3D-printed dual drug delivery nanoparticleloaded hydrogels to combat antibiotic-resistant bacteria
Bibliographic record
Abstract
Implant-associated infections are not easy to diagnose and very difficult to treat, due to the ability of major pathogens, such as Staphylococcus aureus, to develop biofilms and escape the immune response and antibiotic treatment. We, therefore, aimed to develop a 3D-printed dual rifampicin (Rif)- and vancomycin (Van)-loaded polylacticco-glycolic acid (PLGA) nanoparticles (NPs) delivery system based on hydrogels made of gelatin methacrylate (GelMA). The release of Rif and Van from NPs manufactured from different PLGA molecular weights was studied in phosphate-buffered saline for 21 days. Low molecular weight PLGA NPs exhibited the fastest release of Rif and Van within the first 7 days and were selected for antimicrobial evaluation. Four different GelMA-based 3D-printed samples were successfully produced, carrying non-loaded NPs, Rif-NPs, Van-NPs, or alternating layers of Rif-NPs and Van-NP. The exposition of S. aureus against increased concentrations of Rif or Van produced new resistant strains to Rif (RifR ) or Van (VanR ). The GelMA hydrogel co-delivering Rif and Van eradicated S. aureus RN4220 RifR and RN4220 VanR strains. S. aureus RN4220 and S. aureus AMC 201 colonies developed resistance to Rif after contact with the GelMA hydrogel containing only Rif-NPs which appeared to be due to known mutations in the rpoB gene. In conclusion, 3D-printed GelMA hydrogel loaded with PLGA Rif-Van-NPs drug delivery system show promising in vitro results to prevent implant-associated infections caused by antimicrobial-resistant bacteria.
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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".