Nanoscale bismuth infused bioadhesive gelatin methacryloyl electrospun mats demonstrate excellent antibiofilm activity and biocompatibility
Bibliographic record
Abstract
Periodontal diseases affect a large portion of the global population, imposing significant health and economic burdens. Traditional treatments, including antibiotics, face challenges like antibiotic resistance and rapid clearance from target sites. The study addresses these issues using nanoscale antimicrobial bismuth nanoparticles (BiNPs) delivered through electrospun gelatin methacryloyl (GelMA) nanofibrous mats. BiNPs were synthesized via a rapid chemical reduction process, yielding particles with an average size of 30 nm and a stable surface charge of -18 mV. These nanoparticles were incorporated into GelMA fibers through electrospinning and characterized using techniques such as scanning electron microscopy and Fourier transform infrared spectroscopy. The GelMA fibers exhibited a uniform morphology with a diameter of 414 nm, controlled degradation, and sustained BiNPs release. Adhesion to soft tissue was measured at ∼3 N, and the fibers maintained their mechanical strength after BiNPs incorporation. The BiNPs-loaded mats demonstrated potent antimicrobial activity, killing 100 % of Porphyromonas gingivalis, a key periodontal pathogen. Biocompatibility tests with periodontal ligament stem cells confirmed no significant cytotoxicity. This study highlights BiNPs-infused GelMA nanofibrous mats as a promising localized treatment for periodontal diseases, offering sustained antimicrobial activity, and biocompatibility.
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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) | 0.000 | 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".