Biological characterization of a novel biodegradable antimicrobial polymer synthesized with fluoroquinolones
Bibliographic record
Abstract
Biomaterial-related infections continue to represent a significant challenge to the medical community. Several approaches have been utilized to incorporate antimicrobial agents at the surface of implant devices in attempts to delay or eliminate the formation of biofilms. To date, most of these strategies have focused on drug conjugation or diffusion-limited systems for the delivery of such pharmaceutical agents. More recently, work has been presented on the feasibility of incorporating drugs into the backbone of polymers as a main-chain monomer. When sequenced into the backbone of the polymer with other monomers that are hydrolytically sensitive to enzyme-catalyzed breakdown, it is thought that drugs may be able to be selectively released. Specifically, degradable polyurethanes have been synthesized with fluoroquinolone antibiotics and have shown an ability to kill bacteria when released following degradation of the polymer chains by the macrophage-derived enzyme cholesterol esterase. However, specificity of the cleavage sites in the polymer was difficult to control. Since cholesterol esterase has specificity for hydrophobic moieties, it is desirable to alter the formulation of the polyurethanes to incorporate long hydrophobic monomers immediately adjacent to the ciprofloxacin molecule. Hence, the current study focuses on evaluating the enzyme-catalyzed degradation of a degradable polyurethane synthesized with 1,12 diisocyanatododecane as a substitute for 1,6 diisocyanatohexane, which was used in previous work. Validation of specific ciprofloxacin release and the generation of antimicrobial are shown. A preliminary cell study to assess the cytotoxicity of this biodegradable antibiotic polymer shows that the material has no observable effects on cell proliferation or cell membrane structure.
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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 | 0.001 |
| 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".