Effects of Poly(<scp>l</scp>-lysine) Substrates on Attached <i>Escherichia coli</i> Bacteria
Bibliographic record
Abstract
Poly(L-lysine) (PLL) is a cationic polymer that is often used for attaching and immobilizing cells to glass substrates for further investigation by, e.g., AFM techniques. Because of their small size, bacterial attachment is most easily done using thick air-dried PLL coatings--though thinner PLL coatings are also used and are commercially available. Nevertheless, the antimicrobial activity of PLL is well-established. Accordingly, we have investigated the physiological effects of suspended PLL and of PLL coatings on individual Escherichia coli bacteria through the pole-to-pole oscillations of cytoplasmic MinD-GFP fusion proteins. For planktonic bacteria, suspended PLL concentrations at the micromolar level quenched MinD-GFP oscillations and inhibited bacterial growth. On coverslips with PLL coatings prepared by short exposures of the slides to PLL solutions, followed by rinsing, only a fraction of available bacteria attached after hours of settling time. Min oscillations in the attached bacteria, however, were strong and only moderately slowed. On thick PLL coatings, prepared by drying drops on the slides followed by a brief rinse with deionized water, cells attached well within 15 min. With thick coatings, average oscillation periods for bacteria increased significantly, and considerable cell-to-cell variability was also observed; subsequent replacement of buffer with distilled water led to much larger period increases and/or fading of fluorescence intensity. We demonstrate that Min oscillations are a useful metric for bacteria attached to adhesion layers. We suggest that thick PLL coatings should probably be avoided for bacterial attachment, and that even thin PLL coatings can have significant effects on bacterial physiology.
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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) | 0.000 | 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".