Functionalization of poly(lactic‐co‐glycolic acid) nanofibrous membranes with antibiofilm compounds
Bibliographic record
Abstract
Abstract The aim of this research was to investigate the activity of functionalized PLGA electrospun membranes in preventing Streptococcus mutans biofilm formation. PLGA nanofibres were functionalized with the additives Melaleuca alternifolia and Coffea canephora essential oils, furan‐2( 5H )‐one, and a novel synthetic butyrolactam, in three concentrations (0.002%, 0.004%, and 0.008% w/v). Samples were characterized by SEM, FTIR‐ATR, and GC–MS and exposed to S. mutans cultures. Planktonic growth was determined following a 24‐ and 48‐h incubation period by spectrophotometry and the biofilm formation was evaluated by counting colony forming units. Cytotoxicity of the new biomaterials was assessed by MTS assay, through the quantification of viable placenta‐derived stem cells grown over the functionalized nanofibrous membranes. Observation of the electrospun membranes on SEM images revealed the smooth and bead‐free morphology of the nanofibres. No solvent residues were observed by FTIR‐ATR, but the GC–MS results showed that N,N ‐dimethylformamide and dimethylsulfoxide were present as residues in the membranes after functionalization. Functionalization reduced bacterial attachment to membrane surfaces, with best results being obtained for M. alternifolia essential oil, furanone, and butyrolactam. Cytotoxicity results showed that furan‐2( 5H )‐one‐functionalized membranes demonstrated no statistically significant difference in the cell viability compared to the control membranes.
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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) | 0.001 | 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".