Production of Biocompatible and Antimicrobial Bacterial Cellulose Polymers Functionalized by RGDC Grafting Groups and Gentamicin
Bibliographic record
Abstract
Bacterial cellulose (BC), a three-dimensional fibril, is a natural polymer that can be used for many applications. BC effectiveness may be improved by enhancing surface characteristics contributing to a better physiologic interaction with human and animal cells and to intrinsically present antimicrobial agents. In the present study, gentamicin-activated BC membranes were obtained by chemically grafting RGDC peptides (R: arginine; G: glycine; D: aspartic acid; C: cysteine) using coupling agent 3-aminopropyltriethoxysilane (APTES) followed by covalent attachment of gentamicin onto the surface of the BC membrane network. X-ray photoelectron spectroscopy (XPS) analyses showed that the BC-APTES contained 0.7% of silicon in terms of elemental composition, corresponding to a grafting ratio of 1:12. The presence of silicon and nitrogen in the BC-APTES confirmed the surface functionalization of the BC membrane. Fourier-transform infrared (FTIR) analyses show the formation of the secondary amide as supported by the valence bond C═O (ν(C═O)), a characteristic vibrational transition at 1650 cm(-1) which is particularly intense with the BC-RGDC-gentamicin membrane. Energy-dispersive X-ray (EDX) analyses showed a low level of carbon and nitrogen (C + N) in pure BC but a high level of (C + N) in BC-RGDC-gentamicin confirming the surface modification of the BC membrane by RGDC and gentamicin enrichment. Of great interest, the gentamicin-RGDC-grafted BC membranes are bactericidal against Streptococcus mutans but nontoxic to human dermal fibroblasts and thus may be useful for multiple applications such as improved wound healing and drug delivery systems.
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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) | 0.000 | 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 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".