Design of 3D scaffold geometries for optimal biodegradation of poly(lactic acid)-based bone tissue
Bibliographic record
Abstract
Additive manufacturing is utilized to produce different types of scaffolds to mimic the microcellular structure of bone tissue. Characterizing the biodegradation rate of engineered tissue scaffolds and their respective biodegradation patterns against the 3D structure design of the scaffold is of crucial importance. The research focus of this paper is to understand the relationship between the geometry of biomimetic 3D printed microcellular structures and their biodegradation properties through a comparative experimental assessment study. Polylactic acid (PLA) is one of the commonly used materials in tissue engineering and PLA filaments are widely available for additive manufacturing. To use PLA 3D printed structures as tissue scaffolds, a non-toxicity biological test is conducted using a mammalian cell line, to prove the biocompatibility. Then, multiple common as well as novel microcellular structures are manufactured to assess the effect of the geometry of biomimetic 3D printed scaffolds on the pattern and rate of biodegradation. Microcomputed tomography (Micro-CT) technique is used to monitor the morphology evolution in 3D structures before and after each step of biodegradation. Phosphate buffered saline (PBS) medium plus 5% Carbon Dioxide gas in a CO2 incubator is used to simulate an in vitro environment for performing biodegradation tests. The obtained experimental results are promising as they provide a more advanced understanding on how biodegradation and microcellular structure geometries influence each other; and are helpful for manufacture of optimized biomimetic product for specific needs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 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".