, bacterial cell surface disruption, and bacterial ribosomal RNA degradation. The transcriptome analysis reveals that EPGNs can hinder cell motility, induce loss of cell integrity, decrease the resistance of bacteria to oxidative stress, and finally lead to cell death. Moreover, EPGNs can effectively accelerate the bacteria‐infected wound healing. This work provides the first example that nanomaterials can cause bacterial cell expansion by affecting intracellular structures and inhibiting cell division, and it may inspire other researchers to investigate the effect of antibacterial materials on the change of bacterial volume and design unconventional antibacterial materials/strategies.
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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) | 0.000 | 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) | 0.001 | 0.000 |
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".