Study of Bacterial Adhesion on Different Glycopolymer Surfaces by Quartz Crystal Microbalance with Dissipation
Bibliographic record
Abstract
Protein-carbohydrate interactions are involved in a wide variety of cellular recognition processes including cell growth regulation, differentiation and adhesion, the immune response, and viral or bacterial infections. A common way for bacteria to achieve adhesion is through their fimbriae which possess cellular lectins that can bind to complementary carbohydrates on the surface of the host tissues. In this work, we synthesized glycopolymers using reversible addition-fragmentation chain transfer (RAFT) polymerization which were subsequently immobilized on a sensor surface for studies of bacterial adhesion by quartz crystal microbalance with dissipation (QCM-D). Ricinus communis Agglutinin (RCA120), a galactose specific lectin, was first studied by QCM-D to determine the specific lectin interactions to the different glycopolymers-treated surfaces. Subsequently, Pseudomonas aeruginosa PAO1 (a Gram-negative bacterium with galactose-specific binding C-type lectin (PA-IL)) and Escherichia coli K-12 (a Gram-negative bacterium with mannose-specific binding lectin) were then used as model bacteria to study bacterial adhesion mechanisms on different polymer-treated sensor surfaces by the coupled resonance theory. Our results showed that lectin-carbohydrate interactions play significant roles in comparison to the nonspecific interactions, such as electrostatic interactions. A significantly higher amount of P. aeruginosa PAO1 could adhere on the glycopolymer surface with strong contact point stiffness as compared to E. coli K-12 on the same surface. Furthermore, in comparison to E. coli K-12, the adhesion of P. aeruginosa PAO1 to the glycopolymers was found to be highly dependent on the presence of calcium ions due to the specific C-type lectin interactions of PA-IL, and also the enhanced bacterial adhesion is attributed to the stiffer glycopolymer surface in higher ionic strength condition.
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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".