Chlorhexidine-Containing Electrospun Polymeric Nanofibers for Dental Applications: An In Vitro Study
Bibliographic record
Abstract
Chlorhexidine is the most commonly used anti-infective drug in dentistry. To treat infected void areas, a drug-loaded material that swells to fill the void and releases the drug slowly is needed. This study investigated the encapsulation and release of chlorhexidine from cellulose acetate nanofibers for use as an antibacterial treatment for dental bacterial infections by oral bacteria Streptococcus mutans and Enterococcus faecalis. This study used a commercial electrospinning machine to finely control the manufacture of thin, flexible, chlorhexidine-loaded cellulose acetate nanofiber mats with very-small-diameter fibers (measured using SEM). Water absorption was measured gravimetrically, drug release was analyzed by absorbance at 254 nm, and antibiotic effects were measured by halo analysis in agar. Slow electrospinning at lower voltage (14 kV), short target distance (14 cm), slow traverse and rotation, and syringe injection speeds with controlled humidity and temperature allowed for the manufacture of strong, thin films with evenly cross-meshed, uniform low-diameter nanofibers (640 nm) that were flexible and absorbed over 600% in water. Chlorhexidine was encapsulated efficiently and released in a controlled manner. All formulations killed both bacteria and may be used to fill infected voids by swelling for intimate contact with surfaces and hold the drug in the swollen matrix for effective bacterial killing in dental settings.
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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 | 0.001 | 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".