Study of Bacterial Adhesion on Biomimetic Temperature Responsive Glycopolymer Surfaces
Bibliographic record
Abstract
Pseudomonas aeruginosa is an opportunistic pathogen responsible for diseases such as bacteremia, chronic lung infection, and acute ulcerative keratitis. P. aeruginosa induced diseases can be fatal as the exotoxins and endotoxins released by the bacterium continue to damage host tissues even after the administration of antibiotics. As bacterial adhesion on cell surfaces is the first step in bacterial based pathogen infections, the control of bacteria-cell interactions is a worthwhile research target. In this work, thermally responsive poly(N-isopropylacrylamide) [P(NIPAAm)] based biomimetic surfaces were developed to study the two major bacterial infection mechanisms, which is believed to be mediated by hydrophobic or lectin-carbohydrate interactions, using quartz crystal microbalance with dissipation. Although, a greater number of P. aeruginosa adhered to the NIPAAm homopolymer modified surfaces at temperatures higher than the lower critical solution temperature (LCST), the bacterium-substratum bond stiffness was stronger between P. aeruginosa and a galactose based P(NIPAAm) surface. The high bacterial adhesion bond stiffness observed on the galactose based thermally responsive surface at 37 °C might suggest that both hydrophobic and lectin-carbohydrate interactions contribute to bacterial adhesion on cell surfaces. Our investigation also suggests that the lectin-carbohydrate interaction play a significant role in bacterial 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 | 0.000 |
| 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".