Influence of ciprofloxacin‐based additives on the hydrolysis of nanofiber polyurethane membranes
Bibliographic record
Abstract
A degradable polycarbonate urethane (PCNU) and an antimicrobial oligomer (AO) were used to generate anti-infective nanofiber scaffolds through blend electrospinning. The AO consists of two molecules of ciprofloxacin (CF) bound through hydrolysable linkages to triethylene glycol. The membranes were conceived for use as tissue engineering scaffolds for the regeneration of soft tissues for the periodontium, where there would be a need for a local dose of antibiotic to the periodontal space as the scaffold degrades in order to prevent biomaterial-associated infection. Scaffolds were made using AO at 7 and 15% w/w equivalent CF, and compared to scaffolds with 15% w/w CF (with HCl counterion). AO was hydrolyzed and released CF continuously over 28 days, while the 15% w/w CF HCl scaffolds showed a burst release within hours, with no subsequent release in the subsequent 28 day period. Released CF from both the AO and CF HCl scaffolds had a similar minimum inhibitory concentration to that of off-the-shelf CF. Interestingly, the introduction of drug in either form (AO or CF HCl) was found to increase the hydrolytic stability of the electrospun degradable PCNU scaffold matrix itself. The alteration of hydrolysis kinetics was attributed to changes in the hydrogen bonding character and microstructure within the scaffolds, introduced by the presence of CF. This study has revealed that in generating in situ drug release systems, the secondary effects of the added drug on the degradation properties of the polymeric carriers must be considered, particularly for systems that act dually as tissue engineering scaffolds. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 1211-1222, 2018.
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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".