Mechanism of Action and Design of Potent Antibacterial Block Copolymer Nanoparticles
Bibliographic record
Abstract
Self-assembled polymer nanoparticles are promising antibacterials, with nonspherical morphologies of particular interest as recent work has demonstrated enhanced antibacterial activity relative to their spherical counterparts. However, the reasons for this enhancement are currently unclear. We have performed a multifaceted analysis of the antibacterial mechanism of action of 1D nanofibers relative to nanospheres by the use of flow cytometry, high-resolution microscopy, and evaluations of the antibacterial activity of pristine and tetracycline-loaded nanoparticles. Low-length dispersity, fluorescent diblock copolymer nanofibers with a crystalline poly(fluorenetrimethylenecarbonate) (PFTMC) core (length = 104 and 472 nm, height = 7 nm, width = 10–13 nm) and a partially protonated poly(dimethylaminoethyl methacrylate) (PDMAEMA) corona (length = 12 nm) were prepared via seeded growth living crystallization-driven self-assembly. Their behavior was compared to that of analogous nanospheres containing an amorphous PFTMC core (diameter of 12 nm). While all nanoparticles were uptaken into Escherichia coli W3110, crystalline-core nanofibers were observed to cause significant bacterial damage. Drug loading studies indicated that while all nanoparticle antibacterial activity was enhanced in combination with tetracycline, the enhancement was especially prominent when small nanoparticles (ca. 15–25 nm) were employed. Therefore, the identified differences in the mechanism of action and the demonstrated consequences for nanoparticle size and morphology control may be exploited for the future design of potent antibacterial agents for overcoming antibacterial resistance. This study also reinforces the requirement of morphological control over polymer nanoparticles for biomedical applications, as differences in activity are observed depending on their size, shape, and core-crystallinity.
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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) | 0.000 | 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 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".