Immobilization of Polyethyleneimine (PEI) on Flat Surfaces and Nanoparticles Affects Its Ability to Disrupt Bacterial Membranes
Bibliographic record
Abstract
Interactions between a widely used polycationic polymer, polyethyleneimine (PEI), and a Gram-negative bacteria, E. coli, are investigated using atomic force microscopy (AFM) quantitative imaging. The effect of PEI, a known membrane permeabilizer, is characterized by probing both the structure and elasticity of the bacterial cell envelope. At low concentrations, PEI induced nanoscale membrane perturbations all over the bacterial surface. Despite these structural changes, no change in cellular mechanics (Young’s modulus) was detected and the growth of E. coli is barely affected. However, at high PEI concentrations, dramatic changes in both structure and cell mechanics are observed. When immobilized on a flat surface, the ability of PEI to alter the membrane structure and reduce bacterial elasticity is diminished. We further probe this immobilization-induced effect by covalently attaching the polymer to the surface of polydopamine nanoparticles (PDNP). The nanoparticle-immobilized PEI (PDNP-PEI), though not able to induce major structural changes on the outer membrane of E. coli (in contrast to the flat surface), was able to bind to and reduce the Young’s modulus of the bacteria. Taken together, our data demonstrate that the state of polycationic polymers, whether bound or free—which greatly dictates their overall configuration—plays a major role on how they interact with and disrupt bacterial membranes.
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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".