A Nanoporous 3D-Printed Scaffold for Local Antibiotic Delivery
Bibliographic record
Abstract
Limitations of bone defect reconstruction include poor bone healing and osteointegration with acrylic cements, lack of strength with bone putty/paste, and poor osteointegration. Tissue engineering aims to bridge these gaps through the use of bioactive implants. However, there is often a risk of infection and biofilm formation associated with orthopedic implants, which may develop anti-microbial resistance. To promote bone repair while also locally delivering therapeutics, 3D-printed implants serve as a suitable alternative. Soft, nanoporous 3D-printed filaments made from a thermoplastic polyurethane and polyvinyl alcohol blend, LAY-FOMM and LAY-FELT, have shown promise for drug delivery and orthopedic applications. Here, we compare 3D printability and sustained antibiotic release kinetics from two types of commercial 3D-printed porous filaments suitable for bone tissue engineering applications. We found that both LAY-FOMM and LAY-FELT could be consistently printed into scaffolds for drug delivery. Further, the materials could sustainably release Tetracycline over 3 days, independent of material type and infill geometry. The drug-loaded materials did not show any cytotoxicity when cultured with primary human fibroblasts. We conclude that both LAY-FOMM and LAY-FELT 3D-printed scaffolds are suitable devices for local antibiotic delivery applications, and they may have potential applications to prophylactically reduce infections in orthopedic reconstruction surgery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".