3D-printed reservoir-type implants containing poly(lactic acid)/poly(caprolactone) porous membranes for sustained drug delivery
Bibliographic record
Abstract
Implantable drug delivery systems are an interesting alternative to conventional drug delivery systems to achieve local or systemic drug delivery. In this work, we investigated the potential of fused-deposition modelling to prepare reservoir-type implantable devices for sustained drug delivery. An antibiotic was chosen as a model molecule to evaluate the potential of this type of technology to prepare implants on-demand to provide prophylactic antimicrobial treatment after surgery. The first step was to prepare and characterize biodegradable rate-controlling porous membranes based on poly(lactic acid) (PLA) and poly(caprolactone) (PCL). These membranes were prepared using a solvent casting method. The resulting materials contained different PLA/PCL ratios. Cylindrical implants were 3D-printed vertically on top of the membranes. Tetracycline (TC) was loaded inside the implants and drug release was evaluated. The results suggested that membranes containing a PLA/PCL ratio of 50/50 provided drug release over periods of up to 25 days. On the other hand, membranes containing lower PCL content did not show a porous structure and accordingly the drug could not permeate to the same extent. The influence of different parameters on drug release was evaluated. It was established that film thickness, drug content and implant size are critical parameters as they have a direct influence on drug release kinetics. In all cases the implants were capable of providing drug release for at least 25 days. The antimicrobial properties of the implants were evaluated against E. coli and S. aureus. The resulting implants showed antimicrobial properties at day 0 and even after 21 days against both type of microorganisms. Finally, the biocompatibility of the implants was evaluated using endothelial cells. Cells exposed to implants were compared with a control group. There were no differences between both groups in terms of cell proliferation and morphology.
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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 | 0.001 |
| 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 | 0.001 | 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".