Three-Dimensional Printed Polylactic Acid Scaffolds Promote Bone-like Matrix Deposition in Vitro
Bibliographic record
Abstract
Large bone defects represent a significant challenge for clinicians and surgeons. Tissue engineering for bone regeneration represents an innovative solution for this dilemma and may yield attractive alternate bone substitutes. Three-dimensional (3D) printing with inexpensive desktop printers shows promise in generating high-resolution structures mimicking native tissues using biocompatible, biodegradable, and cost-effective thermoplastics, which are already FDA-approved for food use, drug delivery, and many medical devices. Microporous 3D-printed polylactic acid scaffolds, with different pore sizes (500, 750, and 1000 μm), were designed and manufactured using an inexpensive desktop 3D printer, and the mechanical properties were assessed. The scaffolds were compared for cell growth, activity, and bone-like tissue formation using primary human osteoblasts. Osteoblasts showed high proliferation, metabolic activity, and osteogenic matrix protein production, in which 750 μm pore-size scaffolds showed superiority. Further experimentation using human mesenchymal stem cells on 750 μm pore scaffolds showed their ability in supporting osteogenic differentiation. These findings suggest that even in the absence of any surface modifications, low-cost 750 μm pore-size 3D-printed scaffolds may be suitable as a bone substitute for repair of large bone defects.
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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) | 0.001 | 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 | 0.001 |
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".